7933 lines
672 KiB
Plaintext
7933 lines
672 KiB
Plaintext
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I0405 09:46:24.320744 26038 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210405-094622-ab9e/solver.prototxt
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I0405 09:46:24.320940 26038 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
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W0405 09:46:24.320945 26038 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
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I0405 09:46:24.321007 26038 caffe.cpp:218] Using GPUs 0
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I0405 09:46:24.343080 26038 caffe.cpp:223] GPU 0: GeForce GTX TITAN X
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I0405 09:46:24.548560 26038 solver.cpp:44] Initializing solver from parameters:
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test_iter: 51
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test_interval: 102
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base_lr: 1e-05
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display: 12
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max_iter: 20400
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lr_policy: "fixed"
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momentum: 0.9
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weight_decay: 9.9999994e-08
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snapshot: 102
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snapshot_prefix: "snapshot"
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solver_mode: GPU
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device_id: 0
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net: "train_val.prototxt"
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train_state {
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level: 0
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stage: ""
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}
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type: "SGD"
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I0405 09:46:24.549463 26038 solver.cpp:87] Creating training net from net file: train_val.prototxt
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I0405 09:46:24.550098 26038 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
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I0405 09:46:24.550110 26038 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
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I0405 09:46:24.550230 26038 net.cpp:51] Initializing net from parameters:
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state {
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phase: TRAIN
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level: 0
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stage: ""
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}
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layer {
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name: "train-data"
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type: "Data"
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top: "data"
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top: "label"
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include {
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phase: TRAIN
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}
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transform_param {
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mirror: true
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crop_size: 227
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mean_file: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto"
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}
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data_param {
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source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/train_db"
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batch_size: 128
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backend: LMDB
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}
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}
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layer {
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name: "conv1"
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type: "Convolution"
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bottom: "data"
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top: "conv1"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 96
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kernel_size: 11
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stride: 4
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "relu1"
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type: "ReLU"
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bottom: "conv1"
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top: "conv1"
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}
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layer {
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name: "norm1"
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type: "LRN"
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bottom: "conv1"
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top: "norm1"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layer {
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name: "pool1"
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type: "Pooling"
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bottom: "norm1"
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top: "pool1"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "conv2"
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type: "Convolution"
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bottom: "pool1"
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top: "conv2"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 256
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pad: 2
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kernel_size: 5
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group: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0.1
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}
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}
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}
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layer {
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name: "relu2"
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type: "ReLU"
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bottom: "conv2"
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top: "conv2"
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}
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layer {
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name: "norm2"
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type: "LRN"
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bottom: "conv2"
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top: "norm2"
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lrn_param {
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local_size: 5
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alpha: 0.0001
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beta: 0.75
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}
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}
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layer {
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name: "pool2"
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type: "Pooling"
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bottom: "norm2"
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top: "pool2"
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pooling_param {
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pool: MAX
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "conv3"
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type: "Convolution"
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bottom: "pool2"
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top: "conv3"
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param {
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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num_output: 384
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pad: 1
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kernel_size: 3
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weight_filler {
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type: "gaussian"
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std: 0.01
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}
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bias_filler {
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type: "constant"
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value: 0
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}
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}
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}
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layer {
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name: "relu3"
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type: "ReLU"
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bottom: "conv3"
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top: "conv3"
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}
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layer {
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name: "conv4"
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type: "Convolution"
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bottom: "conv3"
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top: "conv4"
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param {
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||
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lr_mult: 1
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decay_mult: 1
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}
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param {
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lr_mult: 2
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decay_mult: 0
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}
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convolution_param {
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||
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num_output: 384
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pad: 1
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||
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kernel_size: 3
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group: 2
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weight_filler {
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||
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type: "gaussian"
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||
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std: 0.01
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||
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|
}
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||
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|
bias_filler {
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||
|
|
type: "constant"
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||
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value: 0.1
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||
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|
}
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||
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|
}
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||
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|
}
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||
|
|
layer {
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||
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|
name: "relu4"
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||
|
|
type: "ReLU"
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||
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|
bottom: "conv4"
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||
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top: "conv4"
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||
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|
}
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||
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|
layer {
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||
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|
name: "conv5"
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||
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|
type: "Convolution"
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||
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|
bottom: "conv4"
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||
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|
top: "conv5"
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||
|
|
param {
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||
|
|
lr_mult: 1
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||
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|
decay_mult: 1
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||
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|
}
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||
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|
param {
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||
|
|
lr_mult: 2
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||
|
|
decay_mult: 0
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||
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|
}
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||
|
|
convolution_param {
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||
|
|
num_output: 256
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||
|
|
pad: 1
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||
|
|
kernel_size: 3
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||
|
|
group: 2
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||
|
|
weight_filler {
|
||
|
|
type: "gaussian"
|
||
|
|
std: 0.01
|
||
|
|
}
|
||
|
|
bias_filler {
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||
|
|
type: "constant"
|
||
|
|
value: 0.1
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||
|
|
}
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||
|
|
}
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||
|
|
}
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||
|
|
layer {
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||
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|
name: "relu5"
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||
|
|
type: "ReLU"
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||
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|
bottom: "conv5"
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||
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|
top: "conv5"
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||
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|
}
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||
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|
layer {
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||
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|
name: "pool5"
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||
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|
type: "Pooling"
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||
|
|
bottom: "conv5"
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||
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|
top: "pool5"
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||
|
|
pooling_param {
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||
|
|
pool: MAX
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||
|
|
kernel_size: 3
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||
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|
stride: 2
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||
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}
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||
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|
}
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|
layer {
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||
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|
name: "fc6"
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||
|
|
type: "InnerProduct"
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||
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|
bottom: "pool5"
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||
|
|
top: "fc6"
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||
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|
param {
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||
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lr_mult: 1
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||
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decay_mult: 1
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||
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|
}
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||
|
|
param {
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||
|
|
lr_mult: 2
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||
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decay_mult: 0
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||
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}
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||
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|
inner_product_param {
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||
|
|
num_output: 4096
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||
|
|
weight_filler {
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||
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|
type: "gaussian"
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||
|
|
std: 0.005
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||
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|
}
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||
|
|
bias_filler {
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||
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type: "constant"
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||
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value: 0.1
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||
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|
}
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||
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|
}
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||
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|
}
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||
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|
layer {
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||
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name: "relu6"
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type: "ReLU"
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bottom: "fc6"
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top: "fc6"
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}
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layer {
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||
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name: "drop6"
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||
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type: "Dropout"
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||
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bottom: "fc6"
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top: "fc6"
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dropout_param {
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||
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dropout_ratio: 0.5
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||
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}
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||
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}
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||
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|
layer {
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||
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|
name: "fc7"
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||
|
|
type: "InnerProduct"
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||
|
|
bottom: "fc6"
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||
|
|
top: "fc7"
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||
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|
param {
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||
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|
lr_mult: 1
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||
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|
decay_mult: 1
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||
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|
}
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||
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|
param {
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||
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|
lr_mult: 2
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||
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decay_mult: 0
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||
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|
}
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||
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|
inner_product_param {
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||
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|
num_output: 4096
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||
|
|
weight_filler {
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||
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|
type: "gaussian"
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||
|
|
std: 0.005
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||
|
|
}
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||
|
|
bias_filler {
|
||
|
|
type: "constant"
|
||
|
|
value: 0.1
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||
|
|
}
|
||
|
|
}
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||
|
|
}
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||
|
|
layer {
|
||
|
|
name: "relu7"
|
||
|
|
type: "ReLU"
|
||
|
|
bottom: "fc7"
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||
|
|
top: "fc7"
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||
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|
}
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||
|
|
layer {
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||
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|
name: "drop7"
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||
|
|
type: "Dropout"
|
||
|
|
bottom: "fc7"
|
||
|
|
top: "fc7"
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||
|
|
dropout_param {
|
||
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|
dropout_ratio: 0.5
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||
|
|
}
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||
|
|
}
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||
|
|
layer {
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||
|
|
name: "fc8"
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||
|
|
type: "InnerProduct"
|
||
|
|
bottom: "fc7"
|
||
|
|
top: "fc8"
|
||
|
|
param {
|
||
|
|
lr_mult: 1
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||
|
|
decay_mult: 1
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||
|
|
}
|
||
|
|
param {
|
||
|
|
lr_mult: 2
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||
|
|
decay_mult: 0
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||
|
|
}
|
||
|
|
inner_product_param {
|
||
|
|
num_output: 196
|
||
|
|
weight_filler {
|
||
|
|
type: "gaussian"
|
||
|
|
std: 0.01
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||
|
|
}
|
||
|
|
bias_filler {
|
||
|
|
type: "constant"
|
||
|
|
value: 0
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "loss"
|
||
|
|
type: "SoftmaxWithLoss"
|
||
|
|
bottom: "fc8"
|
||
|
|
bottom: "label"
|
||
|
|
top: "loss"
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||
|
|
}
|
||
|
|
I0405 09:46:24.550305 26038 layer_factory.hpp:77] Creating layer train-data
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||
|
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I0405 09:46:24.553166 26038 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/train_db
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||
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I0405 09:46:24.553428 26038 net.cpp:84] Creating Layer train-data
|
||
|
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I0405 09:46:24.553442 26038 net.cpp:380] train-data -> data
|
||
|
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I0405 09:46:24.553462 26038 net.cpp:380] train-data -> label
|
||
|
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I0405 09:46:24.553474 26038 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
|
||
|
|
I0405 09:46:24.559177 26038 data_layer.cpp:45] output data size: 128,3,227,227
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||
|
|
I0405 09:46:24.693485 26038 net.cpp:122] Setting up train-data
|
||
|
|
I0405 09:46:24.693507 26038 net.cpp:129] Top shape: 128 3 227 227 (19787136)
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||
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I0405 09:46:24.693511 26038 net.cpp:129] Top shape: 128 (128)
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||
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I0405 09:46:24.693513 26038 net.cpp:137] Memory required for data: 79149056
|
||
|
|
I0405 09:46:24.693522 26038 layer_factory.hpp:77] Creating layer conv1
|
||
|
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I0405 09:46:24.693540 26038 net.cpp:84] Creating Layer conv1
|
||
|
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I0405 09:46:24.693545 26038 net.cpp:406] conv1 <- data
|
||
|
|
I0405 09:46:24.693555 26038 net.cpp:380] conv1 -> conv1
|
||
|
|
I0405 09:46:25.131551 26038 net.cpp:122] Setting up conv1
|
||
|
|
I0405 09:46:25.131573 26038 net.cpp:129] Top shape: 128 96 55 55 (37171200)
|
||
|
|
I0405 09:46:25.131577 26038 net.cpp:137] Memory required for data: 227833856
|
||
|
|
I0405 09:46:25.131597 26038 layer_factory.hpp:77] Creating layer relu1
|
||
|
|
I0405 09:46:25.131606 26038 net.cpp:84] Creating Layer relu1
|
||
|
|
I0405 09:46:25.131609 26038 net.cpp:406] relu1 <- conv1
|
||
|
|
I0405 09:46:25.131615 26038 net.cpp:367] relu1 -> conv1 (in-place)
|
||
|
|
I0405 09:46:25.131886 26038 net.cpp:122] Setting up relu1
|
||
|
|
I0405 09:46:25.131893 26038 net.cpp:129] Top shape: 128 96 55 55 (37171200)
|
||
|
|
I0405 09:46:25.131896 26038 net.cpp:137] Memory required for data: 376518656
|
||
|
|
I0405 09:46:25.131898 26038 layer_factory.hpp:77] Creating layer norm1
|
||
|
|
I0405 09:46:25.131906 26038 net.cpp:84] Creating Layer norm1
|
||
|
|
I0405 09:46:25.131909 26038 net.cpp:406] norm1 <- conv1
|
||
|
|
I0405 09:46:25.131938 26038 net.cpp:380] norm1 -> norm1
|
||
|
|
I0405 09:46:25.132411 26038 net.cpp:122] Setting up norm1
|
||
|
|
I0405 09:46:25.132421 26038 net.cpp:129] Top shape: 128 96 55 55 (37171200)
|
||
|
|
I0405 09:46:25.132423 26038 net.cpp:137] Memory required for data: 525203456
|
||
|
|
I0405 09:46:25.132426 26038 layer_factory.hpp:77] Creating layer pool1
|
||
|
|
I0405 09:46:25.132433 26038 net.cpp:84] Creating Layer pool1
|
||
|
|
I0405 09:46:25.132436 26038 net.cpp:406] pool1 <- norm1
|
||
|
|
I0405 09:46:25.132441 26038 net.cpp:380] pool1 -> pool1
|
||
|
|
I0405 09:46:25.132475 26038 net.cpp:122] Setting up pool1
|
||
|
|
I0405 09:46:25.132481 26038 net.cpp:129] Top shape: 128 96 27 27 (8957952)
|
||
|
|
I0405 09:46:25.132483 26038 net.cpp:137] Memory required for data: 561035264
|
||
|
|
I0405 09:46:25.132485 26038 layer_factory.hpp:77] Creating layer conv2
|
||
|
|
I0405 09:46:25.132495 26038 net.cpp:84] Creating Layer conv2
|
||
|
|
I0405 09:46:25.132498 26038 net.cpp:406] conv2 <- pool1
|
||
|
|
I0405 09:46:25.132501 26038 net.cpp:380] conv2 -> conv2
|
||
|
|
I0405 09:46:25.138785 26038 net.cpp:122] Setting up conv2
|
||
|
|
I0405 09:46:25.138804 26038 net.cpp:129] Top shape: 128 256 27 27 (23887872)
|
||
|
|
I0405 09:46:25.138808 26038 net.cpp:137] Memory required for data: 656586752
|
||
|
|
I0405 09:46:25.138820 26038 layer_factory.hpp:77] Creating layer relu2
|
||
|
|
I0405 09:46:25.138828 26038 net.cpp:84] Creating Layer relu2
|
||
|
|
I0405 09:46:25.138831 26038 net.cpp:406] relu2 <- conv2
|
||
|
|
I0405 09:46:25.138836 26038 net.cpp:367] relu2 -> conv2 (in-place)
|
||
|
|
I0405 09:46:25.139343 26038 net.cpp:122] Setting up relu2
|
||
|
|
I0405 09:46:25.139353 26038 net.cpp:129] Top shape: 128 256 27 27 (23887872)
|
||
|
|
I0405 09:46:25.139355 26038 net.cpp:137] Memory required for data: 752138240
|
||
|
|
I0405 09:46:25.139358 26038 layer_factory.hpp:77] Creating layer norm2
|
||
|
|
I0405 09:46:25.139364 26038 net.cpp:84] Creating Layer norm2
|
||
|
|
I0405 09:46:25.139366 26038 net.cpp:406] norm2 <- conv2
|
||
|
|
I0405 09:46:25.139371 26038 net.cpp:380] norm2 -> norm2
|
||
|
|
I0405 09:46:25.139705 26038 net.cpp:122] Setting up norm2
|
||
|
|
I0405 09:46:25.139712 26038 net.cpp:129] Top shape: 128 256 27 27 (23887872)
|
||
|
|
I0405 09:46:25.139715 26038 net.cpp:137] Memory required for data: 847689728
|
||
|
|
I0405 09:46:25.139717 26038 layer_factory.hpp:77] Creating layer pool2
|
||
|
|
I0405 09:46:25.139725 26038 net.cpp:84] Creating Layer pool2
|
||
|
|
I0405 09:46:25.139729 26038 net.cpp:406] pool2 <- norm2
|
||
|
|
I0405 09:46:25.139732 26038 net.cpp:380] pool2 -> pool2
|
||
|
|
I0405 09:46:25.139760 26038 net.cpp:122] Setting up pool2
|
||
|
|
I0405 09:46:25.139765 26038 net.cpp:129] Top shape: 128 256 13 13 (5537792)
|
||
|
|
I0405 09:46:25.139766 26038 net.cpp:137] Memory required for data: 869840896
|
||
|
|
I0405 09:46:25.139768 26038 layer_factory.hpp:77] Creating layer conv3
|
||
|
|
I0405 09:46:25.139778 26038 net.cpp:84] Creating Layer conv3
|
||
|
|
I0405 09:46:25.139780 26038 net.cpp:406] conv3 <- pool2
|
||
|
|
I0405 09:46:25.139786 26038 net.cpp:380] conv3 -> conv3
|
||
|
|
I0405 09:46:25.150022 26038 net.cpp:122] Setting up conv3
|
||
|
|
I0405 09:46:25.150043 26038 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
||
|
|
I0405 09:46:25.150044 26038 net.cpp:137] Memory required for data: 903067648
|
||
|
|
I0405 09:46:25.150056 26038 layer_factory.hpp:77] Creating layer relu3
|
||
|
|
I0405 09:46:25.150065 26038 net.cpp:84] Creating Layer relu3
|
||
|
|
I0405 09:46:25.150068 26038 net.cpp:406] relu3 <- conv3
|
||
|
|
I0405 09:46:25.150074 26038 net.cpp:367] relu3 -> conv3 (in-place)
|
||
|
|
I0405 09:46:25.150719 26038 net.cpp:122] Setting up relu3
|
||
|
|
I0405 09:46:25.150728 26038 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
||
|
|
I0405 09:46:25.150730 26038 net.cpp:137] Memory required for data: 936294400
|
||
|
|
I0405 09:46:25.150733 26038 layer_factory.hpp:77] Creating layer conv4
|
||
|
|
I0405 09:46:25.150743 26038 net.cpp:84] Creating Layer conv4
|
||
|
|
I0405 09:46:25.150746 26038 net.cpp:406] conv4 <- conv3
|
||
|
|
I0405 09:46:25.150751 26038 net.cpp:380] conv4 -> conv4
|
||
|
|
I0405 09:46:25.160512 26038 net.cpp:122] Setting up conv4
|
||
|
|
I0405 09:46:25.160529 26038 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
||
|
|
I0405 09:46:25.160532 26038 net.cpp:137] Memory required for data: 969521152
|
||
|
|
I0405 09:46:25.160540 26038 layer_factory.hpp:77] Creating layer relu4
|
||
|
|
I0405 09:46:25.160548 26038 net.cpp:84] Creating Layer relu4
|
||
|
|
I0405 09:46:25.160570 26038 net.cpp:406] relu4 <- conv4
|
||
|
|
I0405 09:46:25.160578 26038 net.cpp:367] relu4 -> conv4 (in-place)
|
||
|
|
I0405 09:46:25.160905 26038 net.cpp:122] Setting up relu4
|
||
|
|
I0405 09:46:25.160914 26038 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
||
|
|
I0405 09:46:25.160917 26038 net.cpp:137] Memory required for data: 1002747904
|
||
|
|
I0405 09:46:25.160919 26038 layer_factory.hpp:77] Creating layer conv5
|
||
|
|
I0405 09:46:25.160929 26038 net.cpp:84] Creating Layer conv5
|
||
|
|
I0405 09:46:25.160931 26038 net.cpp:406] conv5 <- conv4
|
||
|
|
I0405 09:46:25.160936 26038 net.cpp:380] conv5 -> conv5
|
||
|
|
I0405 09:46:25.168756 26038 net.cpp:122] Setting up conv5
|
||
|
|
I0405 09:46:25.168777 26038 net.cpp:129] Top shape: 128 256 13 13 (5537792)
|
||
|
|
I0405 09:46:25.168781 26038 net.cpp:137] Memory required for data: 1024899072
|
||
|
|
I0405 09:46:25.168792 26038 layer_factory.hpp:77] Creating layer relu5
|
||
|
|
I0405 09:46:25.168800 26038 net.cpp:84] Creating Layer relu5
|
||
|
|
I0405 09:46:25.168804 26038 net.cpp:406] relu5 <- conv5
|
||
|
|
I0405 09:46:25.168810 26038 net.cpp:367] relu5 -> conv5 (in-place)
|
||
|
|
I0405 09:46:25.169310 26038 net.cpp:122] Setting up relu5
|
||
|
|
I0405 09:46:25.169322 26038 net.cpp:129] Top shape: 128 256 13 13 (5537792)
|
||
|
|
I0405 09:46:25.169323 26038 net.cpp:137] Memory required for data: 1047050240
|
||
|
|
I0405 09:46:25.169327 26038 layer_factory.hpp:77] Creating layer pool5
|
||
|
|
I0405 09:46:25.169332 26038 net.cpp:84] Creating Layer pool5
|
||
|
|
I0405 09:46:25.169334 26038 net.cpp:406] pool5 <- conv5
|
||
|
|
I0405 09:46:25.169340 26038 net.cpp:380] pool5 -> pool5
|
||
|
|
I0405 09:46:25.169374 26038 net.cpp:122] Setting up pool5
|
||
|
|
I0405 09:46:25.169380 26038 net.cpp:129] Top shape: 128 256 6 6 (1179648)
|
||
|
|
I0405 09:46:25.169382 26038 net.cpp:137] Memory required for data: 1051768832
|
||
|
|
I0405 09:46:25.169384 26038 layer_factory.hpp:77] Creating layer fc6
|
||
|
|
I0405 09:46:25.169394 26038 net.cpp:84] Creating Layer fc6
|
||
|
|
I0405 09:46:25.169395 26038 net.cpp:406] fc6 <- pool5
|
||
|
|
I0405 09:46:25.169399 26038 net.cpp:380] fc6 -> fc6
|
||
|
|
I0405 09:46:25.502702 26038 net.cpp:122] Setting up fc6
|
||
|
|
I0405 09:46:25.502727 26038 net.cpp:129] Top shape: 128 4096 (524288)
|
||
|
|
I0405 09:46:25.502729 26038 net.cpp:137] Memory required for data: 1053865984
|
||
|
|
I0405 09:46:25.502737 26038 layer_factory.hpp:77] Creating layer relu6
|
||
|
|
I0405 09:46:25.502745 26038 net.cpp:84] Creating Layer relu6
|
||
|
|
I0405 09:46:25.502748 26038 net.cpp:406] relu6 <- fc6
|
||
|
|
I0405 09:46:25.502753 26038 net.cpp:367] relu6 -> fc6 (in-place)
|
||
|
|
I0405 09:46:25.503378 26038 net.cpp:122] Setting up relu6
|
||
|
|
I0405 09:46:25.503387 26038 net.cpp:129] Top shape: 128 4096 (524288)
|
||
|
|
I0405 09:46:25.503389 26038 net.cpp:137] Memory required for data: 1055963136
|
||
|
|
I0405 09:46:25.503392 26038 layer_factory.hpp:77] Creating layer drop6
|
||
|
|
I0405 09:46:25.503397 26038 net.cpp:84] Creating Layer drop6
|
||
|
|
I0405 09:46:25.503399 26038 net.cpp:406] drop6 <- fc6
|
||
|
|
I0405 09:46:25.503404 26038 net.cpp:367] drop6 -> fc6 (in-place)
|
||
|
|
I0405 09:46:25.503427 26038 net.cpp:122] Setting up drop6
|
||
|
|
I0405 09:46:25.503432 26038 net.cpp:129] Top shape: 128 4096 (524288)
|
||
|
|
I0405 09:46:25.503434 26038 net.cpp:137] Memory required for data: 1058060288
|
||
|
|
I0405 09:46:25.503437 26038 layer_factory.hpp:77] Creating layer fc7
|
||
|
|
I0405 09:46:25.503443 26038 net.cpp:84] Creating Layer fc7
|
||
|
|
I0405 09:46:25.503445 26038 net.cpp:406] fc7 <- fc6
|
||
|
|
I0405 09:46:25.503450 26038 net.cpp:380] fc7 -> fc7
|
||
|
|
I0405 09:46:25.656199 26038 net.cpp:122] Setting up fc7
|
||
|
|
I0405 09:46:25.656219 26038 net.cpp:129] Top shape: 128 4096 (524288)
|
||
|
|
I0405 09:46:25.656221 26038 net.cpp:137] Memory required for data: 1060157440
|
||
|
|
I0405 09:46:25.656230 26038 layer_factory.hpp:77] Creating layer relu7
|
||
|
|
I0405 09:46:25.656239 26038 net.cpp:84] Creating Layer relu7
|
||
|
|
I0405 09:46:25.656242 26038 net.cpp:406] relu7 <- fc7
|
||
|
|
I0405 09:46:25.656247 26038 net.cpp:367] relu7 -> fc7 (in-place)
|
||
|
|
I0405 09:46:25.656610 26038 net.cpp:122] Setting up relu7
|
||
|
|
I0405 09:46:25.656616 26038 net.cpp:129] Top shape: 128 4096 (524288)
|
||
|
|
I0405 09:46:25.656620 26038 net.cpp:137] Memory required for data: 1062254592
|
||
|
|
I0405 09:46:25.656621 26038 layer_factory.hpp:77] Creating layer drop7
|
||
|
|
I0405 09:46:25.656628 26038 net.cpp:84] Creating Layer drop7
|
||
|
|
I0405 09:46:25.656649 26038 net.cpp:406] drop7 <- fc7
|
||
|
|
I0405 09:46:25.656653 26038 net.cpp:367] drop7 -> fc7 (in-place)
|
||
|
|
I0405 09:46:25.656674 26038 net.cpp:122] Setting up drop7
|
||
|
|
I0405 09:46:25.656678 26038 net.cpp:129] Top shape: 128 4096 (524288)
|
||
|
|
I0405 09:46:25.656680 26038 net.cpp:137] Memory required for data: 1064351744
|
||
|
|
I0405 09:46:25.656683 26038 layer_factory.hpp:77] Creating layer fc8
|
||
|
|
I0405 09:46:25.656688 26038 net.cpp:84] Creating Layer fc8
|
||
|
|
I0405 09:46:25.656690 26038 net.cpp:406] fc8 <- fc7
|
||
|
|
I0405 09:46:25.656695 26038 net.cpp:380] fc8 -> fc8
|
||
|
|
I0405 09:46:25.663861 26038 net.cpp:122] Setting up fc8
|
||
|
|
I0405 09:46:25.663877 26038 net.cpp:129] Top shape: 128 196 (25088)
|
||
|
|
I0405 09:46:25.663879 26038 net.cpp:137] Memory required for data: 1064452096
|
||
|
|
I0405 09:46:25.663887 26038 layer_factory.hpp:77] Creating layer loss
|
||
|
|
I0405 09:46:25.663893 26038 net.cpp:84] Creating Layer loss
|
||
|
|
I0405 09:46:25.663897 26038 net.cpp:406] loss <- fc8
|
||
|
|
I0405 09:46:25.663902 26038 net.cpp:406] loss <- label
|
||
|
|
I0405 09:46:25.663906 26038 net.cpp:380] loss -> loss
|
||
|
|
I0405 09:46:25.663916 26038 layer_factory.hpp:77] Creating layer loss
|
||
|
|
I0405 09:46:25.665540 26038 net.cpp:122] Setting up loss
|
||
|
|
I0405 09:46:25.665550 26038 net.cpp:129] Top shape: (1)
|
||
|
|
I0405 09:46:25.665552 26038 net.cpp:132] with loss weight 1
|
||
|
|
I0405 09:46:25.665568 26038 net.cpp:137] Memory required for data: 1064452100
|
||
|
|
I0405 09:46:25.665571 26038 net.cpp:198] loss needs backward computation.
|
||
|
|
I0405 09:46:25.665577 26038 net.cpp:198] fc8 needs backward computation.
|
||
|
|
I0405 09:46:25.665580 26038 net.cpp:198] drop7 needs backward computation.
|
||
|
|
I0405 09:46:25.665582 26038 net.cpp:198] relu7 needs backward computation.
|
||
|
|
I0405 09:46:25.665585 26038 net.cpp:198] fc7 needs backward computation.
|
||
|
|
I0405 09:46:25.665586 26038 net.cpp:198] drop6 needs backward computation.
|
||
|
|
I0405 09:46:25.665588 26038 net.cpp:198] relu6 needs backward computation.
|
||
|
|
I0405 09:46:25.665591 26038 net.cpp:198] fc6 needs backward computation.
|
||
|
|
I0405 09:46:25.665594 26038 net.cpp:198] pool5 needs backward computation.
|
||
|
|
I0405 09:46:25.665596 26038 net.cpp:198] relu5 needs backward computation.
|
||
|
|
I0405 09:46:25.665598 26038 net.cpp:198] conv5 needs backward computation.
|
||
|
|
I0405 09:46:25.665601 26038 net.cpp:198] relu4 needs backward computation.
|
||
|
|
I0405 09:46:25.665603 26038 net.cpp:198] conv4 needs backward computation.
|
||
|
|
I0405 09:46:25.665606 26038 net.cpp:198] relu3 needs backward computation.
|
||
|
|
I0405 09:46:25.665608 26038 net.cpp:198] conv3 needs backward computation.
|
||
|
|
I0405 09:46:25.665611 26038 net.cpp:198] pool2 needs backward computation.
|
||
|
|
I0405 09:46:25.665613 26038 net.cpp:198] norm2 needs backward computation.
|
||
|
|
I0405 09:46:25.665616 26038 net.cpp:198] relu2 needs backward computation.
|
||
|
|
I0405 09:46:25.665617 26038 net.cpp:198] conv2 needs backward computation.
|
||
|
|
I0405 09:46:25.665619 26038 net.cpp:198] pool1 needs backward computation.
|
||
|
|
I0405 09:46:25.665622 26038 net.cpp:198] norm1 needs backward computation.
|
||
|
|
I0405 09:46:25.665624 26038 net.cpp:198] relu1 needs backward computation.
|
||
|
|
I0405 09:46:25.665627 26038 net.cpp:198] conv1 needs backward computation.
|
||
|
|
I0405 09:46:25.665629 26038 net.cpp:200] train-data does not need backward computation.
|
||
|
|
I0405 09:46:25.665632 26038 net.cpp:242] This network produces output loss
|
||
|
|
I0405 09:46:25.665643 26038 net.cpp:255] Network initialization done.
|
||
|
|
I0405 09:46:25.666174 26038 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
|
||
|
|
I0405 09:46:25.666203 26038 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
|
||
|
|
I0405 09:46:25.666332 26038 net.cpp:51] Initializing net from parameters:
|
||
|
|
state {
|
||
|
|
phase: TEST
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "val-data"
|
||
|
|
type: "Data"
|
||
|
|
top: "data"
|
||
|
|
top: "label"
|
||
|
|
include {
|
||
|
|
phase: TEST
|
||
|
|
}
|
||
|
|
transform_param {
|
||
|
|
crop_size: 227
|
||
|
|
mean_file: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto"
|
||
|
|
}
|
||
|
|
data_param {
|
||
|
|
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/val_db"
|
||
|
|
batch_size: 32
|
||
|
|
backend: LMDB
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "conv1"
|
||
|
|
type: "Convolution"
|
||
|
|
bottom: "data"
|
||
|
|
top: "conv1"
|
||
|
|
param {
|
||
|
|
lr_mult: 1
|
||
|
|
decay_mult: 1
|
||
|
|
}
|
||
|
|
param {
|
||
|
|
lr_mult: 2
|
||
|
|
decay_mult: 0
|
||
|
|
}
|
||
|
|
convolution_param {
|
||
|
|
num_output: 96
|
||
|
|
kernel_size: 11
|
||
|
|
stride: 4
|
||
|
|
weight_filler {
|
||
|
|
type: "gaussian"
|
||
|
|
std: 0.01
|
||
|
|
}
|
||
|
|
bias_filler {
|
||
|
|
type: "constant"
|
||
|
|
value: 0
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "relu1"
|
||
|
|
type: "ReLU"
|
||
|
|
bottom: "conv1"
|
||
|
|
top: "conv1"
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "norm1"
|
||
|
|
type: "LRN"
|
||
|
|
bottom: "conv1"
|
||
|
|
top: "norm1"
|
||
|
|
lrn_param {
|
||
|
|
local_size: 5
|
||
|
|
alpha: 0.0001
|
||
|
|
beta: 0.75
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "pool1"
|
||
|
|
type: "Pooling"
|
||
|
|
bottom: "norm1"
|
||
|
|
top: "pool1"
|
||
|
|
pooling_param {
|
||
|
|
pool: MAX
|
||
|
|
kernel_size: 3
|
||
|
|
stride: 2
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "conv2"
|
||
|
|
type: "Convolution"
|
||
|
|
bottom: "pool1"
|
||
|
|
top: "conv2"
|
||
|
|
param {
|
||
|
|
lr_mult: 1
|
||
|
|
decay_mult: 1
|
||
|
|
}
|
||
|
|
param {
|
||
|
|
lr_mult: 2
|
||
|
|
decay_mult: 0
|
||
|
|
}
|
||
|
|
convolution_param {
|
||
|
|
num_output: 256
|
||
|
|
pad: 2
|
||
|
|
kernel_size: 5
|
||
|
|
group: 2
|
||
|
|
weight_filler {
|
||
|
|
type: "gaussian"
|
||
|
|
std: 0.01
|
||
|
|
}
|
||
|
|
bias_filler {
|
||
|
|
type: "constant"
|
||
|
|
value: 0.1
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "relu2"
|
||
|
|
type: "ReLU"
|
||
|
|
bottom: "conv2"
|
||
|
|
top: "conv2"
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "norm2"
|
||
|
|
type: "LRN"
|
||
|
|
bottom: "conv2"
|
||
|
|
top: "norm2"
|
||
|
|
lrn_param {
|
||
|
|
local_size: 5
|
||
|
|
alpha: 0.0001
|
||
|
|
beta: 0.75
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "pool2"
|
||
|
|
type: "Pooling"
|
||
|
|
bottom: "norm2"
|
||
|
|
top: "pool2"
|
||
|
|
pooling_param {
|
||
|
|
pool: MAX
|
||
|
|
kernel_size: 3
|
||
|
|
stride: 2
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "conv3"
|
||
|
|
type: "Convolution"
|
||
|
|
bottom: "pool2"
|
||
|
|
top: "conv3"
|
||
|
|
param {
|
||
|
|
lr_mult: 1
|
||
|
|
decay_mult: 1
|
||
|
|
}
|
||
|
|
param {
|
||
|
|
lr_mult: 2
|
||
|
|
decay_mult: 0
|
||
|
|
}
|
||
|
|
convolution_param {
|
||
|
|
num_output: 384
|
||
|
|
pad: 1
|
||
|
|
kernel_size: 3
|
||
|
|
weight_filler {
|
||
|
|
type: "gaussian"
|
||
|
|
std: 0.01
|
||
|
|
}
|
||
|
|
bias_filler {
|
||
|
|
type: "constant"
|
||
|
|
value: 0
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "relu3"
|
||
|
|
type: "ReLU"
|
||
|
|
bottom: "conv3"
|
||
|
|
top: "conv3"
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "conv4"
|
||
|
|
type: "Convolution"
|
||
|
|
bottom: "conv3"
|
||
|
|
top: "conv4"
|
||
|
|
param {
|
||
|
|
lr_mult: 1
|
||
|
|
decay_mult: 1
|
||
|
|
}
|
||
|
|
param {
|
||
|
|
lr_mult: 2
|
||
|
|
decay_mult: 0
|
||
|
|
}
|
||
|
|
convolution_param {
|
||
|
|
num_output: 384
|
||
|
|
pad: 1
|
||
|
|
kernel_size: 3
|
||
|
|
group: 2
|
||
|
|
weight_filler {
|
||
|
|
type: "gaussian"
|
||
|
|
std: 0.01
|
||
|
|
}
|
||
|
|
bias_filler {
|
||
|
|
type: "constant"
|
||
|
|
value: 0.1
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "relu4"
|
||
|
|
type: "ReLU"
|
||
|
|
bottom: "conv4"
|
||
|
|
top: "conv4"
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "conv5"
|
||
|
|
type: "Convolution"
|
||
|
|
bottom: "conv4"
|
||
|
|
top: "conv5"
|
||
|
|
param {
|
||
|
|
lr_mult: 1
|
||
|
|
decay_mult: 1
|
||
|
|
}
|
||
|
|
param {
|
||
|
|
lr_mult: 2
|
||
|
|
decay_mult: 0
|
||
|
|
}
|
||
|
|
convolution_param {
|
||
|
|
num_output: 256
|
||
|
|
pad: 1
|
||
|
|
kernel_size: 3
|
||
|
|
group: 2
|
||
|
|
weight_filler {
|
||
|
|
type: "gaussian"
|
||
|
|
std: 0.01
|
||
|
|
}
|
||
|
|
bias_filler {
|
||
|
|
type: "constant"
|
||
|
|
value: 0.1
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "relu5"
|
||
|
|
type: "ReLU"
|
||
|
|
bottom: "conv5"
|
||
|
|
top: "conv5"
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "pool5"
|
||
|
|
type: "Pooling"
|
||
|
|
bottom: "conv5"
|
||
|
|
top: "pool5"
|
||
|
|
pooling_param {
|
||
|
|
pool: MAX
|
||
|
|
kernel_size: 3
|
||
|
|
stride: 2
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "fc6"
|
||
|
|
type: "InnerProduct"
|
||
|
|
bottom: "pool5"
|
||
|
|
top: "fc6"
|
||
|
|
param {
|
||
|
|
lr_mult: 1
|
||
|
|
decay_mult: 1
|
||
|
|
}
|
||
|
|
param {
|
||
|
|
lr_mult: 2
|
||
|
|
decay_mult: 0
|
||
|
|
}
|
||
|
|
inner_product_param {
|
||
|
|
num_output: 4096
|
||
|
|
weight_filler {
|
||
|
|
type: "gaussian"
|
||
|
|
std: 0.005
|
||
|
|
}
|
||
|
|
bias_filler {
|
||
|
|
type: "constant"
|
||
|
|
value: 0.1
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "relu6"
|
||
|
|
type: "ReLU"
|
||
|
|
bottom: "fc6"
|
||
|
|
top: "fc6"
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "drop6"
|
||
|
|
type: "Dropout"
|
||
|
|
bottom: "fc6"
|
||
|
|
top: "fc6"
|
||
|
|
dropout_param {
|
||
|
|
dropout_ratio: 0.5
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "fc7"
|
||
|
|
type: "InnerProduct"
|
||
|
|
bottom: "fc6"
|
||
|
|
top: "fc7"
|
||
|
|
param {
|
||
|
|
lr_mult: 1
|
||
|
|
decay_mult: 1
|
||
|
|
}
|
||
|
|
param {
|
||
|
|
lr_mult: 2
|
||
|
|
decay_mult: 0
|
||
|
|
}
|
||
|
|
inner_product_param {
|
||
|
|
num_output: 4096
|
||
|
|
weight_filler {
|
||
|
|
type: "gaussian"
|
||
|
|
std: 0.005
|
||
|
|
}
|
||
|
|
bias_filler {
|
||
|
|
type: "constant"
|
||
|
|
value: 0.1
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "relu7"
|
||
|
|
type: "ReLU"
|
||
|
|
bottom: "fc7"
|
||
|
|
top: "fc7"
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "drop7"
|
||
|
|
type: "Dropout"
|
||
|
|
bottom: "fc7"
|
||
|
|
top: "fc7"
|
||
|
|
dropout_param {
|
||
|
|
dropout_ratio: 0.5
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "fc8"
|
||
|
|
type: "InnerProduct"
|
||
|
|
bottom: "fc7"
|
||
|
|
top: "fc8"
|
||
|
|
param {
|
||
|
|
lr_mult: 1
|
||
|
|
decay_mult: 1
|
||
|
|
}
|
||
|
|
param {
|
||
|
|
lr_mult: 2
|
||
|
|
decay_mult: 0
|
||
|
|
}
|
||
|
|
inner_product_param {
|
||
|
|
num_output: 196
|
||
|
|
weight_filler {
|
||
|
|
type: "gaussian"
|
||
|
|
std: 0.01
|
||
|
|
}
|
||
|
|
bias_filler {
|
||
|
|
type: "constant"
|
||
|
|
value: 0
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "accuracy"
|
||
|
|
type: "Accuracy"
|
||
|
|
bottom: "fc8"
|
||
|
|
bottom: "label"
|
||
|
|
top: "accuracy"
|
||
|
|
include {
|
||
|
|
phase: TEST
|
||
|
|
}
|
||
|
|
}
|
||
|
|
layer {
|
||
|
|
name: "loss"
|
||
|
|
type: "SoftmaxWithLoss"
|
||
|
|
bottom: "fc8"
|
||
|
|
bottom: "label"
|
||
|
|
top: "loss"
|
||
|
|
}
|
||
|
|
I0405 09:46:25.666429 26038 layer_factory.hpp:77] Creating layer val-data
|
||
|
|
I0405 09:46:25.668426 26038 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/val_db
|
||
|
|
I0405 09:46:25.668658 26038 net.cpp:84] Creating Layer val-data
|
||
|
|
I0405 09:46:25.668666 26038 net.cpp:380] val-data -> data
|
||
|
|
I0405 09:46:25.668673 26038 net.cpp:380] val-data -> label
|
||
|
|
I0405 09:46:25.668679 26038 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
|
||
|
|
I0405 09:46:25.672351 26038 data_layer.cpp:45] output data size: 32,3,227,227
|
||
|
|
I0405 09:46:25.739672 26038 net.cpp:122] Setting up val-data
|
||
|
|
I0405 09:46:25.739691 26038 net.cpp:129] Top shape: 32 3 227 227 (4946784)
|
||
|
|
I0405 09:46:25.739696 26038 net.cpp:129] Top shape: 32 (32)
|
||
|
|
I0405 09:46:25.739697 26038 net.cpp:137] Memory required for data: 19787264
|
||
|
|
I0405 09:46:25.739702 26038 layer_factory.hpp:77] Creating layer label_val-data_1_split
|
||
|
|
I0405 09:46:25.739713 26038 net.cpp:84] Creating Layer label_val-data_1_split
|
||
|
|
I0405 09:46:25.739717 26038 net.cpp:406] label_val-data_1_split <- label
|
||
|
|
I0405 09:46:25.739722 26038 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
|
||
|
|
I0405 09:46:25.739729 26038 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
|
||
|
|
I0405 09:46:25.739781 26038 net.cpp:122] Setting up label_val-data_1_split
|
||
|
|
I0405 09:46:25.739785 26038 net.cpp:129] Top shape: 32 (32)
|
||
|
|
I0405 09:46:25.739789 26038 net.cpp:129] Top shape: 32 (32)
|
||
|
|
I0405 09:46:25.739790 26038 net.cpp:137] Memory required for data: 19787520
|
||
|
|
I0405 09:46:25.739792 26038 layer_factory.hpp:77] Creating layer conv1
|
||
|
|
I0405 09:46:25.739804 26038 net.cpp:84] Creating Layer conv1
|
||
|
|
I0405 09:46:25.739806 26038 net.cpp:406] conv1 <- data
|
||
|
|
I0405 09:46:25.739810 26038 net.cpp:380] conv1 -> conv1
|
||
|
|
I0405 09:46:25.742488 26038 net.cpp:122] Setting up conv1
|
||
|
|
I0405 09:46:25.742501 26038 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
||
|
|
I0405 09:46:25.742503 26038 net.cpp:137] Memory required for data: 56958720
|
||
|
|
I0405 09:46:25.742512 26038 layer_factory.hpp:77] Creating layer relu1
|
||
|
|
I0405 09:46:25.742517 26038 net.cpp:84] Creating Layer relu1
|
||
|
|
I0405 09:46:25.742520 26038 net.cpp:406] relu1 <- conv1
|
||
|
|
I0405 09:46:25.742524 26038 net.cpp:367] relu1 -> conv1 (in-place)
|
||
|
|
I0405 09:46:25.742787 26038 net.cpp:122] Setting up relu1
|
||
|
|
I0405 09:46:25.742794 26038 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
||
|
|
I0405 09:46:25.742797 26038 net.cpp:137] Memory required for data: 94129920
|
||
|
|
I0405 09:46:25.742799 26038 layer_factory.hpp:77] Creating layer norm1
|
||
|
|
I0405 09:46:25.742806 26038 net.cpp:84] Creating Layer norm1
|
||
|
|
I0405 09:46:25.742808 26038 net.cpp:406] norm1 <- conv1
|
||
|
|
I0405 09:46:25.742812 26038 net.cpp:380] norm1 -> norm1
|
||
|
|
I0405 09:46:25.743247 26038 net.cpp:122] Setting up norm1
|
||
|
|
I0405 09:46:25.743257 26038 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
||
|
|
I0405 09:46:25.743258 26038 net.cpp:137] Memory required for data: 131301120
|
||
|
|
I0405 09:46:25.743261 26038 layer_factory.hpp:77] Creating layer pool1
|
||
|
|
I0405 09:46:25.743266 26038 net.cpp:84] Creating Layer pool1
|
||
|
|
I0405 09:46:25.743269 26038 net.cpp:406] pool1 <- norm1
|
||
|
|
I0405 09:46:25.743273 26038 net.cpp:380] pool1 -> pool1
|
||
|
|
I0405 09:46:25.743299 26038 net.cpp:122] Setting up pool1
|
||
|
|
I0405 09:46:25.743302 26038 net.cpp:129] Top shape: 32 96 27 27 (2239488)
|
||
|
|
I0405 09:46:25.743304 26038 net.cpp:137] Memory required for data: 140259072
|
||
|
|
I0405 09:46:25.743306 26038 layer_factory.hpp:77] Creating layer conv2
|
||
|
|
I0405 09:46:25.743314 26038 net.cpp:84] Creating Layer conv2
|
||
|
|
I0405 09:46:25.743315 26038 net.cpp:406] conv2 <- pool1
|
||
|
|
I0405 09:46:25.743338 26038 net.cpp:380] conv2 -> conv2
|
||
|
|
I0405 09:46:25.749562 26038 net.cpp:122] Setting up conv2
|
||
|
|
I0405 09:46:25.749580 26038 net.cpp:129] Top shape: 32 256 27 27 (5971968)
|
||
|
|
I0405 09:46:25.749583 26038 net.cpp:137] Memory required for data: 164146944
|
||
|
|
I0405 09:46:25.749593 26038 layer_factory.hpp:77] Creating layer relu2
|
||
|
|
I0405 09:46:25.749600 26038 net.cpp:84] Creating Layer relu2
|
||
|
|
I0405 09:46:25.749603 26038 net.cpp:406] relu2 <- conv2
|
||
|
|
I0405 09:46:25.749608 26038 net.cpp:367] relu2 -> conv2 (in-place)
|
||
|
|
I0405 09:46:25.750118 26038 net.cpp:122] Setting up relu2
|
||
|
|
I0405 09:46:25.750128 26038 net.cpp:129] Top shape: 32 256 27 27 (5971968)
|
||
|
|
I0405 09:46:25.750129 26038 net.cpp:137] Memory required for data: 188034816
|
||
|
|
I0405 09:46:25.750133 26038 layer_factory.hpp:77] Creating layer norm2
|
||
|
|
I0405 09:46:25.750141 26038 net.cpp:84] Creating Layer norm2
|
||
|
|
I0405 09:46:25.750144 26038 net.cpp:406] norm2 <- conv2
|
||
|
|
I0405 09:46:25.750149 26038 net.cpp:380] norm2 -> norm2
|
||
|
|
I0405 09:46:25.750679 26038 net.cpp:122] Setting up norm2
|
||
|
|
I0405 09:46:25.750687 26038 net.cpp:129] Top shape: 32 256 27 27 (5971968)
|
||
|
|
I0405 09:46:25.750690 26038 net.cpp:137] Memory required for data: 211922688
|
||
|
|
I0405 09:46:25.750694 26038 layer_factory.hpp:77] Creating layer pool2
|
||
|
|
I0405 09:46:25.750699 26038 net.cpp:84] Creating Layer pool2
|
||
|
|
I0405 09:46:25.750701 26038 net.cpp:406] pool2 <- norm2
|
||
|
|
I0405 09:46:25.750706 26038 net.cpp:380] pool2 -> pool2
|
||
|
|
I0405 09:46:25.750733 26038 net.cpp:122] Setting up pool2
|
||
|
|
I0405 09:46:25.750738 26038 net.cpp:129] Top shape: 32 256 13 13 (1384448)
|
||
|
|
I0405 09:46:25.750741 26038 net.cpp:137] Memory required for data: 217460480
|
||
|
|
I0405 09:46:25.750742 26038 layer_factory.hpp:77] Creating layer conv3
|
||
|
|
I0405 09:46:25.750751 26038 net.cpp:84] Creating Layer conv3
|
||
|
|
I0405 09:46:25.750753 26038 net.cpp:406] conv3 <- pool2
|
||
|
|
I0405 09:46:25.750757 26038 net.cpp:380] conv3 -> conv3
|
||
|
|
I0405 09:46:25.761060 26038 net.cpp:122] Setting up conv3
|
||
|
|
I0405 09:46:25.761078 26038 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
||
|
|
I0405 09:46:25.761081 26038 net.cpp:137] Memory required for data: 225767168
|
||
|
|
I0405 09:46:25.761094 26038 layer_factory.hpp:77] Creating layer relu3
|
||
|
|
I0405 09:46:25.761101 26038 net.cpp:84] Creating Layer relu3
|
||
|
|
I0405 09:46:25.761106 26038 net.cpp:406] relu3 <- conv3
|
||
|
|
I0405 09:46:25.761111 26038 net.cpp:367] relu3 -> conv3 (in-place)
|
||
|
|
I0405 09:46:25.761615 26038 net.cpp:122] Setting up relu3
|
||
|
|
I0405 09:46:25.761623 26038 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
||
|
|
I0405 09:46:25.761626 26038 net.cpp:137] Memory required for data: 234073856
|
||
|
|
I0405 09:46:25.761628 26038 layer_factory.hpp:77] Creating layer conv4
|
||
|
|
I0405 09:46:25.761638 26038 net.cpp:84] Creating Layer conv4
|
||
|
|
I0405 09:46:25.761641 26038 net.cpp:406] conv4 <- conv3
|
||
|
|
I0405 09:46:25.761646 26038 net.cpp:380] conv4 -> conv4
|
||
|
|
I0405 09:46:25.771039 26038 net.cpp:122] Setting up conv4
|
||
|
|
I0405 09:46:25.771056 26038 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
||
|
|
I0405 09:46:25.771059 26038 net.cpp:137] Memory required for data: 242380544
|
||
|
|
I0405 09:46:25.771067 26038 layer_factory.hpp:77] Creating layer relu4
|
||
|
|
I0405 09:46:25.771076 26038 net.cpp:84] Creating Layer relu4
|
||
|
|
I0405 09:46:25.771080 26038 net.cpp:406] relu4 <- conv4
|
||
|
|
I0405 09:46:25.771085 26038 net.cpp:367] relu4 -> conv4 (in-place)
|
||
|
|
I0405 09:46:25.771415 26038 net.cpp:122] Setting up relu4
|
||
|
|
I0405 09:46:25.771422 26038 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
||
|
|
I0405 09:46:25.771425 26038 net.cpp:137] Memory required for data: 250687232
|
||
|
|
I0405 09:46:25.771427 26038 layer_factory.hpp:77] Creating layer conv5
|
||
|
|
I0405 09:46:25.771440 26038 net.cpp:84] Creating Layer conv5
|
||
|
|
I0405 09:46:25.771441 26038 net.cpp:406] conv5 <- conv4
|
||
|
|
I0405 09:46:25.771446 26038 net.cpp:380] conv5 -> conv5
|
||
|
|
I0405 09:46:25.779601 26038 net.cpp:122] Setting up conv5
|
||
|
|
I0405 09:46:25.779618 26038 net.cpp:129] Top shape: 32 256 13 13 (1384448)
|
||
|
|
I0405 09:46:25.779621 26038 net.cpp:137] Memory required for data: 256225024
|
||
|
|
I0405 09:46:25.779633 26038 layer_factory.hpp:77] Creating layer relu5
|
||
|
|
I0405 09:46:25.779640 26038 net.cpp:84] Creating Layer relu5
|
||
|
|
I0405 09:46:25.779644 26038 net.cpp:406] relu5 <- conv5
|
||
|
|
I0405 09:46:25.779666 26038 net.cpp:367] relu5 -> conv5 (in-place)
|
||
|
|
I0405 09:46:25.780179 26038 net.cpp:122] Setting up relu5
|
||
|
|
I0405 09:46:25.780187 26038 net.cpp:129] Top shape: 32 256 13 13 (1384448)
|
||
|
|
I0405 09:46:25.780190 26038 net.cpp:137] Memory required for data: 261762816
|
||
|
|
I0405 09:46:25.780192 26038 layer_factory.hpp:77] Creating layer pool5
|
||
|
|
I0405 09:46:25.780202 26038 net.cpp:84] Creating Layer pool5
|
||
|
|
I0405 09:46:25.780205 26038 net.cpp:406] pool5 <- conv5
|
||
|
|
I0405 09:46:25.780210 26038 net.cpp:380] pool5 -> pool5
|
||
|
|
I0405 09:46:25.780247 26038 net.cpp:122] Setting up pool5
|
||
|
|
I0405 09:46:25.780256 26038 net.cpp:129] Top shape: 32 256 6 6 (294912)
|
||
|
|
I0405 09:46:25.780258 26038 net.cpp:137] Memory required for data: 262942464
|
||
|
|
I0405 09:46:25.780261 26038 layer_factory.hpp:77] Creating layer fc6
|
||
|
|
I0405 09:46:25.780267 26038 net.cpp:84] Creating Layer fc6
|
||
|
|
I0405 09:46:25.780268 26038 net.cpp:406] fc6 <- pool5
|
||
|
|
I0405 09:46:25.780274 26038 net.cpp:380] fc6 -> fc6
|
||
|
|
I0405 09:46:26.136466 26038 net.cpp:122] Setting up fc6
|
||
|
|
I0405 09:46:26.136485 26038 net.cpp:129] Top shape: 32 4096 (131072)
|
||
|
|
I0405 09:46:26.136488 26038 net.cpp:137] Memory required for data: 263466752
|
||
|
|
I0405 09:46:26.136497 26038 layer_factory.hpp:77] Creating layer relu6
|
||
|
|
I0405 09:46:26.136505 26038 net.cpp:84] Creating Layer relu6
|
||
|
|
I0405 09:46:26.136509 26038 net.cpp:406] relu6 <- fc6
|
||
|
|
I0405 09:46:26.136513 26038 net.cpp:367] relu6 -> fc6 (in-place)
|
||
|
|
I0405 09:46:26.137194 26038 net.cpp:122] Setting up relu6
|
||
|
|
I0405 09:46:26.137203 26038 net.cpp:129] Top shape: 32 4096 (131072)
|
||
|
|
I0405 09:46:26.137205 26038 net.cpp:137] Memory required for data: 263991040
|
||
|
|
I0405 09:46:26.137208 26038 layer_factory.hpp:77] Creating layer drop6
|
||
|
|
I0405 09:46:26.137213 26038 net.cpp:84] Creating Layer drop6
|
||
|
|
I0405 09:46:26.137219 26038 net.cpp:406] drop6 <- fc6
|
||
|
|
I0405 09:46:26.137223 26038 net.cpp:367] drop6 -> fc6 (in-place)
|
||
|
|
I0405 09:46:26.137243 26038 net.cpp:122] Setting up drop6
|
||
|
|
I0405 09:46:26.137248 26038 net.cpp:129] Top shape: 32 4096 (131072)
|
||
|
|
I0405 09:46:26.137249 26038 net.cpp:137] Memory required for data: 264515328
|
||
|
|
I0405 09:46:26.137251 26038 layer_factory.hpp:77] Creating layer fc7
|
||
|
|
I0405 09:46:26.137259 26038 net.cpp:84] Creating Layer fc7
|
||
|
|
I0405 09:46:26.137260 26038 net.cpp:406] fc7 <- fc6
|
||
|
|
I0405 09:46:26.137265 26038 net.cpp:380] fc7 -> fc7
|
||
|
|
I0405 09:46:26.295933 26038 net.cpp:122] Setting up fc7
|
||
|
|
I0405 09:46:26.295956 26038 net.cpp:129] Top shape: 32 4096 (131072)
|
||
|
|
I0405 09:46:26.295960 26038 net.cpp:137] Memory required for data: 265039616
|
||
|
|
I0405 09:46:26.295971 26038 layer_factory.hpp:77] Creating layer relu7
|
||
|
|
I0405 09:46:26.295982 26038 net.cpp:84] Creating Layer relu7
|
||
|
|
I0405 09:46:26.295987 26038 net.cpp:406] relu7 <- fc7
|
||
|
|
I0405 09:46:26.295994 26038 net.cpp:367] relu7 -> fc7 (in-place)
|
||
|
|
I0405 09:46:26.296517 26038 net.cpp:122] Setting up relu7
|
||
|
|
I0405 09:46:26.296527 26038 net.cpp:129] Top shape: 32 4096 (131072)
|
||
|
|
I0405 09:46:26.296530 26038 net.cpp:137] Memory required for data: 265563904
|
||
|
|
I0405 09:46:26.296535 26038 layer_factory.hpp:77] Creating layer drop7
|
||
|
|
I0405 09:46:26.296543 26038 net.cpp:84] Creating Layer drop7
|
||
|
|
I0405 09:46:26.296547 26038 net.cpp:406] drop7 <- fc7
|
||
|
|
I0405 09:46:26.296552 26038 net.cpp:367] drop7 -> fc7 (in-place)
|
||
|
|
I0405 09:46:26.296582 26038 net.cpp:122] Setting up drop7
|
||
|
|
I0405 09:46:26.296588 26038 net.cpp:129] Top shape: 32 4096 (131072)
|
||
|
|
I0405 09:46:26.296591 26038 net.cpp:137] Memory required for data: 266088192
|
||
|
|
I0405 09:46:26.296594 26038 layer_factory.hpp:77] Creating layer fc8
|
||
|
|
I0405 09:46:26.296603 26038 net.cpp:84] Creating Layer fc8
|
||
|
|
I0405 09:46:26.296607 26038 net.cpp:406] fc8 <- fc7
|
||
|
|
I0405 09:46:26.296615 26038 net.cpp:380] fc8 -> fc8
|
||
|
|
I0405 09:46:26.306855 26038 net.cpp:122] Setting up fc8
|
||
|
|
I0405 09:46:26.306869 26038 net.cpp:129] Top shape: 32 196 (6272)
|
||
|
|
I0405 09:46:26.306871 26038 net.cpp:137] Memory required for data: 266113280
|
||
|
|
I0405 09:46:26.306877 26038 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
|
||
|
|
I0405 09:46:26.306883 26038 net.cpp:84] Creating Layer fc8_fc8_0_split
|
||
|
|
I0405 09:46:26.306885 26038 net.cpp:406] fc8_fc8_0_split <- fc8
|
||
|
|
I0405 09:46:26.306908 26038 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
|
||
|
|
I0405 09:46:26.306915 26038 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
|
||
|
|
I0405 09:46:26.306944 26038 net.cpp:122] Setting up fc8_fc8_0_split
|
||
|
|
I0405 09:46:26.306949 26038 net.cpp:129] Top shape: 32 196 (6272)
|
||
|
|
I0405 09:46:26.306952 26038 net.cpp:129] Top shape: 32 196 (6272)
|
||
|
|
I0405 09:46:26.306953 26038 net.cpp:137] Memory required for data: 266163456
|
||
|
|
I0405 09:46:26.306957 26038 layer_factory.hpp:77] Creating layer accuracy
|
||
|
|
I0405 09:46:26.306962 26038 net.cpp:84] Creating Layer accuracy
|
||
|
|
I0405 09:46:26.306963 26038 net.cpp:406] accuracy <- fc8_fc8_0_split_0
|
||
|
|
I0405 09:46:26.306967 26038 net.cpp:406] accuracy <- label_val-data_1_split_0
|
||
|
|
I0405 09:46:26.306970 26038 net.cpp:380] accuracy -> accuracy
|
||
|
|
I0405 09:46:26.306977 26038 net.cpp:122] Setting up accuracy
|
||
|
|
I0405 09:46:26.306979 26038 net.cpp:129] Top shape: (1)
|
||
|
|
I0405 09:46:26.306982 26038 net.cpp:137] Memory required for data: 266163460
|
||
|
|
I0405 09:46:26.306983 26038 layer_factory.hpp:77] Creating layer loss
|
||
|
|
I0405 09:46:26.306988 26038 net.cpp:84] Creating Layer loss
|
||
|
|
I0405 09:46:26.306991 26038 net.cpp:406] loss <- fc8_fc8_0_split_1
|
||
|
|
I0405 09:46:26.306994 26038 net.cpp:406] loss <- label_val-data_1_split_1
|
||
|
|
I0405 09:46:26.306998 26038 net.cpp:380] loss -> loss
|
||
|
|
I0405 09:46:26.307003 26038 layer_factory.hpp:77] Creating layer loss
|
||
|
|
I0405 09:46:26.307636 26038 net.cpp:122] Setting up loss
|
||
|
|
I0405 09:46:26.307644 26038 net.cpp:129] Top shape: (1)
|
||
|
|
I0405 09:46:26.307646 26038 net.cpp:132] with loss weight 1
|
||
|
|
I0405 09:46:26.307655 26038 net.cpp:137] Memory required for data: 266163464
|
||
|
|
I0405 09:46:26.307658 26038 net.cpp:198] loss needs backward computation.
|
||
|
|
I0405 09:46:26.307662 26038 net.cpp:200] accuracy does not need backward computation.
|
||
|
|
I0405 09:46:26.307665 26038 net.cpp:198] fc8_fc8_0_split needs backward computation.
|
||
|
|
I0405 09:46:26.307667 26038 net.cpp:198] fc8 needs backward computation.
|
||
|
|
I0405 09:46:26.307669 26038 net.cpp:198] drop7 needs backward computation.
|
||
|
|
I0405 09:46:26.307672 26038 net.cpp:198] relu7 needs backward computation.
|
||
|
|
I0405 09:46:26.307674 26038 net.cpp:198] fc7 needs backward computation.
|
||
|
|
I0405 09:46:26.307677 26038 net.cpp:198] drop6 needs backward computation.
|
||
|
|
I0405 09:46:26.307678 26038 net.cpp:198] relu6 needs backward computation.
|
||
|
|
I0405 09:46:26.307680 26038 net.cpp:198] fc6 needs backward computation.
|
||
|
|
I0405 09:46:26.307683 26038 net.cpp:198] pool5 needs backward computation.
|
||
|
|
I0405 09:46:26.307687 26038 net.cpp:198] relu5 needs backward computation.
|
||
|
|
I0405 09:46:26.307688 26038 net.cpp:198] conv5 needs backward computation.
|
||
|
|
I0405 09:46:26.307691 26038 net.cpp:198] relu4 needs backward computation.
|
||
|
|
I0405 09:46:26.307693 26038 net.cpp:198] conv4 needs backward computation.
|
||
|
|
I0405 09:46:26.307695 26038 net.cpp:198] relu3 needs backward computation.
|
||
|
|
I0405 09:46:26.307698 26038 net.cpp:198] conv3 needs backward computation.
|
||
|
|
I0405 09:46:26.307700 26038 net.cpp:198] pool2 needs backward computation.
|
||
|
|
I0405 09:46:26.307704 26038 net.cpp:198] norm2 needs backward computation.
|
||
|
|
I0405 09:46:26.307708 26038 net.cpp:198] relu2 needs backward computation.
|
||
|
|
I0405 09:46:26.307709 26038 net.cpp:198] conv2 needs backward computation.
|
||
|
|
I0405 09:46:26.307711 26038 net.cpp:198] pool1 needs backward computation.
|
||
|
|
I0405 09:46:26.307714 26038 net.cpp:198] norm1 needs backward computation.
|
||
|
|
I0405 09:46:26.307716 26038 net.cpp:198] relu1 needs backward computation.
|
||
|
|
I0405 09:46:26.307718 26038 net.cpp:198] conv1 needs backward computation.
|
||
|
|
I0405 09:46:26.307721 26038 net.cpp:200] label_val-data_1_split does not need backward computation.
|
||
|
|
I0405 09:46:26.307724 26038 net.cpp:200] val-data does not need backward computation.
|
||
|
|
I0405 09:46:26.307726 26038 net.cpp:242] This network produces output accuracy
|
||
|
|
I0405 09:46:26.307729 26038 net.cpp:242] This network produces output loss
|
||
|
|
I0405 09:46:26.307744 26038 net.cpp:255] Network initialization done.
|
||
|
|
I0405 09:46:26.307813 26038 solver.cpp:56] Solver scaffolding done.
|
||
|
|
I0405 09:46:26.308213 26038 caffe.cpp:248] Starting Optimization
|
||
|
|
I0405 09:46:26.308220 26038 solver.cpp:272] Solving
|
||
|
|
I0405 09:46:26.308233 26038 solver.cpp:273] Learning Rate Policy: fixed
|
||
|
|
I0405 09:46:26.310075 26038 solver.cpp:330] Iteration 0, Testing net (#0)
|
||
|
|
I0405 09:46:26.310083 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
|
I0405 09:46:26.415949 26038 blocking_queue.cpp:49] Waiting for data
|
||
|
|
I0405 09:46:30.563352 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 09:46:30.611341 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
|
||
|
|
I0405 09:46:30.611392 26038 solver.cpp:397] Test net output #1: loss = 5.27961 (* 1 = 5.27961 loss)
|
||
|
|
I0405 09:46:30.760635 26038 solver.cpp:218] Iteration 0 (2.86652e+37 iter/s, 4.45231s/12 iters), loss = 5.28418
|
||
|
|
I0405 09:46:30.762203 26038 solver.cpp:237] Train net output #0: loss = 5.28418 (* 1 = 5.28418 loss)
|
||
|
|
I0405 09:46:30.762217 26038 sgd_solver.cpp:105] Iteration 0, lr = 1e-05
|
||
|
|
I0405 09:46:34.955109 26038 solver.cpp:218] Iteration 12 (2.86201 iter/s, 4.19286s/12 iters), loss = 5.27147
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I0405 09:46:34.955150 26038 solver.cpp:237] Train net output #0: loss = 5.27147 (* 1 = 5.27147 loss)
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I0405 09:46:34.955155 26038 sgd_solver.cpp:105] Iteration 12, lr = 1e-05
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I0405 09:46:40.482736 26038 solver.cpp:218] Iteration 24 (2.17095 iter/s, 5.52753s/12 iters), loss = 5.28224
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I0405 09:46:40.482791 26038 solver.cpp:237] Train net output #0: loss = 5.28224 (* 1 = 5.28224 loss)
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I0405 09:46:40.482798 26038 sgd_solver.cpp:105] Iteration 24, lr = 1e-05
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I0405 09:46:45.920212 26038 solver.cpp:218] Iteration 36 (2.20695 iter/s, 5.43737s/12 iters), loss = 5.30523
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I0405 09:46:45.920260 26038 solver.cpp:237] Train net output #0: loss = 5.30523 (* 1 = 5.30523 loss)
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I0405 09:46:45.920267 26038 sgd_solver.cpp:105] Iteration 36, lr = 1e-05
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I0405 09:46:51.118223 26038 solver.cpp:218] Iteration 48 (2.30862 iter/s, 5.19792s/12 iters), loss = 5.28805
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I0405 09:46:51.118260 26038 solver.cpp:237] Train net output #0: loss = 5.28805 (* 1 = 5.28805 loss)
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I0405 09:46:51.118265 26038 sgd_solver.cpp:105] Iteration 48, lr = 1e-05
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I0405 09:46:56.381031 26038 solver.cpp:218] Iteration 60 (2.28019 iter/s, 5.26271s/12 iters), loss = 5.2789
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I0405 09:46:56.381104 26038 solver.cpp:237] Train net output #0: loss = 5.2789 (* 1 = 5.2789 loss)
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I0405 09:46:56.381110 26038 sgd_solver.cpp:105] Iteration 60, lr = 1e-05
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I0405 09:47:01.590095 26038 solver.cpp:218] Iteration 72 (2.30373 iter/s, 5.20894s/12 iters), loss = 5.27752
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I0405 09:47:01.590139 26038 solver.cpp:237] Train net output #0: loss = 5.27752 (* 1 = 5.27752 loss)
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I0405 09:47:01.590145 26038 sgd_solver.cpp:105] Iteration 72, lr = 1e-05
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I0405 09:47:06.729065 26038 solver.cpp:218] Iteration 84 (2.33514 iter/s, 5.13888s/12 iters), loss = 5.28533
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I0405 09:47:06.729110 26038 solver.cpp:237] Train net output #0: loss = 5.28533 (* 1 = 5.28533 loss)
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I0405 09:47:06.729115 26038 sgd_solver.cpp:105] Iteration 84, lr = 1e-05
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I0405 09:47:12.064033 26038 solver.cpp:218] Iteration 96 (2.24935 iter/s, 5.33487s/12 iters), loss = 5.26539
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I0405 09:47:12.064074 26038 solver.cpp:237] Train net output #0: loss = 5.26539 (* 1 = 5.26539 loss)
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I0405 09:47:12.064080 26038 sgd_solver.cpp:105] Iteration 96, lr = 1e-05
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I0405 09:47:13.932049 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:47:14.244184 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
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I0405 09:47:17.325955 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
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I0405 09:47:19.628314 26038 solver.cpp:330] Iteration 102, Testing net (#0)
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I0405 09:47:19.628337 26038 net.cpp:676] Ignoring source layer train-data
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I0405 09:47:23.819476 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:47:23.895174 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 09:47:23.895205 26038 solver.cpp:397] Test net output #1: loss = 5.27917 (* 1 = 5.27917 loss)
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I0405 09:47:25.900517 26038 solver.cpp:218] Iteration 108 (0.867282 iter/s, 13.8363s/12 iters), loss = 5.29719
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I0405 09:47:25.900555 26038 solver.cpp:237] Train net output #0: loss = 5.29719 (* 1 = 5.29719 loss)
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I0405 09:47:25.900561 26038 sgd_solver.cpp:105] Iteration 108, lr = 1e-05
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I0405 09:47:31.151537 26038 solver.cpp:218] Iteration 120 (2.28534 iter/s, 5.25087s/12 iters), loss = 5.29667
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I0405 09:47:31.151674 26038 solver.cpp:237] Train net output #0: loss = 5.29667 (* 1 = 5.29667 loss)
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I0405 09:47:31.151681 26038 sgd_solver.cpp:105] Iteration 120, lr = 1e-05
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I0405 09:47:36.313849 26038 solver.cpp:218] Iteration 132 (2.32462 iter/s, 5.16212s/12 iters), loss = 5.30091
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I0405 09:47:36.313891 26038 solver.cpp:237] Train net output #0: loss = 5.30091 (* 1 = 5.30091 loss)
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I0405 09:47:36.313897 26038 sgd_solver.cpp:105] Iteration 132, lr = 1e-05
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I0405 09:47:41.590260 26038 solver.cpp:218] Iteration 144 (2.27432 iter/s, 5.27631s/12 iters), loss = 5.28043
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I0405 09:47:41.590317 26038 solver.cpp:237] Train net output #0: loss = 5.28043 (* 1 = 5.28043 loss)
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I0405 09:47:41.590327 26038 sgd_solver.cpp:105] Iteration 144, lr = 1e-05
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I0405 09:47:46.867033 26038 solver.cpp:218] Iteration 156 (2.27417 iter/s, 5.27666s/12 iters), loss = 5.28365
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I0405 09:47:46.867084 26038 solver.cpp:237] Train net output #0: loss = 5.28365 (* 1 = 5.28365 loss)
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I0405 09:47:46.867094 26038 sgd_solver.cpp:105] Iteration 156, lr = 1e-05
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I0405 09:47:52.243803 26038 solver.cpp:218] Iteration 168 (2.23187 iter/s, 5.37667s/12 iters), loss = 5.29158
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I0405 09:47:52.243849 26038 solver.cpp:237] Train net output #0: loss = 5.29158 (* 1 = 5.29158 loss)
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I0405 09:47:52.243856 26038 sgd_solver.cpp:105] Iteration 168, lr = 1e-05
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I0405 09:47:57.611066 26038 solver.cpp:218] Iteration 180 (2.23584 iter/s, 5.36712s/12 iters), loss = 5.30049
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I0405 09:47:57.611105 26038 solver.cpp:237] Train net output #0: loss = 5.30049 (* 1 = 5.30049 loss)
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I0405 09:47:57.611111 26038 sgd_solver.cpp:105] Iteration 180, lr = 1e-05
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I0405 09:48:02.898890 26038 solver.cpp:218] Iteration 192 (2.2694 iter/s, 5.28773s/12 iters), loss = 5.28148
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I0405 09:48:02.899027 26038 solver.cpp:237] Train net output #0: loss = 5.28148 (* 1 = 5.28148 loss)
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I0405 09:48:02.899039 26038 sgd_solver.cpp:105] Iteration 192, lr = 1e-05
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I0405 09:48:06.939453 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:48:07.649190 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
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I0405 09:48:10.620666 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
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I0405 09:48:12.928709 26038 solver.cpp:330] Iteration 204, Testing net (#0)
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I0405 09:48:12.928731 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 09:48:17.172788 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:48:17.297479 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 09:48:17.297513 26038 solver.cpp:397] Test net output #1: loss = 5.27952 (* 1 = 5.27952 loss)
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I0405 09:48:17.448206 26038 solver.cpp:218] Iteration 204 (0.824795 iter/s, 14.5491s/12 iters), loss = 5.28904
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I0405 09:48:17.450266 26038 solver.cpp:237] Train net output #0: loss = 5.28904 (* 1 = 5.28904 loss)
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I0405 09:48:17.450278 26038 sgd_solver.cpp:105] Iteration 204, lr = 1e-05
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I0405 09:48:21.873507 26038 solver.cpp:218] Iteration 216 (2.71297 iter/s, 4.4232s/12 iters), loss = 5.28052
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I0405 09:48:21.873551 26038 solver.cpp:237] Train net output #0: loss = 5.28052 (* 1 = 5.28052 loss)
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I0405 09:48:21.873558 26038 sgd_solver.cpp:105] Iteration 216, lr = 1e-05
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I0405 09:48:27.117903 26038 solver.cpp:218] Iteration 228 (2.2882 iter/s, 5.24429s/12 iters), loss = 5.29176
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I0405 09:48:27.117947 26038 solver.cpp:237] Train net output #0: loss = 5.29176 (* 1 = 5.29176 loss)
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I0405 09:48:27.117954 26038 sgd_solver.cpp:105] Iteration 228, lr = 1e-05
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I0405 09:48:32.581295 26038 solver.cpp:218] Iteration 240 (2.19648 iter/s, 5.46329s/12 iters), loss = 5.29517
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I0405 09:48:32.581339 26038 solver.cpp:237] Train net output #0: loss = 5.29517 (* 1 = 5.29517 loss)
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I0405 09:48:32.581346 26038 sgd_solver.cpp:105] Iteration 240, lr = 1e-05
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I0405 09:48:38.075706 26038 solver.cpp:218] Iteration 252 (2.18408 iter/s, 5.49431s/12 iters), loss = 5.27183
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I0405 09:48:38.075814 26038 solver.cpp:237] Train net output #0: loss = 5.27183 (* 1 = 5.27183 loss)
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I0405 09:48:38.075820 26038 sgd_solver.cpp:105] Iteration 252, lr = 1e-05
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I0405 09:48:43.311856 26038 solver.cpp:218] Iteration 264 (2.29183 iter/s, 5.23599s/12 iters), loss = 5.29785
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I0405 09:48:43.311898 26038 solver.cpp:237] Train net output #0: loss = 5.29785 (* 1 = 5.29785 loss)
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I0405 09:48:43.311904 26038 sgd_solver.cpp:105] Iteration 264, lr = 1e-05
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I0405 09:48:48.494458 26038 solver.cpp:218] Iteration 276 (2.31548 iter/s, 5.18251s/12 iters), loss = 5.29305
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I0405 09:48:48.494496 26038 solver.cpp:237] Train net output #0: loss = 5.29305 (* 1 = 5.29305 loss)
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I0405 09:48:48.494503 26038 sgd_solver.cpp:105] Iteration 276, lr = 1e-05
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I0405 09:48:53.518990 26038 solver.cpp:218] Iteration 288 (2.38832 iter/s, 5.02444s/12 iters), loss = 5.28293
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I0405 09:48:53.519027 26038 solver.cpp:237] Train net output #0: loss = 5.28293 (* 1 = 5.28293 loss)
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I0405 09:48:53.519033 26038 sgd_solver.cpp:105] Iteration 288, lr = 1e-05
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I0405 09:48:58.640460 26038 solver.cpp:218] Iteration 300 (2.34314 iter/s, 5.12134s/12 iters), loss = 5.27294
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I0405 09:48:58.640498 26038 solver.cpp:237] Train net output #0: loss = 5.27294 (* 1 = 5.27294 loss)
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I0405 09:48:58.640504 26038 sgd_solver.cpp:105] Iteration 300, lr = 1e-05
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I0405 09:48:59.695549 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:49:00.843586 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
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I0405 09:49:03.832720 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
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I0405 09:49:06.152217 26038 solver.cpp:330] Iteration 306, Testing net (#0)
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I0405 09:49:06.152238 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 09:49:10.300885 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:49:10.455603 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 09:49:10.455646 26038 solver.cpp:397] Test net output #1: loss = 5.27899 (* 1 = 5.27899 loss)
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I0405 09:49:12.420683 26038 solver.cpp:218] Iteration 312 (0.870823 iter/s, 13.7801s/12 iters), loss = 5.28221
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I0405 09:49:12.420732 26038 solver.cpp:237] Train net output #0: loss = 5.28221 (* 1 = 5.28221 loss)
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I0405 09:49:12.420737 26038 sgd_solver.cpp:105] Iteration 312, lr = 1e-05
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I0405 09:49:17.594713 26038 solver.cpp:218] Iteration 324 (2.31932 iter/s, 5.17393s/12 iters), loss = 5.29226
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I0405 09:49:17.594753 26038 solver.cpp:237] Train net output #0: loss = 5.29226 (* 1 = 5.29226 loss)
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I0405 09:49:17.594758 26038 sgd_solver.cpp:105] Iteration 324, lr = 1e-05
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I0405 09:49:22.830188 26038 solver.cpp:218] Iteration 336 (2.2921 iter/s, 5.23538s/12 iters), loss = 5.28205
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I0405 09:49:22.830230 26038 solver.cpp:237] Train net output #0: loss = 5.28205 (* 1 = 5.28205 loss)
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I0405 09:49:22.830237 26038 sgd_solver.cpp:105] Iteration 336, lr = 1e-05
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I0405 09:49:28.248906 26038 solver.cpp:218] Iteration 348 (2.21458 iter/s, 5.41862s/12 iters), loss = 5.28064
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I0405 09:49:28.248950 26038 solver.cpp:237] Train net output #0: loss = 5.28064 (* 1 = 5.28064 loss)
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I0405 09:49:28.248958 26038 sgd_solver.cpp:105] Iteration 348, lr = 1e-05
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I0405 09:49:33.771056 26038 solver.cpp:218] Iteration 360 (2.17311 iter/s, 5.52205s/12 iters), loss = 5.29539
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I0405 09:49:33.771100 26038 solver.cpp:237] Train net output #0: loss = 5.29539 (* 1 = 5.29539 loss)
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I0405 09:49:33.771108 26038 sgd_solver.cpp:105] Iteration 360, lr = 1e-05
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I0405 09:49:39.125494 26038 solver.cpp:218] Iteration 372 (2.24117 iter/s, 5.35434s/12 iters), loss = 5.28099
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I0405 09:49:39.125538 26038 solver.cpp:237] Train net output #0: loss = 5.28099 (* 1 = 5.28099 loss)
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I0405 09:49:39.125545 26038 sgd_solver.cpp:105] Iteration 372, lr = 1e-05
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I0405 09:49:44.356000 26038 solver.cpp:218] Iteration 384 (2.29455 iter/s, 5.22978s/12 iters), loss = 5.27269
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I0405 09:49:44.356151 26038 solver.cpp:237] Train net output #0: loss = 5.27269 (* 1 = 5.27269 loss)
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I0405 09:49:44.356158 26038 sgd_solver.cpp:105] Iteration 384, lr = 1e-05
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I0405 09:49:49.739352 26038 solver.cpp:218] Iteration 396 (2.22918 iter/s, 5.38315s/12 iters), loss = 5.28103
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I0405 09:49:49.739382 26038 solver.cpp:237] Train net output #0: loss = 5.28103 (* 1 = 5.28103 loss)
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||
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I0405 09:49:49.739388 26038 sgd_solver.cpp:105] Iteration 396, lr = 1e-05
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I0405 09:49:52.984515 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 09:49:54.461627 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
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||
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I0405 09:49:57.463202 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
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I0405 09:49:59.764478 26038 solver.cpp:330] Iteration 408, Testing net (#0)
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I0405 09:49:59.764498 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 09:50:04.072336 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 09:50:04.290428 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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||
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I0405 09:50:04.290460 26038 solver.cpp:397] Test net output #1: loss = 5.27924 (* 1 = 5.27924 loss)
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I0405 09:50:04.429776 26038 solver.cpp:218] Iteration 408 (0.816867 iter/s, 14.6903s/12 iters), loss = 5.27761
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I0405 09:50:04.429814 26038 solver.cpp:237] Train net output #0: loss = 5.27761 (* 1 = 5.27761 loss)
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I0405 09:50:04.429819 26038 sgd_solver.cpp:105] Iteration 408, lr = 1e-05
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I0405 09:50:08.763351 26038 solver.cpp:218] Iteration 420 (2.76913 iter/s, 4.33349s/12 iters), loss = 5.25424
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I0405 09:50:08.763388 26038 solver.cpp:237] Train net output #0: loss = 5.25424 (* 1 = 5.25424 loss)
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I0405 09:50:08.763394 26038 sgd_solver.cpp:105] Iteration 420, lr = 1e-05
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I0405 09:50:14.013803 26038 solver.cpp:218] Iteration 432 (2.28556 iter/s, 5.25036s/12 iters), loss = 5.2864
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I0405 09:50:14.013844 26038 solver.cpp:237] Train net output #0: loss = 5.2864 (* 1 = 5.2864 loss)
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I0405 09:50:14.013849 26038 sgd_solver.cpp:105] Iteration 432, lr = 1e-05
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I0405 09:50:19.376250 26038 solver.cpp:218] Iteration 444 (2.23782 iter/s, 5.36235s/12 iters), loss = 5.28963
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I0405 09:50:19.376369 26038 solver.cpp:237] Train net output #0: loss = 5.28963 (* 1 = 5.28963 loss)
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I0405 09:50:19.376377 26038 sgd_solver.cpp:105] Iteration 444, lr = 1e-05
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I0405 09:50:24.533679 26038 solver.cpp:218] Iteration 456 (2.32682 iter/s, 5.15725s/12 iters), loss = 5.27467
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I0405 09:50:24.533731 26038 solver.cpp:237] Train net output #0: loss = 5.27467 (* 1 = 5.27467 loss)
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I0405 09:50:24.533736 26038 sgd_solver.cpp:105] Iteration 456, lr = 1e-05
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I0405 09:50:29.910557 26038 solver.cpp:218] Iteration 468 (2.23182 iter/s, 5.37677s/12 iters), loss = 5.28864
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I0405 09:50:29.910610 26038 solver.cpp:237] Train net output #0: loss = 5.28864 (* 1 = 5.28864 loss)
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I0405 09:50:29.910619 26038 sgd_solver.cpp:105] Iteration 468, lr = 1e-05
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I0405 09:50:35.292665 26038 solver.cpp:218] Iteration 480 (2.22966 iter/s, 5.382s/12 iters), loss = 5.28654
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I0405 09:50:35.292707 26038 solver.cpp:237] Train net output #0: loss = 5.28654 (* 1 = 5.28654 loss)
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I0405 09:50:35.292713 26038 sgd_solver.cpp:105] Iteration 480, lr = 1e-05
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I0405 09:50:40.414394 26038 solver.cpp:218] Iteration 492 (2.343 iter/s, 5.12164s/12 iters), loss = 5.28161
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I0405 09:50:40.414430 26038 solver.cpp:237] Train net output #0: loss = 5.28161 (* 1 = 5.28161 loss)
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I0405 09:50:40.414435 26038 sgd_solver.cpp:105] Iteration 492, lr = 1e-05
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I0405 09:50:45.688464 26038 solver.cpp:218] Iteration 504 (2.27532 iter/s, 5.27398s/12 iters), loss = 5.28527
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I0405 09:50:45.688505 26038 solver.cpp:237] Train net output #0: loss = 5.28527 (* 1 = 5.28527 loss)
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I0405 09:50:45.688511 26038 sgd_solver.cpp:105] Iteration 504, lr = 1e-05
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I0405 09:50:45.924849 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 09:50:47.972668 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
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I0405 09:50:50.879148 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
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I0405 09:50:53.172052 26038 solver.cpp:330] Iteration 510, Testing net (#0)
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I0405 09:50:53.172070 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 09:50:57.248225 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 09:50:57.485370 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 09:50:57.485411 26038 solver.cpp:397] Test net output #1: loss = 5.27916 (* 1 = 5.27916 loss)
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I0405 09:50:59.290401 26038 solver.cpp:218] Iteration 516 (0.882237 iter/s, 13.6018s/12 iters), loss = 5.30241
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I0405 09:50:59.290441 26038 solver.cpp:237] Train net output #0: loss = 5.30241 (* 1 = 5.30241 loss)
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I0405 09:50:59.290446 26038 sgd_solver.cpp:105] Iteration 516, lr = 1e-05
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I0405 09:51:04.720156 26038 solver.cpp:218] Iteration 528 (2.21008 iter/s, 5.42966s/12 iters), loss = 5.2734
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I0405 09:51:04.720198 26038 solver.cpp:237] Train net output #0: loss = 5.2734 (* 1 = 5.2734 loss)
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I0405 09:51:04.720204 26038 sgd_solver.cpp:105] Iteration 528, lr = 1e-05
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I0405 09:51:10.117554 26038 solver.cpp:218] Iteration 540 (2.22333 iter/s, 5.3973s/12 iters), loss = 5.28234
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I0405 09:51:10.117595 26038 solver.cpp:237] Train net output #0: loss = 5.28234 (* 1 = 5.28234 loss)
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||
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I0405 09:51:10.117601 26038 sgd_solver.cpp:105] Iteration 540, lr = 1e-05
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I0405 09:51:15.334754 26038 solver.cpp:218] Iteration 552 (2.30013 iter/s, 5.21711s/12 iters), loss = 5.29167
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I0405 09:51:15.334800 26038 solver.cpp:237] Train net output #0: loss = 5.29167 (* 1 = 5.29167 loss)
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||
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I0405 09:51:15.334806 26038 sgd_solver.cpp:105] Iteration 552, lr = 1e-05
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I0405 09:51:20.687193 26038 solver.cpp:218] Iteration 564 (2.24201 iter/s, 5.35234s/12 iters), loss = 5.27768
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||
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I0405 09:51:20.687233 26038 solver.cpp:237] Train net output #0: loss = 5.27768 (* 1 = 5.27768 loss)
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||
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I0405 09:51:20.687238 26038 sgd_solver.cpp:105] Iteration 564, lr = 1e-05
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I0405 09:51:25.872390 26038 solver.cpp:218] Iteration 576 (2.31432 iter/s, 5.1851s/12 iters), loss = 5.29471
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||
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I0405 09:51:25.872488 26038 solver.cpp:237] Train net output #0: loss = 5.29471 (* 1 = 5.29471 loss)
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||
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I0405 09:51:25.872494 26038 sgd_solver.cpp:105] Iteration 576, lr = 1e-05
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I0405 09:51:30.825557 26038 solver.cpp:218] Iteration 588 (2.42277 iter/s, 4.95302s/12 iters), loss = 5.29081
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||
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I0405 09:51:30.825599 26038 solver.cpp:237] Train net output #0: loss = 5.29081 (* 1 = 5.29081 loss)
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||
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I0405 09:51:30.825605 26038 sgd_solver.cpp:105] Iteration 588, lr = 1e-05
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I0405 09:51:36.385205 26038 solver.cpp:218] Iteration 600 (2.15845 iter/s, 5.55955s/12 iters), loss = 5.27795
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||
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I0405 09:51:36.385246 26038 solver.cpp:237] Train net output #0: loss = 5.27795 (* 1 = 5.27795 loss)
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||
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I0405 09:51:36.385251 26038 sgd_solver.cpp:105] Iteration 600, lr = 1e-05
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||
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I0405 09:51:38.967679 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 09:51:41.428992 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
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||
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|
I0405 09:51:44.376121 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
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||
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I0405 09:51:46.674336 26038 solver.cpp:330] Iteration 612, Testing net (#0)
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||
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I0405 09:51:46.674358 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
|
I0405 09:51:50.754554 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 09:51:51.037184 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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||
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|
I0405 09:51:51.037221 26038 solver.cpp:397] Test net output #1: loss = 5.27919 (* 1 = 5.27919 loss)
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||
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I0405 09:51:51.174980 26038 solver.cpp:218] Iteration 612 (0.81138 iter/s, 14.7896s/12 iters), loss = 5.25961
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||
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I0405 09:51:51.176568 26038 solver.cpp:237] Train net output #0: loss = 5.25961 (* 1 = 5.25961 loss)
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||
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I0405 09:51:51.176582 26038 sgd_solver.cpp:105] Iteration 612, lr = 1e-05
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I0405 09:51:55.421881 26038 solver.cpp:218] Iteration 624 (2.82667 iter/s, 4.24528s/12 iters), loss = 5.27155
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||
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I0405 09:51:55.421922 26038 solver.cpp:237] Train net output #0: loss = 5.27155 (* 1 = 5.27155 loss)
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||
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I0405 09:51:55.421928 26038 sgd_solver.cpp:105] Iteration 624, lr = 1e-05
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I0405 09:52:00.877200 26038 solver.cpp:218] Iteration 636 (2.19972 iter/s, 5.45523s/12 iters), loss = 5.2825
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I0405 09:52:00.877311 26038 solver.cpp:237] Train net output #0: loss = 5.2825 (* 1 = 5.2825 loss)
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||
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I0405 09:52:00.877319 26038 sgd_solver.cpp:105] Iteration 636, lr = 1e-05
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I0405 09:52:05.897051 26038 solver.cpp:218] Iteration 648 (2.39059 iter/s, 5.01969s/12 iters), loss = 5.28292
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||
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I0405 09:52:05.897090 26038 solver.cpp:237] Train net output #0: loss = 5.28292 (* 1 = 5.28292 loss)
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||
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I0405 09:52:05.897095 26038 sgd_solver.cpp:105] Iteration 648, lr = 1e-05
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I0405 09:52:11.204233 26038 solver.cpp:218] Iteration 660 (2.26113 iter/s, 5.30709s/12 iters), loss = 5.29702
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I0405 09:52:11.204274 26038 solver.cpp:237] Train net output #0: loss = 5.29702 (* 1 = 5.29702 loss)
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||
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I0405 09:52:11.204280 26038 sgd_solver.cpp:105] Iteration 660, lr = 1e-05
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I0405 09:52:16.548421 26038 solver.cpp:218] Iteration 672 (2.24547 iter/s, 5.34409s/12 iters), loss = 5.2796
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||
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I0405 09:52:16.548462 26038 solver.cpp:237] Train net output #0: loss = 5.2796 (* 1 = 5.2796 loss)
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||
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I0405 09:52:16.548470 26038 sgd_solver.cpp:105] Iteration 672, lr = 1e-05
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I0405 09:52:21.983153 26038 solver.cpp:218] Iteration 684 (2.20806 iter/s, 5.43464s/12 iters), loss = 5.28668
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||
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I0405 09:52:21.983196 26038 solver.cpp:237] Train net output #0: loss = 5.28668 (* 1 = 5.28668 loss)
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||
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I0405 09:52:21.983201 26038 sgd_solver.cpp:105] Iteration 684, lr = 1e-05
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||
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I0405 09:52:22.810134 26038 blocking_queue.cpp:49] Waiting for data
|
||
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I0405 09:52:27.335997 26038 solver.cpp:218] Iteration 696 (2.24184 iter/s, 5.35274s/12 iters), loss = 5.29452
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||
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I0405 09:52:27.336040 26038 solver.cpp:237] Train net output #0: loss = 5.29452 (* 1 = 5.29452 loss)
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||
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I0405 09:52:27.336045 26038 sgd_solver.cpp:105] Iteration 696, lr = 1e-05
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||
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I0405 09:52:32.042591 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 09:52:32.457370 26038 solver.cpp:218] Iteration 708 (2.34316 iter/s, 5.12128s/12 iters), loss = 5.29659
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||
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I0405 09:52:32.457412 26038 solver.cpp:237] Train net output #0: loss = 5.29659 (* 1 = 5.29659 loss)
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||
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I0405 09:52:32.457418 26038 sgd_solver.cpp:105] Iteration 708, lr = 1e-05
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||
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I0405 09:52:34.518733 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
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||
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I0405 09:52:37.506386 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
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I0405 09:52:39.796856 26038 solver.cpp:330] Iteration 714, Testing net (#0)
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I0405 09:52:39.796876 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 09:52:43.836845 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 09:52:44.150934 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
|
||
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I0405 09:52:44.150976 26038 solver.cpp:397] Test net output #1: loss = 5.27879 (* 1 = 5.27879 loss)
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||
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I0405 09:52:45.868984 26038 solver.cpp:218] Iteration 720 (0.894757 iter/s, 13.4115s/12 iters), loss = 5.27616
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I0405 09:52:45.869025 26038 solver.cpp:237] Train net output #0: loss = 5.27616 (* 1 = 5.27616 loss)
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I0405 09:52:45.869031 26038 sgd_solver.cpp:105] Iteration 720, lr = 1e-05
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I0405 09:52:51.120995 26038 solver.cpp:218] Iteration 732 (2.28488 iter/s, 5.25192s/12 iters), loss = 5.27999
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I0405 09:52:51.121034 26038 solver.cpp:237] Train net output #0: loss = 5.27999 (* 1 = 5.27999 loss)
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I0405 09:52:51.121040 26038 sgd_solver.cpp:105] Iteration 732, lr = 1e-05
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I0405 09:52:56.372131 26038 solver.cpp:218] Iteration 744 (2.28526 iter/s, 5.25104s/12 iters), loss = 5.30158
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||
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I0405 09:52:56.372175 26038 solver.cpp:237] Train net output #0: loss = 5.30158 (* 1 = 5.30158 loss)
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||
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I0405 09:52:56.372181 26038 sgd_solver.cpp:105] Iteration 744, lr = 1e-05
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I0405 09:53:01.528120 26038 solver.cpp:218] Iteration 756 (2.32743 iter/s, 5.15589s/12 iters), loss = 5.28942
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||
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I0405 09:53:01.528156 26038 solver.cpp:237] Train net output #0: loss = 5.28942 (* 1 = 5.28942 loss)
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||
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I0405 09:53:01.528162 26038 sgd_solver.cpp:105] Iteration 756, lr = 1e-05
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I0405 09:53:06.930788 26038 solver.cpp:218] Iteration 768 (2.22116 iter/s, 5.40258s/12 iters), loss = 5.2876
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||
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I0405 09:53:06.930920 26038 solver.cpp:237] Train net output #0: loss = 5.2876 (* 1 = 5.2876 loss)
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||
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I0405 09:53:06.930927 26038 sgd_solver.cpp:105] Iteration 768, lr = 1e-05
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||
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I0405 09:53:12.278226 26038 solver.cpp:218] Iteration 780 (2.24415 iter/s, 5.34725s/12 iters), loss = 5.27841
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||
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|
I0405 09:53:12.278283 26038 solver.cpp:237] Train net output #0: loss = 5.27841 (* 1 = 5.27841 loss)
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||
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|
I0405 09:53:12.278293 26038 sgd_solver.cpp:105] Iteration 780, lr = 1e-05
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||
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I0405 09:53:17.656977 26038 solver.cpp:218] Iteration 792 (2.23105 iter/s, 5.37864s/12 iters), loss = 5.28869
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||
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|
I0405 09:53:17.657027 26038 solver.cpp:237] Train net output #0: loss = 5.28869 (* 1 = 5.28869 loss)
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||
|
|
I0405 09:53:17.657034 26038 sgd_solver.cpp:105] Iteration 792, lr = 1e-05
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||
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I0405 09:53:23.043215 26038 solver.cpp:218] Iteration 804 (2.22794 iter/s, 5.38614s/12 iters), loss = 5.2763
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||
|
|
I0405 09:53:23.043256 26038 solver.cpp:237] Train net output #0: loss = 5.2763 (* 1 = 5.2763 loss)
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||
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I0405 09:53:23.043262 26038 sgd_solver.cpp:105] Iteration 804, lr = 1e-05
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||
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I0405 09:53:24.924237 26057 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 09:53:27.996963 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
|
||
|
|
I0405 09:53:30.933933 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
|
||
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|
I0405 09:53:33.238204 26038 solver.cpp:330] Iteration 816, Testing net (#0)
|
||
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|
I0405 09:53:33.238224 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
|
I0405 09:53:37.227178 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 09:53:37.571766 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
|
||
|
|
I0405 09:53:37.571800 26038 solver.cpp:397] Test net output #1: loss = 5.27905 (* 1 = 5.27905 loss)
|
||
|
|
I0405 09:53:37.713543 26038 solver.cpp:218] Iteration 816 (0.817987 iter/s, 14.6702s/12 iters), loss = 5.29254
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||
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I0405 09:53:37.715112 26038 solver.cpp:237] Train net output #0: loss = 5.29254 (* 1 = 5.29254 loss)
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||
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|
I0405 09:53:37.715123 26038 sgd_solver.cpp:105] Iteration 816, lr = 1e-05
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||
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I0405 09:53:42.142156 26038 solver.cpp:218] Iteration 828 (2.71064 iter/s, 4.427s/12 iters), loss = 5.29574
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||
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|
I0405 09:53:42.142195 26038 solver.cpp:237] Train net output #0: loss = 5.29574 (* 1 = 5.29574 loss)
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||
|
|
I0405 09:53:42.142201 26038 sgd_solver.cpp:105] Iteration 828, lr = 1e-05
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||
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I0405 09:53:47.490033 26038 solver.cpp:218] Iteration 840 (2.24392 iter/s, 5.34778s/12 iters), loss = 5.29733
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||
|
|
I0405 09:53:47.490083 26038 solver.cpp:237] Train net output #0: loss = 5.29733 (* 1 = 5.29733 loss)
|
||
|
|
I0405 09:53:47.490090 26038 sgd_solver.cpp:105] Iteration 840, lr = 1e-05
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||
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|
I0405 09:53:52.709600 26038 solver.cpp:218] Iteration 852 (2.29909 iter/s, 5.21946s/12 iters), loss = 5.27907
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||
|
|
I0405 09:53:52.709640 26038 solver.cpp:237] Train net output #0: loss = 5.27907 (* 1 = 5.27907 loss)
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||
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|
I0405 09:53:52.709646 26038 sgd_solver.cpp:105] Iteration 852, lr = 1e-05
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||
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I0405 09:53:57.841394 26038 solver.cpp:218] Iteration 864 (2.3384 iter/s, 5.1317s/12 iters), loss = 5.29108
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||
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I0405 09:53:57.841434 26038 solver.cpp:237] Train net output #0: loss = 5.29108 (* 1 = 5.29108 loss)
|
||
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I0405 09:53:57.841439 26038 sgd_solver.cpp:105] Iteration 864, lr = 1e-05
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||
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I0405 09:54:02.976682 26038 solver.cpp:218] Iteration 876 (2.33681 iter/s, 5.1352s/12 iters), loss = 5.30147
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||
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|
I0405 09:54:02.976727 26038 solver.cpp:237] Train net output #0: loss = 5.30147 (* 1 = 5.30147 loss)
|
||
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I0405 09:54:02.976734 26038 sgd_solver.cpp:105] Iteration 876, lr = 1e-05
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||
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I0405 09:54:08.147668 26038 solver.cpp:218] Iteration 888 (2.3207 iter/s, 5.17084s/12 iters), loss = 5.28596
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||
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|
I0405 09:54:08.147791 26038 solver.cpp:237] Train net output #0: loss = 5.28596 (* 1 = 5.28596 loss)
|
||
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I0405 09:54:08.147804 26038 sgd_solver.cpp:105] Iteration 888, lr = 1e-05
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||
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I0405 09:54:13.366560 26038 solver.cpp:218] Iteration 900 (2.29941 iter/s, 5.21872s/12 iters), loss = 5.301
|
||
|
|
I0405 09:54:13.366611 26038 solver.cpp:237] Train net output #0: loss = 5.301 (* 1 = 5.301 loss)
|
||
|
|
I0405 09:54:13.366621 26038 sgd_solver.cpp:105] Iteration 900, lr = 1e-05
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||
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|
I0405 09:54:17.366012 26057 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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|
I0405 09:54:18.595602 26038 solver.cpp:218] Iteration 912 (2.29492 iter/s, 5.22893s/12 iters), loss = 5.27447
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||
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|
I0405 09:54:18.595645 26038 solver.cpp:237] Train net output #0: loss = 5.27447 (* 1 = 5.27447 loss)
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||
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I0405 09:54:18.595651 26038 sgd_solver.cpp:105] Iteration 912, lr = 1e-05
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||
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I0405 09:54:20.701735 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
|
||
|
|
I0405 09:54:23.781003 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
|
||
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|
I0405 09:54:26.094926 26038 solver.cpp:330] Iteration 918, Testing net (#0)
|
||
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|
I0405 09:54:26.094946 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
|
I0405 09:54:30.005136 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 09:54:30.401588 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
|
||
|
|
I0405 09:54:30.401618 26038 solver.cpp:397] Test net output #1: loss = 5.27905 (* 1 = 5.27905 loss)
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||
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|
I0405 09:54:32.409116 26038 solver.cpp:218] Iteration 924 (0.868725 iter/s, 13.8134s/12 iters), loss = 5.28075
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I0405 09:54:32.409157 26038 solver.cpp:237] Train net output #0: loss = 5.28075 (* 1 = 5.28075 loss)
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||
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I0405 09:54:32.409162 26038 sgd_solver.cpp:105] Iteration 924, lr = 1e-05
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||
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I0405 09:54:37.758810 26038 solver.cpp:218] Iteration 936 (2.24316 iter/s, 5.3496s/12 iters), loss = 5.28884
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||
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|
I0405 09:54:37.758847 26038 solver.cpp:237] Train net output #0: loss = 5.28884 (* 1 = 5.28884 loss)
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||
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I0405 09:54:37.758853 26038 sgd_solver.cpp:105] Iteration 936, lr = 1e-05
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I0405 09:54:43.211032 26038 solver.cpp:218] Iteration 948 (2.20098 iter/s, 5.45213s/12 iters), loss = 5.28876
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I0405 09:54:43.211158 26038 solver.cpp:237] Train net output #0: loss = 5.28876 (* 1 = 5.28876 loss)
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I0405 09:54:43.211165 26038 sgd_solver.cpp:105] Iteration 948, lr = 1e-05
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I0405 09:54:48.502106 26038 solver.cpp:218] Iteration 960 (2.26805 iter/s, 5.29089s/12 iters), loss = 5.28314
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I0405 09:54:48.502164 26038 solver.cpp:237] Train net output #0: loss = 5.28314 (* 1 = 5.28314 loss)
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I0405 09:54:48.502172 26038 sgd_solver.cpp:105] Iteration 960, lr = 1e-05
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I0405 09:54:53.909958 26038 solver.cpp:218] Iteration 972 (2.21904 iter/s, 5.40774s/12 iters), loss = 5.28563
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I0405 09:54:53.910006 26038 solver.cpp:237] Train net output #0: loss = 5.28563 (* 1 = 5.28563 loss)
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I0405 09:54:53.910014 26038 sgd_solver.cpp:105] Iteration 972, lr = 1e-05
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I0405 09:54:59.008563 26038 solver.cpp:218] Iteration 984 (2.35365 iter/s, 5.09846s/12 iters), loss = 5.28517
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I0405 09:54:59.008610 26038 solver.cpp:237] Train net output #0: loss = 5.28517 (* 1 = 5.28517 loss)
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I0405 09:54:59.008616 26038 sgd_solver.cpp:105] Iteration 984, lr = 1e-05
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I0405 09:55:04.352453 26038 solver.cpp:218] Iteration 996 (2.2456 iter/s, 5.34379s/12 iters), loss = 5.27309
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I0405 09:55:04.352499 26038 solver.cpp:237] Train net output #0: loss = 5.27309 (* 1 = 5.27309 loss)
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I0405 09:55:04.352507 26038 sgd_solver.cpp:105] Iteration 996, lr = 1e-05
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I0405 09:55:09.833449 26038 solver.cpp:218] Iteration 1008 (2.18944 iter/s, 5.48086s/12 iters), loss = 5.27333
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I0405 09:55:09.833493 26038 solver.cpp:237] Train net output #0: loss = 5.27333 (* 1 = 5.27333 loss)
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I0405 09:55:09.833498 26038 sgd_solver.cpp:105] Iteration 1008, lr = 1e-05
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I0405 09:55:10.925963 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:55:14.794759 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
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I0405 09:55:17.863153 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
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I0405 09:55:20.203228 26038 solver.cpp:330] Iteration 1020, Testing net (#0)
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I0405 09:55:20.203253 26038 net.cpp:676] Ignoring source layer train-data
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I0405 09:55:24.262845 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:55:24.740041 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 09:55:24.740070 26038 solver.cpp:397] Test net output #1: loss = 5.27902 (* 1 = 5.27902 loss)
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I0405 09:55:24.900919 26038 solver.cpp:218] Iteration 1020 (0.797438 iter/s, 15.0482s/12 iters), loss = 5.28084
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I0405 09:55:24.900967 26038 solver.cpp:237] Train net output #0: loss = 5.28084 (* 1 = 5.28084 loss)
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I0405 09:55:24.900974 26038 sgd_solver.cpp:105] Iteration 1020, lr = 1e-05
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I0405 09:55:29.354869 26038 solver.cpp:218] Iteration 1032 (2.69429 iter/s, 4.45386s/12 iters), loss = 5.28193
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I0405 09:55:29.354919 26038 solver.cpp:237] Train net output #0: loss = 5.28193 (* 1 = 5.28193 loss)
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I0405 09:55:29.354926 26038 sgd_solver.cpp:105] Iteration 1032, lr = 1e-05
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I0405 09:55:34.858810 26038 solver.cpp:218] Iteration 1044 (2.1803 iter/s, 5.50383s/12 iters), loss = 5.29058
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I0405 09:55:34.858866 26038 solver.cpp:237] Train net output #0: loss = 5.29058 (* 1 = 5.29058 loss)
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I0405 09:55:34.858873 26038 sgd_solver.cpp:105] Iteration 1044, lr = 1e-05
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I0405 09:55:40.232324 26038 solver.cpp:218] Iteration 1056 (2.23322 iter/s, 5.37341s/12 iters), loss = 5.27846
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I0405 09:55:40.232362 26038 solver.cpp:237] Train net output #0: loss = 5.27846 (* 1 = 5.27846 loss)
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I0405 09:55:40.232367 26038 sgd_solver.cpp:105] Iteration 1056, lr = 1e-05
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I0405 09:55:45.692142 26038 solver.cpp:218] Iteration 1068 (2.19791 iter/s, 5.45972s/12 iters), loss = 5.30513
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I0405 09:55:45.692286 26038 solver.cpp:237] Train net output #0: loss = 5.30513 (* 1 = 5.30513 loss)
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I0405 09:55:45.692292 26038 sgd_solver.cpp:105] Iteration 1068, lr = 1e-05
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I0405 09:55:51.085690 26038 solver.cpp:218] Iteration 1080 (2.22496 iter/s, 5.39335s/12 iters), loss = 5.29109
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I0405 09:55:51.085732 26038 solver.cpp:237] Train net output #0: loss = 5.29109 (* 1 = 5.29109 loss)
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I0405 09:55:51.085738 26038 sgd_solver.cpp:105] Iteration 1080, lr = 1e-05
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I0405 09:55:56.542295 26038 solver.cpp:218] Iteration 1092 (2.19921 iter/s, 5.45651s/12 iters), loss = 5.27902
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I0405 09:55:56.542340 26038 solver.cpp:237] Train net output #0: loss = 5.27902 (* 1 = 5.27902 loss)
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I0405 09:55:56.542346 26038 sgd_solver.cpp:105] Iteration 1092, lr = 1e-05
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I0405 09:56:01.794258 26038 solver.cpp:218] Iteration 1104 (2.2849 iter/s, 5.25187s/12 iters), loss = 5.28022
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I0405 09:56:01.794296 26038 solver.cpp:237] Train net output #0: loss = 5.28022 (* 1 = 5.28022 loss)
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I0405 09:56:01.794301 26038 sgd_solver.cpp:105] Iteration 1104, lr = 1e-05
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I0405 09:56:05.353439 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:56:07.400099 26038 solver.cpp:218] Iteration 1116 (2.14066 iter/s, 5.60574s/12 iters), loss = 5.27979
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I0405 09:56:07.400156 26038 solver.cpp:237] Train net output #0: loss = 5.27979 (* 1 = 5.27979 loss)
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I0405 09:56:07.400166 26038 sgd_solver.cpp:105] Iteration 1116, lr = 1e-05
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I0405 09:56:09.619311 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
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||
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I0405 09:56:12.627928 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
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I0405 09:56:14.923981 26038 solver.cpp:330] Iteration 1122, Testing net (#0)
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I0405 09:56:14.924002 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 09:56:19.048188 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 09:56:19.561581 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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||
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I0405 09:56:19.561622 26038 solver.cpp:397] Test net output #1: loss = 5.27891 (* 1 = 5.27891 loss)
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I0405 09:56:21.425407 26038 solver.cpp:218] Iteration 1128 (0.855606 iter/s, 14.0251s/12 iters), loss = 5.28058
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I0405 09:56:21.425451 26038 solver.cpp:237] Train net output #0: loss = 5.28058 (* 1 = 5.28058 loss)
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I0405 09:56:21.425457 26038 sgd_solver.cpp:105] Iteration 1128, lr = 1e-05
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I0405 09:56:26.575927 26038 solver.cpp:218] Iteration 1140 (2.32991 iter/s, 5.15042s/12 iters), loss = 5.29059
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I0405 09:56:26.575969 26038 solver.cpp:237] Train net output #0: loss = 5.29059 (* 1 = 5.29059 loss)
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I0405 09:56:26.575975 26038 sgd_solver.cpp:105] Iteration 1140, lr = 1e-05
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I0405 09:56:32.121631 26038 solver.cpp:218] Iteration 1152 (2.16387 iter/s, 5.54561s/12 iters), loss = 5.30109
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I0405 09:56:32.121678 26038 solver.cpp:237] Train net output #0: loss = 5.30109 (* 1 = 5.30109 loss)
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I0405 09:56:32.121685 26038 sgd_solver.cpp:105] Iteration 1152, lr = 1e-05
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I0405 09:56:37.337057 26038 solver.cpp:218] Iteration 1164 (2.30091 iter/s, 5.21532s/12 iters), loss = 5.27159
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I0405 09:56:37.337105 26038 solver.cpp:237] Train net output #0: loss = 5.27159 (* 1 = 5.27159 loss)
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I0405 09:56:37.337113 26038 sgd_solver.cpp:105] Iteration 1164, lr = 1e-05
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I0405 09:56:42.590404 26038 solver.cpp:218] Iteration 1176 (2.2843 iter/s, 5.25324s/12 iters), loss = 5.28989
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I0405 09:56:42.590453 26038 solver.cpp:237] Train net output #0: loss = 5.28989 (* 1 = 5.28989 loss)
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I0405 09:56:42.590461 26038 sgd_solver.cpp:105] Iteration 1176, lr = 1e-05
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I0405 09:56:48.008772 26038 solver.cpp:218] Iteration 1188 (2.21473 iter/s, 5.41827s/12 iters), loss = 5.27977
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I0405 09:56:48.008826 26038 solver.cpp:237] Train net output #0: loss = 5.27977 (* 1 = 5.27977 loss)
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I0405 09:56:48.008834 26038 sgd_solver.cpp:105] Iteration 1188, lr = 1e-05
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I0405 09:56:53.554260 26038 solver.cpp:218] Iteration 1200 (2.16396 iter/s, 5.54538s/12 iters), loss = 5.28314
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I0405 09:56:53.554378 26038 solver.cpp:237] Train net output #0: loss = 5.28314 (* 1 = 5.28314 loss)
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I0405 09:56:53.554384 26038 sgd_solver.cpp:105] Iteration 1200, lr = 1e-05
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I0405 09:56:59.051326 26038 solver.cpp:218] Iteration 1212 (2.18305 iter/s, 5.4969s/12 iters), loss = 5.28408
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I0405 09:56:59.051378 26038 solver.cpp:237] Train net output #0: loss = 5.28408 (* 1 = 5.28408 loss)
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I0405 09:56:59.051385 26038 sgd_solver.cpp:105] Iteration 1212, lr = 1e-05
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I0405 09:56:59.259639 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:57:04.054191 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
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I0405 09:57:07.199832 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
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I0405 09:57:09.534577 26038 solver.cpp:330] Iteration 1224, Testing net (#0)
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I0405 09:57:09.534600 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 09:57:13.517737 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:57:14.021191 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 09:57:14.021229 26038 solver.cpp:397] Test net output #1: loss = 5.27895 (* 1 = 5.27895 loss)
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I0405 09:57:14.158488 26038 solver.cpp:218] Iteration 1224 (0.794334 iter/s, 15.107s/12 iters), loss = 5.28886
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I0405 09:57:14.158562 26038 solver.cpp:237] Train net output #0: loss = 5.28886 (* 1 = 5.28886 loss)
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I0405 09:57:14.158572 26038 sgd_solver.cpp:105] Iteration 1224, lr = 1e-05
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I0405 09:57:18.642649 26038 solver.cpp:218] Iteration 1236 (2.67615 iter/s, 4.48405s/12 iters), loss = 5.29138
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I0405 09:57:18.642693 26038 solver.cpp:237] Train net output #0: loss = 5.29138 (* 1 = 5.29138 loss)
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I0405 09:57:18.642700 26038 sgd_solver.cpp:105] Iteration 1236, lr = 1e-05
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I0405 09:57:23.949165 26038 solver.cpp:218] Iteration 1248 (2.26143 iter/s, 5.30638s/12 iters), loss = 5.27094
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I0405 09:57:23.949282 26038 solver.cpp:237] Train net output #0: loss = 5.27094 (* 1 = 5.27094 loss)
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I0405 09:57:23.949290 26038 sgd_solver.cpp:105] Iteration 1248, lr = 1e-05
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I0405 09:57:29.218066 26038 solver.cpp:218] Iteration 1260 (2.27758 iter/s, 5.26874s/12 iters), loss = 5.29342
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I0405 09:57:29.218109 26038 solver.cpp:237] Train net output #0: loss = 5.29342 (* 1 = 5.29342 loss)
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||
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I0405 09:57:29.218116 26038 sgd_solver.cpp:105] Iteration 1260, lr = 1e-05
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I0405 09:57:34.466115 26038 solver.cpp:218] Iteration 1272 (2.2866 iter/s, 5.24797s/12 iters), loss = 5.28757
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I0405 09:57:34.466152 26038 solver.cpp:237] Train net output #0: loss = 5.28757 (* 1 = 5.28757 loss)
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||
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I0405 09:57:34.466157 26038 sgd_solver.cpp:105] Iteration 1272, lr = 1e-05
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I0405 09:57:39.920269 26038 solver.cpp:218] Iteration 1284 (2.2002 iter/s, 5.45406s/12 iters), loss = 5.29181
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I0405 09:57:39.920336 26038 solver.cpp:237] Train net output #0: loss = 5.29181 (* 1 = 5.29181 loss)
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||
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I0405 09:57:39.920346 26038 sgd_solver.cpp:105] Iteration 1284, lr = 1e-05
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I0405 09:57:45.286168 26038 solver.cpp:218] Iteration 1296 (2.23639 iter/s, 5.36579s/12 iters), loss = 5.27367
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||
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I0405 09:57:45.286212 26038 solver.cpp:237] Train net output #0: loss = 5.27367 (* 1 = 5.27367 loss)
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||
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I0405 09:57:45.286219 26038 sgd_solver.cpp:105] Iteration 1296, lr = 1e-05
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I0405 09:57:50.702857 26038 solver.cpp:218] Iteration 1308 (2.21542 iter/s, 5.41659s/12 iters), loss = 5.28699
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||
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I0405 09:57:50.702908 26038 solver.cpp:237] Train net output #0: loss = 5.28699 (* 1 = 5.28699 loss)
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I0405 09:57:50.702916 26038 sgd_solver.cpp:105] Iteration 1308, lr = 1e-05
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I0405 09:57:53.309494 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:57:56.041586 26038 solver.cpp:218] Iteration 1320 (2.24777 iter/s, 5.33863s/12 iters), loss = 5.29319
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I0405 09:57:56.041755 26038 solver.cpp:237] Train net output #0: loss = 5.29319 (* 1 = 5.29319 loss)
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I0405 09:57:56.041764 26038 sgd_solver.cpp:105] Iteration 1320, lr = 1e-05
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I0405 09:57:58.258646 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
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I0405 09:58:01.300171 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
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I0405 09:58:04.374621 26038 solver.cpp:330] Iteration 1326, Testing net (#0)
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I0405 09:58:04.374645 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 09:58:08.203275 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 09:58:08.818387 26038 solver.cpp:397] Test net output #0: accuracy = 0.00306373
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I0405 09:58:08.818429 26038 solver.cpp:397] Test net output #1: loss = 5.27913 (* 1 = 5.27913 loss)
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I0405 09:58:10.769389 26038 solver.cpp:218] Iteration 1332 (0.8148 iter/s, 14.7275s/12 iters), loss = 5.27695
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I0405 09:58:10.769445 26038 solver.cpp:237] Train net output #0: loss = 5.27695 (* 1 = 5.27695 loss)
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I0405 09:58:10.769455 26038 sgd_solver.cpp:105] Iteration 1332, lr = 1e-05
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I0405 09:58:15.935796 26038 solver.cpp:218] Iteration 1344 (2.32274 iter/s, 5.16631s/12 iters), loss = 5.28194
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I0405 09:58:15.935835 26038 solver.cpp:237] Train net output #0: loss = 5.28194 (* 1 = 5.28194 loss)
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I0405 09:58:15.935840 26038 sgd_solver.cpp:105] Iteration 1344, lr = 1e-05
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I0405 09:58:21.368356 26038 solver.cpp:218] Iteration 1356 (2.20894 iter/s, 5.43247s/12 iters), loss = 5.2908
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I0405 09:58:21.368404 26038 solver.cpp:237] Train net output #0: loss = 5.2908 (* 1 = 5.2908 loss)
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I0405 09:58:21.368409 26038 sgd_solver.cpp:105] Iteration 1356, lr = 1e-05
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I0405 09:58:26.871199 26038 solver.cpp:218] Iteration 1368 (2.18073 iter/s, 5.50274s/12 iters), loss = 5.27649
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I0405 09:58:26.871318 26038 solver.cpp:237] Train net output #0: loss = 5.27649 (* 1 = 5.27649 loss)
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I0405 09:58:26.871327 26038 sgd_solver.cpp:105] Iteration 1368, lr = 1e-05
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I0405 09:58:28.172319 26038 blocking_queue.cpp:49] Waiting for data
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I0405 09:58:32.329926 26038 solver.cpp:218] Iteration 1380 (2.19838 iter/s, 5.45856s/12 iters), loss = 5.27965
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I0405 09:58:32.329983 26038 solver.cpp:237] Train net output #0: loss = 5.27965 (* 1 = 5.27965 loss)
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I0405 09:58:32.329993 26038 sgd_solver.cpp:105] Iteration 1380, lr = 1e-05
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I0405 09:58:37.657153 26038 solver.cpp:218] Iteration 1392 (2.25262 iter/s, 5.32712s/12 iters), loss = 5.2774
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I0405 09:58:37.657197 26038 solver.cpp:237] Train net output #0: loss = 5.2774 (* 1 = 5.2774 loss)
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I0405 09:58:37.657203 26038 sgd_solver.cpp:105] Iteration 1392, lr = 1e-05
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I0405 09:58:43.024662 26038 solver.cpp:218] Iteration 1404 (2.23571 iter/s, 5.36742s/12 iters), loss = 5.28838
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I0405 09:58:43.024705 26038 solver.cpp:237] Train net output #0: loss = 5.28838 (* 1 = 5.28838 loss)
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I0405 09:58:43.024711 26038 sgd_solver.cpp:105] Iteration 1404, lr = 1e-05
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I0405 09:58:47.859889 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:58:48.269639 26038 solver.cpp:218] Iteration 1416 (2.28794 iter/s, 5.24489s/12 iters), loss = 5.28216
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I0405 09:58:48.269682 26038 solver.cpp:237] Train net output #0: loss = 5.28216 (* 1 = 5.28216 loss)
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I0405 09:58:48.269687 26038 sgd_solver.cpp:105] Iteration 1416, lr = 1e-05
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I0405 09:58:52.898777 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
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I0405 09:58:56.092183 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
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I0405 09:58:58.424643 26038 solver.cpp:330] Iteration 1428, Testing net (#0)
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I0405 09:58:58.424741 26038 net.cpp:676] Ignoring source layer train-data
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I0405 09:59:02.434357 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:59:03.145490 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 09:59:03.145530 26038 solver.cpp:397] Test net output #1: loss = 5.279 (* 1 = 5.279 loss)
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I0405 09:59:03.287098 26038 solver.cpp:218] Iteration 1428 (0.799078 iter/s, 15.0173s/12 iters), loss = 5.28058
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I0405 09:59:03.287154 26038 solver.cpp:237] Train net output #0: loss = 5.28058 (* 1 = 5.28058 loss)
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I0405 09:59:03.287163 26038 sgd_solver.cpp:105] Iteration 1428, lr = 1e-05
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I0405 09:59:07.663050 26038 solver.cpp:218] Iteration 1440 (2.74232 iter/s, 4.37586s/12 iters), loss = 5.28158
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I0405 09:59:07.663100 26038 solver.cpp:237] Train net output #0: loss = 5.28158 (* 1 = 5.28158 loss)
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I0405 09:59:07.663107 26038 sgd_solver.cpp:105] Iteration 1440, lr = 1e-05
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I0405 09:59:13.112910 26038 solver.cpp:218] Iteration 1452 (2.20193 iter/s, 5.44976s/12 iters), loss = 5.29175
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I0405 09:59:13.112963 26038 solver.cpp:237] Train net output #0: loss = 5.29175 (* 1 = 5.29175 loss)
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I0405 09:59:13.112972 26038 sgd_solver.cpp:105] Iteration 1452, lr = 1e-05
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I0405 09:59:18.554847 26038 solver.cpp:218] Iteration 1464 (2.20514 iter/s, 5.44184s/12 iters), loss = 5.28229
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I0405 09:59:18.554884 26038 solver.cpp:237] Train net output #0: loss = 5.28229 (* 1 = 5.28229 loss)
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I0405 09:59:18.554889 26038 sgd_solver.cpp:105] Iteration 1464, lr = 1e-05
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I0405 09:59:23.781931 26038 solver.cpp:218] Iteration 1476 (2.29577 iter/s, 5.227s/12 iters), loss = 5.2911
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I0405 09:59:23.781985 26038 solver.cpp:237] Train net output #0: loss = 5.2911 (* 1 = 5.2911 loss)
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I0405 09:59:23.781992 26038 sgd_solver.cpp:105] Iteration 1476, lr = 1e-05
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I0405 09:59:29.255906 26038 solver.cpp:218] Iteration 1488 (2.19223 iter/s, 5.47388s/12 iters), loss = 5.28805
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I0405 09:59:29.255997 26038 solver.cpp:237] Train net output #0: loss = 5.28805 (* 1 = 5.28805 loss)
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I0405 09:59:29.256003 26038 sgd_solver.cpp:105] Iteration 1488, lr = 1e-05
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I0405 09:59:34.497189 26038 solver.cpp:218] Iteration 1500 (2.28957 iter/s, 5.24115s/12 iters), loss = 5.2871
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I0405 09:59:34.497229 26038 solver.cpp:237] Train net output #0: loss = 5.2871 (* 1 = 5.2871 loss)
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I0405 09:59:34.497234 26038 sgd_solver.cpp:105] Iteration 1500, lr = 1e-05
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I0405 09:59:39.844023 26038 solver.cpp:218] Iteration 1512 (2.24436 iter/s, 5.34674s/12 iters), loss = 5.2745
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I0405 09:59:39.844084 26038 solver.cpp:237] Train net output #0: loss = 5.2745 (* 1 = 5.2745 loss)
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I0405 09:59:39.844094 26038 sgd_solver.cpp:105] Iteration 1512, lr = 1e-05
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I0405 09:59:41.816318 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 09:59:45.432615 26038 solver.cpp:218] Iteration 1524 (2.14727 iter/s, 5.58848s/12 iters), loss = 5.29965
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I0405 09:59:45.432662 26038 solver.cpp:237] Train net output #0: loss = 5.29965 (* 1 = 5.29965 loss)
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I0405 09:59:45.432667 26038 sgd_solver.cpp:105] Iteration 1524, lr = 1e-05
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I0405 09:59:47.779515 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
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I0405 09:59:50.917946 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
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I0405 09:59:53.250995 26038 solver.cpp:330] Iteration 1530, Testing net (#0)
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I0405 09:59:53.251017 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 09:59:57.177271 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 09:59:57.842581 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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||
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I0405 09:59:57.842617 26038 solver.cpp:397] Test net output #1: loss = 5.2788 (* 1 = 5.2788 loss)
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I0405 09:59:59.746122 26038 solver.cpp:218] Iteration 1536 (0.838377 iter/s, 14.3134s/12 iters), loss = 5.29303
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I0405 09:59:59.746294 26038 solver.cpp:237] Train net output #0: loss = 5.29303 (* 1 = 5.29303 loss)
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I0405 09:59:59.746302 26038 sgd_solver.cpp:105] Iteration 1536, lr = 1e-05
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I0405 10:00:05.408696 26038 solver.cpp:218] Iteration 1548 (2.11926 iter/s, 5.66236s/12 iters), loss = 5.2819
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I0405 10:00:05.408740 26038 solver.cpp:237] Train net output #0: loss = 5.2819 (* 1 = 5.2819 loss)
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I0405 10:00:05.408746 26038 sgd_solver.cpp:105] Iteration 1548, lr = 1e-05
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I0405 10:00:10.869065 26038 solver.cpp:218] Iteration 1560 (2.19769 iter/s, 5.46028s/12 iters), loss = 5.28679
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I0405 10:00:10.869117 26038 solver.cpp:237] Train net output #0: loss = 5.28679 (* 1 = 5.28679 loss)
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I0405 10:00:10.869125 26038 sgd_solver.cpp:105] Iteration 1560, lr = 1e-05
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I0405 10:00:16.263064 26038 solver.cpp:218] Iteration 1572 (2.22473 iter/s, 5.3939s/12 iters), loss = 5.28932
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I0405 10:00:16.263114 26038 solver.cpp:237] Train net output #0: loss = 5.28932 (* 1 = 5.28932 loss)
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I0405 10:00:16.263123 26038 sgd_solver.cpp:105] Iteration 1572, lr = 1e-05
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I0405 10:00:21.586025 26038 solver.cpp:218] Iteration 1584 (2.25443 iter/s, 5.32287s/12 iters), loss = 5.29815
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I0405 10:00:21.586081 26038 solver.cpp:237] Train net output #0: loss = 5.29815 (* 1 = 5.29815 loss)
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I0405 10:00:21.586089 26038 sgd_solver.cpp:105] Iteration 1584, lr = 1e-05
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I0405 10:00:27.105098 26038 solver.cpp:218] Iteration 1596 (2.17432 iter/s, 5.51897s/12 iters), loss = 5.28393
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I0405 10:00:27.105149 26038 solver.cpp:237] Train net output #0: loss = 5.28393 (* 1 = 5.28393 loss)
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I0405 10:00:27.105156 26038 sgd_solver.cpp:105] Iteration 1596, lr = 1e-05
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I0405 10:00:32.695974 26038 solver.cpp:218] Iteration 1608 (2.14639 iter/s, 5.59078s/12 iters), loss = 5.29044
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I0405 10:00:32.696079 26038 solver.cpp:237] Train net output #0: loss = 5.29044 (* 1 = 5.29044 loss)
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I0405 10:00:32.696085 26038 sgd_solver.cpp:105] Iteration 1608, lr = 1e-05
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I0405 10:00:36.845975 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:00:38.054476 26038 solver.cpp:218] Iteration 1620 (2.23949 iter/s, 5.35835s/12 iters), loss = 5.27931
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I0405 10:00:38.054525 26038 solver.cpp:237] Train net output #0: loss = 5.27931 (* 1 = 5.27931 loss)
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I0405 10:00:38.054531 26038 sgd_solver.cpp:105] Iteration 1620, lr = 1e-05
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I0405 10:00:42.762138 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
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I0405 10:00:45.842988 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
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I0405 10:00:48.154953 26038 solver.cpp:330] Iteration 1632, Testing net (#0)
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I0405 10:00:48.154978 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:00:51.804620 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:00:52.473068 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:00:52.473105 26038 solver.cpp:397] Test net output #1: loss = 5.27912 (* 1 = 5.27912 loss)
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I0405 10:00:52.619371 26038 solver.cpp:218] Iteration 1632 (0.823907 iter/s, 14.5648s/12 iters), loss = 5.29298
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I0405 10:00:52.619429 26038 solver.cpp:237] Train net output #0: loss = 5.29298 (* 1 = 5.29298 loss)
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I0405 10:00:52.619437 26038 sgd_solver.cpp:105] Iteration 1632, lr = 1e-05
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I0405 10:00:57.146893 26038 solver.cpp:218] Iteration 1644 (2.65052 iter/s, 4.52742s/12 iters), loss = 5.29367
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I0405 10:00:57.146939 26038 solver.cpp:237] Train net output #0: loss = 5.29367 (* 1 = 5.29367 loss)
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I0405 10:00:57.146946 26038 sgd_solver.cpp:105] Iteration 1644, lr = 1e-05
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I0405 10:01:02.622287 26038 solver.cpp:218] Iteration 1656 (2.19166 iter/s, 5.4753s/12 iters), loss = 5.28621
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I0405 10:01:02.622346 26038 solver.cpp:237] Train net output #0: loss = 5.28621 (* 1 = 5.28621 loss)
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I0405 10:01:02.622356 26038 sgd_solver.cpp:105] Iteration 1656, lr = 1e-05
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I0405 10:01:08.193393 26038 solver.cpp:218] Iteration 1668 (2.15401 iter/s, 5.571s/12 iters), loss = 5.28487
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I0405 10:01:08.193542 26038 solver.cpp:237] Train net output #0: loss = 5.28487 (* 1 = 5.28487 loss)
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I0405 10:01:08.193549 26038 sgd_solver.cpp:105] Iteration 1668, lr = 1e-05
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I0405 10:01:13.551612 26038 solver.cpp:218] Iteration 1680 (2.23963 iter/s, 5.35802s/12 iters), loss = 5.29189
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I0405 10:01:13.551658 26038 solver.cpp:237] Train net output #0: loss = 5.29189 (* 1 = 5.29189 loss)
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I0405 10:01:13.551666 26038 sgd_solver.cpp:105] Iteration 1680, lr = 1e-05
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I0405 10:01:18.991181 26038 solver.cpp:218] Iteration 1692 (2.2061 iter/s, 5.43947s/12 iters), loss = 5.28209
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I0405 10:01:18.991235 26038 solver.cpp:237] Train net output #0: loss = 5.28209 (* 1 = 5.28209 loss)
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I0405 10:01:18.991243 26038 sgd_solver.cpp:105] Iteration 1692, lr = 1e-05
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I0405 10:01:24.574280 26038 solver.cpp:218] Iteration 1704 (2.14938 iter/s, 5.583s/12 iters), loss = 5.27313
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I0405 10:01:24.574326 26038 solver.cpp:237] Train net output #0: loss = 5.27313 (* 1 = 5.27313 loss)
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I0405 10:01:24.574331 26038 sgd_solver.cpp:105] Iteration 1704, lr = 1e-05
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I0405 10:01:30.177071 26038 solver.cpp:218] Iteration 1716 (2.14183 iter/s, 5.6027s/12 iters), loss = 5.25937
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I0405 10:01:30.177119 26038 solver.cpp:237] Train net output #0: loss = 5.25937 (* 1 = 5.25937 loss)
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I0405 10:01:30.177129 26038 sgd_solver.cpp:105] Iteration 1716, lr = 1e-05
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I0405 10:01:31.242074 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:01:35.466547 26038 solver.cpp:218] Iteration 1728 (2.26919 iter/s, 5.28824s/12 iters), loss = 5.27277
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I0405 10:01:35.466599 26038 solver.cpp:237] Train net output #0: loss = 5.27277 (* 1 = 5.27277 loss)
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I0405 10:01:35.466607 26038 sgd_solver.cpp:105] Iteration 1728, lr = 1e-05
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I0405 10:01:37.704668 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
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||
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I0405 10:01:40.693889 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
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I0405 10:01:43.033141 26038 solver.cpp:330] Iteration 1734, Testing net (#0)
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I0405 10:01:43.033169 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:01:46.876859 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:01:47.641852 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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||
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I0405 10:01:47.641892 26038 solver.cpp:397] Test net output #1: loss = 5.27907 (* 1 = 5.27907 loss)
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I0405 10:01:49.602262 26038 solver.cpp:218] Iteration 1740 (0.848922 iter/s, 14.1356s/12 iters), loss = 5.27076
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I0405 10:01:49.602314 26038 solver.cpp:237] Train net output #0: loss = 5.27076 (* 1 = 5.27076 loss)
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I0405 10:01:49.602322 26038 sgd_solver.cpp:105] Iteration 1740, lr = 1e-05
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I0405 10:01:54.895711 26038 solver.cpp:218] Iteration 1752 (2.26699 iter/s, 5.29336s/12 iters), loss = 5.28349
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I0405 10:01:54.895750 26038 solver.cpp:237] Train net output #0: loss = 5.28349 (* 1 = 5.28349 loss)
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I0405 10:01:54.895756 26038 sgd_solver.cpp:105] Iteration 1752, lr = 1e-05
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I0405 10:02:00.553323 26038 solver.cpp:218] Iteration 1764 (2.12107 iter/s, 5.65753s/12 iters), loss = 5.27944
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I0405 10:02:00.553361 26038 solver.cpp:237] Train net output #0: loss = 5.27944 (* 1 = 5.27944 loss)
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I0405 10:02:00.553366 26038 sgd_solver.cpp:105] Iteration 1764, lr = 1e-05
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I0405 10:02:06.214676 26038 solver.cpp:218] Iteration 1776 (2.11967 iter/s, 5.66127s/12 iters), loss = 5.29663
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I0405 10:02:06.214726 26038 solver.cpp:237] Train net output #0: loss = 5.29663 (* 1 = 5.29663 loss)
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I0405 10:02:06.214733 26038 sgd_solver.cpp:105] Iteration 1776, lr = 1e-05
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I0405 10:02:11.574457 26038 solver.cpp:218] Iteration 1788 (2.23894 iter/s, 5.35968s/12 iters), loss = 5.29077
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I0405 10:02:11.574601 26038 solver.cpp:237] Train net output #0: loss = 5.29077 (* 1 = 5.29077 loss)
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I0405 10:02:11.574612 26038 sgd_solver.cpp:105] Iteration 1788, lr = 1e-05
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I0405 10:02:17.192963 26038 solver.cpp:218] Iteration 1800 (2.13587 iter/s, 5.61831s/12 iters), loss = 5.26385
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I0405 10:02:17.193024 26038 solver.cpp:237] Train net output #0: loss = 5.26385 (* 1 = 5.26385 loss)
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I0405 10:02:17.193032 26038 sgd_solver.cpp:105] Iteration 1800, lr = 1e-05
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I0405 10:02:22.479696 26038 solver.cpp:218] Iteration 1812 (2.26988 iter/s, 5.28663s/12 iters), loss = 5.26547
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I0405 10:02:22.479744 26038 solver.cpp:237] Train net output #0: loss = 5.26547 (* 1 = 5.26547 loss)
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||
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I0405 10:02:22.479750 26038 sgd_solver.cpp:105] Iteration 1812, lr = 1e-05
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I0405 10:02:25.926434 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:02:27.782343 26038 solver.cpp:218] Iteration 1824 (2.26306 iter/s, 5.30255s/12 iters), loss = 5.28806
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I0405 10:02:27.782407 26038 solver.cpp:237] Train net output #0: loss = 5.28806 (* 1 = 5.28806 loss)
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I0405 10:02:27.782416 26038 sgd_solver.cpp:105] Iteration 1824, lr = 1e-05
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I0405 10:02:32.417749 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
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||
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I0405 10:02:35.442530 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
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I0405 10:02:37.793032 26038 solver.cpp:330] Iteration 1836, Testing net (#0)
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||
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I0405 10:02:37.793051 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:02:41.582859 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 10:02:42.395756 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
|
||
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I0405 10:02:42.395788 26038 solver.cpp:397] Test net output #1: loss = 5.27914 (* 1 = 5.27914 loss)
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I0405 10:02:42.537154 26038 solver.cpp:218] Iteration 1836 (0.813303 iter/s, 14.7547s/12 iters), loss = 5.27035
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I0405 10:02:42.537209 26038 solver.cpp:237] Train net output #0: loss = 5.27035 (* 1 = 5.27035 loss)
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I0405 10:02:42.537218 26038 sgd_solver.cpp:105] Iteration 1836, lr = 1e-05
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I0405 10:02:47.135011 26038 solver.cpp:218] Iteration 1848 (2.60997 iter/s, 4.59776s/12 iters), loss = 5.27906
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I0405 10:02:47.135053 26038 solver.cpp:237] Train net output #0: loss = 5.27906 (* 1 = 5.27906 loss)
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I0405 10:02:47.135059 26038 sgd_solver.cpp:105] Iteration 1848, lr = 1e-05
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I0405 10:02:52.512233 26038 solver.cpp:218] Iteration 1860 (2.23167 iter/s, 5.37713s/12 iters), loss = 5.29748
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I0405 10:02:52.512287 26038 solver.cpp:237] Train net output #0: loss = 5.29748 (* 1 = 5.29748 loss)
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I0405 10:02:52.512295 26038 sgd_solver.cpp:105] Iteration 1860, lr = 1e-05
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I0405 10:02:57.493583 26038 solver.cpp:218] Iteration 1872 (2.40903 iter/s, 4.98125s/12 iters), loss = 5.26768
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I0405 10:02:57.493623 26038 solver.cpp:237] Train net output #0: loss = 5.26768 (* 1 = 5.26768 loss)
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I0405 10:02:57.493629 26038 sgd_solver.cpp:105] Iteration 1872, lr = 1e-05
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I0405 10:03:02.843829 26038 solver.cpp:218] Iteration 1884 (2.24293 iter/s, 5.35016s/12 iters), loss = 5.27976
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I0405 10:03:02.843886 26038 solver.cpp:237] Train net output #0: loss = 5.27976 (* 1 = 5.27976 loss)
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I0405 10:03:02.843895 26038 sgd_solver.cpp:105] Iteration 1884, lr = 1e-05
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I0405 10:03:08.456948 26038 solver.cpp:218] Iteration 1896 (2.13789 iter/s, 5.61302s/12 iters), loss = 5.27626
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I0405 10:03:08.457000 26038 solver.cpp:237] Train net output #0: loss = 5.27626 (* 1 = 5.27626 loss)
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I0405 10:03:08.457008 26038 sgd_solver.cpp:105] Iteration 1896, lr = 1e-05
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I0405 10:03:13.956384 26038 solver.cpp:218] Iteration 1908 (2.18208 iter/s, 5.49934s/12 iters), loss = 5.29315
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I0405 10:03:13.956508 26038 solver.cpp:237] Train net output #0: loss = 5.29315 (* 1 = 5.29315 loss)
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I0405 10:03:13.956519 26038 sgd_solver.cpp:105] Iteration 1908, lr = 1e-05
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I0405 10:03:19.465682 26038 solver.cpp:218] Iteration 1920 (2.1782 iter/s, 5.50914s/12 iters), loss = 5.28733
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I0405 10:03:19.465723 26038 solver.cpp:237] Train net output #0: loss = 5.28733 (* 1 = 5.28733 loss)
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I0405 10:03:19.465730 26038 sgd_solver.cpp:105] Iteration 1920, lr = 1e-05
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I0405 10:03:19.840569 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:03:24.876184 26038 solver.cpp:218] Iteration 1932 (2.21794 iter/s, 5.41042s/12 iters), loss = 5.28238
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I0405 10:03:24.876226 26038 solver.cpp:237] Train net output #0: loss = 5.28238 (* 1 = 5.28238 loss)
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I0405 10:03:24.876232 26038 sgd_solver.cpp:105] Iteration 1932, lr = 1e-05
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I0405 10:03:26.984582 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
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I0405 10:03:30.117103 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
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I0405 10:03:32.427834 26038 solver.cpp:330] Iteration 1938, Testing net (#0)
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I0405 10:03:32.427855 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:03:36.160756 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:03:36.981454 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:03:36.981492 26038 solver.cpp:397] Test net output #1: loss = 5.27882 (* 1 = 5.27882 loss)
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I0405 10:03:38.752676 26038 solver.cpp:218] Iteration 1944 (0.86478 iter/s, 13.8764s/12 iters), loss = 5.28528
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I0405 10:03:38.752732 26038 solver.cpp:237] Train net output #0: loss = 5.28528 (* 1 = 5.28528 loss)
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I0405 10:03:38.752739 26038 sgd_solver.cpp:105] Iteration 1944, lr = 1e-05
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I0405 10:03:44.175870 26038 solver.cpp:218] Iteration 1956 (2.21278 iter/s, 5.42305s/12 iters), loss = 5.26432
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I0405 10:03:44.176048 26038 solver.cpp:237] Train net output #0: loss = 5.26432 (* 1 = 5.26432 loss)
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I0405 10:03:44.176057 26038 sgd_solver.cpp:105] Iteration 1956, lr = 1e-05
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I0405 10:03:49.595611 26038 solver.cpp:218] Iteration 1968 (2.21422 iter/s, 5.41952s/12 iters), loss = 5.2932
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I0405 10:03:49.595667 26038 solver.cpp:237] Train net output #0: loss = 5.2932 (* 1 = 5.2932 loss)
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I0405 10:03:49.595676 26038 sgd_solver.cpp:105] Iteration 1968, lr = 1e-05
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I0405 10:03:54.874763 26038 solver.cpp:218] Iteration 1980 (2.27314 iter/s, 5.27905s/12 iters), loss = 5.27999
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I0405 10:03:54.874805 26038 solver.cpp:237] Train net output #0: loss = 5.27999 (* 1 = 5.27999 loss)
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I0405 10:03:54.874810 26038 sgd_solver.cpp:105] Iteration 1980, lr = 1e-05
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I0405 10:04:00.240551 26038 solver.cpp:218] Iteration 1992 (2.23643 iter/s, 5.36569s/12 iters), loss = 5.28556
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I0405 10:04:00.240612 26038 solver.cpp:237] Train net output #0: loss = 5.28556 (* 1 = 5.28556 loss)
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I0405 10:04:00.240622 26038 sgd_solver.cpp:105] Iteration 1992, lr = 1e-05
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I0405 10:04:05.808297 26038 solver.cpp:218] Iteration 2004 (2.15531 iter/s, 5.56765s/12 iters), loss = 5.27667
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I0405 10:04:05.808337 26038 solver.cpp:237] Train net output #0: loss = 5.27667 (* 1 = 5.27667 loss)
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I0405 10:04:05.808342 26038 sgd_solver.cpp:105] Iteration 2004, lr = 1e-05
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I0405 10:04:11.197175 26038 solver.cpp:218] Iteration 2016 (2.22684 iter/s, 5.38879s/12 iters), loss = 5.2797
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I0405 10:04:11.197211 26038 solver.cpp:237] Train net output #0: loss = 5.2797 (* 1 = 5.2797 loss)
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I0405 10:04:11.197216 26038 sgd_solver.cpp:105] Iteration 2016, lr = 1e-05
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I0405 10:04:13.944897 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:04:16.615016 26038 solver.cpp:218] Iteration 2028 (2.21494 iter/s, 5.41776s/12 iters), loss = 5.27518
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I0405 10:04:16.615137 26038 solver.cpp:237] Train net output #0: loss = 5.27518 (* 1 = 5.27518 loss)
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I0405 10:04:16.615145 26038 sgd_solver.cpp:105] Iteration 2028, lr = 1e-05
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I0405 10:04:21.408224 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
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I0405 10:04:24.446297 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
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I0405 10:04:26.776798 26038 solver.cpp:330] Iteration 2040, Testing net (#0)
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I0405 10:04:26.776818 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:04:30.551285 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:04:31.398468 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:04:31.398505 26038 solver.cpp:397] Test net output #1: loss = 5.2789 (* 1 = 5.2789 loss)
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I0405 10:04:31.540201 26038 solver.cpp:218] Iteration 2040 (0.804022 iter/s, 14.925s/12 iters), loss = 5.28567
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I0405 10:04:31.540261 26038 solver.cpp:237] Train net output #0: loss = 5.28567 (* 1 = 5.28567 loss)
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I0405 10:04:31.540268 26038 sgd_solver.cpp:105] Iteration 2040, lr = 1e-05
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I0405 10:04:36.017252 26038 solver.cpp:218] Iteration 2052 (2.6804 iter/s, 4.47695s/12 iters), loss = 5.27981
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I0405 10:04:36.017318 26038 solver.cpp:237] Train net output #0: loss = 5.27981 (* 1 = 5.27981 loss)
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I0405 10:04:36.017326 26038 sgd_solver.cpp:105] Iteration 2052, lr = 1e-05
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I0405 10:04:37.733220 26038 blocking_queue.cpp:49] Waiting for data
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I0405 10:04:41.449457 26038 solver.cpp:218] Iteration 2064 (2.20909 iter/s, 5.43209s/12 iters), loss = 5.28192
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I0405 10:04:41.449520 26038 solver.cpp:237] Train net output #0: loss = 5.28192 (* 1 = 5.28192 loss)
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I0405 10:04:41.449529 26038 sgd_solver.cpp:105] Iteration 2064, lr = 1e-05
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I0405 10:04:46.927695 26038 solver.cpp:218] Iteration 2076 (2.19053 iter/s, 5.47813s/12 iters), loss = 5.28665
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I0405 10:04:46.927875 26038 solver.cpp:237] Train net output #0: loss = 5.28665 (* 1 = 5.28665 loss)
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I0405 10:04:46.927886 26038 sgd_solver.cpp:105] Iteration 2076, lr = 1e-05
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I0405 10:04:52.322302 26038 solver.cpp:218] Iteration 2088 (2.22454 iter/s, 5.39438s/12 iters), loss = 5.27719
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I0405 10:04:52.322345 26038 solver.cpp:237] Train net output #0: loss = 5.27719 (* 1 = 5.27719 loss)
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I0405 10:04:52.322350 26038 sgd_solver.cpp:105] Iteration 2088, lr = 1e-05
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I0405 10:04:57.897701 26038 solver.cpp:237] Train net output #0: loss = 5.28531 (* 1 = 5.28531 loss)
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I0405 10:05:03.315084 26038 solver.cpp:218] Iteration 2112 (2.21511 iter/s, 5.41733s/12 iters), loss = 5.28484
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I0405 10:05:03.315145 26038 solver.cpp:237] Train net output #0: loss = 5.28484 (* 1 = 5.28484 loss)
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I0405 10:05:03.315153 26038 sgd_solver.cpp:105] Iteration 2112, lr = 1e-05
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I0405 10:05:08.352260 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:05:08.729076 26038 solver.cpp:218] Iteration 2124 (2.21652 iter/s, 5.41389s/12 iters), loss = 5.28365
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I0405 10:05:08.729115 26038 solver.cpp:237] Train net output #0: loss = 5.28365 (* 1 = 5.28365 loss)
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I0405 10:05:08.729120 26038 sgd_solver.cpp:105] Iteration 2124, lr = 1e-05
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I0405 10:05:14.235599 26038 solver.cpp:218] Iteration 2136 (2.17927 iter/s, 5.50644s/12 iters), loss = 5.27339
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I0405 10:05:14.235661 26038 solver.cpp:237] Train net output #0: loss = 5.27339 (* 1 = 5.27339 loss)
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I0405 10:05:14.235671 26038 sgd_solver.cpp:105] Iteration 2136, lr = 1e-05
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I0405 10:05:19.518496 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
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I0405 10:05:21.831553 26038 solver.cpp:330] Iteration 2142, Testing net (#0)
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I0405 10:05:21.831574 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:05:25.595319 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:05:26.485406 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:05:26.485442 26038 solver.cpp:397] Test net output #1: loss = 5.27906 (* 1 = 5.27906 loss)
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I0405 10:05:28.511335 26038 solver.cpp:218] Iteration 2148 (0.840596 iter/s, 14.2756s/12 iters), loss = 5.28341
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I0405 10:05:28.511376 26038 solver.cpp:237] Train net output #0: loss = 5.28341 (* 1 = 5.28341 loss)
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I0405 10:05:33.978386 26038 solver.cpp:218] Iteration 2160 (2.195 iter/s, 5.46697s/12 iters), loss = 5.27887
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I0405 10:05:33.978423 26038 solver.cpp:237] Train net output #0: loss = 5.27887 (* 1 = 5.27887 loss)
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I0405 10:05:33.978428 26038 sgd_solver.cpp:105] Iteration 2160, lr = 1e-05
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I0405 10:05:39.185271 26038 solver.cpp:218] Iteration 2172 (2.30468 iter/s, 5.20679s/12 iters), loss = 5.28933
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I0405 10:05:39.185351 26038 solver.cpp:237] Train net output #0: loss = 5.28933 (* 1 = 5.28933 loss)
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I0405 10:05:39.185360 26038 sgd_solver.cpp:105] Iteration 2172, lr = 1e-05
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I0405 10:05:44.524077 26038 solver.cpp:218] Iteration 2184 (2.24774 iter/s, 5.33869s/12 iters), loss = 5.28543
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I0405 10:05:44.524120 26038 solver.cpp:237] Train net output #0: loss = 5.28543 (* 1 = 5.28543 loss)
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I0405 10:05:44.524125 26038 sgd_solver.cpp:105] Iteration 2184, lr = 1e-05
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I0405 10:05:50.169338 26038 solver.cpp:218] Iteration 2196 (2.12577 iter/s, 5.645s/12 iters), loss = 5.28599
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I0405 10:05:50.169466 26038 solver.cpp:237] Train net output #0: loss = 5.28599 (* 1 = 5.28599 loss)
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I0405 10:05:50.169474 26038 sgd_solver.cpp:105] Iteration 2196, lr = 1e-05
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I0405 10:05:55.591517 26038 solver.cpp:218] Iteration 2208 (2.2132 iter/s, 5.42201s/12 iters), loss = 5.2905
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I0405 10:05:55.591574 26038 solver.cpp:237] Train net output #0: loss = 5.2905 (* 1 = 5.2905 loss)
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I0405 10:05:55.591583 26038 sgd_solver.cpp:105] Iteration 2208, lr = 1e-05
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I0405 10:06:01.054980 26038 solver.cpp:218] Iteration 2220 (2.19645 iter/s, 5.46336s/12 iters), loss = 5.2683
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I0405 10:06:01.055020 26038 solver.cpp:237] Train net output #0: loss = 5.2683 (* 1 = 5.2683 loss)
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I0405 10:06:01.055027 26038 sgd_solver.cpp:105] Iteration 2220, lr = 1e-05
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I0405 10:06:02.752115 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:06:06.160838 26038 solver.cpp:218] Iteration 2232 (2.3503 iter/s, 5.10574s/12 iters), loss = 5.29251
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I0405 10:06:06.160874 26038 solver.cpp:237] Train net output #0: loss = 5.29251 (* 1 = 5.29251 loss)
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I0405 10:06:06.160881 26038 sgd_solver.cpp:105] Iteration 2232, lr = 1e-05
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I0405 10:06:11.161087 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
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I0405 10:06:14.303336 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
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I0405 10:06:16.596993 26038 solver.cpp:330] Iteration 2244, Testing net (#0)
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I0405 10:06:16.597016 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:06:20.179869 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:06:21.088140 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:06:21.088181 26038 solver.cpp:397] Test net output #1: loss = 5.27881 (* 1 = 5.27881 loss)
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I0405 10:06:21.229702 26038 solver.cpp:218] Iteration 2244 (0.796351 iter/s, 15.0687s/12 iters), loss = 5.28144
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I0405 10:06:21.229759 26038 solver.cpp:237] Train net output #0: loss = 5.28144 (* 1 = 5.28144 loss)
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I0405 10:06:21.229768 26038 sgd_solver.cpp:105] Iteration 2244, lr = 1e-05
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I0405 10:06:25.663064 26038 solver.cpp:218] Iteration 2256 (2.70681 iter/s, 4.43327s/12 iters), loss = 5.28463
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I0405 10:06:25.663105 26038 solver.cpp:237] Train net output #0: loss = 5.28463 (* 1 = 5.28463 loss)
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I0405 10:06:25.663110 26038 sgd_solver.cpp:105] Iteration 2256, lr = 1e-05
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I0405 10:06:30.933686 26038 solver.cpp:218] Iteration 2268 (2.27681 iter/s, 5.27053s/12 iters), loss = 5.27977
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I0405 10:06:30.933740 26038 solver.cpp:237] Train net output #0: loss = 5.27977 (* 1 = 5.27977 loss)
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I0405 10:06:30.933748 26038 sgd_solver.cpp:105] Iteration 2268, lr = 1e-05
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I0405 10:06:36.398640 26038 solver.cpp:218] Iteration 2280 (2.19585 iter/s, 5.46486s/12 iters), loss = 5.26889
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I0405 10:06:36.398685 26038 solver.cpp:237] Train net output #0: loss = 5.26889 (* 1 = 5.26889 loss)
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I0405 10:06:36.398691 26038 sgd_solver.cpp:105] Iteration 2280, lr = 1e-05
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I0405 10:06:42.007162 26038 solver.cpp:218] Iteration 2292 (2.13964 iter/s, 5.60843s/12 iters), loss = 5.27937
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I0405 10:06:42.007205 26038 solver.cpp:237] Train net output #0: loss = 5.27937 (* 1 = 5.27937 loss)
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I0405 10:06:42.007210 26038 sgd_solver.cpp:105] Iteration 2292, lr = 1e-05
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I0405 10:06:47.450611 26038 solver.cpp:218] Iteration 2304 (2.20452 iter/s, 5.44335s/12 iters), loss = 5.29962
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I0405 10:06:47.450675 26038 solver.cpp:237] Train net output #0: loss = 5.29962 (* 1 = 5.29962 loss)
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I0405 10:06:47.450685 26038 sgd_solver.cpp:105] Iteration 2304, lr = 1e-05
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I0405 10:06:52.847458 26038 solver.cpp:218] Iteration 2316 (2.22356 iter/s, 5.39674s/12 iters), loss = 5.276
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I0405 10:06:52.847565 26038 solver.cpp:237] Train net output #0: loss = 5.276 (* 1 = 5.276 loss)
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I0405 10:06:52.847575 26038 sgd_solver.cpp:105] Iteration 2316, lr = 1e-05
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I0405 10:06:57.082007 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:06:58.258106 26038 solver.cpp:218] Iteration 2328 (2.21791 iter/s, 5.4105s/12 iters), loss = 5.28033
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I0405 10:06:58.258149 26038 solver.cpp:237] Train net output #0: loss = 5.28033 (* 1 = 5.28033 loss)
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I0405 10:06:58.258155 26038 sgd_solver.cpp:105] Iteration 2328, lr = 1e-05
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I0405 10:07:03.748737 26038 solver.cpp:218] Iteration 2340 (2.18558 iter/s, 5.49054s/12 iters), loss = 5.27404
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I0405 10:07:03.748800 26038 solver.cpp:237] Train net output #0: loss = 5.27404 (* 1 = 5.27404 loss)
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I0405 10:07:03.748811 26038 sgd_solver.cpp:105] Iteration 2340, lr = 1e-05
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I0405 10:07:05.951357 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
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I0405 10:07:09.789824 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
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I0405 10:07:12.170295 26038 solver.cpp:330] Iteration 2346, Testing net (#0)
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I0405 10:07:12.170317 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:07:15.825387 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:07:16.921037 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:07:16.921066 26038 solver.cpp:397] Test net output #1: loss = 5.27904 (* 1 = 5.27904 loss)
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I0405 10:07:18.748045 26038 solver.cpp:218] Iteration 2352 (0.800045 iter/s, 14.9992s/12 iters), loss = 5.29755
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I0405 10:07:18.748095 26038 solver.cpp:237] Train net output #0: loss = 5.29755 (* 1 = 5.29755 loss)
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I0405 10:07:18.748103 26038 sgd_solver.cpp:105] Iteration 2352, lr = 1e-05
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I0405 10:07:24.180846 26038 solver.cpp:218] Iteration 2364 (2.20884 iter/s, 5.4327s/12 iters), loss = 5.29607
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I0405 10:07:24.181066 26038 solver.cpp:237] Train net output #0: loss = 5.29607 (* 1 = 5.29607 loss)
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I0405 10:07:24.181075 26038 sgd_solver.cpp:105] Iteration 2364, lr = 1e-05
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I0405 10:07:29.540905 26038 solver.cpp:218] Iteration 2376 (2.23889 iter/s, 5.3598s/12 iters), loss = 5.27353
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I0405 10:07:29.540951 26038 solver.cpp:237] Train net output #0: loss = 5.27353 (* 1 = 5.27353 loss)
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I0405 10:07:29.540958 26038 sgd_solver.cpp:105] Iteration 2376, lr = 1e-05
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I0405 10:07:34.911008 26038 solver.cpp:218] Iteration 2388 (2.23463 iter/s, 5.37001s/12 iters), loss = 5.28054
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I0405 10:07:34.911075 26038 solver.cpp:237] Train net output #0: loss = 5.28054 (* 1 = 5.28054 loss)
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I0405 10:07:34.911084 26038 sgd_solver.cpp:105] Iteration 2388, lr = 1e-05
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I0405 10:07:40.351593 26038 solver.cpp:218] Iteration 2400 (2.20569 iter/s, 5.44047s/12 iters), loss = 5.29164
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I0405 10:07:40.351642 26038 solver.cpp:237] Train net output #0: loss = 5.29164 (* 1 = 5.29164 loss)
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I0405 10:07:40.351649 26038 sgd_solver.cpp:105] Iteration 2400, lr = 1e-05
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I0405 10:07:45.924922 26038 solver.cpp:218] Iteration 2412 (2.15315 iter/s, 5.57324s/12 iters), loss = 5.27698
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I0405 10:07:45.924964 26038 solver.cpp:237] Train net output #0: loss = 5.27698 (* 1 = 5.27698 loss)
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I0405 10:07:45.924971 26038 sgd_solver.cpp:105] Iteration 2412, lr = 1e-05
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I0405 10:07:51.437932 26038 solver.cpp:218] Iteration 2424 (2.17671 iter/s, 5.51292s/12 iters), loss = 5.27403
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I0405 10:07:51.437984 26038 solver.cpp:237] Train net output #0: loss = 5.27403 (* 1 = 5.27403 loss)
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I0405 10:07:51.437992 26038 sgd_solver.cpp:105] Iteration 2424, lr = 1e-05
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I0405 10:07:52.605540 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:07:56.964591 26038 solver.cpp:218] Iteration 2436 (2.17133 iter/s, 5.52657s/12 iters), loss = 5.28584
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I0405 10:07:56.964720 26038 solver.cpp:237] Train net output #0: loss = 5.28584 (* 1 = 5.28584 loss)
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I0405 10:07:56.964728 26038 sgd_solver.cpp:105] Iteration 2436, lr = 1e-05
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I0405 10:08:01.871407 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
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I0405 10:08:04.810627 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
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I0405 10:08:07.114200 26038 solver.cpp:330] Iteration 2448, Testing net (#0)
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I0405 10:08:07.114223 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:08:10.645970 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:08:11.626403 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:08:11.626436 26038 solver.cpp:397] Test net output #1: loss = 5.27884 (* 1 = 5.27884 loss)
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I0405 10:08:11.764201 26038 solver.cpp:218] Iteration 2448 (0.810844 iter/s, 14.7994s/12 iters), loss = 5.28899
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I0405 10:08:11.764928 26038 solver.cpp:237] Train net output #0: loss = 5.28899 (* 1 = 5.28899 loss)
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I0405 10:08:11.764943 26038 sgd_solver.cpp:105] Iteration 2448, lr = 1e-05
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I0405 10:08:16.318142 26038 solver.cpp:218] Iteration 2460 (2.63552 iter/s, 4.55318s/12 iters), loss = 5.28061
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I0405 10:08:16.318199 26038 solver.cpp:237] Train net output #0: loss = 5.28061 (* 1 = 5.28061 loss)
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I0405 10:08:16.318208 26038 sgd_solver.cpp:105] Iteration 2460, lr = 1e-05
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I0405 10:08:21.604862 26038 solver.cpp:218] Iteration 2472 (2.26988 iter/s, 5.28662s/12 iters), loss = 5.2697
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I0405 10:08:21.604907 26038 solver.cpp:237] Train net output #0: loss = 5.2697 (* 1 = 5.2697 loss)
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I0405 10:08:21.604913 26038 sgd_solver.cpp:105] Iteration 2472, lr = 1e-05
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I0405 10:08:27.149410 26038 solver.cpp:218] Iteration 2484 (2.16432 iter/s, 5.54445s/12 iters), loss = 5.29228
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I0405 10:08:27.149578 26038 solver.cpp:237] Train net output #0: loss = 5.29228 (* 1 = 5.29228 loss)
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I0405 10:08:27.149587 26038 sgd_solver.cpp:105] Iteration 2484, lr = 1e-05
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I0405 10:08:32.668001 26038 solver.cpp:218] Iteration 2496 (2.17455 iter/s, 5.51838s/12 iters), loss = 5.29091
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I0405 10:08:32.668054 26038 solver.cpp:237] Train net output #0: loss = 5.29091 (* 1 = 5.29091 loss)
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I0405 10:08:32.668062 26038 sgd_solver.cpp:105] Iteration 2496, lr = 1e-05
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I0405 10:08:37.907426 26038 solver.cpp:218] Iteration 2508 (2.29037 iter/s, 5.23933s/12 iters), loss = 5.26452
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I0405 10:08:37.907466 26038 solver.cpp:237] Train net output #0: loss = 5.26452 (* 1 = 5.26452 loss)
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I0405 10:08:37.907471 26038 sgd_solver.cpp:105] Iteration 2508, lr = 1e-05
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I0405 10:08:43.232161 26038 solver.cpp:218] Iteration 2520 (2.25367 iter/s, 5.32465s/12 iters), loss = 5.26547
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I0405 10:08:43.232199 26038 solver.cpp:237] Train net output #0: loss = 5.26547 (* 1 = 5.26547 loss)
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I0405 10:08:43.232205 26038 sgd_solver.cpp:105] Iteration 2520, lr = 1e-05
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I0405 10:08:46.640185 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:08:48.726680 26038 solver.cpp:218] Iteration 2532 (2.18403 iter/s, 5.49443s/12 iters), loss = 5.29444
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I0405 10:08:48.726717 26038 solver.cpp:237] Train net output #0: loss = 5.29444 (* 1 = 5.29444 loss)
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I0405 10:08:48.726723 26038 sgd_solver.cpp:105] Iteration 2532, lr = 1e-05
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I0405 10:08:54.039531 26038 solver.cpp:218] Iteration 2544 (2.25871 iter/s, 5.31277s/12 iters), loss = 5.27024
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I0405 10:08:54.039572 26038 solver.cpp:237] Train net output #0: loss = 5.27024 (* 1 = 5.27024 loss)
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I0405 10:08:54.039577 26038 sgd_solver.cpp:105] Iteration 2544, lr = 1e-05
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I0405 10:08:56.156973 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
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I0405 10:08:59.627998 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
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I0405 10:09:02.053350 26038 solver.cpp:330] Iteration 2550, Testing net (#0)
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I0405 10:09:02.053370 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:09:05.658289 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:09:06.824182 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:09:06.824220 26038 solver.cpp:397] Test net output #1: loss = 5.27877 (* 1 = 5.27877 loss)
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I0405 10:09:08.848305 26038 solver.cpp:218] Iteration 2556 (0.810339 iter/s, 14.8086s/12 iters), loss = 5.28829
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I0405 10:09:08.848366 26038 solver.cpp:237] Train net output #0: loss = 5.28829 (* 1 = 5.28829 loss)
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I0405 10:09:08.848376 26038 sgd_solver.cpp:105] Iteration 2556, lr = 1e-05
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I0405 10:09:14.166254 26038 solver.cpp:218] Iteration 2568 (2.25655 iter/s, 5.31785s/12 iters), loss = 5.29492
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I0405 10:09:14.166291 26038 solver.cpp:237] Train net output #0: loss = 5.29492 (* 1 = 5.29492 loss)
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I0405 10:09:14.166297 26038 sgd_solver.cpp:105] Iteration 2568, lr = 1e-05
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I0405 10:09:19.489642 26038 solver.cpp:218] Iteration 2580 (2.25424 iter/s, 5.3233s/12 iters), loss = 5.27245
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I0405 10:09:19.489697 26038 solver.cpp:237] Train net output #0: loss = 5.27245 (* 1 = 5.27245 loss)
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I0405 10:09:19.489706 26038 sgd_solver.cpp:105] Iteration 2580, lr = 1e-05
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I0405 10:09:25.029897 26038 solver.cpp:218] Iteration 2592 (2.166 iter/s, 5.54016s/12 iters), loss = 5.28421
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I0405 10:09:25.029943 26038 solver.cpp:237] Train net output #0: loss = 5.28421 (* 1 = 5.28421 loss)
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I0405 10:09:25.029949 26038 sgd_solver.cpp:105] Iteration 2592, lr = 1e-05
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I0405 10:09:30.372727 26038 solver.cpp:218] Iteration 2604 (2.24604 iter/s, 5.34274s/12 iters), loss = 5.2777
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I0405 10:09:30.372872 26038 solver.cpp:237] Train net output #0: loss = 5.2777 (* 1 = 5.2777 loss)
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I0405 10:09:30.372887 26038 sgd_solver.cpp:105] Iteration 2604, lr = 1e-05
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I0405 10:09:35.597707 26038 solver.cpp:218] Iteration 2616 (2.29674 iter/s, 5.2248s/12 iters), loss = 5.28694
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I0405 10:09:35.597759 26038 solver.cpp:237] Train net output #0: loss = 5.28694 (* 1 = 5.28694 loss)
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I0405 10:09:35.597767 26038 sgd_solver.cpp:105] Iteration 2616, lr = 1e-05
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I0405 10:09:40.779315 26038 solver.cpp:218] Iteration 2628 (2.31593 iter/s, 5.18151s/12 iters), loss = 5.28082
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I0405 10:09:40.779369 26038 solver.cpp:237] Train net output #0: loss = 5.28082 (* 1 = 5.28082 loss)
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I0405 10:09:40.779377 26038 sgd_solver.cpp:105] Iteration 2628, lr = 1e-05
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I0405 10:09:41.174182 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:09:46.230281 26038 solver.cpp:218] Iteration 2640 (2.20149 iter/s, 5.45087s/12 iters), loss = 5.28686
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I0405 10:09:46.230342 26038 solver.cpp:237] Train net output #0: loss = 5.28686 (* 1 = 5.28686 loss)
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I0405 10:09:46.230353 26038 sgd_solver.cpp:105] Iteration 2640, lr = 1e-05
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I0405 10:09:50.940141 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
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I0405 10:09:54.121264 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
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I0405 10:09:56.492920 26038 solver.cpp:330] Iteration 2652, Testing net (#0)
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I0405 10:09:56.492946 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:09:59.919368 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:10:01.075624 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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||
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I0405 10:10:01.075717 26038 solver.cpp:397] Test net output #1: loss = 5.27873 (* 1 = 5.27873 loss)
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I0405 10:10:01.217351 26038 solver.cpp:218] Iteration 2652 (0.800698 iter/s, 14.9869s/12 iters), loss = 5.26923
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I0405 10:10:01.217397 26038 solver.cpp:237] Train net output #0: loss = 5.26923 (* 1 = 5.26923 loss)
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I0405 10:10:01.217403 26038 sgd_solver.cpp:105] Iteration 2652, lr = 1e-05
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I0405 10:10:05.787518 26038 solver.cpp:218] Iteration 2664 (2.62578 iter/s, 4.57008s/12 iters), loss = 5.27386
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I0405 10:10:05.787568 26038 solver.cpp:237] Train net output #0: loss = 5.27386 (* 1 = 5.27386 loss)
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I0405 10:10:05.787576 26038 sgd_solver.cpp:105] Iteration 2664, lr = 1e-05
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I0405 10:10:11.277513 26038 solver.cpp:218] Iteration 2676 (2.18583 iter/s, 5.4899s/12 iters), loss = 5.27648
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I0405 10:10:11.277570 26038 solver.cpp:237] Train net output #0: loss = 5.27648 (* 1 = 5.27648 loss)
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I0405 10:10:11.277578 26038 sgd_solver.cpp:105] Iteration 2676, lr = 1e-05
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I0405 10:10:16.823284 26038 solver.cpp:218] Iteration 2688 (2.16385 iter/s, 5.54568s/12 iters), loss = 5.29422
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I0405 10:10:16.823321 26038 solver.cpp:237] Train net output #0: loss = 5.29422 (* 1 = 5.29422 loss)
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I0405 10:10:16.823326 26038 sgd_solver.cpp:105] Iteration 2688, lr = 1e-05
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I0405 10:10:22.106004 26038 solver.cpp:218] Iteration 2700 (2.27159 iter/s, 5.28264s/12 iters), loss = 5.28654
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I0405 10:10:22.106058 26038 solver.cpp:237] Train net output #0: loss = 5.28654 (* 1 = 5.28654 loss)
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I0405 10:10:22.106066 26038 sgd_solver.cpp:105] Iteration 2700, lr = 1e-05
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I0405 10:10:27.502820 26038 solver.cpp:218] Iteration 2712 (2.22357 iter/s, 5.39672s/12 iters), loss = 5.28716
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I0405 10:10:27.502859 26038 solver.cpp:237] Train net output #0: loss = 5.28716 (* 1 = 5.28716 loss)
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I0405 10:10:27.502864 26038 sgd_solver.cpp:105] Iteration 2712, lr = 1e-05
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I0405 10:10:33.040999 26038 solver.cpp:218] Iteration 2724 (2.16681 iter/s, 5.5381s/12 iters), loss = 5.27572
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I0405 10:10:33.041100 26038 solver.cpp:237] Train net output #0: loss = 5.27572 (* 1 = 5.27572 loss)
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I0405 10:10:33.041105 26038 sgd_solver.cpp:105] Iteration 2724, lr = 1e-05
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I0405 10:10:35.893838 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:10:38.537786 26038 solver.cpp:218] Iteration 2736 (2.18315 iter/s, 5.49665s/12 iters), loss = 5.28645
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I0405 10:10:38.537823 26038 solver.cpp:237] Train net output #0: loss = 5.28645 (* 1 = 5.28645 loss)
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I0405 10:10:38.537828 26038 sgd_solver.cpp:105] Iteration 2736, lr = 1e-05
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I0405 10:10:43.831553 26038 solver.cpp:218] Iteration 2748 (2.26685 iter/s, 5.29368s/12 iters), loss = 5.27958
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I0405 10:10:43.831614 26038 solver.cpp:237] Train net output #0: loss = 5.27958 (* 1 = 5.27958 loss)
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I0405 10:10:43.831621 26038 sgd_solver.cpp:105] Iteration 2748, lr = 1e-05
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I0405 10:10:45.944231 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
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||
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I0405 10:10:48.929469 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
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I0405 10:10:51.245620 26038 solver.cpp:330] Iteration 2754, Testing net (#0)
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I0405 10:10:51.245641 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:10:54.403584 26038 blocking_queue.cpp:49] Waiting for data
|
||
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I0405 10:10:54.635226 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:10:55.738513 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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||
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I0405 10:10:55.738550 26038 solver.cpp:397] Test net output #1: loss = 5.27872 (* 1 = 5.27872 loss)
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I0405 10:10:57.764889 26038 solver.cpp:218] Iteration 2760 (0.861253 iter/s, 13.9332s/12 iters), loss = 5.28681
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I0405 10:10:57.764928 26038 solver.cpp:237] Train net output #0: loss = 5.28681 (* 1 = 5.28681 loss)
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I0405 10:10:57.764933 26038 sgd_solver.cpp:105] Iteration 2760, lr = 1e-05
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I0405 10:11:02.870046 26038 solver.cpp:218] Iteration 2772 (2.3506 iter/s, 5.10508s/12 iters), loss = 5.27881
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I0405 10:11:02.870085 26038 solver.cpp:237] Train net output #0: loss = 5.27881 (* 1 = 5.27881 loss)
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I0405 10:11:02.870091 26038 sgd_solver.cpp:105] Iteration 2772, lr = 1e-05
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I0405 10:11:08.143685 26038 solver.cpp:218] Iteration 2784 (2.27551 iter/s, 5.27355s/12 iters), loss = 5.29649
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I0405 10:11:08.143815 26038 solver.cpp:237] Train net output #0: loss = 5.29649 (* 1 = 5.29649 loss)
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I0405 10:11:08.143824 26038 sgd_solver.cpp:105] Iteration 2784, lr = 1e-05
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I0405 10:11:13.603262 26038 solver.cpp:218] Iteration 2796 (2.19804 iter/s, 5.45941s/12 iters), loss = 5.2818
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I0405 10:11:13.603302 26038 solver.cpp:237] Train net output #0: loss = 5.2818 (* 1 = 5.2818 loss)
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I0405 10:11:13.603307 26038 sgd_solver.cpp:105] Iteration 2796, lr = 1e-05
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I0405 10:11:19.120149 26038 solver.cpp:218] Iteration 2808 (2.17517 iter/s, 5.5168s/12 iters), loss = 5.27823
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I0405 10:11:19.120189 26038 solver.cpp:237] Train net output #0: loss = 5.27823 (* 1 = 5.27823 loss)
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I0405 10:11:19.120195 26038 sgd_solver.cpp:105] Iteration 2808, lr = 1e-05
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I0405 10:11:24.836917 26038 solver.cpp:218] Iteration 2820 (2.10088 iter/s, 5.71188s/12 iters), loss = 5.28033
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I0405 10:11:24.836966 26038 solver.cpp:237] Train net output #0: loss = 5.28033 (* 1 = 5.28033 loss)
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I0405 10:11:24.836972 26038 sgd_solver.cpp:105] Iteration 2820, lr = 1e-05
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I0405 10:11:30.137099 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:11:30.471483 26038 solver.cpp:218] Iteration 2832 (2.12975 iter/s, 5.63447s/12 iters), loss = 5.29502
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I0405 10:11:30.471540 26038 solver.cpp:237] Train net output #0: loss = 5.29502 (* 1 = 5.29502 loss)
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I0405 10:11:30.471552 26038 sgd_solver.cpp:105] Iteration 2832, lr = 1e-05
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I0405 10:11:35.869181 26038 solver.cpp:218] Iteration 2844 (2.22321 iter/s, 5.3976s/12 iters), loss = 5.27141
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I0405 10:11:35.875382 26038 solver.cpp:237] Train net output #0: loss = 5.27141 (* 1 = 5.27141 loss)
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I0405 10:11:35.875401 26038 sgd_solver.cpp:105] Iteration 2844, lr = 1e-05
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I0405 10:11:40.549401 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
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I0405 10:11:43.625864 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
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I0405 10:11:45.972003 26038 solver.cpp:330] Iteration 2856, Testing net (#0)
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I0405 10:11:45.972026 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:11:49.453050 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:11:50.598487 26038 solver.cpp:397] Test net output #0: accuracy = 0.00306373
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I0405 10:11:50.598520 26038 solver.cpp:397] Test net output #1: loss = 5.27858 (* 1 = 5.27858 loss)
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I0405 10:11:50.736498 26038 solver.cpp:218] Iteration 2856 (0.807481 iter/s, 14.861s/12 iters), loss = 5.27657
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I0405 10:11:50.738083 26038 solver.cpp:237] Train net output #0: loss = 5.27657 (* 1 = 5.27657 loss)
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I0405 10:11:50.738101 26038 sgd_solver.cpp:105] Iteration 2856, lr = 1e-05
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I0405 10:11:55.033715 26038 solver.cpp:218] Iteration 2868 (2.79355 iter/s, 4.2956s/12 iters), loss = 5.29326
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I0405 10:11:55.033766 26038 solver.cpp:237] Train net output #0: loss = 5.29326 (* 1 = 5.29326 loss)
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I0405 10:11:55.033773 26038 sgd_solver.cpp:105] Iteration 2868, lr = 1e-05
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I0405 10:12:00.522619 26038 solver.cpp:218] Iteration 2880 (2.18626 iter/s, 5.48881s/12 iters), loss = 5.28294
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I0405 10:12:00.522661 26038 solver.cpp:237] Train net output #0: loss = 5.28294 (* 1 = 5.28294 loss)
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I0405 10:12:00.522666 26038 sgd_solver.cpp:105] Iteration 2880, lr = 1e-05
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I0405 10:12:05.755247 26038 solver.cpp:218] Iteration 2892 (2.29334 iter/s, 5.23254s/12 iters), loss = 5.28468
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I0405 10:12:05.761441 26038 solver.cpp:237] Train net output #0: loss = 5.28468 (* 1 = 5.28468 loss)
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I0405 10:12:05.761458 26038 sgd_solver.cpp:105] Iteration 2892, lr = 1e-05
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I0405 10:12:11.252050 26038 solver.cpp:218] Iteration 2904 (2.18556 iter/s, 5.49057s/12 iters), loss = 5.28759
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I0405 10:12:11.255937 26038 solver.cpp:237] Train net output #0: loss = 5.28759 (* 1 = 5.28759 loss)
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I0405 10:12:11.255951 26038 sgd_solver.cpp:105] Iteration 2904, lr = 1e-05
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I0405 10:12:16.597640 26038 solver.cpp:218] Iteration 2916 (2.24649 iter/s, 5.34167s/12 iters), loss = 5.28347
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I0405 10:12:16.597692 26038 solver.cpp:237] Train net output #0: loss = 5.28347 (* 1 = 5.28347 loss)
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I0405 10:12:16.597700 26038 sgd_solver.cpp:105] Iteration 2916, lr = 1e-05
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I0405 10:12:21.976827 26038 solver.cpp:218] Iteration 2928 (2.23086 iter/s, 5.3791s/12 iters), loss = 5.28462
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I0405 10:12:21.976864 26038 solver.cpp:237] Train net output #0: loss = 5.28462 (* 1 = 5.28462 loss)
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I0405 10:12:21.976869 26038 sgd_solver.cpp:105] Iteration 2928, lr = 1e-05
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I0405 10:12:23.931349 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:12:27.451727 26038 solver.cpp:218] Iteration 2940 (2.19186 iter/s, 5.47481s/12 iters), loss = 5.29002
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I0405 10:12:27.451782 26038 solver.cpp:237] Train net output #0: loss = 5.29002 (* 1 = 5.29002 loss)
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I0405 10:12:27.451792 26038 sgd_solver.cpp:105] Iteration 2940, lr = 1e-05
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I0405 10:12:32.861711 26038 solver.cpp:218] Iteration 2952 (2.21816 iter/s, 5.40989s/12 iters), loss = 5.27564
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I0405 10:12:32.861750 26038 solver.cpp:237] Train net output #0: loss = 5.27564 (* 1 = 5.27564 loss)
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I0405 10:12:32.861757 26038 sgd_solver.cpp:105] Iteration 2952, lr = 1e-05
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I0405 10:12:35.046356 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
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I0405 10:12:38.127300 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
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I0405 10:12:40.458096 26038 solver.cpp:330] Iteration 2958, Testing net (#0)
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I0405 10:12:40.458122 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:12:43.823755 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:12:45.162784 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:12:45.162819 26038 solver.cpp:397] Test net output #1: loss = 5.27895 (* 1 = 5.27895 loss)
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I0405 10:12:47.157867 26038 solver.cpp:218] Iteration 2964 (0.839394 iter/s, 14.296s/12 iters), loss = 5.28749
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I0405 10:12:47.157924 26038 solver.cpp:237] Train net output #0: loss = 5.28749 (* 1 = 5.28749 loss)
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I0405 10:12:47.157932 26038 sgd_solver.cpp:105] Iteration 2964, lr = 1e-05
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I0405 10:12:52.684813 26038 solver.cpp:218] Iteration 2976 (2.17122 iter/s, 5.52684s/12 iters), loss = 5.27933
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I0405 10:12:52.684872 26038 solver.cpp:237] Train net output #0: loss = 5.27933 (* 1 = 5.27933 loss)
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I0405 10:12:52.684880 26038 sgd_solver.cpp:105] Iteration 2976, lr = 1e-05
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I0405 10:12:57.882575 26038 solver.cpp:218] Iteration 2988 (2.30873 iter/s, 5.19767s/12 iters), loss = 5.27821
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I0405 10:12:57.882616 26038 solver.cpp:237] Train net output #0: loss = 5.27821 (* 1 = 5.27821 loss)
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I0405 10:12:57.882620 26038 sgd_solver.cpp:105] Iteration 2988, lr = 1e-05
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I0405 10:13:03.135756 26038 solver.cpp:218] Iteration 3000 (2.28437 iter/s, 5.2531s/12 iters), loss = 5.27637
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I0405 10:13:03.135797 26038 solver.cpp:237] Train net output #0: loss = 5.27637 (* 1 = 5.27637 loss)
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I0405 10:13:03.135803 26038 sgd_solver.cpp:105] Iteration 3000, lr = 1e-05
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I0405 10:13:08.528120 26038 solver.cpp:218] Iteration 3012 (2.22541 iter/s, 5.39227s/12 iters), loss = 5.29825
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I0405 10:13:08.528180 26038 solver.cpp:237] Train net output #0: loss = 5.29825 (* 1 = 5.29825 loss)
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I0405 10:13:08.528189 26038 sgd_solver.cpp:105] Iteration 3012, lr = 1e-05
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I0405 10:13:13.956327 26038 solver.cpp:218] Iteration 3024 (2.21072 iter/s, 5.42811s/12 iters), loss = 5.27854
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I0405 10:13:13.956429 26038 solver.cpp:237] Train net output #0: loss = 5.27854 (* 1 = 5.27854 loss)
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I0405 10:13:13.956436 26038 sgd_solver.cpp:105] Iteration 3024, lr = 1e-05
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I0405 10:13:18.340131 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:13:19.529130 26038 solver.cpp:218] Iteration 3036 (2.15337 iter/s, 5.57266s/12 iters), loss = 5.27206
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I0405 10:13:19.529172 26038 solver.cpp:237] Train net output #0: loss = 5.27206 (* 1 = 5.27206 loss)
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I0405 10:13:19.529177 26038 sgd_solver.cpp:105] Iteration 3036, lr = 1e-05
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I0405 10:13:25.064792 26038 solver.cpp:218] Iteration 3048 (2.1678 iter/s, 5.53558s/12 iters), loss = 5.26536
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I0405 10:13:25.064836 26038 solver.cpp:237] Train net output #0: loss = 5.26536 (* 1 = 5.26536 loss)
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I0405 10:13:25.064841 26038 sgd_solver.cpp:105] Iteration 3048, lr = 1e-05
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I0405 10:13:29.934463 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
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I0405 10:13:32.883812 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
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I0405 10:13:35.194645 26038 solver.cpp:330] Iteration 3060, Testing net (#0)
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I0405 10:13:35.194669 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:13:38.530215 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:13:39.858194 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:13:39.858230 26038 solver.cpp:397] Test net output #1: loss = 5.27893 (* 1 = 5.27893 loss)
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I0405 10:13:40.006960 26038 solver.cpp:218] Iteration 3060 (0.803103 iter/s, 14.942s/12 iters), loss = 5.28644
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I0405 10:13:40.008548 26038 solver.cpp:237] Train net output #0: loss = 5.28644 (* 1 = 5.28644 loss)
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I0405 10:13:40.008561 26038 sgd_solver.cpp:105] Iteration 3060, lr = 1e-05
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I0405 10:13:44.367177 26038 solver.cpp:218] Iteration 3072 (2.75318 iter/s, 4.3586s/12 iters), loss = 5.27907
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I0405 10:13:44.367297 26038 solver.cpp:237] Train net output #0: loss = 5.27907 (* 1 = 5.27907 loss)
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I0405 10:13:44.367306 26038 sgd_solver.cpp:105] Iteration 3072, lr = 1e-05
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I0405 10:13:49.712956 26038 solver.cpp:218] Iteration 3084 (2.24483 iter/s, 5.34562s/12 iters), loss = 5.26178
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I0405 10:13:49.712997 26038 solver.cpp:237] Train net output #0: loss = 5.26178 (* 1 = 5.26178 loss)
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I0405 10:13:49.713003 26038 sgd_solver.cpp:105] Iteration 3084, lr = 1e-05
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I0405 10:13:55.277057 26038 solver.cpp:218] Iteration 3096 (2.15672 iter/s, 5.56401s/12 iters), loss = 5.28895
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I0405 10:13:55.283257 26038 solver.cpp:237] Train net output #0: loss = 5.28895 (* 1 = 5.28895 loss)
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I0405 10:13:55.283273 26038 sgd_solver.cpp:105] Iteration 3096, lr = 1e-05
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I0405 10:14:00.663007 26038 solver.cpp:218] Iteration 3108 (2.2306 iter/s, 5.37972s/12 iters), loss = 5.27929
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I0405 10:14:00.663054 26038 solver.cpp:237] Train net output #0: loss = 5.27929 (* 1 = 5.27929 loss)
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I0405 10:14:00.663060 26038 sgd_solver.cpp:105] Iteration 3108, lr = 1e-05
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I0405 10:14:06.149740 26038 solver.cpp:218] Iteration 3120 (2.18713 iter/s, 5.48665s/12 iters), loss = 5.27646
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I0405 10:14:06.149777 26038 solver.cpp:237] Train net output #0: loss = 5.27646 (* 1 = 5.27646 loss)
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I0405 10:14:06.149782 26038 sgd_solver.cpp:105] Iteration 3120, lr = 1e-05
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I0405 10:14:11.500552 26038 solver.cpp:218] Iteration 3132 (2.24268 iter/s, 5.35073s/12 iters), loss = 5.28338
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I0405 10:14:11.500592 26038 solver.cpp:237] Train net output #0: loss = 5.28338 (* 1 = 5.28338 loss)
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||
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I0405 10:14:11.500598 26038 sgd_solver.cpp:105] Iteration 3132, lr = 1e-05
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I0405 10:14:12.609752 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:14:16.961498 26038 solver.cpp:218] Iteration 3144 (2.19746 iter/s, 5.46086s/12 iters), loss = 5.29716
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I0405 10:14:16.961663 26038 solver.cpp:237] Train net output #0: loss = 5.29716 (* 1 = 5.29716 loss)
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I0405 10:14:16.961673 26038 sgd_solver.cpp:105] Iteration 3144, lr = 1e-05
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I0405 10:14:22.336081 26038 solver.cpp:218] Iteration 3156 (2.23282 iter/s, 5.37438s/12 iters), loss = 5.28412
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I0405 10:14:22.336125 26038 solver.cpp:237] Train net output #0: loss = 5.28412 (* 1 = 5.28412 loss)
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I0405 10:14:22.336131 26038 sgd_solver.cpp:105] Iteration 3156, lr = 1e-05
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I0405 10:14:24.505192 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
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||
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I0405 10:14:27.580454 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
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I0405 10:14:29.902586 26038 solver.cpp:330] Iteration 3162, Testing net (#0)
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I0405 10:14:29.902608 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 10:14:33.206115 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:14:34.492370 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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||
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I0405 10:14:34.492408 26038 solver.cpp:397] Test net output #1: loss = 5.27868 (* 1 = 5.27868 loss)
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I0405 10:14:36.437400 26038 solver.cpp:218] Iteration 3168 (0.850993 iter/s, 14.1012s/12 iters), loss = 5.28445
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I0405 10:14:36.437453 26038 solver.cpp:237] Train net output #0: loss = 5.28445 (* 1 = 5.28445 loss)
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I0405 10:14:36.437460 26038 sgd_solver.cpp:105] Iteration 3168, lr = 1e-05
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I0405 10:14:41.680589 26038 solver.cpp:218] Iteration 3180 (2.28872 iter/s, 5.24309s/12 iters), loss = 5.27531
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I0405 10:14:41.680634 26038 solver.cpp:237] Train net output #0: loss = 5.27531 (* 1 = 5.27531 loss)
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I0405 10:14:41.680642 26038 sgd_solver.cpp:105] Iteration 3180, lr = 1e-05
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I0405 10:14:47.112783 26038 solver.cpp:218] Iteration 3192 (2.20909 iter/s, 5.4321s/12 iters), loss = 5.27283
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I0405 10:14:47.112913 26038 solver.cpp:237] Train net output #0: loss = 5.27283 (* 1 = 5.27283 loss)
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I0405 10:14:47.112922 26038 sgd_solver.cpp:105] Iteration 3192, lr = 1e-05
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I0405 10:14:52.668221 26038 solver.cpp:218] Iteration 3204 (2.16012 iter/s, 5.55525s/12 iters), loss = 5.28797
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I0405 10:14:52.668272 26038 solver.cpp:237] Train net output #0: loss = 5.28797 (* 1 = 5.28797 loss)
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I0405 10:14:52.668280 26038 sgd_solver.cpp:105] Iteration 3204, lr = 1e-05
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I0405 10:14:57.937104 26038 solver.cpp:218] Iteration 3216 (2.27756 iter/s, 5.26879s/12 iters), loss = 5.26601
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I0405 10:14:57.937144 26038 solver.cpp:237] Train net output #0: loss = 5.26601 (* 1 = 5.26601 loss)
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I0405 10:14:57.937150 26038 sgd_solver.cpp:105] Iteration 3216, lr = 1e-05
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I0405 10:15:03.543399 26038 solver.cpp:218] Iteration 3228 (2.14048 iter/s, 5.60621s/12 iters), loss = 5.27333
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I0405 10:15:03.543455 26038 solver.cpp:237] Train net output #0: loss = 5.27333 (* 1 = 5.27333 loss)
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I0405 10:15:03.543464 26038 sgd_solver.cpp:105] Iteration 3228, lr = 1e-05
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I0405 10:15:07.219655 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:15:09.206182 26038 solver.cpp:218] Iteration 3240 (2.11914 iter/s, 5.66268s/12 iters), loss = 5.28321
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I0405 10:15:09.206224 26038 solver.cpp:237] Train net output #0: loss = 5.28321 (* 1 = 5.28321 loss)
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I0405 10:15:09.206229 26038 sgd_solver.cpp:105] Iteration 3240, lr = 1e-05
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I0405 10:15:14.565521 26038 solver.cpp:218] Iteration 3252 (2.23912 iter/s, 5.35925s/12 iters), loss = 5.28252
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I0405 10:15:14.565573 26038 solver.cpp:237] Train net output #0: loss = 5.28252 (* 1 = 5.28252 loss)
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I0405 10:15:14.565582 26038 sgd_solver.cpp:105] Iteration 3252, lr = 1e-05
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I0405 10:15:19.667964 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
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I0405 10:15:22.755102 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
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I0405 10:15:25.094553 26038 solver.cpp:330] Iteration 3264, Testing net (#0)
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I0405 10:15:25.094571 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:15:28.238596 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:15:29.568748 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:15:29.568783 26038 solver.cpp:397] Test net output #1: loss = 5.27874 (* 1 = 5.27874 loss)
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I0405 10:15:29.713346 26038 solver.cpp:218] Iteration 3264 (0.792201 iter/s, 15.1477s/12 iters), loss = 5.26997
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I0405 10:15:29.714907 26038 solver.cpp:237] Train net output #0: loss = 5.26997 (* 1 = 5.26997 loss)
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I0405 10:15:29.714920 26038 sgd_solver.cpp:105] Iteration 3264, lr = 1e-05
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I0405 10:15:34.073632 26038 solver.cpp:218] Iteration 3276 (2.75312 iter/s, 4.35869s/12 iters), loss = 5.28322
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I0405 10:15:34.073684 26038 solver.cpp:237] Train net output #0: loss = 5.28322 (* 1 = 5.28322 loss)
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I0405 10:15:34.073693 26038 sgd_solver.cpp:105] Iteration 3276, lr = 1e-05
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I0405 10:15:39.525182 26038 solver.cpp:218] Iteration 3288 (2.20125 iter/s, 5.45145s/12 iters), loss = 5.2876
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I0405 10:15:39.525238 26038 solver.cpp:237] Train net output #0: loss = 5.2876 (* 1 = 5.2876 loss)
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I0405 10:15:39.525246 26038 sgd_solver.cpp:105] Iteration 3288, lr = 1e-05
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I0405 10:15:45.051159 26038 solver.cpp:218] Iteration 3300 (2.1716 iter/s, 5.52588s/12 iters), loss = 5.29454
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I0405 10:15:45.051215 26038 solver.cpp:237] Train net output #0: loss = 5.29454 (* 1 = 5.29454 loss)
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I0405 10:15:45.051223 26038 sgd_solver.cpp:105] Iteration 3300, lr = 1e-05
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I0405 10:15:50.340993 26038 solver.cpp:218] Iteration 3312 (2.26854 iter/s, 5.28974s/12 iters), loss = 5.27843
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I0405 10:15:50.341085 26038 solver.cpp:237] Train net output #0: loss = 5.27843 (* 1 = 5.27843 loss)
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I0405 10:15:50.341091 26038 sgd_solver.cpp:105] Iteration 3312, lr = 1e-05
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I0405 10:15:55.631444 26038 solver.cpp:218] Iteration 3324 (2.2683 iter/s, 5.29031s/12 iters), loss = 5.29529
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I0405 10:15:55.631495 26038 solver.cpp:237] Train net output #0: loss = 5.29529 (* 1 = 5.29529 loss)
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I0405 10:15:55.631502 26038 sgd_solver.cpp:105] Iteration 3324, lr = 1e-05
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I0405 10:16:00.877568 26038 solver.cpp:218] Iteration 3336 (2.28744 iter/s, 5.24603s/12 iters), loss = 5.28076
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I0405 10:16:00.877612 26038 solver.cpp:237] Train net output #0: loss = 5.28076 (* 1 = 5.28076 loss)
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||
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I0405 10:16:00.877617 26038 sgd_solver.cpp:105] Iteration 3336, lr = 1e-05
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||
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I0405 10:16:01.361645 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:16:06.285925 26038 solver.cpp:218] Iteration 3348 (2.21882 iter/s, 5.40827s/12 iters), loss = 5.28051
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I0405 10:16:06.285961 26038 solver.cpp:237] Train net output #0: loss = 5.28051 (* 1 = 5.28051 loss)
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I0405 10:16:06.285967 26038 sgd_solver.cpp:105] Iteration 3348, lr = 1e-05
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I0405 10:16:11.585693 26038 solver.cpp:218] Iteration 3360 (2.26429 iter/s, 5.29968s/12 iters), loss = 5.28548
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I0405 10:16:11.591894 26038 solver.cpp:237] Train net output #0: loss = 5.28548 (* 1 = 5.28548 loss)
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I0405 10:16:11.591912 26038 sgd_solver.cpp:105] Iteration 3360, lr = 1e-05
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||
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I0405 10:16:13.810612 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
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||
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I0405 10:16:16.913599 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
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I0405 10:16:19.250654 26038 solver.cpp:330] Iteration 3366, Testing net (#0)
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||
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I0405 10:16:19.250677 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:16:22.301192 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:16:23.666165 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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||
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I0405 10:16:23.666201 26038 solver.cpp:397] Test net output #1: loss = 5.27908 (* 1 = 5.27908 loss)
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I0405 10:16:25.727843 26038 solver.cpp:218] Iteration 3372 (0.848903 iter/s, 14.1359s/12 iters), loss = 5.26952
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I0405 10:16:25.727885 26038 solver.cpp:237] Train net output #0: loss = 5.26952 (* 1 = 5.26952 loss)
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I0405 10:16:25.727891 26038 sgd_solver.cpp:105] Iteration 3372, lr = 1e-05
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I0405 10:16:30.888696 26038 solver.cpp:218] Iteration 3384 (2.32524 iter/s, 5.16077s/12 iters), loss = 5.2887
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I0405 10:16:30.888741 26038 solver.cpp:237] Train net output #0: loss = 5.2887 (* 1 = 5.2887 loss)
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||
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I0405 10:16:30.888747 26038 sgd_solver.cpp:105] Iteration 3384, lr = 1e-05
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I0405 10:16:36.195272 26038 solver.cpp:218] Iteration 3396 (2.26139 iter/s, 5.30648s/12 iters), loss = 5.29152
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I0405 10:16:36.195317 26038 solver.cpp:237] Train net output #0: loss = 5.29152 (* 1 = 5.29152 loss)
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||
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I0405 10:16:36.195322 26038 sgd_solver.cpp:105] Iteration 3396, lr = 1e-05
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I0405 10:16:41.361757 26038 solver.cpp:218] Iteration 3408 (2.32271 iter/s, 5.16639s/12 iters), loss = 5.27612
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||
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I0405 10:16:41.361809 26038 solver.cpp:237] Train net output #0: loss = 5.27612 (* 1 = 5.27612 loss)
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||
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I0405 10:16:41.361816 26038 sgd_solver.cpp:105] Iteration 3408, lr = 1e-05
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I0405 10:16:46.592420 26038 solver.cpp:218] Iteration 3420 (2.29421 iter/s, 5.23057s/12 iters), loss = 5.28431
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I0405 10:16:46.592473 26038 solver.cpp:237] Train net output #0: loss = 5.28431 (* 1 = 5.28431 loss)
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||
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I0405 10:16:46.592480 26038 sgd_solver.cpp:105] Iteration 3420, lr = 1e-05
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I0405 10:16:52.017581 26038 solver.cpp:218] Iteration 3432 (2.21196 iter/s, 5.42507s/12 iters), loss = 5.27817
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I0405 10:16:52.017633 26038 solver.cpp:237] Train net output #0: loss = 5.27817 (* 1 = 5.27817 loss)
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I0405 10:16:52.017642 26038 sgd_solver.cpp:105] Iteration 3432, lr = 1e-05
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||
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I0405 10:16:54.898562 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:16:57.538472 26038 solver.cpp:218] Iteration 3444 (2.1736 iter/s, 5.52079s/12 iters), loss = 5.29189
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I0405 10:16:57.538530 26038 solver.cpp:237] Train net output #0: loss = 5.29189 (* 1 = 5.29189 loss)
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I0405 10:16:57.538538 26038 sgd_solver.cpp:105] Iteration 3444, lr = 1e-05
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I0405 10:17:02.867619 26038 solver.cpp:218] Iteration 3456 (2.25181 iter/s, 5.32905s/12 iters), loss = 5.29074
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I0405 10:17:02.867671 26038 solver.cpp:237] Train net output #0: loss = 5.29074 (* 1 = 5.29074 loss)
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I0405 10:17:02.867679 26038 sgd_solver.cpp:105] Iteration 3456, lr = 1e-05
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I0405 10:17:07.563444 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
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I0405 10:17:10.694218 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
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I0405 10:17:13.423167 26038 solver.cpp:330] Iteration 3468, Testing net (#0)
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I0405 10:17:13.423192 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:17:13.902810 26038 blocking_queue.cpp:49] Waiting for data
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I0405 10:17:16.577034 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:17:18.087111 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:17:18.087141 26038 solver.cpp:397] Test net output #1: loss = 5.27875 (* 1 = 5.27875 loss)
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I0405 10:17:18.225837 26038 solver.cpp:218] Iteration 3468 (0.781348 iter/s, 15.3581s/12 iters), loss = 5.27326
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I0405 10:17:18.225904 26038 solver.cpp:237] Train net output #0: loss = 5.27326 (* 1 = 5.27326 loss)
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I0405 10:17:18.225914 26038 sgd_solver.cpp:105] Iteration 3468, lr = 1e-05
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I0405 10:17:22.856159 26038 solver.cpp:218] Iteration 3480 (2.59167 iter/s, 4.63021s/12 iters), loss = 5.2783
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I0405 10:17:22.856212 26038 solver.cpp:237] Train net output #0: loss = 5.2783 (* 1 = 5.2783 loss)
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I0405 10:17:22.856220 26038 sgd_solver.cpp:105] Iteration 3480, lr = 1e-05
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I0405 10:17:28.347059 26038 solver.cpp:218] Iteration 3492 (2.18547 iter/s, 5.4908s/12 iters), loss = 5.2932
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I0405 10:17:28.347224 26038 solver.cpp:237] Train net output #0: loss = 5.2932 (* 1 = 5.2932 loss)
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I0405 10:17:28.347234 26038 sgd_solver.cpp:105] Iteration 3492, lr = 1e-05
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I0405 10:17:33.705076 26038 solver.cpp:218] Iteration 3504 (2.23972 iter/s, 5.35781s/12 iters), loss = 5.2874
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I0405 10:17:33.705119 26038 solver.cpp:237] Train net output #0: loss = 5.2874 (* 1 = 5.2874 loss)
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I0405 10:17:33.705125 26038 sgd_solver.cpp:105] Iteration 3504, lr = 1e-05
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I0405 10:17:39.180002 26038 solver.cpp:218] Iteration 3516 (2.19185 iter/s, 5.47483s/12 iters), loss = 5.28868
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I0405 10:17:39.180052 26038 solver.cpp:237] Train net output #0: loss = 5.28868 (* 1 = 5.28868 loss)
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I0405 10:17:39.180058 26038 sgd_solver.cpp:105] Iteration 3516, lr = 1e-05
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I0405 10:17:44.246701 26038 solver.cpp:218] Iteration 3528 (2.36845 iter/s, 5.06661s/12 iters), loss = 5.28253
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I0405 10:17:44.246747 26038 solver.cpp:237] Train net output #0: loss = 5.28253 (* 1 = 5.28253 loss)
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||
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I0405 10:17:44.246752 26038 sgd_solver.cpp:105] Iteration 3528, lr = 1e-05
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I0405 10:17:49.333700 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:17:49.672171 26038 solver.cpp:218] Iteration 3540 (2.21183 iter/s, 5.42538s/12 iters), loss = 5.28603
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I0405 10:17:49.672216 26038 solver.cpp:237] Train net output #0: loss = 5.28603 (* 1 = 5.28603 loss)
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I0405 10:17:49.672224 26038 sgd_solver.cpp:105] Iteration 3540, lr = 1e-05
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I0405 10:17:55.223095 26038 solver.cpp:218] Iteration 3552 (2.16184 iter/s, 5.55083s/12 iters), loss = 5.2676
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I0405 10:17:55.223146 26038 solver.cpp:237] Train net output #0: loss = 5.2676 (* 1 = 5.2676 loss)
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I0405 10:17:55.223153 26038 sgd_solver.cpp:105] Iteration 3552, lr = 1e-05
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I0405 10:18:00.567628 26038 solver.cpp:218] Iteration 3564 (2.24533 iter/s, 5.34444s/12 iters), loss = 5.28888
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I0405 10:18:00.567750 26038 solver.cpp:237] Train net output #0: loss = 5.28888 (* 1 = 5.28888 loss)
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I0405 10:18:00.567759 26038 sgd_solver.cpp:105] Iteration 3564, lr = 1e-05
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I0405 10:18:02.664373 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
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||
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|
I0405 10:18:06.592226 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
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I0405 10:18:08.896194 26038 solver.cpp:330] Iteration 3570, Testing net (#0)
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I0405 10:18:08.896214 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 10:18:11.949293 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 10:18:13.410748 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
|
||
|
|
I0405 10:18:13.410784 26038 solver.cpp:397] Test net output #1: loss = 5.27889 (* 1 = 5.27889 loss)
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||
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I0405 10:18:15.311405 26038 solver.cpp:218] Iteration 3576 (0.813914 iter/s, 14.7436s/12 iters), loss = 5.2833
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||
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I0405 10:18:15.311463 26038 solver.cpp:237] Train net output #0: loss = 5.2833 (* 1 = 5.2833 loss)
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I0405 10:18:15.311475 26038 sgd_solver.cpp:105] Iteration 3576, lr = 1e-05
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I0405 10:18:20.850020 26038 solver.cpp:218] Iteration 3588 (2.16665 iter/s, 5.53851s/12 iters), loss = 5.29405
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||
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I0405 10:18:20.850064 26038 solver.cpp:237] Train net output #0: loss = 5.29405 (* 1 = 5.29405 loss)
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||
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I0405 10:18:20.850068 26038 sgd_solver.cpp:105] Iteration 3588, lr = 1e-05
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I0405 10:18:26.446244 26038 solver.cpp:218] Iteration 3600 (2.14434 iter/s, 5.59613s/12 iters), loss = 5.29213
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I0405 10:18:26.446288 26038 solver.cpp:237] Train net output #0: loss = 5.29213 (* 1 = 5.29213 loss)
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||
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I0405 10:18:26.446293 26038 sgd_solver.cpp:105] Iteration 3600, lr = 1e-05
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I0405 10:18:31.958969 26038 solver.cpp:218] Iteration 3612 (2.17682 iter/s, 5.51263s/12 iters), loss = 5.27485
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I0405 10:18:31.959149 26038 solver.cpp:237] Train net output #0: loss = 5.27485 (* 1 = 5.27485 loss)
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I0405 10:18:31.959159 26038 sgd_solver.cpp:105] Iteration 3612, lr = 1e-05
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I0405 10:18:37.314209 26038 solver.cpp:218] Iteration 3624 (2.24089 iter/s, 5.35502s/12 iters), loss = 5.28842
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I0405 10:18:37.314266 26038 solver.cpp:237] Train net output #0: loss = 5.28842 (* 1 = 5.28842 loss)
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||
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I0405 10:18:37.314275 26038 sgd_solver.cpp:105] Iteration 3624, lr = 1e-05
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I0405 10:18:42.825306 26038 solver.cpp:218] Iteration 3636 (2.17746 iter/s, 5.511s/12 iters), loss = 5.27366
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||
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I0405 10:18:42.825357 26038 solver.cpp:237] Train net output #0: loss = 5.27366 (* 1 = 5.27366 loss)
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||
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I0405 10:18:42.825367 26038 sgd_solver.cpp:105] Iteration 3636, lr = 1e-05
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||
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I0405 10:18:44.777755 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:18:48.292098 26038 solver.cpp:218] Iteration 3648 (2.19511 iter/s, 5.46669s/12 iters), loss = 5.29013
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||
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I0405 10:18:48.292156 26038 solver.cpp:237] Train net output #0: loss = 5.29013 (* 1 = 5.29013 loss)
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||
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I0405 10:18:48.292165 26038 sgd_solver.cpp:105] Iteration 3648, lr = 1e-05
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I0405 10:18:53.666152 26038 solver.cpp:218] Iteration 3660 (2.23299 iter/s, 5.37395s/12 iters), loss = 5.26678
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I0405 10:18:53.666198 26038 solver.cpp:237] Train net output #0: loss = 5.26678 (* 1 = 5.26678 loss)
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I0405 10:18:53.666204 26038 sgd_solver.cpp:105] Iteration 3660, lr = 1e-05
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I0405 10:18:58.538198 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
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||
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I0405 10:19:01.694094 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
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I0405 10:19:04.011106 26038 solver.cpp:330] Iteration 3672, Testing net (#0)
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||
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I0405 10:19:04.011178 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 10:19:07.049696 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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|
I0405 10:19:08.576061 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
|
||
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|
I0405 10:19:08.576092 26038 solver.cpp:397] Test net output #1: loss = 5.27865 (* 1 = 5.27865 loss)
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I0405 10:19:08.717461 26038 solver.cpp:218] Iteration 3672 (0.79728 iter/s, 15.0512s/12 iters), loss = 5.27712
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I0405 10:19:08.717509 26038 solver.cpp:237] Train net output #0: loss = 5.27712 (* 1 = 5.27712 loss)
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I0405 10:19:08.717514 26038 sgd_solver.cpp:105] Iteration 3672, lr = 1e-05
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I0405 10:19:13.223666 26038 solver.cpp:218] Iteration 3684 (2.66305 iter/s, 4.50611s/12 iters), loss = 5.28454
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I0405 10:19:13.223708 26038 solver.cpp:237] Train net output #0: loss = 5.28454 (* 1 = 5.28454 loss)
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||
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I0405 10:19:13.223714 26038 sgd_solver.cpp:105] Iteration 3684, lr = 1e-05
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I0405 10:19:18.566211 26038 solver.cpp:218] Iteration 3696 (2.24616 iter/s, 5.34246s/12 iters), loss = 5.26274
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||
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I0405 10:19:18.566259 26038 solver.cpp:237] Train net output #0: loss = 5.26274 (* 1 = 5.26274 loss)
|
||
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I0405 10:19:18.566267 26038 sgd_solver.cpp:105] Iteration 3696, lr = 1e-05
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I0405 10:19:24.058212 26038 solver.cpp:218] Iteration 3708 (2.18503 iter/s, 5.49191s/12 iters), loss = 5.27655
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I0405 10:19:24.058274 26038 solver.cpp:237] Train net output #0: loss = 5.27655 (* 1 = 5.27655 loss)
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I0405 10:19:24.058284 26038 sgd_solver.cpp:105] Iteration 3708, lr = 1e-05
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I0405 10:19:29.224426 26038 solver.cpp:218] Iteration 3720 (2.32283 iter/s, 5.16611s/12 iters), loss = 5.29455
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I0405 10:19:29.224473 26038 solver.cpp:237] Train net output #0: loss = 5.29455 (* 1 = 5.29455 loss)
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I0405 10:19:29.224480 26038 sgd_solver.cpp:105] Iteration 3720, lr = 1e-05
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I0405 10:19:34.529282 26038 solver.cpp:218] Iteration 3732 (2.26212 iter/s, 5.30476s/12 iters), loss = 5.28031
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I0405 10:19:34.529371 26038 solver.cpp:237] Train net output #0: loss = 5.28031 (* 1 = 5.28031 loss)
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I0405 10:19:34.529377 26038 sgd_solver.cpp:105] Iteration 3732, lr = 1e-05
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I0405 10:19:38.918046 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:19:40.076020 26038 solver.cpp:218] Iteration 3744 (2.16349 iter/s, 5.5466s/12 iters), loss = 5.28048
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I0405 10:19:40.076076 26038 solver.cpp:237] Train net output #0: loss = 5.28048 (* 1 = 5.28048 loss)
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I0405 10:19:40.076083 26038 sgd_solver.cpp:105] Iteration 3744, lr = 1e-05
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I0405 10:19:45.539110 26038 solver.cpp:218] Iteration 3756 (2.1966 iter/s, 5.46299s/12 iters), loss = 5.28123
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I0405 10:19:45.539160 26038 solver.cpp:237] Train net output #0: loss = 5.28123 (* 1 = 5.28123 loss)
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I0405 10:19:45.539167 26038 sgd_solver.cpp:105] Iteration 3756, lr = 1e-05
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I0405 10:19:50.885727 26038 solver.cpp:218] Iteration 3768 (2.24447 iter/s, 5.34647s/12 iters), loss = 5.29235
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I0405 10:19:50.885767 26038 solver.cpp:237] Train net output #0: loss = 5.29235 (* 1 = 5.29235 loss)
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I0405 10:19:50.885773 26038 sgd_solver.cpp:105] Iteration 3768, lr = 1e-05
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I0405 10:19:53.146100 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
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I0405 10:19:56.239637 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
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I0405 10:19:58.598898 26038 solver.cpp:330] Iteration 3774, Testing net (#0)
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I0405 10:19:58.598917 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:20:01.697082 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:20:03.418293 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:20:03.418323 26038 solver.cpp:397] Test net output #1: loss = 5.27889 (* 1 = 5.27889 loss)
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I0405 10:20:05.435575 26038 solver.cpp:218] Iteration 3780 (0.824758 iter/s, 14.5497s/12 iters), loss = 5.28645
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I0405 10:20:05.435705 26038 solver.cpp:237] Train net output #0: loss = 5.28645 (* 1 = 5.28645 loss)
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I0405 10:20:05.435712 26038 sgd_solver.cpp:105] Iteration 3780, lr = 1e-05
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I0405 10:20:10.629076 26038 solver.cpp:218] Iteration 3792 (2.31066 iter/s, 5.19333s/12 iters), loss = 5.27799
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I0405 10:20:10.629128 26038 solver.cpp:237] Train net output #0: loss = 5.27799 (* 1 = 5.27799 loss)
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I0405 10:20:10.629139 26038 sgd_solver.cpp:105] Iteration 3792, lr = 1e-05
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I0405 10:20:16.101114 26038 solver.cpp:218] Iteration 3804 (2.193 iter/s, 5.47194s/12 iters), loss = 5.2962
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I0405 10:20:16.101156 26038 solver.cpp:237] Train net output #0: loss = 5.2962 (* 1 = 5.2962 loss)
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I0405 10:20:16.101162 26038 sgd_solver.cpp:105] Iteration 3804, lr = 1e-05
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I0405 10:20:21.750598 26038 solver.cpp:218] Iteration 3816 (2.12412 iter/s, 5.64939s/12 iters), loss = 5.29083
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I0405 10:20:21.750654 26038 solver.cpp:237] Train net output #0: loss = 5.29083 (* 1 = 5.29083 loss)
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I0405 10:20:21.750661 26038 sgd_solver.cpp:105] Iteration 3816, lr = 1e-05
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I0405 10:20:27.035091 26038 solver.cpp:218] Iteration 3828 (2.27084 iter/s, 5.28439s/12 iters), loss = 5.27549
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I0405 10:20:27.035146 26038 solver.cpp:237] Train net output #0: loss = 5.27549 (* 1 = 5.27549 loss)
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I0405 10:20:27.035154 26038 sgd_solver.cpp:105] Iteration 3828, lr = 1e-05
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I0405 10:20:32.689066 26038 solver.cpp:218] Iteration 3840 (2.12244 iter/s, 5.65388s/12 iters), loss = 5.29066
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I0405 10:20:32.689108 26038 solver.cpp:237] Train net output #0: loss = 5.29066 (* 1 = 5.29066 loss)
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I0405 10:20:32.689117 26038 sgd_solver.cpp:105] Iteration 3840, lr = 1e-05
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I0405 10:20:33.779291 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:20:37.993860 26038 solver.cpp:218] Iteration 3852 (2.26216 iter/s, 5.30467s/12 iters), loss = 5.27699
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I0405 10:20:37.993960 26038 solver.cpp:237] Train net output #0: loss = 5.27699 (* 1 = 5.27699 loss)
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I0405 10:20:37.993969 26038 sgd_solver.cpp:105] Iteration 3852, lr = 1e-05
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I0405 10:20:43.529542 26038 solver.cpp:218] Iteration 3864 (2.16781 iter/s, 5.53553s/12 iters), loss = 5.29444
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I0405 10:20:43.529593 26038 solver.cpp:237] Train net output #0: loss = 5.29444 (* 1 = 5.29444 loss)
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I0405 10:20:43.529601 26038 sgd_solver.cpp:105] Iteration 3864, lr = 1e-05
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I0405 10:20:48.310830 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
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I0405 10:20:51.324733 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
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I0405 10:20:53.619598 26038 solver.cpp:330] Iteration 3876, Testing net (#0)
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I0405 10:20:53.619618 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:20:56.613734 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:20:58.151170 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:20:58.151206 26038 solver.cpp:397] Test net output #1: loss = 5.27879 (* 1 = 5.27879 loss)
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I0405 10:20:58.290864 26038 solver.cpp:218] Iteration 3876 (0.812943 iter/s, 14.7612s/12 iters), loss = 5.28976
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I0405 10:20:58.290923 26038 solver.cpp:237] Train net output #0: loss = 5.28976 (* 1 = 5.28976 loss)
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I0405 10:20:58.290930 26038 sgd_solver.cpp:105] Iteration 3876, lr = 1e-05
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I0405 10:21:02.458889 26038 solver.cpp:218] Iteration 3888 (2.87913 iter/s, 4.16792s/12 iters), loss = 5.26893
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I0405 10:21:02.458933 26038 solver.cpp:237] Train net output #0: loss = 5.26893 (* 1 = 5.26893 loss)
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I0405 10:21:02.458938 26038 sgd_solver.cpp:105] Iteration 3888, lr = 1e-05
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I0405 10:21:07.740779 26038 solver.cpp:218] Iteration 3900 (2.27195 iter/s, 5.2818s/12 iters), loss = 5.28291
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I0405 10:21:07.740867 26038 solver.cpp:237] Train net output #0: loss = 5.28291 (* 1 = 5.28291 loss)
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I0405 10:21:07.740896 26038 sgd_solver.cpp:105] Iteration 3900, lr = 1e-05
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I0405 10:21:13.195755 26038 solver.cpp:218] Iteration 3912 (2.19988 iter/s, 5.45485s/12 iters), loss = 5.2732
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I0405 10:21:13.199507 26038 solver.cpp:237] Train net output #0: loss = 5.2732 (* 1 = 5.2732 loss)
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I0405 10:21:13.199518 26038 sgd_solver.cpp:105] Iteration 3912, lr = 1e-05
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I0405 10:21:18.565724 26038 solver.cpp:218] Iteration 3924 (2.23623 iter/s, 5.36618s/12 iters), loss = 5.263
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I0405 10:21:18.565778 26038 solver.cpp:237] Train net output #0: loss = 5.263 (* 1 = 5.263 loss)
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I0405 10:21:18.565786 26038 sgd_solver.cpp:105] Iteration 3924, lr = 1e-05
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I0405 10:21:23.977583 26038 solver.cpp:218] Iteration 3936 (2.21739 iter/s, 5.41176s/12 iters), loss = 5.28142
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I0405 10:21:23.977632 26038 solver.cpp:237] Train net output #0: loss = 5.28142 (* 1 = 5.28142 loss)
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I0405 10:21:23.977640 26038 sgd_solver.cpp:105] Iteration 3936, lr = 1e-05
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I0405 10:21:27.682000 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:21:29.366760 26038 solver.cpp:218] Iteration 3948 (2.22672 iter/s, 5.38909s/12 iters), loss = 5.27837
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I0405 10:21:29.366803 26038 solver.cpp:237] Train net output #0: loss = 5.27837 (* 1 = 5.27837 loss)
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I0405 10:21:29.366809 26038 sgd_solver.cpp:105] Iteration 3948, lr = 1e-05
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I0405 10:21:34.673740 26038 solver.cpp:218] Iteration 3960 (2.26121 iter/s, 5.30689s/12 iters), loss = 5.27575
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I0405 10:21:34.673784 26038 solver.cpp:237] Train net output #0: loss = 5.27575 (* 1 = 5.27575 loss)
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I0405 10:21:34.673789 26038 sgd_solver.cpp:105] Iteration 3960, lr = 1e-05
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I0405 10:21:40.130420 26038 solver.cpp:218] Iteration 3972 (2.19918 iter/s, 5.45659s/12 iters), loss = 5.27083
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I0405 10:21:40.130475 26038 solver.cpp:237] Train net output #0: loss = 5.27083 (* 1 = 5.27083 loss)
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I0405 10:21:40.130482 26038 sgd_solver.cpp:105] Iteration 3972, lr = 1e-05
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I0405 10:21:42.340025 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
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I0405 10:21:45.453984 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
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I0405 10:21:47.761201 26038 solver.cpp:330] Iteration 3978, Testing net (#0)
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I0405 10:21:47.761220 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:21:50.555693 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:21:52.167531 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:21:52.167578 26038 solver.cpp:397] Test net output #1: loss = 5.27892 (* 1 = 5.27892 loss)
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I0405 10:21:54.267904 26038 solver.cpp:218] Iteration 3984 (0.848816 iter/s, 14.1373s/12 iters), loss = 5.28253
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I0405 10:21:54.267954 26038 solver.cpp:237] Train net output #0: loss = 5.28253 (* 1 = 5.28253 loss)
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I0405 10:21:54.267962 26038 sgd_solver.cpp:105] Iteration 3984, lr = 1e-05
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I0405 10:21:59.892248 26038 solver.cpp:218] Iteration 3996 (2.13362 iter/s, 5.62426s/12 iters), loss = 5.27533
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I0405 10:21:59.892287 26038 solver.cpp:237] Train net output #0: loss = 5.27533 (* 1 = 5.27533 loss)
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I0405 10:21:59.892292 26038 sgd_solver.cpp:105] Iteration 3996, lr = 1e-05
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I0405 10:22:05.400543 26038 solver.cpp:218] Iteration 4008 (2.17856 iter/s, 5.50821s/12 iters), loss = 5.27216
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I0405 10:22:05.400583 26038 solver.cpp:237] Train net output #0: loss = 5.27216 (* 1 = 5.27216 loss)
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I0405 10:22:05.400588 26038 sgd_solver.cpp:105] Iteration 4008, lr = 1e-05
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I0405 10:22:10.937743 26038 solver.cpp:218] Iteration 4020 (2.16719 iter/s, 5.53711s/12 iters), loss = 5.27964
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I0405 10:22:10.937796 26038 solver.cpp:237] Train net output #0: loss = 5.27964 (* 1 = 5.27964 loss)
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I0405 10:22:10.937804 26038 sgd_solver.cpp:105] Iteration 4020, lr = 1e-05
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I0405 10:22:16.412843 26038 solver.cpp:218] Iteration 4032 (2.19178 iter/s, 5.475s/12 iters), loss = 5.29068
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I0405 10:22:16.413031 26038 solver.cpp:237] Train net output #0: loss = 5.29068 (* 1 = 5.29068 loss)
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I0405 10:22:16.413039 26038 sgd_solver.cpp:105] Iteration 4032, lr = 1e-05
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I0405 10:22:21.678874 26038 solver.cpp:218] Iteration 4044 (2.27885 iter/s, 5.26581s/12 iters), loss = 5.28825
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I0405 10:22:21.678915 26038 solver.cpp:237] Train net output #0: loss = 5.28825 (* 1 = 5.28825 loss)
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I0405 10:22:21.678921 26038 sgd_solver.cpp:105] Iteration 4044, lr = 1e-05
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||
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I0405 10:22:22.155025 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:22:26.841307 26038 solver.cpp:218] Iteration 4056 (2.32452 iter/s, 5.16235s/12 iters), loss = 5.28656
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I0405 10:22:26.841346 26038 solver.cpp:237] Train net output #0: loss = 5.28656 (* 1 = 5.28656 loss)
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I0405 10:22:26.841351 26038 sgd_solver.cpp:105] Iteration 4056, lr = 1e-05
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I0405 10:22:32.407135 26038 solver.cpp:218] Iteration 4068 (2.15605 iter/s, 5.56574s/12 iters), loss = 5.28485
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I0405 10:22:32.407189 26038 solver.cpp:237] Train net output #0: loss = 5.28485 (* 1 = 5.28485 loss)
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I0405 10:22:32.407198 26038 sgd_solver.cpp:105] Iteration 4068, lr = 1e-05
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I0405 10:22:37.328279 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
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||
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I0405 10:22:40.417655 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
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I0405 10:22:42.756301 26038 solver.cpp:330] Iteration 4080, Testing net (#0)
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I0405 10:22:42.756326 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:22:45.692957 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:22:47.284507 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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||
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I0405 10:22:47.284615 26038 solver.cpp:397] Test net output #1: loss = 5.27887 (* 1 = 5.27887 loss)
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I0405 10:22:47.439275 26038 solver.cpp:218] Iteration 4080 (0.798297 iter/s, 15.032s/12 iters), loss = 5.2664
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I0405 10:22:47.439328 26038 solver.cpp:237] Train net output #0: loss = 5.2664 (* 1 = 5.2664 loss)
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I0405 10:22:47.439337 26038 sgd_solver.cpp:105] Iteration 4080, lr = 1e-05
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I0405 10:22:52.066037 26038 solver.cpp:218] Iteration 4092 (2.59366 iter/s, 4.62667s/12 iters), loss = 5.29029
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I0405 10:22:52.066082 26038 solver.cpp:237] Train net output #0: loss = 5.29029 (* 1 = 5.29029 loss)
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I0405 10:22:52.066087 26038 sgd_solver.cpp:105] Iteration 4092, lr = 1e-05
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I0405 10:22:57.385035 26038 solver.cpp:218] Iteration 4104 (2.2561 iter/s, 5.31891s/12 iters), loss = 5.28629
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I0405 10:22:57.385079 26038 solver.cpp:237] Train net output #0: loss = 5.28629 (* 1 = 5.28629 loss)
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I0405 10:22:57.385085 26038 sgd_solver.cpp:105] Iteration 4104, lr = 1e-05
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I0405 10:23:02.584470 26038 solver.cpp:218] Iteration 4116 (2.30798 iter/s, 5.19935s/12 iters), loss = 5.28587
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I0405 10:23:02.584517 26038 solver.cpp:237] Train net output #0: loss = 5.28587 (* 1 = 5.28587 loss)
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I0405 10:23:02.584524 26038 sgd_solver.cpp:105] Iteration 4116, lr = 1e-05
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I0405 10:23:08.064617 26038 solver.cpp:218] Iteration 4128 (2.18976 iter/s, 5.48006s/12 iters), loss = 5.28389
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I0405 10:23:08.064656 26038 solver.cpp:237] Train net output #0: loss = 5.28389 (* 1 = 5.28389 loss)
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I0405 10:23:08.064661 26038 sgd_solver.cpp:105] Iteration 4128, lr = 1e-05
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I0405 10:23:13.348193 26038 solver.cpp:218] Iteration 4140 (2.27122 iter/s, 5.28349s/12 iters), loss = 5.29497
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I0405 10:23:13.348234 26038 solver.cpp:237] Train net output #0: loss = 5.29497 (* 1 = 5.29497 loss)
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I0405 10:23:13.348239 26038 sgd_solver.cpp:105] Iteration 4140, lr = 1e-05
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I0405 10:23:16.166728 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:23:18.769367 26038 solver.cpp:218] Iteration 4152 (2.21358 iter/s, 5.42109s/12 iters), loss = 5.28449
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I0405 10:23:18.769510 26038 solver.cpp:237] Train net output #0: loss = 5.28449 (* 1 = 5.28449 loss)
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I0405 10:23:18.769517 26038 sgd_solver.cpp:105] Iteration 4152, lr = 1e-05
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I0405 10:23:20.618403 26038 blocking_queue.cpp:49] Waiting for data
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I0405 10:23:24.162304 26038 solver.cpp:218] Iteration 4164 (2.22521 iter/s, 5.39276s/12 iters), loss = 5.27192
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I0405 10:23:24.162350 26038 solver.cpp:237] Train net output #0: loss = 5.27192 (* 1 = 5.27192 loss)
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I0405 10:23:24.162358 26038 sgd_solver.cpp:105] Iteration 4164, lr = 1e-05
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I0405 10:23:29.108405 26038 solver.cpp:218] Iteration 4176 (2.4262 iter/s, 4.94601s/12 iters), loss = 5.27912
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I0405 10:23:29.108448 26038 solver.cpp:237] Train net output #0: loss = 5.27912 (* 1 = 5.27912 loss)
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I0405 10:23:29.108454 26038 sgd_solver.cpp:105] Iteration 4176, lr = 1e-05
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I0405 10:23:31.203099 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
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I0405 10:23:34.274093 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
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I0405 10:23:36.596123 26038 solver.cpp:330] Iteration 4182, Testing net (#0)
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I0405 10:23:36.596140 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:23:39.324373 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:23:41.127576 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:23:41.127619 26038 solver.cpp:397] Test net output #1: loss = 5.27872 (* 1 = 5.27872 loss)
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I0405 10:23:43.082381 26038 solver.cpp:218] Iteration 4188 (0.858747 iter/s, 13.9738s/12 iters), loss = 5.2856
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I0405 10:23:43.082428 26038 solver.cpp:237] Train net output #0: loss = 5.2856 (* 1 = 5.2856 loss)
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I0405 10:23:43.082434 26038 sgd_solver.cpp:105] Iteration 4188, lr = 1e-05
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I0405 10:23:48.681166 26038 solver.cpp:218] Iteration 4200 (2.14336 iter/s, 5.5987s/12 iters), loss = 5.3084
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I0405 10:23:48.681205 26038 solver.cpp:237] Train net output #0: loss = 5.3084 (* 1 = 5.3084 loss)
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I0405 10:23:48.681210 26038 sgd_solver.cpp:105] Iteration 4200, lr = 1e-05
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I0405 10:23:54.222360 26038 solver.cpp:218] Iteration 4212 (2.16563 iter/s, 5.54111s/12 iters), loss = 5.28483
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I0405 10:23:54.222486 26038 solver.cpp:237] Train net output #0: loss = 5.28483 (* 1 = 5.28483 loss)
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I0405 10:23:54.222496 26038 sgd_solver.cpp:105] Iteration 4212, lr = 1e-05
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I0405 10:23:59.654006 26038 solver.cpp:218] Iteration 4224 (2.20934 iter/s, 5.43148s/12 iters), loss = 5.28054
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I0405 10:23:59.654057 26038 solver.cpp:237] Train net output #0: loss = 5.28054 (* 1 = 5.28054 loss)
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I0405 10:23:59.654062 26038 sgd_solver.cpp:105] Iteration 4224, lr = 1e-05
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I0405 10:24:05.250595 26038 solver.cpp:218] Iteration 4236 (2.1442 iter/s, 5.59649s/12 iters), loss = 5.28436
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I0405 10:24:05.250648 26038 solver.cpp:237] Train net output #0: loss = 5.28436 (* 1 = 5.28436 loss)
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I0405 10:24:05.250654 26038 sgd_solver.cpp:105] Iteration 4236, lr = 1e-05
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I0405 10:24:10.430940 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:24:10.748132 26038 solver.cpp:218] Iteration 4248 (2.18283 iter/s, 5.49744s/12 iters), loss = 5.29447
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I0405 10:24:10.748180 26038 solver.cpp:237] Train net output #0: loss = 5.29447 (* 1 = 5.29447 loss)
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I0405 10:24:10.748188 26038 sgd_solver.cpp:105] Iteration 4248, lr = 1e-05
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I0405 10:24:16.315814 26038 solver.cpp:218] Iteration 4260 (2.15533 iter/s, 5.56759s/12 iters), loss = 5.26635
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I0405 10:24:16.315856 26038 solver.cpp:237] Train net output #0: loss = 5.26635 (* 1 = 5.26635 loss)
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I0405 10:24:16.315861 26038 sgd_solver.cpp:105] Iteration 4260, lr = 1e-05
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I0405 10:24:21.800499 26038 solver.cpp:218] Iteration 4272 (2.18794 iter/s, 5.4846s/12 iters), loss = 5.26481
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I0405 10:24:21.800539 26038 solver.cpp:237] Train net output #0: loss = 5.26481 (* 1 = 5.26481 loss)
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I0405 10:24:21.800545 26038 sgd_solver.cpp:105] Iteration 4272, lr = 1e-05
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I0405 10:24:26.809769 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
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I0405 10:24:29.829632 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
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I0405 10:24:32.130453 26038 solver.cpp:330] Iteration 4284, Testing net (#0)
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I0405 10:24:32.130477 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:24:34.938228 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:24:36.856946 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:24:36.856984 26038 solver.cpp:397] Test net output #1: loss = 5.27891 (* 1 = 5.27891 loss)
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I0405 10:24:37.003604 26038 solver.cpp:218] Iteration 4284 (0.789319 iter/s, 15.203s/12 iters), loss = 5.28791
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I0405 10:24:37.003648 26038 solver.cpp:237] Train net output #0: loss = 5.28791 (* 1 = 5.28791 loss)
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I0405 10:24:37.003654 26038 sgd_solver.cpp:105] Iteration 4284, lr = 1e-05
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I0405 10:24:41.316339 26038 solver.cpp:218] Iteration 4296 (2.78252 iter/s, 4.31264s/12 iters), loss = 5.29001
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I0405 10:24:41.316381 26038 solver.cpp:237] Train net output #0: loss = 5.29001 (* 1 = 5.29001 loss)
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I0405 10:24:41.316388 26038 sgd_solver.cpp:105] Iteration 4296, lr = 1e-05
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I0405 10:24:46.580976 26038 solver.cpp:218] Iteration 4308 (2.27939 iter/s, 5.26456s/12 iters), loss = 5.29562
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I0405 10:24:46.581015 26038 solver.cpp:237] Train net output #0: loss = 5.29562 (* 1 = 5.29562 loss)
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I0405 10:24:46.581022 26038 sgd_solver.cpp:105] Iteration 4308, lr = 1e-05
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I0405 10:24:52.032598 26038 solver.cpp:218] Iteration 4320 (2.20122 iter/s, 5.45153s/12 iters), loss = 5.26135
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I0405 10:24:52.032647 26038 solver.cpp:237] Train net output #0: loss = 5.26135 (* 1 = 5.26135 loss)
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I0405 10:24:52.032655 26038 sgd_solver.cpp:105] Iteration 4320, lr = 1e-05
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I0405 10:24:57.370667 26038 solver.cpp:218] Iteration 4332 (2.24804 iter/s, 5.33798s/12 iters), loss = 5.28165
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I0405 10:24:57.370776 26038 solver.cpp:237] Train net output #0: loss = 5.28165 (* 1 = 5.28165 loss)
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I0405 10:24:57.370784 26038 sgd_solver.cpp:105] Iteration 4332, lr = 1e-05
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I0405 10:25:02.765038 26038 solver.cpp:218] Iteration 4344 (2.22461 iter/s, 5.39421s/12 iters), loss = 5.29065
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I0405 10:25:02.765082 26038 solver.cpp:237] Train net output #0: loss = 5.29065 (* 1 = 5.29065 loss)
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I0405 10:25:02.765087 26038 sgd_solver.cpp:105] Iteration 4344, lr = 1e-05
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I0405 10:25:04.782136 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:25:07.976068 26038 solver.cpp:218] Iteration 4356 (2.30285 iter/s, 5.21094s/12 iters), loss = 5.28457
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I0405 10:25:07.976114 26038 solver.cpp:237] Train net output #0: loss = 5.28457 (* 1 = 5.28457 loss)
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I0405 10:25:07.976121 26038 sgd_solver.cpp:105] Iteration 4356, lr = 1e-05
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I0405 10:25:13.108753 26038 solver.cpp:218] Iteration 4368 (2.338 iter/s, 5.1326s/12 iters), loss = 5.28055
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I0405 10:25:13.108798 26038 solver.cpp:237] Train net output #0: loss = 5.28055 (* 1 = 5.28055 loss)
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I0405 10:25:13.108803 26038 sgd_solver.cpp:105] Iteration 4368, lr = 1e-05
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I0405 10:25:18.477557 26038 solver.cpp:218] Iteration 4380 (2.23517 iter/s, 5.36872s/12 iters), loss = 5.27764
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I0405 10:25:18.477593 26038 solver.cpp:237] Train net output #0: loss = 5.27764 (* 1 = 5.27764 loss)
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I0405 10:25:18.477599 26038 sgd_solver.cpp:105] Iteration 4380, lr = 1e-05
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I0405 10:25:20.825516 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
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I0405 10:25:23.888548 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
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I0405 10:25:26.282528 26038 solver.cpp:330] Iteration 4386, Testing net (#0)
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I0405 10:25:26.282554 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:25:29.072813 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:25:30.851042 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:25:30.851075 26038 solver.cpp:397] Test net output #1: loss = 5.27888 (* 1 = 5.27888 loss)
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I0405 10:25:32.958847 26038 solver.cpp:218] Iteration 4392 (0.828663 iter/s, 14.4812s/12 iters), loss = 5.27757
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I0405 10:25:32.958887 26038 solver.cpp:237] Train net output #0: loss = 5.27757 (* 1 = 5.27757 loss)
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I0405 10:25:32.958894 26038 sgd_solver.cpp:105] Iteration 4392, lr = 1e-05
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I0405 10:25:38.190567 26038 solver.cpp:218] Iteration 4404 (2.29374 iter/s, 5.23163s/12 iters), loss = 5.284
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I0405 10:25:38.190608 26038 solver.cpp:237] Train net output #0: loss = 5.284 (* 1 = 5.284 loss)
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I0405 10:25:38.190613 26038 sgd_solver.cpp:105] Iteration 4404, lr = 1e-05
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I0405 10:25:43.615969 26038 solver.cpp:218] Iteration 4416 (2.21185 iter/s, 5.42531s/12 iters), loss = 5.28656
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I0405 10:25:43.616030 26038 solver.cpp:237] Train net output #0: loss = 5.28656 (* 1 = 5.28656 loss)
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I0405 10:25:43.616039 26038 sgd_solver.cpp:105] Iteration 4416, lr = 1e-05
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I0405 10:25:49.003196 26038 solver.cpp:218] Iteration 4428 (2.22753 iter/s, 5.38712s/12 iters), loss = 5.29076
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I0405 10:25:49.003239 26038 solver.cpp:237] Train net output #0: loss = 5.29076 (* 1 = 5.29076 loss)
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I0405 10:25:49.003245 26038 sgd_solver.cpp:105] Iteration 4428, lr = 1e-05
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I0405 10:25:54.548725 26038 solver.cpp:218] Iteration 4440 (2.16394 iter/s, 5.54544s/12 iters), loss = 5.25605
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I0405 10:25:54.548775 26038 solver.cpp:237] Train net output #0: loss = 5.25605 (* 1 = 5.25605 loss)
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I0405 10:25:54.548782 26038 sgd_solver.cpp:105] Iteration 4440, lr = 1e-05
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I0405 10:25:58.970880 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:26:00.008205 26038 solver.cpp:218] Iteration 4452 (2.19805 iter/s, 5.45939s/12 iters), loss = 5.27747
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I0405 10:26:00.008318 26038 solver.cpp:237] Train net output #0: loss = 5.27747 (* 1 = 5.27747 loss)
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I0405 10:26:00.008325 26038 sgd_solver.cpp:105] Iteration 4452, lr = 1e-05
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I0405 10:26:05.228034 26038 solver.cpp:218] Iteration 4464 (2.29899 iter/s, 5.21967s/12 iters), loss = 5.27689
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I0405 10:26:05.228082 26038 solver.cpp:237] Train net output #0: loss = 5.27689 (* 1 = 5.27689 loss)
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I0405 10:26:05.228087 26038 sgd_solver.cpp:105] Iteration 4464, lr = 1e-05
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I0405 10:26:10.493206 26038 solver.cpp:218] Iteration 4476 (2.27917 iter/s, 5.26508s/12 iters), loss = 5.28719
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I0405 10:26:10.493250 26038 solver.cpp:237] Train net output #0: loss = 5.28719 (* 1 = 5.28719 loss)
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I0405 10:26:10.493257 26038 sgd_solver.cpp:105] Iteration 4476, lr = 1e-05
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I0405 10:26:15.478349 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
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I0405 10:26:18.602116 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
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I0405 10:26:20.918956 26038 solver.cpp:330] Iteration 4488, Testing net (#0)
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I0405 10:26:20.918973 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:26:23.637934 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:26:25.450727 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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||
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I0405 10:26:25.450762 26038 solver.cpp:397] Test net output #1: loss = 5.27868 (* 1 = 5.27868 loss)
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I0405 10:26:25.592407 26038 solver.cpp:218] Iteration 4488 (0.794751 iter/s, 15.0991s/12 iters), loss = 5.28029
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I0405 10:26:25.592455 26038 solver.cpp:237] Train net output #0: loss = 5.28029 (* 1 = 5.28029 loss)
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I0405 10:26:25.592463 26038 sgd_solver.cpp:105] Iteration 4488, lr = 1e-05
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I0405 10:26:30.070267 26038 solver.cpp:218] Iteration 4500 (2.6799 iter/s, 4.47777s/12 iters), loss = 5.27178
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I0405 10:26:30.070405 26038 solver.cpp:237] Train net output #0: loss = 5.27178 (* 1 = 5.27178 loss)
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I0405 10:26:30.070415 26038 sgd_solver.cpp:105] Iteration 4500, lr = 1e-05
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I0405 10:26:35.619107 26038 solver.cpp:218] Iteration 4512 (2.16268 iter/s, 5.54866s/12 iters), loss = 5.29311
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I0405 10:26:35.619158 26038 solver.cpp:237] Train net output #0: loss = 5.29311 (* 1 = 5.29311 loss)
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I0405 10:26:35.619168 26038 sgd_solver.cpp:105] Iteration 4512, lr = 1e-05
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I0405 10:26:41.166865 26038 solver.cpp:218] Iteration 4524 (2.16307 iter/s, 5.54767s/12 iters), loss = 5.27514
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I0405 10:26:41.166906 26038 solver.cpp:237] Train net output #0: loss = 5.27514 (* 1 = 5.27514 loss)
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I0405 10:26:41.166911 26038 sgd_solver.cpp:105] Iteration 4524, lr = 1e-05
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I0405 10:26:46.556149 26038 solver.cpp:218] Iteration 4536 (2.22668 iter/s, 5.38919s/12 iters), loss = 5.27598
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I0405 10:26:46.556202 26038 solver.cpp:237] Train net output #0: loss = 5.27598 (* 1 = 5.27598 loss)
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I0405 10:26:46.556210 26038 sgd_solver.cpp:105] Iteration 4536, lr = 1e-05
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I0405 10:26:51.870800 26038 solver.cpp:218] Iteration 4548 (2.25795 iter/s, 5.31455s/12 iters), loss = 5.29358
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I0405 10:26:51.870862 26038 solver.cpp:237] Train net output #0: loss = 5.29358 (* 1 = 5.29358 loss)
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I0405 10:26:51.870870 26038 sgd_solver.cpp:105] Iteration 4548, lr = 1e-05
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I0405 10:26:53.264765 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:26:57.336261 26038 solver.cpp:218] Iteration 4560 (2.19565 iter/s, 5.46536s/12 iters), loss = 5.2938
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I0405 10:26:57.336300 26038 solver.cpp:237] Train net output #0: loss = 5.2938 (* 1 = 5.2938 loss)
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I0405 10:26:57.336305 26038 sgd_solver.cpp:105] Iteration 4560, lr = 1e-05
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I0405 10:27:02.827734 26038 solver.cpp:218] Iteration 4572 (2.18524 iter/s, 5.49139s/12 iters), loss = 5.2746
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I0405 10:27:02.827922 26038 solver.cpp:237] Train net output #0: loss = 5.2746 (* 1 = 5.2746 loss)
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I0405 10:27:02.827932 26038 sgd_solver.cpp:105] Iteration 4572, lr = 1e-05
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I0405 10:27:08.100791 26038 solver.cpp:218] Iteration 4584 (2.27582 iter/s, 5.27283s/12 iters), loss = 5.27639
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I0405 10:27:08.100841 26038 solver.cpp:237] Train net output #0: loss = 5.27639 (* 1 = 5.27639 loss)
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I0405 10:27:08.100848 26038 sgd_solver.cpp:105] Iteration 4584, lr = 1e-05
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I0405 10:27:10.241473 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
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I0405 10:27:13.329166 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
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I0405 10:27:16.439532 26038 solver.cpp:330] Iteration 4590, Testing net (#0)
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I0405 10:27:16.439549 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:27:19.275805 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:27:21.238867 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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||
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I0405 10:27:21.238893 26038 solver.cpp:397] Test net output #1: loss = 5.2789 (* 1 = 5.2789 loss)
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I0405 10:27:23.187296 26038 solver.cpp:218] Iteration 4596 (0.79542 iter/s, 15.0864s/12 iters), loss = 5.29024
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I0405 10:27:23.187355 26038 solver.cpp:237] Train net output #0: loss = 5.29024 (* 1 = 5.29024 loss)
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I0405 10:27:23.187363 26038 sgd_solver.cpp:105] Iteration 4596, lr = 1e-05
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I0405 10:27:28.765516 26038 solver.cpp:218] Iteration 4608 (2.15126 iter/s, 5.57812s/12 iters), loss = 5.28269
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I0405 10:27:28.765554 26038 solver.cpp:237] Train net output #0: loss = 5.28269 (* 1 = 5.28269 loss)
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I0405 10:27:28.765559 26038 sgd_solver.cpp:105] Iteration 4608, lr = 1e-05
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I0405 10:27:34.177424 26038 solver.cpp:218] Iteration 4620 (2.21737 iter/s, 5.41182s/12 iters), loss = 5.28608
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I0405 10:27:34.177520 26038 solver.cpp:237] Train net output #0: loss = 5.28608 (* 1 = 5.28608 loss)
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I0405 10:27:34.177526 26038 sgd_solver.cpp:105] Iteration 4620, lr = 1e-05
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I0405 10:27:39.422083 26038 solver.cpp:218] Iteration 4632 (2.2881 iter/s, 5.24452s/12 iters), loss = 5.28212
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I0405 10:27:39.422137 26038 solver.cpp:237] Train net output #0: loss = 5.28212 (* 1 = 5.28212 loss)
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I0405 10:27:39.422145 26038 sgd_solver.cpp:105] Iteration 4632, lr = 1e-05
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I0405 10:27:44.685860 26038 solver.cpp:218] Iteration 4644 (2.27977 iter/s, 5.26369s/12 iters), loss = 5.28521
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I0405 10:27:44.685900 26038 solver.cpp:237] Train net output #0: loss = 5.28521 (* 1 = 5.28521 loss)
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I0405 10:27:44.685906 26038 sgd_solver.cpp:105] Iteration 4644, lr = 1e-05
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I0405 10:27:48.374526 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:27:49.976266 26038 solver.cpp:218] Iteration 4656 (2.26829 iter/s, 5.29032s/12 iters), loss = 5.29084
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I0405 10:27:49.976308 26038 solver.cpp:237] Train net output #0: loss = 5.29084 (* 1 = 5.29084 loss)
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I0405 10:27:49.976313 26038 sgd_solver.cpp:105] Iteration 4656, lr = 1e-05
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I0405 10:27:55.581077 26038 solver.cpp:218] Iteration 4668 (2.14105 iter/s, 5.60472s/12 iters), loss = 5.26758
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I0405 10:27:55.581125 26038 solver.cpp:237] Train net output #0: loss = 5.26758 (* 1 = 5.26758 loss)
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I0405 10:27:55.581131 26038 sgd_solver.cpp:105] Iteration 4668, lr = 1e-05
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I0405 10:28:01.161968 26038 solver.cpp:218] Iteration 4680 (2.15023 iter/s, 5.58079s/12 iters), loss = 5.25875
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I0405 10:28:01.162014 26038 solver.cpp:237] Train net output #0: loss = 5.25875 (* 1 = 5.25875 loss)
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I0405 10:28:01.162020 26038 sgd_solver.cpp:105] Iteration 4680, lr = 1e-05
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I0405 10:28:06.109356 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
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I0405 10:28:09.138434 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
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I0405 10:28:12.216219 26038 solver.cpp:330] Iteration 4692, Testing net (#0)
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I0405 10:28:12.216243 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:28:14.846082 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:28:16.892870 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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I0405 10:28:16.892912 26038 solver.cpp:397] Test net output #1: loss = 5.27879 (* 1 = 5.27879 loss)
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I0405 10:28:17.030246 26038 solver.cpp:218] Iteration 4692 (0.756233 iter/s, 15.8681s/12 iters), loss = 5.28462
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I0405 10:28:17.030303 26038 solver.cpp:237] Train net output #0: loss = 5.28462 (* 1 = 5.28462 loss)
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I0405 10:28:17.030310 26038 sgd_solver.cpp:105] Iteration 4692, lr = 1e-05
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I0405 10:28:21.349455 26038 solver.cpp:218] Iteration 4704 (2.77835 iter/s, 4.31911s/12 iters), loss = 5.28386
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I0405 10:28:21.349505 26038 solver.cpp:237] Train net output #0: loss = 5.28386 (* 1 = 5.28386 loss)
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I0405 10:28:21.349514 26038 sgd_solver.cpp:105] Iteration 4704, lr = 1e-05
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I0405 10:28:26.524670 26038 solver.cpp:218] Iteration 4716 (2.31879 iter/s, 5.17512s/12 iters), loss = 5.27926
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I0405 10:28:26.524711 26038 solver.cpp:237] Train net output #0: loss = 5.27926 (* 1 = 5.27926 loss)
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I0405 10:28:26.524716 26038 sgd_solver.cpp:105] Iteration 4716, lr = 1e-05
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I0405 10:28:31.775147 26038 solver.cpp:218] Iteration 4728 (2.28554 iter/s, 5.25039s/12 iters), loss = 5.27959
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I0405 10:28:31.775208 26038 solver.cpp:237] Train net output #0: loss = 5.27959 (* 1 = 5.27959 loss)
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||
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I0405 10:28:31.775218 26038 sgd_solver.cpp:105] Iteration 4728, lr = 1e-05
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I0405 10:28:37.166929 26038 solver.cpp:218] Iteration 4740 (2.22566 iter/s, 5.39167s/12 iters), loss = 5.27695
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I0405 10:28:37.167104 26038 solver.cpp:237] Train net output #0: loss = 5.27695 (* 1 = 5.27695 loss)
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I0405 10:28:37.167115 26038 sgd_solver.cpp:105] Iteration 4740, lr = 1e-05
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I0405 10:28:42.657894 26038 solver.cpp:218] Iteration 4752 (2.18549 iter/s, 5.49075s/12 iters), loss = 5.29634
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I0405 10:28:42.657955 26038 solver.cpp:237] Train net output #0: loss = 5.29634 (* 1 = 5.29634 loss)
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I0405 10:28:42.657963 26038 sgd_solver.cpp:105] Iteration 4752, lr = 1e-05
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I0405 10:28:43.223085 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:28:48.103629 26038 solver.cpp:218] Iteration 4764 (2.2036 iter/s, 5.44563s/12 iters), loss = 5.28538
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I0405 10:28:48.103675 26038 solver.cpp:237] Train net output #0: loss = 5.28538 (* 1 = 5.28538 loss)
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I0405 10:28:48.103682 26038 sgd_solver.cpp:105] Iteration 4764, lr = 1e-05
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I0405 10:28:53.643654 26038 solver.cpp:218] Iteration 4776 (2.16609 iter/s, 5.53993s/12 iters), loss = 5.27456
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I0405 10:28:53.643702 26038 solver.cpp:237] Train net output #0: loss = 5.27456 (* 1 = 5.27456 loss)
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I0405 10:28:53.643708 26038 sgd_solver.cpp:105] Iteration 4776, lr = 1e-05
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I0405 10:28:59.056010 26038 solver.cpp:218] Iteration 4788 (2.21719 iter/s, 5.41226s/12 iters), loss = 5.2824
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I0405 10:28:59.056062 26038 solver.cpp:237] Train net output #0: loss = 5.2824 (* 1 = 5.2824 loss)
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I0405 10:28:59.056069 26038 sgd_solver.cpp:105] Iteration 4788, lr = 1e-05
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I0405 10:29:01.325073 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
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I0405 10:29:04.340328 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
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I0405 10:29:06.645215 26038 solver.cpp:330] Iteration 4794, Testing net (#0)
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I0405 10:29:06.645241 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:29:09.320716 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:29:11.321805 26038 solver.cpp:397] Test net output #0: accuracy = 0.00245098
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||
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I0405 10:29:11.321846 26038 solver.cpp:397] Test net output #1: loss = 5.2786 (* 1 = 5.2786 loss)
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I0405 10:29:13.272914 26038 solver.cpp:218] Iteration 4800 (0.844075 iter/s, 14.2168s/12 iters), loss = 5.28712
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I0405 10:29:13.272966 26038 solver.cpp:237] Train net output #0: loss = 5.28712 (* 1 = 5.28712 loss)
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I0405 10:29:13.272974 26038 sgd_solver.cpp:105] Iteration 4800, lr = 1e-05
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I0405 10:29:18.656756 26038 solver.cpp:218] Iteration 4812 (2.22893 iter/s, 5.38374s/12 iters), loss = 5.27369
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I0405 10:29:18.656806 26038 solver.cpp:237] Train net output #0: loss = 5.27369 (* 1 = 5.27369 loss)
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I0405 10:29:18.656812 26038 sgd_solver.cpp:105] Iteration 4812, lr = 1e-05
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I0405 10:29:23.887712 26038 solver.cpp:218] Iteration 4824 (2.29408 iter/s, 5.23086s/12 iters), loss = 5.29113
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I0405 10:29:23.887753 26038 solver.cpp:237] Train net output #0: loss = 5.29113 (* 1 = 5.29113 loss)
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I0405 10:29:23.887758 26038 sgd_solver.cpp:105] Iteration 4824, lr = 1e-05
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I0405 10:29:29.196107 26038 solver.cpp:218] Iteration 4836 (2.26061 iter/s, 5.30831s/12 iters), loss = 5.26875
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I0405 10:29:29.196151 26038 solver.cpp:237] Train net output #0: loss = 5.26875 (* 1 = 5.26875 loss)
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I0405 10:29:29.196156 26038 sgd_solver.cpp:105] Iteration 4836, lr = 1e-05
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I0405 10:29:31.341683 26038 blocking_queue.cpp:49] Waiting for data
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I0405 10:29:34.661942 26038 solver.cpp:218] Iteration 4848 (2.19549 iter/s, 5.46574s/12 iters), loss = 5.28794
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I0405 10:29:34.662003 26038 solver.cpp:237] Train net output #0: loss = 5.28794 (* 1 = 5.28794 loss)
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I0405 10:29:34.662012 26038 sgd_solver.cpp:105] Iteration 4848, lr = 1e-05
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I0405 10:29:37.668584 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:29:40.375344 26038 solver.cpp:218] Iteration 4860 (2.10036 iter/s, 5.7133s/12 iters), loss = 5.27268
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I0405 10:29:40.375440 26038 solver.cpp:237] Train net output #0: loss = 5.27268 (* 1 = 5.27268 loss)
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I0405 10:29:40.375447 26038 sgd_solver.cpp:105] Iteration 4860, lr = 1e-05
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I0405 10:29:45.798766 26038 solver.cpp:218] Iteration 4872 (2.21268 iter/s, 5.42329s/12 iters), loss = 5.27919
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I0405 10:29:45.798804 26038 solver.cpp:237] Train net output #0: loss = 5.27919 (* 1 = 5.27919 loss)
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I0405 10:29:45.798810 26038 sgd_solver.cpp:105] Iteration 4872, lr = 1e-05
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I0405 10:29:51.285948 26038 solver.cpp:218] Iteration 4884 (2.18695 iter/s, 5.48709s/12 iters), loss = 5.27479
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I0405 10:29:51.297765 26038 solver.cpp:237] Train net output #0: loss = 5.27479 (* 1 = 5.27479 loss)
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I0405 10:29:51.297786 26038 sgd_solver.cpp:105] Iteration 4884, lr = 1e-05
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I0405 10:29:56.150470 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
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I0405 10:29:59.241094 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
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I0405 10:30:01.561969 26038 solver.cpp:330] Iteration 4896, Testing net (#0)
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I0405 10:30:01.561988 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:30:04.267544 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:30:06.172683 26038 solver.cpp:397] Test net output #0: accuracy = 0.00306373
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||
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I0405 10:30:06.172713 26038 solver.cpp:397] Test net output #1: loss = 5.27862 (* 1 = 5.27862 loss)
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I0405 10:30:06.313938 26038 solver.cpp:218] Iteration 4896 (0.799142 iter/s, 15.0161s/12 iters), loss = 5.27889
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I0405 10:30:06.313997 26038 solver.cpp:237] Train net output #0: loss = 5.27889 (* 1 = 5.27889 loss)
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I0405 10:30:06.314007 26038 sgd_solver.cpp:105] Iteration 4896, lr = 1e-05
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I0405 10:30:10.778409 26038 solver.cpp:218] Iteration 4908 (2.68795 iter/s, 4.46437s/12 iters), loss = 5.29813
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I0405 10:30:10.779145 26038 solver.cpp:237] Train net output #0: loss = 5.29813 (* 1 = 5.29813 loss)
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I0405 10:30:10.779155 26038 sgd_solver.cpp:105] Iteration 4908, lr = 1e-05
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I0405 10:30:16.031257 26038 solver.cpp:218] Iteration 4920 (2.28481 iter/s, 5.25207s/12 iters), loss = 5.27173
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I0405 10:30:16.031309 26038 solver.cpp:237] Train net output #0: loss = 5.27173 (* 1 = 5.27173 loss)
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I0405 10:30:16.031317 26038 sgd_solver.cpp:105] Iteration 4920, lr = 1e-05
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I0405 10:30:21.351357 26038 solver.cpp:218] Iteration 4932 (2.25564 iter/s, 5.32s/12 iters), loss = 5.2882
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I0405 10:30:21.351415 26038 solver.cpp:237] Train net output #0: loss = 5.2882 (* 1 = 5.2882 loss)
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I0405 10:30:21.351424 26038 sgd_solver.cpp:105] Iteration 4932, lr = 1e-05
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I0405 10:30:26.797526 26038 solver.cpp:218] Iteration 4944 (2.20342 iter/s, 5.44607s/12 iters), loss = 5.29352
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I0405 10:30:26.797564 26038 solver.cpp:237] Train net output #0: loss = 5.29352 (* 1 = 5.29352 loss)
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||
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I0405 10:30:26.797569 26038 sgd_solver.cpp:105] Iteration 4944, lr = 1e-05
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||
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I0405 10:30:31.755029 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:30:32.004493 26038 solver.cpp:218] Iteration 4956 (2.30464 iter/s, 5.20688s/12 iters), loss = 5.27557
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||
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I0405 10:30:32.004544 26038 solver.cpp:237] Train net output #0: loss = 5.27557 (* 1 = 5.27557 loss)
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||
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I0405 10:30:32.004549 26038 sgd_solver.cpp:105] Iteration 4956, lr = 1e-05
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I0405 10:30:37.426105 26038 solver.cpp:218] Iteration 4968 (2.2134 iter/s, 5.42152s/12 iters), loss = 5.28093
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||
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I0405 10:30:37.426151 26038 solver.cpp:237] Train net output #0: loss = 5.28093 (* 1 = 5.28093 loss)
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||
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I0405 10:30:37.426156 26038 sgd_solver.cpp:105] Iteration 4968, lr = 1e-05
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I0405 10:30:42.830646 26038 solver.cpp:218] Iteration 4980 (2.22039 iter/s, 5.40445s/12 iters), loss = 5.27896
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I0405 10:30:42.830773 26038 solver.cpp:237] Train net output #0: loss = 5.27896 (* 1 = 5.27896 loss)
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I0405 10:30:42.830783 26038 sgd_solver.cpp:105] Iteration 4980, lr = 1e-05
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I0405 10:30:48.279620 26038 solver.cpp:218] Iteration 4992 (2.20232 iter/s, 5.4488s/12 iters), loss = 5.28492
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I0405 10:30:48.279675 26038 solver.cpp:237] Train net output #0: loss = 5.28492 (* 1 = 5.28492 loss)
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I0405 10:30:48.279683 26038 sgd_solver.cpp:105] Iteration 4992, lr = 1e-05
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I0405 10:30:50.490000 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
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||
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I0405 10:30:53.667829 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
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I0405 10:30:55.994289 26038 solver.cpp:330] Iteration 4998, Testing net (#0)
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||
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I0405 10:30:55.994311 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 10:30:58.443536 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 10:31:00.551625 26038 solver.cpp:397] Test net output #0: accuracy = 0.00306373
|
||
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|
I0405 10:31:00.551663 26038 solver.cpp:397] Test net output #1: loss = 5.27859 (* 1 = 5.27859 loss)
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I0405 10:31:02.556474 26038 solver.cpp:218] Iteration 5004 (0.84053 iter/s, 14.2767s/12 iters), loss = 5.27746
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I0405 10:31:02.556526 26038 solver.cpp:237] Train net output #0: loss = 5.27746 (* 1 = 5.27746 loss)
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I0405 10:31:02.556537 26038 sgd_solver.cpp:105] Iteration 5004, lr = 1e-05
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I0405 10:31:07.924737 26038 solver.cpp:218] Iteration 5016 (2.2354 iter/s, 5.36816s/12 iters), loss = 5.28543
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I0405 10:31:07.924788 26038 solver.cpp:237] Train net output #0: loss = 5.28543 (* 1 = 5.28543 loss)
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||
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I0405 10:31:07.924795 26038 sgd_solver.cpp:105] Iteration 5016, lr = 1e-05
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I0405 10:31:13.393573 26038 solver.cpp:218] Iteration 5028 (2.19429 iter/s, 5.46874s/12 iters), loss = 5.27882
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I0405 10:31:13.393678 26038 solver.cpp:237] Train net output #0: loss = 5.27882 (* 1 = 5.27882 loss)
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||
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I0405 10:31:13.393684 26038 sgd_solver.cpp:105] Iteration 5028, lr = 1e-05
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I0405 10:31:18.634853 26038 solver.cpp:218] Iteration 5040 (2.28958 iter/s, 5.24113s/12 iters), loss = 5.27208
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I0405 10:31:18.634898 26038 solver.cpp:237] Train net output #0: loss = 5.27208 (* 1 = 5.27208 loss)
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I0405 10:31:18.634903 26038 sgd_solver.cpp:105] Iteration 5040, lr = 1e-05
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I0405 10:31:24.135277 26038 solver.cpp:218] Iteration 5052 (2.18169 iter/s, 5.50033s/12 iters), loss = 5.28078
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I0405 10:31:24.135319 26038 solver.cpp:237] Train net output #0: loss = 5.28078 (* 1 = 5.28078 loss)
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I0405 10:31:24.135325 26038 sgd_solver.cpp:105] Iteration 5052, lr = 1e-05
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||
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I0405 10:31:26.287432 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:31:29.869019 26038 solver.cpp:218] Iteration 5064 (2.09291 iter/s, 5.73365s/12 iters), loss = 5.28678
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I0405 10:31:29.869069 26038 solver.cpp:237] Train net output #0: loss = 5.28678 (* 1 = 5.28678 loss)
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I0405 10:31:29.869077 26038 sgd_solver.cpp:105] Iteration 5064, lr = 1e-05
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I0405 10:31:35.052995 26038 solver.cpp:218] Iteration 5076 (2.31487 iter/s, 5.18388s/12 iters), loss = 5.27969
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I0405 10:31:35.053043 26038 solver.cpp:237] Train net output #0: loss = 5.27969 (* 1 = 5.27969 loss)
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I0405 10:31:35.053048 26038 sgd_solver.cpp:105] Iteration 5076, lr = 1e-05
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I0405 10:31:40.423638 26038 solver.cpp:218] Iteration 5088 (2.23441 iter/s, 5.37054s/12 iters), loss = 5.27321
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||
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I0405 10:31:40.423705 26038 solver.cpp:237] Train net output #0: loss = 5.27321 (* 1 = 5.27321 loss)
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I0405 10:31:40.423715 26038 sgd_solver.cpp:105] Iteration 5088, lr = 1e-05
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I0405 10:31:45.323475 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
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I0405 10:31:48.349462 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
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I0405 10:31:50.671937 26038 solver.cpp:330] Iteration 5100, Testing net (#0)
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I0405 10:31:50.671962 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:31:53.127032 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:31:55.213733 26038 solver.cpp:397] Test net output #0: accuracy = 0.00367647
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I0405 10:31:55.213765 26038 solver.cpp:397] Test net output #1: loss = 5.27865 (* 1 = 5.27865 loss)
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I0405 10:31:55.355526 26038 solver.cpp:218] Iteration 5100 (0.803658 iter/s, 14.9317s/12 iters), loss = 5.28724
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I0405 10:31:55.355567 26038 solver.cpp:237] Train net output #0: loss = 5.28724 (* 1 = 5.28724 loss)
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I0405 10:31:55.355573 26038 sgd_solver.cpp:105] Iteration 5100, lr = 1e-05
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I0405 10:31:59.974225 26038 solver.cpp:218] Iteration 5112 (2.59818 iter/s, 4.61861s/12 iters), loss = 5.2715
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I0405 10:31:59.974267 26038 solver.cpp:237] Train net output #0: loss = 5.2715 (* 1 = 5.2715 loss)
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I0405 10:31:59.974272 26038 sgd_solver.cpp:105] Iteration 5112, lr = 1e-05
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I0405 10:32:05.387913 26038 solver.cpp:218] Iteration 5124 (2.21664 iter/s, 5.4136s/12 iters), loss = 5.27789
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I0405 10:32:05.387961 26038 solver.cpp:237] Train net output #0: loss = 5.27789 (* 1 = 5.27789 loss)
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I0405 10:32:05.387969 26038 sgd_solver.cpp:105] Iteration 5124, lr = 1e-05
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I0405 10:32:10.727284 26038 solver.cpp:218] Iteration 5136 (2.24749 iter/s, 5.33928s/12 iters), loss = 5.28393
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I0405 10:32:10.727331 26038 solver.cpp:237] Train net output #0: loss = 5.28393 (* 1 = 5.28393 loss)
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I0405 10:32:10.727336 26038 sgd_solver.cpp:105] Iteration 5136, lr = 1e-05
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I0405 10:32:16.024453 26038 solver.cpp:218] Iteration 5148 (2.2654 iter/s, 5.29708s/12 iters), loss = 5.27724
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I0405 10:32:16.024554 26038 solver.cpp:237] Train net output #0: loss = 5.27724 (* 1 = 5.27724 loss)
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I0405 10:32:16.024559 26038 sgd_solver.cpp:105] Iteration 5148, lr = 1e-05
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I0405 10:32:20.374927 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:32:21.392318 26038 solver.cpp:218] Iteration 5160 (2.23559 iter/s, 5.36772s/12 iters), loss = 5.28565
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I0405 10:32:21.392379 26038 solver.cpp:237] Train net output #0: loss = 5.28565 (* 1 = 5.28565 loss)
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I0405 10:32:21.392387 26038 sgd_solver.cpp:105] Iteration 5160, lr = 1e-05
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I0405 10:32:26.805112 26038 solver.cpp:218] Iteration 5172 (2.21701 iter/s, 5.41269s/12 iters), loss = 5.28773
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I0405 10:32:26.805171 26038 solver.cpp:237] Train net output #0: loss = 5.28773 (* 1 = 5.28773 loss)
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I0405 10:32:26.805179 26038 sgd_solver.cpp:105] Iteration 5172, lr = 1e-05
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I0405 10:32:32.162402 26038 solver.cpp:218] Iteration 5184 (2.23998 iter/s, 5.35719s/12 iters), loss = 5.27653
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I0405 10:32:32.162454 26038 solver.cpp:237] Train net output #0: loss = 5.27653 (* 1 = 5.27653 loss)
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I0405 10:32:32.162462 26038 sgd_solver.cpp:105] Iteration 5184, lr = 1e-05
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I0405 10:32:37.493631 26038 solver.cpp:218] Iteration 5196 (2.25093 iter/s, 5.33112s/12 iters), loss = 5.28241
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I0405 10:32:37.493690 26038 solver.cpp:237] Train net output #0: loss = 5.28241 (* 1 = 5.28241 loss)
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I0405 10:32:37.493697 26038 sgd_solver.cpp:105] Iteration 5196, lr = 1e-05
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||
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I0405 10:32:39.637087 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
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||
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I0405 10:32:42.831001 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
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||
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I0405 10:32:45.172154 26038 solver.cpp:330] Iteration 5202, Testing net (#0)
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||
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I0405 10:32:45.172173 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:32:47.764505 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:32:50.057771 26038 solver.cpp:397] Test net output #0: accuracy = 0.00306373
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||
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I0405 10:32:50.057798 26038 solver.cpp:397] Test net output #1: loss = 5.27885 (* 1 = 5.27885 loss)
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||
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I0405 10:32:51.985920 26038 solver.cpp:218] Iteration 5208 (0.828035 iter/s, 14.4921s/12 iters), loss = 5.28256
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||
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I0405 10:32:51.985973 26038 solver.cpp:237] Train net output #0: loss = 5.28256 (* 1 = 5.28256 loss)
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I0405 10:32:51.985981 26038 sgd_solver.cpp:105] Iteration 5208, lr = 1e-05
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I0405 10:32:57.300803 26038 solver.cpp:218] Iteration 5220 (2.25785 iter/s, 5.31478s/12 iters), loss = 5.28342
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I0405 10:32:57.300850 26038 solver.cpp:237] Train net output #0: loss = 5.28342 (* 1 = 5.28342 loss)
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I0405 10:32:57.300858 26038 sgd_solver.cpp:105] Iteration 5220, lr = 1e-05
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I0405 10:33:02.805621 26038 solver.cpp:218] Iteration 5232 (2.17995 iter/s, 5.50472s/12 iters), loss = 5.27994
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I0405 10:33:02.805682 26038 solver.cpp:237] Train net output #0: loss = 5.27994 (* 1 = 5.27994 loss)
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I0405 10:33:02.805693 26038 sgd_solver.cpp:105] Iteration 5232, lr = 1e-05
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I0405 10:33:08.465776 26038 solver.cpp:218] Iteration 5244 (2.12012 iter/s, 5.66005s/12 iters), loss = 5.2894
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I0405 10:33:08.465821 26038 solver.cpp:237] Train net output #0: loss = 5.2894 (* 1 = 5.2894 loss)
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I0405 10:33:08.465826 26038 sgd_solver.cpp:105] Iteration 5244, lr = 1e-05
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I0405 10:33:14.093300 26038 solver.cpp:218] Iteration 5256 (2.13241 iter/s, 5.62743s/12 iters), loss = 5.28277
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I0405 10:33:14.093345 26038 solver.cpp:237] Train net output #0: loss = 5.28277 (* 1 = 5.28277 loss)
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I0405 10:33:14.093351 26038 sgd_solver.cpp:105] Iteration 5256, lr = 1e-05
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||
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I0405 10:33:15.595127 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:33:19.726267 26038 solver.cpp:218] Iteration 5268 (2.13035 iter/s, 5.63288s/12 iters), loss = 5.29965
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I0405 10:33:19.726375 26038 solver.cpp:237] Train net output #0: loss = 5.29965 (* 1 = 5.29965 loss)
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I0405 10:33:19.726384 26038 sgd_solver.cpp:105] Iteration 5268, lr = 1e-05
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I0405 10:33:25.306339 26038 solver.cpp:218] Iteration 5280 (2.15057 iter/s, 5.57992s/12 iters), loss = 5.2939
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I0405 10:33:25.306386 26038 solver.cpp:237] Train net output #0: loss = 5.2939 (* 1 = 5.2939 loss)
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I0405 10:33:25.306394 26038 sgd_solver.cpp:105] Iteration 5280, lr = 1e-05
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I0405 10:33:30.354076 26038 solver.cpp:218] Iteration 5292 (2.37734 iter/s, 5.04765s/12 iters), loss = 5.27305
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I0405 10:33:30.354118 26038 solver.cpp:237] Train net output #0: loss = 5.27305 (* 1 = 5.27305 loss)
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I0405 10:33:30.354125 26038 sgd_solver.cpp:105] Iteration 5292, lr = 1e-05
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I0405 10:33:35.259254 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
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||
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I0405 10:33:38.409312 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
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I0405 10:33:40.710922 26038 solver.cpp:330] Iteration 5304, Testing net (#0)
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I0405 10:33:40.710943 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:33:42.970425 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:33:45.153437 26038 solver.cpp:397] Test net output #0: accuracy = 0.00306373
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I0405 10:33:45.153473 26038 solver.cpp:397] Test net output #1: loss = 5.27897 (* 1 = 5.27897 loss)
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I0405 10:33:45.289906 26038 solver.cpp:218] Iteration 5304 (0.803444 iter/s, 14.9357s/12 iters), loss = 5.27636
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I0405 10:33:45.289968 26038 solver.cpp:237] Train net output #0: loss = 5.27636 (* 1 = 5.27636 loss)
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I0405 10:33:45.289978 26038 sgd_solver.cpp:105] Iteration 5304, lr = 1e-05
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I0405 10:33:49.608335 26038 solver.cpp:218] Iteration 5316 (2.77885 iter/s, 4.31833s/12 iters), loss = 5.29569
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I0405 10:33:49.608383 26038 solver.cpp:237] Train net output #0: loss = 5.29569 (* 1 = 5.29569 loss)
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I0405 10:33:49.608392 26038 sgd_solver.cpp:105] Iteration 5316, lr = 1e-05
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I0405 10:33:55.141386 26038 solver.cpp:218] Iteration 5328 (2.16882 iter/s, 5.53296s/12 iters), loss = 5.28319
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I0405 10:33:55.147682 26038 solver.cpp:237] Train net output #0: loss = 5.28319 (* 1 = 5.28319 loss)
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I0405 10:33:55.147692 26038 sgd_solver.cpp:105] Iteration 5328, lr = 1e-05
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I0405 10:34:00.330433 26038 solver.cpp:218] Iteration 5340 (2.31539 iter/s, 5.18271s/12 iters), loss = 5.26557
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I0405 10:34:00.330485 26038 solver.cpp:237] Train net output #0: loss = 5.26557 (* 1 = 5.26557 loss)
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I0405 10:34:00.330493 26038 sgd_solver.cpp:105] Iteration 5340, lr = 1e-05
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I0405 10:34:06.013154 26038 solver.cpp:218] Iteration 5352 (2.1117 iter/s, 5.68262s/12 iters), loss = 5.2875
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I0405 10:34:06.013211 26038 solver.cpp:237] Train net output #0: loss = 5.2875 (* 1 = 5.2875 loss)
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I0405 10:34:06.013221 26038 sgd_solver.cpp:105] Iteration 5352, lr = 1e-05
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I0405 10:34:09.640403 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:34:11.396134 26038 solver.cpp:218] Iteration 5364 (2.22929 iter/s, 5.38288s/12 iters), loss = 5.28444
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I0405 10:34:11.396176 26038 solver.cpp:237] Train net output #0: loss = 5.28444 (* 1 = 5.28444 loss)
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||
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I0405 10:34:11.396181 26038 sgd_solver.cpp:105] Iteration 5364, lr = 1e-05
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I0405 10:34:17.020627 26038 solver.cpp:218] Iteration 5376 (2.13356 iter/s, 5.6244s/12 iters), loss = 5.27087
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I0405 10:34:17.020668 26038 solver.cpp:237] Train net output #0: loss = 5.27087 (* 1 = 5.27087 loss)
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I0405 10:34:17.020673 26038 sgd_solver.cpp:105] Iteration 5376, lr = 1e-05
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I0405 10:34:22.303903 26038 solver.cpp:218] Iteration 5388 (2.27135 iter/s, 5.28319s/12 iters), loss = 5.25821
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I0405 10:34:22.303941 26038 solver.cpp:237] Train net output #0: loss = 5.25821 (* 1 = 5.25821 loss)
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I0405 10:34:22.303946 26038 sgd_solver.cpp:105] Iteration 5388, lr = 1e-05
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I0405 10:34:27.880713 26038 solver.cpp:218] Iteration 5400 (2.1518 iter/s, 5.57672s/12 iters), loss = 5.27407
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||
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I0405 10:34:27.880816 26038 solver.cpp:237] Train net output #0: loss = 5.27407 (* 1 = 5.27407 loss)
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||
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I0405 10:34:27.880823 26038 sgd_solver.cpp:105] Iteration 5400, lr = 1e-05
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||
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I0405 10:34:29.865916 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
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||
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|
I0405 10:34:33.085492 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
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||
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I0405 10:34:35.377454 26038 solver.cpp:330] Iteration 5406, Testing net (#0)
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||
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I0405 10:34:35.377473 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 10:34:37.797504 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 10:34:40.093847 26038 solver.cpp:397] Test net output #0: accuracy = 0.00306373
|
||
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|
I0405 10:34:40.093879 26038 solver.cpp:397] Test net output #1: loss = 5.27891 (* 1 = 5.27891 loss)
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||
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I0405 10:34:42.062458 26038 solver.cpp:218] Iteration 5412 (0.846169 iter/s, 14.1816s/12 iters), loss = 5.27507
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||
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I0405 10:34:42.062498 26038 solver.cpp:237] Train net output #0: loss = 5.27507 (* 1 = 5.27507 loss)
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||
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I0405 10:34:42.062505 26038 sgd_solver.cpp:105] Iteration 5412, lr = 1e-05
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||
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I0405 10:34:47.437943 26038 solver.cpp:218] Iteration 5424 (2.23239 iter/s, 5.3754s/12 iters), loss = 5.26743
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||
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I0405 10:34:47.437984 26038 solver.cpp:237] Train net output #0: loss = 5.26743 (* 1 = 5.26743 loss)
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||
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I0405 10:34:47.437990 26038 sgd_solver.cpp:105] Iteration 5424, lr = 1e-05
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||
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I0405 10:34:53.133205 26038 solver.cpp:218] Iteration 5436 (2.10705 iter/s, 5.69517s/12 iters), loss = 5.28509
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||
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I0405 10:34:53.133249 26038 solver.cpp:237] Train net output #0: loss = 5.28509 (* 1 = 5.28509 loss)
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||
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I0405 10:34:53.133255 26038 sgd_solver.cpp:105] Iteration 5436, lr = 1e-05
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||
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I0405 10:34:58.382938 26038 solver.cpp:218] Iteration 5448 (2.28587 iter/s, 5.24964s/12 iters), loss = 5.27644
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I0405 10:34:58.383077 26038 solver.cpp:237] Train net output #0: loss = 5.27644 (* 1 = 5.27644 loss)
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||
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I0405 10:34:58.383085 26038 sgd_solver.cpp:105] Iteration 5448, lr = 1e-05
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||
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I0405 10:35:03.622627 26038 solver.cpp:218] Iteration 5460 (2.29029 iter/s, 5.2395s/12 iters), loss = 5.27763
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||
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I0405 10:35:03.622671 26038 solver.cpp:237] Train net output #0: loss = 5.27763 (* 1 = 5.27763 loss)
|
||
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I0405 10:35:03.622676 26038 sgd_solver.cpp:105] Iteration 5460, lr = 1e-05
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||
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I0405 10:35:04.252951 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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|
I0405 10:35:09.134229 26038 solver.cpp:218] Iteration 5472 (2.17726 iter/s, 5.51152s/12 iters), loss = 5.28279
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||
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|
I0405 10:35:09.134269 26038 solver.cpp:237] Train net output #0: loss = 5.28279 (* 1 = 5.28279 loss)
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||
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I0405 10:35:09.134274 26038 sgd_solver.cpp:105] Iteration 5472, lr = 1e-05
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I0405 10:35:14.654959 26038 solver.cpp:218] Iteration 5484 (2.17366 iter/s, 5.52065s/12 iters), loss = 5.27679
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||
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I0405 10:35:14.654994 26038 solver.cpp:237] Train net output #0: loss = 5.27679 (* 1 = 5.27679 loss)
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||
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I0405 10:35:14.654999 26038 sgd_solver.cpp:105] Iteration 5484, lr = 1e-05
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I0405 10:35:20.150772 26038 solver.cpp:218] Iteration 5496 (2.18351 iter/s, 5.49573s/12 iters), loss = 5.27603
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I0405 10:35:20.150831 26038 solver.cpp:237] Train net output #0: loss = 5.27603 (* 1 = 5.27603 loss)
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||
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I0405 10:35:20.150840 26038 sgd_solver.cpp:105] Iteration 5496, lr = 1e-05
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||
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I0405 10:35:25.168983 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
|
||
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|
I0405 10:35:28.273072 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
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||
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I0405 10:35:30.646070 26038 solver.cpp:330] Iteration 5508, Testing net (#0)
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||
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I0405 10:35:30.646165 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 10:35:33.166019 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 10:35:35.474280 26038 solver.cpp:397] Test net output #0: accuracy = 0.00367647
|
||
|
|
I0405 10:35:35.474314 26038 solver.cpp:397] Test net output #1: loss = 5.27874 (* 1 = 5.27874 loss)
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||
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I0405 10:35:35.614159 26038 solver.cpp:218] Iteration 5508 (0.776035 iter/s, 15.4632s/12 iters), loss = 5.29486
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||
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I0405 10:35:35.614217 26038 solver.cpp:237] Train net output #0: loss = 5.29486 (* 1 = 5.29486 loss)
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||
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I0405 10:35:35.614225 26038 sgd_solver.cpp:105] Iteration 5508, lr = 1e-05
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I0405 10:35:39.943747 26038 solver.cpp:218] Iteration 5520 (2.77169 iter/s, 4.32949s/12 iters), loss = 5.28306
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I0405 10:35:39.943787 26038 solver.cpp:237] Train net output #0: loss = 5.28306 (* 1 = 5.28306 loss)
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||
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I0405 10:35:39.943794 26038 sgd_solver.cpp:105] Iteration 5520, lr = 1e-05
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I0405 10:35:42.579715 26038 blocking_queue.cpp:49] Waiting for data
|
||
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I0405 10:35:45.371604 26038 solver.cpp:218] Iteration 5532 (2.21086 iter/s, 5.42776s/12 iters), loss = 5.28283
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||
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I0405 10:35:45.371650 26038 solver.cpp:237] Train net output #0: loss = 5.28283 (* 1 = 5.28283 loss)
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||
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I0405 10:35:45.371655 26038 sgd_solver.cpp:105] Iteration 5532, lr = 1e-05
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I0405 10:35:50.919252 26038 solver.cpp:218] Iteration 5544 (2.16312 iter/s, 5.54755s/12 iters), loss = 5.27241
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||
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I0405 10:35:50.919317 26038 solver.cpp:237] Train net output #0: loss = 5.27241 (* 1 = 5.27241 loss)
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I0405 10:35:50.919325 26038 sgd_solver.cpp:105] Iteration 5544, lr = 1e-05
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I0405 10:35:56.312176 26038 solver.cpp:218] Iteration 5556 (2.22518 iter/s, 5.39282s/12 iters), loss = 5.28386
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I0405 10:35:56.312218 26038 solver.cpp:237] Train net output #0: loss = 5.28386 (* 1 = 5.28386 loss)
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I0405 10:35:56.312224 26038 sgd_solver.cpp:105] Iteration 5556, lr = 1e-05
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I0405 10:35:59.359550 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:36:01.954789 26038 solver.cpp:218] Iteration 5568 (2.12671 iter/s, 5.64252s/12 iters), loss = 5.27313
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I0405 10:36:01.954912 26038 solver.cpp:237] Train net output #0: loss = 5.27313 (* 1 = 5.27313 loss)
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I0405 10:36:01.954918 26038 sgd_solver.cpp:105] Iteration 5568, lr = 1e-05
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I0405 10:36:07.427091 26038 solver.cpp:218] Iteration 5580 (2.19293 iter/s, 5.47213s/12 iters), loss = 5.27933
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I0405 10:36:07.427157 26038 solver.cpp:237] Train net output #0: loss = 5.27933 (* 1 = 5.27933 loss)
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I0405 10:36:07.427166 26038 sgd_solver.cpp:105] Iteration 5580, lr = 1e-05
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I0405 10:36:12.907819 26038 solver.cpp:218] Iteration 5592 (2.18953 iter/s, 5.48062s/12 iters), loss = 5.27911
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I0405 10:36:12.907876 26038 solver.cpp:237] Train net output #0: loss = 5.27911 (* 1 = 5.27911 loss)
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I0405 10:36:12.907884 26038 sgd_solver.cpp:105] Iteration 5592, lr = 1e-05
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I0405 10:36:18.326719 26038 solver.cpp:218] Iteration 5604 (2.21451 iter/s, 5.4188s/12 iters), loss = 5.27316
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I0405 10:36:18.326762 26038 solver.cpp:237] Train net output #0: loss = 5.27316 (* 1 = 5.27316 loss)
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I0405 10:36:18.326768 26038 sgd_solver.cpp:105] Iteration 5604, lr = 1e-05
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I0405 10:36:20.627084 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
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I0405 10:36:23.751534 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
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I0405 10:36:26.055374 26038 solver.cpp:330] Iteration 5610, Testing net (#0)
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I0405 10:36:26.055395 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:36:28.321853 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:36:30.582165 26038 solver.cpp:397] Test net output #0: accuracy = 0.00306373
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I0405 10:36:30.582190 26038 solver.cpp:397] Test net output #1: loss = 5.27898 (* 1 = 5.27898 loss)
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I0405 10:36:32.522747 26038 solver.cpp:218] Iteration 5616 (0.845315 iter/s, 14.1959s/12 iters), loss = 5.29709
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I0405 10:36:32.522903 26038 solver.cpp:237] Train net output #0: loss = 5.29709 (* 1 = 5.29709 loss)
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I0405 10:36:32.522912 26038 sgd_solver.cpp:105] Iteration 5616, lr = 1e-05
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I0405 10:36:37.919454 26038 solver.cpp:218] Iteration 5628 (2.22366 iter/s, 5.39651s/12 iters), loss = 5.28359
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I0405 10:36:37.919513 26038 solver.cpp:237] Train net output #0: loss = 5.28359 (* 1 = 5.28359 loss)
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I0405 10:36:37.919523 26038 sgd_solver.cpp:105] Iteration 5628, lr = 1e-05
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I0405 10:36:43.443485 26038 solver.cpp:218] Iteration 5640 (2.17238 iter/s, 5.52389s/12 iters), loss = 5.29305
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I0405 10:36:43.443533 26038 solver.cpp:237] Train net output #0: loss = 5.29305 (* 1 = 5.29305 loss)
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I0405 10:36:43.443540 26038 sgd_solver.cpp:105] Iteration 5640, lr = 1e-05
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I0405 10:36:48.794296 26038 solver.cpp:218] Iteration 5652 (2.24269 iter/s, 5.35072s/12 iters), loss = 5.27956
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I0405 10:36:48.794350 26038 solver.cpp:237] Train net output #0: loss = 5.27956 (* 1 = 5.27956 loss)
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I0405 10:36:48.794358 26038 sgd_solver.cpp:105] Iteration 5652, lr = 1e-05
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I0405 10:36:53.968451 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:36:54.197897 26038 solver.cpp:218] Iteration 5664 (2.22078 iter/s, 5.4035s/12 iters), loss = 5.27998
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I0405 10:36:54.197947 26038 solver.cpp:237] Train net output #0: loss = 5.27998 (* 1 = 5.27998 loss)
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I0405 10:36:54.197954 26038 sgd_solver.cpp:105] Iteration 5664, lr = 1e-05
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I0405 10:36:59.568297 26038 solver.cpp:218] Iteration 5676 (2.23451 iter/s, 5.37031s/12 iters), loss = 5.29083
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I0405 10:36:59.568333 26038 solver.cpp:237] Train net output #0: loss = 5.29083 (* 1 = 5.29083 loss)
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I0405 10:36:59.568338 26038 sgd_solver.cpp:105] Iteration 5676, lr = 1e-05
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I0405 10:37:04.990142 26038 solver.cpp:218] Iteration 5688 (2.2133 iter/s, 5.42177s/12 iters), loss = 5.28006
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I0405 10:37:04.990299 26038 solver.cpp:237] Train net output #0: loss = 5.28006 (* 1 = 5.28006 loss)
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I0405 10:37:04.990309 26038 sgd_solver.cpp:105] Iteration 5688, lr = 1e-05
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I0405 10:37:10.507655 26038 solver.cpp:218] Iteration 5700 (2.17497 iter/s, 5.51731s/12 iters), loss = 5.28035
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I0405 10:37:10.507697 26038 solver.cpp:237] Train net output #0: loss = 5.28035 (* 1 = 5.28035 loss)
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I0405 10:37:10.507704 26038 sgd_solver.cpp:105] Iteration 5700, lr = 1e-05
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I0405 10:37:15.209941 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
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I0405 10:37:19.061596 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
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I0405 10:37:22.173458 26038 solver.cpp:330] Iteration 5712, Testing net (#0)
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I0405 10:37:22.173478 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:37:24.444190 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:37:26.890159 26038 solver.cpp:397] Test net output #0: accuracy = 0.00367647
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||
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I0405 10:37:26.890198 26038 solver.cpp:397] Test net output #1: loss = 5.27882 (* 1 = 5.27882 loss)
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I0405 10:37:27.036108 26038 solver.cpp:218] Iteration 5712 (0.726027 iter/s, 16.5283s/12 iters), loss = 5.28979
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I0405 10:37:27.036175 26038 solver.cpp:237] Train net output #0: loss = 5.28979 (* 1 = 5.28979 loss)
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I0405 10:37:27.036185 26038 sgd_solver.cpp:105] Iteration 5712, lr = 1e-05
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I0405 10:37:31.406833 26038 solver.cpp:218] Iteration 5724 (2.74561 iter/s, 4.37062s/12 iters), loss = 5.27569
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I0405 10:37:31.406879 26038 solver.cpp:237] Train net output #0: loss = 5.27569 (* 1 = 5.27569 loss)
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I0405 10:37:31.406884 26038 sgd_solver.cpp:105] Iteration 5724, lr = 1e-05
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I0405 10:37:36.786008 26038 solver.cpp:218] Iteration 5736 (2.23087 iter/s, 5.37908s/12 iters), loss = 5.28791
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I0405 10:37:36.786146 26038 solver.cpp:237] Train net output #0: loss = 5.28791 (* 1 = 5.28791 loss)
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I0405 10:37:36.786156 26038 sgd_solver.cpp:105] Iteration 5736, lr = 1e-05
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I0405 10:37:42.029497 26038 solver.cpp:218] Iteration 5748 (2.28863 iter/s, 5.24331s/12 iters), loss = 5.29254
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I0405 10:37:42.029537 26038 solver.cpp:237] Train net output #0: loss = 5.29254 (* 1 = 5.29254 loss)
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I0405 10:37:42.029543 26038 sgd_solver.cpp:105] Iteration 5748, lr = 1e-05
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I0405 10:37:47.574458 26038 solver.cpp:218] Iteration 5760 (2.16416 iter/s, 5.54487s/12 iters), loss = 5.28713
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I0405 10:37:47.574507 26038 solver.cpp:237] Train net output #0: loss = 5.28713 (* 1 = 5.28713 loss)
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I0405 10:37:47.574515 26038 sgd_solver.cpp:105] Iteration 5760, lr = 1e-05
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I0405 10:37:49.529547 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:37:52.834748 26038 solver.cpp:218] Iteration 5772 (2.28557 iter/s, 5.25033s/12 iters), loss = 5.29062
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I0405 10:37:52.834794 26038 solver.cpp:237] Train net output #0: loss = 5.29062 (* 1 = 5.29062 loss)
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I0405 10:37:52.834801 26038 sgd_solver.cpp:105] Iteration 5772, lr = 1e-05
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I0405 10:37:58.161834 26038 solver.cpp:218] Iteration 5784 (2.25268 iter/s, 5.32699s/12 iters), loss = 5.27777
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I0405 10:37:58.161895 26038 solver.cpp:237] Train net output #0: loss = 5.27777 (* 1 = 5.27777 loss)
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I0405 10:37:58.161906 26038 sgd_solver.cpp:105] Iteration 5784, lr = 1e-05
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I0405 10:38:03.754072 26038 solver.cpp:218] Iteration 5796 (2.14587 iter/s, 5.59214s/12 iters), loss = 5.28131
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I0405 10:38:03.754112 26038 solver.cpp:237] Train net output #0: loss = 5.28131 (* 1 = 5.28131 loss)
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I0405 10:38:03.754118 26038 sgd_solver.cpp:105] Iteration 5796, lr = 1e-05
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I0405 10:38:09.219696 26038 solver.cpp:218] Iteration 5808 (2.19558 iter/s, 5.46554s/12 iters), loss = 5.27976
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I0405 10:38:09.219818 26038 solver.cpp:237] Train net output #0: loss = 5.27976 (* 1 = 5.27976 loss)
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I0405 10:38:09.219828 26038 sgd_solver.cpp:105] Iteration 5808, lr = 1e-05
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I0405 10:38:11.310916 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
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||
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I0405 10:38:14.358726 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
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I0405 10:38:16.717970 26038 solver.cpp:330] Iteration 5814, Testing net (#0)
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I0405 10:38:16.717993 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:38:18.753535 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:38:21.227190 26038 solver.cpp:397] Test net output #0: accuracy = 0.00490196
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||
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I0405 10:38:21.227226 26038 solver.cpp:397] Test net output #1: loss = 5.27871 (* 1 = 5.27871 loss)
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I0405 10:38:22.977170 26038 solver.cpp:218] Iteration 5820 (0.872266 iter/s, 13.7573s/12 iters), loss = 5.27191
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I0405 10:38:22.977221 26038 solver.cpp:237] Train net output #0: loss = 5.27191 (* 1 = 5.27191 loss)
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I0405 10:38:22.977229 26038 sgd_solver.cpp:105] Iteration 5820, lr = 1e-05
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I0405 10:38:28.572067 26038 solver.cpp:218] Iteration 5832 (2.14485 iter/s, 5.5948s/12 iters), loss = 5.27741
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I0405 10:38:28.572124 26038 solver.cpp:237] Train net output #0: loss = 5.27741 (* 1 = 5.27741 loss)
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I0405 10:38:28.572131 26038 sgd_solver.cpp:105] Iteration 5832, lr = 1e-05
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I0405 10:38:33.875545 26038 solver.cpp:218] Iteration 5844 (2.26271 iter/s, 5.30337s/12 iters), loss = 5.2773
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I0405 10:38:33.875605 26038 solver.cpp:237] Train net output #0: loss = 5.2773 (* 1 = 5.2773 loss)
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||
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I0405 10:38:33.875614 26038 sgd_solver.cpp:105] Iteration 5844, lr = 1e-05
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I0405 10:38:39.519170 26038 solver.cpp:218] Iteration 5856 (2.12633 iter/s, 5.64353s/12 iters), loss = 5.2791
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I0405 10:38:39.519315 26038 solver.cpp:237] Train net output #0: loss = 5.2791 (* 1 = 5.2791 loss)
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||
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I0405 10:38:39.519323 26038 sgd_solver.cpp:105] Iteration 5856, lr = 1e-05
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||
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I0405 10:38:44.125682 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:38:45.019824 26038 solver.cpp:218] Iteration 5868 (2.18163 iter/s, 5.50047s/12 iters), loss = 5.27426
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||
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I0405 10:38:45.019873 26038 solver.cpp:237] Train net output #0: loss = 5.27426 (* 1 = 5.27426 loss)
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||
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I0405 10:38:45.019881 26038 sgd_solver.cpp:105] Iteration 5868, lr = 1e-05
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||
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I0405 10:38:50.341091 26038 solver.cpp:218] Iteration 5880 (2.25514 iter/s, 5.32117s/12 iters), loss = 5.28403
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||
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I0405 10:38:50.341135 26038 solver.cpp:237] Train net output #0: loss = 5.28403 (* 1 = 5.28403 loss)
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||
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I0405 10:38:50.341140 26038 sgd_solver.cpp:105] Iteration 5880, lr = 1e-05
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||
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I0405 10:38:55.583863 26038 solver.cpp:218] Iteration 5892 (2.2889 iter/s, 5.24268s/12 iters), loss = 5.27653
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||
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I0405 10:38:55.583910 26038 solver.cpp:237] Train net output #0: loss = 5.27653 (* 1 = 5.27653 loss)
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||
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I0405 10:38:55.583917 26038 sgd_solver.cpp:105] Iteration 5892, lr = 1e-05
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I0405 10:39:00.986966 26038 solver.cpp:218] Iteration 5904 (2.22098 iter/s, 5.40301s/12 iters), loss = 5.30309
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||
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I0405 10:39:00.987007 26038 solver.cpp:237] Train net output #0: loss = 5.30309 (* 1 = 5.30309 loss)
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||
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I0405 10:39:00.987013 26038 sgd_solver.cpp:105] Iteration 5904, lr = 1e-05
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||
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I0405 10:39:05.973790 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
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||
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|
I0405 10:39:08.964452 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
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||
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I0405 10:39:11.262784 26038 solver.cpp:330] Iteration 5916, Testing net (#0)
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||
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I0405 10:39:11.262859 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 10:39:13.433001 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 10:39:15.728279 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 10:39:15.728327 26038 solver.cpp:397] Test net output #1: loss = 5.27896 (* 1 = 5.27896 loss)
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||
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I0405 10:39:15.866547 26038 solver.cpp:218] Iteration 5916 (0.806482 iter/s, 14.8794s/12 iters), loss = 5.2751
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||
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I0405 10:39:15.866600 26038 solver.cpp:237] Train net output #0: loss = 5.2751 (* 1 = 5.2751 loss)
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||
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I0405 10:39:15.866608 26038 sgd_solver.cpp:105] Iteration 5916, lr = 1e-05
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||
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I0405 10:39:20.516606 26038 solver.cpp:218] Iteration 5928 (2.58066 iter/s, 4.64997s/12 iters), loss = 5.27001
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||
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I0405 10:39:20.516646 26038 solver.cpp:237] Train net output #0: loss = 5.27001 (* 1 = 5.27001 loss)
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||
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I0405 10:39:20.516652 26038 sgd_solver.cpp:105] Iteration 5928, lr = 1e-05
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||
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I0405 10:39:25.919729 26038 solver.cpp:218] Iteration 5940 (2.22097 iter/s, 5.40304s/12 iters), loss = 5.27342
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||
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I0405 10:39:25.919775 26038 solver.cpp:237] Train net output #0: loss = 5.27342 (* 1 = 5.27342 loss)
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||
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I0405 10:39:25.919781 26038 sgd_solver.cpp:105] Iteration 5940, lr = 1e-05
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||
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I0405 10:39:30.940459 26038 solver.cpp:218] Iteration 5952 (2.39014 iter/s, 5.02063s/12 iters), loss = 5.28038
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||
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I0405 10:39:30.940522 26038 solver.cpp:237] Train net output #0: loss = 5.28038 (* 1 = 5.28038 loss)
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||
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I0405 10:39:30.940531 26038 sgd_solver.cpp:105] Iteration 5952, lr = 1e-05
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||
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I0405 10:39:36.539650 26038 solver.cpp:218] Iteration 5964 (2.14321 iter/s, 5.59908s/12 iters), loss = 5.26669
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||
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I0405 10:39:36.539705 26038 solver.cpp:237] Train net output #0: loss = 5.26669 (* 1 = 5.26669 loss)
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||
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I0405 10:39:36.539713 26038 sgd_solver.cpp:105] Iteration 5964, lr = 1e-05
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||
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I0405 10:39:38.013557 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:39:42.042877 26038 solver.cpp:218] Iteration 5976 (2.18058 iter/s, 5.50313s/12 iters), loss = 5.28351
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||
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I0405 10:39:42.043063 26038 solver.cpp:237] Train net output #0: loss = 5.28351 (* 1 = 5.28351 loss)
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||
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I0405 10:39:42.043072 26038 sgd_solver.cpp:105] Iteration 5976, lr = 1e-05
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||
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I0405 10:39:47.522541 26038 solver.cpp:218] Iteration 5988 (2.19 iter/s, 5.47944s/12 iters), loss = 5.29683
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||
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I0405 10:39:47.522586 26038 solver.cpp:237] Train net output #0: loss = 5.29683 (* 1 = 5.29683 loss)
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||
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I0405 10:39:47.522593 26038 sgd_solver.cpp:105] Iteration 5988, lr = 1e-05
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||
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I0405 10:39:53.147043 26038 solver.cpp:218] Iteration 6000 (2.13356 iter/s, 5.62441s/12 iters), loss = 5.27988
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I0405 10:39:53.147096 26038 solver.cpp:237] Train net output #0: loss = 5.27988 (* 1 = 5.27988 loss)
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||
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I0405 10:39:53.147104 26038 sgd_solver.cpp:105] Iteration 6000, lr = 1e-05
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I0405 10:39:58.449273 26038 solver.cpp:218] Iteration 6012 (2.26324 iter/s, 5.30214s/12 iters), loss = 5.26632
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||
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I0405 10:39:58.449328 26038 solver.cpp:237] Train net output #0: loss = 5.26632 (* 1 = 5.26632 loss)
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||
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I0405 10:39:58.449335 26038 sgd_solver.cpp:105] Iteration 6012, lr = 1e-05
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||
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I0405 10:40:00.585569 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
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||
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I0405 10:40:03.607329 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
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I0405 10:40:05.912210 26038 solver.cpp:330] Iteration 6018, Testing net (#0)
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I0405 10:40:05.912230 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:40:07.899412 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:40:10.395171 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 10:40:10.395210 26038 solver.cpp:397] Test net output #1: loss = 5.27876 (* 1 = 5.27876 loss)
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I0405 10:40:12.406008 26038 solver.cpp:218] Iteration 6024 (0.859808 iter/s, 13.9566s/12 iters), loss = 5.28447
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I0405 10:40:12.406154 26038 solver.cpp:237] Train net output #0: loss = 5.28447 (* 1 = 5.28447 loss)
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I0405 10:40:12.406162 26038 sgd_solver.cpp:105] Iteration 6024, lr = 1e-05
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I0405 10:40:17.770656 26038 solver.cpp:218] Iteration 6036 (2.23695 iter/s, 5.36446s/12 iters), loss = 5.26593
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I0405 10:40:17.770717 26038 solver.cpp:237] Train net output #0: loss = 5.26593 (* 1 = 5.26593 loss)
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I0405 10:40:17.770725 26038 sgd_solver.cpp:105] Iteration 6036, lr = 1e-05
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I0405 10:40:22.828423 26038 solver.cpp:218] Iteration 6048 (2.37264 iter/s, 5.05767s/12 iters), loss = 5.2733
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I0405 10:40:22.828476 26038 solver.cpp:237] Train net output #0: loss = 5.2733 (* 1 = 5.2733 loss)
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I0405 10:40:22.828485 26038 sgd_solver.cpp:105] Iteration 6048, lr = 1e-05
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I0405 10:40:28.380228 26038 solver.cpp:218] Iteration 6060 (2.1615 iter/s, 5.55171s/12 iters), loss = 5.27711
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I0405 10:40:28.380275 26038 solver.cpp:237] Train net output #0: loss = 5.27711 (* 1 = 5.27711 loss)
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I0405 10:40:28.380282 26038 sgd_solver.cpp:105] Iteration 6060, lr = 1e-05
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I0405 10:40:32.052410 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:40:33.557103 26038 solver.cpp:218] Iteration 6072 (2.31804 iter/s, 5.17679s/12 iters), loss = 5.26669
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I0405 10:40:33.557142 26038 solver.cpp:237] Train net output #0: loss = 5.26669 (* 1 = 5.26669 loss)
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I0405 10:40:33.557147 26038 sgd_solver.cpp:105] Iteration 6072, lr = 1e-05
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I0405 10:40:38.947816 26038 solver.cpp:218] Iteration 6084 (2.22608 iter/s, 5.39064s/12 iters), loss = 5.26885
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I0405 10:40:38.947855 26038 solver.cpp:237] Train net output #0: loss = 5.26885 (* 1 = 5.26885 loss)
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I0405 10:40:38.947860 26038 sgd_solver.cpp:105] Iteration 6084, lr = 1e-05
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I0405 10:40:44.252694 26038 solver.cpp:218] Iteration 6096 (2.2621 iter/s, 5.3048s/12 iters), loss = 5.2861
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I0405 10:40:44.252830 26038 solver.cpp:237] Train net output #0: loss = 5.2861 (* 1 = 5.2861 loss)
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I0405 10:40:44.252838 26038 sgd_solver.cpp:105] Iteration 6096, lr = 1e-05
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I0405 10:40:49.806566 26038 solver.cpp:218] Iteration 6108 (2.16073 iter/s, 5.55369s/12 iters), loss = 5.26692
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I0405 10:40:49.806624 26038 solver.cpp:237] Train net output #0: loss = 5.26692 (* 1 = 5.26692 loss)
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||
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I0405 10:40:49.806635 26038 sgd_solver.cpp:105] Iteration 6108, lr = 1e-05
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I0405 10:40:54.450914 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
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||
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I0405 10:40:57.544512 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
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I0405 10:40:59.854988 26038 solver.cpp:330] Iteration 6120, Testing net (#0)
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I0405 10:40:59.855010 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:41:01.923820 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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|
I0405 10:41:04.430892 26038 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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||
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I0405 10:41:04.430923 26038 solver.cpp:397] Test net output #1: loss = 5.27868 (* 1 = 5.27868 loss)
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I0405 10:41:04.575366 26038 solver.cpp:218] Iteration 6120 (0.812531 iter/s, 14.7687s/12 iters), loss = 5.27723
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||
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I0405 10:41:04.575426 26038 solver.cpp:237] Train net output #0: loss = 5.27723 (* 1 = 5.27723 loss)
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||
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I0405 10:41:04.575433 26038 sgd_solver.cpp:105] Iteration 6120, lr = 1e-05
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I0405 10:41:09.153496 26038 solver.cpp:218] Iteration 6132 (2.62121 iter/s, 4.57803s/12 iters), loss = 5.26594
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I0405 10:41:09.153537 26038 solver.cpp:237] Train net output #0: loss = 5.26594 (* 1 = 5.26594 loss)
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||
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I0405 10:41:09.153542 26038 sgd_solver.cpp:105] Iteration 6132, lr = 1e-05
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I0405 10:41:14.372555 26038 solver.cpp:218] Iteration 6144 (2.2993 iter/s, 5.21897s/12 iters), loss = 5.28263
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||
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I0405 10:41:14.372689 26038 solver.cpp:237] Train net output #0: loss = 5.28263 (* 1 = 5.28263 loss)
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||
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I0405 10:41:14.372699 26038 sgd_solver.cpp:105] Iteration 6144, lr = 1e-05
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I0405 10:41:19.829766 26038 solver.cpp:218] Iteration 6156 (2.199 iter/s, 5.45704s/12 iters), loss = 5.27829
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I0405 10:41:19.829815 26038 solver.cpp:237] Train net output #0: loss = 5.27829 (* 1 = 5.27829 loss)
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||
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I0405 10:41:19.829823 26038 sgd_solver.cpp:105] Iteration 6156, lr = 1e-05
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I0405 10:41:25.315268 26038 solver.cpp:218] Iteration 6168 (2.18762 iter/s, 5.48541s/12 iters), loss = 5.27567
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||
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I0405 10:41:25.321468 26038 solver.cpp:237] Train net output #0: loss = 5.27567 (* 1 = 5.27567 loss)
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||
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I0405 10:41:25.321486 26038 sgd_solver.cpp:105] Iteration 6168, lr = 1e-05
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I0405 10:41:25.909072 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:41:30.850742 26038 solver.cpp:218] Iteration 6180 (2.17028 iter/s, 5.52923s/12 iters), loss = 5.27543
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||
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I0405 10:41:30.850823 26038 solver.cpp:237] Train net output #0: loss = 5.27543 (* 1 = 5.27543 loss)
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||
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I0405 10:41:30.850834 26038 sgd_solver.cpp:105] Iteration 6180, lr = 1e-05
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I0405 10:41:36.335343 26038 solver.cpp:218] Iteration 6192 (2.18799 iter/s, 5.48448s/12 iters), loss = 5.27839
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I0405 10:41:36.335391 26038 solver.cpp:237] Train net output #0: loss = 5.27839 (* 1 = 5.27839 loss)
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||
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I0405 10:41:36.335399 26038 sgd_solver.cpp:105] Iteration 6192, lr = 1e-05
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I0405 10:41:41.494360 26038 solver.cpp:218] Iteration 6204 (2.32606 iter/s, 5.15893s/12 iters), loss = 5.26399
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I0405 10:41:41.494415 26038 solver.cpp:237] Train net output #0: loss = 5.26399 (* 1 = 5.26399 loss)
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||
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I0405 10:41:41.494421 26038 sgd_solver.cpp:105] Iteration 6204, lr = 1e-05
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I0405 10:41:47.052601 26038 solver.cpp:218] Iteration 6216 (2.15899 iter/s, 5.55814s/12 iters), loss = 5.28485
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I0405 10:41:47.052745 26038 solver.cpp:237] Train net output #0: loss = 5.28485 (* 1 = 5.28485 loss)
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I0405 10:41:47.052752 26038 sgd_solver.cpp:105] Iteration 6216, lr = 1e-05
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I0405 10:41:49.268196 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
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I0405 10:41:52.350946 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
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I0405 10:41:54.719017 26038 solver.cpp:330] Iteration 6222, Testing net (#0)
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I0405 10:41:54.719044 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:41:56.732961 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:41:58.020128 26038 blocking_queue.cpp:49] Waiting for data
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||
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I0405 10:41:59.286996 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 10:41:59.287034 26038 solver.cpp:397] Test net output #1: loss = 5.2789 (* 1 = 5.2789 loss)
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I0405 10:42:01.265625 26038 solver.cpp:218] Iteration 6228 (0.844309 iter/s, 14.2128s/12 iters), loss = 5.27827
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I0405 10:42:01.265681 26038 solver.cpp:237] Train net output #0: loss = 5.27827 (* 1 = 5.27827 loss)
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I0405 10:42:01.265687 26038 sgd_solver.cpp:105] Iteration 6228, lr = 1e-05
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I0405 10:42:06.622987 26038 solver.cpp:218] Iteration 6240 (2.23995 iter/s, 5.35726s/12 iters), loss = 5.28633
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I0405 10:42:06.623044 26038 solver.cpp:237] Train net output #0: loss = 5.28633 (* 1 = 5.28633 loss)
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I0405 10:42:06.623054 26038 sgd_solver.cpp:105] Iteration 6240, lr = 1e-05
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I0405 10:42:11.873942 26038 solver.cpp:218] Iteration 6252 (2.28534 iter/s, 5.25086s/12 iters), loss = 5.28095
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I0405 10:42:11.873987 26038 solver.cpp:237] Train net output #0: loss = 5.28095 (* 1 = 5.28095 loss)
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||
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I0405 10:42:11.873993 26038 sgd_solver.cpp:105] Iteration 6252, lr = 1e-05
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I0405 10:42:17.283589 26038 solver.cpp:218] Iteration 6264 (2.2183 iter/s, 5.40956s/12 iters), loss = 5.2846
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I0405 10:42:17.283718 26038 solver.cpp:237] Train net output #0: loss = 5.2846 (* 1 = 5.2846 loss)
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||
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I0405 10:42:17.283726 26038 sgd_solver.cpp:105] Iteration 6264, lr = 1e-05
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I0405 10:42:20.217339 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:42:22.773350 26038 solver.cpp:218] Iteration 6276 (2.18595 iter/s, 5.48959s/12 iters), loss = 5.28083
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I0405 10:42:22.773391 26038 solver.cpp:237] Train net output #0: loss = 5.28083 (* 1 = 5.28083 loss)
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I0405 10:42:22.773396 26038 sgd_solver.cpp:105] Iteration 6276, lr = 1e-05
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I0405 10:42:28.435508 26038 solver.cpp:218] Iteration 6288 (2.11936 iter/s, 5.66207s/12 iters), loss = 5.26481
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I0405 10:42:28.435550 26038 solver.cpp:237] Train net output #0: loss = 5.26481 (* 1 = 5.26481 loss)
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||
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I0405 10:42:28.435556 26038 sgd_solver.cpp:105] Iteration 6288, lr = 1e-05
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I0405 10:42:33.785959 26038 solver.cpp:218] Iteration 6300 (2.24284 iter/s, 5.35037s/12 iters), loss = 5.27658
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||
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I0405 10:42:33.786012 26038 solver.cpp:237] Train net output #0: loss = 5.27658 (* 1 = 5.27658 loss)
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||
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I0405 10:42:33.786020 26038 sgd_solver.cpp:105] Iteration 6300, lr = 1e-05
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I0405 10:42:39.240698 26038 solver.cpp:218] Iteration 6312 (2.19996 iter/s, 5.45465s/12 iters), loss = 5.27977
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I0405 10:42:39.240739 26038 solver.cpp:237] Train net output #0: loss = 5.27977 (* 1 = 5.27977 loss)
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||
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I0405 10:42:39.240746 26038 sgd_solver.cpp:105] Iteration 6312, lr = 1e-05
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||
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I0405 10:42:44.093919 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
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||
|
|
I0405 10:42:47.207561 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
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||
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|
I0405 10:42:49.552453 26038 solver.cpp:330] Iteration 6324, Testing net (#0)
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||
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I0405 10:42:49.552551 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
|
I0405 10:42:51.546411 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 10:42:54.196522 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 10:42:54.196560 26038 solver.cpp:397] Test net output #1: loss = 5.27878 (* 1 = 5.27878 loss)
|
||
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I0405 10:42:54.334429 26038 solver.cpp:218] Iteration 6324 (0.795038 iter/s, 15.0936s/12 iters), loss = 5.27962
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||
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I0405 10:42:54.334481 26038 solver.cpp:237] Train net output #0: loss = 5.27962 (* 1 = 5.27962 loss)
|
||
|
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I0405 10:42:54.334489 26038 sgd_solver.cpp:105] Iteration 6324, lr = 1e-05
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||
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I0405 10:42:58.812407 26038 solver.cpp:218] Iteration 6336 (2.67984 iter/s, 4.47788s/12 iters), loss = 5.29417
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||
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I0405 10:42:58.812464 26038 solver.cpp:237] Train net output #0: loss = 5.29417 (* 1 = 5.29417 loss)
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||
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I0405 10:42:58.812472 26038 sgd_solver.cpp:105] Iteration 6336, lr = 1e-05
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||
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I0405 10:43:04.221827 26038 solver.cpp:218] Iteration 6348 (2.21839 iter/s, 5.40932s/12 iters), loss = 5.27596
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||
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I0405 10:43:04.221879 26038 solver.cpp:237] Train net output #0: loss = 5.27596 (* 1 = 5.27596 loss)
|
||
|
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I0405 10:43:04.221886 26038 sgd_solver.cpp:105] Iteration 6348, lr = 1e-05
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||
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I0405 10:43:09.669867 26038 solver.cpp:218] Iteration 6360 (2.20267 iter/s, 5.44794s/12 iters), loss = 5.28985
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||
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|
I0405 10:43:09.669921 26038 solver.cpp:237] Train net output #0: loss = 5.28985 (* 1 = 5.28985 loss)
|
||
|
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I0405 10:43:09.669929 26038 sgd_solver.cpp:105] Iteration 6360, lr = 1e-05
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||
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I0405 10:43:14.797264 26057 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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|
I0405 10:43:14.992508 26038 solver.cpp:218] Iteration 6372 (2.25456 iter/s, 5.32255s/12 iters), loss = 5.28255
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||
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I0405 10:43:14.992568 26038 solver.cpp:237] Train net output #0: loss = 5.28255 (* 1 = 5.28255 loss)
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||
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I0405 10:43:14.992578 26038 sgd_solver.cpp:105] Iteration 6372, lr = 1e-05
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||
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I0405 10:43:20.533274 26038 solver.cpp:218] Iteration 6384 (2.1658 iter/s, 5.54067s/12 iters), loss = 5.28167
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||
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|
I0405 10:43:20.533411 26038 solver.cpp:237] Train net output #0: loss = 5.28167 (* 1 = 5.28167 loss)
|
||
|
|
I0405 10:43:20.533419 26038 sgd_solver.cpp:105] Iteration 6384, lr = 1e-05
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||
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|
I0405 10:43:25.810395 26038 solver.cpp:218] Iteration 6396 (2.27404 iter/s, 5.27695s/12 iters), loss = 5.26936
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||
|
|
I0405 10:43:25.810432 26038 solver.cpp:237] Train net output #0: loss = 5.26936 (* 1 = 5.26936 loss)
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||
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|
I0405 10:43:25.810438 26038 sgd_solver.cpp:105] Iteration 6396, lr = 1e-05
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||
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I0405 10:43:31.341863 26038 solver.cpp:218] Iteration 6408 (2.16944 iter/s, 5.53139s/12 iters), loss = 5.29572
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||
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|
I0405 10:43:31.341908 26038 solver.cpp:237] Train net output #0: loss = 5.29572 (* 1 = 5.29572 loss)
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||
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I0405 10:43:31.341914 26038 sgd_solver.cpp:105] Iteration 6408, lr = 1e-05
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||
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I0405 10:43:36.836256 26038 solver.cpp:218] Iteration 6420 (2.18408 iter/s, 5.49431s/12 iters), loss = 5.27778
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||
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|
I0405 10:43:36.836297 26038 solver.cpp:237] Train net output #0: loss = 5.27778 (* 1 = 5.27778 loss)
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||
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|
I0405 10:43:36.836302 26038 sgd_solver.cpp:105] Iteration 6420, lr = 1e-05
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||
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I0405 10:43:38.907907 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
|
||
|
|
I0405 10:43:41.984349 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
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||
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I0405 10:43:44.298913 26038 solver.cpp:330] Iteration 6426, Testing net (#0)
|
||
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I0405 10:43:44.298938 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
|
I0405 10:43:46.155328 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 10:43:48.792292 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 10:43:48.792322 26038 solver.cpp:397] Test net output #1: loss = 5.27881 (* 1 = 5.27881 loss)
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||
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I0405 10:43:50.712113 26038 solver.cpp:218] Iteration 6432 (0.864819 iter/s, 13.8757s/12 iters), loss = 5.27632
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||
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I0405 10:43:50.712215 26038 solver.cpp:237] Train net output #0: loss = 5.27632 (* 1 = 5.27632 loss)
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||
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I0405 10:43:50.712224 26038 sgd_solver.cpp:105] Iteration 6432, lr = 1e-05
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||
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I0405 10:43:56.245589 26038 solver.cpp:218] Iteration 6444 (2.16868 iter/s, 5.53333s/12 iters), loss = 5.2715
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||
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I0405 10:43:56.245636 26038 solver.cpp:237] Train net output #0: loss = 5.2715 (* 1 = 5.2715 loss)
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||
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I0405 10:43:56.245642 26038 sgd_solver.cpp:105] Iteration 6444, lr = 1e-05
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||
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I0405 10:44:01.757670 26038 solver.cpp:218] Iteration 6456 (2.17707 iter/s, 5.51199s/12 iters), loss = 5.27921
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||
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I0405 10:44:01.757714 26038 solver.cpp:237] Train net output #0: loss = 5.27921 (* 1 = 5.27921 loss)
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||
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I0405 10:44:01.757721 26038 sgd_solver.cpp:105] Iteration 6456, lr = 1e-05
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||
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I0405 10:44:07.132236 26038 solver.cpp:218] Iteration 6468 (2.23277 iter/s, 5.37448s/12 iters), loss = 5.29565
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||
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I0405 10:44:07.138446 26038 solver.cpp:237] Train net output #0: loss = 5.29565 (* 1 = 5.29565 loss)
|
||
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I0405 10:44:07.138466 26038 sgd_solver.cpp:105] Iteration 6468, lr = 1e-05
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I0405 10:44:09.258612 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:44:12.683507 26038 solver.cpp:218] Iteration 6480 (2.1641 iter/s, 5.54504s/12 iters), loss = 5.29101
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I0405 10:44:12.683544 26038 solver.cpp:237] Train net output #0: loss = 5.29101 (* 1 = 5.29101 loss)
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I0405 10:44:12.683550 26038 sgd_solver.cpp:105] Iteration 6480, lr = 1e-05
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I0405 10:44:17.847724 26038 solver.cpp:218] Iteration 6492 (2.32372 iter/s, 5.16414s/12 iters), loss = 5.27105
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I0405 10:44:17.853914 26038 solver.cpp:237] Train net output #0: loss = 5.27105 (* 1 = 5.27105 loss)
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I0405 10:44:17.853927 26038 sgd_solver.cpp:105] Iteration 6492, lr = 1e-05
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I0405 10:44:23.367056 26038 solver.cpp:218] Iteration 6504 (2.17663 iter/s, 5.51311s/12 iters), loss = 5.27194
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I0405 10:44:23.367215 26038 solver.cpp:237] Train net output #0: loss = 5.27194 (* 1 = 5.27194 loss)
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I0405 10:44:23.367224 26038 sgd_solver.cpp:105] Iteration 6504, lr = 1e-05
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I0405 10:44:28.784396 26038 solver.cpp:218] Iteration 6516 (2.21519 iter/s, 5.41715s/12 iters), loss = 5.26649
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I0405 10:44:28.784435 26038 solver.cpp:237] Train net output #0: loss = 5.26649 (* 1 = 5.26649 loss)
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I0405 10:44:28.784440 26038 sgd_solver.cpp:105] Iteration 6516, lr = 1e-05
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I0405 10:44:33.618914 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
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I0405 10:44:36.711306 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
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I0405 10:44:39.013908 26038 solver.cpp:330] Iteration 6528, Testing net (#0)
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I0405 10:44:39.013929 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:44:40.942842 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:44:43.568905 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 10:44:43.568939 26038 solver.cpp:397] Test net output #1: loss = 5.27889 (* 1 = 5.27889 loss)
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I0405 10:44:43.703855 26038 solver.cpp:218] Iteration 6528 (0.804335 iter/s, 14.9191s/12 iters), loss = 5.28652
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I0405 10:44:43.703908 26038 solver.cpp:237] Train net output #0: loss = 5.28652 (* 1 = 5.28652 loss)
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I0405 10:44:43.703917 26038 sgd_solver.cpp:105] Iteration 6528, lr = 1e-05
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I0405 10:44:48.204378 26038 solver.cpp:218] Iteration 6540 (2.66641 iter/s, 4.50043s/12 iters), loss = 5.26653
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I0405 10:44:48.204421 26038 solver.cpp:237] Train net output #0: loss = 5.26653 (* 1 = 5.26653 loss)
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I0405 10:44:48.204428 26038 sgd_solver.cpp:105] Iteration 6540, lr = 1e-05
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I0405 10:44:53.704104 26038 solver.cpp:218] Iteration 6552 (2.18196 iter/s, 5.49965s/12 iters), loss = 5.28743
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I0405 10:44:53.704221 26038 solver.cpp:237] Train net output #0: loss = 5.28743 (* 1 = 5.28743 loss)
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I0405 10:44:53.704229 26038 sgd_solver.cpp:105] Iteration 6552, lr = 1e-05
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I0405 10:44:59.064038 26038 solver.cpp:218] Iteration 6564 (2.2389 iter/s, 5.35978s/12 iters), loss = 5.26029
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I0405 10:44:59.064085 26038 solver.cpp:237] Train net output #0: loss = 5.26029 (* 1 = 5.26029 loss)
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I0405 10:44:59.064091 26038 sgd_solver.cpp:105] Iteration 6564, lr = 1e-05
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I0405 10:45:03.429551 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:45:04.318926 26038 solver.cpp:218] Iteration 6576 (2.28363 iter/s, 5.2548s/12 iters), loss = 5.27515
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I0405 10:45:04.318970 26038 solver.cpp:237] Train net output #0: loss = 5.27515 (* 1 = 5.27515 loss)
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I0405 10:45:04.318975 26038 sgd_solver.cpp:105] Iteration 6576, lr = 1e-05
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I0405 10:45:09.719954 26038 solver.cpp:218] Iteration 6588 (2.22183 iter/s, 5.40095s/12 iters), loss = 5.2748
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I0405 10:45:09.719988 26038 solver.cpp:237] Train net output #0: loss = 5.2748 (* 1 = 5.2748 loss)
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I0405 10:45:09.719995 26038 sgd_solver.cpp:105] Iteration 6588, lr = 1e-05
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I0405 10:45:15.011384 26038 solver.cpp:218] Iteration 6600 (2.26787 iter/s, 5.29131s/12 iters), loss = 5.28692
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I0405 10:45:15.011426 26038 solver.cpp:237] Train net output #0: loss = 5.28692 (* 1 = 5.28692 loss)
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I0405 10:45:15.011431 26038 sgd_solver.cpp:105] Iteration 6600, lr = 1e-05
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I0405 10:45:20.516486 26038 solver.cpp:218] Iteration 6612 (2.17983 iter/s, 5.50502s/12 iters), loss = 5.28476
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I0405 10:45:20.516544 26038 solver.cpp:237] Train net output #0: loss = 5.28476 (* 1 = 5.28476 loss)
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I0405 10:45:20.516554 26038 sgd_solver.cpp:105] Iteration 6612, lr = 1e-05
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I0405 10:45:26.061933 26038 solver.cpp:218] Iteration 6624 (2.16397 iter/s, 5.54535s/12 iters), loss = 5.27481
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I0405 10:45:26.062054 26038 solver.cpp:237] Train net output #0: loss = 5.27481 (* 1 = 5.27481 loss)
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I0405 10:45:26.062060 26038 sgd_solver.cpp:105] Iteration 6624, lr = 1e-05
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I0405 10:45:28.142020 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
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I0405 10:45:31.276533 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
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I0405 10:45:33.582351 26038 solver.cpp:330] Iteration 6630, Testing net (#0)
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I0405 10:45:33.582372 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:45:35.401710 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:45:38.172696 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 10:45:38.172725 26038 solver.cpp:397] Test net output #1: loss = 5.27859 (* 1 = 5.27859 loss)
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I0405 10:45:40.303357 26038 solver.cpp:218] Iteration 6636 (0.842624 iter/s, 14.2412s/12 iters), loss = 5.26887
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I0405 10:45:40.303421 26038 solver.cpp:237] Train net output #0: loss = 5.26887 (* 1 = 5.26887 loss)
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I0405 10:45:40.303431 26038 sgd_solver.cpp:105] Iteration 6636, lr = 1e-05
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I0405 10:45:45.694006 26038 solver.cpp:218] Iteration 6648 (2.22612 iter/s, 5.39054s/12 iters), loss = 5.2666
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I0405 10:45:45.694053 26038 solver.cpp:237] Train net output #0: loss = 5.2666 (* 1 = 5.2666 loss)
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I0405 10:45:45.694059 26038 sgd_solver.cpp:105] Iteration 6648, lr = 1e-05
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I0405 10:45:51.160646 26038 solver.cpp:218] Iteration 6660 (2.19517 iter/s, 5.46654s/12 iters), loss = 5.26997
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I0405 10:45:51.160709 26038 solver.cpp:237] Train net output #0: loss = 5.26997 (* 1 = 5.26997 loss)
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I0405 10:45:51.160718 26038 sgd_solver.cpp:105] Iteration 6660, lr = 1e-05
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I0405 10:45:56.632448 26038 solver.cpp:218] Iteration 6672 (2.1931 iter/s, 5.4717s/12 iters), loss = 5.25574
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I0405 10:45:56.632562 26038 solver.cpp:237] Train net output #0: loss = 5.25574 (* 1 = 5.25574 loss)
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I0405 10:45:56.632568 26038 sgd_solver.cpp:105] Iteration 6672, lr = 1e-05
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I0405 10:45:58.088428 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:46:02.068347 26038 solver.cpp:218] Iteration 6684 (2.20761 iter/s, 5.43574s/12 iters), loss = 5.28777
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I0405 10:46:02.068400 26038 solver.cpp:237] Train net output #0: loss = 5.28777 (* 1 = 5.28777 loss)
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I0405 10:46:02.068408 26038 sgd_solver.cpp:105] Iteration 6684, lr = 1e-05
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I0405 10:46:07.622843 26038 solver.cpp:218] Iteration 6696 (2.16045 iter/s, 5.55439s/12 iters), loss = 5.27362
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I0405 10:46:07.622917 26038 solver.cpp:237] Train net output #0: loss = 5.27362 (* 1 = 5.27362 loss)
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I0405 10:46:07.622925 26038 sgd_solver.cpp:105] Iteration 6696, lr = 1e-05
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I0405 10:46:13.022272 26038 solver.cpp:218] Iteration 6708 (2.2225 iter/s, 5.39933s/12 iters), loss = 5.27473
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I0405 10:46:13.022310 26038 solver.cpp:237] Train net output #0: loss = 5.27473 (* 1 = 5.27473 loss)
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I0405 10:46:13.022315 26038 sgd_solver.cpp:105] Iteration 6708, lr = 1e-05
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I0405 10:46:18.438503 26038 solver.cpp:218] Iteration 6720 (2.2156 iter/s, 5.41615s/12 iters), loss = 5.27304
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I0405 10:46:18.438553 26038 solver.cpp:237] Train net output #0: loss = 5.27304 (* 1 = 5.27304 loss)
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I0405 10:46:18.438560 26038 sgd_solver.cpp:105] Iteration 6720, lr = 1e-05
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I0405 10:46:23.212766 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
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||
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I0405 10:46:26.834450 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
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I0405 10:46:29.179551 26038 solver.cpp:330] Iteration 6732, Testing net (#0)
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I0405 10:46:29.179572 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:46:30.882863 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:46:33.772552 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 10:46:33.772588 26038 solver.cpp:397] Test net output #1: loss = 5.27891 (* 1 = 5.27891 loss)
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I0405 10:46:33.910751 26038 solver.cpp:218] Iteration 6732 (0.775589 iter/s, 15.4721s/12 iters), loss = 5.29523
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I0405 10:46:33.910811 26038 solver.cpp:237] Train net output #0: loss = 5.29523 (* 1 = 5.29523 loss)
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I0405 10:46:33.910818 26038 sgd_solver.cpp:105] Iteration 6732, lr = 1e-05
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I0405 10:46:38.444449 26038 solver.cpp:218] Iteration 6744 (2.6469 iter/s, 4.5336s/12 iters), loss = 5.27403
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I0405 10:46:38.444485 26038 solver.cpp:237] Train net output #0: loss = 5.27403 (* 1 = 5.27403 loss)
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I0405 10:46:38.444490 26038 sgd_solver.cpp:105] Iteration 6744, lr = 1e-05
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I0405 10:46:43.951880 26038 solver.cpp:218] Iteration 6756 (2.17891 iter/s, 5.50735s/12 iters), loss = 5.27694
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I0405 10:46:43.951934 26038 solver.cpp:237] Train net output #0: loss = 5.27694 (* 1 = 5.27694 loss)
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I0405 10:46:43.951942 26038 sgd_solver.cpp:105] Iteration 6756, lr = 1e-05
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||
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I0405 10:46:49.422878 26038 solver.cpp:218] Iteration 6768 (2.19342 iter/s, 5.4709s/12 iters), loss = 5.28052
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I0405 10:46:49.422926 26038 solver.cpp:237] Train net output #0: loss = 5.28052 (* 1 = 5.28052 loss)
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||
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I0405 10:46:49.422932 26038 sgd_solver.cpp:105] Iteration 6768, lr = 1e-05
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||
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I0405 10:46:53.191130 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:46:54.870416 26038 solver.cpp:218] Iteration 6780 (2.20287 iter/s, 5.44745s/12 iters), loss = 5.26246
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I0405 10:46:54.870460 26038 solver.cpp:237] Train net output #0: loss = 5.26246 (* 1 = 5.26246 loss)
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I0405 10:46:54.870465 26038 sgd_solver.cpp:105] Iteration 6780, lr = 1e-05
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I0405 10:47:00.376065 26038 solver.cpp:218] Iteration 6792 (2.17961 iter/s, 5.50556s/12 iters), loss = 5.27247
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||
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I0405 10:47:00.376160 26038 solver.cpp:237] Train net output #0: loss = 5.27247 (* 1 = 5.27247 loss)
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||
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I0405 10:47:00.376168 26038 sgd_solver.cpp:105] Iteration 6792, lr = 1e-05
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I0405 10:47:05.716185 26038 solver.cpp:218] Iteration 6804 (2.2472 iter/s, 5.33998s/12 iters), loss = 5.2812
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||
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I0405 10:47:05.716240 26038 solver.cpp:237] Train net output #0: loss = 5.2812 (* 1 = 5.2812 loss)
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||
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I0405 10:47:05.716248 26038 sgd_solver.cpp:105] Iteration 6804, lr = 1e-05
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I0405 10:47:10.904217 26038 solver.cpp:218] Iteration 6816 (2.31306 iter/s, 5.18793s/12 iters), loss = 5.27294
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||
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I0405 10:47:10.904258 26038 solver.cpp:237] Train net output #0: loss = 5.27294 (* 1 = 5.27294 loss)
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||
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I0405 10:47:10.904263 26038 sgd_solver.cpp:105] Iteration 6816, lr = 1e-05
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||
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I0405 10:47:16.247030 26038 solver.cpp:218] Iteration 6828 (2.24605 iter/s, 5.34272s/12 iters), loss = 5.27355
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||
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I0405 10:47:16.247088 26038 solver.cpp:237] Train net output #0: loss = 5.27355 (* 1 = 5.27355 loss)
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||
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I0405 10:47:16.247097 26038 sgd_solver.cpp:105] Iteration 6828, lr = 1e-05
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||
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I0405 10:47:18.372329 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
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||
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I0405 10:47:21.436584 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
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||
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I0405 10:47:24.141796 26038 solver.cpp:330] Iteration 6834, Testing net (#0)
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||
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I0405 10:47:24.141819 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
|
I0405 10:47:25.897325 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 10:47:28.856468 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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|
I0405 10:47:28.856499 26038 solver.cpp:397] Test net output #1: loss = 5.27885 (* 1 = 5.27885 loss)
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||
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I0405 10:47:30.714102 26038 solver.cpp:218] Iteration 6840 (0.829478 iter/s, 14.4669s/12 iters), loss = 5.27529
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||
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I0405 10:47:30.714269 26038 solver.cpp:237] Train net output #0: loss = 5.27529 (* 1 = 5.27529 loss)
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||
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I0405 10:47:30.714278 26038 sgd_solver.cpp:105] Iteration 6840, lr = 1e-05
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||
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I0405 10:47:36.259936 26038 solver.cpp:218] Iteration 6852 (2.16387 iter/s, 5.54563s/12 iters), loss = 5.28953
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||
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I0405 10:47:36.259981 26038 solver.cpp:237] Train net output #0: loss = 5.28953 (* 1 = 5.28953 loss)
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||
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I0405 10:47:36.259989 26038 sgd_solver.cpp:105] Iteration 6852, lr = 1e-05
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I0405 10:47:41.680480 26038 solver.cpp:218] Iteration 6864 (2.21383 iter/s, 5.42046s/12 iters), loss = 5.26878
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||
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I0405 10:47:41.680522 26038 solver.cpp:237] Train net output #0: loss = 5.26878 (* 1 = 5.26878 loss)
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||
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I0405 10:47:41.680528 26038 sgd_solver.cpp:105] Iteration 6864, lr = 1e-05
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I0405 10:47:47.130556 26038 solver.cpp:218] Iteration 6876 (2.20184 iter/s, 5.44999s/12 iters), loss = 5.29692
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I0405 10:47:47.130604 26038 solver.cpp:237] Train net output #0: loss = 5.29692 (* 1 = 5.29692 loss)
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||
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I0405 10:47:47.130611 26038 sgd_solver.cpp:105] Iteration 6876, lr = 1e-05
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I0405 10:47:47.823261 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:47:52.756546 26038 solver.cpp:218] Iteration 6888 (2.13299 iter/s, 5.6259s/12 iters), loss = 5.27214
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||
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I0405 10:47:52.756580 26038 solver.cpp:237] Train net output #0: loss = 5.27214 (* 1 = 5.27214 loss)
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||
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I0405 10:47:52.756587 26038 sgd_solver.cpp:105] Iteration 6888, lr = 1e-05
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||
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I0405 10:47:58.056197 26038 solver.cpp:218] Iteration 6900 (2.26433 iter/s, 5.29957s/12 iters), loss = 5.26858
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||
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I0405 10:47:58.056237 26038 solver.cpp:237] Train net output #0: loss = 5.26858 (* 1 = 5.26858 loss)
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||
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I0405 10:47:58.056243 26038 sgd_solver.cpp:105] Iteration 6900, lr = 1e-05
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||
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I0405 10:48:03.578094 26038 solver.cpp:218] Iteration 6912 (2.1732 iter/s, 5.52181s/12 iters), loss = 5.2694
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||
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I0405 10:48:03.584301 26038 solver.cpp:237] Train net output #0: loss = 5.2694 (* 1 = 5.2694 loss)
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||
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I0405 10:48:03.584319 26038 sgd_solver.cpp:105] Iteration 6912, lr = 1e-05
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I0405 10:48:09.063771 26038 solver.cpp:218] Iteration 6924 (2.19 iter/s, 5.47944s/12 iters), loss = 5.28504
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||
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I0405 10:48:09.063823 26038 solver.cpp:237] Train net output #0: loss = 5.28504 (* 1 = 5.28504 loss)
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||
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I0405 10:48:09.063830 26038 sgd_solver.cpp:105] Iteration 6924, lr = 1e-05
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||
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I0405 10:48:13.873814 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
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||
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I0405 10:48:16.920648 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
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I0405 10:48:19.240775 26038 solver.cpp:330] Iteration 6936, Testing net (#0)
|
||
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I0405 10:48:19.240800 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 10:48:19.830935 26038 blocking_queue.cpp:49] Waiting for data
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I0405 10:48:20.973493 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:48:23.721714 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 10:48:23.721758 26038 solver.cpp:397] Test net output #1: loss = 5.27901 (* 1 = 5.27901 loss)
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I0405 10:48:23.863871 26038 solver.cpp:218] Iteration 6936 (0.810813 iter/s, 14.8s/12 iters), loss = 5.26868
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I0405 10:48:23.863922 26038 solver.cpp:237] Train net output #0: loss = 5.26868 (* 1 = 5.26868 loss)
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I0405 10:48:23.863929 26038 sgd_solver.cpp:105] Iteration 6936, lr = 1e-05
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I0405 10:48:28.450286 26038 solver.cpp:218] Iteration 6948 (2.61647 iter/s, 4.58633s/12 iters), loss = 5.28085
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I0405 10:48:28.450335 26038 solver.cpp:237] Train net output #0: loss = 5.28085 (* 1 = 5.28085 loss)
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I0405 10:48:28.450342 26038 sgd_solver.cpp:105] Iteration 6948, lr = 1e-05
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I0405 10:48:33.804352 26038 solver.cpp:218] Iteration 6960 (2.24132 iter/s, 5.35398s/12 iters), loss = 5.27248
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I0405 10:48:33.804494 26038 solver.cpp:237] Train net output #0: loss = 5.27248 (* 1 = 5.27248 loss)
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I0405 10:48:33.804502 26038 sgd_solver.cpp:105] Iteration 6960, lr = 1e-05
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I0405 10:48:39.153965 26038 solver.cpp:218] Iteration 6972 (2.24323 iter/s, 5.34943s/12 iters), loss = 5.28597
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I0405 10:48:39.154026 26038 solver.cpp:237] Train net output #0: loss = 5.28597 (* 1 = 5.28597 loss)
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I0405 10:48:39.154034 26038 sgd_solver.cpp:105] Iteration 6972, lr = 1e-05
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I0405 10:48:42.125010 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:48:44.503727 26038 solver.cpp:218] Iteration 6984 (2.24313 iter/s, 5.34967s/12 iters), loss = 5.27226
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I0405 10:48:44.503777 26038 solver.cpp:237] Train net output #0: loss = 5.27226 (* 1 = 5.27226 loss)
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I0405 10:48:44.503784 26038 sgd_solver.cpp:105] Iteration 6984, lr = 1e-05
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I0405 10:48:49.767285 26038 solver.cpp:218] Iteration 6996 (2.2799 iter/s, 5.26338s/12 iters), loss = 5.27345
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I0405 10:48:49.767328 26038 solver.cpp:237] Train net output #0: loss = 5.27345 (* 1 = 5.27345 loss)
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I0405 10:48:49.767334 26038 sgd_solver.cpp:105] Iteration 6996, lr = 1e-05
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I0405 10:48:54.978955 26038 solver.cpp:218] Iteration 7008 (2.30256 iter/s, 5.21159s/12 iters), loss = 5.28791
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I0405 10:48:54.979018 26038 solver.cpp:237] Train net output #0: loss = 5.28791 (* 1 = 5.28791 loss)
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I0405 10:48:54.979027 26038 sgd_solver.cpp:105] Iteration 7008, lr = 1e-05
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I0405 10:49:00.540758 26038 solver.cpp:218] Iteration 7020 (2.15761 iter/s, 5.5617s/12 iters), loss = 5.28144
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I0405 10:49:00.540799 26038 solver.cpp:237] Train net output #0: loss = 5.28144 (* 1 = 5.28144 loss)
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I0405 10:49:00.540804 26038 sgd_solver.cpp:105] Iteration 7020, lr = 1e-05
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I0405 10:49:05.785218 26038 solver.cpp:218] Iteration 7032 (2.28817 iter/s, 5.24438s/12 iters), loss = 5.28756
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I0405 10:49:05.785347 26038 solver.cpp:237] Train net output #0: loss = 5.28756 (* 1 = 5.28756 loss)
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I0405 10:49:05.785356 26038 sgd_solver.cpp:105] Iteration 7032, lr = 1e-05
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I0405 10:49:07.853371 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
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||
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I0405 10:49:11.114745 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
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I0405 10:49:13.413414 26038 solver.cpp:330] Iteration 7038, Testing net (#0)
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I0405 10:49:13.413431 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:49:15.074309 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:49:18.060134 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 10:49:18.060163 26038 solver.cpp:397] Test net output #1: loss = 5.27881 (* 1 = 5.27881 loss)
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I0405 10:49:19.991600 26038 solver.cpp:218] Iteration 7044 (0.844703 iter/s, 14.2062s/12 iters), loss = 5.27924
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I0405 10:49:19.991667 26038 solver.cpp:237] Train net output #0: loss = 5.27924 (* 1 = 5.27924 loss)
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I0405 10:49:19.991678 26038 sgd_solver.cpp:105] Iteration 7044, lr = 1e-05
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I0405 10:49:25.241178 26038 solver.cpp:218] Iteration 7056 (2.28594 iter/s, 5.24948s/12 iters), loss = 5.28812
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I0405 10:49:25.241216 26038 solver.cpp:237] Train net output #0: loss = 5.28812 (* 1 = 5.28812 loss)
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||
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I0405 10:49:25.241221 26038 sgd_solver.cpp:105] Iteration 7056, lr = 1e-05
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I0405 10:49:30.531339 26038 solver.cpp:218] Iteration 7068 (2.2684 iter/s, 5.29008s/12 iters), loss = 5.28619
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||
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I0405 10:49:30.531380 26038 solver.cpp:237] Train net output #0: loss = 5.28619 (* 1 = 5.28619 loss)
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||
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I0405 10:49:30.531386 26038 sgd_solver.cpp:105] Iteration 7068, lr = 1e-05
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I0405 10:49:36.004051 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:49:36.169848 26038 solver.cpp:218] Iteration 7080 (2.12825 iter/s, 5.63843s/12 iters), loss = 5.28991
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I0405 10:49:36.169900 26038 solver.cpp:237] Train net output #0: loss = 5.28991 (* 1 = 5.28991 loss)
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I0405 10:49:36.169909 26038 sgd_solver.cpp:105] Iteration 7080, lr = 1e-05
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I0405 10:49:41.794852 26038 solver.cpp:218] Iteration 7092 (2.13337 iter/s, 5.62491s/12 iters), loss = 5.29055
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I0405 10:49:41.794894 26038 solver.cpp:237] Train net output #0: loss = 5.29055 (* 1 = 5.29055 loss)
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I0405 10:49:41.794899 26038 sgd_solver.cpp:105] Iteration 7092, lr = 1e-05
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I0405 10:49:47.107900 26038 solver.cpp:218] Iteration 7104 (2.25863 iter/s, 5.31296s/12 iters), loss = 5.27647
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I0405 10:49:47.107954 26038 solver.cpp:237] Train net output #0: loss = 5.27647 (* 1 = 5.27647 loss)
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I0405 10:49:47.107962 26038 sgd_solver.cpp:105] Iteration 7104, lr = 1e-05
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I0405 10:49:52.550248 26038 solver.cpp:218] Iteration 7116 (2.20497 iter/s, 5.44226s/12 iters), loss = 5.28089
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I0405 10:49:52.550297 26038 solver.cpp:237] Train net output #0: loss = 5.28089 (* 1 = 5.28089 loss)
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I0405 10:49:52.550305 26038 sgd_solver.cpp:105] Iteration 7116, lr = 1e-05
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I0405 10:49:58.190369 26038 solver.cpp:218] Iteration 7128 (2.12765 iter/s, 5.64003s/12 iters), loss = 5.26613
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I0405 10:49:58.190412 26038 solver.cpp:237] Train net output #0: loss = 5.26613 (* 1 = 5.26613 loss)
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I0405 10:49:58.190416 26038 sgd_solver.cpp:105] Iteration 7128, lr = 1e-05
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I0405 10:50:03.063541 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
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||
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I0405 10:50:06.139619 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
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I0405 10:50:08.464174 26038 solver.cpp:330] Iteration 7140, Testing net (#0)
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I0405 10:50:08.464190 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:50:10.072247 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:50:13.077510 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 10:50:13.077543 26038 solver.cpp:397] Test net output #1: loss = 5.27864 (* 1 = 5.27864 loss)
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I0405 10:50:13.212270 26038 solver.cpp:218] Iteration 7140 (0.79884 iter/s, 15.0218s/12 iters), loss = 5.26623
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I0405 10:50:13.212322 26038 solver.cpp:237] Train net output #0: loss = 5.26623 (* 1 = 5.26623 loss)
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I0405 10:50:13.212330 26038 sgd_solver.cpp:105] Iteration 7140, lr = 1e-05
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I0405 10:50:17.657277 26038 solver.cpp:218] Iteration 7152 (2.69971 iter/s, 4.44492s/12 iters), loss = 5.27649
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I0405 10:50:17.657313 26038 solver.cpp:237] Train net output #0: loss = 5.27649 (* 1 = 5.27649 loss)
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I0405 10:50:17.657318 26038 sgd_solver.cpp:105] Iteration 7152, lr = 1e-05
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I0405 10:50:23.242086 26038 solver.cpp:218] Iteration 7164 (2.14872 iter/s, 5.58472s/12 iters), loss = 5.27901
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I0405 10:50:23.242138 26038 solver.cpp:237] Train net output #0: loss = 5.27901 (* 1 = 5.27901 loss)
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I0405 10:50:23.242146 26038 sgd_solver.cpp:105] Iteration 7164, lr = 1e-05
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I0405 10:50:28.655086 26038 solver.cpp:218] Iteration 7176 (2.21692 iter/s, 5.4129s/12 iters), loss = 5.28224
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I0405 10:50:28.655138 26038 solver.cpp:237] Train net output #0: loss = 5.28224 (* 1 = 5.28224 loss)
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I0405 10:50:28.655144 26038 sgd_solver.cpp:105] Iteration 7176, lr = 1e-05
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I0405 10:50:30.905220 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:50:34.209837 26038 solver.cpp:218] Iteration 7188 (2.16035 iter/s, 5.55466s/12 iters), loss = 5.27705
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I0405 10:50:34.209885 26038 solver.cpp:237] Train net output #0: loss = 5.27705 (* 1 = 5.27705 loss)
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I0405 10:50:34.209892 26038 sgd_solver.cpp:105] Iteration 7188, lr = 1e-05
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I0405 10:50:39.747999 26038 solver.cpp:218] Iteration 7200 (2.16682 iter/s, 5.53807s/12 iters), loss = 5.28419
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I0405 10:50:39.748100 26038 solver.cpp:237] Train net output #0: loss = 5.28419 (* 1 = 5.28419 loss)
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||
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I0405 10:50:39.748106 26038 sgd_solver.cpp:105] Iteration 7200, lr = 1e-05
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I0405 10:50:45.138960 26038 solver.cpp:218] Iteration 7212 (2.22601 iter/s, 5.39082s/12 iters), loss = 5.27405
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I0405 10:50:45.139003 26038 solver.cpp:237] Train net output #0: loss = 5.27405 (* 1 = 5.27405 loss)
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||
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I0405 10:50:45.139008 26038 sgd_solver.cpp:105] Iteration 7212, lr = 1e-05
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I0405 10:50:50.500692 26038 solver.cpp:218] Iteration 7224 (2.23812 iter/s, 5.36165s/12 iters), loss = 5.26755
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I0405 10:50:50.500730 26038 solver.cpp:237] Train net output #0: loss = 5.26755 (* 1 = 5.26755 loss)
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||
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I0405 10:50:50.500735 26038 sgd_solver.cpp:105] Iteration 7224, lr = 1e-05
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I0405 10:50:55.993988 26038 solver.cpp:218] Iteration 7236 (2.18452 iter/s, 5.49321s/12 iters), loss = 5.2711
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I0405 10:50:55.994047 26038 solver.cpp:237] Train net output #0: loss = 5.2711 (* 1 = 5.2711 loss)
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||
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I0405 10:50:55.994056 26038 sgd_solver.cpp:105] Iteration 7236, lr = 1e-05
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||
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I0405 10:50:58.131467 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
|
||
|
|
I0405 10:51:01.189698 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
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||
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I0405 10:51:03.512693 26038 solver.cpp:330] Iteration 7242, Testing net (#0)
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||
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|
I0405 10:51:03.512715 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
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I0405 10:51:05.063354 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 10:51:08.205513 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 10:51:08.205551 26038 solver.cpp:397] Test net output #1: loss = 5.27873 (* 1 = 5.27873 loss)
|
||
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I0405 10:51:10.163710 26038 solver.cpp:218] Iteration 7248 (0.846884 iter/s, 14.1696s/12 iters), loss = 5.28363
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||
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I0405 10:51:10.163868 26038 solver.cpp:237] Train net output #0: loss = 5.28363 (* 1 = 5.28363 loss)
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||
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I0405 10:51:10.163877 26038 sgd_solver.cpp:105] Iteration 7248, lr = 1e-05
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||
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I0405 10:51:15.514763 26038 solver.cpp:218] Iteration 7260 (2.24263 iter/s, 5.35085s/12 iters), loss = 5.27464
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||
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I0405 10:51:15.514824 26038 solver.cpp:237] Train net output #0: loss = 5.27464 (* 1 = 5.27464 loss)
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||
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I0405 10:51:15.514833 26038 sgd_solver.cpp:105] Iteration 7260, lr = 1e-05
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||
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I0405 10:51:21.007649 26038 solver.cpp:218] Iteration 7272 (2.18468 iter/s, 5.49278s/12 iters), loss = 5.26713
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||
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I0405 10:51:21.007691 26038 solver.cpp:237] Train net output #0: loss = 5.26713 (* 1 = 5.26713 loss)
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||
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I0405 10:51:21.007696 26038 sgd_solver.cpp:105] Iteration 7272, lr = 1e-05
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||
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I0405 10:51:25.627092 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:51:26.498633 26038 solver.cpp:218] Iteration 7284 (2.18543 iter/s, 5.4909s/12 iters), loss = 5.28055
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||
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I0405 10:51:26.498677 26038 solver.cpp:237] Train net output #0: loss = 5.28055 (* 1 = 5.28055 loss)
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||
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I0405 10:51:26.498682 26038 sgd_solver.cpp:105] Iteration 7284, lr = 1e-05
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||
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I0405 10:51:31.945780 26038 solver.cpp:218] Iteration 7296 (2.20303 iter/s, 5.44705s/12 iters), loss = 5.26484
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||
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I0405 10:51:31.945824 26038 solver.cpp:237] Train net output #0: loss = 5.26484 (* 1 = 5.26484 loss)
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||
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I0405 10:51:31.945830 26038 sgd_solver.cpp:105] Iteration 7296, lr = 1e-05
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||
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I0405 10:51:37.362080 26038 solver.cpp:218] Iteration 7308 (2.21557 iter/s, 5.41622s/12 iters), loss = 5.29736
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||
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I0405 10:51:37.362120 26038 solver.cpp:237] Train net output #0: loss = 5.29736 (* 1 = 5.29736 loss)
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||
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I0405 10:51:37.362125 26038 sgd_solver.cpp:105] Iteration 7308, lr = 1e-05
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||
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I0405 10:51:42.813133 26038 solver.cpp:218] Iteration 7320 (2.20144 iter/s, 5.45097s/12 iters), loss = 5.28453
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||
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|
I0405 10:51:42.813268 26038 solver.cpp:237] Train net output #0: loss = 5.28453 (* 1 = 5.28453 loss)
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||
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I0405 10:51:42.813278 26038 sgd_solver.cpp:105] Iteration 7320, lr = 1e-05
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||
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I0405 10:51:48.217849 26038 solver.cpp:218] Iteration 7332 (2.22036 iter/s, 5.40454s/12 iters), loss = 5.27734
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||
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I0405 10:51:48.217903 26038 solver.cpp:237] Train net output #0: loss = 5.27734 (* 1 = 5.27734 loss)
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||
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I0405 10:51:48.217912 26038 sgd_solver.cpp:105] Iteration 7332, lr = 1e-05
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||
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I0405 10:51:53.135462 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
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||
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I0405 10:51:56.303483 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
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||
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I0405 10:51:58.654301 26038 solver.cpp:330] Iteration 7344, Testing net (#0)
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||
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I0405 10:51:58.654317 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 10:52:00.250543 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 10:52:03.193166 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 10:52:03.193194 26038 solver.cpp:397] Test net output #1: loss = 5.27906 (* 1 = 5.27906 loss)
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||
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I0405 10:52:03.328397 26038 solver.cpp:218] Iteration 7344 (0.794155 iter/s, 15.1104s/12 iters), loss = 5.26339
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I0405 10:52:03.328462 26038 solver.cpp:237] Train net output #0: loss = 5.26339 (* 1 = 5.26339 loss)
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||
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I0405 10:52:03.328471 26038 sgd_solver.cpp:105] Iteration 7344, lr = 1e-05
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||
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I0405 10:52:07.986812 26038 solver.cpp:218] Iteration 7356 (2.57604 iter/s, 4.65832s/12 iters), loss = 5.28598
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||
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I0405 10:52:07.986871 26038 solver.cpp:237] Train net output #0: loss = 5.28598 (* 1 = 5.28598 loss)
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||
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I0405 10:52:07.986879 26038 sgd_solver.cpp:105] Iteration 7356, lr = 1e-05
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I0405 10:52:13.575466 26038 solver.cpp:218] Iteration 7368 (2.14725 iter/s, 5.58856s/12 iters), loss = 5.2834
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I0405 10:52:13.576521 26038 solver.cpp:237] Train net output #0: loss = 5.2834 (* 1 = 5.2834 loss)
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I0405 10:52:13.576529 26038 sgd_solver.cpp:105] Iteration 7368, lr = 1e-05
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||
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I0405 10:52:18.978214 26038 solver.cpp:218] Iteration 7380 (2.22154 iter/s, 5.40165s/12 iters), loss = 5.28054
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I0405 10:52:18.978258 26038 solver.cpp:237] Train net output #0: loss = 5.28054 (* 1 = 5.28054 loss)
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||
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I0405 10:52:18.978263 26038 sgd_solver.cpp:105] Iteration 7380, lr = 1e-05
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||
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I0405 10:52:20.420864 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:52:24.415843 26038 solver.cpp:218] Iteration 7392 (2.20688 iter/s, 5.43754s/12 iters), loss = 5.29888
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I0405 10:52:24.415890 26038 solver.cpp:237] Train net output #0: loss = 5.29888 (* 1 = 5.29888 loss)
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I0405 10:52:24.415897 26038 sgd_solver.cpp:105] Iteration 7392, lr = 1e-05
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I0405 10:52:29.765890 26038 solver.cpp:218] Iteration 7404 (2.24301 iter/s, 5.34996s/12 iters), loss = 5.27686
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I0405 10:52:29.765947 26038 solver.cpp:237] Train net output #0: loss = 5.27686 (* 1 = 5.27686 loss)
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I0405 10:52:29.765957 26038 sgd_solver.cpp:105] Iteration 7404, lr = 1e-05
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I0405 10:52:34.921358 26038 solver.cpp:218] Iteration 7416 (2.32767 iter/s, 5.15537s/12 iters), loss = 5.27466
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I0405 10:52:34.921401 26038 solver.cpp:237] Train net output #0: loss = 5.27466 (* 1 = 5.27466 loss)
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I0405 10:52:34.921406 26038 sgd_solver.cpp:105] Iteration 7416, lr = 1e-05
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I0405 10:52:40.190824 26038 solver.cpp:218] Iteration 7428 (2.27731 iter/s, 5.26937s/12 iters), loss = 5.27208
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I0405 10:52:40.190904 26038 solver.cpp:237] Train net output #0: loss = 5.27208 (* 1 = 5.27208 loss)
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I0405 10:52:40.190917 26038 sgd_solver.cpp:105] Iteration 7428, lr = 1e-05
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I0405 10:52:45.809142 26038 solver.cpp:218] Iteration 7440 (2.13592 iter/s, 5.61819s/12 iters), loss = 5.2883
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I0405 10:52:45.809285 26038 solver.cpp:237] Train net output #0: loss = 5.2883 (* 1 = 5.2883 loss)
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I0405 10:52:45.809294 26038 sgd_solver.cpp:105] Iteration 7440, lr = 1e-05
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I0405 10:52:48.051611 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
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I0405 10:52:51.289351 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
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I0405 10:52:53.634397 26038 solver.cpp:330] Iteration 7446, Testing net (#0)
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I0405 10:52:53.634418 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:52:55.139286 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:52:58.171394 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 10:52:58.171437 26038 solver.cpp:397] Test net output #1: loss = 5.27873 (* 1 = 5.27873 loss)
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I0405 10:53:00.109033 26038 solver.cpp:218] Iteration 7452 (0.83918 iter/s, 14.2997s/12 iters), loss = 5.2732
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I0405 10:53:00.109091 26038 solver.cpp:237] Train net output #0: loss = 5.2732 (* 1 = 5.2732 loss)
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I0405 10:53:00.109098 26038 sgd_solver.cpp:105] Iteration 7452, lr = 1e-05
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I0405 10:53:05.601330 26038 solver.cpp:218] Iteration 7464 (2.18492 iter/s, 5.49219s/12 iters), loss = 5.27159
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I0405 10:53:05.601370 26038 solver.cpp:237] Train net output #0: loss = 5.27159 (* 1 = 5.27159 loss)
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I0405 10:53:05.601375 26038 sgd_solver.cpp:105] Iteration 7464, lr = 1e-05
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I0405 10:53:11.174883 26038 solver.cpp:218] Iteration 7476 (2.15306 iter/s, 5.57346s/12 iters), loss = 5.29765
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I0405 10:53:11.174932 26038 solver.cpp:237] Train net output #0: loss = 5.29765 (* 1 = 5.29765 loss)
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||
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I0405 10:53:11.174939 26038 sgd_solver.cpp:105] Iteration 7476, lr = 1e-05
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||
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I0405 10:53:14.734838 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:53:16.360276 26038 solver.cpp:218] Iteration 7488 (2.31423 iter/s, 5.18531s/12 iters), loss = 5.27354
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||
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I0405 10:53:16.360410 26038 solver.cpp:237] Train net output #0: loss = 5.27354 (* 1 = 5.27354 loss)
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||
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I0405 10:53:16.360417 26038 sgd_solver.cpp:105] Iteration 7488, lr = 1e-05
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I0405 10:53:21.720757 26038 solver.cpp:218] Iteration 7500 (2.23868 iter/s, 5.36031s/12 iters), loss = 5.27664
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||
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I0405 10:53:21.720794 26038 solver.cpp:237] Train net output #0: loss = 5.27664 (* 1 = 5.27664 loss)
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||
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I0405 10:53:21.720800 26038 sgd_solver.cpp:105] Iteration 7500, lr = 1e-05
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I0405 10:53:27.191089 26038 solver.cpp:218] Iteration 7512 (2.19368 iter/s, 5.47025s/12 iters), loss = 5.28568
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I0405 10:53:27.191138 26038 solver.cpp:237] Train net output #0: loss = 5.28568 (* 1 = 5.28568 loss)
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I0405 10:53:27.191145 26038 sgd_solver.cpp:105] Iteration 7512, lr = 1e-05
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I0405 10:53:32.786659 26038 solver.cpp:218] Iteration 7524 (2.14459 iter/s, 5.59547s/12 iters), loss = 5.2673
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I0405 10:53:32.786723 26038 solver.cpp:237] Train net output #0: loss = 5.2673 (* 1 = 5.2673 loss)
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||
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I0405 10:53:32.786731 26038 sgd_solver.cpp:105] Iteration 7524, lr = 1e-05
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I0405 10:53:38.176739 26038 solver.cpp:218] Iteration 7536 (2.22636 iter/s, 5.38998s/12 iters), loss = 5.27678
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I0405 10:53:38.176785 26038 solver.cpp:237] Train net output #0: loss = 5.27678 (* 1 = 5.27678 loss)
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||
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I0405 10:53:38.176791 26038 sgd_solver.cpp:105] Iteration 7536, lr = 1e-05
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I0405 10:53:43.101095 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
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I0405 10:53:46.149257 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
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I0405 10:53:48.459792 26038 solver.cpp:330] Iteration 7548, Testing net (#0)
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||
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I0405 10:53:48.459867 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:53:49.856808 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:53:53.077545 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 10:53:53.077579 26038 solver.cpp:397] Test net output #1: loss = 5.27883 (* 1 = 5.27883 loss)
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I0405 10:53:53.224261 26038 solver.cpp:218] Iteration 7548 (0.797481 iter/s, 15.0474s/12 iters), loss = 5.26898
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I0405 10:53:53.224321 26038 solver.cpp:237] Train net output #0: loss = 5.26898 (* 1 = 5.26898 loss)
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||
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I0405 10:53:53.224329 26038 sgd_solver.cpp:105] Iteration 7548, lr = 1e-05
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I0405 10:53:57.770841 26038 solver.cpp:218] Iteration 7560 (2.63941 iter/s, 4.54648s/12 iters), loss = 5.27541
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I0405 10:53:57.770892 26038 solver.cpp:237] Train net output #0: loss = 5.27541 (* 1 = 5.27541 loss)
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||
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I0405 10:53:57.770900 26038 sgd_solver.cpp:105] Iteration 7560, lr = 1e-05
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I0405 10:54:03.179289 26038 solver.cpp:218] Iteration 7572 (2.21879 iter/s, 5.40835s/12 iters), loss = 5.25931
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I0405 10:54:03.179342 26038 solver.cpp:237] Train net output #0: loss = 5.25931 (* 1 = 5.25931 loss)
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||
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I0405 10:54:03.179350 26038 sgd_solver.cpp:105] Iteration 7572, lr = 1e-05
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I0405 10:54:08.658845 26038 solver.cpp:218] Iteration 7584 (2.19 iter/s, 5.47946s/12 iters), loss = 5.27396
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I0405 10:54:08.658915 26038 solver.cpp:237] Train net output #0: loss = 5.27396 (* 1 = 5.27396 loss)
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||
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I0405 10:54:08.658927 26038 sgd_solver.cpp:105] Iteration 7584, lr = 1e-05
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I0405 10:54:09.333081 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:54:14.023260 26038 solver.cpp:218] Iteration 7596 (2.23701 iter/s, 5.36431s/12 iters), loss = 5.28564
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I0405 10:54:14.023305 26038 solver.cpp:237] Train net output #0: loss = 5.28564 (* 1 = 5.28564 loss)
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I0405 10:54:14.023311 26038 sgd_solver.cpp:105] Iteration 7596, lr = 1e-05
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I0405 10:54:19.218179 26038 solver.cpp:218] Iteration 7608 (2.30999 iter/s, 5.19483s/12 iters), loss = 5.2755
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I0405 10:54:19.218305 26038 solver.cpp:237] Train net output #0: loss = 5.2755 (* 1 = 5.2755 loss)
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I0405 10:54:19.218314 26038 sgd_solver.cpp:105] Iteration 7608, lr = 1e-05
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I0405 10:54:24.824951 26038 solver.cpp:218] Iteration 7620 (2.14033 iter/s, 5.60661s/12 iters), loss = 5.2754
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I0405 10:54:24.825008 26038 solver.cpp:237] Train net output #0: loss = 5.2754 (* 1 = 5.2754 loss)
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||
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I0405 10:54:24.825017 26038 sgd_solver.cpp:105] Iteration 7620, lr = 1e-05
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||
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I0405 10:54:27.558622 26038 blocking_queue.cpp:49] Waiting for data
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I0405 10:54:30.315279 26038 solver.cpp:218] Iteration 7632 (2.1857 iter/s, 5.49023s/12 iters), loss = 5.27831
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I0405 10:54:30.315315 26038 solver.cpp:237] Train net output #0: loss = 5.27831 (* 1 = 5.27831 loss)
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I0405 10:54:30.315320 26038 sgd_solver.cpp:105] Iteration 7632, lr = 1e-05
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I0405 10:54:35.872686 26038 solver.cpp:218] Iteration 7644 (2.15931 iter/s, 5.55732s/12 iters), loss = 5.29154
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I0405 10:54:35.872740 26038 solver.cpp:237] Train net output #0: loss = 5.29154 (* 1 = 5.29154 loss)
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I0405 10:54:35.872746 26038 sgd_solver.cpp:105] Iteration 7644, lr = 1e-05
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I0405 10:54:38.142524 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
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||
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I0405 10:54:41.235920 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
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I0405 10:54:43.541430 26038 solver.cpp:330] Iteration 7650, Testing net (#0)
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||
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I0405 10:54:43.541455 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:54:45.057778 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:54:48.259402 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 10:54:48.259441 26038 solver.cpp:397] Test net output #1: loss = 5.27892 (* 1 = 5.27892 loss)
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||
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I0405 10:54:50.239113 26038 solver.cpp:218] Iteration 7656 (0.835289 iter/s, 14.3663s/12 iters), loss = 5.27576
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I0405 10:54:50.239250 26038 solver.cpp:237] Train net output #0: loss = 5.27576 (* 1 = 5.27576 loss)
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I0405 10:54:50.239257 26038 sgd_solver.cpp:105] Iteration 7656, lr = 1e-05
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I0405 10:54:55.651899 26038 solver.cpp:218] Iteration 7668 (2.21704 iter/s, 5.41261s/12 iters), loss = 5.27706
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I0405 10:54:55.651947 26038 solver.cpp:237] Train net output #0: loss = 5.27706 (* 1 = 5.27706 loss)
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I0405 10:54:55.651953 26038 sgd_solver.cpp:105] Iteration 7668, lr = 1e-05
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I0405 10:55:01.044873 26038 solver.cpp:218] Iteration 7680 (2.22515 iter/s, 5.39289s/12 iters), loss = 5.27037
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I0405 10:55:01.044919 26038 solver.cpp:237] Train net output #0: loss = 5.27037 (* 1 = 5.27037 loss)
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||
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I0405 10:55:01.044924 26038 sgd_solver.cpp:105] Iteration 7680, lr = 1e-05
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||
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I0405 10:55:04.084892 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:55:06.479321 26038 solver.cpp:218] Iteration 7692 (2.20817 iter/s, 5.43436s/12 iters), loss = 5.28061
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||
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I0405 10:55:06.479373 26038 solver.cpp:237] Train net output #0: loss = 5.28061 (* 1 = 5.28061 loss)
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||
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I0405 10:55:06.479382 26038 sgd_solver.cpp:105] Iteration 7692, lr = 1e-05
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I0405 10:55:11.633869 26038 solver.cpp:218] Iteration 7704 (2.32808 iter/s, 5.15446s/12 iters), loss = 5.26487
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||
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I0405 10:55:11.633917 26038 solver.cpp:237] Train net output #0: loss = 5.26487 (* 1 = 5.26487 loss)
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||
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I0405 10:55:11.633926 26038 sgd_solver.cpp:105] Iteration 7704, lr = 1e-05
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||
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I0405 10:55:17.306017 26038 solver.cpp:218] Iteration 7716 (2.11563 iter/s, 5.67206s/12 iters), loss = 5.28378
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||
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I0405 10:55:17.306061 26038 solver.cpp:237] Train net output #0: loss = 5.28378 (* 1 = 5.28378 loss)
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||
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I0405 10:55:17.306066 26038 sgd_solver.cpp:105] Iteration 7716, lr = 1e-05
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||
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I0405 10:55:22.729727 26038 solver.cpp:218] Iteration 7728 (2.21254 iter/s, 5.42362s/12 iters), loss = 5.2848
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||
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|
I0405 10:55:22.729846 26038 solver.cpp:237] Train net output #0: loss = 5.2848 (* 1 = 5.2848 loss)
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||
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I0405 10:55:22.729853 26038 sgd_solver.cpp:105] Iteration 7728, lr = 1e-05
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||
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I0405 10:55:28.354677 26038 solver.cpp:218] Iteration 7740 (2.13342 iter/s, 5.62478s/12 iters), loss = 5.2861
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||
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I0405 10:55:28.354743 26038 solver.cpp:237] Train net output #0: loss = 5.2861 (* 1 = 5.2861 loss)
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||
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I0405 10:55:28.354753 26038 sgd_solver.cpp:105] Iteration 7740, lr = 1e-05
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||
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I0405 10:55:33.273620 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
|
||
|
|
I0405 10:55:36.307858 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
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||
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I0405 10:55:38.659564 26038 solver.cpp:330] Iteration 7752, Testing net (#0)
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||
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|
I0405 10:55:38.659590 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
|
I0405 10:55:40.154124 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 10:55:43.306418 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 10:55:43.306454 26038 solver.cpp:397] Test net output #1: loss = 5.27878 (* 1 = 5.27878 loss)
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||
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I0405 10:55:43.443814 26038 solver.cpp:218] Iteration 7752 (0.795282 iter/s, 15.089s/12 iters), loss = 5.27777
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||
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I0405 10:55:43.443873 26038 solver.cpp:237] Train net output #0: loss = 5.27777 (* 1 = 5.27777 loss)
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||
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I0405 10:55:43.443881 26038 sgd_solver.cpp:105] Iteration 7752, lr = 1e-05
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||
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I0405 10:55:48.136219 26038 solver.cpp:218] Iteration 7764 (2.55738 iter/s, 4.6923s/12 iters), loss = 5.29479
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||
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I0405 10:55:48.136278 26038 solver.cpp:237] Train net output #0: loss = 5.29479 (* 1 = 5.29479 loss)
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||
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I0405 10:55:48.136286 26038 sgd_solver.cpp:105] Iteration 7764, lr = 1e-05
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||
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I0405 10:55:53.432180 26038 solver.cpp:218] Iteration 7776 (2.26592 iter/s, 5.29586s/12 iters), loss = 5.28392
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||
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I0405 10:55:53.432327 26038 solver.cpp:237] Train net output #0: loss = 5.28392 (* 1 = 5.28392 loss)
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||
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I0405 10:55:53.432335 26038 sgd_solver.cpp:105] Iteration 7776, lr = 1e-05
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||
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I0405 10:55:58.748355 26038 solver.cpp:218] Iteration 7788 (2.25734 iter/s, 5.31599s/12 iters), loss = 5.28067
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||
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I0405 10:55:58.748401 26038 solver.cpp:237] Train net output #0: loss = 5.28067 (* 1 = 5.28067 loss)
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||
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I0405 10:55:58.748409 26038 sgd_solver.cpp:105] Iteration 7788, lr = 1e-05
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||
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I0405 10:55:58.754854 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:56:04.419137 26038 solver.cpp:218] Iteration 7800 (2.11615 iter/s, 5.67069s/12 iters), loss = 5.27623
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||
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I0405 10:56:04.419199 26038 solver.cpp:237] Train net output #0: loss = 5.27623 (* 1 = 5.27623 loss)
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||
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I0405 10:56:04.419209 26038 sgd_solver.cpp:105] Iteration 7800, lr = 1e-05
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||
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I0405 10:56:09.871837 26038 solver.cpp:218] Iteration 7812 (2.20079 iter/s, 5.4526s/12 iters), loss = 5.28278
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||
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I0405 10:56:09.871889 26038 solver.cpp:237] Train net output #0: loss = 5.28278 (* 1 = 5.28278 loss)
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||
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I0405 10:56:09.871898 26038 sgd_solver.cpp:105] Iteration 7812, lr = 1e-05
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||
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I0405 10:56:15.310724 26038 solver.cpp:218] Iteration 7824 (2.20637 iter/s, 5.43879s/12 iters), loss = 5.26412
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||
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I0405 10:56:15.310783 26038 solver.cpp:237] Train net output #0: loss = 5.26412 (* 1 = 5.26412 loss)
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||
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I0405 10:56:15.310792 26038 sgd_solver.cpp:105] Iteration 7824, lr = 1e-05
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||
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I0405 10:56:20.671315 26038 solver.cpp:218] Iteration 7836 (2.2386 iter/s, 5.36049s/12 iters), loss = 5.28632
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||
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I0405 10:56:20.671370 26038 solver.cpp:237] Train net output #0: loss = 5.28632 (* 1 = 5.28632 loss)
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||
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I0405 10:56:20.671378 26038 sgd_solver.cpp:105] Iteration 7836, lr = 1e-05
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||
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I0405 10:56:26.225625 26038 solver.cpp:218] Iteration 7848 (2.16052 iter/s, 5.55421s/12 iters), loss = 5.28826
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||
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I0405 10:56:26.225733 26038 solver.cpp:237] Train net output #0: loss = 5.28826 (* 1 = 5.28826 loss)
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||
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I0405 10:56:26.225741 26038 sgd_solver.cpp:105] Iteration 7848, lr = 1e-05
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||
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I0405 10:56:28.377566 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
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||
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I0405 10:56:32.107129 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
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||
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I0405 10:56:34.806479 26038 solver.cpp:330] Iteration 7854, Testing net (#0)
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||
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I0405 10:56:34.806500 26038 net.cpp:676] Ignoring source layer train-data
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I0405 10:56:36.104733 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:56:39.403156 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 10:56:39.403195 26038 solver.cpp:397] Test net output #1: loss = 5.27908 (* 1 = 5.27908 loss)
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I0405 10:56:41.348217 26038 solver.cpp:218] Iteration 7860 (0.793525 iter/s, 15.1224s/12 iters), loss = 5.26118
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I0405 10:56:41.348275 26038 solver.cpp:237] Train net output #0: loss = 5.26118 (* 1 = 5.26118 loss)
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I0405 10:56:41.348284 26038 sgd_solver.cpp:105] Iteration 7860, lr = 1e-05
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I0405 10:56:46.810238 26038 solver.cpp:218] Iteration 7872 (2.19703 iter/s, 5.46192s/12 iters), loss = 5.29066
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I0405 10:56:46.810289 26038 solver.cpp:237] Train net output #0: loss = 5.29066 (* 1 = 5.29066 loss)
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I0405 10:56:46.810297 26038 sgd_solver.cpp:105] Iteration 7872, lr = 1e-05
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I0405 10:56:51.969266 26038 solver.cpp:218] Iteration 7884 (2.32606 iter/s, 5.15893s/12 iters), loss = 5.27662
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I0405 10:56:51.975500 26038 solver.cpp:237] Train net output #0: loss = 5.27662 (* 1 = 5.27662 loss)
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I0405 10:56:51.975522 26038 sgd_solver.cpp:105] Iteration 7884, lr = 1e-05
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I0405 10:56:54.301692 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 10:56:57.441325 26038 solver.cpp:218] Iteration 7896 (2.19547 iter/s, 5.4658s/12 iters), loss = 5.27811
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I0405 10:56:57.441483 26038 solver.cpp:237] Train net output #0: loss = 5.27811 (* 1 = 5.27811 loss)
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I0405 10:56:57.441493 26038 sgd_solver.cpp:105] Iteration 7896, lr = 1e-05
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I0405 10:57:02.727771 26038 solver.cpp:218] Iteration 7908 (2.27004 iter/s, 5.28625s/12 iters), loss = 5.27932
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I0405 10:57:02.727815 26038 solver.cpp:237] Train net output #0: loss = 5.27932 (* 1 = 5.27932 loss)
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I0405 10:57:02.727823 26038 sgd_solver.cpp:105] Iteration 7908, lr = 1e-05
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I0405 10:57:08.306005 26038 solver.cpp:218] Iteration 7920 (2.15125 iter/s, 5.57815s/12 iters), loss = 5.27637
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I0405 10:57:08.306051 26038 solver.cpp:237] Train net output #0: loss = 5.27637 (* 1 = 5.27637 loss)
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I0405 10:57:08.306059 26038 sgd_solver.cpp:105] Iteration 7920, lr = 1e-05
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I0405 10:57:13.733443 26038 solver.cpp:218] Iteration 7932 (2.21102 iter/s, 5.42735s/12 iters), loss = 5.26465
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I0405 10:57:13.733480 26038 solver.cpp:237] Train net output #0: loss = 5.26465 (* 1 = 5.26465 loss)
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I0405 10:57:13.733486 26038 sgd_solver.cpp:105] Iteration 7932, lr = 1e-05
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I0405 10:57:19.251219 26038 solver.cpp:218] Iteration 7944 (2.17482 iter/s, 5.51769s/12 iters), loss = 5.27191
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I0405 10:57:19.251288 26038 solver.cpp:237] Train net output #0: loss = 5.27191 (* 1 = 5.27191 loss)
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I0405 10:57:19.251298 26038 sgd_solver.cpp:105] Iteration 7944, lr = 1e-05
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||
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I0405 10:57:24.165275 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
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||
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|
I0405 10:57:27.232550 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
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||
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I0405 10:57:29.591233 26038 solver.cpp:330] Iteration 7956, Testing net (#0)
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||
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I0405 10:57:29.591291 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:57:30.835942 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:57:33.986726 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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I0405 10:57:33.986753 26038 solver.cpp:397] Test net output #1: loss = 5.27898 (* 1 = 5.27898 loss)
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||
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I0405 10:57:34.118870 26038 solver.cpp:218] Iteration 7956 (0.80713 iter/s, 14.8675s/12 iters), loss = 5.27916
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||
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I0405 10:57:34.120438 26038 solver.cpp:237] Train net output #0: loss = 5.27916 (* 1 = 5.27916 loss)
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||
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I0405 10:57:34.120450 26038 sgd_solver.cpp:105] Iteration 7956, lr = 1e-05
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I0405 10:57:38.668346 26038 solver.cpp:218] Iteration 7968 (2.6386 iter/s, 4.54787s/12 iters), loss = 5.26889
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I0405 10:57:38.668390 26038 solver.cpp:237] Train net output #0: loss = 5.26889 (* 1 = 5.26889 loss)
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||
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I0405 10:57:38.668395 26038 sgd_solver.cpp:105] Iteration 7968, lr = 1e-05
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I0405 10:57:44.003492 26038 solver.cpp:218] Iteration 7980 (2.24927 iter/s, 5.33506s/12 iters), loss = 5.26829
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||
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I0405 10:57:44.003552 26038 solver.cpp:237] Train net output #0: loss = 5.26829 (* 1 = 5.26829 loss)
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||
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I0405 10:57:44.003561 26038 sgd_solver.cpp:105] Iteration 7980, lr = 1e-05
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||
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I0405 10:57:48.498996 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:57:49.358786 26038 solver.cpp:218] Iteration 7992 (2.24082 iter/s, 5.35519s/12 iters), loss = 5.27872
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||
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I0405 10:57:49.358839 26038 solver.cpp:237] Train net output #0: loss = 5.27872 (* 1 = 5.27872 loss)
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I0405 10:57:49.358847 26038 sgd_solver.cpp:105] Iteration 7992, lr = 1e-05
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I0405 10:57:54.913609 26038 solver.cpp:218] Iteration 8004 (2.16032 iter/s, 5.55472s/12 iters), loss = 5.26904
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I0405 10:57:54.913668 26038 solver.cpp:237] Train net output #0: loss = 5.26904 (* 1 = 5.26904 loss)
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I0405 10:57:54.913678 26038 sgd_solver.cpp:105] Iteration 8004, lr = 1e-05
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I0405 10:58:00.271049 26038 solver.cpp:218] Iteration 8016 (2.23992 iter/s, 5.35734s/12 iters), loss = 5.29104
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I0405 10:58:00.271174 26038 solver.cpp:237] Train net output #0: loss = 5.29104 (* 1 = 5.29104 loss)
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I0405 10:58:00.271181 26038 sgd_solver.cpp:105] Iteration 8016, lr = 1e-05
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I0405 10:58:05.641705 26038 solver.cpp:218] Iteration 8028 (2.23443 iter/s, 5.37049s/12 iters), loss = 5.28012
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I0405 10:58:05.641744 26038 solver.cpp:237] Train net output #0: loss = 5.28012 (* 1 = 5.28012 loss)
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I0405 10:58:05.641749 26038 sgd_solver.cpp:105] Iteration 8028, lr = 1e-05
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I0405 10:58:10.697189 26038 solver.cpp:218] Iteration 8040 (2.3737 iter/s, 5.05541s/12 iters), loss = 5.27133
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I0405 10:58:10.697224 26038 solver.cpp:237] Train net output #0: loss = 5.27133 (* 1 = 5.27133 loss)
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I0405 10:58:10.697230 26038 sgd_solver.cpp:105] Iteration 8040, lr = 1e-05
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I0405 10:58:15.936060 26038 solver.cpp:218] Iteration 8052 (2.29061 iter/s, 5.23879s/12 iters), loss = 5.25869
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I0405 10:58:15.936105 26038 solver.cpp:237] Train net output #0: loss = 5.25869 (* 1 = 5.25869 loss)
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I0405 10:58:15.936110 26038 sgd_solver.cpp:105] Iteration 8052, lr = 1e-05
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I0405 10:58:18.192288 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
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||
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I0405 10:58:21.269170 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
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I0405 10:58:23.584434 26038 solver.cpp:330] Iteration 8058, Testing net (#0)
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||
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I0405 10:58:23.584452 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 10:58:24.987504 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:58:28.455886 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 10:58:28.455924 26038 solver.cpp:397] Test net output #1: loss = 5.27913 (* 1 = 5.27913 loss)
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I0405 10:58:30.376013 26038 solver.cpp:218] Iteration 8064 (0.831035 iter/s, 14.4398s/12 iters), loss = 5.28075
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I0405 10:58:30.376148 26038 solver.cpp:237] Train net output #0: loss = 5.28075 (* 1 = 5.28075 loss)
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I0405 10:58:30.376157 26038 sgd_solver.cpp:105] Iteration 8064, lr = 1e-05
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I0405 10:58:35.855417 26038 solver.cpp:218] Iteration 8076 (2.19009 iter/s, 5.47922s/12 iters), loss = 5.27318
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I0405 10:58:35.855469 26038 solver.cpp:237] Train net output #0: loss = 5.27318 (* 1 = 5.27318 loss)
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I0405 10:58:35.855475 26038 sgd_solver.cpp:105] Iteration 8076, lr = 1e-05
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I0405 10:58:41.626578 26038 solver.cpp:218] Iteration 8088 (2.07934 iter/s, 5.77106s/12 iters), loss = 5.26579
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I0405 10:58:41.626619 26038 solver.cpp:237] Train net output #0: loss = 5.26579 (* 1 = 5.26579 loss)
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||
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I0405 10:58:41.626623 26038 sgd_solver.cpp:105] Iteration 8088, lr = 1e-05
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||
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I0405 10:58:43.064052 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 10:58:47.338438 26038 solver.cpp:218] Iteration 8100 (2.10093 iter/s, 5.71177s/12 iters), loss = 5.28259
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I0405 10:58:47.338497 26038 solver.cpp:237] Train net output #0: loss = 5.28259 (* 1 = 5.28259 loss)
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I0405 10:58:47.338506 26038 sgd_solver.cpp:105] Iteration 8100, lr = 1e-05
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I0405 10:58:53.029958 26038 solver.cpp:218] Iteration 8112 (2.10844 iter/s, 5.69142s/12 iters), loss = 5.2839
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I0405 10:58:53.029994 26038 solver.cpp:237] Train net output #0: loss = 5.2839 (* 1 = 5.2839 loss)
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I0405 10:58:53.029999 26038 sgd_solver.cpp:105] Iteration 8112, lr = 1e-05
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I0405 10:58:58.443869 26038 solver.cpp:218] Iteration 8124 (2.21655 iter/s, 5.41383s/12 iters), loss = 5.28301
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I0405 10:58:58.443914 26038 solver.cpp:237] Train net output #0: loss = 5.28301 (* 1 = 5.28301 loss)
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||
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I0405 10:58:58.443919 26038 sgd_solver.cpp:105] Iteration 8124, lr = 1e-05
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I0405 10:59:03.783636 26038 solver.cpp:218] Iteration 8136 (2.24733 iter/s, 5.33967s/12 iters), loss = 5.28487
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||
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I0405 10:59:03.783813 26038 solver.cpp:237] Train net output #0: loss = 5.28487 (* 1 = 5.28487 loss)
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||
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I0405 10:59:03.783823 26038 sgd_solver.cpp:105] Iteration 8136, lr = 1e-05
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I0405 10:59:09.288612 26038 solver.cpp:218] Iteration 8148 (2.17993 iter/s, 5.50476s/12 iters), loss = 5.29406
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||
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I0405 10:59:09.288671 26038 solver.cpp:237] Train net output #0: loss = 5.29406 (* 1 = 5.29406 loss)
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||
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I0405 10:59:09.288681 26038 sgd_solver.cpp:105] Iteration 8148, lr = 1e-05
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||
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I0405 10:59:14.307816 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
|
||
|
|
I0405 10:59:17.230361 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
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||
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I0405 10:59:19.539438 26038 solver.cpp:330] Iteration 8160, Testing net (#0)
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||
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I0405 10:59:19.539458 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
|
I0405 10:59:20.795938 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 10:59:24.077234 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 10:59:24.077268 26038 solver.cpp:397] Test net output #1: loss = 5.27906 (* 1 = 5.27906 loss)
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||
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I0405 10:59:24.215006 26038 solver.cpp:218] Iteration 8160 (0.803953 iter/s, 14.9262s/12 iters), loss = 5.27651
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||
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I0405 10:59:24.215065 26038 solver.cpp:237] Train net output #0: loss = 5.27651 (* 1 = 5.27651 loss)
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||
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I0405 10:59:24.215072 26038 sgd_solver.cpp:105] Iteration 8160, lr = 1e-05
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||
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I0405 10:59:28.456321 26038 solver.cpp:218] Iteration 8172 (2.82938 iter/s, 4.24121s/12 iters), loss = 5.26939
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||
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I0405 10:59:28.456387 26038 solver.cpp:237] Train net output #0: loss = 5.26939 (* 1 = 5.26939 loss)
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||
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I0405 10:59:28.456395 26038 sgd_solver.cpp:105] Iteration 8172, lr = 1e-05
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||
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I0405 10:59:33.945963 26038 solver.cpp:218] Iteration 8184 (2.18598 iter/s, 5.48954s/12 iters), loss = 5.28556
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||
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I0405 10:59:33.946072 26038 solver.cpp:237] Train net output #0: loss = 5.28556 (* 1 = 5.28556 loss)
|
||
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I0405 10:59:33.946080 26038 sgd_solver.cpp:105] Iteration 8184, lr = 1e-05
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||
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I0405 10:59:37.770642 26057 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 10:59:39.334738 26038 solver.cpp:218] Iteration 8196 (2.22691 iter/s, 5.38862s/12 iters), loss = 5.26002
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||
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I0405 10:59:39.334789 26038 solver.cpp:237] Train net output #0: loss = 5.26002 (* 1 = 5.26002 loss)
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||
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I0405 10:59:39.334797 26038 sgd_solver.cpp:105] Iteration 8196, lr = 1e-05
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||
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I0405 10:59:44.838052 26038 solver.cpp:218] Iteration 8208 (2.18054 iter/s, 5.50322s/12 iters), loss = 5.26828
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||
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I0405 10:59:44.838099 26038 solver.cpp:237] Train net output #0: loss = 5.26828 (* 1 = 5.26828 loss)
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||
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I0405 10:59:44.838105 26038 sgd_solver.cpp:105] Iteration 8208, lr = 1e-05
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||
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I0405 10:59:50.193034 26038 solver.cpp:218] Iteration 8220 (2.24094 iter/s, 5.3549s/12 iters), loss = 5.27549
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||
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I0405 10:59:50.193073 26038 solver.cpp:237] Train net output #0: loss = 5.27549 (* 1 = 5.27549 loss)
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||
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I0405 10:59:50.193078 26038 sgd_solver.cpp:105] Iteration 8220, lr = 1e-05
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||
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I0405 10:59:55.806295 26038 solver.cpp:218] Iteration 8232 (2.13783 iter/s, 5.61318s/12 iters), loss = 5.26735
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||
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I0405 10:59:55.806344 26038 solver.cpp:237] Train net output #0: loss = 5.26735 (* 1 = 5.26735 loss)
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||
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|
I0405 10:59:55.806350 26038 sgd_solver.cpp:105] Iteration 8232, lr = 1e-05
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||
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I0405 11:00:01.337507 26038 solver.cpp:218] Iteration 8244 (2.16954 iter/s, 5.53112s/12 iters), loss = 5.28029
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||
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I0405 11:00:01.337555 26038 solver.cpp:237] Train net output #0: loss = 5.28029 (* 1 = 5.28029 loss)
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||
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I0405 11:00:01.337561 26038 sgd_solver.cpp:105] Iteration 8244, lr = 1e-05
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I0405 11:00:06.426062 26038 solver.cpp:218] Iteration 8256 (2.35828 iter/s, 5.08846s/12 iters), loss = 5.26691
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||
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I0405 11:00:06.426182 26038 solver.cpp:237] Train net output #0: loss = 5.26691 (* 1 = 5.26691 loss)
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||
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I0405 11:00:06.426190 26038 sgd_solver.cpp:105] Iteration 8256, lr = 1e-05
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||
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I0405 11:00:08.638350 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
|
||
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|
I0405 11:00:11.680586 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
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||
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I0405 11:00:13.994150 26038 solver.cpp:330] Iteration 8262, Testing net (#0)
|
||
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I0405 11:00:13.994174 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
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I0405 11:00:15.145824 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 11:00:18.450475 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 11:00:18.450508 26038 solver.cpp:397] Test net output #1: loss = 5.27898 (* 1 = 5.27898 loss)
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||
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I0405 11:00:20.440593 26038 solver.cpp:218] Iteration 8268 (0.856267 iter/s, 14.0143s/12 iters), loss = 5.28026
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||
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I0405 11:00:20.440654 26038 solver.cpp:237] Train net output #0: loss = 5.28026 (* 1 = 5.28026 loss)
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||
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I0405 11:00:20.440662 26038 sgd_solver.cpp:105] Iteration 8268, lr = 1e-05
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I0405 11:00:25.969329 26038 solver.cpp:218] Iteration 8280 (2.17052 iter/s, 5.52863s/12 iters), loss = 5.26951
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I0405 11:00:25.969370 26038 solver.cpp:237] Train net output #0: loss = 5.26951 (* 1 = 5.26951 loss)
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I0405 11:00:25.969375 26038 sgd_solver.cpp:105] Iteration 8280, lr = 1e-05
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I0405 11:00:31.330585 26038 solver.cpp:218] Iteration 8292 (2.23832 iter/s, 5.36117s/12 iters), loss = 5.27508
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I0405 11:00:31.330627 26038 solver.cpp:237] Train net output #0: loss = 5.27508 (* 1 = 5.27508 loss)
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||
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I0405 11:00:31.330633 26038 sgd_solver.cpp:105] Iteration 8292, lr = 1e-05
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||
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I0405 11:00:32.053454 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:00:36.523906 26038 solver.cpp:218] Iteration 8304 (2.31073 iter/s, 5.19316s/12 iters), loss = 5.27983
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I0405 11:00:36.524056 26038 solver.cpp:237] Train net output #0: loss = 5.27983 (* 1 = 5.27983 loss)
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I0405 11:00:36.524065 26038 sgd_solver.cpp:105] Iteration 8304, lr = 1e-05
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||
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I0405 11:00:39.425210 26038 blocking_queue.cpp:49] Waiting for data
|
||
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I0405 11:00:41.946590 26038 solver.cpp:218] Iteration 8316 (2.21301 iter/s, 5.42249s/12 iters), loss = 5.27283
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I0405 11:00:41.946640 26038 solver.cpp:237] Train net output #0: loss = 5.27283 (* 1 = 5.27283 loss)
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I0405 11:00:41.946647 26038 sgd_solver.cpp:105] Iteration 8316, lr = 1e-05
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I0405 11:00:47.471737 26038 solver.cpp:218] Iteration 8328 (2.17192 iter/s, 5.52506s/12 iters), loss = 5.26918
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I0405 11:00:47.471778 26038 solver.cpp:237] Train net output #0: loss = 5.26918 (* 1 = 5.26918 loss)
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I0405 11:00:47.471783 26038 sgd_solver.cpp:105] Iteration 8328, lr = 1e-05
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I0405 11:00:53.077962 26038 solver.cpp:218] Iteration 8340 (2.14051 iter/s, 5.60613s/12 iters), loss = 5.27627
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I0405 11:00:53.078014 26038 solver.cpp:237] Train net output #0: loss = 5.27627 (* 1 = 5.27627 loss)
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I0405 11:00:53.078022 26038 sgd_solver.cpp:105] Iteration 8340, lr = 1e-05
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I0405 11:00:58.344914 26038 solver.cpp:218] Iteration 8352 (2.2784 iter/s, 5.26686s/12 iters), loss = 5.28113
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I0405 11:00:58.344967 26038 solver.cpp:237] Train net output #0: loss = 5.28113 (* 1 = 5.28113 loss)
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I0405 11:00:58.344975 26038 sgd_solver.cpp:105] Iteration 8352, lr = 1e-05
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I0405 11:01:03.063501 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
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I0405 11:01:06.146497 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
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I0405 11:01:08.449893 26038 solver.cpp:330] Iteration 8364, Testing net (#0)
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I0405 11:01:08.449970 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:01:09.506230 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:01:12.973665 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:01:12.973706 26038 solver.cpp:397] Test net output #1: loss = 5.27909 (* 1 = 5.27909 loss)
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I0405 11:01:13.115770 26038 solver.cpp:218] Iteration 8364 (0.812418 iter/s, 14.7707s/12 iters), loss = 5.28335
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I0405 11:01:13.115844 26038 solver.cpp:237] Train net output #0: loss = 5.28335 (* 1 = 5.28335 loss)
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I0405 11:01:13.115852 26038 sgd_solver.cpp:105] Iteration 8364, lr = 1e-05
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I0405 11:01:17.781414 26038 solver.cpp:218] Iteration 8376 (2.57205 iter/s, 4.66553s/12 iters), loss = 5.27246
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I0405 11:01:17.781453 26038 solver.cpp:237] Train net output #0: loss = 5.27246 (* 1 = 5.27246 loss)
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I0405 11:01:17.781458 26038 sgd_solver.cpp:105] Iteration 8376, lr = 1e-05
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I0405 11:01:23.404904 26038 solver.cpp:218] Iteration 8388 (2.13394 iter/s, 5.62339s/12 iters), loss = 5.29195
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I0405 11:01:23.404953 26038 solver.cpp:237] Train net output #0: loss = 5.29195 (* 1 = 5.29195 loss)
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I0405 11:01:23.404960 26038 sgd_solver.cpp:105] Iteration 8388, lr = 1e-05
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I0405 11:01:26.258426 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:01:28.735050 26038 solver.cpp:218] Iteration 8400 (2.25138 iter/s, 5.33006s/12 iters), loss = 5.28072
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I0405 11:01:28.735095 26038 solver.cpp:237] Train net output #0: loss = 5.28072 (* 1 = 5.28072 loss)
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I0405 11:01:28.735100 26038 sgd_solver.cpp:105] Iteration 8400, lr = 1e-05
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I0405 11:01:34.006889 26038 solver.cpp:218] Iteration 8412 (2.27628 iter/s, 5.27175s/12 iters), loss = 5.28148
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I0405 11:01:34.006930 26038 solver.cpp:237] Train net output #0: loss = 5.28148 (* 1 = 5.28148 loss)
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I0405 11:01:34.006935 26038 sgd_solver.cpp:105] Iteration 8412, lr = 1e-05
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I0405 11:01:39.513454 26038 solver.cpp:218] Iteration 8424 (2.17925 iter/s, 5.50647s/12 iters), loss = 5.29396
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I0405 11:01:39.513600 26038 solver.cpp:237] Train net output #0: loss = 5.29396 (* 1 = 5.29396 loss)
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I0405 11:01:39.513609 26038 sgd_solver.cpp:105] Iteration 8424, lr = 1e-05
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I0405 11:01:45.103976 26038 solver.cpp:218] Iteration 8436 (2.14656 iter/s, 5.59033s/12 iters), loss = 5.29558
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I0405 11:01:45.104022 26038 solver.cpp:237] Train net output #0: loss = 5.29558 (* 1 = 5.29558 loss)
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I0405 11:01:45.104029 26038 sgd_solver.cpp:105] Iteration 8436, lr = 1e-05
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I0405 11:01:50.575114 26038 solver.cpp:218] Iteration 8448 (2.19336 iter/s, 5.47105s/12 iters), loss = 5.28636
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I0405 11:01:50.575157 26038 solver.cpp:237] Train net output #0: loss = 5.28636 (* 1 = 5.28636 loss)
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I0405 11:01:50.575162 26038 sgd_solver.cpp:105] Iteration 8448, lr = 1e-05
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I0405 11:01:55.998433 26038 solver.cpp:218] Iteration 8460 (2.2127 iter/s, 5.42323s/12 iters), loss = 5.28257
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I0405 11:01:55.998499 26038 solver.cpp:237] Train net output #0: loss = 5.28257 (* 1 = 5.28257 loss)
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I0405 11:01:55.998510 26038 sgd_solver.cpp:105] Iteration 8460, lr = 1e-05
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I0405 11:01:58.195108 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
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I0405 11:02:01.420912 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
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I0405 11:02:03.730898 26038 solver.cpp:330] Iteration 8466, Testing net (#0)
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I0405 11:02:03.730921 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:02:04.844626 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:02:08.266055 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:02:08.266086 26038 solver.cpp:397] Test net output #1: loss = 5.27896 (* 1 = 5.27896 loss)
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I0405 11:02:10.236735 26038 solver.cpp:218] Iteration 8472 (0.842806 iter/s, 14.2382s/12 iters), loss = 5.28335
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I0405 11:02:10.236865 26038 solver.cpp:237] Train net output #0: loss = 5.28335 (* 1 = 5.28335 loss)
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I0405 11:02:10.236872 26038 sgd_solver.cpp:105] Iteration 8472, lr = 1e-05
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I0405 11:02:15.595479 26038 solver.cpp:218] Iteration 8484 (2.2394 iter/s, 5.35857s/12 iters), loss = 5.28504
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I0405 11:02:15.595528 26038 solver.cpp:237] Train net output #0: loss = 5.28504 (* 1 = 5.28504 loss)
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I0405 11:02:15.595536 26038 sgd_solver.cpp:105] Iteration 8484, lr = 1e-05
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I0405 11:02:21.246433 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:02:21.256491 26038 solver.cpp:218] Iteration 8496 (2.1198 iter/s, 5.66092s/12 iters), loss = 5.28544
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I0405 11:02:21.256543 26038 solver.cpp:237] Train net output #0: loss = 5.28544 (* 1 = 5.28544 loss)
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I0405 11:02:21.256553 26038 sgd_solver.cpp:105] Iteration 8496, lr = 1e-05
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I0405 11:02:26.511453 26038 solver.cpp:218] Iteration 8508 (2.2836 iter/s, 5.25487s/12 iters), loss = 5.29563
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I0405 11:02:26.511502 26038 solver.cpp:237] Train net output #0: loss = 5.29563 (* 1 = 5.29563 loss)
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I0405 11:02:26.511512 26038 sgd_solver.cpp:105] Iteration 8508, lr = 1e-05
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I0405 11:02:32.133446 26038 solver.cpp:218] Iteration 8520 (2.13451 iter/s, 5.6219s/12 iters), loss = 5.26911
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I0405 11:02:32.133502 26038 solver.cpp:237] Train net output #0: loss = 5.26911 (* 1 = 5.26911 loss)
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I0405 11:02:32.133508 26038 sgd_solver.cpp:105] Iteration 8520, lr = 1e-05
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I0405 11:02:37.629813 26038 solver.cpp:218] Iteration 8532 (2.1833 iter/s, 5.49627s/12 iters), loss = 5.27634
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I0405 11:02:37.629868 26038 solver.cpp:237] Train net output #0: loss = 5.27634 (* 1 = 5.27634 loss)
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I0405 11:02:37.629876 26038 sgd_solver.cpp:105] Iteration 8532, lr = 1e-05
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I0405 11:02:42.981276 26038 solver.cpp:218] Iteration 8544 (2.24242 iter/s, 5.35137s/12 iters), loss = 5.27483
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I0405 11:02:42.981407 26038 solver.cpp:237] Train net output #0: loss = 5.27483 (* 1 = 5.27483 loss)
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I0405 11:02:42.981415 26038 sgd_solver.cpp:105] Iteration 8544, lr = 1e-05
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I0405 11:02:48.412945 26038 solver.cpp:218] Iteration 8556 (2.20934 iter/s, 5.43149s/12 iters), loss = 5.28284
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I0405 11:02:48.412998 26038 solver.cpp:237] Train net output #0: loss = 5.28284 (* 1 = 5.28284 loss)
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I0405 11:02:48.413007 26038 sgd_solver.cpp:105] Iteration 8556, lr = 1e-05
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I0405 11:02:53.412648 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
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I0405 11:02:56.397085 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
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I0405 11:02:58.726806 26038 solver.cpp:330] Iteration 8568, Testing net (#0)
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I0405 11:02:58.726824 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:02:59.862047 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:03:03.241305 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:03:03.241348 26038 solver.cpp:397] Test net output #1: loss = 5.27917 (* 1 = 5.27917 loss)
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I0405 11:03:03.381916 26038 solver.cpp:218] Iteration 8568 (0.801666 iter/s, 14.9688s/12 iters), loss = 5.26669
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I0405 11:03:03.383472 26038 solver.cpp:237] Train net output #0: loss = 5.26669 (* 1 = 5.26669 loss)
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I0405 11:03:03.383484 26038 sgd_solver.cpp:105] Iteration 8568, lr = 1e-05
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I0405 11:03:07.697644 26038 solver.cpp:218] Iteration 8580 (2.78155 iter/s, 4.31413s/12 iters), loss = 5.28879
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I0405 11:03:07.697700 26038 solver.cpp:237] Train net output #0: loss = 5.28879 (* 1 = 5.28879 loss)
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I0405 11:03:07.697707 26038 sgd_solver.cpp:105] Iteration 8580, lr = 1e-05
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I0405 11:03:13.026921 26038 solver.cpp:218] Iteration 8592 (2.25175 iter/s, 5.32918s/12 iters), loss = 5.275
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I0405 11:03:13.027031 26038 solver.cpp:237] Train net output #0: loss = 5.275 (* 1 = 5.275 loss)
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I0405 11:03:13.027040 26038 sgd_solver.cpp:105] Iteration 8592, lr = 1e-05
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I0405 11:03:15.293808 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:03:18.442287 26038 solver.cpp:218] Iteration 8604 (2.21598 iter/s, 5.41521s/12 iters), loss = 5.27721
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I0405 11:03:18.442345 26038 solver.cpp:237] Train net output #0: loss = 5.27721 (* 1 = 5.27721 loss)
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I0405 11:03:18.442353 26038 sgd_solver.cpp:105] Iteration 8604, lr = 1e-05
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I0405 11:03:23.943600 26038 solver.cpp:218] Iteration 8616 (2.18134 iter/s, 5.50121s/12 iters), loss = 5.28429
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I0405 11:03:23.943650 26038 solver.cpp:237] Train net output #0: loss = 5.28429 (* 1 = 5.28429 loss)
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I0405 11:03:23.943657 26038 sgd_solver.cpp:105] Iteration 8616, lr = 1e-05
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I0405 11:03:29.632287 26038 solver.cpp:218] Iteration 8628 (2.10948 iter/s, 5.6886s/12 iters), loss = 5.28389
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I0405 11:03:29.632324 26038 solver.cpp:237] Train net output #0: loss = 5.28389 (* 1 = 5.28389 loss)
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||
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I0405 11:03:29.632329 26038 sgd_solver.cpp:105] Iteration 8628, lr = 1e-05
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I0405 11:03:34.836738 26038 solver.cpp:218] Iteration 8640 (2.30575 iter/s, 5.20437s/12 iters), loss = 5.28657
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I0405 11:03:34.836786 26038 solver.cpp:237] Train net output #0: loss = 5.28657 (* 1 = 5.28657 loss)
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||
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I0405 11:03:34.836793 26038 sgd_solver.cpp:105] Iteration 8640, lr = 1e-05
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I0405 11:03:40.289367 26038 solver.cpp:218] Iteration 8652 (2.20081 iter/s, 5.45254s/12 iters), loss = 5.2748
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I0405 11:03:40.289415 26038 solver.cpp:237] Train net output #0: loss = 5.2748 (* 1 = 5.2748 loss)
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I0405 11:03:40.289420 26038 sgd_solver.cpp:105] Iteration 8652, lr = 1e-05
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I0405 11:03:45.620290 26038 solver.cpp:218] Iteration 8664 (2.25105 iter/s, 5.33084s/12 iters), loss = 5.27001
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I0405 11:03:45.620430 26038 solver.cpp:237] Train net output #0: loss = 5.27001 (* 1 = 5.27001 loss)
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||
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I0405 11:03:45.620438 26038 sgd_solver.cpp:105] Iteration 8664, lr = 1e-05
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I0405 11:03:47.768780 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
|
||
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I0405 11:03:50.856312 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
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||
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I0405 11:03:53.174978 26038 solver.cpp:330] Iteration 8670, Testing net (#0)
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||
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I0405 11:03:53.174998 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 11:03:54.198366 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 11:03:57.620170 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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I0405 11:03:57.620213 26038 solver.cpp:397] Test net output #1: loss = 5.27903 (* 1 = 5.27903 loss)
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||
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I0405 11:03:59.658054 26038 solver.cpp:218] Iteration 8676 (0.85485 iter/s, 14.0375s/12 iters), loss = 5.29557
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I0405 11:03:59.658097 26038 solver.cpp:237] Train net output #0: loss = 5.29557 (* 1 = 5.29557 loss)
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I0405 11:03:59.658103 26038 sgd_solver.cpp:105] Iteration 8676, lr = 1e-05
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I0405 11:04:04.863188 26038 solver.cpp:218] Iteration 8688 (2.30545 iter/s, 5.20505s/12 iters), loss = 5.26929
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I0405 11:04:04.863229 26038 solver.cpp:237] Train net output #0: loss = 5.26929 (* 1 = 5.26929 loss)
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||
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I0405 11:04:04.863234 26038 sgd_solver.cpp:105] Iteration 8688, lr = 1e-05
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||
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I0405 11:04:09.274222 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:04:10.047480 26038 solver.cpp:218] Iteration 8700 (2.31472 iter/s, 5.18422s/12 iters), loss = 5.27682
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I0405 11:04:10.047521 26038 solver.cpp:237] Train net output #0: loss = 5.27682 (* 1 = 5.27682 loss)
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I0405 11:04:10.047528 26038 sgd_solver.cpp:105] Iteration 8700, lr = 1e-05
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I0405 11:04:15.409525 26038 solver.cpp:218] Iteration 8712 (2.23799 iter/s, 5.36196s/12 iters), loss = 5.25832
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||
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I0405 11:04:15.409572 26038 solver.cpp:237] Train net output #0: loss = 5.25832 (* 1 = 5.25832 loss)
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||
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I0405 11:04:15.409580 26038 sgd_solver.cpp:105] Iteration 8712, lr = 1e-05
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I0405 11:04:20.824419 26038 solver.cpp:218] Iteration 8724 (2.21615 iter/s, 5.41481s/12 iters), loss = 5.28609
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||
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I0405 11:04:20.824574 26038 solver.cpp:237] Train net output #0: loss = 5.28609 (* 1 = 5.28609 loss)
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||
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I0405 11:04:20.824589 26038 sgd_solver.cpp:105] Iteration 8724, lr = 1e-05
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||
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I0405 11:04:25.911672 26038 solver.cpp:218] Iteration 8736 (2.35892 iter/s, 5.08707s/12 iters), loss = 5.28622
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||
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I0405 11:04:25.911711 26038 solver.cpp:237] Train net output #0: loss = 5.28622 (* 1 = 5.28622 loss)
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||
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I0405 11:04:25.911716 26038 sgd_solver.cpp:105] Iteration 8736, lr = 1e-05
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||
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I0405 11:04:31.289149 26038 solver.cpp:218] Iteration 8748 (2.23156 iter/s, 5.37739s/12 iters), loss = 5.27108
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||
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I0405 11:04:31.289196 26038 solver.cpp:237] Train net output #0: loss = 5.27108 (* 1 = 5.27108 loss)
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||
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I0405 11:04:31.289201 26038 sgd_solver.cpp:105] Iteration 8748, lr = 1e-05
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||
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I0405 11:04:36.692569 26038 solver.cpp:218] Iteration 8760 (2.22085 iter/s, 5.40333s/12 iters), loss = 5.2556
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||
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I0405 11:04:36.692606 26038 solver.cpp:237] Train net output #0: loss = 5.2556 (* 1 = 5.2556 loss)
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||
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I0405 11:04:36.692611 26038 sgd_solver.cpp:105] Iteration 8760, lr = 1e-05
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||
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I0405 11:04:41.448314 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
|
||
|
|
I0405 11:04:44.437114 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
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||
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I0405 11:04:46.753363 26038 solver.cpp:330] Iteration 8772, Testing net (#0)
|
||
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I0405 11:04:46.753391 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 11:04:47.682128 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 11:04:51.078629 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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I0405 11:04:51.078789 26038 solver.cpp:397] Test net output #1: loss = 5.27916 (* 1 = 5.27916 loss)
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I0405 11:04:51.220626 26038 solver.cpp:218] Iteration 8772 (0.825995 iter/s, 14.5279s/12 iters), loss = 5.27252
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I0405 11:04:51.220690 26038 solver.cpp:237] Train net output #0: loss = 5.27252 (* 1 = 5.27252 loss)
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I0405 11:04:51.220697 26038 sgd_solver.cpp:105] Iteration 8772, lr = 1e-05
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I0405 11:04:55.548516 26038 solver.cpp:218] Iteration 8784 (2.77278 iter/s, 4.32779s/12 iters), loss = 5.27098
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I0405 11:04:55.548565 26038 solver.cpp:237] Train net output #0: loss = 5.27098 (* 1 = 5.27098 loss)
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I0405 11:04:55.548573 26038 sgd_solver.cpp:105] Iteration 8784, lr = 1e-05
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I0405 11:05:00.948469 26038 solver.cpp:218] Iteration 8796 (2.22228 iter/s, 5.39986s/12 iters), loss = 5.27461
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I0405 11:05:00.948511 26038 solver.cpp:237] Train net output #0: loss = 5.27461 (* 1 = 5.27461 loss)
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I0405 11:05:00.948518 26038 sgd_solver.cpp:105] Iteration 8796, lr = 1e-05
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I0405 11:05:02.505050 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:05:06.253638 26038 solver.cpp:218] Iteration 8808 (2.26198 iter/s, 5.30509s/12 iters), loss = 5.28391
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I0405 11:05:06.253679 26038 solver.cpp:237] Train net output #0: loss = 5.28391 (* 1 = 5.28391 loss)
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I0405 11:05:06.253684 26038 sgd_solver.cpp:105] Iteration 8808, lr = 1e-05
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I0405 11:05:11.329761 26038 solver.cpp:218] Iteration 8820 (2.36404 iter/s, 5.07605s/12 iters), loss = 5.27957
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I0405 11:05:11.329798 26038 solver.cpp:237] Train net output #0: loss = 5.27957 (* 1 = 5.27957 loss)
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I0405 11:05:11.329804 26038 sgd_solver.cpp:105] Iteration 8820, lr = 1e-05
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I0405 11:05:16.547677 26038 solver.cpp:218] Iteration 8832 (2.29981 iter/s, 5.21783s/12 iters), loss = 5.28317
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I0405 11:05:16.547719 26038 solver.cpp:237] Train net output #0: loss = 5.28317 (* 1 = 5.28317 loss)
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I0405 11:05:16.547724 26038 sgd_solver.cpp:105] Iteration 8832, lr = 1e-05
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I0405 11:05:21.938244 26038 solver.cpp:218] Iteration 8844 (2.22615 iter/s, 5.39048s/12 iters), loss = 5.26564
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I0405 11:05:21.938403 26038 solver.cpp:237] Train net output #0: loss = 5.26564 (* 1 = 5.26564 loss)
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I0405 11:05:21.938412 26038 sgd_solver.cpp:105] Iteration 8844, lr = 1e-05
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I0405 11:05:27.087141 26038 solver.cpp:218] Iteration 8856 (2.33069 iter/s, 5.1487s/12 iters), loss = 5.29172
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I0405 11:05:27.087198 26038 solver.cpp:237] Train net output #0: loss = 5.29172 (* 1 = 5.29172 loss)
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I0405 11:05:27.087204 26038 sgd_solver.cpp:105] Iteration 8856, lr = 1e-05
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I0405 11:05:32.381500 26038 solver.cpp:218] Iteration 8868 (2.2666 iter/s, 5.29427s/12 iters), loss = 5.27489
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I0405 11:05:32.381556 26038 solver.cpp:237] Train net output #0: loss = 5.27489 (* 1 = 5.27489 loss)
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I0405 11:05:32.381564 26038 sgd_solver.cpp:105] Iteration 8868, lr = 1e-05
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I0405 11:05:34.428349 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
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I0405 11:05:37.830056 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
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I0405 11:05:40.276362 26038 solver.cpp:330] Iteration 8874, Testing net (#0)
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I0405 11:05:40.276384 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:05:41.133973 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:05:44.703449 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:05:44.703485 26038 solver.cpp:397] Test net output #1: loss = 5.27898 (* 1 = 5.27898 loss)
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I0405 11:05:46.627888 26038 solver.cpp:218] Iteration 8880 (0.842326 iter/s, 14.2463s/12 iters), loss = 5.28964
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I0405 11:05:46.627933 26038 solver.cpp:237] Train net output #0: loss = 5.28964 (* 1 = 5.28964 loss)
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I0405 11:05:46.627938 26038 sgd_solver.cpp:105] Iteration 8880, lr = 1e-05
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I0405 11:05:51.952750 26038 solver.cpp:218] Iteration 8892 (2.25362 iter/s, 5.32477s/12 iters), loss = 5.28846
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I0405 11:05:51.952855 26038 solver.cpp:237] Train net output #0: loss = 5.28846 (* 1 = 5.28846 loss)
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I0405 11:05:51.952863 26038 sgd_solver.cpp:105] Iteration 8892, lr = 1e-05
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I0405 11:05:55.853533 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:05:57.352807 26038 solver.cpp:218] Iteration 8904 (2.22226 iter/s, 5.39992s/12 iters), loss = 5.27254
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I0405 11:05:57.352851 26038 solver.cpp:237] Train net output #0: loss = 5.27254 (* 1 = 5.27254 loss)
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I0405 11:05:57.352859 26038 sgd_solver.cpp:105] Iteration 8904, lr = 1e-05
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I0405 11:06:02.561897 26038 solver.cpp:218] Iteration 8916 (2.3037 iter/s, 5.209s/12 iters), loss = 5.27816
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I0405 11:06:02.561944 26038 solver.cpp:237] Train net output #0: loss = 5.27816 (* 1 = 5.27816 loss)
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I0405 11:06:02.561951 26038 sgd_solver.cpp:105] Iteration 8916, lr = 1e-05
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I0405 11:06:07.973727 26038 solver.cpp:218] Iteration 8928 (2.2174 iter/s, 5.41175s/12 iters), loss = 5.28733
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I0405 11:06:07.973767 26038 solver.cpp:237] Train net output #0: loss = 5.28733 (* 1 = 5.28733 loss)
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I0405 11:06:07.973771 26038 sgd_solver.cpp:105] Iteration 8928, lr = 1e-05
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I0405 11:06:13.323134 26038 solver.cpp:218] Iteration 8940 (2.24327 iter/s, 5.34933s/12 iters), loss = 5.27299
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I0405 11:06:13.323176 26038 solver.cpp:237] Train net output #0: loss = 5.27299 (* 1 = 5.27299 loss)
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I0405 11:06:13.323181 26038 sgd_solver.cpp:105] Iteration 8940, lr = 1e-05
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I0405 11:06:18.644168 26038 solver.cpp:218] Iteration 8952 (2.25524 iter/s, 5.32095s/12 iters), loss = 5.29099
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I0405 11:06:18.644207 26038 solver.cpp:237] Train net output #0: loss = 5.29099 (* 1 = 5.29099 loss)
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I0405 11:06:18.644212 26038 sgd_solver.cpp:105] Iteration 8952, lr = 1e-05
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I0405 11:06:23.930740 26038 solver.cpp:218] Iteration 8964 (2.26994 iter/s, 5.28649s/12 iters), loss = 5.27796
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I0405 11:06:23.931303 26038 solver.cpp:237] Train net output #0: loss = 5.27796 (* 1 = 5.27796 loss)
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I0405 11:06:23.931309 26038 sgd_solver.cpp:105] Iteration 8964, lr = 1e-05
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I0405 11:06:28.644248 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
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I0405 11:06:31.662267 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
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I0405 11:06:34.263723 26038 solver.cpp:330] Iteration 8976, Testing net (#0)
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I0405 11:06:34.263747 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:06:35.105075 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:06:38.599273 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:06:38.599310 26038 solver.cpp:397] Test net output #1: loss = 5.27883 (* 1 = 5.27883 loss)
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I0405 11:06:38.741060 26038 solver.cpp:218] Iteration 8976 (0.810281 iter/s, 14.8097s/12 iters), loss = 5.27128
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I0405 11:06:38.741109 26038 solver.cpp:237] Train net output #0: loss = 5.27128 (* 1 = 5.27128 loss)
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I0405 11:06:38.741117 26038 sgd_solver.cpp:105] Iteration 8976, lr = 1e-05
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I0405 11:06:43.239737 26038 solver.cpp:218] Iteration 8988 (2.66751 iter/s, 4.49858s/12 iters), loss = 5.26072
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I0405 11:06:43.239789 26038 solver.cpp:237] Train net output #0: loss = 5.26072 (* 1 = 5.26072 loss)
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I0405 11:06:43.239797 26038 sgd_solver.cpp:105] Iteration 8988, lr = 1e-05
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I0405 11:06:46.667222 26038 blocking_queue.cpp:49] Waiting for data
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I0405 11:06:48.550576 26038 solver.cpp:218] Iteration 9000 (2.25957 iter/s, 5.31075s/12 iters), loss = 5.27134
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I0405 11:06:48.550616 26038 solver.cpp:237] Train net output #0: loss = 5.27134 (* 1 = 5.27134 loss)
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I0405 11:06:48.550621 26038 sgd_solver.cpp:105] Iteration 9000, lr = 1e-05
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I0405 11:06:49.272732 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:06:53.957909 26038 solver.cpp:218] Iteration 9012 (2.21924 iter/s, 5.40725s/12 iters), loss = 5.27093
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I0405 11:06:53.958037 26038 solver.cpp:237] Train net output #0: loss = 5.27093 (* 1 = 5.27093 loss)
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I0405 11:06:53.958046 26038 sgd_solver.cpp:105] Iteration 9012, lr = 1e-05
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I0405 11:06:59.135689 26038 solver.cpp:218] Iteration 9024 (2.31767 iter/s, 5.17761s/12 iters), loss = 5.28516
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I0405 11:06:59.135741 26038 solver.cpp:237] Train net output #0: loss = 5.28516 (* 1 = 5.28516 loss)
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I0405 11:06:59.135748 26038 sgd_solver.cpp:105] Iteration 9024, lr = 1e-05
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I0405 11:07:04.603823 26038 solver.cpp:218] Iteration 9036 (2.19457 iter/s, 5.46804s/12 iters), loss = 5.27678
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I0405 11:07:04.603869 26038 solver.cpp:237] Train net output #0: loss = 5.27678 (* 1 = 5.27678 loss)
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I0405 11:07:04.603876 26038 sgd_solver.cpp:105] Iteration 9036, lr = 1e-05
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I0405 11:07:09.947181 26038 solver.cpp:218] Iteration 9048 (2.24582 iter/s, 5.34326s/12 iters), loss = 5.28899
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I0405 11:07:09.947237 26038 solver.cpp:237] Train net output #0: loss = 5.28899 (* 1 = 5.28899 loss)
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I0405 11:07:09.947245 26038 sgd_solver.cpp:105] Iteration 9048, lr = 1e-05
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I0405 11:07:15.306218 26038 solver.cpp:218] Iteration 9060 (2.23925 iter/s, 5.35894s/12 iters), loss = 5.28372
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I0405 11:07:15.306274 26038 solver.cpp:237] Train net output #0: loss = 5.28372 (* 1 = 5.28372 loss)
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I0405 11:07:15.306282 26038 sgd_solver.cpp:105] Iteration 9060, lr = 1e-05
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I0405 11:07:20.636276 26038 solver.cpp:218] Iteration 9072 (2.25142 iter/s, 5.32996s/12 iters), loss = 5.28273
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I0405 11:07:20.636310 26038 solver.cpp:237] Train net output #0: loss = 5.28273 (* 1 = 5.28273 loss)
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I0405 11:07:20.636317 26038 sgd_solver.cpp:105] Iteration 9072, lr = 1e-05
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I0405 11:07:22.689252 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
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I0405 11:07:25.712941 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
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I0405 11:07:28.016563 26038 solver.cpp:330] Iteration 9078, Testing net (#0)
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I0405 11:07:28.016582 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:07:28.795679 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:07:32.456948 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:07:32.456990 26038 solver.cpp:397] Test net output #1: loss = 5.27922 (* 1 = 5.27922 loss)
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I0405 11:07:34.391153 26038 solver.cpp:218] Iteration 9084 (0.872425 iter/s, 13.7548s/12 iters), loss = 5.28233
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I0405 11:07:34.391193 26038 solver.cpp:237] Train net output #0: loss = 5.28233 (* 1 = 5.28233 loss)
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I0405 11:07:34.391199 26038 sgd_solver.cpp:105] Iteration 9084, lr = 1e-05
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I0405 11:07:39.819088 26038 solver.cpp:218] Iteration 9096 (2.21082 iter/s, 5.42785s/12 iters), loss = 5.26617
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I0405 11:07:39.819129 26038 solver.cpp:237] Train net output #0: loss = 5.26617 (* 1 = 5.26617 loss)
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I0405 11:07:39.819135 26038 sgd_solver.cpp:105] Iteration 9096, lr = 1e-05
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I0405 11:07:42.706216 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:07:44.920967 26038 solver.cpp:218] Iteration 9108 (2.35211 iter/s, 5.10179s/12 iters), loss = 5.28458
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I0405 11:07:44.921021 26038 solver.cpp:237] Train net output #0: loss = 5.28458 (* 1 = 5.28458 loss)
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I0405 11:07:44.921030 26038 sgd_solver.cpp:105] Iteration 9108, lr = 1e-05
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I0405 11:07:50.199252 26038 solver.cpp:218] Iteration 9120 (2.27351 iter/s, 5.27819s/12 iters), loss = 5.27501
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I0405 11:07:50.199291 26038 solver.cpp:237] Train net output #0: loss = 5.27501 (* 1 = 5.27501 loss)
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I0405 11:07:50.199296 26038 sgd_solver.cpp:105] Iteration 9120, lr = 1e-05
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I0405 11:07:55.583662 26038 solver.cpp:218] Iteration 9132 (2.22869 iter/s, 5.38433s/12 iters), loss = 5.27196
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I0405 11:07:55.583703 26038 solver.cpp:237] Train net output #0: loss = 5.27196 (* 1 = 5.27196 loss)
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I0405 11:07:55.583709 26038 sgd_solver.cpp:105] Iteration 9132, lr = 1e-05
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I0405 11:08:00.997892 26038 solver.cpp:218] Iteration 9144 (2.21642 iter/s, 5.41414s/12 iters), loss = 5.28427
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I0405 11:08:00.998023 26038 solver.cpp:237] Train net output #0: loss = 5.28427 (* 1 = 5.28427 loss)
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I0405 11:08:00.998031 26038 sgd_solver.cpp:105] Iteration 9144, lr = 1e-05
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I0405 11:08:06.355087 26038 solver.cpp:218] Iteration 9156 (2.24005 iter/s, 5.35703s/12 iters), loss = 5.2831
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I0405 11:08:06.355127 26038 solver.cpp:237] Train net output #0: loss = 5.2831 (* 1 = 5.2831 loss)
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I0405 11:08:06.355132 26038 sgd_solver.cpp:105] Iteration 9156, lr = 1e-05
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I0405 11:08:11.670586 26038 solver.cpp:218] Iteration 9168 (2.25758 iter/s, 5.31542s/12 iters), loss = 5.27416
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I0405 11:08:11.670629 26038 solver.cpp:237] Train net output #0: loss = 5.27416 (* 1 = 5.27416 loss)
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I0405 11:08:11.670634 26038 sgd_solver.cpp:105] Iteration 9168, lr = 1e-05
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I0405 11:08:16.439734 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
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||
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I0405 11:08:19.459749 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
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I0405 11:08:21.781359 26038 solver.cpp:330] Iteration 9180, Testing net (#0)
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I0405 11:08:21.781379 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:08:22.537276 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:08:26.070636 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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I0405 11:08:26.070674 26038 solver.cpp:397] Test net output #1: loss = 5.27898 (* 1 = 5.27898 loss)
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I0405 11:08:26.209641 26038 solver.cpp:218] Iteration 9180 (0.82537 iter/s, 14.5389s/12 iters), loss = 5.28501
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I0405 11:08:26.209687 26038 solver.cpp:237] Train net output #0: loss = 5.28501 (* 1 = 5.28501 loss)
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I0405 11:08:26.209694 26038 sgd_solver.cpp:105] Iteration 9180, lr = 1e-05
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I0405 11:08:30.453904 26038 solver.cpp:218] Iteration 9192 (2.8274 iter/s, 4.24418s/12 iters), loss = 5.27926
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I0405 11:08:30.453944 26038 solver.cpp:237] Train net output #0: loss = 5.27926 (* 1 = 5.27926 loss)
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I0405 11:08:30.453949 26038 sgd_solver.cpp:105] Iteration 9192, lr = 1e-05
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I0405 11:08:35.633714 26038 solver.cpp:218] Iteration 9204 (2.31673 iter/s, 5.17972s/12 iters), loss = 5.26981
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I0405 11:08:35.633878 26038 solver.cpp:237] Train net output #0: loss = 5.26981 (* 1 = 5.26981 loss)
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I0405 11:08:35.633888 26038 sgd_solver.cpp:105] Iteration 9204, lr = 1e-05
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I0405 11:08:35.695808 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:08:41.013605 26038 solver.cpp:218] Iteration 9216 (2.23061 iter/s, 5.37969s/12 iters), loss = 5.26921
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I0405 11:08:41.013646 26038 solver.cpp:237] Train net output #0: loss = 5.26921 (* 1 = 5.26921 loss)
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I0405 11:08:41.013651 26038 sgd_solver.cpp:105] Iteration 9216, lr = 1e-05
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I0405 11:08:46.279278 26038 solver.cpp:218] Iteration 9228 (2.27895 iter/s, 5.26559s/12 iters), loss = 5.26939
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I0405 11:08:46.279320 26038 solver.cpp:237] Train net output #0: loss = 5.26939 (* 1 = 5.26939 loss)
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I0405 11:08:46.279326 26038 sgd_solver.cpp:105] Iteration 9228, lr = 1e-05
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I0405 11:08:51.419896 26038 solver.cpp:218] Iteration 9240 (2.33439 iter/s, 5.14053s/12 iters), loss = 5.27867
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I0405 11:08:51.419939 26038 solver.cpp:237] Train net output #0: loss = 5.27867 (* 1 = 5.27867 loss)
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I0405 11:08:51.419945 26038 sgd_solver.cpp:105] Iteration 9240, lr = 1e-05
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I0405 11:08:56.623592 26038 solver.cpp:218] Iteration 9252 (2.30609 iter/s, 5.20361s/12 iters), loss = 5.28131
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I0405 11:08:56.623646 26038 solver.cpp:237] Train net output #0: loss = 5.28131 (* 1 = 5.28131 loss)
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I0405 11:08:56.623653 26038 sgd_solver.cpp:105] Iteration 9252, lr = 1e-05
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I0405 11:09:02.089020 26038 solver.cpp:218] Iteration 9264 (2.19566 iter/s, 5.46533s/12 iters), loss = 5.28183
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I0405 11:09:02.089069 26038 solver.cpp:237] Train net output #0: loss = 5.28183 (* 1 = 5.28183 loss)
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I0405 11:09:02.089076 26038 sgd_solver.cpp:105] Iteration 9264, lr = 1e-05
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I0405 11:09:07.115491 26038 solver.cpp:218] Iteration 9276 (2.3874 iter/s, 5.02638s/12 iters), loss = 5.27588
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I0405 11:09:07.115597 26038 solver.cpp:237] Train net output #0: loss = 5.27588 (* 1 = 5.27588 loss)
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I0405 11:09:07.115603 26038 sgd_solver.cpp:105] Iteration 9276, lr = 1e-05
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I0405 11:09:09.342661 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
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I0405 11:09:12.304695 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
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I0405 11:09:14.610924 26038 solver.cpp:330] Iteration 9282, Testing net (#0)
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I0405 11:09:14.610944 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:09:15.348946 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:09:19.053927 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:09:19.053966 26038 solver.cpp:397] Test net output #1: loss = 5.27905 (* 1 = 5.27905 loss)
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I0405 11:09:21.131019 26038 solver.cpp:218] Iteration 9288 (0.856204 iter/s, 14.0153s/12 iters), loss = 5.27373
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I0405 11:09:21.131057 26038 solver.cpp:237] Train net output #0: loss = 5.27373 (* 1 = 5.27373 loss)
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I0405 11:09:21.131062 26038 sgd_solver.cpp:105] Iteration 9288, lr = 1e-05
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I0405 11:09:26.404158 26038 solver.cpp:218] Iteration 9300 (2.27572 iter/s, 5.27305s/12 iters), loss = 5.27744
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I0405 11:09:26.404211 26038 solver.cpp:237] Train net output #0: loss = 5.27744 (* 1 = 5.27744 loss)
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I0405 11:09:26.404219 26038 sgd_solver.cpp:105] Iteration 9300, lr = 1e-05
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I0405 11:09:28.762697 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:09:31.615695 26038 solver.cpp:218] Iteration 9312 (2.30262 iter/s, 5.21145s/12 iters), loss = 5.26403
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I0405 11:09:31.615731 26038 solver.cpp:237] Train net output #0: loss = 5.26403 (* 1 = 5.26403 loss)
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I0405 11:09:31.615736 26038 sgd_solver.cpp:105] Iteration 9312, lr = 1e-05
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I0405 11:09:36.812714 26038 solver.cpp:218] Iteration 9324 (2.30905 iter/s, 5.19694s/12 iters), loss = 5.27835
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I0405 11:09:36.812767 26038 solver.cpp:237] Train net output #0: loss = 5.27835 (* 1 = 5.27835 loss)
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I0405 11:09:36.812774 26038 sgd_solver.cpp:105] Iteration 9324, lr = 1e-05
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I0405 11:09:42.000213 26038 solver.cpp:218] Iteration 9336 (2.3133 iter/s, 5.1874s/12 iters), loss = 5.27688
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I0405 11:09:42.000365 26038 solver.cpp:237] Train net output #0: loss = 5.27688 (* 1 = 5.27688 loss)
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I0405 11:09:42.000375 26038 sgd_solver.cpp:105] Iteration 9336, lr = 1e-05
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I0405 11:09:46.948179 26038 solver.cpp:218] Iteration 9348 (2.42533 iter/s, 4.94777s/12 iters), loss = 5.27294
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I0405 11:09:46.948243 26038 solver.cpp:237] Train net output #0: loss = 5.27294 (* 1 = 5.27294 loss)
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I0405 11:09:46.948254 26038 sgd_solver.cpp:105] Iteration 9348, lr = 1e-05
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I0405 11:09:52.371356 26038 solver.cpp:218] Iteration 9360 (2.21277 iter/s, 5.42307s/12 iters), loss = 5.26607
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I0405 11:09:52.371402 26038 solver.cpp:237] Train net output #0: loss = 5.26607 (* 1 = 5.26607 loss)
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I0405 11:09:52.371408 26038 sgd_solver.cpp:105] Iteration 9360, lr = 1e-05
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I0405 11:09:57.820201 26038 solver.cpp:218] Iteration 9372 (2.20234 iter/s, 5.44875s/12 iters), loss = 5.28166
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I0405 11:09:57.820248 26038 solver.cpp:237] Train net output #0: loss = 5.28166 (* 1 = 5.28166 loss)
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I0405 11:09:57.820255 26038 sgd_solver.cpp:105] Iteration 9372, lr = 1e-05
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I0405 11:10:02.578070 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
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||
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I0405 11:10:05.588996 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
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I0405 11:10:07.894109 26038 solver.cpp:330] Iteration 9384, Testing net (#0)
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I0405 11:10:07.894137 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:10:08.617689 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:10:12.325794 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:10:12.325899 26038 solver.cpp:397] Test net output #1: loss = 5.27915 (* 1 = 5.27915 loss)
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I0405 11:10:12.461112 26038 solver.cpp:218] Iteration 9384 (0.819629 iter/s, 14.6408s/12 iters), loss = 5.26597
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I0405 11:10:12.461165 26038 solver.cpp:237] Train net output #0: loss = 5.26597 (* 1 = 5.26597 loss)
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I0405 11:10:12.461174 26038 sgd_solver.cpp:105] Iteration 9384, lr = 1e-05
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I0405 11:10:16.684541 26038 solver.cpp:218] Iteration 9396 (2.84135 iter/s, 4.22334s/12 iters), loss = 5.26425
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I0405 11:10:16.684581 26038 solver.cpp:237] Train net output #0: loss = 5.26425 (* 1 = 5.26425 loss)
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I0405 11:10:16.684587 26038 sgd_solver.cpp:105] Iteration 9396, lr = 1e-05
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I0405 11:10:21.234557 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:10:21.965963 26038 solver.cpp:218] Iteration 9408 (2.27215 iter/s, 5.28134s/12 iters), loss = 5.27103
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I0405 11:10:21.966001 26038 solver.cpp:237] Train net output #0: loss = 5.27103 (* 1 = 5.27103 loss)
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I0405 11:10:21.966006 26038 sgd_solver.cpp:105] Iteration 9408, lr = 1e-05
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I0405 11:10:27.343498 26038 solver.cpp:218] Iteration 9420 (2.23154 iter/s, 5.37745s/12 iters), loss = 5.26039
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I0405 11:10:27.343554 26038 solver.cpp:237] Train net output #0: loss = 5.26039 (* 1 = 5.26039 loss)
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I0405 11:10:27.343560 26038 sgd_solver.cpp:105] Iteration 9420, lr = 1e-05
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I0405 11:10:32.770953 26038 solver.cpp:218] Iteration 9432 (2.21102 iter/s, 5.42736s/12 iters), loss = 5.29941
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I0405 11:10:32.770998 26038 solver.cpp:237] Train net output #0: loss = 5.29941 (* 1 = 5.29941 loss)
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I0405 11:10:32.771003 26038 sgd_solver.cpp:105] Iteration 9432, lr = 1e-05
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I0405 11:10:37.912041 26038 solver.cpp:218] Iteration 9444 (2.33417 iter/s, 5.141s/12 iters), loss = 5.2883
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I0405 11:10:37.912082 26038 solver.cpp:237] Train net output #0: loss = 5.2883 (* 1 = 5.2883 loss)
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I0405 11:10:37.912087 26038 sgd_solver.cpp:105] Iteration 9444, lr = 1e-05
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I0405 11:10:43.237182 26038 solver.cpp:218] Iteration 9456 (2.2535 iter/s, 5.32506s/12 iters), loss = 5.28749
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I0405 11:10:43.237345 26038 solver.cpp:237] Train net output #0: loss = 5.28749 (* 1 = 5.28749 loss)
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I0405 11:10:43.237355 26038 sgd_solver.cpp:105] Iteration 9456, lr = 1e-05
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I0405 11:10:48.534683 26038 solver.cpp:218] Iteration 9468 (2.2653 iter/s, 5.2973s/12 iters), loss = 5.25563
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I0405 11:10:48.534718 26038 solver.cpp:237] Train net output #0: loss = 5.25563 (* 1 = 5.25563 loss)
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I0405 11:10:48.534723 26038 sgd_solver.cpp:105] Iteration 9468, lr = 1e-05
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I0405 11:10:53.666966 26038 solver.cpp:218] Iteration 9480 (2.33818 iter/s, 5.1322s/12 iters), loss = 5.28619
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I0405 11:10:53.667012 26038 solver.cpp:237] Train net output #0: loss = 5.28619 (* 1 = 5.28619 loss)
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I0405 11:10:53.667019 26038 sgd_solver.cpp:105] Iteration 9480, lr = 1e-05
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I0405 11:10:55.647095 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
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||
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I0405 11:10:58.688364 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
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I0405 11:11:00.991672 26038 solver.cpp:330] Iteration 9486, Testing net (#0)
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I0405 11:11:00.991693 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:11:01.637209 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:11:05.561069 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:11:05.561106 26038 solver.cpp:397] Test net output #1: loss = 5.27901 (* 1 = 5.27901 loss)
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I0405 11:11:07.358388 26038 solver.cpp:218] Iteration 9492 (0.876469 iter/s, 13.6913s/12 iters), loss = 5.2824
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I0405 11:11:07.358440 26038 solver.cpp:237] Train net output #0: loss = 5.2824 (* 1 = 5.2824 loss)
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I0405 11:11:07.358448 26038 sgd_solver.cpp:105] Iteration 9492, lr = 1e-05
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I0405 11:11:12.838843 26038 solver.cpp:218] Iteration 9504 (2.18964 iter/s, 5.48036s/12 iters), loss = 5.27312
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I0405 11:11:12.838889 26038 solver.cpp:237] Train net output #0: loss = 5.27312 (* 1 = 5.27312 loss)
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I0405 11:11:12.838896 26038 sgd_solver.cpp:105] Iteration 9504, lr = 1e-05
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I0405 11:11:14.423307 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:11:18.183544 26038 solver.cpp:218] Iteration 9516 (2.24525 iter/s, 5.34462s/12 iters), loss = 5.28675
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I0405 11:11:18.183581 26038 solver.cpp:237] Train net output #0: loss = 5.28675 (* 1 = 5.28675 loss)
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I0405 11:11:18.183586 26038 sgd_solver.cpp:105] Iteration 9516, lr = 1e-05
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I0405 11:11:23.343633 26038 solver.cpp:218] Iteration 9528 (2.32558 iter/s, 5.16001s/12 iters), loss = 5.27277
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I0405 11:11:23.343675 26038 solver.cpp:237] Train net output #0: loss = 5.27277 (* 1 = 5.27277 loss)
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I0405 11:11:23.343681 26038 sgd_solver.cpp:105] Iteration 9528, lr = 1e-05
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I0405 11:11:28.536950 26038 solver.cpp:218] Iteration 9540 (2.3107 iter/s, 5.19323s/12 iters), loss = 5.27216
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I0405 11:11:28.537001 26038 solver.cpp:237] Train net output #0: loss = 5.27216 (* 1 = 5.27216 loss)
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I0405 11:11:28.537009 26038 sgd_solver.cpp:105] Iteration 9540, lr = 1e-05
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I0405 11:11:33.806075 26038 solver.cpp:218] Iteration 9552 (2.27746 iter/s, 5.26904s/12 iters), loss = 5.2729
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I0405 11:11:33.806113 26038 solver.cpp:237] Train net output #0: loss = 5.2729 (* 1 = 5.2729 loss)
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||
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I0405 11:11:33.806118 26038 sgd_solver.cpp:105] Iteration 9552, lr = 1e-05
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I0405 11:11:38.983865 26038 solver.cpp:218] Iteration 9564 (2.31763 iter/s, 5.17771s/12 iters), loss = 5.2805
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I0405 11:11:38.983919 26038 solver.cpp:237] Train net output #0: loss = 5.2805 (* 1 = 5.2805 loss)
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I0405 11:11:38.983927 26038 sgd_solver.cpp:105] Iteration 9564, lr = 1e-05
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I0405 11:11:44.219197 26038 solver.cpp:218] Iteration 9576 (2.29216 iter/s, 5.23523s/12 iters), loss = 5.2773
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I0405 11:11:44.219249 26038 solver.cpp:237] Train net output #0: loss = 5.2773 (* 1 = 5.2773 loss)
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I0405 11:11:44.219256 26038 sgd_solver.cpp:105] Iteration 9576, lr = 1e-05
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||
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I0405 11:11:48.924099 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
|
||
|
|
I0405 11:11:52.013850 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
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||
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I0405 11:11:54.376677 26038 solver.cpp:330] Iteration 9588, Testing net (#0)
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||
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I0405 11:11:54.376696 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
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I0405 11:11:55.025107 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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|
I0405 11:11:58.804112 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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I0405 11:11:58.804150 26038 solver.cpp:397] Test net output #1: loss = 5.27932 (* 1 = 5.27932 loss)
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||
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I0405 11:11:58.945609 26038 solver.cpp:218] Iteration 9588 (0.81487 iter/s, 14.7263s/12 iters), loss = 5.2823
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I0405 11:11:58.945657 26038 solver.cpp:237] Train net output #0: loss = 5.2823 (* 1 = 5.2823 loss)
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I0405 11:11:58.945664 26038 sgd_solver.cpp:105] Iteration 9588, lr = 1e-05
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I0405 11:12:03.109700 26038 solver.cpp:218] Iteration 9600 (2.88184 iter/s, 4.164s/12 iters), loss = 5.28613
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I0405 11:12:03.109747 26038 solver.cpp:237] Train net output #0: loss = 5.28613 (* 1 = 5.28613 loss)
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I0405 11:12:03.109753 26038 sgd_solver.cpp:105] Iteration 9600, lr = 1e-05
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||
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I0405 11:12:06.837586 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:12:08.400928 26038 solver.cpp:218] Iteration 9612 (2.26794 iter/s, 5.29114s/12 iters), loss = 5.25966
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I0405 11:12:08.400972 26038 solver.cpp:237] Train net output #0: loss = 5.25966 (* 1 = 5.25966 loss)
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I0405 11:12:08.400977 26038 sgd_solver.cpp:105] Iteration 9612, lr = 1e-05
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I0405 11:12:13.585855 26038 solver.cpp:218] Iteration 9624 (2.31444 iter/s, 5.18484s/12 iters), loss = 5.26136
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I0405 11:12:13.585896 26038 solver.cpp:237] Train net output #0: loss = 5.26136 (* 1 = 5.26136 loss)
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I0405 11:12:13.585902 26038 sgd_solver.cpp:105] Iteration 9624, lr = 1e-05
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I0405 11:12:18.875362 26038 solver.cpp:218] Iteration 9636 (2.26868 iter/s, 5.28942s/12 iters), loss = 5.26662
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I0405 11:12:18.875408 26038 solver.cpp:237] Train net output #0: loss = 5.26662 (* 1 = 5.26662 loss)
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I0405 11:12:18.875416 26038 sgd_solver.cpp:105] Iteration 9636, lr = 1e-05
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I0405 11:12:24.186087 26038 solver.cpp:218] Iteration 9648 (2.25962 iter/s, 5.31063s/12 iters), loss = 5.27823
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I0405 11:12:24.186198 26038 solver.cpp:237] Train net output #0: loss = 5.27823 (* 1 = 5.27823 loss)
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||
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I0405 11:12:24.186206 26038 sgd_solver.cpp:105] Iteration 9648, lr = 1e-05
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||
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I0405 11:12:29.476994 26038 solver.cpp:218] Iteration 9660 (2.26811 iter/s, 5.29076s/12 iters), loss = 5.28673
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||
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I0405 11:12:29.477036 26038 solver.cpp:237] Train net output #0: loss = 5.28673 (* 1 = 5.28673 loss)
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||
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I0405 11:12:29.477042 26038 sgd_solver.cpp:105] Iteration 9660, lr = 1e-05
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||
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I0405 11:12:34.610877 26038 solver.cpp:218] Iteration 9672 (2.33745 iter/s, 5.1338s/12 iters), loss = 5.28036
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||
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I0405 11:12:34.610914 26038 solver.cpp:237] Train net output #0: loss = 5.28036 (* 1 = 5.28036 loss)
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||
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I0405 11:12:34.610919 26038 sgd_solver.cpp:105] Iteration 9672, lr = 1e-05
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I0405 11:12:39.943004 26038 solver.cpp:218] Iteration 9684 (2.25054 iter/s, 5.33204s/12 iters), loss = 5.27694
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I0405 11:12:39.943048 26038 solver.cpp:237] Train net output #0: loss = 5.27694 (* 1 = 5.27694 loss)
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||
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I0405 11:12:39.943054 26038 sgd_solver.cpp:105] Iteration 9684, lr = 1e-05
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||
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I0405 11:12:42.046695 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
|
||
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I0405 11:12:45.104797 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
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||
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I0405 11:12:47.408579 26038 solver.cpp:330] Iteration 9690, Testing net (#0)
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||
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I0405 11:12:47.408599 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 11:12:48.000368 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:12:50.757092 26038 blocking_queue.cpp:49] Waiting for data
|
||
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I0405 11:12:51.731775 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:12:51.731810 26038 solver.cpp:397] Test net output #1: loss = 5.27934 (* 1 = 5.27934 loss)
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I0405 11:12:53.672983 26038 solver.cpp:218] Iteration 9696 (0.874008 iter/s, 13.7299s/12 iters), loss = 5.27137
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I0405 11:12:53.673034 26038 solver.cpp:237] Train net output #0: loss = 5.27137 (* 1 = 5.27137 loss)
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I0405 11:12:53.673043 26038 sgd_solver.cpp:105] Iteration 9696, lr = 1e-05
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I0405 11:12:58.840507 26038 solver.cpp:218] Iteration 9708 (2.32224 iter/s, 5.16743s/12 iters), loss = 5.27478
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I0405 11:12:58.840636 26038 solver.cpp:237] Train net output #0: loss = 5.27478 (* 1 = 5.27478 loss)
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I0405 11:12:58.840643 26038 sgd_solver.cpp:105] Iteration 9708, lr = 1e-05
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I0405 11:12:59.652762 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:13:04.251154 26038 solver.cpp:218] Iteration 9720 (2.21792 iter/s, 5.41048s/12 iters), loss = 5.27374
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I0405 11:13:04.251206 26038 solver.cpp:237] Train net output #0: loss = 5.27374 (* 1 = 5.27374 loss)
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I0405 11:13:04.251214 26038 sgd_solver.cpp:105] Iteration 9720, lr = 1e-05
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I0405 11:13:09.544646 26038 solver.cpp:218] Iteration 9732 (2.26697 iter/s, 5.2934s/12 iters), loss = 5.26437
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I0405 11:13:09.544688 26038 solver.cpp:237] Train net output #0: loss = 5.26437 (* 1 = 5.26437 loss)
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I0405 11:13:09.544693 26038 sgd_solver.cpp:105] Iteration 9732, lr = 1e-05
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I0405 11:13:14.899163 26038 solver.cpp:218] Iteration 9744 (2.24113 iter/s, 5.35443s/12 iters), loss = 5.27615
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I0405 11:13:14.899217 26038 solver.cpp:237] Train net output #0: loss = 5.27615 (* 1 = 5.27615 loss)
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I0405 11:13:14.899226 26038 sgd_solver.cpp:105] Iteration 9744, lr = 1e-05
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I0405 11:13:20.368103 26038 solver.cpp:218] Iteration 9756 (2.19425 iter/s, 5.46884s/12 iters), loss = 5.26491
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I0405 11:13:20.368145 26038 solver.cpp:237] Train net output #0: loss = 5.26491 (* 1 = 5.26491 loss)
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I0405 11:13:20.368151 26038 sgd_solver.cpp:105] Iteration 9756, lr = 1e-05
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I0405 11:13:25.580354 26038 solver.cpp:218] Iteration 9768 (2.30231 iter/s, 5.21217s/12 iters), loss = 5.30656
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I0405 11:13:25.580392 26038 solver.cpp:237] Train net output #0: loss = 5.30656 (* 1 = 5.30656 loss)
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I0405 11:13:25.580397 26038 sgd_solver.cpp:105] Iteration 9768, lr = 1e-05
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I0405 11:13:30.849038 26038 solver.cpp:218] Iteration 9780 (2.27765 iter/s, 5.2686s/12 iters), loss = 5.27629
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I0405 11:13:30.849164 26038 solver.cpp:237] Train net output #0: loss = 5.27629 (* 1 = 5.27629 loss)
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I0405 11:13:30.849174 26038 sgd_solver.cpp:105] Iteration 9780, lr = 1e-05
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I0405 11:13:35.729274 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
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I0405 11:13:39.199401 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
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I0405 11:13:41.517876 26038 solver.cpp:330] Iteration 9792, Testing net (#0)
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I0405 11:13:41.517896 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:13:42.037106 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:13:45.846946 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:13:45.846979 26038 solver.cpp:397] Test net output #1: loss = 5.27917 (* 1 = 5.27917 loss)
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I0405 11:13:45.988801 26038 solver.cpp:218] Iteration 9792 (0.792626 iter/s, 15.1396s/12 iters), loss = 5.2831
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I0405 11:13:45.988848 26038 solver.cpp:237] Train net output #0: loss = 5.2831 (* 1 = 5.2831 loss)
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I0405 11:13:45.988855 26038 sgd_solver.cpp:105] Iteration 9792, lr = 1e-05
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I0405 11:13:50.390125 26038 solver.cpp:218] Iteration 9804 (2.72651 iter/s, 4.40123s/12 iters), loss = 5.2875
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I0405 11:13:50.390180 26038 solver.cpp:237] Train net output #0: loss = 5.2875 (* 1 = 5.2875 loss)
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I0405 11:13:50.390188 26038 sgd_solver.cpp:105] Iteration 9804, lr = 1e-05
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I0405 11:13:53.558986 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:13:55.639762 26038 solver.cpp:218] Iteration 9816 (2.28592 iter/s, 5.24954s/12 iters), loss = 5.27119
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I0405 11:13:55.639822 26038 solver.cpp:237] Train net output #0: loss = 5.27119 (* 1 = 5.27119 loss)
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I0405 11:13:55.639830 26038 sgd_solver.cpp:105] Iteration 9816, lr = 1e-05
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I0405 11:14:01.116482 26038 solver.cpp:218] Iteration 9828 (2.19113 iter/s, 5.47662s/12 iters), loss = 5.2709
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I0405 11:14:01.116586 26038 solver.cpp:237] Train net output #0: loss = 5.2709 (* 1 = 5.2709 loss)
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I0405 11:14:01.116593 26038 sgd_solver.cpp:105] Iteration 9828, lr = 1e-05
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I0405 11:14:06.578917 26038 solver.cpp:218] Iteration 9840 (2.19688 iter/s, 5.46229s/12 iters), loss = 5.2856
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I0405 11:14:06.578961 26038 solver.cpp:237] Train net output #0: loss = 5.2856 (* 1 = 5.2856 loss)
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I0405 11:14:06.578966 26038 sgd_solver.cpp:105] Iteration 9840, lr = 1e-05
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I0405 11:14:11.925379 26038 solver.cpp:218] Iteration 9852 (2.24451 iter/s, 5.34637s/12 iters), loss = 5.28374
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I0405 11:14:11.925418 26038 solver.cpp:237] Train net output #0: loss = 5.28374 (* 1 = 5.28374 loss)
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I0405 11:14:11.925424 26038 sgd_solver.cpp:105] Iteration 9852, lr = 1e-05
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I0405 11:14:17.225982 26038 solver.cpp:218] Iteration 9864 (2.26393 iter/s, 5.30052s/12 iters), loss = 5.27689
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I0405 11:14:17.226019 26038 solver.cpp:237] Train net output #0: loss = 5.27689 (* 1 = 5.27689 loss)
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I0405 11:14:17.226024 26038 sgd_solver.cpp:105] Iteration 9864, lr = 1e-05
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I0405 11:14:22.267640 26038 solver.cpp:218] Iteration 9876 (2.38021 iter/s, 5.04158s/12 iters), loss = 5.28098
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I0405 11:14:22.267688 26038 solver.cpp:237] Train net output #0: loss = 5.28098 (* 1 = 5.28098 loss)
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I0405 11:14:22.267696 26038 sgd_solver.cpp:105] Iteration 9876, lr = 1e-05
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I0405 11:14:27.608639 26038 solver.cpp:218] Iteration 9888 (2.24681 iter/s, 5.3409s/12 iters), loss = 5.28813
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I0405 11:14:27.608693 26038 solver.cpp:237] Train net output #0: loss = 5.28813 (* 1 = 5.28813 loss)
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I0405 11:14:27.608701 26038 sgd_solver.cpp:105] Iteration 9888, lr = 1e-05
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I0405 11:14:29.722198 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
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I0405 11:14:33.037140 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
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I0405 11:14:35.418268 26038 solver.cpp:330] Iteration 9894, Testing net (#0)
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I0405 11:14:35.418292 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:14:35.957147 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:14:39.987237 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:14:39.987272 26038 solver.cpp:397] Test net output #1: loss = 5.27932 (* 1 = 5.27932 loss)
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I0405 11:14:41.932355 26038 solver.cpp:218] Iteration 9900 (0.837779 iter/s, 14.3236s/12 iters), loss = 5.2869
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I0405 11:14:41.932406 26038 solver.cpp:237] Train net output #0: loss = 5.2869 (* 1 = 5.2869 loss)
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I0405 11:14:41.932415 26038 sgd_solver.cpp:105] Iteration 9900, lr = 1e-05
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I0405 11:14:47.193477 26038 solver.cpp:218] Iteration 9912 (2.28092 iter/s, 5.26103s/12 iters), loss = 5.28115
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I0405 11:14:47.193521 26038 solver.cpp:237] Train net output #0: loss = 5.28115 (* 1 = 5.28115 loss)
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I0405 11:14:47.193526 26038 sgd_solver.cpp:105] Iteration 9912, lr = 1e-05
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I0405 11:14:47.292819 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:14:52.582600 26038 solver.cpp:218] Iteration 9924 (2.22674 iter/s, 5.38904s/12 iters), loss = 5.26401
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I0405 11:14:52.582643 26038 solver.cpp:237] Train net output #0: loss = 5.26401 (* 1 = 5.26401 loss)
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I0405 11:14:52.582648 26038 sgd_solver.cpp:105] Iteration 9924, lr = 1e-05
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I0405 11:14:57.973985 26038 solver.cpp:218] Iteration 9936 (2.22581 iter/s, 5.3913s/12 iters), loss = 5.26135
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I0405 11:14:57.974025 26038 solver.cpp:237] Train net output #0: loss = 5.26135 (* 1 = 5.26135 loss)
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I0405 11:14:57.974030 26038 sgd_solver.cpp:105] Iteration 9936, lr = 1e-05
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I0405 11:15:03.278291 26038 solver.cpp:218] Iteration 9948 (2.26235 iter/s, 5.30422s/12 iters), loss = 5.26789
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I0405 11:15:03.278417 26038 solver.cpp:237] Train net output #0: loss = 5.26789 (* 1 = 5.26789 loss)
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I0405 11:15:03.278424 26038 sgd_solver.cpp:105] Iteration 9948, lr = 1e-05
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I0405 11:15:08.605402 26038 solver.cpp:218] Iteration 9960 (2.2527 iter/s, 5.32694s/12 iters), loss = 5.28286
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I0405 11:15:08.605453 26038 solver.cpp:237] Train net output #0: loss = 5.28286 (* 1 = 5.28286 loss)
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I0405 11:15:08.605460 26038 sgd_solver.cpp:105] Iteration 9960, lr = 1e-05
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I0405 11:15:13.931346 26038 solver.cpp:218] Iteration 9972 (2.25316 iter/s, 5.32585s/12 iters), loss = 5.27252
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I0405 11:15:13.931393 26038 solver.cpp:237] Train net output #0: loss = 5.27252 (* 1 = 5.27252 loss)
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I0405 11:15:13.931401 26038 sgd_solver.cpp:105] Iteration 9972, lr = 1e-05
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I0405 11:15:19.076691 26038 solver.cpp:218] Iteration 9984 (2.33224 iter/s, 5.14526s/12 iters), loss = 5.26958
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I0405 11:15:19.076738 26038 solver.cpp:237] Train net output #0: loss = 5.26958 (* 1 = 5.26958 loss)
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I0405 11:15:19.076746 26038 sgd_solver.cpp:105] Iteration 9984, lr = 1e-05
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I0405 11:15:23.772881 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
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I0405 11:15:26.880448 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
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I0405 11:15:29.203476 26038 solver.cpp:330] Iteration 9996, Testing net (#0)
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I0405 11:15:29.203500 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:15:29.633319 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:15:33.684384 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:15:33.684468 26038 solver.cpp:397] Test net output #1: loss = 5.27917 (* 1 = 5.27917 loss)
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I0405 11:15:33.826179 26038 solver.cpp:218] Iteration 9996 (0.813595 iter/s, 14.7494s/12 iters), loss = 5.28471
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I0405 11:15:33.826221 26038 solver.cpp:237] Train net output #0: loss = 5.28471 (* 1 = 5.28471 loss)
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I0405 11:15:33.826226 26038 sgd_solver.cpp:105] Iteration 9996, lr = 1e-05
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I0405 11:15:38.284469 26038 solver.cpp:218] Iteration 10008 (2.69166 iter/s, 4.45821s/12 iters), loss = 5.27095
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I0405 11:15:38.284524 26038 solver.cpp:237] Train net output #0: loss = 5.27095 (* 1 = 5.27095 loss)
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I0405 11:15:38.284534 26038 sgd_solver.cpp:105] Iteration 10008, lr = 1e-05
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I0405 11:15:40.667990 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:15:43.680444 26038 solver.cpp:218] Iteration 10020 (2.22392 iter/s, 5.39587s/12 iters), loss = 5.27201
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I0405 11:15:43.680491 26038 solver.cpp:237] Train net output #0: loss = 5.27201 (* 1 = 5.27201 loss)
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I0405 11:15:43.680498 26038 sgd_solver.cpp:105] Iteration 10020, lr = 1e-05
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I0405 11:15:48.977319 26038 solver.cpp:218] Iteration 10032 (2.26553 iter/s, 5.29678s/12 iters), loss = 5.27102
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I0405 11:15:48.977362 26038 solver.cpp:237] Train net output #0: loss = 5.27102 (* 1 = 5.27102 loss)
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I0405 11:15:48.977368 26038 sgd_solver.cpp:105] Iteration 10032, lr = 1e-05
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I0405 11:15:54.225883 26038 solver.cpp:218] Iteration 10044 (2.28638 iter/s, 5.24848s/12 iters), loss = 5.28346
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I0405 11:15:54.225926 26038 solver.cpp:237] Train net output #0: loss = 5.28346 (* 1 = 5.28346 loss)
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I0405 11:15:54.225931 26038 sgd_solver.cpp:105] Iteration 10044, lr = 1e-05
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I0405 11:15:59.671675 26038 solver.cpp:218] Iteration 10056 (2.20357 iter/s, 5.44571s/12 iters), loss = 5.26715
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I0405 11:15:59.671726 26038 solver.cpp:237] Train net output #0: loss = 5.26715 (* 1 = 5.26715 loss)
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I0405 11:15:59.671734 26038 sgd_solver.cpp:105] Iteration 10056, lr = 1e-05
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I0405 11:16:05.122979 26038 solver.cpp:218] Iteration 10068 (2.20135 iter/s, 5.45121s/12 iters), loss = 5.28213
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I0405 11:16:05.123104 26038 solver.cpp:237] Train net output #0: loss = 5.28213 (* 1 = 5.28213 loss)
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I0405 11:16:05.123114 26038 sgd_solver.cpp:105] Iteration 10068, lr = 1e-05
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I0405 11:16:10.544286 26038 solver.cpp:218] Iteration 10080 (2.21355 iter/s, 5.42115s/12 iters), loss = 5.29839
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I0405 11:16:10.544325 26038 solver.cpp:237] Train net output #0: loss = 5.29839 (* 1 = 5.29839 loss)
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I0405 11:16:10.544332 26038 sgd_solver.cpp:105] Iteration 10080, lr = 1e-05
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I0405 11:16:16.001168 26038 solver.cpp:218] Iteration 10092 (2.19909 iter/s, 5.45679s/12 iters), loss = 5.2896
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I0405 11:16:16.001226 26038 solver.cpp:237] Train net output #0: loss = 5.2896 (* 1 = 5.2896 loss)
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I0405 11:16:16.001235 26038 sgd_solver.cpp:105] Iteration 10092, lr = 1e-05
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I0405 11:16:18.104060 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
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I0405 11:16:21.104382 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
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I0405 11:16:23.401082 26038 solver.cpp:330] Iteration 10098, Testing net (#0)
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I0405 11:16:23.401103 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:16:23.884649 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:16:27.915060 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:16:27.915102 26038 solver.cpp:397] Test net output #1: loss = 5.27923 (* 1 = 5.27923 loss)
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I0405 11:16:29.872805 26038 solver.cpp:218] Iteration 10104 (0.865083 iter/s, 13.8715s/12 iters), loss = 5.26643
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I0405 11:16:29.872846 26038 solver.cpp:237] Train net output #0: loss = 5.26643 (* 1 = 5.26643 loss)
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I0405 11:16:29.872851 26038 sgd_solver.cpp:105] Iteration 10104, lr = 1e-05
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I0405 11:16:34.434722 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:16:35.087898 26038 solver.cpp:218] Iteration 10116 (2.30105 iter/s, 5.21501s/12 iters), loss = 5.26463
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I0405 11:16:35.087935 26038 solver.cpp:237] Train net output #0: loss = 5.26463 (* 1 = 5.26463 loss)
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I0405 11:16:35.087940 26038 sgd_solver.cpp:105] Iteration 10116, lr = 1e-05
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I0405 11:16:40.222548 26038 solver.cpp:218] Iteration 10128 (2.3371 iter/s, 5.13457s/12 iters), loss = 5.26119
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I0405 11:16:40.222681 26038 solver.cpp:237] Train net output #0: loss = 5.26119 (* 1 = 5.26119 loss)
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I0405 11:16:40.222689 26038 sgd_solver.cpp:105] Iteration 10128, lr = 1e-05
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I0405 11:16:45.765280 26038 solver.cpp:218] Iteration 10140 (2.16507 iter/s, 5.54256s/12 iters), loss = 5.2783
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I0405 11:16:45.765321 26038 solver.cpp:237] Train net output #0: loss = 5.2783 (* 1 = 5.2783 loss)
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I0405 11:16:45.765326 26038 sgd_solver.cpp:105] Iteration 10140, lr = 1e-05
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I0405 11:16:51.053755 26038 solver.cpp:218] Iteration 10152 (2.26912 iter/s, 5.28839s/12 iters), loss = 5.28592
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I0405 11:16:51.053802 26038 solver.cpp:237] Train net output #0: loss = 5.28592 (* 1 = 5.28592 loss)
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I0405 11:16:51.053808 26038 sgd_solver.cpp:105] Iteration 10152, lr = 1e-05
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I0405 11:16:56.318245 26038 solver.cpp:218] Iteration 10164 (2.27946 iter/s, 5.2644s/12 iters), loss = 5.2846
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I0405 11:16:56.318300 26038 solver.cpp:237] Train net output #0: loss = 5.2846 (* 1 = 5.2846 loss)
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I0405 11:16:56.318308 26038 sgd_solver.cpp:105] Iteration 10164, lr = 1e-05
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I0405 11:17:01.565285 26038 solver.cpp:218] Iteration 10176 (2.28705 iter/s, 5.24694s/12 iters), loss = 5.26179
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I0405 11:17:01.565344 26038 solver.cpp:237] Train net output #0: loss = 5.26179 (* 1 = 5.26179 loss)
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I0405 11:17:01.565356 26038 sgd_solver.cpp:105] Iteration 10176, lr = 1e-05
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I0405 11:17:06.973430 26038 solver.cpp:218] Iteration 10188 (2.21892 iter/s, 5.40805s/12 iters), loss = 5.28059
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I0405 11:17:06.973470 26038 solver.cpp:237] Train net output #0: loss = 5.28059 (* 1 = 5.28059 loss)
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I0405 11:17:06.973475 26038 sgd_solver.cpp:105] Iteration 10188, lr = 1e-05
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I0405 11:17:11.805670 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
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I0405 11:17:14.868960 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
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I0405 11:17:17.204478 26038 solver.cpp:330] Iteration 10200, Testing net (#0)
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I0405 11:17:17.204501 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:17:17.553520 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:17:21.475515 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:17:21.475550 26038 solver.cpp:397] Test net output #1: loss = 5.27892 (* 1 = 5.27892 loss)
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I0405 11:17:21.615033 26038 solver.cpp:218] Iteration 10200 (0.819589 iter/s, 14.6415s/12 iters), loss = 5.27641
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I0405 11:17:21.615073 26038 solver.cpp:237] Train net output #0: loss = 5.27641 (* 1 = 5.27641 loss)
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I0405 11:17:21.615078 26038 sgd_solver.cpp:105] Iteration 10200, lr = 1e-05
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I0405 11:17:25.935331 26038 solver.cpp:218] Iteration 10212 (2.77763 iter/s, 4.32022s/12 iters), loss = 5.26489
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I0405 11:17:25.935369 26038 solver.cpp:237] Train net output #0: loss = 5.26489 (* 1 = 5.26489 loss)
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I0405 11:17:25.935374 26038 sgd_solver.cpp:105] Iteration 10212, lr = 1e-05
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I0405 11:17:27.549731 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:17:31.313302 26038 solver.cpp:218] Iteration 10224 (2.23136 iter/s, 5.37789s/12 iters), loss = 5.29326
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I0405 11:17:31.313364 26038 solver.cpp:237] Train net output #0: loss = 5.29326 (* 1 = 5.29326 loss)
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I0405 11:17:31.313372 26038 sgd_solver.cpp:105] Iteration 10224, lr = 1e-05
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I0405 11:17:36.578568 26038 solver.cpp:218] Iteration 10236 (2.27913 iter/s, 5.26517s/12 iters), loss = 5.25498
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I0405 11:17:36.578614 26038 solver.cpp:237] Train net output #0: loss = 5.25498 (* 1 = 5.25498 loss)
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I0405 11:17:36.578621 26038 sgd_solver.cpp:105] Iteration 10236, lr = 1e-05
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I0405 11:17:41.688073 26038 solver.cpp:218] Iteration 10248 (2.3486 iter/s, 5.10942s/12 iters), loss = 5.28811
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I0405 11:17:41.688122 26038 solver.cpp:237] Train net output #0: loss = 5.28811 (* 1 = 5.28811 loss)
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I0405 11:17:41.688133 26038 sgd_solver.cpp:105] Iteration 10248, lr = 1e-05
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I0405 11:17:46.969132 26038 solver.cpp:218] Iteration 10260 (2.27231 iter/s, 5.28097s/12 iters), loss = 5.26379
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I0405 11:17:46.969305 26038 solver.cpp:237] Train net output #0: loss = 5.26379 (* 1 = 5.26379 loss)
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I0405 11:17:46.969314 26038 sgd_solver.cpp:105] Iteration 10260, lr = 1e-05
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I0405 11:17:52.370882 26038 solver.cpp:218] Iteration 10272 (2.22159 iter/s, 5.40154s/12 iters), loss = 5.27931
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I0405 11:17:52.370925 26038 solver.cpp:237] Train net output #0: loss = 5.27931 (* 1 = 5.27931 loss)
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I0405 11:17:52.370931 26038 sgd_solver.cpp:105] Iteration 10272, lr = 1e-05
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I0405 11:17:57.853318 26038 solver.cpp:218] Iteration 10284 (2.18884 iter/s, 5.48235s/12 iters), loss = 5.27242
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I0405 11:17:57.853369 26038 solver.cpp:237] Train net output #0: loss = 5.27242 (* 1 = 5.27242 loss)
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I0405 11:17:57.853377 26038 sgd_solver.cpp:105] Iteration 10284, lr = 1e-05
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I0405 11:18:03.121019 26038 solver.cpp:218] Iteration 10296 (2.27807 iter/s, 5.26761s/12 iters), loss = 5.27073
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I0405 11:18:03.121062 26038 solver.cpp:237] Train net output #0: loss = 5.27073 (* 1 = 5.27073 loss)
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I0405 11:18:03.121068 26038 sgd_solver.cpp:105] Iteration 10296, lr = 1e-05
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I0405 11:18:05.211324 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10302.caffemodel
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I0405 11:18:08.226603 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10302.solverstate
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I0405 11:18:10.541460 26038 solver.cpp:330] Iteration 10302, Testing net (#0)
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I0405 11:18:10.541481 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:18:10.931362 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:18:15.033459 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:18:15.033499 26038 solver.cpp:397] Test net output #1: loss = 5.27919 (* 1 = 5.27919 loss)
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I0405 11:18:17.036412 26038 solver.cpp:218] Iteration 10308 (0.862362 iter/s, 13.9153s/12 iters), loss = 5.28777
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I0405 11:18:17.036520 26038 solver.cpp:237] Train net output #0: loss = 5.28777 (* 1 = 5.28777 loss)
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I0405 11:18:17.036530 26038 sgd_solver.cpp:105] Iteration 10308, lr = 1e-05
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I0405 11:18:20.888983 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:18:22.400188 26038 solver.cpp:218] Iteration 10320 (2.23729 iter/s, 5.36363s/12 iters), loss = 5.2821
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I0405 11:18:22.400247 26038 solver.cpp:237] Train net output #0: loss = 5.2821 (* 1 = 5.2821 loss)
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I0405 11:18:22.400256 26038 sgd_solver.cpp:105] Iteration 10320, lr = 1e-05
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I0405 11:18:27.768795 26038 solver.cpp:218] Iteration 10332 (2.23526 iter/s, 5.36851s/12 iters), loss = 5.27135
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I0405 11:18:27.768842 26038 solver.cpp:237] Train net output #0: loss = 5.27135 (* 1 = 5.27135 loss)
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I0405 11:18:27.768848 26038 sgd_solver.cpp:105] Iteration 10332, lr = 1e-05
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I0405 11:18:32.909121 26038 solver.cpp:218] Iteration 10344 (2.33452 iter/s, 5.14023s/12 iters), loss = 5.28304
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I0405 11:18:32.909168 26038 solver.cpp:237] Train net output #0: loss = 5.28304 (* 1 = 5.28304 loss)
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I0405 11:18:32.909175 26038 sgd_solver.cpp:105] Iteration 10344, lr = 1e-05
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I0405 11:18:38.007347 26038 solver.cpp:218] Iteration 10356 (2.3538 iter/s, 5.09814s/12 iters), loss = 5.28036
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I0405 11:18:38.007385 26038 solver.cpp:237] Train net output #0: loss = 5.28036 (* 1 = 5.28036 loss)
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I0405 11:18:38.007390 26038 sgd_solver.cpp:105] Iteration 10356, lr = 1e-05
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I0405 11:18:43.025841 26038 solver.cpp:218] Iteration 10368 (2.39119 iter/s, 5.01841s/12 iters), loss = 5.28456
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I0405 11:18:43.025884 26038 solver.cpp:237] Train net output #0: loss = 5.28456 (* 1 = 5.28456 loss)
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I0405 11:18:43.025889 26038 sgd_solver.cpp:105] Iteration 10368, lr = 1e-05
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I0405 11:18:48.641842 26038 solver.cpp:218] Iteration 10380 (2.13679 iter/s, 5.61591s/12 iters), loss = 5.27183
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I0405 11:18:48.641975 26038 solver.cpp:237] Train net output #0: loss = 5.27183 (* 1 = 5.27183 loss)
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I0405 11:18:48.641984 26038 sgd_solver.cpp:105] Iteration 10380, lr = 1e-05
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I0405 11:18:53.841670 26038 solver.cpp:218] Iteration 10392 (2.30785 iter/s, 5.19965s/12 iters), loss = 5.28805
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I0405 11:18:53.841728 26038 solver.cpp:237] Train net output #0: loss = 5.28805 (* 1 = 5.28805 loss)
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I0405 11:18:53.841738 26038 sgd_solver.cpp:105] Iteration 10392, lr = 1e-05
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I0405 11:18:58.493104 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10404.caffemodel
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I0405 11:19:01.582458 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10404.solverstate
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I0405 11:19:03.887989 26038 solver.cpp:330] Iteration 10404, Testing net (#0)
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I0405 11:19:03.888013 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:19:04.176964 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:19:04.645409 26038 blocking_queue.cpp:49] Waiting for data
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I0405 11:19:08.181116 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:19:08.181151 26038 solver.cpp:397] Test net output #1: loss = 5.27912 (* 1 = 5.27912 loss)
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I0405 11:19:08.321750 26038 solver.cpp:218] Iteration 10404 (0.828733 iter/s, 14.4799s/12 iters), loss = 5.26312
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I0405 11:19:08.321802 26038 solver.cpp:237] Train net output #0: loss = 5.26312 (* 1 = 5.26312 loss)
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I0405 11:19:08.321810 26038 sgd_solver.cpp:105] Iteration 10404, lr = 1e-05
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I0405 11:19:12.615389 26038 solver.cpp:218] Iteration 10416 (2.79489 iter/s, 4.29355s/12 iters), loss = 5.28549
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I0405 11:19:12.615437 26038 solver.cpp:237] Train net output #0: loss = 5.28549 (* 1 = 5.28549 loss)
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I0405 11:19:12.615442 26038 sgd_solver.cpp:105] Iteration 10416, lr = 1e-05
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I0405 11:19:13.567239 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:19:18.047237 26038 solver.cpp:218] Iteration 10428 (2.20923 iter/s, 5.43176s/12 iters), loss = 5.28812
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I0405 11:19:18.047278 26038 solver.cpp:237] Train net output #0: loss = 5.28812 (* 1 = 5.28812 loss)
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I0405 11:19:18.047284 26038 sgd_solver.cpp:105] Iteration 10428, lr = 1e-05
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I0405 11:19:23.397101 26038 solver.cpp:218] Iteration 10440 (2.24309 iter/s, 5.34977s/12 iters), loss = 5.27353
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I0405 11:19:23.397231 26038 solver.cpp:237] Train net output #0: loss = 5.27353 (* 1 = 5.27353 loss)
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I0405 11:19:23.397241 26038 sgd_solver.cpp:105] Iteration 10440, lr = 1e-05
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I0405 11:19:28.928520 26038 solver.cpp:218] Iteration 10452 (2.16949 iter/s, 5.53125s/12 iters), loss = 5.27095
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I0405 11:19:28.928563 26038 solver.cpp:237] Train net output #0: loss = 5.27095 (* 1 = 5.27095 loss)
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I0405 11:19:28.928570 26038 sgd_solver.cpp:105] Iteration 10452, lr = 1e-05
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I0405 11:19:34.050755 26038 solver.cpp:218] Iteration 10464 (2.34277 iter/s, 5.12215s/12 iters), loss = 5.29282
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I0405 11:19:34.050796 26038 solver.cpp:237] Train net output #0: loss = 5.29282 (* 1 = 5.29282 loss)
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I0405 11:19:34.050802 26038 sgd_solver.cpp:105] Iteration 10464, lr = 1e-05
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I0405 11:19:39.537182 26038 solver.cpp:218] Iteration 10476 (2.18725 iter/s, 5.48634s/12 iters), loss = 5.27388
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I0405 11:19:39.537222 26038 solver.cpp:237] Train net output #0: loss = 5.27388 (* 1 = 5.27388 loss)
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I0405 11:19:39.537227 26038 sgd_solver.cpp:105] Iteration 10476, lr = 1e-05
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I0405 11:19:44.965900 26038 solver.cpp:218] Iteration 10488 (2.2105 iter/s, 5.42863s/12 iters), loss = 5.27309
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I0405 11:19:44.965950 26038 solver.cpp:237] Train net output #0: loss = 5.27309 (* 1 = 5.27309 loss)
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I0405 11:19:44.965960 26038 sgd_solver.cpp:105] Iteration 10488, lr = 1e-05
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I0405 11:19:50.348181 26038 solver.cpp:218] Iteration 10500 (2.22958 iter/s, 5.38219s/12 iters), loss = 5.27544
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I0405 11:19:50.348220 26038 solver.cpp:237] Train net output #0: loss = 5.27544 (* 1 = 5.27544 loss)
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I0405 11:19:50.348227 26038 sgd_solver.cpp:105] Iteration 10500, lr = 1e-05
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I0405 11:19:52.425355 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10506.caffemodel
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||
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I0405 11:19:55.432993 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10506.solverstate
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I0405 11:19:57.736289 26038 solver.cpp:330] Iteration 10506, Testing net (#0)
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I0405 11:19:57.736307 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:19:58.011977 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:20:02.136857 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:20:02.136914 26038 solver.cpp:397] Test net output #1: loss = 5.27933 (* 1 = 5.27933 loss)
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I0405 11:20:03.990159 26038 solver.cpp:218] Iteration 10512 (0.879646 iter/s, 13.6419s/12 iters), loss = 5.28099
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I0405 11:20:03.990206 26038 solver.cpp:237] Train net output #0: loss = 5.28099 (* 1 = 5.28099 loss)
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I0405 11:20:03.990212 26038 sgd_solver.cpp:105] Iteration 10512, lr = 1e-05
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I0405 11:20:07.149140 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:20:09.190472 26038 solver.cpp:218] Iteration 10524 (2.30759 iter/s, 5.20022s/12 iters), loss = 5.27762
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I0405 11:20:09.190516 26038 solver.cpp:237] Train net output #0: loss = 5.27762 (* 1 = 5.27762 loss)
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I0405 11:20:09.190523 26038 sgd_solver.cpp:105] Iteration 10524, lr = 1e-05
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I0405 11:20:14.447026 26038 solver.cpp:218] Iteration 10536 (2.2829 iter/s, 5.25647s/12 iters), loss = 5.27564
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I0405 11:20:14.447067 26038 solver.cpp:237] Train net output #0: loss = 5.27564 (* 1 = 5.27564 loss)
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I0405 11:20:14.447072 26038 sgd_solver.cpp:105] Iteration 10536, lr = 1e-05
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I0405 11:20:19.836748 26038 solver.cpp:218] Iteration 10548 (2.2265 iter/s, 5.38963s/12 iters), loss = 5.28779
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I0405 11:20:19.836799 26038 solver.cpp:237] Train net output #0: loss = 5.28779 (* 1 = 5.28779 loss)
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I0405 11:20:19.836807 26038 sgd_solver.cpp:105] Iteration 10548, lr = 1e-05
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I0405 11:20:25.114583 26038 solver.cpp:218] Iteration 10560 (2.2737 iter/s, 5.27774s/12 iters), loss = 5.29393
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I0405 11:20:25.114627 26038 solver.cpp:237] Train net output #0: loss = 5.29393 (* 1 = 5.29393 loss)
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I0405 11:20:25.114634 26038 sgd_solver.cpp:105] Iteration 10560, lr = 1e-05
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I0405 11:20:30.498514 26038 solver.cpp:218] Iteration 10572 (2.22889 iter/s, 5.38385s/12 iters), loss = 5.27895
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I0405 11:20:30.498617 26038 solver.cpp:237] Train net output #0: loss = 5.27895 (* 1 = 5.27895 loss)
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I0405 11:20:30.498626 26038 sgd_solver.cpp:105] Iteration 10572, lr = 1e-05
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I0405 11:20:35.811867 26038 solver.cpp:218] Iteration 10584 (2.25852 iter/s, 5.31321s/12 iters), loss = 5.28737
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I0405 11:20:35.811915 26038 solver.cpp:237] Train net output #0: loss = 5.28737 (* 1 = 5.28737 loss)
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I0405 11:20:35.811923 26038 sgd_solver.cpp:105] Iteration 10584, lr = 1e-05
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I0405 11:20:41.155493 26038 solver.cpp:218] Iteration 10596 (2.2457 iter/s, 5.34354s/12 iters), loss = 5.27278
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I0405 11:20:41.155534 26038 solver.cpp:237] Train net output #0: loss = 5.27278 (* 1 = 5.27278 loss)
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I0405 11:20:41.155539 26038 sgd_solver.cpp:105] Iteration 10596, lr = 1e-05
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I0405 11:20:45.913478 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10608.caffemodel
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||
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I0405 11:20:48.942813 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10608.solverstate
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I0405 11:20:51.271224 26038 solver.cpp:330] Iteration 10608, Testing net (#0)
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I0405 11:20:51.271246 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 11:20:51.509092 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:20:55.737932 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:20:55.737963 26038 solver.cpp:397] Test net output #1: loss = 5.27937 (* 1 = 5.27937 loss)
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I0405 11:20:55.877897 26038 solver.cpp:218] Iteration 10608 (0.815092 iter/s, 14.7223s/12 iters), loss = 5.27375
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I0405 11:20:55.877969 26038 solver.cpp:237] Train net output #0: loss = 5.27375 (* 1 = 5.27375 loss)
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I0405 11:20:55.877977 26038 sgd_solver.cpp:105] Iteration 10608, lr = 1e-05
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I0405 11:21:00.332444 26038 solver.cpp:218] Iteration 10620 (2.69395 iter/s, 4.45443s/12 iters), loss = 5.28709
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I0405 11:21:00.332504 26038 solver.cpp:237] Train net output #0: loss = 5.28709 (* 1 = 5.28709 loss)
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I0405 11:21:00.332511 26038 sgd_solver.cpp:105] Iteration 10620, lr = 1e-05
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I0405 11:21:00.450286 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:21:05.695283 26038 solver.cpp:218] Iteration 10632 (2.23766 iter/s, 5.36273s/12 iters), loss = 5.27583
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I0405 11:21:05.695461 26038 solver.cpp:237] Train net output #0: loss = 5.27583 (* 1 = 5.27583 loss)
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I0405 11:21:05.695470 26038 sgd_solver.cpp:105] Iteration 10632, lr = 1e-05
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I0405 11:21:10.954090 26038 solver.cpp:218] Iteration 10644 (2.28198 iter/s, 5.25859s/12 iters), loss = 5.27653
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I0405 11:21:10.954149 26038 solver.cpp:237] Train net output #0: loss = 5.27653 (* 1 = 5.27653 loss)
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I0405 11:21:10.954157 26038 sgd_solver.cpp:105] Iteration 10644, lr = 1e-05
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I0405 11:21:16.296494 26038 solver.cpp:218] Iteration 10656 (2.24622 iter/s, 5.3423s/12 iters), loss = 5.27596
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I0405 11:21:16.296546 26038 solver.cpp:237] Train net output #0: loss = 5.27596 (* 1 = 5.27596 loss)
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I0405 11:21:16.296555 26038 sgd_solver.cpp:105] Iteration 10656, lr = 1e-05
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I0405 11:21:21.696686 26038 solver.cpp:218] Iteration 10668 (2.22218 iter/s, 5.40009s/12 iters), loss = 5.27041
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I0405 11:21:21.696730 26038 solver.cpp:237] Train net output #0: loss = 5.27041 (* 1 = 5.27041 loss)
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I0405 11:21:21.696738 26038 sgd_solver.cpp:105] Iteration 10668, lr = 1e-05
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I0405 11:21:27.183657 26038 solver.cpp:218] Iteration 10680 (2.18703 iter/s, 5.48688s/12 iters), loss = 5.27123
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I0405 11:21:27.183715 26038 solver.cpp:237] Train net output #0: loss = 5.27123 (* 1 = 5.27123 loss)
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I0405 11:21:27.183723 26038 sgd_solver.cpp:105] Iteration 10680, lr = 1e-05
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I0405 11:21:32.621554 26038 solver.cpp:218] Iteration 10692 (2.20678 iter/s, 5.4378s/12 iters), loss = 5.26759
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I0405 11:21:32.621595 26038 solver.cpp:237] Train net output #0: loss = 5.26759 (* 1 = 5.26759 loss)
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I0405 11:21:32.621600 26038 sgd_solver.cpp:105] Iteration 10692, lr = 1e-05
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I0405 11:21:37.779151 26038 solver.cpp:218] Iteration 10704 (2.3267 iter/s, 5.15752s/12 iters), loss = 5.27963
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I0405 11:21:37.779243 26038 solver.cpp:237] Train net output #0: loss = 5.27963 (* 1 = 5.27963 loss)
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I0405 11:21:37.779249 26038 sgd_solver.cpp:105] Iteration 10704, lr = 1e-05
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I0405 11:21:40.005739 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10710.caffemodel
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I0405 11:21:43.022855 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10710.solverstate
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I0405 11:21:45.316478 26038 solver.cpp:330] Iteration 10710, Testing net (#0)
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I0405 11:21:45.316495 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:21:45.479890 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:21:49.758996 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:21:49.759033 26038 solver.cpp:397] Test net output #1: loss = 5.27941 (* 1 = 5.27941 loss)
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I0405 11:21:51.555454 26038 solver.cpp:218] Iteration 10716 (0.871072 iter/s, 13.7761s/12 iters), loss = 5.26283
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I0405 11:21:51.555495 26038 solver.cpp:237] Train net output #0: loss = 5.26283 (* 1 = 5.26283 loss)
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I0405 11:21:51.555500 26038 sgd_solver.cpp:105] Iteration 10716, lr = 1e-05
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I0405 11:21:54.084004 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:21:57.136731 26038 solver.cpp:218] Iteration 10728 (2.15008 iter/s, 5.5812s/12 iters), loss = 5.28361
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I0405 11:21:57.136765 26038 solver.cpp:237] Train net output #0: loss = 5.28361 (* 1 = 5.28361 loss)
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I0405 11:21:57.136770 26038 sgd_solver.cpp:105] Iteration 10728, lr = 1e-05
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I0405 11:22:02.524530 26038 solver.cpp:218] Iteration 10740 (2.22729 iter/s, 5.38772s/12 iters), loss = 5.2955
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I0405 11:22:02.524567 26038 solver.cpp:237] Train net output #0: loss = 5.2955 (* 1 = 5.2955 loss)
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I0405 11:22:02.524572 26038 sgd_solver.cpp:105] Iteration 10740, lr = 1e-05
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I0405 11:22:07.911329 26038 solver.cpp:218] Iteration 10752 (2.2277 iter/s, 5.38672s/12 iters), loss = 5.27221
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I0405 11:22:07.911437 26038 solver.cpp:237] Train net output #0: loss = 5.27221 (* 1 = 5.27221 loss)
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I0405 11:22:07.911443 26038 sgd_solver.cpp:105] Iteration 10752, lr = 1e-05
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I0405 11:22:13.378600 26038 solver.cpp:218] Iteration 10764 (2.19494 iter/s, 5.46712s/12 iters), loss = 5.27588
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I0405 11:22:13.378638 26038 solver.cpp:237] Train net output #0: loss = 5.27588 (* 1 = 5.27588 loss)
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I0405 11:22:13.378644 26038 sgd_solver.cpp:105] Iteration 10764, lr = 1e-05
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I0405 11:22:18.601526 26038 solver.cpp:218] Iteration 10776 (2.2976 iter/s, 5.22285s/12 iters), loss = 5.28562
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I0405 11:22:18.601564 26038 solver.cpp:237] Train net output #0: loss = 5.28562 (* 1 = 5.28562 loss)
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I0405 11:22:18.601569 26038 sgd_solver.cpp:105] Iteration 10776, lr = 1e-05
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I0405 11:22:23.883347 26038 solver.cpp:218] Iteration 10788 (2.27198 iter/s, 5.28174s/12 iters), loss = 5.28723
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I0405 11:22:23.883389 26038 solver.cpp:237] Train net output #0: loss = 5.28723 (* 1 = 5.28723 loss)
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I0405 11:22:23.883394 26038 sgd_solver.cpp:105] Iteration 10788, lr = 1e-05
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I0405 11:22:29.049810 26038 solver.cpp:218] Iteration 10800 (2.32271 iter/s, 5.16637s/12 iters), loss = 5.27271
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I0405 11:22:29.049851 26038 solver.cpp:237] Train net output #0: loss = 5.27271 (* 1 = 5.27271 loss)
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I0405 11:22:29.049857 26038 sgd_solver.cpp:105] Iteration 10800, lr = 1e-05
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I0405 11:22:33.561730 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10812.caffemodel
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I0405 11:22:36.616066 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10812.solverstate
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I0405 11:22:38.922720 26038 solver.cpp:330] Iteration 10812, Testing net (#0)
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I0405 11:22:38.922792 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:22:39.060343 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:22:43.386193 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:22:43.386229 26038 solver.cpp:397] Test net output #1: loss = 5.27923 (* 1 = 5.27923 loss)
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I0405 11:22:43.527633 26038 solver.cpp:218] Iteration 10812 (0.828861 iter/s, 14.4777s/12 iters), loss = 5.27863
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I0405 11:22:43.527683 26038 solver.cpp:237] Train net output #0: loss = 5.27863 (* 1 = 5.27863 loss)
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I0405 11:22:43.527689 26038 sgd_solver.cpp:105] Iteration 10812, lr = 1e-05
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I0405 11:22:47.389676 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:22:48.085911 26038 solver.cpp:218] Iteration 10824 (2.63262 iter/s, 4.55819s/12 iters), loss = 5.28663
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I0405 11:22:48.085959 26038 solver.cpp:237] Train net output #0: loss = 5.28663 (* 1 = 5.28663 loss)
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I0405 11:22:48.085966 26038 sgd_solver.cpp:105] Iteration 10824, lr = 1e-05
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I0405 11:22:53.337776 26038 solver.cpp:218] Iteration 10836 (2.28494 iter/s, 5.25177s/12 iters), loss = 5.25638
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I0405 11:22:53.337821 26038 solver.cpp:237] Train net output #0: loss = 5.25638 (* 1 = 5.25638 loss)
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I0405 11:22:53.337827 26038 sgd_solver.cpp:105] Iteration 10836, lr = 1e-05
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I0405 11:22:58.618782 26038 solver.cpp:218] Iteration 10848 (2.27233 iter/s, 5.28092s/12 iters), loss = 5.27708
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I0405 11:22:58.618824 26038 solver.cpp:237] Train net output #0: loss = 5.27708 (* 1 = 5.27708 loss)
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I0405 11:22:58.618830 26038 sgd_solver.cpp:105] Iteration 10848, lr = 1e-05
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I0405 11:23:04.152591 26038 solver.cpp:218] Iteration 10860 (2.16852 iter/s, 5.53372s/12 iters), loss = 5.3022
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I0405 11:23:04.152635 26038 solver.cpp:237] Train net output #0: loss = 5.3022 (* 1 = 5.3022 loss)
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I0405 11:23:04.152640 26038 sgd_solver.cpp:105] Iteration 10860, lr = 1e-05
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I0405 11:23:09.057528 26038 solver.cpp:218] Iteration 10872 (2.44656 iter/s, 4.90485s/12 iters), loss = 5.2999
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I0405 11:23:09.057662 26038 solver.cpp:237] Train net output #0: loss = 5.2999 (* 1 = 5.2999 loss)
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I0405 11:23:09.057667 26038 sgd_solver.cpp:105] Iteration 10872, lr = 1e-05
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I0405 11:23:14.076658 26038 solver.cpp:218] Iteration 10884 (2.39093 iter/s, 5.01896s/12 iters), loss = 5.24962
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I0405 11:23:14.076694 26038 solver.cpp:237] Train net output #0: loss = 5.24962 (* 1 = 5.24962 loss)
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I0405 11:23:14.076699 26038 sgd_solver.cpp:105] Iteration 10884, lr = 1e-05
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I0405 11:23:19.202009 26038 solver.cpp:218] Iteration 10896 (2.34134 iter/s, 5.12527s/12 iters), loss = 5.28244
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I0405 11:23:19.202054 26038 solver.cpp:237] Train net output #0: loss = 5.28244 (* 1 = 5.28244 loss)
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I0405 11:23:19.202059 26038 sgd_solver.cpp:105] Iteration 10896, lr = 1e-05
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I0405 11:23:24.232913 26038 solver.cpp:218] Iteration 10908 (2.3853 iter/s, 5.03082s/12 iters), loss = 5.29179
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I0405 11:23:24.232955 26038 solver.cpp:237] Train net output #0: loss = 5.29179 (* 1 = 5.29179 loss)
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I0405 11:23:24.232960 26038 sgd_solver.cpp:105] Iteration 10908, lr = 1e-05
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I0405 11:23:26.353808 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10914.caffemodel
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I0405 11:23:29.449893 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10914.solverstate
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I0405 11:23:32.509479 26038 solver.cpp:330] Iteration 10914, Testing net (#0)
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I0405 11:23:32.509500 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:23:32.603688 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:23:36.946030 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:23:36.946069 26038 solver.cpp:397] Test net output #1: loss = 5.27924 (* 1 = 5.27924 loss)
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I0405 11:23:38.884323 26038 solver.cpp:218] Iteration 10920 (0.819041 iter/s, 14.6513s/12 iters), loss = 5.26876
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I0405 11:23:38.884378 26038 solver.cpp:237] Train net output #0: loss = 5.26876 (* 1 = 5.26876 loss)
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I0405 11:23:38.884387 26038 sgd_solver.cpp:105] Iteration 10920, lr = 1e-05
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I0405 11:23:40.538906 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:23:44.142253 26038 solver.cpp:218] Iteration 10932 (2.28231 iter/s, 5.25783s/12 iters), loss = 5.27724
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I0405 11:23:44.142295 26038 solver.cpp:237] Train net output #0: loss = 5.27724 (* 1 = 5.27724 loss)
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I0405 11:23:44.142302 26038 sgd_solver.cpp:105] Iteration 10932, lr = 1e-05
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I0405 11:23:49.491865 26038 solver.cpp:218] Iteration 10944 (2.24319 iter/s, 5.34952s/12 iters), loss = 5.28018
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I0405 11:23:49.491912 26038 solver.cpp:237] Train net output #0: loss = 5.28018 (* 1 = 5.28018 loss)
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I0405 11:23:49.491917 26038 sgd_solver.cpp:105] Iteration 10944, lr = 1e-05
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I0405 11:23:54.618278 26038 solver.cpp:218] Iteration 10956 (2.34086 iter/s, 5.12632s/12 iters), loss = 5.28009
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I0405 11:23:54.618331 26038 solver.cpp:237] Train net output #0: loss = 5.28009 (* 1 = 5.28009 loss)
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I0405 11:23:54.618340 26038 sgd_solver.cpp:105] Iteration 10956, lr = 1e-05
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I0405 11:23:59.826841 26038 solver.cpp:218] Iteration 10968 (2.30394 iter/s, 5.20846s/12 iters), loss = 5.27974
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I0405 11:23:59.826900 26038 solver.cpp:237] Train net output #0: loss = 5.27974 (* 1 = 5.27974 loss)
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||
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I0405 11:23:59.826908 26038 sgd_solver.cpp:105] Iteration 10968, lr = 1e-05
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I0405 11:24:05.243175 26038 solver.cpp:218] Iteration 10980 (2.21556 iter/s, 5.41624s/12 iters), loss = 5.28023
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I0405 11:24:05.243216 26038 solver.cpp:237] Train net output #0: loss = 5.28023 (* 1 = 5.28023 loss)
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I0405 11:24:05.243221 26038 sgd_solver.cpp:105] Iteration 10980, lr = 1e-05
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I0405 11:24:10.509392 26038 solver.cpp:218] Iteration 10992 (2.27871 iter/s, 5.26613s/12 iters), loss = 5.27326
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I0405 11:24:10.509429 26038 solver.cpp:237] Train net output #0: loss = 5.27326 (* 1 = 5.27326 loss)
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I0405 11:24:10.509434 26038 sgd_solver.cpp:105] Iteration 10992, lr = 1e-05
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I0405 11:24:15.675303 26038 solver.cpp:218] Iteration 11004 (2.32296 iter/s, 5.16583s/12 iters), loss = 5.26339
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I0405 11:24:15.675431 26038 solver.cpp:237] Train net output #0: loss = 5.26339 (* 1 = 5.26339 loss)
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I0405 11:24:15.675436 26038 sgd_solver.cpp:105] Iteration 11004, lr = 1e-05
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I0405 11:24:20.324193 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11016.caffemodel
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||
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I0405 11:24:23.369153 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11016.solverstate
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I0405 11:24:25.702842 26038 solver.cpp:330] Iteration 11016, Testing net (#0)
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I0405 11:24:25.702867 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:24:25.756095 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:24:30.013077 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:24:30.013116 26038 solver.cpp:397] Test net output #1: loss = 5.27931 (* 1 = 5.27931 loss)
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I0405 11:24:30.152268 26038 solver.cpp:218] Iteration 11016 (0.828915 iter/s, 14.4768s/12 iters), loss = 5.27154
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I0405 11:24:30.152312 26038 solver.cpp:237] Train net output #0: loss = 5.27154 (* 1 = 5.27154 loss)
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I0405 11:24:30.152318 26038 sgd_solver.cpp:105] Iteration 11016, lr = 1e-05
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I0405 11:24:30.503878 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:24:33.186790 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:24:34.522802 26038 solver.cpp:218] Iteration 11028 (2.74571 iter/s, 4.37045s/12 iters), loss = 5.26701
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I0405 11:24:34.522845 26038 solver.cpp:237] Train net output #0: loss = 5.26701 (* 1 = 5.26701 loss)
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I0405 11:24:34.522851 26038 sgd_solver.cpp:105] Iteration 11028, lr = 1e-05
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I0405 11:24:39.906538 26038 solver.cpp:218] Iteration 11040 (2.22897 iter/s, 5.38365s/12 iters), loss = 5.25598
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I0405 11:24:39.906574 26038 solver.cpp:237] Train net output #0: loss = 5.25598 (* 1 = 5.25598 loss)
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I0405 11:24:39.906579 26038 sgd_solver.cpp:105] Iteration 11040, lr = 1e-05
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I0405 11:24:45.293812 26038 solver.cpp:218] Iteration 11052 (2.22751 iter/s, 5.38719s/12 iters), loss = 5.26098
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I0405 11:24:45.293862 26038 solver.cpp:237] Train net output #0: loss = 5.26098 (* 1 = 5.26098 loss)
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I0405 11:24:45.293870 26038 sgd_solver.cpp:105] Iteration 11052, lr = 1e-05
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I0405 11:24:50.751617 26038 solver.cpp:218] Iteration 11064 (2.19872 iter/s, 5.45772s/12 iters), loss = 5.2618
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I0405 11:24:50.751703 26038 solver.cpp:237] Train net output #0: loss = 5.2618 (* 1 = 5.2618 loss)
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I0405 11:24:50.751709 26038 sgd_solver.cpp:105] Iteration 11064, lr = 1e-05
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I0405 11:24:56.291275 26038 solver.cpp:218] Iteration 11076 (2.16625 iter/s, 5.53952s/12 iters), loss = 5.26989
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I0405 11:24:56.291316 26038 solver.cpp:237] Train net output #0: loss = 5.26989 (* 1 = 5.26989 loss)
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I0405 11:24:56.291322 26038 sgd_solver.cpp:105] Iteration 11076, lr = 1e-05
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I0405 11:25:01.467388 26038 solver.cpp:218] Iteration 11088 (2.31838 iter/s, 5.17603s/12 iters), loss = 5.26963
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I0405 11:25:01.467437 26038 solver.cpp:237] Train net output #0: loss = 5.26963 (* 1 = 5.26963 loss)
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I0405 11:25:01.467443 26038 sgd_solver.cpp:105] Iteration 11088, lr = 1e-05
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I0405 11:25:04.875424 26038 blocking_queue.cpp:49] Waiting for data
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I0405 11:25:06.670179 26038 solver.cpp:218] Iteration 11100 (2.30649 iter/s, 5.2027s/12 iters), loss = 5.28575
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I0405 11:25:06.670229 26038 solver.cpp:237] Train net output #0: loss = 5.28575 (* 1 = 5.28575 loss)
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I0405 11:25:06.670238 26038 sgd_solver.cpp:105] Iteration 11100, lr = 1e-05
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I0405 11:25:12.027827 26038 solver.cpp:218] Iteration 11112 (2.23983 iter/s, 5.35755s/12 iters), loss = 5.26988
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I0405 11:25:12.027873 26038 solver.cpp:237] Train net output #0: loss = 5.26988 (* 1 = 5.26988 loss)
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I0405 11:25:12.027880 26038 sgd_solver.cpp:105] Iteration 11112, lr = 1e-05
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I0405 11:25:14.124714 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11118.caffemodel
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I0405 11:25:17.160331 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11118.solverstate
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I0405 11:25:19.455690 26038 solver.cpp:330] Iteration 11118, Testing net (#0)
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I0405 11:25:19.455713 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:25:23.833284 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:25:23.833451 26038 solver.cpp:397] Test net output #1: loss = 5.27912 (* 1 = 5.27912 loss)
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I0405 11:25:24.382640 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:25:25.715642 26038 solver.cpp:218] Iteration 11124 (0.876701 iter/s, 13.6877s/12 iters), loss = 5.27109
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I0405 11:25:25.715682 26038 solver.cpp:237] Train net output #0: loss = 5.27109 (* 1 = 5.27109 loss)
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I0405 11:25:25.715688 26038 sgd_solver.cpp:105] Iteration 11124, lr = 1e-05
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I0405 11:25:26.664116 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:25:31.098146 26038 solver.cpp:218] Iteration 11136 (2.22948 iter/s, 5.38242s/12 iters), loss = 5.28826
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I0405 11:25:31.098204 26038 solver.cpp:237] Train net output #0: loss = 5.28826 (* 1 = 5.28826 loss)
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I0405 11:25:31.098213 26038 sgd_solver.cpp:105] Iteration 11136, lr = 1e-05
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I0405 11:25:36.403117 26038 solver.cpp:218] Iteration 11148 (2.26208 iter/s, 5.30486s/12 iters), loss = 5.27593
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I0405 11:25:36.403177 26038 solver.cpp:237] Train net output #0: loss = 5.27593 (* 1 = 5.27593 loss)
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I0405 11:25:36.403187 26038 sgd_solver.cpp:105] Iteration 11148, lr = 1e-05
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I0405 11:25:41.638202 26038 solver.cpp:218] Iteration 11160 (2.29227 iter/s, 5.23498s/12 iters), loss = 5.28344
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I0405 11:25:41.638242 26038 solver.cpp:237] Train net output #0: loss = 5.28344 (* 1 = 5.28344 loss)
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I0405 11:25:41.638247 26038 sgd_solver.cpp:105] Iteration 11160, lr = 1e-05
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I0405 11:25:47.248715 26038 solver.cpp:218] Iteration 11172 (2.13887 iter/s, 5.61043s/12 iters), loss = 5.28531
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I0405 11:25:47.248755 26038 solver.cpp:237] Train net output #0: loss = 5.28531 (* 1 = 5.28531 loss)
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I0405 11:25:47.248760 26038 sgd_solver.cpp:105] Iteration 11172, lr = 1e-05
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I0405 11:25:52.589574 26038 solver.cpp:218] Iteration 11184 (2.24687 iter/s, 5.34077s/12 iters), loss = 5.29464
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I0405 11:25:52.589622 26038 solver.cpp:237] Train net output #0: loss = 5.29464 (* 1 = 5.29464 loss)
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I0405 11:25:52.589628 26038 sgd_solver.cpp:105] Iteration 11184, lr = 1e-05
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I0405 11:25:57.606593 26038 solver.cpp:218] Iteration 11196 (2.3919 iter/s, 5.01693s/12 iters), loss = 5.27603
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I0405 11:25:57.606683 26038 solver.cpp:237] Train net output #0: loss = 5.27603 (* 1 = 5.27603 loss)
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I0405 11:25:57.606688 26038 sgd_solver.cpp:105] Iteration 11196, lr = 1e-05
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I0405 11:26:02.995101 26038 solver.cpp:218] Iteration 11208 (2.22702 iter/s, 5.38837s/12 iters), loss = 5.2592
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I0405 11:26:02.995146 26038 solver.cpp:237] Train net output #0: loss = 5.2592 (* 1 = 5.2592 loss)
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I0405 11:26:02.995152 26038 sgd_solver.cpp:105] Iteration 11208, lr = 1e-05
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I0405 11:26:07.538033 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11220.caffemodel
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I0405 11:26:10.584465 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11220.solverstate
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I0405 11:26:12.940237 26038 solver.cpp:330] Iteration 11220, Testing net (#0)
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I0405 11:26:12.940255 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:26:17.232388 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:26:17.232425 26038 solver.cpp:397] Test net output #1: loss = 5.27927 (* 1 = 5.27927 loss)
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I0405 11:26:17.371507 26038 solver.cpp:218] Iteration 11220 (0.834709 iter/s, 14.3763s/12 iters), loss = 5.28874
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I0405 11:26:17.373070 26038 solver.cpp:237] Train net output #0: loss = 5.28874 (* 1 = 5.28874 loss)
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I0405 11:26:17.373082 26038 sgd_solver.cpp:105] Iteration 11220, lr = 1e-05
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I0405 11:26:17.496671 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:26:19.666293 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:26:21.782270 26038 solver.cpp:218] Iteration 11232 (2.7216 iter/s, 4.40917s/12 iters), loss = 5.27244
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I0405 11:26:21.782310 26038 solver.cpp:237] Train net output #0: loss = 5.27244 (* 1 = 5.27244 loss)
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I0405 11:26:21.782315 26038 sgd_solver.cpp:105] Iteration 11232, lr = 1e-05
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I0405 11:26:27.118664 26038 solver.cpp:218] Iteration 11244 (2.24875 iter/s, 5.33631s/12 iters), loss = 5.27732
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I0405 11:26:27.118708 26038 solver.cpp:237] Train net output #0: loss = 5.27732 (* 1 = 5.27732 loss)
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I0405 11:26:27.118714 26038 sgd_solver.cpp:105] Iteration 11244, lr = 1e-05
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I0405 11:26:32.537919 26038 solver.cpp:218] Iteration 11256 (2.21436 iter/s, 5.41916s/12 iters), loss = 5.27305
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I0405 11:26:32.538087 26038 solver.cpp:237] Train net output #0: loss = 5.27305 (* 1 = 5.27305 loss)
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I0405 11:26:32.538095 26038 sgd_solver.cpp:105] Iteration 11256, lr = 1e-05
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I0405 11:26:37.844089 26038 solver.cpp:218] Iteration 11268 (2.26161 iter/s, 5.30596s/12 iters), loss = 5.29645
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I0405 11:26:37.844128 26038 solver.cpp:237] Train net output #0: loss = 5.29645 (* 1 = 5.29645 loss)
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I0405 11:26:37.844135 26038 sgd_solver.cpp:105] Iteration 11268, lr = 1e-05
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I0405 11:26:43.209491 26038 solver.cpp:218] Iteration 11280 (2.23659 iter/s, 5.36531s/12 iters), loss = 5.2761
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I0405 11:26:43.209538 26038 solver.cpp:237] Train net output #0: loss = 5.2761 (* 1 = 5.2761 loss)
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I0405 11:26:43.209543 26038 sgd_solver.cpp:105] Iteration 11280, lr = 1e-05
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I0405 11:26:48.605669 26038 solver.cpp:218] Iteration 11292 (2.22383 iter/s, 5.39609s/12 iters), loss = 5.27309
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I0405 11:26:48.605707 26038 solver.cpp:237] Train net output #0: loss = 5.27309 (* 1 = 5.27309 loss)
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I0405 11:26:48.605712 26038 sgd_solver.cpp:105] Iteration 11292, lr = 1e-05
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I0405 11:26:53.979523 26038 solver.cpp:218] Iteration 11304 (2.23307 iter/s, 5.37377s/12 iters), loss = 5.28033
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I0405 11:26:53.979578 26038 solver.cpp:237] Train net output #0: loss = 5.28033 (* 1 = 5.28033 loss)
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I0405 11:26:53.979585 26038 sgd_solver.cpp:105] Iteration 11304, lr = 1e-05
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I0405 11:26:59.486265 26038 solver.cpp:218] Iteration 11316 (2.17918 iter/s, 5.50665s/12 iters), loss = 5.27757
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I0405 11:26:59.486307 26038 solver.cpp:237] Train net output #0: loss = 5.27757 (* 1 = 5.27757 loss)
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I0405 11:26:59.486312 26038 sgd_solver.cpp:105] Iteration 11316, lr = 1e-05
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I0405 11:27:01.634172 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11322.caffemodel
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I0405 11:27:04.655629 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11322.solverstate
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I0405 11:27:06.959764 26038 solver.cpp:330] Iteration 11322, Testing net (#0)
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I0405 11:27:06.959785 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:27:11.386883 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:27:11.386917 26038 solver.cpp:397] Test net output #1: loss = 5.27938 (* 1 = 5.27938 loss)
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I0405 11:27:11.760541 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:27:13.314429 26038 solver.cpp:218] Iteration 11328 (0.867803 iter/s, 13.828s/12 iters), loss = 5.28626
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I0405 11:27:13.314479 26038 solver.cpp:237] Train net output #0: loss = 5.28626 (* 1 = 5.28626 loss)
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I0405 11:27:13.314486 26038 sgd_solver.cpp:105] Iteration 11328, lr = 1e-05
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I0405 11:27:13.459709 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:27:18.704510 26038 solver.cpp:218] Iteration 11340 (2.22635 iter/s, 5.38999s/12 iters), loss = 5.27986
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I0405 11:27:18.704560 26038 solver.cpp:237] Train net output #0: loss = 5.27986 (* 1 = 5.27986 loss)
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I0405 11:27:18.704566 26038 sgd_solver.cpp:105] Iteration 11340, lr = 1e-05
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I0405 11:27:23.786523 26038 solver.cpp:218] Iteration 11352 (2.36131 iter/s, 5.08192s/12 iters), loss = 5.28315
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I0405 11:27:23.786571 26038 solver.cpp:237] Train net output #0: loss = 5.28315 (* 1 = 5.28315 loss)
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I0405 11:27:23.786577 26038 sgd_solver.cpp:105] Iteration 11352, lr = 1e-05
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I0405 11:27:29.154645 26038 solver.cpp:218] Iteration 11364 (2.23546 iter/s, 5.36803s/12 iters), loss = 5.28713
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I0405 11:27:29.154683 26038 solver.cpp:237] Train net output #0: loss = 5.28713 (* 1 = 5.28713 loss)
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I0405 11:27:29.154690 26038 sgd_solver.cpp:105] Iteration 11364, lr = 1e-05
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I0405 11:27:34.430656 26038 solver.cpp:218] Iteration 11376 (2.27448 iter/s, 5.27593s/12 iters), loss = 5.27074
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I0405 11:27:34.430693 26038 solver.cpp:237] Train net output #0: loss = 5.27074 (* 1 = 5.27074 loss)
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I0405 11:27:34.430699 26038 sgd_solver.cpp:105] Iteration 11376, lr = 1e-05
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I0405 11:27:39.710952 26038 solver.cpp:218] Iteration 11388 (2.27264 iter/s, 5.28021s/12 iters), loss = 5.27363
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I0405 11:27:39.711117 26038 solver.cpp:237] Train net output #0: loss = 5.27363 (* 1 = 5.27363 loss)
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I0405 11:27:39.711127 26038 sgd_solver.cpp:105] Iteration 11388, lr = 1e-05
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I0405 11:27:45.064584 26038 solver.cpp:218] Iteration 11400 (2.24155 iter/s, 5.35343s/12 iters), loss = 5.28304
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I0405 11:27:45.064625 26038 solver.cpp:237] Train net output #0: loss = 5.28304 (* 1 = 5.28304 loss)
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I0405 11:27:45.064630 26038 sgd_solver.cpp:105] Iteration 11400, lr = 1e-05
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I0405 11:27:50.262550 26038 solver.cpp:218] Iteration 11412 (2.30863 iter/s, 5.19788s/12 iters), loss = 5.27765
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I0405 11:27:50.262588 26038 solver.cpp:237] Train net output #0: loss = 5.27765 (* 1 = 5.27765 loss)
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I0405 11:27:50.262593 26038 sgd_solver.cpp:105] Iteration 11412, lr = 1e-05
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I0405 11:27:55.029891 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11424.caffemodel
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||
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I0405 11:27:58.042189 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11424.solverstate
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I0405 11:28:00.354660 26038 solver.cpp:330] Iteration 11424, Testing net (#0)
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I0405 11:28:00.354681 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:28:04.768899 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:28:04.768931 26038 solver.cpp:397] Test net output #1: loss = 5.27925 (* 1 = 5.27925 loss)
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I0405 11:28:04.908812 26038 solver.cpp:218] Iteration 11424 (0.819329 iter/s, 14.6461s/12 iters), loss = 5.27111
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I0405 11:28:04.908879 26038 solver.cpp:237] Train net output #0: loss = 5.27111 (* 1 = 5.27111 loss)
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I0405 11:28:04.908895 26038 sgd_solver.cpp:105] Iteration 11424, lr = 1e-05
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I0405 11:28:05.107383 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:28:06.285282 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:28:09.089066 26038 solver.cpp:218] Iteration 11436 (2.87071 iter/s, 4.18015s/12 iters), loss = 5.2784
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I0405 11:28:09.089105 26038 solver.cpp:237] Train net output #0: loss = 5.2784 (* 1 = 5.2784 loss)
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I0405 11:28:09.089110 26038 sgd_solver.cpp:105] Iteration 11436, lr = 1e-05
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I0405 11:28:14.287994 26038 solver.cpp:218] Iteration 11448 (2.30821 iter/s, 5.19884s/12 iters), loss = 5.25846
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I0405 11:28:14.288120 26038 solver.cpp:237] Train net output #0: loss = 5.25846 (* 1 = 5.25846 loss)
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I0405 11:28:14.288128 26038 sgd_solver.cpp:105] Iteration 11448, lr = 1e-05
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I0405 11:28:19.513938 26038 solver.cpp:218] Iteration 11460 (2.29631 iter/s, 5.22578s/12 iters), loss = 5.27693
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I0405 11:28:19.513979 26038 solver.cpp:237] Train net output #0: loss = 5.27693 (* 1 = 5.27693 loss)
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I0405 11:28:19.513984 26038 sgd_solver.cpp:105] Iteration 11460, lr = 1e-05
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I0405 11:28:24.807852 26038 solver.cpp:218] Iteration 11472 (2.26679 iter/s, 5.29383s/12 iters), loss = 5.26845
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I0405 11:28:24.807895 26038 solver.cpp:237] Train net output #0: loss = 5.26845 (* 1 = 5.26845 loss)
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I0405 11:28:24.807901 26038 sgd_solver.cpp:105] Iteration 11472, lr = 1e-05
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I0405 11:28:30.073892 26038 solver.cpp:218] Iteration 11484 (2.27879 iter/s, 5.26595s/12 iters), loss = 5.25784
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I0405 11:28:30.073940 26038 solver.cpp:237] Train net output #0: loss = 5.25784 (* 1 = 5.25784 loss)
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I0405 11:28:30.073946 26038 sgd_solver.cpp:105] Iteration 11484, lr = 1e-05
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I0405 11:28:35.417119 26038 solver.cpp:218] Iteration 11496 (2.24587 iter/s, 5.34314s/12 iters), loss = 5.28719
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I0405 11:28:35.417155 26038 solver.cpp:237] Train net output #0: loss = 5.28719 (* 1 = 5.28719 loss)
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I0405 11:28:35.417160 26038 sgd_solver.cpp:105] Iteration 11496, lr = 1e-05
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I0405 11:28:40.613024 26038 solver.cpp:218] Iteration 11508 (2.30955 iter/s, 5.19583s/12 iters), loss = 5.27078
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||
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I0405 11:28:40.613065 26038 solver.cpp:237] Train net output #0: loss = 5.27078 (* 1 = 5.27078 loss)
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I0405 11:28:40.613070 26038 sgd_solver.cpp:105] Iteration 11508, lr = 1e-05
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I0405 11:28:45.899207 26038 solver.cpp:218] Iteration 11520 (2.27011 iter/s, 5.2861s/12 iters), loss = 5.27845
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I0405 11:28:45.899351 26038 solver.cpp:237] Train net output #0: loss = 5.27845 (* 1 = 5.27845 loss)
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I0405 11:28:45.899358 26038 sgd_solver.cpp:105] Iteration 11520, lr = 1e-05
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I0405 11:28:48.074569 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11526.caffemodel
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||
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I0405 11:28:51.068853 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11526.solverstate
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I0405 11:28:53.365870 26038 solver.cpp:330] Iteration 11526, Testing net (#0)
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I0405 11:28:53.365888 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 11:28:57.815623 26038 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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||
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I0405 11:28:57.815658 26038 solver.cpp:397] Test net output #1: loss = 5.27939 (* 1 = 5.27939 loss)
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I0405 11:28:57.922602 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:28:59.249511 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:28:59.930179 26038 solver.cpp:218] Iteration 11532 (0.855265 iter/s, 14.0307s/12 iters), loss = 5.28428
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I0405 11:28:59.930231 26038 solver.cpp:237] Train net output #0: loss = 5.28428 (* 1 = 5.28428 loss)
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I0405 11:28:59.930239 26038 sgd_solver.cpp:105] Iteration 11532, lr = 1e-05
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I0405 11:29:05.108814 26038 solver.cpp:218] Iteration 11544 (2.31726 iter/s, 5.17854s/12 iters), loss = 5.25326
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I0405 11:29:05.108861 26038 solver.cpp:237] Train net output #0: loss = 5.25326 (* 1 = 5.25326 loss)
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I0405 11:29:05.108868 26038 sgd_solver.cpp:105] Iteration 11544, lr = 1e-05
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I0405 11:29:10.277302 26038 solver.cpp:218] Iteration 11556 (2.3218 iter/s, 5.1684s/12 iters), loss = 5.27438
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I0405 11:29:10.277354 26038 solver.cpp:237] Train net output #0: loss = 5.27438 (* 1 = 5.27438 loss)
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I0405 11:29:10.277361 26038 sgd_solver.cpp:105] Iteration 11556, lr = 1e-05
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I0405 11:29:15.603538 26038 solver.cpp:218] Iteration 11568 (2.25304 iter/s, 5.32614s/12 iters), loss = 5.28111
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I0405 11:29:15.603570 26038 solver.cpp:237] Train net output #0: loss = 5.28111 (* 1 = 5.28111 loss)
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I0405 11:29:15.603576 26038 sgd_solver.cpp:105] Iteration 11568, lr = 1e-05
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I0405 11:29:21.007382 26038 solver.cpp:218] Iteration 11580 (2.22067 iter/s, 5.40377s/12 iters), loss = 5.28509
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I0405 11:29:21.007469 26038 solver.cpp:237] Train net output #0: loss = 5.28509 (* 1 = 5.28509 loss)
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I0405 11:29:21.007477 26038 sgd_solver.cpp:105] Iteration 11580, lr = 1e-05
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I0405 11:29:26.239869 26038 solver.cpp:218] Iteration 11592 (2.29342 iter/s, 5.23235s/12 iters), loss = 5.2637
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I0405 11:29:26.239921 26038 solver.cpp:237] Train net output #0: loss = 5.2637 (* 1 = 5.2637 loss)
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I0405 11:29:26.239929 26038 sgd_solver.cpp:105] Iteration 11592, lr = 1e-05
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I0405 11:29:31.415724 26038 solver.cpp:218] Iteration 11604 (2.3185 iter/s, 5.17576s/12 iters), loss = 5.28244
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I0405 11:29:31.415766 26038 solver.cpp:237] Train net output #0: loss = 5.28244 (* 1 = 5.28244 loss)
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I0405 11:29:31.415771 26038 sgd_solver.cpp:105] Iteration 11604, lr = 1e-05
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I0405 11:29:36.752717 26038 solver.cpp:218] Iteration 11616 (2.24849 iter/s, 5.33691s/12 iters), loss = 5.27078
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I0405 11:29:36.752756 26038 solver.cpp:237] Train net output #0: loss = 5.27078 (* 1 = 5.27078 loss)
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I0405 11:29:36.752761 26038 sgd_solver.cpp:105] Iteration 11616, lr = 1e-05
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I0405 11:29:41.608870 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11628.caffemodel
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||
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I0405 11:29:44.602421 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11628.solverstate
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I0405 11:29:46.920545 26038 solver.cpp:330] Iteration 11628, Testing net (#0)
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I0405 11:29:46.920567 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:29:51.355443 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:29:51.355577 26038 solver.cpp:397] Test net output #1: loss = 5.27941 (* 1 = 5.27941 loss)
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I0405 11:29:51.417290 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:29:51.495460 26038 solver.cpp:218] Iteration 11628 (0.813967 iter/s, 14.7426s/12 iters), loss = 5.27254
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I0405 11:29:51.497020 26038 solver.cpp:237] Train net output #0: loss = 5.27254 (* 1 = 5.27254 loss)
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I0405 11:29:51.497031 26038 sgd_solver.cpp:105] Iteration 11628, lr = 1e-05
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I0405 11:29:52.210146 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:29:55.950225 26038 solver.cpp:218] Iteration 11640 (2.69471 iter/s, 4.45318s/12 iters), loss = 5.27413
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I0405 11:29:55.950266 26038 solver.cpp:237] Train net output #0: loss = 5.27413 (* 1 = 5.27413 loss)
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I0405 11:29:55.950273 26038 sgd_solver.cpp:105] Iteration 11640, lr = 1e-05
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I0405 11:30:01.122933 26038 solver.cpp:218] Iteration 11652 (2.31991 iter/s, 5.17262s/12 iters), loss = 5.28368
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I0405 11:30:01.122977 26038 solver.cpp:237] Train net output #0: loss = 5.28368 (* 1 = 5.28368 loss)
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I0405 11:30:01.122982 26038 sgd_solver.cpp:105] Iteration 11652, lr = 1e-05
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I0405 11:30:06.137079 26038 solver.cpp:218] Iteration 11664 (2.39327 iter/s, 5.01406s/12 iters), loss = 5.27707
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I0405 11:30:06.137115 26038 solver.cpp:237] Train net output #0: loss = 5.27707 (* 1 = 5.27707 loss)
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I0405 11:30:06.137120 26038 sgd_solver.cpp:105] Iteration 11664, lr = 1e-05
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I0405 11:30:11.418109 26038 solver.cpp:218] Iteration 11676 (2.27232 iter/s, 5.28094s/12 iters), loss = 5.26174
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I0405 11:30:11.418164 26038 solver.cpp:237] Train net output #0: loss = 5.26174 (* 1 = 5.26174 loss)
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I0405 11:30:11.418174 26038 sgd_solver.cpp:105] Iteration 11676, lr = 1e-05
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I0405 11:30:16.577172 26038 solver.cpp:218] Iteration 11688 (2.32604 iter/s, 5.15897s/12 iters), loss = 5.27617
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I0405 11:30:16.577209 26038 solver.cpp:237] Train net output #0: loss = 5.27617 (* 1 = 5.27617 loss)
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||
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I0405 11:30:16.577214 26038 sgd_solver.cpp:105] Iteration 11688, lr = 1e-05
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I0405 11:30:21.948065 26038 solver.cpp:218] Iteration 11700 (2.2343 iter/s, 5.37081s/12 iters), loss = 5.27006
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||
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I0405 11:30:21.948185 26038 solver.cpp:237] Train net output #0: loss = 5.27006 (* 1 = 5.27006 loss)
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||
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I0405 11:30:21.948194 26038 sgd_solver.cpp:105] Iteration 11700, lr = 1e-05
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I0405 11:30:27.305318 26038 solver.cpp:218] Iteration 11712 (2.24002 iter/s, 5.35709s/12 iters), loss = 5.2758
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||
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I0405 11:30:27.305361 26038 solver.cpp:237] Train net output #0: loss = 5.2758 (* 1 = 5.2758 loss)
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I0405 11:30:27.305366 26038 sgd_solver.cpp:105] Iteration 11712, lr = 1e-05
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I0405 11:30:32.576434 26038 solver.cpp:218] Iteration 11724 (2.27659 iter/s, 5.27103s/12 iters), loss = 5.27824
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I0405 11:30:32.576480 26038 solver.cpp:237] Train net output #0: loss = 5.27824 (* 1 = 5.27824 loss)
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||
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I0405 11:30:32.576488 26038 sgd_solver.cpp:105] Iteration 11724, lr = 1e-05
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I0405 11:30:34.812945 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11730.caffemodel
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||
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I0405 11:30:37.806845 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11730.solverstate
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I0405 11:30:40.497197 26038 solver.cpp:330] Iteration 11730, Testing net (#0)
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||
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I0405 11:30:40.497221 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:30:44.942155 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:30:44.972482 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:30:44.972515 26038 solver.cpp:397] Test net output #1: loss = 5.27948 (* 1 = 5.27948 loss)
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||
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I0405 11:30:45.551209 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:30:46.843643 26038 solver.cpp:218] Iteration 11736 (0.841098 iter/s, 14.2671s/12 iters), loss = 5.25928
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I0405 11:30:46.843688 26038 solver.cpp:237] Train net output #0: loss = 5.25928 (* 1 = 5.25928 loss)
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I0405 11:30:46.843693 26038 sgd_solver.cpp:105] Iteration 11736, lr = 1e-05
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I0405 11:30:52.261184 26038 solver.cpp:218] Iteration 11748 (2.21506 iter/s, 5.41745s/12 iters), loss = 5.27407
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I0405 11:30:52.261314 26038 solver.cpp:237] Train net output #0: loss = 5.27407 (* 1 = 5.27407 loss)
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I0405 11:30:52.261322 26038 sgd_solver.cpp:105] Iteration 11748, lr = 1e-05
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I0405 11:30:57.632217 26038 solver.cpp:218] Iteration 11760 (2.23428 iter/s, 5.37086s/12 iters), loss = 5.28189
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I0405 11:30:57.632277 26038 solver.cpp:237] Train net output #0: loss = 5.28189 (* 1 = 5.28189 loss)
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I0405 11:30:57.632285 26038 sgd_solver.cpp:105] Iteration 11760, lr = 1e-05
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I0405 11:31:03.121625 26038 solver.cpp:218] Iteration 11772 (2.18607 iter/s, 5.4893s/12 iters), loss = 5.27171
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I0405 11:31:03.121681 26038 solver.cpp:237] Train net output #0: loss = 5.27171 (* 1 = 5.27171 loss)
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I0405 11:31:03.121690 26038 sgd_solver.cpp:105] Iteration 11772, lr = 1e-05
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I0405 11:31:07.140012 26038 blocking_queue.cpp:49] Waiting for data
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I0405 11:31:08.648236 26038 solver.cpp:218] Iteration 11784 (2.17135 iter/s, 5.52651s/12 iters), loss = 5.28325
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I0405 11:31:08.648277 26038 solver.cpp:237] Train net output #0: loss = 5.28325 (* 1 = 5.28325 loss)
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I0405 11:31:08.648281 26038 sgd_solver.cpp:105] Iteration 11784, lr = 1e-05
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I0405 11:31:14.067353 26038 solver.cpp:218] Iteration 11796 (2.21442 iter/s, 5.41903s/12 iters), loss = 5.27717
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I0405 11:31:14.067394 26038 solver.cpp:237] Train net output #0: loss = 5.27717 (* 1 = 5.27717 loss)
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I0405 11:31:14.067399 26038 sgd_solver.cpp:105] Iteration 11796, lr = 1e-05
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I0405 11:31:19.242699 26038 solver.cpp:218] Iteration 11808 (2.31873 iter/s, 5.17526s/12 iters), loss = 5.28359
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I0405 11:31:19.242750 26038 solver.cpp:237] Train net output #0: loss = 5.28359 (* 1 = 5.28359 loss)
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I0405 11:31:19.242755 26038 sgd_solver.cpp:105] Iteration 11808, lr = 1e-05
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I0405 11:31:24.522579 26038 solver.cpp:218] Iteration 11820 (2.27282 iter/s, 5.27978s/12 iters), loss = 5.26102
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I0405 11:31:24.522811 26038 solver.cpp:237] Train net output #0: loss = 5.26102 (* 1 = 5.26102 loss)
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||
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I0405 11:31:24.522819 26038 sgd_solver.cpp:105] Iteration 11820, lr = 1e-05
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||
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I0405 11:31:29.220741 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11832.caffemodel
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||
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|
I0405 11:31:33.042800 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11832.solverstate
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||
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I0405 11:31:35.360575 26038 solver.cpp:330] Iteration 11832, Testing net (#0)
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||
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I0405 11:31:35.360599 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
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I0405 11:31:39.696362 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 11:31:39.758850 26038 solver.cpp:397] Test net output #0: accuracy = 0.00612745
|
||
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|
I0405 11:31:39.758882 26038 solver.cpp:397] Test net output #1: loss = 5.27942 (* 1 = 5.27942 loss)
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||
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I0405 11:31:39.894217 26038 solver.cpp:218] Iteration 11832 (0.780675 iter/s, 15.3713s/12 iters), loss = 5.26998
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||
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I0405 11:31:39.894268 26038 solver.cpp:237] Train net output #0: loss = 5.26998 (* 1 = 5.26998 loss)
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||
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I0405 11:31:39.894275 26038 sgd_solver.cpp:105] Iteration 11832, lr = 1e-05
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||
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I0405 11:31:40.001907 26057 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 11:31:44.343901 26038 solver.cpp:218] Iteration 11844 (2.69688 iter/s, 4.44959s/12 iters), loss = 5.28691
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||
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I0405 11:31:44.343950 26038 solver.cpp:237] Train net output #0: loss = 5.28691 (* 1 = 5.28691 loss)
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||
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I0405 11:31:44.343957 26038 sgd_solver.cpp:105] Iteration 11844, lr = 1e-05
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||
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I0405 11:31:49.691109 26038 solver.cpp:218] Iteration 11856 (2.2442 iter/s, 5.34711s/12 iters), loss = 5.28112
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||
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I0405 11:31:49.691155 26038 solver.cpp:237] Train net output #0: loss = 5.28112 (* 1 = 5.28112 loss)
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||
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I0405 11:31:49.691162 26038 sgd_solver.cpp:105] Iteration 11856, lr = 1e-05
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||
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I0405 11:31:55.173233 26038 solver.cpp:218] Iteration 11868 (2.18897 iter/s, 5.48204s/12 iters), loss = 5.26617
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||
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I0405 11:31:55.173360 26038 solver.cpp:237] Train net output #0: loss = 5.26617 (* 1 = 5.26617 loss)
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||
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I0405 11:31:55.173367 26038 sgd_solver.cpp:105] Iteration 11868, lr = 1e-05
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||
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I0405 11:32:00.278292 26038 solver.cpp:218] Iteration 11880 (2.35069 iter/s, 5.10489s/12 iters), loss = 5.28354
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||
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I0405 11:32:00.278338 26038 solver.cpp:237] Train net output #0: loss = 5.28354 (* 1 = 5.28354 loss)
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||
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I0405 11:32:00.278345 26038 sgd_solver.cpp:105] Iteration 11880, lr = 1e-05
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||
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I0405 11:32:05.520829 26038 solver.cpp:218] Iteration 11892 (2.28901 iter/s, 5.24245s/12 iters), loss = 5.29089
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||
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I0405 11:32:05.520876 26038 solver.cpp:237] Train net output #0: loss = 5.29089 (* 1 = 5.29089 loss)
|
||
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I0405 11:32:05.520890 26038 sgd_solver.cpp:105] Iteration 11892, lr = 1e-05
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||
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I0405 11:32:10.818817 26038 solver.cpp:218] Iteration 11904 (2.26505 iter/s, 5.2979s/12 iters), loss = 5.27516
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||
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I0405 11:32:10.818867 26038 solver.cpp:237] Train net output #0: loss = 5.27516 (* 1 = 5.27516 loss)
|
||
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I0405 11:32:10.818876 26038 sgd_solver.cpp:105] Iteration 11904, lr = 1e-05
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||
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I0405 11:32:16.220649 26038 solver.cpp:218] Iteration 11916 (2.22151 iter/s, 5.40174s/12 iters), loss = 5.26813
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||
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I0405 11:32:16.220687 26038 solver.cpp:237] Train net output #0: loss = 5.26813 (* 1 = 5.26813 loss)
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||
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I0405 11:32:16.220693 26038 sgd_solver.cpp:105] Iteration 11916, lr = 1e-05
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||
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I0405 11:32:21.513907 26038 solver.cpp:218] Iteration 11928 (2.26707 iter/s, 5.29317s/12 iters), loss = 5.2879
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||
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I0405 11:32:21.513949 26038 solver.cpp:237] Train net output #0: loss = 5.2879 (* 1 = 5.2879 loss)
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||
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I0405 11:32:21.513954 26038 sgd_solver.cpp:105] Iteration 11928, lr = 1e-05
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||
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I0405 11:32:23.720439 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11934.caffemodel
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||
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|
I0405 11:32:24.512485 26057 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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|
I0405 11:32:26.673650 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11934.solverstate
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||
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I0405 11:32:28.967552 26038 solver.cpp:330] Iteration 11934, Testing net (#0)
|
||
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I0405 11:32:28.967571 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 11:32:33.169013 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 11:32:33.274318 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 11:32:33.274353 26038 solver.cpp:397] Test net output #1: loss = 5.27953 (* 1 = 5.27953 loss)
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||
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I0405 11:32:35.189036 26038 solver.cpp:218] Iteration 11940 (0.877514 iter/s, 13.675s/12 iters), loss = 5.27962
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||
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I0405 11:32:35.189103 26038 solver.cpp:237] Train net output #0: loss = 5.27962 (* 1 = 5.27962 loss)
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||
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I0405 11:32:35.189113 26038 sgd_solver.cpp:105] Iteration 11940, lr = 1e-05
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I0405 11:32:40.642789 26038 solver.cpp:218] Iteration 11952 (2.20036 iter/s, 5.45364s/12 iters), loss = 5.27967
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||
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I0405 11:32:40.642830 26038 solver.cpp:237] Train net output #0: loss = 5.27967 (* 1 = 5.27967 loss)
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||
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I0405 11:32:40.642835 26038 sgd_solver.cpp:105] Iteration 11952, lr = 1e-05
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||
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I0405 11:32:46.036334 26038 solver.cpp:218] Iteration 11964 (2.22492 iter/s, 5.39346s/12 iters), loss = 5.27131
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I0405 11:32:46.036375 26038 solver.cpp:237] Train net output #0: loss = 5.27131 (* 1 = 5.27131 loss)
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||
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I0405 11:32:46.036381 26038 sgd_solver.cpp:105] Iteration 11964, lr = 1e-05
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||
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I0405 11:32:51.320466 26038 solver.cpp:218] Iteration 11976 (2.27099 iter/s, 5.28404s/12 iters), loss = 5.28578
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||
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I0405 11:32:51.320508 26038 solver.cpp:237] Train net output #0: loss = 5.28578 (* 1 = 5.28578 loss)
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||
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I0405 11:32:51.320513 26038 sgd_solver.cpp:105] Iteration 11976, lr = 1e-05
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||
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I0405 11:32:56.735204 26038 solver.cpp:218] Iteration 11988 (2.21621 iter/s, 5.41466s/12 iters), loss = 5.27719
|
||
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I0405 11:32:56.735344 26038 solver.cpp:237] Train net output #0: loss = 5.27719 (* 1 = 5.27719 loss)
|
||
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I0405 11:32:56.735350 26038 sgd_solver.cpp:105] Iteration 11988, lr = 1e-05
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I0405 11:33:01.935626 26038 solver.cpp:218] Iteration 12000 (2.30759 iter/s, 5.20024s/12 iters), loss = 5.29319
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I0405 11:33:01.935678 26038 solver.cpp:237] Train net output #0: loss = 5.29319 (* 1 = 5.29319 loss)
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I0405 11:33:01.935686 26038 sgd_solver.cpp:105] Iteration 12000, lr = 1e-05
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I0405 11:33:06.994124 26038 solver.cpp:218] Iteration 12012 (2.37229 iter/s, 5.0584s/12 iters), loss = 5.27117
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I0405 11:33:06.994168 26038 solver.cpp:237] Train net output #0: loss = 5.27117 (* 1 = 5.27117 loss)
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I0405 11:33:06.994174 26038 sgd_solver.cpp:105] Iteration 12012, lr = 1e-05
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I0405 11:33:12.256896 26038 solver.cpp:218] Iteration 12024 (2.28021 iter/s, 5.26268s/12 iters), loss = 5.28232
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I0405 11:33:12.256942 26038 solver.cpp:237] Train net output #0: loss = 5.28232 (* 1 = 5.28232 loss)
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I0405 11:33:12.256948 26038 sgd_solver.cpp:105] Iteration 12024, lr = 1e-05
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I0405 11:33:17.177732 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12036.caffemodel
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I0405 11:33:17.763891 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:33:20.192698 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12036.solverstate
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I0405 11:33:22.501384 26038 solver.cpp:330] Iteration 12036, Testing net (#0)
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I0405 11:33:22.501402 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:33:26.788151 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:33:26.926103 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:33:26.926151 26038 solver.cpp:397] Test net output #1: loss = 5.27947 (* 1 = 5.27947 loss)
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I0405 11:33:27.065820 26038 solver.cpp:218] Iteration 12036 (0.81033 iter/s, 14.8088s/12 iters), loss = 5.28016
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I0405 11:33:27.065881 26038 solver.cpp:237] Train net output #0: loss = 5.28016 (* 1 = 5.28016 loss)
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I0405 11:33:27.065891 26038 sgd_solver.cpp:105] Iteration 12036, lr = 1e-05
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I0405 11:33:31.394994 26038 solver.cpp:218] Iteration 12048 (2.77196 iter/s, 4.32907s/12 iters), loss = 5.27698
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I0405 11:33:31.395035 26038 solver.cpp:237] Train net output #0: loss = 5.27698 (* 1 = 5.27698 loss)
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I0405 11:33:31.395040 26038 sgd_solver.cpp:105] Iteration 12048, lr = 1e-05
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I0405 11:33:36.619633 26038 solver.cpp:218] Iteration 12060 (2.29685 iter/s, 5.22456s/12 iters), loss = 5.28102
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I0405 11:33:36.619673 26038 solver.cpp:237] Train net output #0: loss = 5.28102 (* 1 = 5.28102 loss)
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I0405 11:33:36.619678 26038 sgd_solver.cpp:105] Iteration 12060, lr = 1e-05
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I0405 11:33:41.975648 26038 solver.cpp:218] Iteration 12072 (2.24051 iter/s, 5.35593s/12 iters), loss = 5.26906
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I0405 11:33:41.975682 26038 solver.cpp:237] Train net output #0: loss = 5.26906 (* 1 = 5.26906 loss)
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I0405 11:33:41.975687 26038 sgd_solver.cpp:105] Iteration 12072, lr = 1e-05
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I0405 11:33:47.260602 26038 solver.cpp:218] Iteration 12084 (2.27063 iter/s, 5.28487s/12 iters), loss = 5.28475
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I0405 11:33:47.260656 26038 solver.cpp:237] Train net output #0: loss = 5.28475 (* 1 = 5.28475 loss)
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I0405 11:33:47.260663 26038 sgd_solver.cpp:105] Iteration 12084, lr = 1e-05
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I0405 11:33:52.474447 26038 solver.cpp:218] Iteration 12096 (2.30161 iter/s, 5.21375s/12 iters), loss = 5.27703
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I0405 11:33:52.474491 26038 solver.cpp:237] Train net output #0: loss = 5.27703 (* 1 = 5.27703 loss)
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I0405 11:33:52.474496 26038 sgd_solver.cpp:105] Iteration 12096, lr = 1e-05
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I0405 11:33:57.724654 26038 solver.cpp:218] Iteration 12108 (2.28566 iter/s, 5.25012s/12 iters), loss = 5.26467
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I0405 11:33:57.724740 26038 solver.cpp:237] Train net output #0: loss = 5.26467 (* 1 = 5.26467 loss)
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I0405 11:33:57.724746 26038 sgd_solver.cpp:105] Iteration 12108, lr = 1e-05
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I0405 11:34:03.073941 26038 solver.cpp:218] Iteration 12120 (2.24334 iter/s, 5.34916s/12 iters), loss = 5.29025
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I0405 11:34:03.073988 26038 solver.cpp:237] Train net output #0: loss = 5.29025 (* 1 = 5.29025 loss)
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I0405 11:34:03.073994 26038 sgd_solver.cpp:105] Iteration 12120, lr = 1e-05
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I0405 11:34:08.344446 26038 solver.cpp:218] Iteration 12132 (2.27686 iter/s, 5.27041s/12 iters), loss = 5.27894
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I0405 11:34:08.344485 26038 solver.cpp:237] Train net output #0: loss = 5.27894 (* 1 = 5.27894 loss)
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I0405 11:34:08.344491 26038 sgd_solver.cpp:105] Iteration 12132, lr = 1e-05
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I0405 11:34:10.490664 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12138.caffemodel
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||
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I0405 11:34:10.809446 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:34:13.592634 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12138.solverstate
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I0405 11:34:15.953953 26038 solver.cpp:330] Iteration 12138, Testing net (#0)
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I0405 11:34:15.953974 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:34:20.081557 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:34:20.252336 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:34:20.252374 26038 solver.cpp:397] Test net output #1: loss = 5.27947 (* 1 = 5.27947 loss)
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I0405 11:34:22.189996 26038 solver.cpp:218] Iteration 12144 (0.866713 iter/s, 13.8454s/12 iters), loss = 5.2677
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I0405 11:34:22.190039 26038 solver.cpp:237] Train net output #0: loss = 5.2677 (* 1 = 5.2677 loss)
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I0405 11:34:22.190044 26038 sgd_solver.cpp:105] Iteration 12144, lr = 1e-05
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I0405 11:34:27.517357 26038 solver.cpp:218] Iteration 12156 (2.25256 iter/s, 5.32727s/12 iters), loss = 5.27736
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I0405 11:34:27.517408 26038 solver.cpp:237] Train net output #0: loss = 5.27736 (* 1 = 5.27736 loss)
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I0405 11:34:27.517416 26038 sgd_solver.cpp:105] Iteration 12156, lr = 1e-05
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I0405 11:34:32.880316 26038 solver.cpp:218] Iteration 12168 (2.23761 iter/s, 5.36287s/12 iters), loss = 5.27436
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I0405 11:34:32.880447 26038 solver.cpp:237] Train net output #0: loss = 5.27436 (* 1 = 5.27436 loss)
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I0405 11:34:32.880455 26038 sgd_solver.cpp:105] Iteration 12168, lr = 1e-05
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I0405 11:34:38.132616 26038 solver.cpp:218] Iteration 12180 (2.28479 iter/s, 5.25213s/12 iters), loss = 5.28096
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I0405 11:34:38.132654 26038 solver.cpp:237] Train net output #0: loss = 5.28096 (* 1 = 5.28096 loss)
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I0405 11:34:38.132660 26038 sgd_solver.cpp:105] Iteration 12180, lr = 1e-05
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I0405 11:34:43.327981 26038 solver.cpp:218] Iteration 12192 (2.30979 iter/s, 5.19528s/12 iters), loss = 5.26823
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I0405 11:34:43.328039 26038 solver.cpp:237] Train net output #0: loss = 5.26823 (* 1 = 5.26823 loss)
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I0405 11:34:43.328048 26038 sgd_solver.cpp:105] Iteration 12192, lr = 1e-05
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I0405 11:34:48.851229 26038 solver.cpp:218] Iteration 12204 (2.17267 iter/s, 5.52315s/12 iters), loss = 5.27159
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I0405 11:34:48.851280 26038 solver.cpp:237] Train net output #0: loss = 5.27159 (* 1 = 5.27159 loss)
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I0405 11:34:48.851289 26038 sgd_solver.cpp:105] Iteration 12204, lr = 1e-05
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I0405 11:34:54.105098 26038 solver.cpp:218] Iteration 12216 (2.28407 iter/s, 5.25378s/12 iters), loss = 5.27994
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I0405 11:34:54.105134 26038 solver.cpp:237] Train net output #0: loss = 5.27994 (* 1 = 5.27994 loss)
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I0405 11:34:54.105139 26038 sgd_solver.cpp:105] Iteration 12216, lr = 1e-05
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I0405 11:34:59.449033 26038 solver.cpp:218] Iteration 12228 (2.24557 iter/s, 5.34385s/12 iters), loss = 5.25467
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I0405 11:34:59.449080 26038 solver.cpp:237] Train net output #0: loss = 5.25467 (* 1 = 5.25467 loss)
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I0405 11:34:59.449086 26038 sgd_solver.cpp:105] Iteration 12228, lr = 1e-05
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I0405 11:35:04.164808 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:35:04.252496 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12240.caffemodel
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I0405 11:35:07.283545 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12240.solverstate
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I0405 11:35:09.616410 26038 solver.cpp:330] Iteration 12240, Testing net (#0)
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I0405 11:35:09.616436 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:35:13.702693 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:35:13.921308 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:35:13.921346 26038 solver.cpp:397] Test net output #1: loss = 5.27945 (* 1 = 5.27945 loss)
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I0405 11:35:14.063355 26038 solver.cpp:218] Iteration 12240 (0.82112 iter/s, 14.6142s/12 iters), loss = 5.28389
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I0405 11:35:14.064926 26038 solver.cpp:237] Train net output #0: loss = 5.28389 (* 1 = 5.28389 loss)
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I0405 11:35:14.064940 26038 sgd_solver.cpp:105] Iteration 12240, lr = 1e-05
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I0405 11:35:18.402632 26038 solver.cpp:218] Iteration 12252 (2.76646 iter/s, 4.33767s/12 iters), loss = 5.23424
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I0405 11:35:18.402671 26038 solver.cpp:237] Train net output #0: loss = 5.23424 (* 1 = 5.23424 loss)
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I0405 11:35:18.402678 26038 sgd_solver.cpp:105] Iteration 12252, lr = 1e-05
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I0405 11:35:23.785953 26038 solver.cpp:218] Iteration 12264 (2.22914 iter/s, 5.38323s/12 iters), loss = 5.27167
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I0405 11:35:23.786005 26038 solver.cpp:237] Train net output #0: loss = 5.27167 (* 1 = 5.27167 loss)
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I0405 11:35:23.786011 26038 sgd_solver.cpp:105] Iteration 12264, lr = 1e-05
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I0405 11:35:29.304643 26038 solver.cpp:218] Iteration 12276 (2.17447 iter/s, 5.51859s/12 iters), loss = 5.27739
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I0405 11:35:29.304695 26038 solver.cpp:237] Train net output #0: loss = 5.27739 (* 1 = 5.27739 loss)
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I0405 11:35:29.304702 26038 sgd_solver.cpp:105] Iteration 12276, lr = 1e-05
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I0405 11:35:34.349582 26038 solver.cpp:218] Iteration 12288 (2.37866 iter/s, 5.04485s/12 iters), loss = 5.29467
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I0405 11:35:34.349732 26038 solver.cpp:237] Train net output #0: loss = 5.29467 (* 1 = 5.29467 loss)
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I0405 11:35:34.349742 26038 sgd_solver.cpp:105] Iteration 12288, lr = 1e-05
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I0405 11:35:39.751642 26038 solver.cpp:218] Iteration 12300 (2.22145 iter/s, 5.40187s/12 iters), loss = 5.25479
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I0405 11:35:39.751680 26038 solver.cpp:237] Train net output #0: loss = 5.25479 (* 1 = 5.25479 loss)
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||
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I0405 11:35:39.751686 26038 sgd_solver.cpp:105] Iteration 12300, lr = 1e-05
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I0405 11:35:45.153338 26038 solver.cpp:218] Iteration 12312 (2.22156 iter/s, 5.40162s/12 iters), loss = 5.28456
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||
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I0405 11:35:45.153378 26038 solver.cpp:237] Train net output #0: loss = 5.28456 (* 1 = 5.28456 loss)
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||
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I0405 11:35:45.153383 26038 sgd_solver.cpp:105] Iteration 12312, lr = 1e-05
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||
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I0405 11:35:50.613960 26038 solver.cpp:218] Iteration 12324 (2.19759 iter/s, 5.46053s/12 iters), loss = 5.27745
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||
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I0405 11:35:50.614002 26038 solver.cpp:237] Train net output #0: loss = 5.27745 (* 1 = 5.27745 loss)
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||
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I0405 11:35:50.614007 26038 sgd_solver.cpp:105] Iteration 12324, lr = 1e-05
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I0405 11:35:55.710681 26038 solver.cpp:218] Iteration 12336 (2.3545 iter/s, 5.09663s/12 iters), loss = 5.27926
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||
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I0405 11:35:55.710728 26038 solver.cpp:237] Train net output #0: loss = 5.27926 (* 1 = 5.27926 loss)
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||
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I0405 11:35:55.710733 26038 sgd_solver.cpp:105] Iteration 12336, lr = 1e-05
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||
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I0405 11:35:57.513482 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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|
I0405 11:35:57.863759 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12342.caffemodel
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||
|
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I0405 11:36:00.837481 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12342.solverstate
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||
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I0405 11:36:03.139494 26038 solver.cpp:330] Iteration 12342, Testing net (#0)
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||
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I0405 11:36:03.139515 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 11:36:07.621939 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 11:36:07.919025 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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|
I0405 11:36:07.919062 26038 solver.cpp:397] Test net output #1: loss = 5.27956 (* 1 = 5.27956 loss)
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||
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I0405 11:36:09.773730 26038 solver.cpp:218] Iteration 12348 (0.853308 iter/s, 14.0629s/12 iters), loss = 5.28543
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I0405 11:36:09.773773 26038 solver.cpp:237] Train net output #0: loss = 5.28543 (* 1 = 5.28543 loss)
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I0405 11:36:09.773778 26038 sgd_solver.cpp:105] Iteration 12348, lr = 1e-05
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I0405 11:36:15.170591 26038 solver.cpp:218] Iteration 12360 (2.22355 iter/s, 5.39677s/12 iters), loss = 5.25995
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I0405 11:36:15.170634 26038 solver.cpp:237] Train net output #0: loss = 5.25995 (* 1 = 5.25995 loss)
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I0405 11:36:15.170639 26038 sgd_solver.cpp:105] Iteration 12360, lr = 1e-05
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I0405 11:36:20.663744 26038 solver.cpp:218] Iteration 12372 (2.18457 iter/s, 5.49306s/12 iters), loss = 5.28481
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||
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I0405 11:36:20.663794 26038 solver.cpp:237] Train net output #0: loss = 5.28481 (* 1 = 5.28481 loss)
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I0405 11:36:20.663801 26038 sgd_solver.cpp:105] Iteration 12372, lr = 1e-05
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I0405 11:36:25.820930 26038 solver.cpp:218] Iteration 12384 (2.32689 iter/s, 5.15709s/12 iters), loss = 5.27741
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I0405 11:36:25.820971 26038 solver.cpp:237] Train net output #0: loss = 5.27741 (* 1 = 5.27741 loss)
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||
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I0405 11:36:25.820976 26038 sgd_solver.cpp:105] Iteration 12384, lr = 1e-05
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||
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I0405 11:36:31.264043 26038 solver.cpp:218] Iteration 12396 (2.20466 iter/s, 5.44302s/12 iters), loss = 5.29034
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I0405 11:36:31.264101 26038 solver.cpp:237] Train net output #0: loss = 5.29034 (* 1 = 5.29034 loss)
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||
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I0405 11:36:31.264109 26038 sgd_solver.cpp:105] Iteration 12396, lr = 1e-05
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||
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I0405 11:36:36.629559 26038 solver.cpp:218] Iteration 12408 (2.23655 iter/s, 5.36542s/12 iters), loss = 5.27125
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||
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I0405 11:36:36.629604 26038 solver.cpp:237] Train net output #0: loss = 5.27125 (* 1 = 5.27125 loss)
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||
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I0405 11:36:36.629609 26038 sgd_solver.cpp:105] Iteration 12408, lr = 1e-05
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||
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I0405 11:36:41.926712 26038 solver.cpp:218] Iteration 12420 (2.26541 iter/s, 5.29706s/12 iters), loss = 5.28292
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||
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I0405 11:36:41.926867 26038 solver.cpp:237] Train net output #0: loss = 5.28292 (* 1 = 5.28292 loss)
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||
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I0405 11:36:41.926875 26038 sgd_solver.cpp:105] Iteration 12420, lr = 1e-05
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||
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I0405 11:36:47.233902 26038 solver.cpp:218] Iteration 12432 (2.26117 iter/s, 5.30699s/12 iters), loss = 5.27628
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I0405 11:36:47.233952 26038 solver.cpp:237] Train net output #0: loss = 5.27628 (* 1 = 5.27628 loss)
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||
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I0405 11:36:47.233958 26038 sgd_solver.cpp:105] Iteration 12432, lr = 1e-05
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||
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I0405 11:36:51.313935 26057 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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|
I0405 11:36:52.073025 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12444.caffemodel
|
||
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I0405 11:36:55.157181 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12444.solverstate
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||
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I0405 11:36:57.476383 26038 solver.cpp:330] Iteration 12444, Testing net (#0)
|
||
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I0405 11:36:57.476400 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 11:37:01.437420 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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|
I0405 11:37:01.733640 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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I0405 11:37:01.733676 26038 solver.cpp:397] Test net output #1: loss = 5.27947 (* 1 = 5.27947 loss)
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I0405 11:37:01.869570 26038 solver.cpp:218] Iteration 12444 (0.819923 iter/s, 14.6355s/12 iters), loss = 5.27566
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I0405 11:37:01.869624 26038 solver.cpp:237] Train net output #0: loss = 5.27566 (* 1 = 5.27566 loss)
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I0405 11:37:01.869632 26038 sgd_solver.cpp:105] Iteration 12444, lr = 1e-05
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I0405 11:37:06.186306 26038 solver.cpp:218] Iteration 12456 (2.77993 iter/s, 4.31665s/12 iters), loss = 5.27347
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I0405 11:37:06.186345 26038 solver.cpp:237] Train net output #0: loss = 5.27347 (* 1 = 5.27347 loss)
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I0405 11:37:06.186350 26038 sgd_solver.cpp:105] Iteration 12456, lr = 1e-05
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I0405 11:37:10.583600 26038 blocking_queue.cpp:49] Waiting for data
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I0405 11:37:11.470611 26038 solver.cpp:218] Iteration 12468 (2.27091 iter/s, 5.28421s/12 iters), loss = 5.28414
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I0405 11:37:11.470664 26038 solver.cpp:237] Train net output #0: loss = 5.28414 (* 1 = 5.28414 loss)
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I0405 11:37:11.470671 26038 sgd_solver.cpp:105] Iteration 12468, lr = 1e-05
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I0405 11:37:16.501376 26038 solver.cpp:218] Iteration 12480 (2.38537 iter/s, 5.03067s/12 iters), loss = 5.27007
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I0405 11:37:16.501471 26038 solver.cpp:237] Train net output #0: loss = 5.27007 (* 1 = 5.27007 loss)
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I0405 11:37:16.501477 26038 sgd_solver.cpp:105] Iteration 12480, lr = 1e-05
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I0405 11:37:21.752737 26038 solver.cpp:218] Iteration 12492 (2.28518 iter/s, 5.25123s/12 iters), loss = 5.27887
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I0405 11:37:21.752779 26038 solver.cpp:237] Train net output #0: loss = 5.27887 (* 1 = 5.27887 loss)
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I0405 11:37:21.752789 26038 sgd_solver.cpp:105] Iteration 12492, lr = 1e-05
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I0405 11:37:27.160290 26038 solver.cpp:218] Iteration 12504 (2.21915 iter/s, 5.40747s/12 iters), loss = 5.26666
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I0405 11:37:27.160332 26038 solver.cpp:237] Train net output #0: loss = 5.26666 (* 1 = 5.26666 loss)
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I0405 11:37:27.160338 26038 sgd_solver.cpp:105] Iteration 12504, lr = 1e-05
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I0405 11:37:32.508520 26038 solver.cpp:218] Iteration 12516 (2.24377 iter/s, 5.34814s/12 iters), loss = 5.27412
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I0405 11:37:32.508559 26038 solver.cpp:237] Train net output #0: loss = 5.27412 (* 1 = 5.27412 loss)
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I0405 11:37:32.508564 26038 sgd_solver.cpp:105] Iteration 12516, lr = 1e-05
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I0405 11:37:37.751726 26038 solver.cpp:218] Iteration 12528 (2.28871 iter/s, 5.24312s/12 iters), loss = 5.27671
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I0405 11:37:37.751765 26038 solver.cpp:237] Train net output #0: loss = 5.27671 (* 1 = 5.27671 loss)
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I0405 11:37:37.751770 26038 sgd_solver.cpp:105] Iteration 12528, lr = 1e-05
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I0405 11:37:42.992799 26038 solver.cpp:218] Iteration 12540 (2.28965 iter/s, 5.24099s/12 iters), loss = 5.27438
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I0405 11:37:42.992842 26038 solver.cpp:237] Train net output #0: loss = 5.27438 (* 1 = 5.27438 loss)
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I0405 11:37:42.992848 26038 sgd_solver.cpp:105] Iteration 12540, lr = 1e-05
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I0405 11:37:44.043256 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:37:45.187654 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12546.caffemodel
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I0405 11:37:48.164328 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12546.solverstate
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I0405 11:37:51.227602 26038 solver.cpp:330] Iteration 12546, Testing net (#0)
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I0405 11:37:51.227622 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:37:55.235636 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:37:55.565493 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:37:55.565531 26038 solver.cpp:397] Test net output #1: loss = 5.27935 (* 1 = 5.27935 loss)
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I0405 11:37:57.364953 26038 solver.cpp:218] Iteration 12552 (0.834956 iter/s, 14.372s/12 iters), loss = 5.2754
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I0405 11:37:57.364998 26038 solver.cpp:237] Train net output #0: loss = 5.2754 (* 1 = 5.2754 loss)
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I0405 11:37:57.365005 26038 sgd_solver.cpp:105] Iteration 12552, lr = 1e-05
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I0405 11:38:02.608186 26038 solver.cpp:218] Iteration 12564 (2.2887 iter/s, 5.24314s/12 iters), loss = 5.27378
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I0405 11:38:02.608227 26038 solver.cpp:237] Train net output #0: loss = 5.27378 (* 1 = 5.27378 loss)
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I0405 11:38:02.608232 26038 sgd_solver.cpp:105] Iteration 12564, lr = 1e-05
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I0405 11:38:07.890446 26038 solver.cpp:218] Iteration 12576 (2.27179 iter/s, 5.28217s/12 iters), loss = 5.27515
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I0405 11:38:07.890489 26038 solver.cpp:237] Train net output #0: loss = 5.27515 (* 1 = 5.27515 loss)
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I0405 11:38:07.890493 26038 sgd_solver.cpp:105] Iteration 12576, lr = 1e-05
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I0405 11:38:13.363209 26038 solver.cpp:218] Iteration 12588 (2.19271 iter/s, 5.47267s/12 iters), loss = 5.2792
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I0405 11:38:13.363261 26038 solver.cpp:237] Train net output #0: loss = 5.2792 (* 1 = 5.2792 loss)
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I0405 11:38:13.363270 26038 sgd_solver.cpp:105] Iteration 12588, lr = 1e-05
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I0405 11:38:18.749467 26038 solver.cpp:218] Iteration 12600 (2.22793 iter/s, 5.38616s/12 iters), loss = 5.28937
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I0405 11:38:18.749603 26038 solver.cpp:237] Train net output #0: loss = 5.28937 (* 1 = 5.28937 loss)
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I0405 11:38:18.749611 26038 sgd_solver.cpp:105] Iteration 12600, lr = 1e-05
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I0405 11:38:24.083333 26038 solver.cpp:218] Iteration 12612 (2.24985 iter/s, 5.33369s/12 iters), loss = 5.25526
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I0405 11:38:24.083384 26038 solver.cpp:237] Train net output #0: loss = 5.25526 (* 1 = 5.25526 loss)
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I0405 11:38:24.083389 26038 sgd_solver.cpp:105] Iteration 12612, lr = 1e-05
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I0405 11:38:29.588443 26038 solver.cpp:218] Iteration 12624 (2.17983 iter/s, 5.50501s/12 iters), loss = 5.26654
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I0405 11:38:29.588490 26038 solver.cpp:237] Train net output #0: loss = 5.26654 (* 1 = 5.26654 loss)
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I0405 11:38:29.588496 26038 sgd_solver.cpp:105] Iteration 12624, lr = 1e-05
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I0405 11:38:34.894446 26038 solver.cpp:218] Iteration 12636 (2.26163 iter/s, 5.30591s/12 iters), loss = 5.2863
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I0405 11:38:34.894502 26038 solver.cpp:237] Train net output #0: loss = 5.2863 (* 1 = 5.2863 loss)
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I0405 11:38:34.894511 26038 sgd_solver.cpp:105] Iteration 12636, lr = 1e-05
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I0405 11:38:38.192688 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:38:39.738782 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12648.caffemodel
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I0405 11:38:42.765568 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12648.solverstate
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I0405 11:38:45.126263 26038 solver.cpp:330] Iteration 12648, Testing net (#0)
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I0405 11:38:45.126281 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:38:49.169849 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:38:49.546617 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:38:49.546653 26038 solver.cpp:397] Test net output #1: loss = 5.27963 (* 1 = 5.27963 loss)
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I0405 11:38:49.688591 26038 solver.cpp:218] Iteration 12648 (0.81114 iter/s, 14.794s/12 iters), loss = 5.27661
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I0405 11:38:49.688644 26038 solver.cpp:237] Train net output #0: loss = 5.27661 (* 1 = 5.27661 loss)
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I0405 11:38:49.688652 26038 sgd_solver.cpp:105] Iteration 12648, lr = 1e-05
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I0405 11:38:54.008072 26038 solver.cpp:218] Iteration 12660 (2.77817 iter/s, 4.31939s/12 iters), loss = 5.27615
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I0405 11:38:54.008111 26038 solver.cpp:237] Train net output #0: loss = 5.27615 (* 1 = 5.27615 loss)
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I0405 11:38:54.008114 26038 sgd_solver.cpp:105] Iteration 12660, lr = 1e-05
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I0405 11:38:59.235550 26038 solver.cpp:218] Iteration 12672 (2.2956 iter/s, 5.22739s/12 iters), loss = 5.28126
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I0405 11:38:59.235590 26038 solver.cpp:237] Train net output #0: loss = 5.28126 (* 1 = 5.28126 loss)
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I0405 11:38:59.235596 26038 sgd_solver.cpp:105] Iteration 12672, lr = 1e-05
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I0405 11:39:04.616087 26038 solver.cpp:218] Iteration 12684 (2.2303 iter/s, 5.38045s/12 iters), loss = 5.30049
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I0405 11:39:04.616128 26038 solver.cpp:237] Train net output #0: loss = 5.30049 (* 1 = 5.30049 loss)
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I0405 11:39:04.616134 26038 sgd_solver.cpp:105] Iteration 12684, lr = 1e-05
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I0405 11:39:09.847096 26038 solver.cpp:218] Iteration 12696 (2.29405 iter/s, 5.23092s/12 iters), loss = 5.28069
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I0405 11:39:09.847141 26038 solver.cpp:237] Train net output #0: loss = 5.28069 (* 1 = 5.28069 loss)
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I0405 11:39:09.847146 26038 sgd_solver.cpp:105] Iteration 12696, lr = 1e-05
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I0405 11:39:14.902521 26038 solver.cpp:218] Iteration 12708 (2.37373 iter/s, 5.05533s/12 iters), loss = 5.27008
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I0405 11:39:14.902573 26038 solver.cpp:237] Train net output #0: loss = 5.27008 (* 1 = 5.27008 loss)
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I0405 11:39:14.902580 26038 sgd_solver.cpp:105] Iteration 12708, lr = 1e-05
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I0405 11:39:20.131506 26038 solver.cpp:218] Iteration 12720 (2.29494 iter/s, 5.22889s/12 iters), loss = 5.28759
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I0405 11:39:20.131623 26038 solver.cpp:237] Train net output #0: loss = 5.28759 (* 1 = 5.28759 loss)
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I0405 11:39:20.131630 26038 sgd_solver.cpp:105] Iteration 12720, lr = 1e-05
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I0405 11:39:25.241506 26038 solver.cpp:218] Iteration 12732 (2.34841 iter/s, 5.10984s/12 iters), loss = 5.27666
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I0405 11:39:25.241544 26038 solver.cpp:237] Train net output #0: loss = 5.27666 (* 1 = 5.27666 loss)
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I0405 11:39:25.241549 26038 sgd_solver.cpp:105] Iteration 12732, lr = 1e-05
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I0405 11:39:30.748613 26038 solver.cpp:218] Iteration 12744 (2.17904 iter/s, 5.50702s/12 iters), loss = 5.2664
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I0405 11:39:30.748670 26038 solver.cpp:237] Train net output #0: loss = 5.2664 (* 1 = 5.2664 loss)
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I0405 11:39:30.748679 26038 sgd_solver.cpp:105] Iteration 12744, lr = 1e-05
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I0405 11:39:30.955765 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:39:33.018677 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12750.caffemodel
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I0405 11:39:35.930733 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12750.solverstate
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I0405 11:39:39.757485 26038 solver.cpp:330] Iteration 12750, Testing net (#0)
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I0405 11:39:39.757505 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:39:43.639479 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:39:44.051437 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:39:44.051476 26038 solver.cpp:397] Test net output #1: loss = 5.27942 (* 1 = 5.27942 loss)
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I0405 11:39:45.901073 26038 solver.cpp:218] Iteration 12756 (0.791959 iter/s, 15.1523s/12 iters), loss = 5.27886
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I0405 11:39:45.901115 26038 solver.cpp:237] Train net output #0: loss = 5.27886 (* 1 = 5.27886 loss)
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||
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I0405 11:39:45.901121 26038 sgd_solver.cpp:105] Iteration 12756, lr = 1e-05
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I0405 11:39:51.049846 26038 solver.cpp:218] Iteration 12768 (2.33069 iter/s, 5.14868s/12 iters), loss = 5.27176
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I0405 11:39:51.050004 26038 solver.cpp:237] Train net output #0: loss = 5.27176 (* 1 = 5.27176 loss)
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I0405 11:39:51.050014 26038 sgd_solver.cpp:105] Iteration 12768, lr = 1e-05
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I0405 11:39:56.392839 26038 solver.cpp:218] Iteration 12780 (2.24602 iter/s, 5.34279s/12 iters), loss = 5.26556
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I0405 11:39:56.392901 26038 solver.cpp:237] Train net output #0: loss = 5.26556 (* 1 = 5.26556 loss)
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||
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I0405 11:39:56.392910 26038 sgd_solver.cpp:105] Iteration 12780, lr = 1e-05
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I0405 11:40:01.661602 26038 solver.cpp:218] Iteration 12792 (2.27762 iter/s, 5.26866s/12 iters), loss = 5.29067
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I0405 11:40:01.661643 26038 solver.cpp:237] Train net output #0: loss = 5.29067 (* 1 = 5.29067 loss)
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I0405 11:40:01.661648 26038 sgd_solver.cpp:105] Iteration 12792, lr = 1e-05
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I0405 11:40:07.092089 26038 solver.cpp:218] Iteration 12804 (2.20978 iter/s, 5.43039s/12 iters), loss = 5.28139
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I0405 11:40:07.092144 26038 solver.cpp:237] Train net output #0: loss = 5.28139 (* 1 = 5.28139 loss)
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||
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I0405 11:40:07.092151 26038 sgd_solver.cpp:105] Iteration 12804, lr = 1e-05
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||
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I0405 11:40:12.472172 26038 solver.cpp:218] Iteration 12816 (2.23049 iter/s, 5.37998s/12 iters), loss = 5.28711
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||
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I0405 11:40:12.472213 26038 solver.cpp:237] Train net output #0: loss = 5.28711 (* 1 = 5.28711 loss)
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||
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I0405 11:40:12.472218 26038 sgd_solver.cpp:105] Iteration 12816, lr = 1e-05
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I0405 11:40:17.864524 26038 solver.cpp:218] Iteration 12828 (2.22541 iter/s, 5.39226s/12 iters), loss = 5.28618
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||
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I0405 11:40:17.864568 26038 solver.cpp:237] Train net output #0: loss = 5.28618 (* 1 = 5.28618 loss)
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||
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I0405 11:40:17.864573 26038 sgd_solver.cpp:105] Iteration 12828, lr = 1e-05
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I0405 11:40:23.388248 26038 solver.cpp:218] Iteration 12840 (2.17248 iter/s, 5.52363s/12 iters), loss = 5.26966
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||
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I0405 11:40:23.388367 26038 solver.cpp:237] Train net output #0: loss = 5.26966 (* 1 = 5.26966 loss)
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I0405 11:40:23.388376 26038 sgd_solver.cpp:105] Iteration 12840, lr = 1e-05
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I0405 11:40:25.782969 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:40:28.214917 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12852.caffemodel
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||
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I0405 11:40:31.350451 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12852.solverstate
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||
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I0405 11:40:33.659941 26038 solver.cpp:330] Iteration 12852, Testing net (#0)
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I0405 11:40:33.659962 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 11:40:37.587237 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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|
I0405 11:40:38.041352 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:40:38.041388 26038 solver.cpp:397] Test net output #1: loss = 5.27961 (* 1 = 5.27961 loss)
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||
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I0405 11:40:38.183390 26038 solver.cpp:218] Iteration 12852 (0.811089 iter/s, 14.7949s/12 iters), loss = 5.27872
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||
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I0405 11:40:38.183439 26038 solver.cpp:237] Train net output #0: loss = 5.27872 (* 1 = 5.27872 loss)
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||
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I0405 11:40:38.183445 26038 sgd_solver.cpp:105] Iteration 12852, lr = 1e-05
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I0405 11:40:42.724613 26038 solver.cpp:218] Iteration 12864 (2.64251 iter/s, 4.54114s/12 iters), loss = 5.26952
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I0405 11:40:42.724654 26038 solver.cpp:237] Train net output #0: loss = 5.26952 (* 1 = 5.26952 loss)
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||
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I0405 11:40:42.724660 26038 sgd_solver.cpp:105] Iteration 12864, lr = 1e-05
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I0405 11:40:48.360436 26038 solver.cpp:218] Iteration 12876 (2.12927 iter/s, 5.63573s/12 iters), loss = 5.27854
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I0405 11:40:48.360473 26038 solver.cpp:237] Train net output #0: loss = 5.27854 (* 1 = 5.27854 loss)
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||
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I0405 11:40:48.360479 26038 sgd_solver.cpp:105] Iteration 12876, lr = 1e-05
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I0405 11:40:53.704237 26038 solver.cpp:218] Iteration 12888 (2.24563 iter/s, 5.34372s/12 iters), loss = 5.27053
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I0405 11:40:53.704372 26038 solver.cpp:237] Train net output #0: loss = 5.27053 (* 1 = 5.27053 loss)
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||
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I0405 11:40:53.704378 26038 sgd_solver.cpp:105] Iteration 12888, lr = 1e-05
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I0405 11:40:59.180929 26038 solver.cpp:218] Iteration 12900 (2.19132 iter/s, 5.47615s/12 iters), loss = 5.27022
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I0405 11:40:59.180979 26038 solver.cpp:237] Train net output #0: loss = 5.27022 (* 1 = 5.27022 loss)
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I0405 11:40:59.180986 26038 sgd_solver.cpp:105] Iteration 12900, lr = 1e-05
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I0405 11:41:04.541219 26038 solver.cpp:218] Iteration 12912 (2.23873 iter/s, 5.36019s/12 iters), loss = 5.28449
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I0405 11:41:04.541270 26038 solver.cpp:237] Train net output #0: loss = 5.28449 (* 1 = 5.28449 loss)
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I0405 11:41:04.541280 26038 sgd_solver.cpp:105] Iteration 12912, lr = 1e-05
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I0405 11:41:09.821846 26038 solver.cpp:218] Iteration 12924 (2.2725 iter/s, 5.28053s/12 iters), loss = 5.29275
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I0405 11:41:09.821887 26038 solver.cpp:237] Train net output #0: loss = 5.29275 (* 1 = 5.29275 loss)
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I0405 11:41:09.821893 26038 sgd_solver.cpp:105] Iteration 12924, lr = 1e-05
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I0405 11:41:15.313809 26038 solver.cpp:218] Iteration 12936 (2.18505 iter/s, 5.49187s/12 iters), loss = 5.27891
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I0405 11:41:15.313868 26038 solver.cpp:237] Train net output #0: loss = 5.27891 (* 1 = 5.27891 loss)
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I0405 11:41:15.313876 26038 sgd_solver.cpp:105] Iteration 12936, lr = 1e-05
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I0405 11:41:19.949018 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:41:20.575331 26038 solver.cpp:218] Iteration 12948 (2.28075 iter/s, 5.26142s/12 iters), loss = 5.28964
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I0405 11:41:20.575376 26038 solver.cpp:237] Train net output #0: loss = 5.28964 (* 1 = 5.28964 loss)
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I0405 11:41:20.575382 26038 sgd_solver.cpp:105] Iteration 12948, lr = 1e-05
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I0405 11:41:22.729147 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12954.caffemodel
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I0405 11:41:25.943100 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12954.solverstate
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I0405 11:41:28.244405 26038 solver.cpp:330] Iteration 12954, Testing net (#0)
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I0405 11:41:28.244426 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:41:32.067715 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:41:32.630506 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:41:32.630543 26038 solver.cpp:397] Test net output #1: loss = 5.27947 (* 1 = 5.27947 loss)
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I0405 11:41:34.486464 26038 solver.cpp:218] Iteration 12960 (0.862627 iter/s, 13.911s/12 iters), loss = 5.26192
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I0405 11:41:34.486510 26038 solver.cpp:237] Train net output #0: loss = 5.26192 (* 1 = 5.26192 loss)
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I0405 11:41:34.486515 26038 sgd_solver.cpp:105] Iteration 12960, lr = 1e-05
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I0405 11:41:39.726414 26038 solver.cpp:218] Iteration 12972 (2.29014 iter/s, 5.23986s/12 iters), loss = 5.28821
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I0405 11:41:39.726459 26038 solver.cpp:237] Train net output #0: loss = 5.28821 (* 1 = 5.28821 loss)
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I0405 11:41:39.726464 26038 sgd_solver.cpp:105] Iteration 12972, lr = 1e-05
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I0405 11:41:45.277040 26038 solver.cpp:218] Iteration 12984 (2.16196 iter/s, 5.55053s/12 iters), loss = 5.29457
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I0405 11:41:45.277091 26038 solver.cpp:237] Train net output #0: loss = 5.29457 (* 1 = 5.29457 loss)
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I0405 11:41:45.277101 26038 sgd_solver.cpp:105] Iteration 12984, lr = 1e-05
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I0405 11:41:50.824613 26038 solver.cpp:218] Iteration 12996 (2.16315 iter/s, 5.54747s/12 iters), loss = 5.28361
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I0405 11:41:50.824663 26038 solver.cpp:237] Train net output #0: loss = 5.28361 (* 1 = 5.28361 loss)
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I0405 11:41:50.824671 26038 sgd_solver.cpp:105] Iteration 12996, lr = 1e-05
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I0405 11:41:56.428488 26038 solver.cpp:218] Iteration 13008 (2.14141 iter/s, 5.60377s/12 iters), loss = 5.26857
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I0405 11:41:56.428614 26038 solver.cpp:237] Train net output #0: loss = 5.26857 (* 1 = 5.26857 loss)
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I0405 11:41:56.428623 26038 sgd_solver.cpp:105] Iteration 13008, lr = 1e-05
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I0405 11:42:01.657536 26038 solver.cpp:218] Iteration 13020 (2.29495 iter/s, 5.22888s/12 iters), loss = 5.27958
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||
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I0405 11:42:01.657582 26038 solver.cpp:237] Train net output #0: loss = 5.27958 (* 1 = 5.27958 loss)
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||
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I0405 11:42:01.657588 26038 sgd_solver.cpp:105] Iteration 13020, lr = 1e-05
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I0405 11:42:06.986485 26038 solver.cpp:218] Iteration 13032 (2.25189 iter/s, 5.32885s/12 iters), loss = 5.27287
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I0405 11:42:06.986532 26038 solver.cpp:237] Train net output #0: loss = 5.27287 (* 1 = 5.27287 loss)
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||
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I0405 11:42:06.986539 26038 sgd_solver.cpp:105] Iteration 13032, lr = 1e-05
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I0405 11:42:12.510825 26038 solver.cpp:218] Iteration 13044 (2.17224 iter/s, 5.52425s/12 iters), loss = 5.28878
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I0405 11:42:12.510864 26038 solver.cpp:237] Train net output #0: loss = 5.28878 (* 1 = 5.28878 loss)
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||
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I0405 11:42:12.510869 26038 sgd_solver.cpp:105] Iteration 13044, lr = 1e-05
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I0405 11:42:14.415203 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:42:17.548300 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13056.caffemodel
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||
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I0405 11:42:20.569535 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13056.solverstate
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I0405 11:42:22.874666 26038 solver.cpp:330] Iteration 13056, Testing net (#0)
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I0405 11:42:22.874688 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:42:26.975173 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:42:27.581435 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:42:27.581472 26038 solver.cpp:397] Test net output #1: loss = 5.27939 (* 1 = 5.27939 loss)
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I0405 11:42:27.720913 26038 solver.cpp:218] Iteration 13056 (0.788957 iter/s, 15.2099s/12 iters), loss = 5.27332
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I0405 11:42:27.720955 26038 solver.cpp:237] Train net output #0: loss = 5.27332 (* 1 = 5.27332 loss)
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I0405 11:42:27.720961 26038 sgd_solver.cpp:105] Iteration 13056, lr = 1e-05
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I0405 11:42:32.256484 26038 solver.cpp:218] Iteration 13068 (2.64581 iter/s, 4.53548s/12 iters), loss = 5.26131
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I0405 11:42:32.262816 26038 solver.cpp:237] Train net output #0: loss = 5.26131 (* 1 = 5.26131 loss)
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I0405 11:42:32.262831 26038 sgd_solver.cpp:105] Iteration 13068, lr = 1e-05
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I0405 11:42:37.851224 26038 solver.cpp:218] Iteration 13080 (2.14732 iter/s, 5.58837s/12 iters), loss = 5.27142
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||
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I0405 11:42:37.851269 26038 solver.cpp:237] Train net output #0: loss = 5.27142 (* 1 = 5.27142 loss)
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I0405 11:42:37.851275 26038 sgd_solver.cpp:105] Iteration 13080, lr = 1e-05
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I0405 11:42:43.362324 26038 solver.cpp:218] Iteration 13092 (2.17746 iter/s, 5.511s/12 iters), loss = 5.26719
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I0405 11:42:43.362380 26038 solver.cpp:237] Train net output #0: loss = 5.26719 (* 1 = 5.26719 loss)
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I0405 11:42:43.362388 26038 sgd_solver.cpp:105] Iteration 13092, lr = 1e-05
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I0405 11:42:49.134016 26038 solver.cpp:218] Iteration 13104 (2.07915 iter/s, 5.77159s/12 iters), loss = 5.28575
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I0405 11:42:49.134061 26038 solver.cpp:237] Train net output #0: loss = 5.28575 (* 1 = 5.28575 loss)
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||
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I0405 11:42:49.134068 26038 sgd_solver.cpp:105] Iteration 13104, lr = 1e-05
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I0405 11:42:54.474839 26038 solver.cpp:218] Iteration 13116 (2.24689 iter/s, 5.34072s/12 iters), loss = 5.28009
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I0405 11:42:54.474893 26038 solver.cpp:237] Train net output #0: loss = 5.28009 (* 1 = 5.28009 loss)
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I0405 11:42:54.474902 26038 sgd_solver.cpp:105] Iteration 13116, lr = 1e-05
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I0405 11:43:00.122767 26038 solver.cpp:218] Iteration 13128 (2.12471 iter/s, 5.64782s/12 iters), loss = 5.29685
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I0405 11:43:00.122884 26038 solver.cpp:237] Train net output #0: loss = 5.29685 (* 1 = 5.29685 loss)
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I0405 11:43:00.122892 26038 sgd_solver.cpp:105] Iteration 13128, lr = 1e-05
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I0405 11:43:05.513657 26038 solver.cpp:218] Iteration 13140 (2.22605 iter/s, 5.39072s/12 iters), loss = 5.26286
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I0405 11:43:05.513703 26038 solver.cpp:237] Train net output #0: loss = 5.26286 (* 1 = 5.26286 loss)
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||
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I0405 11:43:05.513708 26038 sgd_solver.cpp:105] Iteration 13140, lr = 1e-05
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I0405 11:43:09.750144 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:43:11.003327 26038 solver.cpp:218] Iteration 13152 (2.18596 iter/s, 5.48957s/12 iters), loss = 5.27353
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I0405 11:43:11.003383 26038 solver.cpp:237] Train net output #0: loss = 5.27353 (* 1 = 5.27353 loss)
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I0405 11:43:11.003398 26038 sgd_solver.cpp:105] Iteration 13152, lr = 1e-05
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I0405 11:43:13.267271 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13158.caffemodel
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||
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I0405 11:43:16.304296 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13158.solverstate
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I0405 11:43:18.664562 26038 solver.cpp:330] Iteration 13158, Testing net (#0)
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I0405 11:43:18.664587 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:43:22.406199 26038 blocking_queue.cpp:49] Waiting for data
|
||
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I0405 11:43:22.635543 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:43:23.233126 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:43:23.233167 26038 solver.cpp:397] Test net output #1: loss = 5.27964 (* 1 = 5.27964 loss)
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I0405 11:43:25.385274 26038 solver.cpp:218] Iteration 13164 (0.834388 iter/s, 14.3818s/12 iters), loss = 5.27004
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I0405 11:43:25.385318 26038 solver.cpp:237] Train net output #0: loss = 5.27004 (* 1 = 5.27004 loss)
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I0405 11:43:25.385324 26038 sgd_solver.cpp:105] Iteration 13164, lr = 1e-05
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I0405 11:43:30.877004 26038 solver.cpp:218] Iteration 13176 (2.18514 iter/s, 5.49164s/12 iters), loss = 5.28011
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I0405 11:43:30.877183 26038 solver.cpp:237] Train net output #0: loss = 5.28011 (* 1 = 5.28011 loss)
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I0405 11:43:30.877192 26038 sgd_solver.cpp:105] Iteration 13176, lr = 1e-05
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I0405 11:43:36.462577 26038 solver.cpp:218] Iteration 13188 (2.14848 iter/s, 5.58534s/12 iters), loss = 5.27377
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||
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I0405 11:43:36.462636 26038 solver.cpp:237] Train net output #0: loss = 5.27377 (* 1 = 5.27377 loss)
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||
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I0405 11:43:36.462648 26038 sgd_solver.cpp:105] Iteration 13188, lr = 1e-05
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||
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I0405 11:43:41.802523 26038 solver.cpp:218] Iteration 13200 (2.24726 iter/s, 5.33984s/12 iters), loss = 5.26665
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I0405 11:43:41.802561 26038 solver.cpp:237] Train net output #0: loss = 5.26665 (* 1 = 5.26665 loss)
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||
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I0405 11:43:41.802567 26038 sgd_solver.cpp:105] Iteration 13200, lr = 1e-05
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I0405 11:43:46.962177 26038 solver.cpp:218] Iteration 13212 (2.32578 iter/s, 5.15956s/12 iters), loss = 5.30006
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||
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I0405 11:43:46.962222 26038 solver.cpp:237] Train net output #0: loss = 5.30006 (* 1 = 5.30006 loss)
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||
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I0405 11:43:46.962229 26038 sgd_solver.cpp:105] Iteration 13212, lr = 1e-05
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||
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I0405 11:43:52.447391 26038 solver.cpp:218] Iteration 13224 (2.18774 iter/s, 5.48512s/12 iters), loss = 5.26728
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||
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I0405 11:43:52.447443 26038 solver.cpp:237] Train net output #0: loss = 5.26728 (* 1 = 5.26728 loss)
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||
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I0405 11:43:52.447450 26038 sgd_solver.cpp:105] Iteration 13224, lr = 1e-05
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||
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I0405 11:43:58.040370 26038 solver.cpp:218] Iteration 13236 (2.14558 iter/s, 5.59288s/12 iters), loss = 5.27534
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||
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I0405 11:43:58.040410 26038 solver.cpp:237] Train net output #0: loss = 5.27534 (* 1 = 5.27534 loss)
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||
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|
I0405 11:43:58.040414 26038 sgd_solver.cpp:105] Iteration 13236, lr = 1e-05
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||
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I0405 11:44:03.461473 26038 solver.cpp:218] Iteration 13248 (2.21361 iter/s, 5.42101s/12 iters), loss = 5.25958
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||
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I0405 11:44:03.461597 26038 solver.cpp:237] Train net output #0: loss = 5.25958 (* 1 = 5.25958 loss)
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||
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I0405 11:44:03.461606 26038 sgd_solver.cpp:105] Iteration 13248, lr = 1e-05
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||
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I0405 11:44:04.418107 26057 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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|
I0405 11:44:08.419756 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13260.caffemodel
|
||
|
|
I0405 11:44:11.525966 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13260.solverstate
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||
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I0405 11:44:13.865236 26038 solver.cpp:330] Iteration 13260, Testing net (#0)
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||
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I0405 11:44:13.865262 26038 net.cpp:676] Ignoring source layer train-data
|
||
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|
I0405 11:44:17.675762 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 11:44:18.274873 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 11:44:18.274906 26038 solver.cpp:397] Test net output #1: loss = 5.27971 (* 1 = 5.27971 loss)
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||
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I0405 11:44:18.413728 26038 solver.cpp:218] Iteration 13260 (0.802567 iter/s, 14.952s/12 iters), loss = 5.26823
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||
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I0405 11:44:18.413779 26038 solver.cpp:237] Train net output #0: loss = 5.26823 (* 1 = 5.26823 loss)
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||
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I0405 11:44:18.413785 26038 sgd_solver.cpp:105] Iteration 13260, lr = 1e-05
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||
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I0405 11:44:22.873401 26038 solver.cpp:218] Iteration 13272 (2.69084 iter/s, 4.45957s/12 iters), loss = 5.26995
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||
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I0405 11:44:22.873463 26038 solver.cpp:237] Train net output #0: loss = 5.26995 (* 1 = 5.26995 loss)
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||
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I0405 11:44:22.873471 26038 sgd_solver.cpp:105] Iteration 13272, lr = 1e-05
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||
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I0405 11:44:28.532079 26038 solver.cpp:218] Iteration 13284 (2.12068 iter/s, 5.65856s/12 iters), loss = 5.27821
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||
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I0405 11:44:28.532138 26038 solver.cpp:237] Train net output #0: loss = 5.27821 (* 1 = 5.27821 loss)
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||
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I0405 11:44:28.532146 26038 sgd_solver.cpp:105] Iteration 13284, lr = 1e-05
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||
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I0405 11:44:33.880951 26038 solver.cpp:218] Iteration 13296 (2.24351 iter/s, 5.34876s/12 iters), loss = 5.27415
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||
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I0405 11:44:33.881116 26038 solver.cpp:237] Train net output #0: loss = 5.27415 (* 1 = 5.27415 loss)
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||
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I0405 11:44:33.881125 26038 sgd_solver.cpp:105] Iteration 13296, lr = 1e-05
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I0405 11:44:39.350565 26038 solver.cpp:218] Iteration 13308 (2.19402 iter/s, 5.46941s/12 iters), loss = 5.28426
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I0405 11:44:39.350607 26038 solver.cpp:237] Train net output #0: loss = 5.28426 (* 1 = 5.28426 loss)
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||
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I0405 11:44:39.350612 26038 sgd_solver.cpp:105] Iteration 13308, lr = 1e-05
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I0405 11:44:44.770437 26038 solver.cpp:218] Iteration 13320 (2.21411 iter/s, 5.41978s/12 iters), loss = 5.27405
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I0405 11:44:44.770484 26038 solver.cpp:237] Train net output #0: loss = 5.27405 (* 1 = 5.27405 loss)
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||
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I0405 11:44:44.770493 26038 sgd_solver.cpp:105] Iteration 13320, lr = 1e-05
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||
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I0405 11:44:50.242483 26038 solver.cpp:218] Iteration 13332 (2.193 iter/s, 5.47195s/12 iters), loss = 5.24785
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||
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I0405 11:44:50.242537 26038 solver.cpp:237] Train net output #0: loss = 5.24785 (* 1 = 5.24785 loss)
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||
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I0405 11:44:50.242545 26038 sgd_solver.cpp:105] Iteration 13332, lr = 1e-05
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||
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I0405 11:44:55.555766 26038 solver.cpp:218] Iteration 13344 (2.25854 iter/s, 5.31317s/12 iters), loss = 5.27481
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||
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I0405 11:44:55.555830 26038 solver.cpp:237] Train net output #0: loss = 5.27481 (* 1 = 5.27481 loss)
|
||
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I0405 11:44:55.555840 26038 sgd_solver.cpp:105] Iteration 13344, lr = 1e-05
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||
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I0405 11:44:59.139147 26057 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 11:45:01.224648 26038 solver.cpp:218] Iteration 13356 (2.11686 iter/s, 5.66877s/12 iters), loss = 5.27129
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I0405 11:45:01.224700 26038 solver.cpp:237] Train net output #0: loss = 5.27129 (* 1 = 5.27129 loss)
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||
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I0405 11:45:01.224709 26038 sgd_solver.cpp:105] Iteration 13356, lr = 1e-05
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||
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I0405 11:45:03.413570 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13362.caffemodel
|
||
|
|
I0405 11:45:06.561218 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13362.solverstate
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||
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I0405 11:45:08.937026 26038 solver.cpp:330] Iteration 13362, Testing net (#0)
|
||
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I0405 11:45:08.937045 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
|
I0405 11:45:12.867391 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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|
I0405 11:45:13.585683 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
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I0405 11:45:13.585711 26038 solver.cpp:397] Test net output #1: loss = 5.2795 (* 1 = 5.2795 loss)
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I0405 11:45:15.645946 26038 solver.cpp:218] Iteration 13368 (0.832111 iter/s, 14.4211s/12 iters), loss = 5.27116
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I0405 11:45:15.645988 26038 solver.cpp:237] Train net output #0: loss = 5.27116 (* 1 = 5.27116 loss)
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I0405 11:45:15.645993 26038 sgd_solver.cpp:105] Iteration 13368, lr = 1e-05
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I0405 11:45:21.147874 26038 solver.cpp:218] Iteration 13380 (2.18109 iter/s, 5.50183s/12 iters), loss = 5.28244
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I0405 11:45:21.147915 26038 solver.cpp:237] Train net output #0: loss = 5.28244 (* 1 = 5.28244 loss)
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I0405 11:45:21.147922 26038 sgd_solver.cpp:105] Iteration 13380, lr = 1e-05
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I0405 11:45:26.556053 26038 solver.cpp:218] Iteration 13392 (2.2189 iter/s, 5.40809s/12 iters), loss = 5.28372
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I0405 11:45:26.556094 26038 solver.cpp:237] Train net output #0: loss = 5.28372 (* 1 = 5.28372 loss)
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I0405 11:45:26.556099 26038 sgd_solver.cpp:105] Iteration 13392, lr = 1e-05
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I0405 11:45:32.013200 26038 solver.cpp:218] Iteration 13404 (2.19899 iter/s, 5.45705s/12 iters), loss = 5.25481
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I0405 11:45:32.013258 26038 solver.cpp:237] Train net output #0: loss = 5.25481 (* 1 = 5.25481 loss)
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I0405 11:45:32.013267 26038 sgd_solver.cpp:105] Iteration 13404, lr = 1e-05
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I0405 11:45:37.609874 26038 solver.cpp:218] Iteration 13416 (2.14417 iter/s, 5.59657s/12 iters), loss = 5.27795
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I0405 11:45:37.610013 26038 solver.cpp:237] Train net output #0: loss = 5.27795 (* 1 = 5.27795 loss)
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I0405 11:45:37.610018 26038 sgd_solver.cpp:105] Iteration 13416, lr = 1e-05
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I0405 11:45:43.111918 26038 solver.cpp:218] Iteration 13428 (2.18108 iter/s, 5.50185s/12 iters), loss = 5.26322
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I0405 11:45:43.111971 26038 solver.cpp:237] Train net output #0: loss = 5.26322 (* 1 = 5.26322 loss)
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I0405 11:45:43.111979 26038 sgd_solver.cpp:105] Iteration 13428, lr = 1e-05
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I0405 11:45:48.585161 26038 solver.cpp:218] Iteration 13440 (2.19253 iter/s, 5.47314s/12 iters), loss = 5.26438
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I0405 11:45:48.585217 26038 solver.cpp:237] Train net output #0: loss = 5.26438 (* 1 = 5.26438 loss)
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I0405 11:45:48.585227 26038 sgd_solver.cpp:105] Iteration 13440, lr = 1e-05
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I0405 11:45:53.958937 26038 solver.cpp:218] Iteration 13452 (2.23311 iter/s, 5.37367s/12 iters), loss = 5.2851
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I0405 11:45:53.958981 26038 solver.cpp:237] Train net output #0: loss = 5.2851 (* 1 = 5.2851 loss)
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I0405 11:45:53.958986 26038 sgd_solver.cpp:105] Iteration 13452, lr = 1e-05
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I0405 11:45:54.188467 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:45:58.597070 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13464.caffemodel
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I0405 11:46:01.645714 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13464.solverstate
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I0405 11:46:03.958060 26038 solver.cpp:330] Iteration 13464, Testing net (#0)
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I0405 11:46:03.958081 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:46:07.709533 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:46:08.479066 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:46:08.479113 26038 solver.cpp:397] Test net output #1: loss = 5.2797 (* 1 = 5.2797 loss)
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I0405 11:46:08.620303 26038 solver.cpp:218] Iteration 13464 (0.818486 iter/s, 14.6612s/12 iters), loss = 5.26175
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I0405 11:46:08.620348 26038 solver.cpp:237] Train net output #0: loss = 5.26175 (* 1 = 5.26175 loss)
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I0405 11:46:08.620354 26038 sgd_solver.cpp:105] Iteration 13464, lr = 1e-05
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I0405 11:46:13.069164 26038 solver.cpp:218] Iteration 13476 (2.69737 iter/s, 4.44877s/12 iters), loss = 5.28944
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I0405 11:46:13.069205 26038 solver.cpp:237] Train net output #0: loss = 5.28944 (* 1 = 5.28944 loss)
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I0405 11:46:13.069211 26038 sgd_solver.cpp:105] Iteration 13476, lr = 1e-05
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I0405 11:46:18.406252 26038 solver.cpp:218] Iteration 13488 (2.24845 iter/s, 5.337s/12 iters), loss = 5.26653
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I0405 11:46:18.406291 26038 solver.cpp:237] Train net output #0: loss = 5.26653 (* 1 = 5.26653 loss)
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I0405 11:46:18.406296 26038 sgd_solver.cpp:105] Iteration 13488, lr = 1e-05
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I0405 11:46:23.560092 26038 solver.cpp:218] Iteration 13500 (2.3284 iter/s, 5.15375s/12 iters), loss = 5.29433
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I0405 11:46:23.560137 26038 solver.cpp:237] Train net output #0: loss = 5.29433 (* 1 = 5.29433 loss)
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I0405 11:46:23.560142 26038 sgd_solver.cpp:105] Iteration 13500, lr = 1e-05
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I0405 11:46:28.789477 26038 solver.cpp:218] Iteration 13512 (2.29476 iter/s, 5.22929s/12 iters), loss = 5.27882
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I0405 11:46:28.789521 26038 solver.cpp:237] Train net output #0: loss = 5.27882 (* 1 = 5.27882 loss)
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I0405 11:46:28.789527 26038 sgd_solver.cpp:105] Iteration 13512, lr = 1e-05
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I0405 11:46:34.160851 26038 solver.cpp:218] Iteration 13524 (2.2341 iter/s, 5.37128s/12 iters), loss = 5.27438
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I0405 11:46:34.160897 26038 solver.cpp:237] Train net output #0: loss = 5.27438 (* 1 = 5.27438 loss)
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I0405 11:46:34.160904 26038 sgd_solver.cpp:105] Iteration 13524, lr = 1e-05
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I0405 11:46:39.137135 26038 solver.cpp:218] Iteration 13536 (2.41148 iter/s, 4.97619s/12 iters), loss = 5.29072
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I0405 11:46:39.137264 26038 solver.cpp:237] Train net output #0: loss = 5.29072 (* 1 = 5.29072 loss)
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I0405 11:46:39.137270 26038 sgd_solver.cpp:105] Iteration 13536, lr = 1e-05
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I0405 11:46:44.563938 26038 solver.cpp:218] Iteration 13548 (2.21132 iter/s, 5.42663s/12 iters), loss = 5.26563
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I0405 11:46:44.563979 26038 solver.cpp:237] Train net output #0: loss = 5.26563 (* 1 = 5.26563 loss)
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I0405 11:46:44.563984 26038 sgd_solver.cpp:105] Iteration 13548, lr = 1e-05
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I0405 11:46:47.245884 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:46:50.213953 26038 solver.cpp:218] Iteration 13560 (2.12393 iter/s, 5.64992s/12 iters), loss = 5.26713
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I0405 11:46:50.214004 26038 solver.cpp:237] Train net output #0: loss = 5.26713 (* 1 = 5.26713 loss)
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I0405 11:46:50.214012 26038 sgd_solver.cpp:105] Iteration 13560, lr = 1e-05
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I0405 11:46:52.469357 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13566.caffemodel
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I0405 11:46:56.245832 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13566.solverstate
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I0405 11:46:58.562561 26038 solver.cpp:330] Iteration 13566, Testing net (#0)
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I0405 11:46:58.562583 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:47:02.175388 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:47:02.994230 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:47:02.994269 26038 solver.cpp:397] Test net output #1: loss = 5.27987 (* 1 = 5.27987 loss)
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I0405 11:47:05.076790 26038 solver.cpp:218] Iteration 13572 (0.807391 iter/s, 14.8627s/12 iters), loss = 5.28203
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I0405 11:47:05.076846 26038 solver.cpp:237] Train net output #0: loss = 5.28203 (* 1 = 5.28203 loss)
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I0405 11:47:05.076855 26038 sgd_solver.cpp:105] Iteration 13572, lr = 1e-05
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I0405 11:47:10.370309 26038 solver.cpp:218] Iteration 13584 (2.26697 iter/s, 5.29342s/12 iters), loss = 5.28439
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I0405 11:47:10.370451 26038 solver.cpp:237] Train net output #0: loss = 5.28439 (* 1 = 5.28439 loss)
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I0405 11:47:10.370458 26038 sgd_solver.cpp:105] Iteration 13584, lr = 1e-05
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I0405 11:47:15.427714 26038 solver.cpp:218] Iteration 13596 (2.37285 iter/s, 5.05722s/12 iters), loss = 5.27066
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I0405 11:47:15.427762 26038 solver.cpp:237] Train net output #0: loss = 5.27066 (* 1 = 5.27066 loss)
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I0405 11:47:15.427770 26038 sgd_solver.cpp:105] Iteration 13596, lr = 1e-05
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I0405 11:47:20.896163 26038 solver.cpp:218] Iteration 13608 (2.19445 iter/s, 5.46835s/12 iters), loss = 5.2677
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I0405 11:47:20.896225 26038 solver.cpp:237] Train net output #0: loss = 5.2677 (* 1 = 5.2677 loss)
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I0405 11:47:20.896234 26038 sgd_solver.cpp:105] Iteration 13608, lr = 1e-05
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I0405 11:47:26.354823 26038 solver.cpp:218] Iteration 13620 (2.19838 iter/s, 5.45855s/12 iters), loss = 5.28178
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I0405 11:47:26.354863 26038 solver.cpp:237] Train net output #0: loss = 5.28178 (* 1 = 5.28178 loss)
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I0405 11:47:26.354869 26038 sgd_solver.cpp:105] Iteration 13620, lr = 1e-05
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I0405 11:47:31.688012 26038 solver.cpp:218] Iteration 13632 (2.2501 iter/s, 5.3331s/12 iters), loss = 5.27426
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I0405 11:47:31.688071 26038 solver.cpp:237] Train net output #0: loss = 5.27426 (* 1 = 5.27426 loss)
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I0405 11:47:31.688079 26038 sgd_solver.cpp:105] Iteration 13632, lr = 1e-05
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I0405 11:47:37.060138 26038 solver.cpp:218] Iteration 13644 (2.23379 iter/s, 5.37203s/12 iters), loss = 5.26358
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I0405 11:47:37.060178 26038 solver.cpp:237] Train net output #0: loss = 5.26358 (* 1 = 5.26358 loss)
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I0405 11:47:37.060184 26038 sgd_solver.cpp:105] Iteration 13644, lr = 1e-05
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I0405 11:47:41.601976 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:47:42.024106 26038 solver.cpp:218] Iteration 13656 (2.41746 iter/s, 4.96388s/12 iters), loss = 5.29318
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I0405 11:47:42.024156 26038 solver.cpp:237] Train net output #0: loss = 5.29318 (* 1 = 5.29318 loss)
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I0405 11:47:42.024164 26038 sgd_solver.cpp:105] Iteration 13656, lr = 1e-05
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I0405 11:47:46.675971 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13668.caffemodel
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I0405 11:47:49.786288 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13668.solverstate
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I0405 11:47:52.085197 26038 solver.cpp:330] Iteration 13668, Testing net (#0)
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I0405 11:47:52.085217 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:47:55.728826 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:47:56.483867 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:47:56.483906 26038 solver.cpp:397] Test net output #1: loss = 5.27973 (* 1 = 5.27973 loss)
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I0405 11:47:56.625682 26038 solver.cpp:218] Iteration 13668 (0.821837 iter/s, 14.6014s/12 iters), loss = 5.26552
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I0405 11:47:56.625723 26038 solver.cpp:237] Train net output #0: loss = 5.26552 (* 1 = 5.26552 loss)
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I0405 11:47:56.625728 26038 sgd_solver.cpp:105] Iteration 13668, lr = 1e-05
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I0405 11:48:01.100423 26038 solver.cpp:218] Iteration 13680 (2.68177 iter/s, 4.47465s/12 iters), loss = 5.27825
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I0405 11:48:01.100466 26038 solver.cpp:237] Train net output #0: loss = 5.27825 (* 1 = 5.27825 loss)
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I0405 11:48:01.100471 26038 sgd_solver.cpp:105] Iteration 13680, lr = 1e-05
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I0405 11:48:06.536129 26038 solver.cpp:218] Iteration 13692 (2.20766 iter/s, 5.43561s/12 iters), loss = 5.28843
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I0405 11:48:06.536171 26038 solver.cpp:237] Train net output #0: loss = 5.28843 (* 1 = 5.28843 loss)
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||
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I0405 11:48:06.536177 26038 sgd_solver.cpp:105] Iteration 13692, lr = 1e-05
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I0405 11:48:11.834913 26038 solver.cpp:218] Iteration 13704 (2.26471 iter/s, 5.29869s/12 iters), loss = 5.29239
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I0405 11:48:11.835072 26038 solver.cpp:237] Train net output #0: loss = 5.29239 (* 1 = 5.29239 loss)
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I0405 11:48:11.835081 26038 sgd_solver.cpp:105] Iteration 13704, lr = 1e-05
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I0405 11:48:17.296617 26038 solver.cpp:218] Iteration 13716 (2.1972 iter/s, 5.4615s/12 iters), loss = 5.27045
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I0405 11:48:17.296654 26038 solver.cpp:237] Train net output #0: loss = 5.27045 (* 1 = 5.27045 loss)
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I0405 11:48:17.296659 26038 sgd_solver.cpp:105] Iteration 13716, lr = 1e-05
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I0405 11:48:22.375764 26038 solver.cpp:218] Iteration 13728 (2.36264 iter/s, 5.07906s/12 iters), loss = 5.28259
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I0405 11:48:22.375803 26038 solver.cpp:237] Train net output #0: loss = 5.28259 (* 1 = 5.28259 loss)
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||
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I0405 11:48:22.375809 26038 sgd_solver.cpp:105] Iteration 13728, lr = 1e-05
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I0405 11:48:27.824060 26038 solver.cpp:218] Iteration 13740 (2.20256 iter/s, 5.44821s/12 iters), loss = 5.27138
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||
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I0405 11:48:27.824100 26038 solver.cpp:237] Train net output #0: loss = 5.27138 (* 1 = 5.27138 loss)
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||
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I0405 11:48:27.824106 26038 sgd_solver.cpp:105] Iteration 13740, lr = 1e-05
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I0405 11:48:33.112008 26038 solver.cpp:218] Iteration 13752 (2.26935 iter/s, 5.28786s/12 iters), loss = 5.2688
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||
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I0405 11:48:33.112044 26038 solver.cpp:237] Train net output #0: loss = 5.2688 (* 1 = 5.2688 loss)
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||
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I0405 11:48:33.112049 26038 sgd_solver.cpp:105] Iteration 13752, lr = 1e-05
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I0405 11:48:34.907490 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:48:38.266259 26038 solver.cpp:218] Iteration 13764 (2.32821 iter/s, 5.15417s/12 iters), loss = 5.28787
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I0405 11:48:38.266299 26038 solver.cpp:237] Train net output #0: loss = 5.28787 (* 1 = 5.28787 loss)
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I0405 11:48:38.266305 26038 sgd_solver.cpp:105] Iteration 13764, lr = 1e-05
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||
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I0405 11:48:40.489501 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13770.caffemodel
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||
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I0405 11:48:43.445616 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13770.solverstate
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||
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I0405 11:48:45.780709 26038 solver.cpp:330] Iteration 13770, Testing net (#0)
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||
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I0405 11:48:45.780740 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 11:48:49.396034 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 11:48:50.192873 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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|
I0405 11:48:50.192929 26038 solver.cpp:397] Test net output #1: loss = 5.27948 (* 1 = 5.27948 loss)
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I0405 11:48:52.005466 26038 solver.cpp:218] Iteration 13776 (0.873422 iter/s, 13.7391s/12 iters), loss = 5.26957
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I0405 11:48:52.005522 26038 solver.cpp:237] Train net output #0: loss = 5.26957 (* 1 = 5.26957 loss)
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I0405 11:48:52.005529 26038 sgd_solver.cpp:105] Iteration 13776, lr = 1e-05
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I0405 11:48:57.385829 26038 solver.cpp:218] Iteration 13788 (2.23037 iter/s, 5.38026s/12 iters), loss = 5.28578
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I0405 11:48:57.385869 26038 solver.cpp:237] Train net output #0: loss = 5.28578 (* 1 = 5.28578 loss)
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I0405 11:48:57.385874 26038 sgd_solver.cpp:105] Iteration 13788, lr = 1e-05
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I0405 11:49:02.637169 26038 solver.cpp:218] Iteration 13800 (2.28517 iter/s, 5.25125s/12 iters), loss = 5.26768
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I0405 11:49:02.637218 26038 solver.cpp:237] Train net output #0: loss = 5.26768 (* 1 = 5.26768 loss)
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I0405 11:49:02.637225 26038 sgd_solver.cpp:105] Iteration 13800, lr = 1e-05
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I0405 11:49:07.893483 26038 solver.cpp:218] Iteration 13812 (2.28301 iter/s, 5.25621s/12 iters), loss = 5.28035
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I0405 11:49:07.893544 26038 solver.cpp:237] Train net output #0: loss = 5.28035 (* 1 = 5.28035 loss)
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I0405 11:49:07.893554 26038 sgd_solver.cpp:105] Iteration 13812, lr = 1e-05
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I0405 11:49:13.188589 26038 solver.cpp:218] Iteration 13824 (2.26629 iter/s, 5.295s/12 iters), loss = 5.25889
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I0405 11:49:13.188645 26038 solver.cpp:237] Train net output #0: loss = 5.25889 (* 1 = 5.25889 loss)
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I0405 11:49:13.188654 26038 sgd_solver.cpp:105] Iteration 13824, lr = 1e-05
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I0405 11:49:18.571557 26038 solver.cpp:218] Iteration 13836 (2.2293 iter/s, 5.38286s/12 iters), loss = 5.27057
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I0405 11:49:18.571724 26038 solver.cpp:237] Train net output #0: loss = 5.27057 (* 1 = 5.27057 loss)
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I0405 11:49:18.571733 26038 sgd_solver.cpp:105] Iteration 13836, lr = 1e-05
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I0405 11:49:23.823786 26038 solver.cpp:218] Iteration 13848 (2.28484 iter/s, 5.25202s/12 iters), loss = 5.27388
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I0405 11:49:23.823837 26038 solver.cpp:237] Train net output #0: loss = 5.27388 (* 1 = 5.27388 loss)
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I0405 11:49:23.823845 26038 sgd_solver.cpp:105] Iteration 13848, lr = 1e-05
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I0405 11:49:27.845139 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:49:29.012957 26038 solver.cpp:218] Iteration 13860 (2.31255 iter/s, 5.18907s/12 iters), loss = 5.28252
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I0405 11:49:29.012995 26038 solver.cpp:237] Train net output #0: loss = 5.28252 (* 1 = 5.28252 loss)
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I0405 11:49:29.013000 26038 sgd_solver.cpp:105] Iteration 13860, lr = 1e-05
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I0405 11:49:33.799331 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13872.caffemodel
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I0405 11:49:36.812604 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13872.solverstate
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I0405 11:49:39.868729 26038 solver.cpp:330] Iteration 13872, Testing net (#0)
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I0405 11:49:39.868750 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:49:40.808853 26038 blocking_queue.cpp:49] Waiting for data
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I0405 11:49:43.336792 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:49:44.173596 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:49:44.173630 26038 solver.cpp:397] Test net output #1: loss = 5.2799 (* 1 = 5.2799 loss)
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I0405 11:49:44.315659 26038 solver.cpp:218] Iteration 13872 (0.784183 iter/s, 15.3026s/12 iters), loss = 5.26714
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I0405 11:49:44.315722 26038 solver.cpp:237] Train net output #0: loss = 5.26714 (* 1 = 5.26714 loss)
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I0405 11:49:44.315732 26038 sgd_solver.cpp:105] Iteration 13872, lr = 1e-05
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I0405 11:49:48.527312 26038 solver.cpp:218] Iteration 13884 (2.84931 iter/s, 4.21155s/12 iters), loss = 5.27672
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I0405 11:49:48.527349 26038 solver.cpp:237] Train net output #0: loss = 5.27672 (* 1 = 5.27672 loss)
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I0405 11:49:48.527354 26038 sgd_solver.cpp:105] Iteration 13884, lr = 1e-05
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I0405 11:49:53.610200 26038 solver.cpp:218] Iteration 13896 (2.3609 iter/s, 5.08281s/12 iters), loss = 5.27246
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I0405 11:49:53.610314 26038 solver.cpp:237] Train net output #0: loss = 5.27246 (* 1 = 5.27246 loss)
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I0405 11:49:53.610321 26038 sgd_solver.cpp:105] Iteration 13896, lr = 1e-05
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I0405 11:49:59.105628 26038 solver.cpp:218] Iteration 13908 (2.1837 iter/s, 5.49527s/12 iters), loss = 5.28024
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I0405 11:49:59.105674 26038 solver.cpp:237] Train net output #0: loss = 5.28024 (* 1 = 5.28024 loss)
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I0405 11:49:59.105680 26038 sgd_solver.cpp:105] Iteration 13908, lr = 1e-05
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I0405 11:50:04.586647 26038 solver.cpp:218] Iteration 13920 (2.18941 iter/s, 5.48092s/12 iters), loss = 5.2611
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I0405 11:50:04.586699 26038 solver.cpp:237] Train net output #0: loss = 5.2611 (* 1 = 5.2611 loss)
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I0405 11:50:04.586707 26038 sgd_solver.cpp:105] Iteration 13920, lr = 1e-05
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I0405 11:50:10.025604 26038 solver.cpp:218] Iteration 13932 (2.20635 iter/s, 5.43886s/12 iters), loss = 5.27969
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I0405 11:50:10.025645 26038 solver.cpp:237] Train net output #0: loss = 5.27969 (* 1 = 5.27969 loss)
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I0405 11:50:10.025651 26038 sgd_solver.cpp:105] Iteration 13932, lr = 1e-05
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I0405 11:50:15.414422 26038 solver.cpp:218] Iteration 13944 (2.22687 iter/s, 5.38873s/12 iters), loss = 5.25796
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I0405 11:50:15.414479 26038 solver.cpp:237] Train net output #0: loss = 5.25796 (* 1 = 5.25796 loss)
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||
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I0405 11:50:15.414489 26038 sgd_solver.cpp:105] Iteration 13944, lr = 1e-05
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I0405 11:50:20.755863 26038 solver.cpp:218] Iteration 13956 (2.24663 iter/s, 5.34134s/12 iters), loss = 5.27528
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I0405 11:50:20.755903 26038 solver.cpp:237] Train net output #0: loss = 5.27528 (* 1 = 5.27528 loss)
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I0405 11:50:20.755910 26038 sgd_solver.cpp:105] Iteration 13956, lr = 1e-05
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I0405 11:50:21.782984 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:50:26.130470 26038 solver.cpp:218] Iteration 13968 (2.23276 iter/s, 5.37452s/12 iters), loss = 5.26903
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I0405 11:50:26.130616 26038 solver.cpp:237] Train net output #0: loss = 5.26903 (* 1 = 5.26903 loss)
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I0405 11:50:26.130623 26038 sgd_solver.cpp:105] Iteration 13968, lr = 1e-05
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I0405 11:50:28.296685 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13974.caffemodel
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||
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I0405 11:50:31.315582 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13974.solverstate
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I0405 11:50:33.627811 26038 solver.cpp:330] Iteration 13974, Testing net (#0)
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I0405 11:50:33.627835 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:50:37.175542 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:50:38.043344 26038 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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||
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I0405 11:50:38.043390 26038 solver.cpp:397] Test net output #1: loss = 5.27951 (* 1 = 5.27951 loss)
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I0405 11:50:39.906172 26038 solver.cpp:218] Iteration 13980 (0.871114 iter/s, 13.7755s/12 iters), loss = 5.27752
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I0405 11:50:39.906225 26038 solver.cpp:237] Train net output #0: loss = 5.27752 (* 1 = 5.27752 loss)
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I0405 11:50:39.906232 26038 sgd_solver.cpp:105] Iteration 13980, lr = 1e-05
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I0405 11:50:45.129114 26038 solver.cpp:218] Iteration 13992 (2.2976 iter/s, 5.22284s/12 iters), loss = 5.27639
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I0405 11:50:45.129166 26038 solver.cpp:237] Train net output #0: loss = 5.27639 (* 1 = 5.27639 loss)
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I0405 11:50:45.129173 26038 sgd_solver.cpp:105] Iteration 13992, lr = 1e-05
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I0405 11:50:50.365185 26038 solver.cpp:218] Iteration 14004 (2.29184 iter/s, 5.23597s/12 iters), loss = 5.27885
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||
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I0405 11:50:50.365228 26038 solver.cpp:237] Train net output #0: loss = 5.27885 (* 1 = 5.27885 loss)
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||
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I0405 11:50:50.365234 26038 sgd_solver.cpp:105] Iteration 14004, lr = 1e-05
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I0405 11:50:55.753173 26038 solver.cpp:218] Iteration 14016 (2.22722 iter/s, 5.38789s/12 iters), loss = 5.28164
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I0405 11:50:55.753243 26038 solver.cpp:237] Train net output #0: loss = 5.28164 (* 1 = 5.28164 loss)
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||
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I0405 11:50:55.753249 26038 sgd_solver.cpp:105] Iteration 14016, lr = 1e-05
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I0405 11:51:00.909917 26038 solver.cpp:218] Iteration 14028 (2.3271 iter/s, 5.15663s/12 iters), loss = 5.27329
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||
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I0405 11:51:00.910056 26038 solver.cpp:237] Train net output #0: loss = 5.27329 (* 1 = 5.27329 loss)
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I0405 11:51:00.910065 26038 sgd_solver.cpp:105] Iteration 14028, lr = 1e-05
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I0405 11:51:06.292435 26038 solver.cpp:218] Iteration 14040 (2.22952 iter/s, 5.38233s/12 iters), loss = 5.27457
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||
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I0405 11:51:06.292485 26038 solver.cpp:237] Train net output #0: loss = 5.27457 (* 1 = 5.27457 loss)
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||
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I0405 11:51:06.292493 26038 sgd_solver.cpp:105] Iteration 14040, lr = 1e-05
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I0405 11:51:11.607980 26038 solver.cpp:218] Iteration 14052 (2.25757 iter/s, 5.31544s/12 iters), loss = 5.27284
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||
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I0405 11:51:11.608032 26038 solver.cpp:237] Train net output #0: loss = 5.27284 (* 1 = 5.27284 loss)
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||
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I0405 11:51:11.608040 26038 sgd_solver.cpp:105] Iteration 14052, lr = 1e-05
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I0405 11:51:14.984357 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:51:17.019004 26038 solver.cpp:218] Iteration 14064 (2.21773 iter/s, 5.41093s/12 iters), loss = 5.28967
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I0405 11:51:17.019042 26038 solver.cpp:237] Train net output #0: loss = 5.28967 (* 1 = 5.28967 loss)
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I0405 11:51:17.019047 26038 sgd_solver.cpp:105] Iteration 14064, lr = 1e-05
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I0405 11:51:21.972016 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14076.caffemodel
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||
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I0405 11:51:24.918205 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14076.solverstate
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I0405 11:51:27.209594 26038 solver.cpp:330] Iteration 14076, Testing net (#0)
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I0405 11:51:27.209614 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:51:30.613727 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:51:31.550387 26038 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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||
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I0405 11:51:31.550504 26038 solver.cpp:397] Test net output #1: loss = 5.27954 (* 1 = 5.27954 loss)
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I0405 11:51:31.692332 26038 solver.cpp:218] Iteration 14076 (0.817818 iter/s, 14.6732s/12 iters), loss = 5.28092
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I0405 11:51:31.692389 26038 solver.cpp:237] Train net output #0: loss = 5.28092 (* 1 = 5.28092 loss)
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I0405 11:51:31.692395 26038 sgd_solver.cpp:105] Iteration 14076, lr = 1e-05
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I0405 11:51:36.089839 26038 solver.cpp:218] Iteration 14088 (2.72888 iter/s, 4.3974s/12 iters), loss = 5.27929
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I0405 11:51:36.089902 26038 solver.cpp:237] Train net output #0: loss = 5.27929 (* 1 = 5.27929 loss)
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I0405 11:51:36.089911 26038 sgd_solver.cpp:105] Iteration 14088, lr = 1e-05
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I0405 11:51:41.494387 26038 solver.cpp:218] Iteration 14100 (2.2204 iter/s, 5.40444s/12 iters), loss = 5.27963
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I0405 11:51:41.494424 26038 solver.cpp:237] Train net output #0: loss = 5.27963 (* 1 = 5.27963 loss)
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||
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I0405 11:51:41.494429 26038 sgd_solver.cpp:105] Iteration 14100, lr = 1e-05
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I0405 11:51:46.574299 26038 solver.cpp:218] Iteration 14112 (2.36229 iter/s, 5.07982s/12 iters), loss = 5.27124
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I0405 11:51:46.574353 26038 solver.cpp:237] Train net output #0: loss = 5.27124 (* 1 = 5.27124 loss)
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I0405 11:51:46.574364 26038 sgd_solver.cpp:105] Iteration 14112, lr = 1e-05
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I0405 11:51:51.814391 26038 solver.cpp:218] Iteration 14124 (2.29008 iter/s, 5.23999s/12 iters), loss = 5.27651
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I0405 11:51:51.814443 26038 solver.cpp:237] Train net output #0: loss = 5.27651 (* 1 = 5.27651 loss)
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||
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I0405 11:51:51.814452 26038 sgd_solver.cpp:105] Iteration 14124, lr = 1e-05
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I0405 11:51:56.971004 26038 solver.cpp:218] Iteration 14136 (2.32715 iter/s, 5.15651s/12 iters), loss = 5.2695
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I0405 11:51:56.971046 26038 solver.cpp:237] Train net output #0: loss = 5.2695 (* 1 = 5.2695 loss)
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||
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I0405 11:51:56.971051 26038 sgd_solver.cpp:105] Iteration 14136, lr = 1e-05
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I0405 11:52:02.179199 26038 solver.cpp:218] Iteration 14148 (2.3041 iter/s, 5.2081s/12 iters), loss = 5.29057
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||
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I0405 11:52:02.179317 26038 solver.cpp:237] Train net output #0: loss = 5.29057 (* 1 = 5.29057 loss)
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||
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I0405 11:52:02.179327 26038 sgd_solver.cpp:105] Iteration 14148, lr = 1e-05
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I0405 11:52:07.535041 26038 solver.cpp:218] Iteration 14160 (2.24061 iter/s, 5.35568s/12 iters), loss = 5.27768
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||
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I0405 11:52:07.535082 26038 solver.cpp:237] Train net output #0: loss = 5.27768 (* 1 = 5.27768 loss)
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||
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I0405 11:52:07.535089 26038 sgd_solver.cpp:105] Iteration 14160, lr = 1e-05
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I0405 11:52:07.761756 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:52:12.977032 26038 solver.cpp:218] Iteration 14172 (2.20511 iter/s, 5.4419s/12 iters), loss = 5.28341
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||
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I0405 11:52:12.977072 26038 solver.cpp:237] Train net output #0: loss = 5.28341 (* 1 = 5.28341 loss)
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||
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I0405 11:52:12.977077 26038 sgd_solver.cpp:105] Iteration 14172, lr = 1e-05
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||
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I0405 11:52:15.126725 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14178.caffemodel
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||
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|
I0405 11:52:18.160724 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14178.solverstate
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||
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I0405 11:52:20.488258 26038 solver.cpp:330] Iteration 14178, Testing net (#0)
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||
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I0405 11:52:20.488277 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 11:52:23.875669 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 11:52:24.820081 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 11:52:24.820111 26038 solver.cpp:397] Test net output #1: loss = 5.2797 (* 1 = 5.2797 loss)
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||
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I0405 11:52:26.614027 26038 solver.cpp:218] Iteration 14184 (0.879968 iter/s, 13.6369s/12 iters), loss = 5.28221
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||
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I0405 11:52:26.614076 26038 solver.cpp:237] Train net output #0: loss = 5.28221 (* 1 = 5.28221 loss)
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||
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I0405 11:52:26.614084 26038 sgd_solver.cpp:105] Iteration 14184, lr = 1e-05
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I0405 11:52:31.920176 26038 solver.cpp:218] Iteration 14196 (2.26156 iter/s, 5.30606s/12 iters), loss = 5.26436
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I0405 11:52:31.920212 26038 solver.cpp:237] Train net output #0: loss = 5.26436 (* 1 = 5.26436 loss)
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||
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I0405 11:52:31.920217 26038 sgd_solver.cpp:105] Iteration 14196, lr = 1e-05
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I0405 11:52:37.044952 26038 solver.cpp:218] Iteration 14208 (2.34161 iter/s, 5.12469s/12 iters), loss = 5.27136
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||
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I0405 11:52:37.045101 26038 solver.cpp:237] Train net output #0: loss = 5.27136 (* 1 = 5.27136 loss)
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||
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I0405 11:52:37.045110 26038 sgd_solver.cpp:105] Iteration 14208, lr = 1e-05
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I0405 11:52:42.485886 26038 solver.cpp:218] Iteration 14220 (2.20558 iter/s, 5.44074s/12 iters), loss = 5.28518
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||
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I0405 11:52:42.485924 26038 solver.cpp:237] Train net output #0: loss = 5.28518 (* 1 = 5.28518 loss)
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||
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I0405 11:52:42.485929 26038 sgd_solver.cpp:105] Iteration 14220, lr = 1e-05
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I0405 11:52:47.990020 26038 solver.cpp:218] Iteration 14232 (2.18022 iter/s, 5.50404s/12 iters), loss = 5.27433
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||
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I0405 11:52:47.990077 26038 solver.cpp:237] Train net output #0: loss = 5.27433 (* 1 = 5.27433 loss)
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||
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I0405 11:52:47.990084 26038 sgd_solver.cpp:105] Iteration 14232, lr = 1e-05
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||
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I0405 11:52:53.441076 26038 solver.cpp:218] Iteration 14244 (2.20145 iter/s, 5.45095s/12 iters), loss = 5.28039
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||
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I0405 11:52:53.441124 26038 solver.cpp:237] Train net output #0: loss = 5.28039 (* 1 = 5.28039 loss)
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||
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I0405 11:52:53.441131 26038 sgd_solver.cpp:105] Iteration 14244, lr = 1e-05
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||
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I0405 11:52:58.771447 26038 solver.cpp:218] Iteration 14256 (2.25129 iter/s, 5.33027s/12 iters), loss = 5.28373
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||
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I0405 11:52:58.771497 26038 solver.cpp:237] Train net output #0: loss = 5.28373 (* 1 = 5.28373 loss)
|
||
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I0405 11:52:58.771505 26038 sgd_solver.cpp:105] Iteration 14256, lr = 1e-05
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||
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I0405 11:53:01.233078 26057 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 11:53:03.999266 26038 solver.cpp:218] Iteration 14268 (2.29545 iter/s, 5.22773s/12 iters), loss = 5.28976
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I0405 11:53:03.999312 26038 solver.cpp:237] Train net output #0: loss = 5.28976 (* 1 = 5.28976 loss)
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||
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I0405 11:53:03.999320 26038 sgd_solver.cpp:105] Iteration 14268, lr = 1e-05
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||
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I0405 11:53:08.868757 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14280.caffemodel
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||
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|
I0405 11:53:11.851321 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14280.solverstate
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||
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I0405 11:53:14.154590 26038 solver.cpp:330] Iteration 14280, Testing net (#0)
|
||
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I0405 11:53:14.154608 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
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I0405 11:53:17.523999 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
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I0405 11:53:18.618325 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 11:53:18.618364 26038 solver.cpp:397] Test net output #1: loss = 5.27969 (* 1 = 5.27969 loss)
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||
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I0405 11:53:18.760124 26038 solver.cpp:218] Iteration 14280 (0.812969 iter/s, 14.7607s/12 iters), loss = 5.27346
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I0405 11:53:18.760172 26038 solver.cpp:237] Train net output #0: loss = 5.27346 (* 1 = 5.27346 loss)
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I0405 11:53:18.760177 26038 sgd_solver.cpp:105] Iteration 14280, lr = 1e-05
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I0405 11:53:23.224490 26038 solver.cpp:218] Iteration 14292 (2.68801 iter/s, 4.46428s/12 iters), loss = 5.26734
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I0405 11:53:23.224532 26038 solver.cpp:237] Train net output #0: loss = 5.26734 (* 1 = 5.26734 loss)
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I0405 11:53:23.224539 26038 sgd_solver.cpp:105] Iteration 14292, lr = 1e-05
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I0405 11:53:28.475569 26038 solver.cpp:218] Iteration 14304 (2.28529 iter/s, 5.25098s/12 iters), loss = 5.28334
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I0405 11:53:28.475622 26038 solver.cpp:237] Train net output #0: loss = 5.28334 (* 1 = 5.28334 loss)
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I0405 11:53:28.475631 26038 sgd_solver.cpp:105] Iteration 14304, lr = 1e-05
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I0405 11:53:33.680773 26038 solver.cpp:218] Iteration 14316 (2.30543 iter/s, 5.2051s/12 iters), loss = 5.27322
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I0405 11:53:33.680825 26038 solver.cpp:237] Train net output #0: loss = 5.27322 (* 1 = 5.27322 loss)
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I0405 11:53:33.680833 26038 sgd_solver.cpp:105] Iteration 14316, lr = 1e-05
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I0405 11:53:39.080356 26038 solver.cpp:218] Iteration 14328 (2.22244 iter/s, 5.39948s/12 iters), loss = 5.2773
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I0405 11:53:39.080516 26038 solver.cpp:237] Train net output #0: loss = 5.2773 (* 1 = 5.2773 loss)
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I0405 11:53:39.080524 26038 sgd_solver.cpp:105] Iteration 14328, lr = 1e-05
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I0405 11:53:44.410001 26038 solver.cpp:218] Iteration 14340 (2.25165 iter/s, 5.32944s/12 iters), loss = 5.27848
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I0405 11:53:44.410053 26038 solver.cpp:237] Train net output #0: loss = 5.27848 (* 1 = 5.27848 loss)
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I0405 11:53:44.410060 26038 sgd_solver.cpp:105] Iteration 14340, lr = 1e-05
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I0405 11:53:49.841739 26038 solver.cpp:218] Iteration 14352 (2.20928 iter/s, 5.43164s/12 iters), loss = 5.27533
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I0405 11:53:49.841786 26038 solver.cpp:237] Train net output #0: loss = 5.27533 (* 1 = 5.27533 loss)
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I0405 11:53:49.841794 26038 sgd_solver.cpp:105] Iteration 14352, lr = 1e-05
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I0405 11:53:54.549430 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:53:54.935848 26038 solver.cpp:218] Iteration 14364 (2.35571 iter/s, 5.09401s/12 iters), loss = 5.27983
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I0405 11:53:54.935907 26038 solver.cpp:237] Train net output #0: loss = 5.27983 (* 1 = 5.27983 loss)
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I0405 11:53:54.935916 26038 sgd_solver.cpp:105] Iteration 14364, lr = 1e-05
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I0405 11:54:00.289014 26038 solver.cpp:218] Iteration 14376 (2.24171 iter/s, 5.35306s/12 iters), loss = 5.26795
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I0405 11:54:00.289075 26038 solver.cpp:237] Train net output #0: loss = 5.26795 (* 1 = 5.26795 loss)
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I0405 11:54:00.289084 26038 sgd_solver.cpp:105] Iteration 14376, lr = 1e-05
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I0405 11:54:02.490933 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14382.caffemodel
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I0405 11:54:05.485852 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14382.solverstate
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I0405 11:54:07.788471 26038 solver.cpp:330] Iteration 14382, Testing net (#0)
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I0405 11:54:07.788492 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:54:11.289618 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:54:12.518982 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:54:12.519013 26038 solver.cpp:397] Test net output #1: loss = 5.27975 (* 1 = 5.27975 loss)
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I0405 11:54:14.378509 26038 solver.cpp:218] Iteration 14388 (0.851708 iter/s, 14.0893s/12 iters), loss = 5.2669
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I0405 11:54:14.378551 26038 solver.cpp:237] Train net output #0: loss = 5.2669 (* 1 = 5.2669 loss)
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I0405 11:54:14.378557 26038 sgd_solver.cpp:105] Iteration 14388, lr = 1e-05
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I0405 11:54:19.982900 26038 solver.cpp:218] Iteration 14400 (2.14122 iter/s, 5.60429s/12 iters), loss = 5.27278
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I0405 11:54:19.982952 26038 solver.cpp:237] Train net output #0: loss = 5.27278 (* 1 = 5.27278 loss)
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I0405 11:54:19.982959 26038 sgd_solver.cpp:105] Iteration 14400, lr = 1e-05
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I0405 11:54:25.531198 26038 solver.cpp:218] Iteration 14412 (2.16286 iter/s, 5.5482s/12 iters), loss = 5.27904
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I0405 11:54:25.531239 26038 solver.cpp:237] Train net output #0: loss = 5.27904 (* 1 = 5.27904 loss)
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I0405 11:54:25.531244 26038 sgd_solver.cpp:105] Iteration 14412, lr = 1e-05
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I0405 11:54:30.684888 26038 solver.cpp:218] Iteration 14424 (2.32847 iter/s, 5.1536s/12 iters), loss = 5.27692
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I0405 11:54:30.684942 26038 solver.cpp:237] Train net output #0: loss = 5.27692 (* 1 = 5.27692 loss)
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I0405 11:54:30.684949 26038 sgd_solver.cpp:105] Iteration 14424, lr = 1e-05
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I0405 11:54:35.900364 26038 solver.cpp:218] Iteration 14436 (2.30089 iter/s, 5.21537s/12 iters), loss = 5.27305
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I0405 11:54:35.900409 26038 solver.cpp:237] Train net output #0: loss = 5.27305 (* 1 = 5.27305 loss)
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I0405 11:54:35.900415 26038 sgd_solver.cpp:105] Iteration 14436, lr = 1e-05
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I0405 11:54:41.202865 26038 solver.cpp:218] Iteration 14448 (2.26312 iter/s, 5.30241s/12 iters), loss = 5.27564
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I0405 11:54:41.202904 26038 solver.cpp:237] Train net output #0: loss = 5.27564 (* 1 = 5.27564 loss)
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I0405 11:54:41.202909 26038 sgd_solver.cpp:105] Iteration 14448, lr = 1e-05
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I0405 11:54:46.497167 26038 solver.cpp:218] Iteration 14460 (2.26663 iter/s, 5.29421s/12 iters), loss = 5.27833
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I0405 11:54:46.497330 26038 solver.cpp:237] Train net output #0: loss = 5.27833 (* 1 = 5.27833 loss)
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I0405 11:54:46.497340 26038 sgd_solver.cpp:105] Iteration 14460, lr = 1e-05
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I0405 11:54:48.344698 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:54:51.825733 26038 solver.cpp:218] Iteration 14472 (2.2521 iter/s, 5.32836s/12 iters), loss = 5.28389
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I0405 11:54:51.825774 26038 solver.cpp:237] Train net output #0: loss = 5.28389 (* 1 = 5.28389 loss)
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I0405 11:54:51.825779 26038 sgd_solver.cpp:105] Iteration 14472, lr = 1e-05
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I0405 11:54:56.652011 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14484.caffemodel
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I0405 11:54:59.647955 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14484.solverstate
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I0405 11:55:02.439570 26038 solver.cpp:330] Iteration 14484, Testing net (#0)
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I0405 11:55:02.439595 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:55:05.821015 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:55:06.888628 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 11:55:06.888656 26038 solver.cpp:397] Test net output #1: loss = 5.2796 (* 1 = 5.2796 loss)
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I0405 11:55:07.028800 26038 solver.cpp:218] Iteration 14484 (0.789322 iter/s, 15.2029s/12 iters), loss = 5.2669
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I0405 11:55:07.028849 26038 solver.cpp:237] Train net output #0: loss = 5.2669 (* 1 = 5.2669 loss)
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I0405 11:55:07.028858 26038 sgd_solver.cpp:105] Iteration 14484, lr = 1e-05
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I0405 11:55:11.678649 26038 solver.cpp:218] Iteration 14496 (2.58078 iter/s, 4.64976s/12 iters), loss = 5.2874
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I0405 11:55:11.678694 26038 solver.cpp:237] Train net output #0: loss = 5.2874 (* 1 = 5.2874 loss)
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I0405 11:55:11.678699 26038 sgd_solver.cpp:105] Iteration 14496, lr = 1e-05
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I0405 11:55:17.058473 26038 solver.cpp:218] Iteration 14508 (2.23059 iter/s, 5.37974s/12 iters), loss = 5.27199
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I0405 11:55:17.058553 26038 solver.cpp:237] Train net output #0: loss = 5.27199 (* 1 = 5.27199 loss)
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I0405 11:55:17.058560 26038 sgd_solver.cpp:105] Iteration 14508, lr = 1e-05
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I0405 11:55:22.179792 26038 solver.cpp:218] Iteration 14520 (2.34321 iter/s, 5.12119s/12 iters), loss = 5.26686
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I0405 11:55:22.179841 26038 solver.cpp:237] Train net output #0: loss = 5.26686 (* 1 = 5.26686 loss)
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I0405 11:55:22.179848 26038 sgd_solver.cpp:105] Iteration 14520, lr = 1e-05
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I0405 11:55:27.461426 26038 solver.cpp:218] Iteration 14532 (2.27207 iter/s, 5.28154s/12 iters), loss = 5.2617
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I0405 11:55:27.461467 26038 solver.cpp:237] Train net output #0: loss = 5.2617 (* 1 = 5.2617 loss)
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I0405 11:55:27.461472 26038 sgd_solver.cpp:105] Iteration 14532, lr = 1e-05
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I0405 11:55:32.852854 26038 solver.cpp:218] Iteration 14544 (2.2258 iter/s, 5.39133s/12 iters), loss = 5.27531
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I0405 11:55:32.852916 26038 solver.cpp:237] Train net output #0: loss = 5.27531 (* 1 = 5.27531 loss)
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I0405 11:55:32.852923 26038 sgd_solver.cpp:105] Iteration 14544, lr = 1e-05
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I0405 11:55:38.325578 26038 solver.cpp:218] Iteration 14556 (2.19274 iter/s, 5.47262s/12 iters), loss = 5.27361
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I0405 11:55:38.325618 26038 solver.cpp:237] Train net output #0: loss = 5.27361 (* 1 = 5.27361 loss)
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I0405 11:55:38.325623 26038 sgd_solver.cpp:105] Iteration 14556, lr = 1e-05
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I0405 11:55:42.597389 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:55:42.864141 26038 blocking_queue.cpp:49] Waiting for data
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I0405 11:55:43.766691 26038 solver.cpp:218] Iteration 14568 (2.20547 iter/s, 5.44102s/12 iters), loss = 5.27765
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I0405 11:55:43.766741 26038 solver.cpp:237] Train net output #0: loss = 5.27765 (* 1 = 5.27765 loss)
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I0405 11:55:43.766749 26038 sgd_solver.cpp:105] Iteration 14568, lr = 1e-05
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I0405 11:55:48.984906 26038 solver.cpp:218] Iteration 14580 (2.29968 iter/s, 5.21811s/12 iters), loss = 5.27202
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I0405 11:55:48.985049 26038 solver.cpp:237] Train net output #0: loss = 5.27202 (* 1 = 5.27202 loss)
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I0405 11:55:48.985055 26038 sgd_solver.cpp:105] Iteration 14580, lr = 1e-05
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I0405 11:55:51.056536 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14586.caffemodel
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I0405 11:55:54.107101 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14586.solverstate
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I0405 11:55:57.075006 26038 solver.cpp:330] Iteration 14586, Testing net (#0)
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I0405 11:55:57.075028 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 11:56:00.448805 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:56:01.694134 26038 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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||
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I0405 11:56:01.694172 26038 solver.cpp:397] Test net output #1: loss = 5.27954 (* 1 = 5.27954 loss)
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||
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I0405 11:56:03.771703 26038 solver.cpp:218] Iteration 14592 (0.811548 iter/s, 14.7866s/12 iters), loss = 5.28317
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I0405 11:56:03.771764 26038 solver.cpp:237] Train net output #0: loss = 5.28317 (* 1 = 5.28317 loss)
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I0405 11:56:03.771773 26038 sgd_solver.cpp:105] Iteration 14592, lr = 1e-05
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I0405 11:56:09.145969 26038 solver.cpp:218] Iteration 14604 (2.23291 iter/s, 5.37416s/12 iters), loss = 5.27716
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I0405 11:56:09.146018 26038 solver.cpp:237] Train net output #0: loss = 5.27716 (* 1 = 5.27716 loss)
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||
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I0405 11:56:09.146026 26038 sgd_solver.cpp:105] Iteration 14604, lr = 1e-05
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I0405 11:56:14.386499 26038 solver.cpp:218] Iteration 14616 (2.28989 iter/s, 5.24044s/12 iters), loss = 5.27992
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I0405 11:56:14.386538 26038 solver.cpp:237] Train net output #0: loss = 5.27992 (* 1 = 5.27992 loss)
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||
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I0405 11:56:14.386543 26038 sgd_solver.cpp:105] Iteration 14616, lr = 1e-05
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I0405 11:56:19.668366 26038 solver.cpp:218] Iteration 14628 (2.27196 iter/s, 5.28178s/12 iters), loss = 5.27114
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I0405 11:56:19.668478 26038 solver.cpp:237] Train net output #0: loss = 5.27114 (* 1 = 5.27114 loss)
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||
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I0405 11:56:19.668484 26038 sgd_solver.cpp:105] Iteration 14628, lr = 1e-05
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||
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I0405 11:56:25.017207 26038 solver.cpp:218] Iteration 14640 (2.24354 iter/s, 5.34868s/12 iters), loss = 5.2696
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I0405 11:56:25.017251 26038 solver.cpp:237] Train net output #0: loss = 5.2696 (* 1 = 5.2696 loss)
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||
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I0405 11:56:25.017256 26038 sgd_solver.cpp:105] Iteration 14640, lr = 1e-05
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||
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I0405 11:56:30.054580 26038 solver.cpp:218] Iteration 14652 (2.38223 iter/s, 5.03729s/12 iters), loss = 5.27079
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I0405 11:56:30.054620 26038 solver.cpp:237] Train net output #0: loss = 5.27079 (* 1 = 5.27079 loss)
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||
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I0405 11:56:30.054625 26038 sgd_solver.cpp:105] Iteration 14652, lr = 1e-05
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||
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I0405 11:56:35.401978 26038 solver.cpp:218] Iteration 14664 (2.24412 iter/s, 5.3473s/12 iters), loss = 5.28675
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||
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I0405 11:56:35.402037 26038 solver.cpp:237] Train net output #0: loss = 5.28675 (* 1 = 5.28675 loss)
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||
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I0405 11:56:35.402045 26038 sgd_solver.cpp:105] Iteration 14664, lr = 1e-05
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||
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I0405 11:56:36.501281 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 11:56:40.655373 26038 solver.cpp:218] Iteration 14676 (2.28428 iter/s, 5.25329s/12 iters), loss = 5.28698
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I0405 11:56:40.655413 26038 solver.cpp:237] Train net output #0: loss = 5.28698 (* 1 = 5.28698 loss)
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I0405 11:56:40.655418 26038 sgd_solver.cpp:105] Iteration 14676, lr = 1e-05
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||
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I0405 11:56:45.421761 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14688.caffemodel
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||
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|
I0405 11:56:48.377319 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14688.solverstate
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||
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I0405 11:56:50.676122 26038 solver.cpp:330] Iteration 14688, Testing net (#0)
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||
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I0405 11:56:50.676218 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 11:56:53.924177 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
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I0405 11:56:55.057255 26038 solver.cpp:397] Test net output #0: accuracy = 0.00612745
|
||
|
|
I0405 11:56:55.057289 26038 solver.cpp:397] Test net output #1: loss = 5.27948 (* 1 = 5.27948 loss)
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||
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I0405 11:56:55.199374 26038 solver.cpp:218] Iteration 14688 (0.82509 iter/s, 14.5439s/12 iters), loss = 5.29885
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I0405 11:56:55.199427 26038 solver.cpp:237] Train net output #0: loss = 5.29885 (* 1 = 5.29885 loss)
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||
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I0405 11:56:55.199434 26038 sgd_solver.cpp:105] Iteration 14688, lr = 1e-05
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I0405 11:56:59.709502 26038 solver.cpp:218] Iteration 14700 (2.66074 iter/s, 4.51003s/12 iters), loss = 5.26395
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||
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I0405 11:56:59.709550 26038 solver.cpp:237] Train net output #0: loss = 5.26395 (* 1 = 5.26395 loss)
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||
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I0405 11:56:59.709558 26038 sgd_solver.cpp:105] Iteration 14700, lr = 1e-05
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I0405 11:57:05.160178 26038 solver.cpp:218] Iteration 14712 (2.2016 iter/s, 5.45058s/12 iters), loss = 5.26664
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I0405 11:57:05.160216 26038 solver.cpp:237] Train net output #0: loss = 5.26664 (* 1 = 5.26664 loss)
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||
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I0405 11:57:05.160221 26038 sgd_solver.cpp:105] Iteration 14712, lr = 1e-05
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||
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I0405 11:57:10.600098 26038 solver.cpp:218] Iteration 14724 (2.20595 iter/s, 5.43983s/12 iters), loss = 5.28727
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I0405 11:57:10.600157 26038 solver.cpp:237] Train net output #0: loss = 5.28727 (* 1 = 5.28727 loss)
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||
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I0405 11:57:10.600169 26038 sgd_solver.cpp:105] Iteration 14724, lr = 1e-05
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I0405 11:57:15.763828 26038 solver.cpp:218] Iteration 14736 (2.32395 iter/s, 5.16362s/12 iters), loss = 5.2747
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I0405 11:57:15.763887 26038 solver.cpp:237] Train net output #0: loss = 5.2747 (* 1 = 5.2747 loss)
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||
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I0405 11:57:15.763897 26038 sgd_solver.cpp:105] Iteration 14736, lr = 1e-05
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I0405 11:57:21.152415 26038 solver.cpp:218] Iteration 14748 (2.22697 iter/s, 5.38848s/12 iters), loss = 5.26122
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I0405 11:57:21.152524 26038 solver.cpp:237] Train net output #0: loss = 5.26122 (* 1 = 5.26122 loss)
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I0405 11:57:21.152531 26038 sgd_solver.cpp:105] Iteration 14748, lr = 1e-05
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I0405 11:57:26.574577 26038 solver.cpp:218] Iteration 14760 (2.2132 iter/s, 5.422s/12 iters), loss = 5.27043
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I0405 11:57:26.574617 26038 solver.cpp:237] Train net output #0: loss = 5.27043 (* 1 = 5.27043 loss)
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I0405 11:57:26.574622 26038 sgd_solver.cpp:105] Iteration 14760, lr = 1e-05
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I0405 11:57:29.970872 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:57:31.877374 26038 solver.cpp:218] Iteration 14772 (2.26299 iter/s, 5.30271s/12 iters), loss = 5.27532
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I0405 11:57:31.877424 26038 solver.cpp:237] Train net output #0: loss = 5.27532 (* 1 = 5.27532 loss)
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I0405 11:57:31.877429 26038 sgd_solver.cpp:105] Iteration 14772, lr = 1e-05
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I0405 11:57:37.043370 26038 solver.cpp:218] Iteration 14784 (2.32293 iter/s, 5.1659s/12 iters), loss = 5.26566
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I0405 11:57:37.043411 26038 solver.cpp:237] Train net output #0: loss = 5.26566 (* 1 = 5.26566 loss)
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I0405 11:57:37.043416 26038 sgd_solver.cpp:105] Iteration 14784, lr = 1e-05
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I0405 11:57:39.232735 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14790.caffemodel
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I0405 11:57:42.239511 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14790.solverstate
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I0405 11:57:44.550979 26038 solver.cpp:330] Iteration 14790, Testing net (#0)
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I0405 11:57:44.551000 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:57:47.918785 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:57:49.103559 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:57:49.103591 26038 solver.cpp:397] Test net output #1: loss = 5.27977 (* 1 = 5.27977 loss)
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I0405 11:57:51.141765 26038 solver.cpp:218] Iteration 14796 (0.851169 iter/s, 14.0983s/12 iters), loss = 5.26329
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I0405 11:57:51.141810 26038 solver.cpp:237] Train net output #0: loss = 5.26329 (* 1 = 5.26329 loss)
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I0405 11:57:51.141816 26038 sgd_solver.cpp:105] Iteration 14796, lr = 1e-05
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I0405 11:57:56.450124 26038 solver.cpp:218] Iteration 14808 (2.26063 iter/s, 5.30826s/12 iters), loss = 5.2749
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I0405 11:57:56.450567 26038 solver.cpp:237] Train net output #0: loss = 5.2749 (* 1 = 5.2749 loss)
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I0405 11:57:56.450577 26038 sgd_solver.cpp:105] Iteration 14808, lr = 1e-05
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I0405 11:58:01.856391 26038 solver.cpp:218] Iteration 14820 (2.21985 iter/s, 5.40578s/12 iters), loss = 5.26858
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I0405 11:58:01.856446 26038 solver.cpp:237] Train net output #0: loss = 5.26858 (* 1 = 5.26858 loss)
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I0405 11:58:01.856454 26038 sgd_solver.cpp:105] Iteration 14820, lr = 1e-05
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I0405 11:58:07.215333 26038 solver.cpp:218] Iteration 14832 (2.23929 iter/s, 5.35884s/12 iters), loss = 5.28061
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I0405 11:58:07.215371 26038 solver.cpp:237] Train net output #0: loss = 5.28061 (* 1 = 5.28061 loss)
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I0405 11:58:07.215376 26038 sgd_solver.cpp:105] Iteration 14832, lr = 1e-05
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I0405 11:58:12.596238 26038 solver.cpp:218] Iteration 14844 (2.23014 iter/s, 5.38082s/12 iters), loss = 5.26354
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I0405 11:58:12.596292 26038 solver.cpp:237] Train net output #0: loss = 5.26354 (* 1 = 5.26354 loss)
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I0405 11:58:12.596300 26038 sgd_solver.cpp:105] Iteration 14844, lr = 1e-05
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I0405 11:58:18.070034 26038 solver.cpp:218] Iteration 14856 (2.1923 iter/s, 5.4737s/12 iters), loss = 5.26453
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I0405 11:58:18.070070 26038 solver.cpp:237] Train net output #0: loss = 5.26453 (* 1 = 5.26453 loss)
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I0405 11:58:18.070075 26038 sgd_solver.cpp:105] Iteration 14856, lr = 1e-05
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I0405 11:58:23.333624 26038 solver.cpp:218] Iteration 14868 (2.27985 iter/s, 5.26351s/12 iters), loss = 5.27787
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I0405 11:58:23.333674 26038 solver.cpp:237] Train net output #0: loss = 5.27787 (* 1 = 5.27787 loss)
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I0405 11:58:23.333680 26038 sgd_solver.cpp:105] Iteration 14868, lr = 1e-05
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I0405 11:58:23.646363 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:58:28.821801 26038 solver.cpp:218] Iteration 14880 (2.18656 iter/s, 5.48808s/12 iters), loss = 5.28708
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I0405 11:58:28.821936 26038 solver.cpp:237] Train net output #0: loss = 5.28708 (* 1 = 5.28708 loss)
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I0405 11:58:28.821943 26038 sgd_solver.cpp:105] Iteration 14880, lr = 1e-05
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I0405 11:58:33.495617 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14892.caffemodel
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I0405 11:58:36.564486 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14892.solverstate
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I0405 11:58:39.457036 26038 solver.cpp:330] Iteration 14892, Testing net (#0)
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I0405 11:58:39.457060 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:58:42.672189 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:58:43.912493 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 11:58:43.912534 26038 solver.cpp:397] Test net output #1: loss = 5.27987 (* 1 = 5.27987 loss)
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I0405 11:58:44.054251 26038 solver.cpp:218] Iteration 14892 (0.787804 iter/s, 15.2322s/12 iters), loss = 5.28645
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I0405 11:58:44.054312 26038 solver.cpp:237] Train net output #0: loss = 5.28645 (* 1 = 5.28645 loss)
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I0405 11:58:44.054322 26038 sgd_solver.cpp:105] Iteration 14892, lr = 1e-05
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I0405 11:58:48.624191 26038 solver.cpp:218] Iteration 14904 (2.62591 iter/s, 4.56984s/12 iters), loss = 5.26577
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I0405 11:58:48.624248 26038 solver.cpp:237] Train net output #0: loss = 5.26577 (* 1 = 5.26577 loss)
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I0405 11:58:48.624258 26038 sgd_solver.cpp:105] Iteration 14904, lr = 1e-05
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I0405 11:58:54.063797 26038 solver.cpp:218] Iteration 14916 (2.20608 iter/s, 5.4395s/12 iters), loss = 5.26553
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I0405 11:58:54.063848 26038 solver.cpp:237] Train net output #0: loss = 5.26553 (* 1 = 5.26553 loss)
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I0405 11:58:54.063855 26038 sgd_solver.cpp:105] Iteration 14916, lr = 1e-05
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I0405 11:58:59.468685 26038 solver.cpp:218] Iteration 14928 (2.22025 iter/s, 5.40479s/12 iters), loss = 5.27955
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I0405 11:58:59.468782 26038 solver.cpp:237] Train net output #0: loss = 5.27955 (* 1 = 5.27955 loss)
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I0405 11:58:59.468788 26038 sgd_solver.cpp:105] Iteration 14928, lr = 1e-05
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I0405 11:59:04.871178 26038 solver.cpp:218] Iteration 14940 (2.22126 iter/s, 5.40235s/12 iters), loss = 5.26975
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I0405 11:59:04.871222 26038 solver.cpp:237] Train net output #0: loss = 5.26975 (* 1 = 5.26975 loss)
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I0405 11:59:04.871227 26038 sgd_solver.cpp:105] Iteration 14940, lr = 1e-05
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I0405 11:59:10.265213 26038 solver.cpp:218] Iteration 14952 (2.22472 iter/s, 5.39394s/12 iters), loss = 5.28483
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I0405 11:59:10.265271 26038 solver.cpp:237] Train net output #0: loss = 5.28483 (* 1 = 5.28483 loss)
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I0405 11:59:10.265281 26038 sgd_solver.cpp:105] Iteration 14952, lr = 1e-05
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I0405 11:59:15.702929 26038 solver.cpp:218] Iteration 14964 (2.20685 iter/s, 5.43761s/12 iters), loss = 5.26096
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I0405 11:59:15.702988 26038 solver.cpp:237] Train net output #0: loss = 5.26096 (* 1 = 5.26096 loss)
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I0405 11:59:15.702996 26038 sgd_solver.cpp:105] Iteration 14964, lr = 1e-05
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I0405 11:59:18.348191 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:59:21.061966 26038 solver.cpp:218] Iteration 14976 (2.23925 iter/s, 5.35894s/12 iters), loss = 5.29018
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I0405 11:59:21.062009 26038 solver.cpp:237] Train net output #0: loss = 5.29018 (* 1 = 5.29018 loss)
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I0405 11:59:21.062017 26038 sgd_solver.cpp:105] Iteration 14976, lr = 1e-05
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I0405 11:59:26.450064 26038 solver.cpp:218] Iteration 14988 (2.22717 iter/s, 5.38801s/12 iters), loss = 5.27467
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I0405 11:59:26.450105 26038 solver.cpp:237] Train net output #0: loss = 5.27467 (* 1 = 5.27467 loss)
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I0405 11:59:26.450111 26038 sgd_solver.cpp:105] Iteration 14988, lr = 1e-05
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I0405 11:59:28.604008 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14994.caffemodel
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I0405 11:59:31.633200 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14994.solverstate
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I0405 11:59:33.947851 26038 solver.cpp:330] Iteration 14994, Testing net (#0)
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I0405 11:59:33.947870 26038 net.cpp:676] Ignoring source layer train-data
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I0405 11:59:37.027747 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 11:59:38.297238 26038 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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I0405 11:59:38.297267 26038 solver.cpp:397] Test net output #1: loss = 5.27949 (* 1 = 5.27949 loss)
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I0405 11:59:40.178036 26038 solver.cpp:218] Iteration 15000 (0.874136 iter/s, 13.7278s/12 iters), loss = 5.27183
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I0405 11:59:40.178076 26038 solver.cpp:237] Train net output #0: loss = 5.27183 (* 1 = 5.27183 loss)
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I0405 11:59:40.178082 26038 sgd_solver.cpp:105] Iteration 15000, lr = 1e-05
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I0405 11:59:45.428483 26038 solver.cpp:218] Iteration 15012 (2.28556 iter/s, 5.25036s/12 iters), loss = 5.27906
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I0405 11:59:45.428521 26038 solver.cpp:237] Train net output #0: loss = 5.27906 (* 1 = 5.27906 loss)
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I0405 11:59:45.428527 26038 sgd_solver.cpp:105] Iteration 15012, lr = 1e-05
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I0405 11:59:50.595430 26038 solver.cpp:218] Iteration 15024 (2.32249 iter/s, 5.16686s/12 iters), loss = 5.27995
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I0405 11:59:50.595480 26038 solver.cpp:237] Train net output #0: loss = 5.27995 (* 1 = 5.27995 loss)
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I0405 11:59:50.595489 26038 sgd_solver.cpp:105] Iteration 15024, lr = 1e-05
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I0405 11:59:55.891333 26038 solver.cpp:218] Iteration 15036 (2.26595 iter/s, 5.2958s/12 iters), loss = 5.29566
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I0405 11:59:55.891388 26038 solver.cpp:237] Train net output #0: loss = 5.29566 (* 1 = 5.29566 loss)
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I0405 11:59:55.891396 26038 sgd_solver.cpp:105] Iteration 15036, lr = 1e-05
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I0405 12:00:01.498173 26038 solver.cpp:218] Iteration 15048 (2.14028 iter/s, 5.60673s/12 iters), loss = 5.2901
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I0405 12:00:01.498239 26038 solver.cpp:237] Train net output #0: loss = 5.2901 (* 1 = 5.2901 loss)
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I0405 12:00:01.498248 26038 sgd_solver.cpp:105] Iteration 15048, lr = 1e-05
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I0405 12:00:06.879372 26038 solver.cpp:218] Iteration 15060 (2.23003 iter/s, 5.38109s/12 iters), loss = 5.28663
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I0405 12:00:06.879504 26038 solver.cpp:237] Train net output #0: loss = 5.28663 (* 1 = 5.28663 loss)
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I0405 12:00:06.879515 26038 sgd_solver.cpp:105] Iteration 15060, lr = 1e-05
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I0405 12:00:11.754578 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:00:12.111510 26038 solver.cpp:218] Iteration 15072 (2.29359 iter/s, 5.23197s/12 iters), loss = 5.28416
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I0405 12:00:12.111552 26038 solver.cpp:237] Train net output #0: loss = 5.28416 (* 1 = 5.28416 loss)
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I0405 12:00:12.111558 26038 sgd_solver.cpp:105] Iteration 15072, lr = 1e-05
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I0405 12:00:17.793751 26038 solver.cpp:218] Iteration 15084 (2.11188 iter/s, 5.68215s/12 iters), loss = 5.26916
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I0405 12:00:17.793787 26038 solver.cpp:237] Train net output #0: loss = 5.26916 (* 1 = 5.26916 loss)
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I0405 12:00:17.793792 26038 sgd_solver.cpp:105] Iteration 15084, lr = 1e-05
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I0405 12:00:22.694453 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15096.caffemodel
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I0405 12:00:25.652379 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15096.solverstate
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I0405 12:00:27.968132 26038 solver.cpp:330] Iteration 15096, Testing net (#0)
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I0405 12:00:27.968159 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:00:31.242322 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:00:32.557349 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:00:32.557386 26038 solver.cpp:397] Test net output #1: loss = 5.27983 (* 1 = 5.27983 loss)
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I0405 12:00:32.697243 26038 solver.cpp:218] Iteration 15096 (0.805188 iter/s, 14.9034s/12 iters), loss = 5.27872
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I0405 12:00:32.698855 26038 solver.cpp:237] Train net output #0: loss = 5.27872 (* 1 = 5.27872 loss)
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I0405 12:00:32.698868 26038 sgd_solver.cpp:105] Iteration 15096, lr = 1e-05
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I0405 12:00:37.235756 26038 solver.cpp:218] Iteration 15108 (2.645 iter/s, 4.53687s/12 iters), loss = 5.29056
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I0405 12:00:37.236238 26038 solver.cpp:237] Train net output #0: loss = 5.29056 (* 1 = 5.29056 loss)
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I0405 12:00:37.236245 26038 sgd_solver.cpp:105] Iteration 15108, lr = 1e-05
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I0405 12:00:42.292416 26038 solver.cpp:218] Iteration 15120 (2.37336 iter/s, 5.05613s/12 iters), loss = 5.27236
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I0405 12:00:42.292471 26038 solver.cpp:237] Train net output #0: loss = 5.27236 (* 1 = 5.27236 loss)
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I0405 12:00:42.292479 26038 sgd_solver.cpp:105] Iteration 15120, lr = 1e-05
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I0405 12:00:47.481249 26038 solver.cpp:218] Iteration 15132 (2.31271 iter/s, 5.18873s/12 iters), loss = 5.27079
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I0405 12:00:47.481307 26038 solver.cpp:237] Train net output #0: loss = 5.27079 (* 1 = 5.27079 loss)
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I0405 12:00:47.481315 26038 sgd_solver.cpp:105] Iteration 15132, lr = 1e-05
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I0405 12:00:52.809222 26038 solver.cpp:218] Iteration 15144 (2.25231 iter/s, 5.32787s/12 iters), loss = 5.27414
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I0405 12:00:52.809262 26038 solver.cpp:237] Train net output #0: loss = 5.27414 (* 1 = 5.27414 loss)
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I0405 12:00:52.809267 26038 sgd_solver.cpp:105] Iteration 15144, lr = 1e-05
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I0405 12:00:58.225018 26038 solver.cpp:218] Iteration 15156 (2.21578 iter/s, 5.41571s/12 iters), loss = 5.26676
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I0405 12:00:58.225059 26038 solver.cpp:237] Train net output #0: loss = 5.26676 (* 1 = 5.26676 loss)
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I0405 12:00:58.225066 26038 sgd_solver.cpp:105] Iteration 15156, lr = 1e-05
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I0405 12:01:03.525897 26038 solver.cpp:218] Iteration 15168 (2.26381 iter/s, 5.30079s/12 iters), loss = 5.27476
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I0405 12:01:03.525947 26038 solver.cpp:237] Train net output #0: loss = 5.27476 (* 1 = 5.27476 loss)
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I0405 12:01:03.525954 26038 sgd_solver.cpp:105] Iteration 15168, lr = 1e-05
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I0405 12:01:05.471457 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:01:08.873370 26038 solver.cpp:218] Iteration 15180 (2.24409 iter/s, 5.34738s/12 iters), loss = 5.2787
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I0405 12:01:08.873471 26038 solver.cpp:237] Train net output #0: loss = 5.2787 (* 1 = 5.2787 loss)
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I0405 12:01:08.873478 26038 sgd_solver.cpp:105] Iteration 15180, lr = 1e-05
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I0405 12:01:14.254534 26038 solver.cpp:218] Iteration 15192 (2.23006 iter/s, 5.38101s/12 iters), loss = 5.24764
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I0405 12:01:14.254585 26038 solver.cpp:237] Train net output #0: loss = 5.24764 (* 1 = 5.24764 loss)
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I0405 12:01:14.254595 26038 sgd_solver.cpp:105] Iteration 15192, lr = 1e-05
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I0405 12:01:16.597003 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15198.caffemodel
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||
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I0405 12:01:19.600193 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15198.solverstate
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I0405 12:01:21.932029 26038 solver.cpp:330] Iteration 15198, Testing net (#0)
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I0405 12:01:21.932049 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 12:01:24.927078 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 12:01:26.266172 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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I0405 12:01:26.266209 26038 solver.cpp:397] Test net output #1: loss = 5.28005 (* 1 = 5.28005 loss)
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I0405 12:01:28.346911 26038 solver.cpp:218] Iteration 15204 (0.851533 iter/s, 14.0922s/12 iters), loss = 5.28135
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I0405 12:01:28.346971 26038 solver.cpp:237] Train net output #0: loss = 5.28135 (* 1 = 5.28135 loss)
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I0405 12:01:28.346979 26038 sgd_solver.cpp:105] Iteration 15204, lr = 1e-05
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I0405 12:01:33.546630 26038 solver.cpp:218] Iteration 15216 (2.30786 iter/s, 5.19961s/12 iters), loss = 5.26578
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I0405 12:01:33.546669 26038 solver.cpp:237] Train net output #0: loss = 5.26578 (* 1 = 5.26578 loss)
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I0405 12:01:33.546674 26038 sgd_solver.cpp:105] Iteration 15216, lr = 1e-05
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I0405 12:01:38.887578 26038 solver.cpp:218] Iteration 15228 (2.24683 iter/s, 5.34085s/12 iters), loss = 5.27177
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I0405 12:01:38.887763 26038 solver.cpp:237] Train net output #0: loss = 5.27177 (* 1 = 5.27177 loss)
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I0405 12:01:38.887773 26038 sgd_solver.cpp:105] Iteration 15228, lr = 1e-05
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I0405 12:01:44.314726 26038 solver.cpp:218] Iteration 15240 (2.2112 iter/s, 5.42691s/12 iters), loss = 5.26788
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I0405 12:01:44.314780 26038 solver.cpp:237] Train net output #0: loss = 5.26788 (* 1 = 5.26788 loss)
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I0405 12:01:44.314788 26038 sgd_solver.cpp:105] Iteration 15240, lr = 1e-05
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I0405 12:01:49.301438 26038 blocking_queue.cpp:49] Waiting for data
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I0405 12:01:49.846503 26038 solver.cpp:218] Iteration 15252 (2.16932 iter/s, 5.53168s/12 iters), loss = 5.27229
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I0405 12:01:49.846554 26038 solver.cpp:237] Train net output #0: loss = 5.27229 (* 1 = 5.27229 loss)
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I0405 12:01:49.846561 26038 sgd_solver.cpp:105] Iteration 15252, lr = 1e-05
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I0405 12:01:55.080440 26038 solver.cpp:218] Iteration 15264 (2.29277 iter/s, 5.23384s/12 iters), loss = 5.26263
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I0405 12:01:55.080484 26038 solver.cpp:237] Train net output #0: loss = 5.26263 (* 1 = 5.26263 loss)
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I0405 12:01:55.080490 26038 sgd_solver.cpp:105] Iteration 15264, lr = 1e-05
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I0405 12:01:59.428318 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:02:00.599016 26038 solver.cpp:218] Iteration 15276 (2.17451 iter/s, 5.51849s/12 iters), loss = 5.26082
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I0405 12:02:00.599061 26038 solver.cpp:237] Train net output #0: loss = 5.26082 (* 1 = 5.26082 loss)
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I0405 12:02:00.599068 26038 sgd_solver.cpp:105] Iteration 15276, lr = 1e-05
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I0405 12:02:05.778674 26038 solver.cpp:218] Iteration 15288 (2.3168 iter/s, 5.17956s/12 iters), loss = 5.27019
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I0405 12:02:05.778721 26038 solver.cpp:237] Train net output #0: loss = 5.27019 (* 1 = 5.27019 loss)
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I0405 12:02:05.778729 26038 sgd_solver.cpp:105] Iteration 15288, lr = 1e-05
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I0405 12:02:10.786679 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15300.caffemodel
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I0405 12:02:13.795258 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15300.solverstate
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I0405 12:02:16.087477 26038 solver.cpp:330] Iteration 15300, Testing net (#0)
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I0405 12:02:16.087497 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:02:19.148010 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:02:20.653942 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:02:20.653973 26038 solver.cpp:397] Test net output #1: loss = 5.27981 (* 1 = 5.27981 loss)
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I0405 12:02:20.795812 26038 solver.cpp:218] Iteration 15300 (0.799095 iter/s, 15.017s/12 iters), loss = 5.27958
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I0405 12:02:20.795855 26038 solver.cpp:237] Train net output #0: loss = 5.27958 (* 1 = 5.27958 loss)
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I0405 12:02:20.795859 26038 sgd_solver.cpp:105] Iteration 15300, lr = 1e-05
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I0405 12:02:25.311827 26038 solver.cpp:218] Iteration 15312 (2.65727 iter/s, 4.51592s/12 iters), loss = 5.27265
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I0405 12:02:25.311872 26038 solver.cpp:237] Train net output #0: loss = 5.27265 (* 1 = 5.27265 loss)
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I0405 12:02:25.311877 26038 sgd_solver.cpp:105] Iteration 15312, lr = 1e-05
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I0405 12:02:30.765705 26038 solver.cpp:218] Iteration 15324 (2.20031 iter/s, 5.45379s/12 iters), loss = 5.28137
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I0405 12:02:30.765748 26038 solver.cpp:237] Train net output #0: loss = 5.28137 (* 1 = 5.28137 loss)
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I0405 12:02:30.765753 26038 sgd_solver.cpp:105] Iteration 15324, lr = 1e-05
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I0405 12:02:36.183952 26038 solver.cpp:218] Iteration 15336 (2.21478 iter/s, 5.41815s/12 iters), loss = 5.28426
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I0405 12:02:36.184008 26038 solver.cpp:237] Train net output #0: loss = 5.28426 (* 1 = 5.28426 loss)
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I0405 12:02:36.184018 26038 sgd_solver.cpp:105] Iteration 15336, lr = 1e-05
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I0405 12:02:41.580745 26038 solver.cpp:218] Iteration 15348 (2.22358 iter/s, 5.39669s/12 iters), loss = 5.27315
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I0405 12:02:41.580878 26038 solver.cpp:237] Train net output #0: loss = 5.27315 (* 1 = 5.27315 loss)
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I0405 12:02:41.580890 26038 sgd_solver.cpp:105] Iteration 15348, lr = 1e-05
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I0405 12:02:46.891486 26038 solver.cpp:218] Iteration 15360 (2.25965 iter/s, 5.31055s/12 iters), loss = 5.2789
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I0405 12:02:46.891539 26038 solver.cpp:237] Train net output #0: loss = 5.2789 (* 1 = 5.2789 loss)
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I0405 12:02:46.891548 26038 sgd_solver.cpp:105] Iteration 15360, lr = 1e-05
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I0405 12:02:52.151850 26038 solver.cpp:218] Iteration 15372 (2.28125 iter/s, 5.26026s/12 iters), loss = 5.26875
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I0405 12:02:52.151890 26038 solver.cpp:237] Train net output #0: loss = 5.26875 (* 1 = 5.26875 loss)
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I0405 12:02:52.151896 26038 sgd_solver.cpp:105] Iteration 15372, lr = 1e-05
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I0405 12:02:53.322765 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:02:57.583657 26038 solver.cpp:218] Iteration 15384 (2.20925 iter/s, 5.43171s/12 iters), loss = 5.28097
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I0405 12:02:57.583710 26038 solver.cpp:237] Train net output #0: loss = 5.28097 (* 1 = 5.28097 loss)
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I0405 12:02:57.583719 26038 sgd_solver.cpp:105] Iteration 15384, lr = 1e-05
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I0405 12:03:03.002315 26038 solver.cpp:218] Iteration 15396 (2.21461 iter/s, 5.41856s/12 iters), loss = 5.28794
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I0405 12:03:03.002359 26038 solver.cpp:237] Train net output #0: loss = 5.28794 (* 1 = 5.28794 loss)
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I0405 12:03:03.002365 26038 sgd_solver.cpp:105] Iteration 15396, lr = 1e-05
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I0405 12:03:05.260799 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15402.caffemodel
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I0405 12:03:08.293073 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15402.solverstate
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I0405 12:03:10.597975 26038 solver.cpp:330] Iteration 15402, Testing net (#0)
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I0405 12:03:10.597995 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:03:13.461813 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:03:14.977579 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:03:14.977620 26038 solver.cpp:397] Test net output #1: loss = 5.27973 (* 1 = 5.27973 loss)
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I0405 12:03:16.925732 26038 solver.cpp:218] Iteration 15408 (0.861866 iter/s, 13.9233s/12 iters), loss = 5.27548
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I0405 12:03:16.925776 26038 solver.cpp:237] Train net output #0: loss = 5.27548 (* 1 = 5.27548 loss)
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I0405 12:03:16.925781 26038 sgd_solver.cpp:105] Iteration 15408, lr = 1e-05
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I0405 12:03:22.070345 26038 solver.cpp:218] Iteration 15420 (2.33258 iter/s, 5.14452s/12 iters), loss = 5.25638
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I0405 12:03:22.070384 26038 solver.cpp:237] Train net output #0: loss = 5.25638 (* 1 = 5.25638 loss)
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I0405 12:03:22.070389 26038 sgd_solver.cpp:105] Iteration 15420, lr = 1e-05
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I0405 12:03:27.272751 26038 solver.cpp:218] Iteration 15432 (2.30667 iter/s, 5.20231s/12 iters), loss = 5.28899
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I0405 12:03:27.272805 26038 solver.cpp:237] Train net output #0: loss = 5.28899 (* 1 = 5.28899 loss)
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I0405 12:03:27.272814 26038 sgd_solver.cpp:105] Iteration 15432, lr = 1e-05
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I0405 12:03:32.614114 26038 solver.cpp:218] Iteration 15444 (2.24666 iter/s, 5.34126s/12 iters), loss = 5.27408
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I0405 12:03:32.614153 26038 solver.cpp:237] Train net output #0: loss = 5.27408 (* 1 = 5.27408 loss)
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I0405 12:03:32.614159 26038 sgd_solver.cpp:105] Iteration 15444, lr = 1e-05
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I0405 12:03:37.822028 26038 solver.cpp:218] Iteration 15456 (2.30422 iter/s, 5.20783s/12 iters), loss = 5.2632
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I0405 12:03:37.822069 26038 solver.cpp:237] Train net output #0: loss = 5.2632 (* 1 = 5.2632 loss)
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I0405 12:03:37.822075 26038 sgd_solver.cpp:105] Iteration 15456, lr = 1e-05
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I0405 12:03:43.164577 26038 solver.cpp:218] Iteration 15468 (2.24616 iter/s, 5.34245s/12 iters), loss = 5.27812
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I0405 12:03:43.164625 26038 solver.cpp:237] Train net output #0: loss = 5.27812 (* 1 = 5.27812 loss)
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I0405 12:03:43.164633 26038 sgd_solver.cpp:105] Iteration 15468, lr = 1e-05
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I0405 12:03:46.647763 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:03:48.622921 26038 solver.cpp:218] Iteration 15480 (2.19851 iter/s, 5.45825s/12 iters), loss = 5.27097
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I0405 12:03:48.622975 26038 solver.cpp:237] Train net output #0: loss = 5.27097 (* 1 = 5.27097 loss)
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I0405 12:03:48.622983 26038 sgd_solver.cpp:105] Iteration 15480, lr = 1e-05
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I0405 12:03:54.068527 26038 solver.cpp:218] Iteration 15492 (2.20365 iter/s, 5.4455s/12 iters), loss = 5.27685
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I0405 12:03:54.068572 26038 solver.cpp:237] Train net output #0: loss = 5.27685 (* 1 = 5.27685 loss)
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I0405 12:03:54.068578 26038 sgd_solver.cpp:105] Iteration 15492, lr = 1e-05
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I0405 12:03:59.011008 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15504.caffemodel
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I0405 12:04:02.028462 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15504.solverstate
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I0405 12:04:04.356523 26038 solver.cpp:330] Iteration 15504, Testing net (#0)
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I0405 12:04:04.356545 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:04:07.269971 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:04:08.857837 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:04:08.857867 26038 solver.cpp:397] Test net output #1: loss = 5.27998 (* 1 = 5.27998 loss)
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I0405 12:04:08.999521 26038 solver.cpp:218] Iteration 15504 (0.803705 iter/s, 14.9309s/12 iters), loss = 5.27073
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I0405 12:04:08.999578 26038 solver.cpp:237] Train net output #0: loss = 5.27073 (* 1 = 5.27073 loss)
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I0405 12:04:08.999584 26038 sgd_solver.cpp:105] Iteration 15504, lr = 1e-05
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I0405 12:04:13.127542 26038 solver.cpp:218] Iteration 15516 (2.90703 iter/s, 4.12792s/12 iters), loss = 5.27239
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I0405 12:04:13.127580 26038 solver.cpp:237] Train net output #0: loss = 5.27239 (* 1 = 5.27239 loss)
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I0405 12:04:13.127586 26038 sgd_solver.cpp:105] Iteration 15516, lr = 1e-05
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I0405 12:04:18.399531 26038 solver.cpp:218] Iteration 15528 (2.27622 iter/s, 5.27191s/12 iters), loss = 5.27718
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I0405 12:04:18.399642 26038 solver.cpp:237] Train net output #0: loss = 5.27718 (* 1 = 5.27718 loss)
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I0405 12:04:18.399649 26038 sgd_solver.cpp:105] Iteration 15528, lr = 1e-05
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I0405 12:04:23.790799 26038 solver.cpp:218] Iteration 15540 (2.22589 iter/s, 5.39111s/12 iters), loss = 5.26496
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I0405 12:04:23.790849 26038 solver.cpp:237] Train net output #0: loss = 5.26496 (* 1 = 5.26496 loss)
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I0405 12:04:23.790856 26038 sgd_solver.cpp:105] Iteration 15540, lr = 1e-05
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I0405 12:04:28.853695 26038 solver.cpp:218] Iteration 15552 (2.37023 iter/s, 5.0628s/12 iters), loss = 5.26617
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I0405 12:04:28.853734 26038 solver.cpp:237] Train net output #0: loss = 5.26617 (* 1 = 5.26617 loss)
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I0405 12:04:28.853740 26038 sgd_solver.cpp:105] Iteration 15552, lr = 1e-05
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I0405 12:04:34.086257 26038 solver.cpp:218] Iteration 15564 (2.29337 iter/s, 5.23248s/12 iters), loss = 5.27829
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I0405 12:04:34.086299 26038 solver.cpp:237] Train net output #0: loss = 5.27829 (* 1 = 5.27829 loss)
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I0405 12:04:34.086304 26038 sgd_solver.cpp:105] Iteration 15564, lr = 1e-05
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I0405 12:04:39.368552 26038 solver.cpp:218] Iteration 15576 (2.27178 iter/s, 5.28221s/12 iters), loss = 5.28142
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I0405 12:04:39.368597 26038 solver.cpp:237] Train net output #0: loss = 5.28142 (* 1 = 5.28142 loss)
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I0405 12:04:39.368602 26038 sgd_solver.cpp:105] Iteration 15576, lr = 1e-05
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I0405 12:04:39.836931 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:04:44.760324 26038 solver.cpp:218] Iteration 15588 (2.22565 iter/s, 5.39167s/12 iters), loss = 5.29023
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I0405 12:04:44.760373 26038 solver.cpp:237] Train net output #0: loss = 5.29023 (* 1 = 5.29023 loss)
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I0405 12:04:44.760381 26038 sgd_solver.cpp:105] Iteration 15588, lr = 1e-05
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I0405 12:04:49.999102 26038 solver.cpp:218] Iteration 15600 (2.29065 iter/s, 5.23869s/12 iters), loss = 5.28585
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I0405 12:04:49.999241 26038 solver.cpp:237] Train net output #0: loss = 5.28585 (* 1 = 5.28585 loss)
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I0405 12:04:49.999251 26038 sgd_solver.cpp:105] Iteration 15600, lr = 1e-05
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I0405 12:04:52.097339 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15606.caffemodel
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I0405 12:04:55.176458 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15606.solverstate
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I0405 12:04:57.484467 26038 solver.cpp:330] Iteration 15606, Testing net (#0)
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I0405 12:04:57.484496 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:05:00.395223 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:05:02.024677 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:05:02.024708 26038 solver.cpp:397] Test net output #1: loss = 5.2799 (* 1 = 5.2799 loss)
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I0405 12:05:04.086323 26038 solver.cpp:218] Iteration 15612 (0.85185 iter/s, 14.087s/12 iters), loss = 5.27577
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I0405 12:05:04.086367 26038 solver.cpp:237] Train net output #0: loss = 5.27577 (* 1 = 5.27577 loss)
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I0405 12:05:04.086372 26038 sgd_solver.cpp:105] Iteration 15612, lr = 1e-05
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I0405 12:05:09.240548 26038 solver.cpp:218] Iteration 15624 (2.32823 iter/s, 5.15413s/12 iters), loss = 5.28716
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I0405 12:05:09.240597 26038 solver.cpp:237] Train net output #0: loss = 5.28716 (* 1 = 5.28716 loss)
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I0405 12:05:09.240603 26038 sgd_solver.cpp:105] Iteration 15624, lr = 1e-05
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I0405 12:05:14.737874 26038 solver.cpp:218] Iteration 15636 (2.18292 iter/s, 5.49723s/12 iters), loss = 5.27453
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I0405 12:05:14.737912 26038 solver.cpp:237] Train net output #0: loss = 5.27453 (* 1 = 5.27453 loss)
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I0405 12:05:14.737917 26038 sgd_solver.cpp:105] Iteration 15636, lr = 1e-05
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I0405 12:05:20.092991 26038 solver.cpp:218] Iteration 15648 (2.24089 iter/s, 5.35502s/12 iters), loss = 5.27354
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I0405 12:05:20.093286 26038 solver.cpp:237] Train net output #0: loss = 5.27354 (* 1 = 5.27354 loss)
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I0405 12:05:20.093297 26038 sgd_solver.cpp:105] Iteration 15648, lr = 1e-05
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I0405 12:05:25.382068 26038 solver.cpp:218] Iteration 15660 (2.26897 iter/s, 5.28874s/12 iters), loss = 5.2792
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I0405 12:05:25.382123 26038 solver.cpp:237] Train net output #0: loss = 5.2792 (* 1 = 5.2792 loss)
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I0405 12:05:25.382131 26038 sgd_solver.cpp:105] Iteration 15660, lr = 1e-05
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I0405 12:05:30.759744 26038 solver.cpp:218] Iteration 15672 (2.23149 iter/s, 5.37757s/12 iters), loss = 5.27845
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I0405 12:05:30.759794 26038 solver.cpp:237] Train net output #0: loss = 5.27845 (* 1 = 5.27845 loss)
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I0405 12:05:30.759804 26038 sgd_solver.cpp:105] Iteration 15672, lr = 1e-05
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I0405 12:05:33.523609 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:05:36.261360 26038 solver.cpp:218] Iteration 15684 (2.18122 iter/s, 5.50152s/12 iters), loss = 5.29453
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I0405 12:05:36.261413 26038 solver.cpp:237] Train net output #0: loss = 5.29453 (* 1 = 5.29453 loss)
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I0405 12:05:36.261422 26038 sgd_solver.cpp:105] Iteration 15684, lr = 1e-05
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I0405 12:05:41.447185 26038 solver.cpp:218] Iteration 15696 (2.31404 iter/s, 5.18573s/12 iters), loss = 5.26622
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I0405 12:05:41.447223 26038 solver.cpp:237] Train net output #0: loss = 5.26622 (* 1 = 5.26622 loss)
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I0405 12:05:41.447228 26038 sgd_solver.cpp:105] Iteration 15696, lr = 1e-05
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I0405 12:05:46.245952 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15708.caffemodel
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I0405 12:05:49.298368 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15708.solverstate
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I0405 12:05:51.605818 26038 solver.cpp:330] Iteration 15708, Testing net (#0)
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I0405 12:05:51.605897 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:05:54.390734 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:05:55.927588 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:05:55.927628 26038 solver.cpp:397] Test net output #1: loss = 5.27979 (* 1 = 5.27979 loss)
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I0405 12:05:56.068308 26038 solver.cpp:218] Iteration 15708 (0.820738 iter/s, 14.621s/12 iters), loss = 5.2757
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I0405 12:05:56.068353 26038 solver.cpp:237] Train net output #0: loss = 5.2757 (* 1 = 5.2757 loss)
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I0405 12:05:56.068361 26038 sgd_solver.cpp:105] Iteration 15708, lr = 1e-05
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I0405 12:06:00.480463 26038 solver.cpp:218] Iteration 15720 (2.71982 iter/s, 4.41206s/12 iters), loss = 5.2802
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I0405 12:06:00.480515 26038 solver.cpp:237] Train net output #0: loss = 5.2802 (* 1 = 5.2802 loss)
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I0405 12:06:00.480525 26038 sgd_solver.cpp:105] Iteration 15720, lr = 1e-05
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I0405 12:06:05.937024 26038 solver.cpp:218] Iteration 15732 (2.19923 iter/s, 5.45646s/12 iters), loss = 5.28133
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I0405 12:06:05.937060 26038 solver.cpp:237] Train net output #0: loss = 5.28133 (* 1 = 5.28133 loss)
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I0405 12:06:05.937067 26038 sgd_solver.cpp:105] Iteration 15732, lr = 1e-05
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I0405 12:06:11.182950 26038 solver.cpp:218] Iteration 15744 (2.28753 iter/s, 5.24584s/12 iters), loss = 5.28031
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I0405 12:06:11.183002 26038 solver.cpp:237] Train net output #0: loss = 5.28031 (* 1 = 5.28031 loss)
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I0405 12:06:11.183010 26038 sgd_solver.cpp:105] Iteration 15744, lr = 1e-05
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I0405 12:06:16.607228 26038 solver.cpp:218] Iteration 15756 (2.21232 iter/s, 5.42418s/12 iters), loss = 5.28124
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I0405 12:06:16.607268 26038 solver.cpp:237] Train net output #0: loss = 5.28124 (* 1 = 5.28124 loss)
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I0405 12:06:16.607273 26038 sgd_solver.cpp:105] Iteration 15756, lr = 1e-05
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I0405 12:06:21.951679 26038 solver.cpp:218] Iteration 15768 (2.24535 iter/s, 5.34437s/12 iters), loss = 5.2706
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I0405 12:06:21.951841 26038 solver.cpp:237] Train net output #0: loss = 5.2706 (* 1 = 5.2706 loss)
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I0405 12:06:21.951851 26038 sgd_solver.cpp:105] Iteration 15768, lr = 1e-05
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I0405 12:06:27.018862 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:06:27.370879 26038 solver.cpp:218] Iteration 15780 (2.21444 iter/s, 5.41899s/12 iters), loss = 5.28109
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I0405 12:06:27.370934 26038 solver.cpp:237] Train net output #0: loss = 5.28109 (* 1 = 5.28109 loss)
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I0405 12:06:27.370942 26038 sgd_solver.cpp:105] Iteration 15780, lr = 1e-05
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I0405 12:06:32.523756 26038 solver.cpp:218] Iteration 15792 (2.32884 iter/s, 5.15278s/12 iters), loss = 5.24976
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I0405 12:06:32.523795 26038 solver.cpp:237] Train net output #0: loss = 5.24976 (* 1 = 5.24976 loss)
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I0405 12:06:32.523800 26038 sgd_solver.cpp:105] Iteration 15792, lr = 1e-05
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I0405 12:06:37.762919 26038 solver.cpp:218] Iteration 15804 (2.29048 iter/s, 5.23907s/12 iters), loss = 5.26201
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I0405 12:06:37.762967 26038 solver.cpp:237] Train net output #0: loss = 5.26201 (* 1 = 5.26201 loss)
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I0405 12:06:37.762975 26038 sgd_solver.cpp:105] Iteration 15804, lr = 1e-05
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I0405 12:06:39.721884 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15810.caffemodel
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I0405 12:06:42.763391 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15810.solverstate
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I0405 12:06:45.071967 26038 solver.cpp:330] Iteration 15810, Testing net (#0)
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I0405 12:06:45.071991 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:06:47.980692 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:06:49.646139 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:06:49.646181 26038 solver.cpp:397] Test net output #1: loss = 5.27978 (* 1 = 5.27978 loss)
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I0405 12:06:51.555567 26038 solver.cpp:218] Iteration 15816 (0.870037 iter/s, 13.7925s/12 iters), loss = 5.28567
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I0405 12:06:51.555610 26038 solver.cpp:237] Train net output #0: loss = 5.28567 (* 1 = 5.28567 loss)
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I0405 12:06:51.555616 26038 sgd_solver.cpp:105] Iteration 15816, lr = 1e-05
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I0405 12:06:56.917858 26038 solver.cpp:218] Iteration 15828 (2.23789 iter/s, 5.3622s/12 iters), loss = 5.27687
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I0405 12:06:56.917953 26038 solver.cpp:237] Train net output #0: loss = 5.27687 (* 1 = 5.27687 loss)
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I0405 12:06:56.917959 26038 sgd_solver.cpp:105] Iteration 15828, lr = 1e-05
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I0405 12:07:02.202270 26038 solver.cpp:218] Iteration 15840 (2.27089 iter/s, 5.28427s/12 iters), loss = 5.27227
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I0405 12:07:02.202313 26038 solver.cpp:237] Train net output #0: loss = 5.27227 (* 1 = 5.27227 loss)
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I0405 12:07:02.202319 26038 sgd_solver.cpp:105] Iteration 15840, lr = 1e-05
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I0405 12:07:07.486586 26038 solver.cpp:218] Iteration 15852 (2.27091 iter/s, 5.28422s/12 iters), loss = 5.28197
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I0405 12:07:07.486642 26038 solver.cpp:237] Train net output #0: loss = 5.28197 (* 1 = 5.28197 loss)
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I0405 12:07:07.486651 26038 sgd_solver.cpp:105] Iteration 15852, lr = 1e-05
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I0405 12:07:12.988390 26038 solver.cpp:218] Iteration 15864 (2.18114 iter/s, 5.5017s/12 iters), loss = 5.2805
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I0405 12:07:12.988427 26038 solver.cpp:237] Train net output #0: loss = 5.2805 (* 1 = 5.2805 loss)
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I0405 12:07:12.988433 26038 sgd_solver.cpp:105] Iteration 15864, lr = 1e-05
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I0405 12:07:18.295872 26038 solver.cpp:218] Iteration 15876 (2.261 iter/s, 5.30739s/12 iters), loss = 5.26809
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I0405 12:07:18.295914 26038 solver.cpp:237] Train net output #0: loss = 5.26809 (* 1 = 5.26809 loss)
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I0405 12:07:18.295922 26038 sgd_solver.cpp:105] Iteration 15876, lr = 1e-05
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I0405 12:07:20.314335 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:07:23.757244 26038 solver.cpp:218] Iteration 15888 (2.19729 iter/s, 5.46128s/12 iters), loss = 5.30064
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I0405 12:07:23.757297 26038 solver.cpp:237] Train net output #0: loss = 5.30064 (* 1 = 5.30064 loss)
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I0405 12:07:23.757304 26038 sgd_solver.cpp:105] Iteration 15888, lr = 1e-05
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I0405 12:07:29.044736 26038 solver.cpp:218] Iteration 15900 (2.26955 iter/s, 5.28739s/12 iters), loss = 5.26916
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I0405 12:07:29.044924 26038 solver.cpp:237] Train net output #0: loss = 5.26916 (* 1 = 5.26916 loss)
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I0405 12:07:29.044934 26038 sgd_solver.cpp:105] Iteration 15900, lr = 1e-05
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I0405 12:07:33.705854 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15912.caffemodel
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I0405 12:07:37.529295 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15912.solverstate
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I0405 12:07:39.875043 26038 solver.cpp:330] Iteration 15912, Testing net (#0)
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I0405 12:07:39.875069 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:07:42.714730 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:07:44.324940 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:07:44.324977 26038 solver.cpp:397] Test net output #1: loss = 5.28009 (* 1 = 5.28009 loss)
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I0405 12:07:44.466508 26038 solver.cpp:218] Iteration 15912 (0.778136 iter/s, 15.4215s/12 iters), loss = 5.28494
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I0405 12:07:44.466565 26038 solver.cpp:237] Train net output #0: loss = 5.28494 (* 1 = 5.28494 loss)
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I0405 12:07:44.466573 26038 sgd_solver.cpp:105] Iteration 15912, lr = 1e-05
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I0405 12:07:48.962929 26038 solver.cpp:218] Iteration 15924 (2.66885 iter/s, 4.49632s/12 iters), loss = 5.27959
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I0405 12:07:48.962963 26038 solver.cpp:237] Train net output #0: loss = 5.27959 (* 1 = 5.27959 loss)
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I0405 12:07:48.962970 26038 sgd_solver.cpp:105] Iteration 15924, lr = 1e-05
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I0405 12:07:54.272328 26038 solver.cpp:218] Iteration 15936 (2.26018 iter/s, 5.30931s/12 iters), loss = 5.26247
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I0405 12:07:54.272387 26038 solver.cpp:237] Train net output #0: loss = 5.26247 (* 1 = 5.26247 loss)
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I0405 12:07:54.272394 26038 sgd_solver.cpp:105] Iteration 15936, lr = 1e-05
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I0405 12:07:54.272646 26038 blocking_queue.cpp:49] Waiting for data
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I0405 12:07:59.497470 26038 solver.cpp:218] Iteration 15948 (2.29664 iter/s, 5.22504s/12 iters), loss = 5.26418
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I0405 12:07:59.497596 26038 solver.cpp:237] Train net output #0: loss = 5.26418 (* 1 = 5.26418 loss)
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I0405 12:07:59.497606 26038 sgd_solver.cpp:105] Iteration 15948, lr = 1e-05
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I0405 12:08:04.718909 26038 solver.cpp:218] Iteration 15960 (2.29829 iter/s, 5.22127s/12 iters), loss = 5.26708
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I0405 12:08:04.718962 26038 solver.cpp:237] Train net output #0: loss = 5.26708 (* 1 = 5.26708 loss)
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I0405 12:08:04.718971 26038 sgd_solver.cpp:105] Iteration 15960, lr = 1e-05
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I0405 12:08:09.919718 26038 solver.cpp:218] Iteration 15972 (2.30737 iter/s, 5.20072s/12 iters), loss = 5.25924
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I0405 12:08:09.919757 26038 solver.cpp:237] Train net output #0: loss = 5.25924 (* 1 = 5.25924 loss)
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I0405 12:08:09.919762 26038 sgd_solver.cpp:105] Iteration 15972, lr = 1e-05
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I0405 12:08:14.171437 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:08:15.239969 26038 solver.cpp:218] Iteration 15984 (2.25557 iter/s, 5.32016s/12 iters), loss = 5.25976
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I0405 12:08:15.240020 26038 solver.cpp:237] Train net output #0: loss = 5.25976 (* 1 = 5.25976 loss)
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I0405 12:08:15.240027 26038 sgd_solver.cpp:105] Iteration 15984, lr = 1e-05
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I0405 12:08:20.527145 26038 solver.cpp:218] Iteration 15996 (2.26968 iter/s, 5.28708s/12 iters), loss = 5.26853
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I0405 12:08:20.527184 26038 solver.cpp:237] Train net output #0: loss = 5.26853 (* 1 = 5.26853 loss)
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I0405 12:08:20.527189 26038 sgd_solver.cpp:105] Iteration 15996, lr = 1e-05
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I0405 12:08:25.473106 26038 solver.cpp:218] Iteration 16008 (2.42626 iter/s, 4.94588s/12 iters), loss = 5.26746
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I0405 12:08:25.473147 26038 solver.cpp:237] Train net output #0: loss = 5.26746 (* 1 = 5.26746 loss)
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I0405 12:08:25.473153 26038 sgd_solver.cpp:105] Iteration 16008, lr = 1e-05
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I0405 12:08:27.593916 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16014.caffemodel
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||
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I0405 12:08:30.630877 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16014.solverstate
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I0405 12:08:32.931478 26038 solver.cpp:330] Iteration 16014, Testing net (#0)
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I0405 12:08:32.931496 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:08:35.681161 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:08:37.332105 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:08:37.332140 26038 solver.cpp:397] Test net output #1: loss = 5.28001 (* 1 = 5.28001 loss)
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I0405 12:08:39.274478 26038 solver.cpp:218] Iteration 16020 (0.869488 iter/s, 13.8012s/12 iters), loss = 5.26859
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I0405 12:08:39.274529 26038 solver.cpp:237] Train net output #0: loss = 5.26859 (* 1 = 5.26859 loss)
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I0405 12:08:39.274538 26038 sgd_solver.cpp:105] Iteration 16020, lr = 1e-05
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I0405 12:08:44.647454 26038 solver.cpp:218] Iteration 16032 (2.23344 iter/s, 5.37287s/12 iters), loss = 5.29074
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I0405 12:08:44.647496 26038 solver.cpp:237] Train net output #0: loss = 5.29074 (* 1 = 5.29074 loss)
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||
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I0405 12:08:44.647501 26038 sgd_solver.cpp:105] Iteration 16032, lr = 1e-05
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I0405 12:08:50.085568 26038 solver.cpp:218] Iteration 16044 (2.20668 iter/s, 5.43802s/12 iters), loss = 5.28238
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I0405 12:08:50.085623 26038 solver.cpp:237] Train net output #0: loss = 5.28238 (* 1 = 5.28238 loss)
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||
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I0405 12:08:50.085633 26038 sgd_solver.cpp:105] Iteration 16044, lr = 1e-05
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I0405 12:08:55.408289 26038 solver.cpp:218] Iteration 16056 (2.25453 iter/s, 5.32262s/12 iters), loss = 5.2829
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I0405 12:08:55.408349 26038 solver.cpp:237] Train net output #0: loss = 5.2829 (* 1 = 5.2829 loss)
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I0405 12:08:55.408357 26038 sgd_solver.cpp:105] Iteration 16056, lr = 1e-05
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I0405 12:09:00.792744 26038 solver.cpp:218] Iteration 16068 (2.22868 iter/s, 5.38435s/12 iters), loss = 5.26961
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I0405 12:09:00.792843 26038 solver.cpp:237] Train net output #0: loss = 5.26961 (* 1 = 5.26961 loss)
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||
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I0405 12:09:00.792850 26038 sgd_solver.cpp:105] Iteration 16068, lr = 1e-05
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I0405 12:09:05.847970 26038 solver.cpp:218] Iteration 16080 (2.37385 iter/s, 5.05508s/12 iters), loss = 5.28976
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||
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I0405 12:09:05.848016 26038 solver.cpp:237] Train net output #0: loss = 5.28976 (* 1 = 5.28976 loss)
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||
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I0405 12:09:05.848023 26038 sgd_solver.cpp:105] Iteration 16080, lr = 1e-05
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||
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I0405 12:09:06.919111 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:09:11.204440 26038 solver.cpp:218] Iteration 16092 (2.24032 iter/s, 5.35638s/12 iters), loss = 5.28906
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I0405 12:09:11.204495 26038 solver.cpp:237] Train net output #0: loss = 5.28906 (* 1 = 5.28906 loss)
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I0405 12:09:11.204505 26038 sgd_solver.cpp:105] Iteration 16092, lr = 1e-05
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I0405 12:09:16.447331 26038 solver.cpp:218] Iteration 16104 (2.28886 iter/s, 5.24279s/12 iters), loss = 5.28193
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I0405 12:09:16.447384 26038 solver.cpp:237] Train net output #0: loss = 5.28193 (* 1 = 5.28193 loss)
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I0405 12:09:16.447391 26038 sgd_solver.cpp:105] Iteration 16104, lr = 1e-05
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||
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I0405 12:09:21.328356 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16116.caffemodel
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||
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I0405 12:09:24.336743 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16116.solverstate
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||
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I0405 12:09:26.643916 26038 solver.cpp:330] Iteration 16116, Testing net (#0)
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||
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I0405 12:09:26.643942 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 12:09:29.318998 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 12:09:31.010255 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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I0405 12:09:31.010392 26038 solver.cpp:397] Test net output #1: loss = 5.28012 (* 1 = 5.28012 loss)
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I0405 12:09:31.151425 26038 solver.cpp:218] Iteration 16116 (0.816108 iter/s, 14.7039s/12 iters), loss = 5.26211
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I0405 12:09:31.151473 26038 solver.cpp:237] Train net output #0: loss = 5.26211 (* 1 = 5.26211 loss)
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I0405 12:09:31.151479 26038 sgd_solver.cpp:105] Iteration 16116, lr = 1e-05
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I0405 12:09:35.675531 26038 solver.cpp:218] Iteration 16128 (2.65251 iter/s, 4.52401s/12 iters), loss = 5.25967
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I0405 12:09:35.675575 26038 solver.cpp:237] Train net output #0: loss = 5.25967 (* 1 = 5.25967 loss)
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I0405 12:09:35.675582 26038 sgd_solver.cpp:105] Iteration 16128, lr = 1e-05
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I0405 12:09:41.088335 26038 solver.cpp:218] Iteration 16140 (2.217 iter/s, 5.41272s/12 iters), loss = 5.28851
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I0405 12:09:41.088376 26038 solver.cpp:237] Train net output #0: loss = 5.28851 (* 1 = 5.28851 loss)
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I0405 12:09:41.088387 26038 sgd_solver.cpp:105] Iteration 16140, lr = 1e-05
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I0405 12:09:46.454241 26038 solver.cpp:218] Iteration 16152 (2.23638 iter/s, 5.36581s/12 iters), loss = 5.26705
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I0405 12:09:46.454300 26038 solver.cpp:237] Train net output #0: loss = 5.26705 (* 1 = 5.26705 loss)
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I0405 12:09:46.454309 26038 sgd_solver.cpp:105] Iteration 16152, lr = 1e-05
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I0405 12:09:51.883289 26038 solver.cpp:218] Iteration 16164 (2.21037 iter/s, 5.42894s/12 iters), loss = 5.26802
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I0405 12:09:51.883334 26038 solver.cpp:237] Train net output #0: loss = 5.26802 (* 1 = 5.26802 loss)
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I0405 12:09:51.883340 26038 sgd_solver.cpp:105] Iteration 16164, lr = 1e-05
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I0405 12:09:57.155675 26038 solver.cpp:218] Iteration 16176 (2.27605 iter/s, 5.27229s/12 iters), loss = 5.28623
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I0405 12:09:57.155725 26038 solver.cpp:237] Train net output #0: loss = 5.28623 (* 1 = 5.28623 loss)
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I0405 12:09:57.155731 26038 sgd_solver.cpp:105] Iteration 16176, lr = 1e-05
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I0405 12:10:00.427242 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:10:02.363873 26038 solver.cpp:218] Iteration 16188 (2.3041 iter/s, 5.2081s/12 iters), loss = 5.25742
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I0405 12:10:02.363976 26038 solver.cpp:237] Train net output #0: loss = 5.25742 (* 1 = 5.25742 loss)
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I0405 12:10:02.363986 26038 sgd_solver.cpp:105] Iteration 16188, lr = 1e-05
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I0405 12:10:07.888458 26038 solver.cpp:218] Iteration 16200 (2.17217 iter/s, 5.52443s/12 iters), loss = 5.26921
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I0405 12:10:07.888510 26038 solver.cpp:237] Train net output #0: loss = 5.26921 (* 1 = 5.26921 loss)
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I0405 12:10:07.888518 26038 sgd_solver.cpp:105] Iteration 16200, lr = 1e-05
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I0405 12:10:13.122113 26038 solver.cpp:218] Iteration 16212 (2.2929 iter/s, 5.23356s/12 iters), loss = 5.27343
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I0405 12:10:13.122159 26038 solver.cpp:237] Train net output #0: loss = 5.27343 (* 1 = 5.27343 loss)
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I0405 12:10:13.122164 26038 sgd_solver.cpp:105] Iteration 16212, lr = 1e-05
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I0405 12:10:15.183575 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16218.caffemodel
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I0405 12:10:18.903561 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16218.solverstate
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I0405 12:10:21.238714 26038 solver.cpp:330] Iteration 16218, Testing net (#0)
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I0405 12:10:21.238736 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:10:23.851090 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:10:25.589319 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:10:25.589356 26038 solver.cpp:397] Test net output #1: loss = 5.27983 (* 1 = 5.27983 loss)
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I0405 12:10:27.649725 26038 solver.cpp:218] Iteration 16224 (0.826022 iter/s, 14.5275s/12 iters), loss = 5.2689
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I0405 12:10:27.649780 26038 solver.cpp:237] Train net output #0: loss = 5.2689 (* 1 = 5.2689 loss)
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I0405 12:10:27.649787 26038 sgd_solver.cpp:105] Iteration 16224, lr = 1e-05
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I0405 12:10:33.008553 26038 solver.cpp:218] Iteration 16236 (2.23934 iter/s, 5.35872s/12 iters), loss = 5.27802
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I0405 12:10:33.008669 26038 solver.cpp:237] Train net output #0: loss = 5.27802 (* 1 = 5.27802 loss)
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I0405 12:10:33.008675 26038 sgd_solver.cpp:105] Iteration 16236, lr = 1e-05
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I0405 12:10:38.250852 26038 solver.cpp:218] Iteration 16248 (2.28914 iter/s, 5.24214s/12 iters), loss = 5.26074
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I0405 12:10:38.250890 26038 solver.cpp:237] Train net output #0: loss = 5.26074 (* 1 = 5.26074 loss)
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I0405 12:10:38.250895 26038 sgd_solver.cpp:105] Iteration 16248, lr = 1e-05
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I0405 12:10:43.486474 26038 solver.cpp:218] Iteration 16260 (2.29203 iter/s, 5.23553s/12 iters), loss = 5.26585
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I0405 12:10:43.486522 26038 solver.cpp:237] Train net output #0: loss = 5.26585 (* 1 = 5.26585 loss)
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I0405 12:10:43.486528 26038 sgd_solver.cpp:105] Iteration 16260, lr = 1e-05
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I0405 12:10:48.721802 26038 solver.cpp:218] Iteration 16272 (2.29216 iter/s, 5.23523s/12 iters), loss = 5.27194
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I0405 12:10:48.721858 26038 solver.cpp:237] Train net output #0: loss = 5.27194 (* 1 = 5.27194 loss)
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I0405 12:10:48.721866 26038 sgd_solver.cpp:105] Iteration 16272, lr = 1e-05
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I0405 12:10:53.884264 26038 solver.cpp:218] Iteration 16284 (2.32452 iter/s, 5.16236s/12 iters), loss = 5.25881
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I0405 12:10:53.884303 26038 solver.cpp:237] Train net output #0: loss = 5.25881 (* 1 = 5.25881 loss)
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I0405 12:10:53.884310 26038 sgd_solver.cpp:105] Iteration 16284, lr = 1e-05
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I0405 12:10:54.385957 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:10:59.348965 26038 solver.cpp:218] Iteration 16296 (2.19595 iter/s, 5.46461s/12 iters), loss = 5.27883
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I0405 12:10:59.349020 26038 solver.cpp:237] Train net output #0: loss = 5.27883 (* 1 = 5.27883 loss)
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I0405 12:10:59.349031 26038 sgd_solver.cpp:105] Iteration 16296, lr = 1e-05
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I0405 12:11:04.579727 26038 solver.cpp:218] Iteration 16308 (2.29417 iter/s, 5.23066s/12 iters), loss = 5.2739
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I0405 12:11:04.579849 26038 solver.cpp:237] Train net output #0: loss = 5.2739 (* 1 = 5.2739 loss)
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I0405 12:11:04.579857 26038 sgd_solver.cpp:105] Iteration 16308, lr = 1e-05
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I0405 12:11:09.477886 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16320.caffemodel
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I0405 12:11:12.469763 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16320.solverstate
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I0405 12:11:14.770522 26038 solver.cpp:330] Iteration 16320, Testing net (#0)
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I0405 12:11:14.770545 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:11:17.369633 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:11:19.240638 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:11:19.240674 26038 solver.cpp:397] Test net output #1: loss = 5.28001 (* 1 = 5.28001 loss)
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I0405 12:11:19.382396 26038 solver.cpp:218] Iteration 16320 (0.810677 iter/s, 14.8024s/12 iters), loss = 5.28063
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I0405 12:11:19.383955 26038 solver.cpp:237] Train net output #0: loss = 5.28063 (* 1 = 5.28063 loss)
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I0405 12:11:19.383965 26038 sgd_solver.cpp:105] Iteration 16320, lr = 1e-05
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I0405 12:11:23.735203 26038 solver.cpp:218] Iteration 16332 (2.75785 iter/s, 4.35121s/12 iters), loss = 5.28077
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I0405 12:11:23.735246 26038 solver.cpp:237] Train net output #0: loss = 5.28077 (* 1 = 5.28077 loss)
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I0405 12:11:23.735252 26038 sgd_solver.cpp:105] Iteration 16332, lr = 1e-05
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I0405 12:11:29.046978 26038 solver.cpp:218] Iteration 16344 (2.25917 iter/s, 5.31168s/12 iters), loss = 5.28357
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I0405 12:11:29.047025 26038 solver.cpp:237] Train net output #0: loss = 5.28357 (* 1 = 5.28357 loss)
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I0405 12:11:29.047032 26038 sgd_solver.cpp:105] Iteration 16344, lr = 1e-05
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I0405 12:11:34.425274 26038 solver.cpp:218] Iteration 16356 (2.23123 iter/s, 5.3782s/12 iters), loss = 5.2757
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I0405 12:11:34.425330 26038 solver.cpp:237] Train net output #0: loss = 5.2757 (* 1 = 5.2757 loss)
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I0405 12:11:34.425343 26038 sgd_solver.cpp:105] Iteration 16356, lr = 1e-05
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I0405 12:11:39.860986 26038 solver.cpp:218] Iteration 16368 (2.20767 iter/s, 5.43561s/12 iters), loss = 5.26417
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I0405 12:11:39.861155 26038 solver.cpp:237] Train net output #0: loss = 5.26417 (* 1 = 5.26417 loss)
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I0405 12:11:39.861166 26038 sgd_solver.cpp:105] Iteration 16368, lr = 1e-05
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I0405 12:11:45.121635 26038 solver.cpp:218] Iteration 16380 (2.28118 iter/s, 5.26044s/12 iters), loss = 5.27656
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I0405 12:11:45.121677 26038 solver.cpp:237] Train net output #0: loss = 5.27656 (* 1 = 5.27656 loss)
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I0405 12:11:45.121682 26038 sgd_solver.cpp:105] Iteration 16380, lr = 1e-05
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I0405 12:11:47.890594 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:11:50.546999 26038 solver.cpp:218] Iteration 16392 (2.21187 iter/s, 5.42528s/12 iters), loss = 5.28462
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I0405 12:11:50.547039 26038 solver.cpp:237] Train net output #0: loss = 5.28462 (* 1 = 5.28462 loss)
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I0405 12:11:50.547045 26038 sgd_solver.cpp:105] Iteration 16392, lr = 1e-05
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I0405 12:11:56.048911 26038 solver.cpp:218] Iteration 16404 (2.1811 iter/s, 5.50182s/12 iters), loss = 5.26251
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I0405 12:11:56.048959 26038 solver.cpp:237] Train net output #0: loss = 5.26251 (* 1 = 5.26251 loss)
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I0405 12:11:56.048966 26038 sgd_solver.cpp:105] Iteration 16404, lr = 1e-05
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I0405 12:12:01.083974 26038 solver.cpp:218] Iteration 16416 (2.38333 iter/s, 5.03497s/12 iters), loss = 5.27047
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I0405 12:12:01.084017 26038 solver.cpp:237] Train net output #0: loss = 5.27047 (* 1 = 5.27047 loss)
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I0405 12:12:01.084022 26038 sgd_solver.cpp:105] Iteration 16416, lr = 1e-05
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I0405 12:12:03.229570 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16422.caffemodel
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I0405 12:12:06.180030 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16422.solverstate
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I0405 12:12:08.483556 26038 solver.cpp:330] Iteration 16422, Testing net (#0)
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I0405 12:12:08.483573 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:12:11.039069 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:12:12.894014 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:12:12.894048 26038 solver.cpp:397] Test net output #1: loss = 5.27997 (* 1 = 5.27997 loss)
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I0405 12:12:14.898787 26038 solver.cpp:218] Iteration 16428 (0.868642 iter/s, 13.8147s/12 iters), loss = 5.28637
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I0405 12:12:14.898838 26038 solver.cpp:237] Train net output #0: loss = 5.28637 (* 1 = 5.28637 loss)
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I0405 12:12:14.898844 26038 sgd_solver.cpp:105] Iteration 16428, lr = 1e-05
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I0405 12:12:20.325330 26038 solver.cpp:218] Iteration 16440 (2.21139 iter/s, 5.42644s/12 iters), loss = 5.2821
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I0405 12:12:20.325379 26038 solver.cpp:237] Train net output #0: loss = 5.2821 (* 1 = 5.2821 loss)
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I0405 12:12:20.325388 26038 sgd_solver.cpp:105] Iteration 16440, lr = 1e-05
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I0405 12:12:25.570235 26038 solver.cpp:218] Iteration 16452 (2.28798 iter/s, 5.24481s/12 iters), loss = 5.2859
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I0405 12:12:25.570276 26038 solver.cpp:237] Train net output #0: loss = 5.2859 (* 1 = 5.2859 loss)
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I0405 12:12:25.570281 26038 sgd_solver.cpp:105] Iteration 16452, lr = 1e-05
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I0405 12:12:30.726068 26038 solver.cpp:218] Iteration 16464 (2.3275 iter/s, 5.15574s/12 iters), loss = 5.28716
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I0405 12:12:30.726109 26038 solver.cpp:237] Train net output #0: loss = 5.28716 (* 1 = 5.28716 loss)
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I0405 12:12:30.726115 26038 sgd_solver.cpp:105] Iteration 16464, lr = 1e-05
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I0405 12:12:35.921597 26038 solver.cpp:218] Iteration 16476 (2.30972 iter/s, 5.19544s/12 iters), loss = 5.29962
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I0405 12:12:35.921640 26038 solver.cpp:237] Train net output #0: loss = 5.29962 (* 1 = 5.29962 loss)
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I0405 12:12:35.921646 26038 sgd_solver.cpp:105] Iteration 16476, lr = 1e-05
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I0405 12:12:40.994194 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:12:41.303558 26038 solver.cpp:218] Iteration 16488 (2.22971 iter/s, 5.38187s/12 iters), loss = 5.28209
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I0405 12:12:41.303742 26038 solver.cpp:237] Train net output #0: loss = 5.28209 (* 1 = 5.28209 loss)
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I0405 12:12:41.303755 26038 sgd_solver.cpp:105] Iteration 16488, lr = 1e-05
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I0405 12:12:46.712589 26038 solver.cpp:218] Iteration 16500 (2.21861 iter/s, 5.4088s/12 iters), loss = 5.25901
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I0405 12:12:46.712635 26038 solver.cpp:237] Train net output #0: loss = 5.25901 (* 1 = 5.25901 loss)
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I0405 12:12:46.712641 26038 sgd_solver.cpp:105] Iteration 16500, lr = 1e-05
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I0405 12:12:52.051425 26038 solver.cpp:218] Iteration 16512 (2.24772 iter/s, 5.33874s/12 iters), loss = 5.2704
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I0405 12:12:52.051466 26038 solver.cpp:237] Train net output #0: loss = 5.2704 (* 1 = 5.2704 loss)
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I0405 12:12:52.051472 26038 sgd_solver.cpp:105] Iteration 16512, lr = 1e-05
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I0405 12:12:56.995961 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16524.caffemodel
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I0405 12:13:00.783187 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16524.solverstate
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I0405 12:13:03.114457 26038 solver.cpp:330] Iteration 16524, Testing net (#0)
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I0405 12:13:03.114481 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:13:05.612700 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:13:07.446238 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:13:07.446274 26038 solver.cpp:397] Test net output #1: loss = 5.27999 (* 1 = 5.27999 loss)
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I0405 12:13:07.583433 26038 solver.cpp:218] Iteration 16524 (0.772606 iter/s, 15.5319s/12 iters), loss = 5.2874
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I0405 12:13:07.583490 26038 solver.cpp:237] Train net output #0: loss = 5.2874 (* 1 = 5.2874 loss)
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I0405 12:13:07.583498 26038 sgd_solver.cpp:105] Iteration 16524, lr = 1e-05
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I0405 12:13:12.039716 26038 solver.cpp:218] Iteration 16536 (2.69289 iter/s, 4.45618s/12 iters), loss = 5.27929
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I0405 12:13:12.039816 26038 solver.cpp:237] Train net output #0: loss = 5.27929 (* 1 = 5.27929 loss)
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I0405 12:13:12.039822 26038 sgd_solver.cpp:105] Iteration 16536, lr = 1e-05
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I0405 12:13:17.483325 26038 solver.cpp:218] Iteration 16548 (2.20448 iter/s, 5.44345s/12 iters), loss = 5.28234
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I0405 12:13:17.483377 26038 solver.cpp:237] Train net output #0: loss = 5.28234 (* 1 = 5.28234 loss)
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I0405 12:13:17.483386 26038 sgd_solver.cpp:105] Iteration 16548, lr = 1e-05
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I0405 12:13:22.733120 26038 solver.cpp:218] Iteration 16560 (2.28585 iter/s, 5.24969s/12 iters), loss = 5.25817
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I0405 12:13:22.733168 26038 solver.cpp:237] Train net output #0: loss = 5.25817 (* 1 = 5.25817 loss)
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I0405 12:13:22.733176 26038 sgd_solver.cpp:105] Iteration 16560, lr = 1e-05
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I0405 12:13:28.159369 26038 solver.cpp:218] Iteration 16572 (2.21151 iter/s, 5.42615s/12 iters), loss = 5.27186
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I0405 12:13:28.159435 26038 solver.cpp:237] Train net output #0: loss = 5.27186 (* 1 = 5.27186 loss)
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I0405 12:13:28.159444 26038 sgd_solver.cpp:105] Iteration 16572, lr = 1e-05
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I0405 12:13:33.504722 26038 solver.cpp:218] Iteration 16584 (2.24499 iter/s, 5.34524s/12 iters), loss = 5.27624
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I0405 12:13:33.504763 26038 solver.cpp:237] Train net output #0: loss = 5.27624 (* 1 = 5.27624 loss)
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I0405 12:13:33.504768 26038 sgd_solver.cpp:105] Iteration 16584, lr = 1e-05
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I0405 12:13:35.349891 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:13:38.645805 26038 solver.cpp:218] Iteration 16596 (2.33418 iter/s, 5.141s/12 iters), loss = 5.28694
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I0405 12:13:38.645844 26038 solver.cpp:237] Train net output #0: loss = 5.28694 (* 1 = 5.28694 loss)
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I0405 12:13:38.645849 26038 sgd_solver.cpp:105] Iteration 16596, lr = 1e-05
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I0405 12:13:44.011746 26038 solver.cpp:218] Iteration 16608 (2.23636 iter/s, 5.36585s/12 iters), loss = 5.26385
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I0405 12:13:44.011837 26038 solver.cpp:237] Train net output #0: loss = 5.26385 (* 1 = 5.26385 loss)
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I0405 12:13:44.011843 26038 sgd_solver.cpp:105] Iteration 16608, lr = 1e-05
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I0405 12:13:49.499373 26038 solver.cpp:218] Iteration 16620 (2.18679 iter/s, 5.48749s/12 iters), loss = 5.27626
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I0405 12:13:49.499413 26038 solver.cpp:237] Train net output #0: loss = 5.27626 (* 1 = 5.27626 loss)
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I0405 12:13:49.499418 26038 sgd_solver.cpp:105] Iteration 16620, lr = 1e-05
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I0405 12:13:51.679179 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16626.caffemodel
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I0405 12:13:54.741277 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16626.solverstate
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I0405 12:13:57.059259 26038 solver.cpp:330] Iteration 16626, Testing net (#0)
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I0405 12:13:57.059284 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:13:59.656471 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:14:00.938691 26038 blocking_queue.cpp:49] Waiting for data
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I0405 12:14:01.557117 26038 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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I0405 12:14:01.557152 26038 solver.cpp:397] Test net output #1: loss = 5.27964 (* 1 = 5.27964 loss)
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I0405 12:14:03.424818 26038 solver.cpp:218] Iteration 16632 (0.86174 iter/s, 13.9253s/12 iters), loss = 5.27149
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I0405 12:14:03.424862 26038 solver.cpp:237] Train net output #0: loss = 5.27149 (* 1 = 5.27149 loss)
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I0405 12:14:03.424868 26038 sgd_solver.cpp:105] Iteration 16632, lr = 1e-05
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I0405 12:14:08.812551 26038 solver.cpp:218] Iteration 16644 (2.22732 iter/s, 5.38764s/12 iters), loss = 5.26231
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I0405 12:14:08.812592 26038 solver.cpp:237] Train net output #0: loss = 5.26231 (* 1 = 5.26231 loss)
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I0405 12:14:08.812597 26038 sgd_solver.cpp:105] Iteration 16644, lr = 1e-05
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I0405 12:14:14.170544 26038 solver.cpp:218] Iteration 16656 (2.23968 iter/s, 5.3579s/12 iters), loss = 5.25473
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I0405 12:14:14.170668 26038 solver.cpp:237] Train net output #0: loss = 5.25473 (* 1 = 5.25473 loss)
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I0405 12:14:14.170675 26038 sgd_solver.cpp:105] Iteration 16656, lr = 1e-05
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I0405 12:14:19.629673 26038 solver.cpp:218] Iteration 16668 (2.19822 iter/s, 5.45896s/12 iters), loss = 5.26819
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I0405 12:14:19.629714 26038 solver.cpp:237] Train net output #0: loss = 5.26819 (* 1 = 5.26819 loss)
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I0405 12:14:19.629719 26038 sgd_solver.cpp:105] Iteration 16668, lr = 1e-05
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I0405 12:14:25.032281 26038 solver.cpp:218] Iteration 16680 (2.22119 iter/s, 5.40252s/12 iters), loss = 5.26253
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I0405 12:14:25.032326 26038 solver.cpp:237] Train net output #0: loss = 5.26253 (* 1 = 5.26253 loss)
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I0405 12:14:25.032333 26038 sgd_solver.cpp:105] Iteration 16680, lr = 1e-05
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I0405 12:14:29.269662 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:14:30.398033 26038 solver.cpp:218] Iteration 16692 (2.23644 iter/s, 5.36566s/12 iters), loss = 5.26472
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I0405 12:14:30.398074 26038 solver.cpp:237] Train net output #0: loss = 5.26472 (* 1 = 5.26472 loss)
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I0405 12:14:30.398079 26038 sgd_solver.cpp:105] Iteration 16692, lr = 1e-05
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I0405 12:14:35.839144 26038 solver.cpp:218] Iteration 16704 (2.20547 iter/s, 5.44102s/12 iters), loss = 5.26478
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I0405 12:14:35.839197 26038 solver.cpp:237] Train net output #0: loss = 5.26478 (* 1 = 5.26478 loss)
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I0405 12:14:35.839205 26038 sgd_solver.cpp:105] Iteration 16704, lr = 1e-05
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I0405 12:14:40.933878 26038 solver.cpp:218] Iteration 16716 (2.35542 iter/s, 5.09464s/12 iters), loss = 5.29772
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I0405 12:14:40.933914 26038 solver.cpp:237] Train net output #0: loss = 5.29772 (* 1 = 5.29772 loss)
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I0405 12:14:40.933920 26038 sgd_solver.cpp:105] Iteration 16716, lr = 1e-05
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I0405 12:14:45.751266 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16728.caffemodel
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I0405 12:14:48.712671 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16728.solverstate
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I0405 12:14:51.019098 26038 solver.cpp:330] Iteration 16728, Testing net (#0)
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I0405 12:14:51.019129 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:14:53.423724 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:14:55.377269 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:14:55.377312 26038 solver.cpp:397] Test net output #1: loss = 5.27994 (* 1 = 5.27994 loss)
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I0405 12:14:55.519183 26038 solver.cpp:218] Iteration 16728 (0.822754 iter/s, 14.5852s/12 iters), loss = 5.27943
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I0405 12:14:55.519227 26038 solver.cpp:237] Train net output #0: loss = 5.27943 (* 1 = 5.27943 loss)
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I0405 12:14:55.519232 26038 sgd_solver.cpp:105] Iteration 16728, lr = 1e-05
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I0405 12:14:59.879964 26038 solver.cpp:218] Iteration 16740 (2.75186 iter/s, 4.36069s/12 iters), loss = 5.2813
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I0405 12:14:59.880008 26038 solver.cpp:237] Train net output #0: loss = 5.2813 (* 1 = 5.2813 loss)
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I0405 12:14:59.880014 26038 sgd_solver.cpp:105] Iteration 16740, lr = 1e-05
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I0405 12:15:05.176192 26038 solver.cpp:218] Iteration 16752 (2.2658 iter/s, 5.29614s/12 iters), loss = 5.28478
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I0405 12:15:05.176229 26038 solver.cpp:237] Train net output #0: loss = 5.28478 (* 1 = 5.28478 loss)
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I0405 12:15:05.176236 26038 sgd_solver.cpp:105] Iteration 16752, lr = 1e-05
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I0405 12:15:10.544837 26038 solver.cpp:218] Iteration 16764 (2.23524 iter/s, 5.36856s/12 iters), loss = 5.28466
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I0405 12:15:10.544895 26038 solver.cpp:237] Train net output #0: loss = 5.28466 (* 1 = 5.28466 loss)
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I0405 12:15:10.544904 26038 sgd_solver.cpp:105] Iteration 16764, lr = 1e-05
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I0405 12:15:15.758581 26038 solver.cpp:218] Iteration 16776 (2.30165 iter/s, 5.21365s/12 iters), loss = 5.26617
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I0405 12:15:15.758723 26038 solver.cpp:237] Train net output #0: loss = 5.26617 (* 1 = 5.26617 loss)
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I0405 12:15:15.758731 26038 sgd_solver.cpp:105] Iteration 16776, lr = 1e-05
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I0405 12:15:21.169173 26038 solver.cpp:218] Iteration 16788 (2.21795 iter/s, 5.41041s/12 iters), loss = 5.26913
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I0405 12:15:21.169219 26038 solver.cpp:237] Train net output #0: loss = 5.26913 (* 1 = 5.26913 loss)
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I0405 12:15:21.169226 26038 sgd_solver.cpp:105] Iteration 16788, lr = 1e-05
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I0405 12:15:22.318222 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:15:26.468803 26038 solver.cpp:218] Iteration 16800 (2.26435 iter/s, 5.29954s/12 iters), loss = 5.29378
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I0405 12:15:26.468847 26038 solver.cpp:237] Train net output #0: loss = 5.29378 (* 1 = 5.29378 loss)
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I0405 12:15:26.468852 26038 sgd_solver.cpp:105] Iteration 16800, lr = 1e-05
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I0405 12:15:31.915732 26038 solver.cpp:218] Iteration 16812 (2.20311 iter/s, 5.44684s/12 iters), loss = 5.27328
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I0405 12:15:31.915781 26038 solver.cpp:237] Train net output #0: loss = 5.27328 (* 1 = 5.27328 loss)
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I0405 12:15:31.915788 26038 sgd_solver.cpp:105] Iteration 16812, lr = 1e-05
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I0405 12:15:37.117825 26038 solver.cpp:218] Iteration 16824 (2.30681 iter/s, 5.202s/12 iters), loss = 5.26063
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I0405 12:15:37.117864 26038 solver.cpp:237] Train net output #0: loss = 5.26063 (* 1 = 5.26063 loss)
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I0405 12:15:37.117870 26038 sgd_solver.cpp:105] Iteration 16824, lr = 1e-05
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I0405 12:15:39.264660 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16830.caffemodel
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I0405 12:15:42.405686 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16830.solverstate
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I0405 12:15:44.751629 26038 solver.cpp:330] Iteration 16830, Testing net (#0)
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I0405 12:15:44.751650 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:15:47.224444 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:15:49.373126 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:15:49.373163 26038 solver.cpp:397] Test net output #1: loss = 5.27979 (* 1 = 5.27979 loss)
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I0405 12:15:51.286247 26038 solver.cpp:218] Iteration 16836 (0.846962 iter/s, 14.1683s/12 iters), loss = 5.27209
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I0405 12:15:51.286309 26038 solver.cpp:237] Train net output #0: loss = 5.27209 (* 1 = 5.27209 loss)
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I0405 12:15:51.286317 26038 sgd_solver.cpp:105] Iteration 16836, lr = 1e-05
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I0405 12:15:56.742738 26038 solver.cpp:218] Iteration 16848 (2.19926 iter/s, 5.45638s/12 iters), loss = 5.28238
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I0405 12:15:56.742789 26038 solver.cpp:237] Train net output #0: loss = 5.28238 (* 1 = 5.28238 loss)
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I0405 12:15:56.742799 26038 sgd_solver.cpp:105] Iteration 16848, lr = 1e-05
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I0405 12:16:01.988574 26038 solver.cpp:218] Iteration 16860 (2.28757 iter/s, 5.24574s/12 iters), loss = 5.28594
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I0405 12:16:01.988626 26038 solver.cpp:237] Train net output #0: loss = 5.28594 (* 1 = 5.28594 loss)
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I0405 12:16:01.988634 26038 sgd_solver.cpp:105] Iteration 16860, lr = 1e-05
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I0405 12:16:07.381243 26038 solver.cpp:218] Iteration 16872 (2.22528 iter/s, 5.39257s/12 iters), loss = 5.25549
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I0405 12:16:07.381287 26038 solver.cpp:237] Train net output #0: loss = 5.25549 (* 1 = 5.25549 loss)
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I0405 12:16:07.381292 26038 sgd_solver.cpp:105] Iteration 16872, lr = 1e-05
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I0405 12:16:12.350056 26038 solver.cpp:218] Iteration 16884 (2.41511 iter/s, 4.96872s/12 iters), loss = 5.2982
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I0405 12:16:12.350106 26038 solver.cpp:237] Train net output #0: loss = 5.2982 (* 1 = 5.2982 loss)
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I0405 12:16:12.350112 26038 sgd_solver.cpp:105] Iteration 16884, lr = 1e-05
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I0405 12:16:15.705869 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:16:17.457998 26038 solver.cpp:218] Iteration 16896 (2.34933 iter/s, 5.10785s/12 iters), loss = 5.26567
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I0405 12:16:17.458168 26038 solver.cpp:237] Train net output #0: loss = 5.26567 (* 1 = 5.26567 loss)
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I0405 12:16:17.458178 26038 sgd_solver.cpp:105] Iteration 16896, lr = 1e-05
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I0405 12:16:22.840121 26038 solver.cpp:218] Iteration 16908 (2.22969 iter/s, 5.38191s/12 iters), loss = 5.26095
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I0405 12:16:22.840162 26038 solver.cpp:237] Train net output #0: loss = 5.26095 (* 1 = 5.26095 loss)
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I0405 12:16:22.840167 26038 sgd_solver.cpp:105] Iteration 16908, lr = 1e-05
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I0405 12:16:28.018333 26038 solver.cpp:218] Iteration 16920 (2.31744 iter/s, 5.17812s/12 iters), loss = 5.2678
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I0405 12:16:28.018384 26038 solver.cpp:237] Train net output #0: loss = 5.2678 (* 1 = 5.2678 loss)
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I0405 12:16:28.018391 26038 sgd_solver.cpp:105] Iteration 16920, lr = 1e-05
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I0405 12:16:32.753793 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16932.caffemodel
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||
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I0405 12:16:35.791957 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16932.solverstate
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||
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I0405 12:16:38.094825 26038 solver.cpp:330] Iteration 16932, Testing net (#0)
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||
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I0405 12:16:38.094846 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
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I0405 12:16:40.508667 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 12:16:42.512115 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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|
I0405 12:16:42.512156 26038 solver.cpp:397] Test net output #1: loss = 5.27982 (* 1 = 5.27982 loss)
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I0405 12:16:42.653903 26038 solver.cpp:218] Iteration 16932 (0.819929 iter/s, 14.6354s/12 iters), loss = 5.28412
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I0405 12:16:42.653955 26038 solver.cpp:237] Train net output #0: loss = 5.28412 (* 1 = 5.28412 loss)
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||
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I0405 12:16:42.653962 26038 sgd_solver.cpp:105] Iteration 16932, lr = 1e-05
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I0405 12:16:47.033681 26038 solver.cpp:218] Iteration 16944 (2.73993 iter/s, 4.37968s/12 iters), loss = 5.27632
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I0405 12:16:47.033733 26038 solver.cpp:237] Train net output #0: loss = 5.27632 (* 1 = 5.27632 loss)
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I0405 12:16:47.033741 26038 sgd_solver.cpp:105] Iteration 16944, lr = 1e-05
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I0405 12:16:52.504149 26038 solver.cpp:218] Iteration 16956 (2.19364 iter/s, 5.47036s/12 iters), loss = 5.25568
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I0405 12:16:52.504269 26038 solver.cpp:237] Train net output #0: loss = 5.25568 (* 1 = 5.25568 loss)
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||
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I0405 12:16:52.504279 26038 sgd_solver.cpp:105] Iteration 16956, lr = 1e-05
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I0405 12:16:57.857926 26038 solver.cpp:218] Iteration 16968 (2.24148 iter/s, 5.35361s/12 iters), loss = 5.2638
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I0405 12:16:57.857975 26038 solver.cpp:237] Train net output #0: loss = 5.2638 (* 1 = 5.2638 loss)
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I0405 12:16:57.857981 26038 sgd_solver.cpp:105] Iteration 16968, lr = 1e-05
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I0405 12:17:03.307726 26038 solver.cpp:218] Iteration 16980 (2.20196 iter/s, 5.4497s/12 iters), loss = 5.2718
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I0405 12:17:03.307780 26038 solver.cpp:237] Train net output #0: loss = 5.2718 (* 1 = 5.2718 loss)
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I0405 12:17:03.307787 26038 sgd_solver.cpp:105] Iteration 16980, lr = 1e-05
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I0405 12:17:08.639175 26038 solver.cpp:218] Iteration 16992 (2.25084 iter/s, 5.33135s/12 iters), loss = 5.27513
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I0405 12:17:08.639214 26038 solver.cpp:237] Train net output #0: loss = 5.27513 (* 1 = 5.27513 loss)
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||
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I0405 12:17:08.639219 26038 sgd_solver.cpp:105] Iteration 16992, lr = 1e-05
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||
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I0405 12:17:09.166059 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:17:14.003310 26038 solver.cpp:218] Iteration 17004 (2.23712 iter/s, 5.36404s/12 iters), loss = 5.30526
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I0405 12:17:14.003367 26038 solver.cpp:237] Train net output #0: loss = 5.30526 (* 1 = 5.30526 loss)
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||
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I0405 12:17:14.003376 26038 sgd_solver.cpp:105] Iteration 17004, lr = 1e-05
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I0405 12:17:19.372870 26038 solver.cpp:218] Iteration 17016 (2.23486 iter/s, 5.36945s/12 iters), loss = 5.28117
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||
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I0405 12:17:19.372938 26038 solver.cpp:237] Train net output #0: loss = 5.28117 (* 1 = 5.28117 loss)
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||
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I0405 12:17:19.372948 26038 sgd_solver.cpp:105] Iteration 17016, lr = 1e-05
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I0405 12:17:24.764127 26038 solver.cpp:218] Iteration 17028 (2.22587 iter/s, 5.39115s/12 iters), loss = 5.26696
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I0405 12:17:24.764241 26038 solver.cpp:237] Train net output #0: loss = 5.26696 (* 1 = 5.26696 loss)
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||
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I0405 12:17:24.764248 26038 sgd_solver.cpp:105] Iteration 17028, lr = 1e-05
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||
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I0405 12:17:26.835413 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17034.caffemodel
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||
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|
I0405 12:17:29.971339 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17034.solverstate
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||
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I0405 12:17:32.369657 26038 solver.cpp:330] Iteration 17034, Testing net (#0)
|
||
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I0405 12:17:32.369679 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
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I0405 12:17:34.743412 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 12:17:36.782202 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 12:17:36.782236 26038 solver.cpp:397] Test net output #1: loss = 5.28 (* 1 = 5.28 loss)
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I0405 12:17:38.711232 26038 solver.cpp:218] Iteration 17040 (0.860407 iter/s, 13.9469s/12 iters), loss = 5.27019
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I0405 12:17:38.711283 26038 solver.cpp:237] Train net output #0: loss = 5.27019 (* 1 = 5.27019 loss)
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I0405 12:17:38.711290 26038 sgd_solver.cpp:105] Iteration 17040, lr = 1e-05
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I0405 12:17:44.032604 26038 solver.cpp:218] Iteration 17052 (2.2551 iter/s, 5.32127s/12 iters), loss = 5.26585
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I0405 12:17:44.032656 26038 solver.cpp:237] Train net output #0: loss = 5.26585 (* 1 = 5.26585 loss)
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I0405 12:17:44.032665 26038 sgd_solver.cpp:105] Iteration 17052, lr = 1e-05
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I0405 12:17:49.472568 26038 solver.cpp:218] Iteration 17064 (2.20594 iter/s, 5.43986s/12 iters), loss = 5.28268
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I0405 12:17:49.472620 26038 solver.cpp:237] Train net output #0: loss = 5.28268 (* 1 = 5.28268 loss)
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I0405 12:17:49.472628 26038 sgd_solver.cpp:105] Iteration 17064, lr = 1e-05
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I0405 12:17:54.857729 26038 solver.cpp:218] Iteration 17076 (2.22839 iter/s, 5.38506s/12 iters), loss = 5.26586
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I0405 12:17:54.860431 26038 solver.cpp:237] Train net output #0: loss = 5.26586 (* 1 = 5.26586 loss)
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I0405 12:17:54.860443 26038 sgd_solver.cpp:105] Iteration 17076, lr = 1e-05
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I0405 12:18:00.168685 26038 solver.cpp:218] Iteration 17088 (2.26065 iter/s, 5.30821s/12 iters), loss = 5.27652
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I0405 12:18:00.168730 26038 solver.cpp:237] Train net output #0: loss = 5.27652 (* 1 = 5.27652 loss)
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I0405 12:18:00.168735 26038 sgd_solver.cpp:105] Iteration 17088, lr = 1e-05
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I0405 12:18:02.924755 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:18:05.497061 26038 solver.cpp:218] Iteration 17100 (2.25213 iter/s, 5.32828s/12 iters), loss = 5.28963
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I0405 12:18:05.497117 26038 solver.cpp:237] Train net output #0: loss = 5.28963 (* 1 = 5.28963 loss)
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I0405 12:18:05.497126 26038 sgd_solver.cpp:105] Iteration 17100, lr = 1e-05
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I0405 12:18:11.017946 26038 solver.cpp:218] Iteration 17112 (2.17361 iter/s, 5.52078s/12 iters), loss = 5.27387
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I0405 12:18:11.017997 26038 solver.cpp:237] Train net output #0: loss = 5.27387 (* 1 = 5.27387 loss)
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I0405 12:18:11.018005 26038 sgd_solver.cpp:105] Iteration 17112, lr = 1e-05
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I0405 12:18:16.147418 26038 solver.cpp:218] Iteration 17124 (2.33947 iter/s, 5.12937s/12 iters), loss = 5.27132
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I0405 12:18:16.147464 26038 solver.cpp:237] Train net output #0: loss = 5.27132 (* 1 = 5.27132 loss)
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I0405 12:18:16.147471 26038 sgd_solver.cpp:105] Iteration 17124, lr = 1e-05
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I0405 12:18:21.161767 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17136.caffemodel
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I0405 12:18:24.264040 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17136.solverstate
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I0405 12:18:26.563441 26038 solver.cpp:330] Iteration 17136, Testing net (#0)
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I0405 12:18:26.563565 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:18:28.796073 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:18:30.874078 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:18:30.874115 26038 solver.cpp:397] Test net output #1: loss = 5.28036 (* 1 = 5.28036 loss)
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I0405 12:18:31.016077 26038 solver.cpp:218] Iteration 17136 (0.807075 iter/s, 14.8685s/12 iters), loss = 5.28625
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I0405 12:18:31.016132 26038 solver.cpp:237] Train net output #0: loss = 5.28625 (* 1 = 5.28625 loss)
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I0405 12:18:31.016139 26038 sgd_solver.cpp:105] Iteration 17136, lr = 1e-05
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I0405 12:18:35.742889 26038 solver.cpp:218] Iteration 17148 (2.53876 iter/s, 4.72671s/12 iters), loss = 5.28792
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I0405 12:18:35.742928 26038 solver.cpp:237] Train net output #0: loss = 5.28792 (* 1 = 5.28792 loss)
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I0405 12:18:35.742933 26038 sgd_solver.cpp:105] Iteration 17148, lr = 1e-05
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I0405 12:18:41.246922 26038 solver.cpp:218] Iteration 17160 (2.18026 iter/s, 5.50394s/12 iters), loss = 5.28611
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I0405 12:18:41.246968 26038 solver.cpp:237] Train net output #0: loss = 5.28611 (* 1 = 5.28611 loss)
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I0405 12:18:41.246974 26038 sgd_solver.cpp:105] Iteration 17160, lr = 1e-05
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I0405 12:18:46.536155 26038 solver.cpp:218] Iteration 17172 (2.2688 iter/s, 5.28914s/12 iters), loss = 5.28372
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I0405 12:18:46.536192 26038 solver.cpp:237] Train net output #0: loss = 5.28372 (* 1 = 5.28372 loss)
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I0405 12:18:46.536198 26038 sgd_solver.cpp:105] Iteration 17172, lr = 1e-05
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I0405 12:18:52.080942 26038 solver.cpp:218] Iteration 17184 (2.16423 iter/s, 5.54469s/12 iters), loss = 5.27868
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I0405 12:18:52.081001 26038 solver.cpp:237] Train net output #0: loss = 5.27868 (* 1 = 5.27868 loss)
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I0405 12:18:52.081009 26038 sgd_solver.cpp:105] Iteration 17184, lr = 1e-05
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I0405 12:18:57.099195 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:18:57.381587 26038 solver.cpp:218] Iteration 17196 (2.26392 iter/s, 5.30054s/12 iters), loss = 5.30001
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I0405 12:18:57.381631 26038 solver.cpp:237] Train net output #0: loss = 5.30001 (* 1 = 5.30001 loss)
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I0405 12:18:57.381636 26038 sgd_solver.cpp:105] Iteration 17196, lr = 1e-05
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I0405 12:19:02.716377 26038 solver.cpp:218] Iteration 17208 (2.24942 iter/s, 5.3347s/12 iters), loss = 5.26483
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I0405 12:19:02.716426 26038 solver.cpp:237] Train net output #0: loss = 5.26483 (* 1 = 5.26483 loss)
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I0405 12:19:02.716434 26038 sgd_solver.cpp:105] Iteration 17208, lr = 1e-05
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I0405 12:19:08.052388 26038 solver.cpp:218] Iteration 17220 (2.24891 iter/s, 5.33592s/12 iters), loss = 5.27081
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I0405 12:19:08.052428 26038 solver.cpp:237] Train net output #0: loss = 5.27081 (* 1 = 5.27081 loss)
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I0405 12:19:08.052433 26038 sgd_solver.cpp:105] Iteration 17220, lr = 1e-05
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I0405 12:19:13.493083 26038 solver.cpp:218] Iteration 17232 (2.20564 iter/s, 5.4406s/12 iters), loss = 5.28823
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I0405 12:19:13.493124 26038 solver.cpp:237] Train net output #0: loss = 5.28823 (* 1 = 5.28823 loss)
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I0405 12:19:13.493129 26038 sgd_solver.cpp:105] Iteration 17232, lr = 1e-05
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I0405 12:19:15.498200 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17238.caffemodel
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||
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I0405 12:19:18.594241 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17238.solverstate
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I0405 12:19:20.900890 26038 solver.cpp:330] Iteration 17238, Testing net (#0)
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I0405 12:19:20.900909 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:19:23.243129 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:19:25.403072 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:19:25.403112 26038 solver.cpp:397] Test net output #1: loss = 5.28007 (* 1 = 5.28007 loss)
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I0405 12:19:27.315711 26038 solver.cpp:218] Iteration 17244 (0.868151 iter/s, 13.8225s/12 iters), loss = 5.26979
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I0405 12:19:27.315881 26038 solver.cpp:237] Train net output #0: loss = 5.26979 (* 1 = 5.26979 loss)
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I0405 12:19:27.315891 26038 sgd_solver.cpp:105] Iteration 17244, lr = 1e-05
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I0405 12:19:32.481876 26038 solver.cpp:218] Iteration 17256 (2.3229 iter/s, 5.16595s/12 iters), loss = 5.27159
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I0405 12:19:32.481916 26038 solver.cpp:237] Train net output #0: loss = 5.27159 (* 1 = 5.27159 loss)
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I0405 12:19:32.481921 26038 sgd_solver.cpp:105] Iteration 17256, lr = 1e-05
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I0405 12:19:37.727874 26038 solver.cpp:218] Iteration 17268 (2.2875 iter/s, 5.24591s/12 iters), loss = 5.26997
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I0405 12:19:37.727921 26038 solver.cpp:237] Train net output #0: loss = 5.26997 (* 1 = 5.26997 loss)
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I0405 12:19:37.727928 26038 sgd_solver.cpp:105] Iteration 17268, lr = 1e-05
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I0405 12:19:42.911823 26038 solver.cpp:218] Iteration 17280 (2.31488 iter/s, 5.18385s/12 iters), loss = 5.28161
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I0405 12:19:42.911885 26038 solver.cpp:237] Train net output #0: loss = 5.28161 (* 1 = 5.28161 loss)
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I0405 12:19:42.911892 26038 sgd_solver.cpp:105] Iteration 17280, lr = 1e-05
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I0405 12:19:48.317631 26038 solver.cpp:218] Iteration 17292 (2.21988 iter/s, 5.4057s/12 iters), loss = 5.28478
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I0405 12:19:48.317673 26038 solver.cpp:237] Train net output #0: loss = 5.28478 (* 1 = 5.28478 loss)
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||
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I0405 12:19:48.317682 26038 sgd_solver.cpp:105] Iteration 17292, lr = 1e-05
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I0405 12:19:50.335009 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:19:53.646101 26038 solver.cpp:218] Iteration 17304 (2.25209 iter/s, 5.32838s/12 iters), loss = 5.29962
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I0405 12:19:53.646143 26038 solver.cpp:237] Train net output #0: loss = 5.29962 (* 1 = 5.29962 loss)
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I0405 12:19:53.646148 26038 sgd_solver.cpp:105] Iteration 17304, lr = 1e-05
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I0405 12:19:59.044997 26038 solver.cpp:218] Iteration 17316 (2.22271 iter/s, 5.39881s/12 iters), loss = 5.27502
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I0405 12:19:59.045120 26038 solver.cpp:237] Train net output #0: loss = 5.27502 (* 1 = 5.27502 loss)
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I0405 12:19:59.045130 26038 sgd_solver.cpp:105] Iteration 17316, lr = 1e-05
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I0405 12:20:04.424965 26038 solver.cpp:218] Iteration 17328 (2.23057 iter/s, 5.3798s/12 iters), loss = 5.28424
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I0405 12:20:04.425009 26038 solver.cpp:237] Train net output #0: loss = 5.28424 (* 1 = 5.28424 loss)
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I0405 12:20:04.425014 26038 sgd_solver.cpp:105] Iteration 17328, lr = 1e-05
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I0405 12:20:09.347255 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17340.caffemodel
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I0405 12:20:12.406414 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17340.solverstate
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I0405 12:20:14.722090 26038 solver.cpp:330] Iteration 17340, Testing net (#0)
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I0405 12:20:14.722107 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:20:15.916712 26038 blocking_queue.cpp:49] Waiting for data
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I0405 12:20:17.017366 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:20:19.239014 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:20:19.239053 26038 solver.cpp:397] Test net output #1: loss = 5.28028 (* 1 = 5.28028 loss)
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I0405 12:20:19.380970 26038 solver.cpp:218] Iteration 17340 (0.802361 iter/s, 14.9559s/12 iters), loss = 5.26293
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I0405 12:20:19.381011 26038 solver.cpp:237] Train net output #0: loss = 5.26293 (* 1 = 5.26293 loss)
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I0405 12:20:19.381016 26038 sgd_solver.cpp:105] Iteration 17340, lr = 1e-05
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I0405 12:20:23.980509 26038 solver.cpp:218] Iteration 17352 (2.60901 iter/s, 4.59945s/12 iters), loss = 5.25967
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I0405 12:20:23.980552 26038 solver.cpp:237] Train net output #0: loss = 5.25967 (* 1 = 5.25967 loss)
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||
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I0405 12:20:23.980558 26038 sgd_solver.cpp:105] Iteration 17352, lr = 1e-05
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I0405 12:20:29.193272 26038 solver.cpp:218] Iteration 17364 (2.30208 iter/s, 5.21267s/12 iters), loss = 5.26661
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||
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I0405 12:20:29.193401 26038 solver.cpp:237] Train net output #0: loss = 5.26661 (* 1 = 5.26661 loss)
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||
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I0405 12:20:29.193409 26038 sgd_solver.cpp:105] Iteration 17364, lr = 1e-05
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I0405 12:20:34.394028 26038 solver.cpp:218] Iteration 17376 (2.30743 iter/s, 5.20058s/12 iters), loss = 5.27772
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||
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I0405 12:20:34.394078 26038 solver.cpp:237] Train net output #0: loss = 5.27772 (* 1 = 5.27772 loss)
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||
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I0405 12:20:34.394085 26038 sgd_solver.cpp:105] Iteration 17376, lr = 1e-05
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I0405 12:20:39.691630 26038 solver.cpp:218] Iteration 17388 (2.26522 iter/s, 5.2975s/12 iters), loss = 5.28614
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||
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I0405 12:20:39.691684 26038 solver.cpp:237] Train net output #0: loss = 5.28614 (* 1 = 5.28614 loss)
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||
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I0405 12:20:39.691694 26038 sgd_solver.cpp:105] Iteration 17388, lr = 1e-05
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||
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I0405 12:20:43.993155 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:20:45.081595 26038 solver.cpp:218] Iteration 17400 (2.2264 iter/s, 5.38986s/12 iters), loss = 5.27377
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||
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I0405 12:20:45.081647 26038 solver.cpp:237] Train net output #0: loss = 5.27377 (* 1 = 5.27377 loss)
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||
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I0405 12:20:45.081657 26038 sgd_solver.cpp:105] Iteration 17400, lr = 1e-05
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||
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I0405 12:20:50.436785 26038 solver.cpp:218] Iteration 17412 (2.24086 iter/s, 5.35509s/12 iters), loss = 5.27514
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I0405 12:20:50.436823 26038 solver.cpp:237] Train net output #0: loss = 5.27514 (* 1 = 5.27514 loss)
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||
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I0405 12:20:50.436828 26038 sgd_solver.cpp:105] Iteration 17412, lr = 1e-05
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I0405 12:20:55.662004 26038 solver.cpp:218] Iteration 17424 (2.2966 iter/s, 5.22513s/12 iters), loss = 5.28011
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I0405 12:20:55.662061 26038 solver.cpp:237] Train net output #0: loss = 5.28011 (* 1 = 5.28011 loss)
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||
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I0405 12:20:55.662070 26038 sgd_solver.cpp:105] Iteration 17424, lr = 1e-05
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I0405 12:21:01.177624 26038 solver.cpp:218] Iteration 17436 (2.17568 iter/s, 5.51552s/12 iters), loss = 5.27614
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I0405 12:21:01.177789 26038 solver.cpp:237] Train net output #0: loss = 5.27614 (* 1 = 5.27614 loss)
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||
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I0405 12:21:01.177799 26038 sgd_solver.cpp:105] Iteration 17436, lr = 1e-05
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||
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I0405 12:21:03.320603 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17442.caffemodel
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||
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|
I0405 12:21:06.344106 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17442.solverstate
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||
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I0405 12:21:08.658828 26038 solver.cpp:330] Iteration 17442, Testing net (#0)
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||
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I0405 12:21:08.658847 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 12:21:10.754868 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 12:21:12.967617 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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|
I0405 12:21:12.967654 26038 solver.cpp:397] Test net output #1: loss = 5.27996 (* 1 = 5.27996 loss)
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||
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I0405 12:21:14.917389 26038 solver.cpp:218] Iteration 17448 (0.873394 iter/s, 13.7395s/12 iters), loss = 5.27703
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||
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I0405 12:21:14.917433 26038 solver.cpp:237] Train net output #0: loss = 5.27703 (* 1 = 5.27703 loss)
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||
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I0405 12:21:14.917438 26038 sgd_solver.cpp:105] Iteration 17448, lr = 1e-05
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I0405 12:21:20.429102 26038 solver.cpp:218] Iteration 17460 (2.17722 iter/s, 5.51161s/12 iters), loss = 5.27118
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I0405 12:21:20.429157 26038 solver.cpp:237] Train net output #0: loss = 5.27118 (* 1 = 5.27118 loss)
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||
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I0405 12:21:20.429164 26038 sgd_solver.cpp:105] Iteration 17460, lr = 1e-05
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I0405 12:21:25.805336 26038 solver.cpp:218] Iteration 17472 (2.23209 iter/s, 5.37613s/12 iters), loss = 5.27517
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I0405 12:21:25.805384 26038 solver.cpp:237] Train net output #0: loss = 5.27517 (* 1 = 5.27517 loss)
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||
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I0405 12:21:25.805390 26038 sgd_solver.cpp:105] Iteration 17472, lr = 1e-05
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I0405 12:21:31.082029 26038 solver.cpp:218] Iteration 17484 (2.27419 iter/s, 5.2766s/12 iters), loss = 5.28196
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I0405 12:21:31.082075 26038 solver.cpp:237] Train net output #0: loss = 5.28196 (* 1 = 5.28196 loss)
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||
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I0405 12:21:31.082080 26038 sgd_solver.cpp:105] Iteration 17484, lr = 1e-05
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I0405 12:21:36.274824 26038 solver.cpp:218] Iteration 17496 (2.31094 iter/s, 5.1927s/12 iters), loss = 5.26673
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I0405 12:21:36.274917 26038 solver.cpp:237] Train net output #0: loss = 5.26673 (* 1 = 5.26673 loss)
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||
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I0405 12:21:36.274924 26038 sgd_solver.cpp:105] Iteration 17496, lr = 1e-05
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||
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I0405 12:21:37.501410 26057 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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I0405 12:21:41.496202 26038 solver.cpp:218] Iteration 17508 (2.29831 iter/s, 5.22123s/12 iters), loss = 5.28559
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I0405 12:21:41.496244 26038 solver.cpp:237] Train net output #0: loss = 5.28559 (* 1 = 5.28559 loss)
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I0405 12:21:41.496250 26038 sgd_solver.cpp:105] Iteration 17508, lr = 1e-05
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I0405 12:21:46.789636 26038 solver.cpp:218] Iteration 17520 (2.267 iter/s, 5.29335s/12 iters), loss = 5.27281
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I0405 12:21:46.789667 26038 solver.cpp:237] Train net output #0: loss = 5.27281 (* 1 = 5.27281 loss)
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I0405 12:21:46.789672 26038 sgd_solver.cpp:105] Iteration 17520, lr = 1e-05
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I0405 12:21:51.862064 26038 solver.cpp:218] Iteration 17532 (2.36577 iter/s, 5.07234s/12 iters), loss = 5.27205
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I0405 12:21:51.862115 26038 solver.cpp:237] Train net output #0: loss = 5.27205 (* 1 = 5.27205 loss)
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I0405 12:21:51.862121 26038 sgd_solver.cpp:105] Iteration 17532, lr = 1e-05
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I0405 12:21:56.772751 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17544.caffemodel
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I0405 12:21:59.859728 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17544.solverstate
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I0405 12:22:02.353116 26038 solver.cpp:330] Iteration 17544, Testing net (#0)
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I0405 12:22:02.353132 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:22:04.506924 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:22:06.740911 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:22:06.741052 26038 solver.cpp:397] Test net output #1: loss = 5.28021 (* 1 = 5.28021 loss)
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I0405 12:22:06.882417 26038 solver.cpp:218] Iteration 17544 (0.798924 iter/s, 15.0202s/12 iters), loss = 5.27296
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I0405 12:22:06.882463 26038 solver.cpp:237] Train net output #0: loss = 5.27296 (* 1 = 5.27296 loss)
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I0405 12:22:06.882468 26038 sgd_solver.cpp:105] Iteration 17544, lr = 1e-05
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I0405 12:22:11.245646 26038 solver.cpp:218] Iteration 17556 (2.75032 iter/s, 4.36314s/12 iters), loss = 5.28521
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I0405 12:22:11.245699 26038 solver.cpp:237] Train net output #0: loss = 5.28521 (* 1 = 5.28521 loss)
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I0405 12:22:11.245707 26038 sgd_solver.cpp:105] Iteration 17556, lr = 1e-05
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I0405 12:22:16.417964 26038 solver.cpp:218] Iteration 17568 (2.32009 iter/s, 5.17222s/12 iters), loss = 5.28969
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I0405 12:22:16.418025 26038 solver.cpp:237] Train net output #0: loss = 5.28969 (* 1 = 5.28969 loss)
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I0405 12:22:16.418033 26038 sgd_solver.cpp:105] Iteration 17568, lr = 1e-05
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I0405 12:22:21.706497 26038 solver.cpp:218] Iteration 17580 (2.26911 iter/s, 5.28843s/12 iters), loss = 5.27653
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I0405 12:22:21.706552 26038 solver.cpp:237] Train net output #0: loss = 5.27653 (* 1 = 5.27653 loss)
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I0405 12:22:21.706559 26038 sgd_solver.cpp:105] Iteration 17580, lr = 1e-05
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I0405 12:22:27.012415 26038 solver.cpp:218] Iteration 17592 (2.26167 iter/s, 5.30581s/12 iters), loss = 5.2959
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I0405 12:22:27.012460 26038 solver.cpp:237] Train net output #0: loss = 5.2959 (* 1 = 5.2959 loss)
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I0405 12:22:27.012466 26038 sgd_solver.cpp:105] Iteration 17592, lr = 1e-05
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I0405 12:22:30.658776 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:22:32.407924 26038 solver.cpp:218] Iteration 17604 (2.22411 iter/s, 5.39541s/12 iters), loss = 5.25554
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I0405 12:22:32.407977 26038 solver.cpp:237] Train net output #0: loss = 5.25554 (* 1 = 5.25554 loss)
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I0405 12:22:32.407986 26038 sgd_solver.cpp:105] Iteration 17604, lr = 1e-05
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I0405 12:22:37.859422 26038 solver.cpp:218] Iteration 17616 (2.20127 iter/s, 5.45139s/12 iters), loss = 5.26361
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I0405 12:22:37.859561 26038 solver.cpp:237] Train net output #0: loss = 5.26361 (* 1 = 5.26361 loss)
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I0405 12:22:37.859571 26038 sgd_solver.cpp:105] Iteration 17616, lr = 1e-05
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I0405 12:22:43.175812 26038 solver.cpp:218] Iteration 17628 (2.25725 iter/s, 5.31621s/12 iters), loss = 5.26747
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I0405 12:22:43.175869 26038 solver.cpp:237] Train net output #0: loss = 5.26747 (* 1 = 5.26747 loss)
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I0405 12:22:43.175879 26038 sgd_solver.cpp:105] Iteration 17628, lr = 1e-05
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I0405 12:22:48.452817 26038 solver.cpp:218] Iteration 17640 (2.27406 iter/s, 5.2769s/12 iters), loss = 5.2817
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I0405 12:22:48.452870 26038 solver.cpp:237] Train net output #0: loss = 5.2817 (* 1 = 5.2817 loss)
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I0405 12:22:48.452878 26038 sgd_solver.cpp:105] Iteration 17640, lr = 1e-05
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I0405 12:22:50.581779 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17646.caffemodel
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||
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I0405 12:22:53.580056 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17646.solverstate
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I0405 12:22:55.907563 26038 solver.cpp:330] Iteration 17646, Testing net (#0)
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I0405 12:22:55.907586 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:22:57.936986 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:23:00.260375 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:23:00.260421 26038 solver.cpp:397] Test net output #1: loss = 5.27991 (* 1 = 5.27991 loss)
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I0405 12:23:02.104405 26038 solver.cpp:218] Iteration 17652 (0.879028 iter/s, 13.6514s/12 iters), loss = 5.27942
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I0405 12:23:02.104446 26038 solver.cpp:237] Train net output #0: loss = 5.27942 (* 1 = 5.27942 loss)
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I0405 12:23:02.104452 26038 sgd_solver.cpp:105] Iteration 17652, lr = 1e-05
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I0405 12:23:07.422417 26038 solver.cpp:218] Iteration 17664 (2.25652 iter/s, 5.31792s/12 iters), loss = 5.26684
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I0405 12:23:07.422462 26038 solver.cpp:237] Train net output #0: loss = 5.26684 (* 1 = 5.26684 loss)
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I0405 12:23:07.422467 26038 sgd_solver.cpp:105] Iteration 17664, lr = 1e-05
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I0405 12:23:12.690779 26038 solver.cpp:218] Iteration 17676 (2.27779 iter/s, 5.26827s/12 iters), loss = 5.2811
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I0405 12:23:12.690941 26038 solver.cpp:237] Train net output #0: loss = 5.2811 (* 1 = 5.2811 loss)
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I0405 12:23:12.690950 26038 sgd_solver.cpp:105] Iteration 17676, lr = 1e-05
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I0405 12:23:17.794939 26038 solver.cpp:218] Iteration 17688 (2.35112 iter/s, 5.10395s/12 iters), loss = 5.28495
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I0405 12:23:17.794981 26038 solver.cpp:237] Train net output #0: loss = 5.28495 (* 1 = 5.28495 loss)
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I0405 12:23:17.794986 26038 sgd_solver.cpp:105] Iteration 17688, lr = 1e-05
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I0405 12:23:23.134630 26038 solver.cpp:218] Iteration 17700 (2.24736 iter/s, 5.33959s/12 iters), loss = 5.29471
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I0405 12:23:23.134693 26038 solver.cpp:237] Train net output #0: loss = 5.29471 (* 1 = 5.29471 loss)
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I0405 12:23:23.134702 26038 sgd_solver.cpp:105] Iteration 17700, lr = 1e-05
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I0405 12:23:23.701294 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:23:28.445317 26038 solver.cpp:218] Iteration 17712 (2.25964 iter/s, 5.31058s/12 iters), loss = 5.28665
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I0405 12:23:28.445360 26038 solver.cpp:237] Train net output #0: loss = 5.28665 (* 1 = 5.28665 loss)
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I0405 12:23:28.445366 26038 sgd_solver.cpp:105] Iteration 17712, lr = 1e-05
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I0405 12:23:34.022780 26038 solver.cpp:218] Iteration 17724 (2.15155 iter/s, 5.57737s/12 iters), loss = 5.28282
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I0405 12:23:34.022831 26038 solver.cpp:237] Train net output #0: loss = 5.28282 (* 1 = 5.28282 loss)
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I0405 12:23:34.022840 26038 sgd_solver.cpp:105] Iteration 17724, lr = 1e-05
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I0405 12:23:39.390692 26038 solver.cpp:218] Iteration 17736 (2.23555 iter/s, 5.36781s/12 iters), loss = 5.26944
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I0405 12:23:39.390741 26038 solver.cpp:237] Train net output #0: loss = 5.26944 (* 1 = 5.26944 loss)
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I0405 12:23:39.390750 26038 sgd_solver.cpp:105] Iteration 17736, lr = 1e-05
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I0405 12:23:44.155269 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17748.caffemodel
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I0405 12:23:47.102228 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17748.solverstate
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I0405 12:23:49.426717 26038 solver.cpp:330] Iteration 17748, Testing net (#0)
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I0405 12:23:49.426740 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:23:51.465842 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:23:53.841908 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:23:53.841958 26038 solver.cpp:397] Test net output #1: loss = 5.28029 (* 1 = 5.28029 loss)
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I0405 12:23:53.984375 26038 solver.cpp:218] Iteration 17748 (0.822282 iter/s, 14.5935s/12 iters), loss = 5.26604
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I0405 12:23:53.984417 26038 solver.cpp:237] Train net output #0: loss = 5.26604 (* 1 = 5.26604 loss)
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I0405 12:23:53.984424 26038 sgd_solver.cpp:105] Iteration 17748, lr = 1e-05
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I0405 12:23:58.484973 26038 solver.cpp:218] Iteration 17760 (2.66636 iter/s, 4.50051s/12 iters), loss = 5.27478
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I0405 12:23:58.485024 26038 solver.cpp:237] Train net output #0: loss = 5.27478 (* 1 = 5.27478 loss)
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I0405 12:23:58.485033 26038 sgd_solver.cpp:105] Iteration 17760, lr = 1e-05
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I0405 12:24:03.816212 26038 solver.cpp:218] Iteration 17772 (2.25092 iter/s, 5.33114s/12 iters), loss = 5.27235
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I0405 12:24:03.816249 26038 solver.cpp:237] Train net output #0: loss = 5.27235 (* 1 = 5.27235 loss)
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I0405 12:24:03.816255 26038 sgd_solver.cpp:105] Iteration 17772, lr = 1e-05
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I0405 12:24:09.174010 26038 solver.cpp:218] Iteration 17784 (2.23976 iter/s, 5.35771s/12 iters), loss = 5.28075
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I0405 12:24:09.174068 26038 solver.cpp:237] Train net output #0: loss = 5.28075 (* 1 = 5.28075 loss)
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I0405 12:24:09.174077 26038 sgd_solver.cpp:105] Iteration 17784, lr = 1e-05
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I0405 12:24:14.589646 26038 solver.cpp:218] Iteration 17796 (2.21585 iter/s, 5.41553s/12 iters), loss = 5.27699
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I0405 12:24:14.589818 26038 solver.cpp:237] Train net output #0: loss = 5.27699 (* 1 = 5.27699 loss)
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I0405 12:24:14.589828 26038 sgd_solver.cpp:105] Iteration 17796, lr = 1e-05
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I0405 12:24:17.434273 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:24:19.878160 26038 solver.cpp:218] Iteration 17808 (2.26916 iter/s, 5.2883s/12 iters), loss = 5.26448
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I0405 12:24:19.878199 26038 solver.cpp:237] Train net output #0: loss = 5.26448 (* 1 = 5.26448 loss)
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I0405 12:24:19.878206 26038 sgd_solver.cpp:105] Iteration 17808, lr = 1e-05
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I0405 12:24:25.094543 26038 solver.cpp:218] Iteration 17820 (2.30048 iter/s, 5.21629s/12 iters), loss = 5.26587
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I0405 12:24:25.094596 26038 solver.cpp:237] Train net output #0: loss = 5.26587 (* 1 = 5.26587 loss)
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I0405 12:24:25.094605 26038 sgd_solver.cpp:105] Iteration 17820, lr = 1e-05
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I0405 12:24:30.454844 26038 solver.cpp:218] Iteration 17832 (2.23872 iter/s, 5.3602s/12 iters), loss = 5.26612
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I0405 12:24:30.454885 26038 solver.cpp:237] Train net output #0: loss = 5.26612 (* 1 = 5.26612 loss)
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I0405 12:24:30.454891 26038 sgd_solver.cpp:105] Iteration 17832, lr = 1e-05
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I0405 12:24:35.703696 26038 solver.cpp:218] Iteration 17844 (2.28625 iter/s, 5.24876s/12 iters), loss = 5.27902
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I0405 12:24:35.703744 26038 solver.cpp:237] Train net output #0: loss = 5.27902 (* 1 = 5.27902 loss)
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I0405 12:24:35.703752 26038 sgd_solver.cpp:105] Iteration 17844, lr = 1e-05
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I0405 12:24:37.981317 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17850.caffemodel
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||
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I0405 12:24:40.999763 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17850.solverstate
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I0405 12:24:43.294999 26038 solver.cpp:330] Iteration 17850, Testing net (#0)
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||
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I0405 12:24:43.295019 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 12:24:45.249698 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
|
|
I0405 12:24:47.604238 26038 solver.cpp:397] Test net output #0: accuracy = 0.00612745
|
||
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|
I0405 12:24:47.604274 26038 solver.cpp:397] Test net output #1: loss = 5.27979 (* 1 = 5.27979 loss)
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||
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I0405 12:24:49.533272 26038 solver.cpp:218] Iteration 17856 (0.867714 iter/s, 13.8294s/12 iters), loss = 5.28793
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I0405 12:24:49.533313 26038 solver.cpp:237] Train net output #0: loss = 5.28793 (* 1 = 5.28793 loss)
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||
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I0405 12:24:49.533318 26038 sgd_solver.cpp:105] Iteration 17856, lr = 1e-05
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||
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I0405 12:24:54.766217 26038 solver.cpp:218] Iteration 17868 (2.2932 iter/s, 5.23286s/12 iters), loss = 5.28013
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I0405 12:24:54.766258 26038 solver.cpp:237] Train net output #0: loss = 5.28013 (* 1 = 5.28013 loss)
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I0405 12:24:54.766263 26038 sgd_solver.cpp:105] Iteration 17868, lr = 1e-05
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I0405 12:25:00.143615 26038 solver.cpp:218] Iteration 17880 (2.2316 iter/s, 5.37731s/12 iters), loss = 5.29097
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I0405 12:25:00.143653 26038 solver.cpp:237] Train net output #0: loss = 5.29097 (* 1 = 5.29097 loss)
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I0405 12:25:00.143659 26038 sgd_solver.cpp:105] Iteration 17880, lr = 1e-05
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I0405 12:25:05.432155 26038 solver.cpp:218] Iteration 17892 (2.2691 iter/s, 5.28845s/12 iters), loss = 5.28523
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I0405 12:25:05.432214 26038 solver.cpp:237] Train net output #0: loss = 5.28523 (* 1 = 5.28523 loss)
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I0405 12:25:05.432222 26038 sgd_solver.cpp:105] Iteration 17892, lr = 1e-05
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I0405 12:25:10.661942 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:25:10.912624 26038 solver.cpp:218] Iteration 17904 (2.18964 iter/s, 5.48036s/12 iters), loss = 5.27655
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I0405 12:25:10.912673 26038 solver.cpp:237] Train net output #0: loss = 5.27655 (* 1 = 5.27655 loss)
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||
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I0405 12:25:10.912681 26038 sgd_solver.cpp:105] Iteration 17904, lr = 1e-05
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||
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I0405 12:25:16.080564 26038 solver.cpp:218] Iteration 17916 (2.32205 iter/s, 5.16784s/12 iters), loss = 5.26718
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||
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I0405 12:25:16.080731 26038 solver.cpp:237] Train net output #0: loss = 5.26718 (* 1 = 5.26718 loss)
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||
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I0405 12:25:16.080741 26038 sgd_solver.cpp:105] Iteration 17916, lr = 1e-05
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I0405 12:25:21.446097 26038 solver.cpp:218] Iteration 17928 (2.23658 iter/s, 5.36533s/12 iters), loss = 5.27241
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||
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I0405 12:25:21.446133 26038 solver.cpp:237] Train net output #0: loss = 5.27241 (* 1 = 5.27241 loss)
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||
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I0405 12:25:21.446139 26038 sgd_solver.cpp:105] Iteration 17928, lr = 1e-05
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I0405 12:25:26.784379 26038 solver.cpp:218] Iteration 17940 (2.24795 iter/s, 5.3382s/12 iters), loss = 5.28879
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||
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I0405 12:25:26.784421 26038 solver.cpp:237] Train net output #0: loss = 5.28879 (* 1 = 5.28879 loss)
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||
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I0405 12:25:26.784427 26038 sgd_solver.cpp:105] Iteration 17940, lr = 1e-05
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||
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I0405 12:25:31.630823 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17952.caffemodel
|
||
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I0405 12:25:34.654374 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17952.solverstate
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||
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I0405 12:25:36.951354 26038 solver.cpp:330] Iteration 17952, Testing net (#0)
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||
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I0405 12:25:36.951375 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
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I0405 12:25:39.019932 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 12:25:41.521780 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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|
I0405 12:25:41.521813 26038 solver.cpp:397] Test net output #1: loss = 5.27985 (* 1 = 5.27985 loss)
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||
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I0405 12:25:41.663653 26038 solver.cpp:218] Iteration 17952 (0.806499 iter/s, 14.8791s/12 iters), loss = 5.27326
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||
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I0405 12:25:41.663697 26038 solver.cpp:237] Train net output #0: loss = 5.27326 (* 1 = 5.27326 loss)
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||
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I0405 12:25:41.663702 26038 sgd_solver.cpp:105] Iteration 17952, lr = 1e-05
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I0405 12:25:45.956636 26038 solver.cpp:218] Iteration 17964 (2.79532 iter/s, 4.29289s/12 iters), loss = 5.26654
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I0405 12:25:45.956694 26038 solver.cpp:237] Train net output #0: loss = 5.26654 (* 1 = 5.26654 loss)
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I0405 12:25:45.956705 26038 sgd_solver.cpp:105] Iteration 17964, lr = 1e-05
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I0405 12:25:51.151432 26038 solver.cpp:218] Iteration 17976 (2.31005 iter/s, 5.19469s/12 iters), loss = 5.2758
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I0405 12:25:51.151549 26038 solver.cpp:237] Train net output #0: loss = 5.2758 (* 1 = 5.2758 loss)
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I0405 12:25:51.151557 26038 sgd_solver.cpp:105] Iteration 17976, lr = 1e-05
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I0405 12:25:56.568430 26038 solver.cpp:218] Iteration 17988 (2.21532 iter/s, 5.41683s/12 iters), loss = 5.29162
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I0405 12:25:56.568486 26038 solver.cpp:237] Train net output #0: loss = 5.29162 (* 1 = 5.29162 loss)
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I0405 12:25:56.568495 26038 sgd_solver.cpp:105] Iteration 17988, lr = 1e-05
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I0405 12:26:01.948483 26038 solver.cpp:218] Iteration 18000 (2.2305 iter/s, 5.37995s/12 iters), loss = 5.28047
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I0405 12:26:01.948539 26038 solver.cpp:237] Train net output #0: loss = 5.28047 (* 1 = 5.28047 loss)
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I0405 12:26:01.948549 26038 sgd_solver.cpp:105] Iteration 18000, lr = 1e-05
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I0405 12:26:03.994859 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:26:07.259968 26038 solver.cpp:218] Iteration 18012 (2.2593 iter/s, 5.31138s/12 iters), loss = 5.29724
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I0405 12:26:07.260008 26038 solver.cpp:237] Train net output #0: loss = 5.29724 (* 1 = 5.29724 loss)
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I0405 12:26:07.260015 26038 sgd_solver.cpp:105] Iteration 18012, lr = 1e-05
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I0405 12:26:12.292392 26038 solver.cpp:218] Iteration 18024 (2.38458 iter/s, 5.03234s/12 iters), loss = 5.26914
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I0405 12:26:12.292454 26038 solver.cpp:237] Train net output #0: loss = 5.26914 (* 1 = 5.26914 loss)
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I0405 12:26:12.292464 26038 sgd_solver.cpp:105] Iteration 18024, lr = 1e-05
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I0405 12:26:17.629973 26038 solver.cpp:218] Iteration 18036 (2.24825 iter/s, 5.33748s/12 iters), loss = 5.27303
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I0405 12:26:17.630009 26038 solver.cpp:237] Train net output #0: loss = 5.27303 (* 1 = 5.27303 loss)
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I0405 12:26:17.630014 26038 sgd_solver.cpp:105] Iteration 18036, lr = 1e-05
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I0405 12:26:17.630188 26038 blocking_queue.cpp:49] Waiting for data
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I0405 12:26:22.932608 26038 solver.cpp:218] Iteration 18048 (2.26306 iter/s, 5.30255s/12 iters), loss = 5.25262
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I0405 12:26:22.932734 26038 solver.cpp:237] Train net output #0: loss = 5.25262 (* 1 = 5.25262 loss)
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I0405 12:26:22.932741 26038 sgd_solver.cpp:105] Iteration 18048, lr = 1e-05
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I0405 12:26:25.095048 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18054.caffemodel
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I0405 12:26:28.105196 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18054.solverstate
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I0405 12:26:30.994536 26038 solver.cpp:330] Iteration 18054, Testing net (#0)
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I0405 12:26:30.994572 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:26:32.940796 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:26:35.367908 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:26:35.367942 26038 solver.cpp:397] Test net output #1: loss = 5.28046 (* 1 = 5.28046 loss)
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I0405 12:26:37.270589 26038 solver.cpp:218] Iteration 18060 (0.836951 iter/s, 14.3378s/12 iters), loss = 5.25798
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I0405 12:26:37.270630 26038 solver.cpp:237] Train net output #0: loss = 5.25798 (* 1 = 5.25798 loss)
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I0405 12:26:37.270637 26038 sgd_solver.cpp:105] Iteration 18060, lr = 1e-05
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I0405 12:26:42.748435 26038 solver.cpp:218] Iteration 18072 (2.19068 iter/s, 5.47775s/12 iters), loss = 5.26765
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I0405 12:26:42.748476 26038 solver.cpp:237] Train net output #0: loss = 5.26765 (* 1 = 5.26765 loss)
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I0405 12:26:42.748481 26038 sgd_solver.cpp:105] Iteration 18072, lr = 1e-05
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I0405 12:26:48.029070 26038 solver.cpp:218] Iteration 18084 (2.27249 iter/s, 5.28054s/12 iters), loss = 5.28419
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I0405 12:26:48.029114 26038 solver.cpp:237] Train net output #0: loss = 5.28419 (* 1 = 5.28419 loss)
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I0405 12:26:48.029119 26038 sgd_solver.cpp:105] Iteration 18084, lr = 1e-05
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I0405 12:26:53.246687 26038 solver.cpp:218] Iteration 18096 (2.29994 iter/s, 5.21753s/12 iters), loss = 5.26915
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I0405 12:26:53.246803 26038 solver.cpp:237] Train net output #0: loss = 5.26915 (* 1 = 5.26915 loss)
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I0405 12:26:53.246809 26038 sgd_solver.cpp:105] Iteration 18096, lr = 1e-05
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I0405 12:26:57.375295 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:26:58.387841 26038 solver.cpp:218] Iteration 18108 (2.33418 iter/s, 5.14099s/12 iters), loss = 5.27147
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I0405 12:26:58.387887 26038 solver.cpp:237] Train net output #0: loss = 5.27147 (* 1 = 5.27147 loss)
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I0405 12:26:58.387894 26038 sgd_solver.cpp:105] Iteration 18108, lr = 1e-05
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I0405 12:27:03.684177 26038 solver.cpp:218] Iteration 18120 (2.26576 iter/s, 5.29625s/12 iters), loss = 5.26874
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I0405 12:27:03.684214 26038 solver.cpp:237] Train net output #0: loss = 5.26874 (* 1 = 5.26874 loss)
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I0405 12:27:03.684219 26038 sgd_solver.cpp:105] Iteration 18120, lr = 1e-05
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I0405 12:27:08.756981 26038 solver.cpp:218] Iteration 18132 (2.36559 iter/s, 5.07272s/12 iters), loss = 5.2966
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I0405 12:27:08.757021 26038 solver.cpp:237] Train net output #0: loss = 5.2966 (* 1 = 5.2966 loss)
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I0405 12:27:08.757026 26038 sgd_solver.cpp:105] Iteration 18132, lr = 1e-05
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I0405 12:27:14.047469 26038 solver.cpp:218] Iteration 18144 (2.26826 iter/s, 5.2904s/12 iters), loss = 5.27891
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I0405 12:27:14.047510 26038 solver.cpp:237] Train net output #0: loss = 5.27891 (* 1 = 5.27891 loss)
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I0405 12:27:14.047516 26038 sgd_solver.cpp:105] Iteration 18144, lr = 1e-05
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I0405 12:27:18.687764 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18156.caffemodel
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I0405 12:27:21.757441 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18156.solverstate
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I0405 12:27:24.072613 26038 solver.cpp:330] Iteration 18156, Testing net (#0)
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I0405 12:27:24.072732 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:27:25.921411 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:27:28.563165 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:27:28.563201 26038 solver.cpp:397] Test net output #1: loss = 5.28008 (* 1 = 5.28008 loss)
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I0405 12:27:28.704751 26038 solver.cpp:218] Iteration 18156 (0.818714 iter/s, 14.6571s/12 iters), loss = 5.2767
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I0405 12:27:28.704798 26038 solver.cpp:237] Train net output #0: loss = 5.2767 (* 1 = 5.2767 loss)
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I0405 12:27:28.704807 26038 sgd_solver.cpp:105] Iteration 18156, lr = 1e-05
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I0405 12:27:33.072901 26038 solver.cpp:218] Iteration 18168 (2.74722 iter/s, 4.36806s/12 iters), loss = 5.26619
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I0405 12:27:33.072945 26038 solver.cpp:237] Train net output #0: loss = 5.26619 (* 1 = 5.26619 loss)
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I0405 12:27:33.072952 26038 sgd_solver.cpp:105] Iteration 18168, lr = 1e-05
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I0405 12:27:38.479843 26038 solver.cpp:218] Iteration 18180 (2.21941 iter/s, 5.40685s/12 iters), loss = 5.27022
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I0405 12:27:38.479883 26038 solver.cpp:237] Train net output #0: loss = 5.27022 (* 1 = 5.27022 loss)
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I0405 12:27:38.479887 26038 sgd_solver.cpp:105] Iteration 18180, lr = 1e-05
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I0405 12:27:43.764003 26038 solver.cpp:218] Iteration 18192 (2.27098 iter/s, 5.28407s/12 iters), loss = 5.27073
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I0405 12:27:43.764043 26038 solver.cpp:237] Train net output #0: loss = 5.27073 (* 1 = 5.27073 loss)
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I0405 12:27:43.764050 26038 sgd_solver.cpp:105] Iteration 18192, lr = 1e-05
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I0405 12:27:49.162487 26038 solver.cpp:218] Iteration 18204 (2.22288 iter/s, 5.39839s/12 iters), loss = 5.27072
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I0405 12:27:49.162523 26038 solver.cpp:237] Train net output #0: loss = 5.27072 (* 1 = 5.27072 loss)
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I0405 12:27:49.162529 26038 sgd_solver.cpp:105] Iteration 18204, lr = 1e-05
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I0405 12:27:50.452965 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:27:54.492919 26038 solver.cpp:218] Iteration 18216 (2.25127 iter/s, 5.33034s/12 iters), loss = 5.29303
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I0405 12:27:54.493043 26038 solver.cpp:237] Train net output #0: loss = 5.29303 (* 1 = 5.29303 loss)
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I0405 12:27:54.493053 26038 sgd_solver.cpp:105] Iteration 18216, lr = 1e-05
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I0405 12:27:59.784162 26038 solver.cpp:218] Iteration 18228 (2.26797 iter/s, 5.29107s/12 iters), loss = 5.28516
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I0405 12:27:59.784217 26038 solver.cpp:237] Train net output #0: loss = 5.28516 (* 1 = 5.28516 loss)
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I0405 12:27:59.784225 26038 sgd_solver.cpp:105] Iteration 18228, lr = 1e-05
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I0405 12:28:05.115231 26038 solver.cpp:218] Iteration 18240 (2.251 iter/s, 5.33097s/12 iters), loss = 5.27078
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I0405 12:28:05.115272 26038 solver.cpp:237] Train net output #0: loss = 5.27078 (* 1 = 5.27078 loss)
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I0405 12:28:05.115278 26038 sgd_solver.cpp:105] Iteration 18240, lr = 1e-05
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I0405 12:28:10.429183 26038 solver.cpp:218] Iteration 18252 (2.25825 iter/s, 5.31386s/12 iters), loss = 5.26918
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I0405 12:28:10.429227 26038 solver.cpp:237] Train net output #0: loss = 5.26918 (* 1 = 5.26918 loss)
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I0405 12:28:10.429234 26038 sgd_solver.cpp:105] Iteration 18252, lr = 1e-05
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I0405 12:28:12.671912 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18258.caffemodel
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I0405 12:28:15.626906 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18258.solverstate
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I0405 12:28:17.956923 26038 solver.cpp:330] Iteration 18258, Testing net (#0)
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I0405 12:28:17.956941 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:28:19.787727 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:28:22.386410 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:28:22.386442 26038 solver.cpp:397] Test net output #1: loss = 5.28018 (* 1 = 5.28018 loss)
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I0405 12:28:24.413703 26038 solver.cpp:218] Iteration 18264 (0.858101 iter/s, 13.9844s/12 iters), loss = 5.29448
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I0405 12:28:24.413755 26038 solver.cpp:237] Train net output #0: loss = 5.29448 (* 1 = 5.29448 loss)
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I0405 12:28:24.413762 26038 sgd_solver.cpp:105] Iteration 18264, lr = 1e-05
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I0405 12:28:29.668402 26038 solver.cpp:218] Iteration 18276 (2.28371 iter/s, 5.2546s/12 iters), loss = 5.26484
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I0405 12:28:29.668534 26038 solver.cpp:237] Train net output #0: loss = 5.26484 (* 1 = 5.26484 loss)
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I0405 12:28:29.668540 26038 sgd_solver.cpp:105] Iteration 18276, lr = 1e-05
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I0405 12:28:34.935894 26038 solver.cpp:218] Iteration 18288 (2.2782 iter/s, 5.26731s/12 iters), loss = 5.27497
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I0405 12:28:34.935940 26038 solver.cpp:237] Train net output #0: loss = 5.27497 (* 1 = 5.27497 loss)
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I0405 12:28:34.935945 26038 sgd_solver.cpp:105] Iteration 18288, lr = 1e-05
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I0405 12:28:40.456872 26038 solver.cpp:218] Iteration 18300 (2.17357 iter/s, 5.52088s/12 iters), loss = 5.27263
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I0405 12:28:40.456967 26038 solver.cpp:237] Train net output #0: loss = 5.27263 (* 1 = 5.27263 loss)
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||
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I0405 12:28:40.456975 26038 sgd_solver.cpp:105] Iteration 18300, lr = 1e-05
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I0405 12:28:44.016513 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:28:45.720530 26038 solver.cpp:218] Iteration 18312 (2.27984 iter/s, 5.26352s/12 iters), loss = 5.26455
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I0405 12:28:45.720567 26038 solver.cpp:237] Train net output #0: loss = 5.26455 (* 1 = 5.26455 loss)
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I0405 12:28:45.720572 26038 sgd_solver.cpp:105] Iteration 18312, lr = 1e-05
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I0405 12:28:51.117041 26038 solver.cpp:218] Iteration 18324 (2.2237 iter/s, 5.39642s/12 iters), loss = 5.27239
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I0405 12:28:51.117096 26038 solver.cpp:237] Train net output #0: loss = 5.27239 (* 1 = 5.27239 loss)
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I0405 12:28:51.117105 26038 sgd_solver.cpp:105] Iteration 18324, lr = 1e-05
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I0405 12:28:56.489670 26038 solver.cpp:218] Iteration 18336 (2.23359 iter/s, 5.37253s/12 iters), loss = 5.27467
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I0405 12:28:56.489711 26038 solver.cpp:237] Train net output #0: loss = 5.27467 (* 1 = 5.27467 loss)
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I0405 12:28:56.489717 26038 sgd_solver.cpp:105] Iteration 18336, lr = 1e-05
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I0405 12:29:01.904646 26038 solver.cpp:218] Iteration 18348 (2.21611 iter/s, 5.41489s/12 iters), loss = 5.26745
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I0405 12:29:01.904747 26038 solver.cpp:237] Train net output #0: loss = 5.26745 (* 1 = 5.26745 loss)
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I0405 12:29:01.904755 26038 sgd_solver.cpp:105] Iteration 18348, lr = 1e-05
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I0405 12:29:06.451098 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18360.caffemodel
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||
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I0405 12:29:09.448438 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18360.solverstate
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I0405 12:29:11.757375 26038 solver.cpp:330] Iteration 18360, Testing net (#0)
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I0405 12:29:11.757402 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:29:13.612030 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:29:16.252112 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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|
I0405 12:29:16.252144 26038 solver.cpp:397] Test net output #1: loss = 5.2801 (* 1 = 5.2801 loss)
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I0405 12:29:16.392088 26038 solver.cpp:218] Iteration 18360 (0.828315 iter/s, 14.4872s/12 iters), loss = 5.26819
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I0405 12:29:16.392135 26038 solver.cpp:237] Train net output #0: loss = 5.26819 (* 1 = 5.26819 loss)
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I0405 12:29:16.392140 26038 sgd_solver.cpp:105] Iteration 18360, lr = 1e-05
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I0405 12:29:20.826234 26038 solver.cpp:218] Iteration 18372 (2.70632 iter/s, 4.43406s/12 iters), loss = 5.25414
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I0405 12:29:20.826284 26038 solver.cpp:237] Train net output #0: loss = 5.25414 (* 1 = 5.25414 loss)
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I0405 12:29:20.826292 26038 sgd_solver.cpp:105] Iteration 18372, lr = 1e-05
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I0405 12:29:26.105513 26038 solver.cpp:218] Iteration 18384 (2.27308 iter/s, 5.27918s/12 iters), loss = 5.27128
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I0405 12:29:26.105556 26038 solver.cpp:237] Train net output #0: loss = 5.27128 (* 1 = 5.27128 loss)
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I0405 12:29:26.105561 26038 sgd_solver.cpp:105] Iteration 18384, lr = 1e-05
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I0405 12:29:31.351857 26038 solver.cpp:218] Iteration 18396 (2.28735 iter/s, 5.24625s/12 iters), loss = 5.26696
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I0405 12:29:31.351918 26038 solver.cpp:237] Train net output #0: loss = 5.26696 (* 1 = 5.26696 loss)
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I0405 12:29:31.351928 26038 sgd_solver.cpp:105] Iteration 18396, lr = 1e-05
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I0405 12:29:36.706941 26038 solver.cpp:218] Iteration 18408 (2.24091 iter/s, 5.35498s/12 iters), loss = 5.28688
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I0405 12:29:36.707095 26038 solver.cpp:237] Train net output #0: loss = 5.28688 (* 1 = 5.28688 loss)
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I0405 12:29:36.707104 26038 sgd_solver.cpp:105] Iteration 18408, lr = 1e-05
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I0405 12:29:37.219115 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:29:41.940035 26038 solver.cpp:218] Iteration 18420 (2.29319 iter/s, 5.2329s/12 iters), loss = 5.28149
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I0405 12:29:41.940084 26038 solver.cpp:237] Train net output #0: loss = 5.28149 (* 1 = 5.28149 loss)
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I0405 12:29:41.940093 26038 sgd_solver.cpp:105] Iteration 18420, lr = 1e-05
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I0405 12:29:47.064461 26038 solver.cpp:218] Iteration 18432 (2.34177 iter/s, 5.12432s/12 iters), loss = 5.28557
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I0405 12:29:47.064512 26038 solver.cpp:237] Train net output #0: loss = 5.28557 (* 1 = 5.28557 loss)
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I0405 12:29:47.064519 26038 sgd_solver.cpp:105] Iteration 18432, lr = 1e-05
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I0405 12:29:52.175470 26038 solver.cpp:218] Iteration 18444 (2.34792 iter/s, 5.11091s/12 iters), loss = 5.27058
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I0405 12:29:52.175515 26038 solver.cpp:237] Train net output #0: loss = 5.27058 (* 1 = 5.27058 loss)
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I0405 12:29:52.175523 26038 sgd_solver.cpp:105] Iteration 18444, lr = 1e-05
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I0405 12:29:57.547356 26038 solver.cpp:218] Iteration 18456 (2.23389 iter/s, 5.3718s/12 iters), loss = 5.28032
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I0405 12:29:57.547399 26038 solver.cpp:237] Train net output #0: loss = 5.28032 (* 1 = 5.28032 loss)
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I0405 12:29:57.547403 26038 sgd_solver.cpp:105] Iteration 18456, lr = 1e-05
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I0405 12:29:59.762662 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18462.caffemodel
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I0405 12:30:02.844996 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18462.solverstate
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I0405 12:30:05.188560 26038 solver.cpp:330] Iteration 18462, Testing net (#0)
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I0405 12:30:05.188583 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:30:06.950904 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:30:09.701436 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:30:09.701462 26038 solver.cpp:397] Test net output #1: loss = 5.28007 (* 1 = 5.28007 loss)
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I0405 12:30:11.728103 26038 solver.cpp:218] Iteration 18468 (0.846226 iter/s, 14.1806s/12 iters), loss = 5.26148
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I0405 12:30:11.728147 26038 solver.cpp:237] Train net output #0: loss = 5.26148 (* 1 = 5.26148 loss)
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I0405 12:30:11.728152 26038 sgd_solver.cpp:105] Iteration 18468, lr = 1e-05
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I0405 12:30:17.229444 26038 solver.cpp:218] Iteration 18480 (2.18132 iter/s, 5.50125s/12 iters), loss = 5.28634
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I0405 12:30:17.229482 26038 solver.cpp:237] Train net output #0: loss = 5.28634 (* 1 = 5.28634 loss)
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I0405 12:30:17.229488 26038 sgd_solver.cpp:105] Iteration 18480, lr = 1e-05
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I0405 12:30:22.678057 26038 solver.cpp:218] Iteration 18492 (2.20243 iter/s, 5.44853s/12 iters), loss = 5.27541
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I0405 12:30:22.678095 26038 solver.cpp:237] Train net output #0: loss = 5.27541 (* 1 = 5.27541 loss)
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I0405 12:30:22.678100 26038 sgd_solver.cpp:105] Iteration 18492, lr = 1e-05
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I0405 12:30:27.687711 26038 solver.cpp:218] Iteration 18504 (2.39542 iter/s, 5.00957s/12 iters), loss = 5.26953
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I0405 12:30:27.687750 26038 solver.cpp:237] Train net output #0: loss = 5.26953 (* 1 = 5.26953 loss)
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I0405 12:30:27.687757 26038 sgd_solver.cpp:105] Iteration 18504, lr = 1e-05
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I0405 12:30:30.582206 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:30:33.005916 26038 solver.cpp:218] Iteration 18516 (2.25644 iter/s, 5.31812s/12 iters), loss = 5.26734
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I0405 12:30:33.005955 26038 solver.cpp:237] Train net output #0: loss = 5.26734 (* 1 = 5.26734 loss)
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I0405 12:30:33.005960 26038 sgd_solver.cpp:105] Iteration 18516, lr = 1e-05
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I0405 12:30:38.424759 26038 solver.cpp:218] Iteration 18528 (2.21453 iter/s, 5.41875s/12 iters), loss = 5.26631
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I0405 12:30:38.424890 26038 solver.cpp:237] Train net output #0: loss = 5.26631 (* 1 = 5.26631 loss)
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I0405 12:30:38.424899 26038 sgd_solver.cpp:105] Iteration 18528, lr = 1e-05
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I0405 12:30:43.807646 26038 solver.cpp:218] Iteration 18540 (2.22936 iter/s, 5.38271s/12 iters), loss = 5.27701
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I0405 12:30:43.807698 26038 solver.cpp:237] Train net output #0: loss = 5.27701 (* 1 = 5.27701 loss)
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I0405 12:30:43.807708 26038 sgd_solver.cpp:105] Iteration 18540, lr = 1e-05
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I0405 12:30:49.187095 26038 solver.cpp:218] Iteration 18552 (2.23075 iter/s, 5.37935s/12 iters), loss = 5.27732
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I0405 12:30:49.187130 26038 solver.cpp:237] Train net output #0: loss = 5.27732 (* 1 = 5.27732 loss)
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I0405 12:30:49.187136 26038 sgd_solver.cpp:105] Iteration 18552, lr = 1e-05
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I0405 12:30:53.822312 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18564.caffemodel
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I0405 12:30:56.856304 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18564.solverstate
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I0405 12:30:59.161607 26038 solver.cpp:330] Iteration 18564, Testing net (#0)
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I0405 12:30:59.161626 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:31:00.884922 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:31:03.631709 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:31:03.631747 26038 solver.cpp:397] Test net output #1: loss = 5.2803 (* 1 = 5.2803 loss)
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I0405 12:31:03.773440 26038 solver.cpp:218] Iteration 18564 (0.822695 iter/s, 14.5862s/12 iters), loss = 5.26502
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I0405 12:31:03.773494 26038 solver.cpp:237] Train net output #0: loss = 5.26502 (* 1 = 5.26502 loss)
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I0405 12:31:03.773504 26038 sgd_solver.cpp:105] Iteration 18564, lr = 1e-05
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I0405 12:31:08.289770 26038 solver.cpp:218] Iteration 18576 (2.65708 iter/s, 4.51623s/12 iters), loss = 5.27663
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I0405 12:31:08.289810 26038 solver.cpp:237] Train net output #0: loss = 5.27663 (* 1 = 5.27663 loss)
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I0405 12:31:08.289816 26038 sgd_solver.cpp:105] Iteration 18576, lr = 1e-05
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I0405 12:31:13.742597 26038 solver.cpp:218] Iteration 18588 (2.20073 iter/s, 5.45273s/12 iters), loss = 5.28047
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I0405 12:31:13.742808 26038 solver.cpp:237] Train net output #0: loss = 5.28047 (* 1 = 5.28047 loss)
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I0405 12:31:13.742825 26038 sgd_solver.cpp:105] Iteration 18588, lr = 1e-05
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I0405 12:31:18.961037 26038 solver.cpp:218] Iteration 18600 (2.29965 iter/s, 5.21819s/12 iters), loss = 5.27406
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I0405 12:31:18.961086 26038 solver.cpp:237] Train net output #0: loss = 5.27406 (* 1 = 5.27406 loss)
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I0405 12:31:18.961092 26038 sgd_solver.cpp:105] Iteration 18600, lr = 1e-05
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I0405 12:31:24.164283 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:31:24.386322 26038 solver.cpp:218] Iteration 18612 (2.2119 iter/s, 5.42519s/12 iters), loss = 5.28408
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I0405 12:31:24.386368 26038 solver.cpp:237] Train net output #0: loss = 5.28408 (* 1 = 5.28408 loss)
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I0405 12:31:24.386373 26038 sgd_solver.cpp:105] Iteration 18612, lr = 1e-05
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I0405 12:31:29.552918 26038 solver.cpp:218] Iteration 18624 (2.32265 iter/s, 5.1665s/12 iters), loss = 5.26049
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I0405 12:31:29.552971 26038 solver.cpp:237] Train net output #0: loss = 5.26049 (* 1 = 5.26049 loss)
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I0405 12:31:29.552981 26038 sgd_solver.cpp:105] Iteration 18624, lr = 1e-05
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I0405 12:31:34.773128 26038 solver.cpp:218] Iteration 18636 (2.2988 iter/s, 5.22011s/12 iters), loss = 5.27942
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I0405 12:31:34.773185 26038 solver.cpp:237] Train net output #0: loss = 5.27942 (* 1 = 5.27942 loss)
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I0405 12:31:34.773193 26038 sgd_solver.cpp:105] Iteration 18636, lr = 1e-05
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I0405 12:31:40.205220 26038 solver.cpp:218] Iteration 18648 (2.20914 iter/s, 5.43199s/12 iters), loss = 5.28141
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I0405 12:31:40.205276 26038 solver.cpp:237] Train net output #0: loss = 5.28141 (* 1 = 5.28141 loss)
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I0405 12:31:40.205283 26038 sgd_solver.cpp:105] Iteration 18648, lr = 1e-05
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I0405 12:31:45.539474 26038 solver.cpp:218] Iteration 18660 (2.24966 iter/s, 5.33415s/12 iters), loss = 5.27204
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I0405 12:31:45.539630 26038 solver.cpp:237] Train net output #0: loss = 5.27204 (* 1 = 5.27204 loss)
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I0405 12:31:45.539640 26038 sgd_solver.cpp:105] Iteration 18660, lr = 1e-05
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I0405 12:31:47.810567 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18666.caffemodel
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I0405 12:31:50.784698 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18666.solverstate
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I0405 12:31:53.093775 26038 solver.cpp:330] Iteration 18666, Testing net (#0)
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I0405 12:31:53.093797 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:31:54.738955 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:31:57.465756 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:31:57.465796 26038 solver.cpp:397] Test net output #1: loss = 5.28022 (* 1 = 5.28022 loss)
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I0405 12:31:59.346230 26038 solver.cpp:218] Iteration 18672 (0.869155 iter/s, 13.8065s/12 iters), loss = 5.26851
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I0405 12:31:59.346274 26038 solver.cpp:237] Train net output #0: loss = 5.26851 (* 1 = 5.26851 loss)
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I0405 12:31:59.346280 26038 sgd_solver.cpp:105] Iteration 18672, lr = 1e-05
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I0405 12:32:04.816947 26038 solver.cpp:218] Iteration 18684 (2.19353 iter/s, 5.47063s/12 iters), loss = 5.28142
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I0405 12:32:04.816984 26038 solver.cpp:237] Train net output #0: loss = 5.28142 (* 1 = 5.28142 loss)
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I0405 12:32:04.816990 26038 sgd_solver.cpp:105] Iteration 18684, lr = 1e-05
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I0405 12:32:10.278194 26038 solver.cpp:218] Iteration 18696 (2.19733 iter/s, 5.46116s/12 iters), loss = 5.27565
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I0405 12:32:10.278236 26038 solver.cpp:237] Train net output #0: loss = 5.27565 (* 1 = 5.27565 loss)
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I0405 12:32:10.278241 26038 sgd_solver.cpp:105] Iteration 18696, lr = 1e-05
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I0405 12:32:15.553701 26038 solver.cpp:218] Iteration 18708 (2.2747 iter/s, 5.27541s/12 iters), loss = 5.28338
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I0405 12:32:15.553887 26038 solver.cpp:237] Train net output #0: loss = 5.28338 (* 1 = 5.28338 loss)
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I0405 12:32:15.553897 26038 sgd_solver.cpp:105] Iteration 18708, lr = 1e-05
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I0405 12:32:17.613308 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:32:20.899245 26038 solver.cpp:218] Iteration 18720 (2.24496 iter/s, 5.34532s/12 iters), loss = 5.29021
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I0405 12:32:20.899281 26038 solver.cpp:237] Train net output #0: loss = 5.29021 (* 1 = 5.29021 loss)
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I0405 12:32:20.899286 26038 sgd_solver.cpp:105] Iteration 18720, lr = 1e-05
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I0405 12:32:21.310734 26038 blocking_queue.cpp:49] Waiting for data
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I0405 12:32:26.418532 26038 solver.cpp:218] Iteration 18732 (2.17423 iter/s, 5.5192s/12 iters), loss = 5.26939
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I0405 12:32:26.418588 26038 solver.cpp:237] Train net output #0: loss = 5.26939 (* 1 = 5.26939 loss)
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||
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I0405 12:32:26.418596 26038 sgd_solver.cpp:105] Iteration 18732, lr = 1e-05
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I0405 12:32:31.930845 26038 solver.cpp:218] Iteration 18744 (2.17699 iter/s, 5.51221s/12 iters), loss = 5.28407
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I0405 12:32:31.930886 26038 solver.cpp:237] Train net output #0: loss = 5.28407 (* 1 = 5.28407 loss)
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||
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I0405 12:32:31.930891 26038 sgd_solver.cpp:105] Iteration 18744, lr = 1e-05
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I0405 12:32:37.369382 26038 solver.cpp:218] Iteration 18756 (2.20651 iter/s, 5.43845s/12 iters), loss = 5.27982
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I0405 12:32:37.369421 26038 solver.cpp:237] Train net output #0: loss = 5.27982 (* 1 = 5.27982 loss)
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||
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I0405 12:32:37.369427 26038 sgd_solver.cpp:105] Iteration 18756, lr = 1e-05
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||
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I0405 12:32:42.131337 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18768.caffemodel
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||
|
|
I0405 12:32:45.100097 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18768.solverstate
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||
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I0405 12:32:47.406810 26038 solver.cpp:330] Iteration 18768, Testing net (#0)
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||
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I0405 12:32:47.406870 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
|
I0405 12:32:49.003638 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 12:32:51.693984 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 12:32:51.694025 26038 solver.cpp:397] Test net output #1: loss = 5.28023 (* 1 = 5.28023 loss)
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||
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I0405 12:32:51.835758 26038 solver.cpp:218] Iteration 18768 (0.829518 iter/s, 14.4662s/12 iters), loss = 5.25329
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||
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I0405 12:32:51.837373 26038 solver.cpp:237] Train net output #0: loss = 5.25329 (* 1 = 5.25329 loss)
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||
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I0405 12:32:51.837388 26038 sgd_solver.cpp:105] Iteration 18768, lr = 1e-05
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I0405 12:32:56.355059 26038 solver.cpp:218] Iteration 18780 (2.65625 iter/s, 4.51765s/12 iters), loss = 5.25729
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||
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I0405 12:32:56.355111 26038 solver.cpp:237] Train net output #0: loss = 5.25729 (* 1 = 5.25729 loss)
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||
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I0405 12:32:56.355120 26038 sgd_solver.cpp:105] Iteration 18780, lr = 1e-05
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I0405 12:33:01.402845 26038 solver.cpp:218] Iteration 18792 (2.37732 iter/s, 5.04769s/12 iters), loss = 5.26573
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I0405 12:33:01.402886 26038 solver.cpp:237] Train net output #0: loss = 5.26573 (* 1 = 5.26573 loss)
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||
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I0405 12:33:01.402891 26038 sgd_solver.cpp:105] Iteration 18792, lr = 1e-05
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||
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I0405 12:33:06.759240 26038 solver.cpp:218] Iteration 18804 (2.24035 iter/s, 5.35631s/12 iters), loss = 5.26933
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||
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I0405 12:33:06.759296 26038 solver.cpp:237] Train net output #0: loss = 5.26933 (* 1 = 5.26933 loss)
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||
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I0405 12:33:06.759305 26038 sgd_solver.cpp:105] Iteration 18804, lr = 1e-05
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||
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I0405 12:33:11.105490 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:33:12.005163 26038 solver.cpp:218] Iteration 18816 (2.28753 iter/s, 5.24583s/12 iters), loss = 5.26145
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||
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I0405 12:33:12.005198 26038 solver.cpp:237] Train net output #0: loss = 5.26145 (* 1 = 5.26145 loss)
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I0405 12:33:12.005203 26038 sgd_solver.cpp:105] Iteration 18816, lr = 1e-05
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||
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I0405 12:33:17.300738 26038 solver.cpp:218] Iteration 18828 (2.26608 iter/s, 5.29549s/12 iters), loss = 5.26811
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||
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I0405 12:33:17.300778 26038 solver.cpp:237] Train net output #0: loss = 5.26811 (* 1 = 5.26811 loss)
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||
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I0405 12:33:17.300784 26038 sgd_solver.cpp:105] Iteration 18828, lr = 1e-05
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||
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I0405 12:33:22.581614 26038 solver.cpp:218] Iteration 18840 (2.27239 iter/s, 5.28079s/12 iters), loss = 5.30203
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||
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I0405 12:33:22.581748 26038 solver.cpp:237] Train net output #0: loss = 5.30203 (* 1 = 5.30203 loss)
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||
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I0405 12:33:22.581754 26038 sgd_solver.cpp:105] Iteration 18840, lr = 1e-05
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||
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I0405 12:33:28.032141 26038 solver.cpp:218] Iteration 18852 (2.20169 iter/s, 5.45035s/12 iters), loss = 5.28586
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||
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|
I0405 12:33:28.032187 26038 solver.cpp:237] Train net output #0: loss = 5.28586 (* 1 = 5.28586 loss)
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||
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I0405 12:33:28.032192 26038 sgd_solver.cpp:105] Iteration 18852, lr = 1e-05
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I0405 12:33:33.377226 26038 solver.cpp:218] Iteration 18864 (2.24509 iter/s, 5.34499s/12 iters), loss = 5.27474
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||
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I0405 12:33:33.377270 26038 solver.cpp:237] Train net output #0: loss = 5.27474 (* 1 = 5.27474 loss)
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||
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I0405 12:33:33.377276 26038 sgd_solver.cpp:105] Iteration 18864, lr = 1e-05
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||
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I0405 12:33:35.263516 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18870.caffemodel
|
||
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|
I0405 12:33:38.370842 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18870.solverstate
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||
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I0405 12:33:40.682937 26038 solver.cpp:330] Iteration 18870, Testing net (#0)
|
||
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I0405 12:33:40.682955 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
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I0405 12:33:42.322060 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 12:33:45.126472 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 12:33:45.126503 26038 solver.cpp:397] Test net output #1: loss = 5.28013 (* 1 = 5.28013 loss)
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||
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I0405 12:33:46.985424 26038 solver.cpp:218] Iteration 18876 (0.88183 iter/s, 13.6081s/12 iters), loss = 5.26228
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||
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I0405 12:33:46.985462 26038 solver.cpp:237] Train net output #0: loss = 5.26228 (* 1 = 5.26228 loss)
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I0405 12:33:46.985468 26038 sgd_solver.cpp:105] Iteration 18876, lr = 1e-05
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I0405 12:33:52.337678 26038 solver.cpp:218] Iteration 18888 (2.24208 iter/s, 5.35216s/12 iters), loss = 5.29335
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I0405 12:33:52.337731 26038 solver.cpp:237] Train net output #0: loss = 5.29335 (* 1 = 5.29335 loss)
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I0405 12:33:52.337739 26038 sgd_solver.cpp:105] Iteration 18888, lr = 1e-05
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I0405 12:33:57.453814 26038 solver.cpp:218] Iteration 18900 (2.34557 iter/s, 5.11604s/12 iters), loss = 5.26509
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I0405 12:33:57.453941 26038 solver.cpp:237] Train net output #0: loss = 5.26509 (* 1 = 5.26509 loss)
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I0405 12:33:57.453950 26038 sgd_solver.cpp:105] Iteration 18900, lr = 1e-05
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I0405 12:34:02.812479 26038 solver.cpp:218] Iteration 18912 (2.23944 iter/s, 5.35849s/12 iters), loss = 5.28865
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I0405 12:34:02.812530 26038 solver.cpp:237] Train net output #0: loss = 5.28865 (* 1 = 5.28865 loss)
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I0405 12:34:02.812536 26038 sgd_solver.cpp:105] Iteration 18912, lr = 1e-05
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I0405 12:34:04.259806 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:34:08.218796 26038 solver.cpp:218] Iteration 18924 (2.21967 iter/s, 5.40622s/12 iters), loss = 5.28262
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I0405 12:34:08.218847 26038 solver.cpp:237] Train net output #0: loss = 5.28262 (* 1 = 5.28262 loss)
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I0405 12:34:08.218854 26038 sgd_solver.cpp:105] Iteration 18924, lr = 1e-05
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I0405 12:34:13.650830 26038 solver.cpp:218] Iteration 18936 (2.20916 iter/s, 5.43194s/12 iters), loss = 5.27694
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I0405 12:34:13.650872 26038 solver.cpp:237] Train net output #0: loss = 5.27694 (* 1 = 5.27694 loss)
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I0405 12:34:13.650878 26038 sgd_solver.cpp:105] Iteration 18936, lr = 1e-05
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I0405 12:34:18.864228 26038 solver.cpp:218] Iteration 18948 (2.3018 iter/s, 5.21331s/12 iters), loss = 5.26985
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I0405 12:34:18.864286 26038 solver.cpp:237] Train net output #0: loss = 5.26985 (* 1 = 5.26985 loss)
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I0405 12:34:18.864295 26038 sgd_solver.cpp:105] Iteration 18948, lr = 1e-05
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I0405 12:34:24.117396 26038 solver.cpp:218] Iteration 18960 (2.28438 iter/s, 5.25306s/12 iters), loss = 5.28309
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I0405 12:34:24.117440 26038 solver.cpp:237] Train net output #0: loss = 5.28309 (* 1 = 5.28309 loss)
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I0405 12:34:24.117446 26038 sgd_solver.cpp:105] Iteration 18960, lr = 1e-05
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I0405 12:34:28.798193 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18972.caffemodel
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I0405 12:34:31.814155 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18972.solverstate
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I0405 12:34:34.121165 26038 solver.cpp:330] Iteration 18972, Testing net (#0)
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I0405 12:34:34.121186 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:34:35.688499 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:34:38.544339 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:34:38.544368 26038 solver.cpp:397] Test net output #1: loss = 5.28013 (* 1 = 5.28013 loss)
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I0405 12:34:38.685153 26038 solver.cpp:218] Iteration 18972 (0.823745 iter/s, 14.5676s/12 iters), loss = 5.2862
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I0405 12:34:38.685211 26038 solver.cpp:237] Train net output #0: loss = 5.2862 (* 1 = 5.2862 loss)
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I0405 12:34:38.685218 26038 sgd_solver.cpp:105] Iteration 18972, lr = 1e-05
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I0405 12:34:43.029963 26038 solver.cpp:218] Iteration 18984 (2.76198 iter/s, 4.34471s/12 iters), loss = 5.2718
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I0405 12:34:43.030004 26038 solver.cpp:237] Train net output #0: loss = 5.2718 (* 1 = 5.2718 loss)
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I0405 12:34:43.030010 26038 sgd_solver.cpp:105] Iteration 18984, lr = 1e-05
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I0405 12:34:48.174765 26038 solver.cpp:218] Iteration 18996 (2.33249 iter/s, 5.14471s/12 iters), loss = 5.26942
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I0405 12:34:48.174810 26038 solver.cpp:237] Train net output #0: loss = 5.26942 (* 1 = 5.26942 loss)
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I0405 12:34:48.174815 26038 sgd_solver.cpp:105] Iteration 18996, lr = 1e-05
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I0405 12:34:53.554085 26038 solver.cpp:218] Iteration 19008 (2.2308 iter/s, 5.37923s/12 iters), loss = 5.27551
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I0405 12:34:53.554126 26038 solver.cpp:237] Train net output #0: loss = 5.27551 (* 1 = 5.27551 loss)
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I0405 12:34:53.554132 26038 sgd_solver.cpp:105] Iteration 19008, lr = 1e-05
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I0405 12:34:57.108398 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:34:58.613425 26038 solver.cpp:218] Iteration 19020 (2.37189 iter/s, 5.05925s/12 iters), loss = 5.25367
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I0405 12:34:58.613468 26038 solver.cpp:237] Train net output #0: loss = 5.25367 (* 1 = 5.25367 loss)
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I0405 12:34:58.613474 26038 sgd_solver.cpp:105] Iteration 19020, lr = 1e-05
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I0405 12:35:04.040536 26038 solver.cpp:218] Iteration 19032 (2.21116 iter/s, 5.42702s/12 iters), loss = 5.26329
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I0405 12:35:04.040647 26038 solver.cpp:237] Train net output #0: loss = 5.26329 (* 1 = 5.26329 loss)
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I0405 12:35:04.040652 26038 sgd_solver.cpp:105] Iteration 19032, lr = 1e-05
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I0405 12:35:09.121063 26038 solver.cpp:218] Iteration 19044 (2.36203 iter/s, 5.08037s/12 iters), loss = 5.2623
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I0405 12:35:09.121110 26038 solver.cpp:237] Train net output #0: loss = 5.2623 (* 1 = 5.2623 loss)
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I0405 12:35:09.121117 26038 sgd_solver.cpp:105] Iteration 19044, lr = 1e-05
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I0405 12:35:14.318063 26038 solver.cpp:218] Iteration 19056 (2.30906 iter/s, 5.19691s/12 iters), loss = 5.26889
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I0405 12:35:14.318104 26038 solver.cpp:237] Train net output #0: loss = 5.26889 (* 1 = 5.26889 loss)
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I0405 12:35:14.318109 26038 sgd_solver.cpp:105] Iteration 19056, lr = 1e-05
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I0405 12:35:19.802155 26038 solver.cpp:218] Iteration 19068 (2.18818 iter/s, 5.484s/12 iters), loss = 5.28019
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I0405 12:35:19.802219 26038 solver.cpp:237] Train net output #0: loss = 5.28019 (* 1 = 5.28019 loss)
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I0405 12:35:19.802230 26038 sgd_solver.cpp:105] Iteration 19068, lr = 1e-05
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I0405 12:35:21.958118 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19074.caffemodel
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I0405 12:35:24.986935 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19074.solverstate
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I0405 12:35:28.156921 26038 solver.cpp:330] Iteration 19074, Testing net (#0)
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I0405 12:35:28.156945 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:35:29.738751 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:35:32.553066 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:35:32.553107 26038 solver.cpp:397] Test net output #1: loss = 5.28019 (* 1 = 5.28019 loss)
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I0405 12:35:34.482647 26038 solver.cpp:218] Iteration 19080 (0.81742 iter/s, 14.6803s/12 iters), loss = 5.24741
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I0405 12:35:34.482800 26038 solver.cpp:237] Train net output #0: loss = 5.24741 (* 1 = 5.24741 loss)
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I0405 12:35:34.482810 26038 sgd_solver.cpp:105] Iteration 19080, lr = 1e-05
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I0405 12:35:39.384366 26038 solver.cpp:218] Iteration 19092 (2.44822 iter/s, 4.90153s/12 iters), loss = 5.28452
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I0405 12:35:39.384405 26038 solver.cpp:237] Train net output #0: loss = 5.28452 (* 1 = 5.28452 loss)
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I0405 12:35:39.384411 26038 sgd_solver.cpp:105] Iteration 19092, lr = 1e-05
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I0405 12:35:44.582159 26038 solver.cpp:218] Iteration 19104 (2.30871 iter/s, 5.1977s/12 iters), loss = 5.26926
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I0405 12:35:44.582223 26038 solver.cpp:237] Train net output #0: loss = 5.26926 (* 1 = 5.26926 loss)
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I0405 12:35:44.582232 26038 sgd_solver.cpp:105] Iteration 19104, lr = 1e-05
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I0405 12:35:49.995004 26038 solver.cpp:218] Iteration 19116 (2.21699 iter/s, 5.41274s/12 iters), loss = 5.28162
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I0405 12:35:49.995041 26038 solver.cpp:237] Train net output #0: loss = 5.28162 (* 1 = 5.28162 loss)
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I0405 12:35:49.995045 26038 sgd_solver.cpp:105] Iteration 19116, lr = 1e-05
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I0405 12:35:50.621101 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:35:55.400967 26038 solver.cpp:218] Iteration 19128 (2.21981 iter/s, 5.40588s/12 iters), loss = 5.27386
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I0405 12:35:55.401010 26038 solver.cpp:237] Train net output #0: loss = 5.27386 (* 1 = 5.27386 loss)
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I0405 12:35:55.401016 26038 sgd_solver.cpp:105] Iteration 19128, lr = 1e-05
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I0405 12:36:00.638556 26038 solver.cpp:218] Iteration 19140 (2.29117 iter/s, 5.2375s/12 iters), loss = 5.27852
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I0405 12:36:00.638602 26038 solver.cpp:237] Train net output #0: loss = 5.27852 (* 1 = 5.27852 loss)
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I0405 12:36:00.638608 26038 sgd_solver.cpp:105] Iteration 19140, lr = 1e-05
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I0405 12:36:05.946565 26038 solver.cpp:218] Iteration 19152 (2.26077 iter/s, 5.30792s/12 iters), loss = 5.26543
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I0405 12:36:05.946681 26038 solver.cpp:237] Train net output #0: loss = 5.26543 (* 1 = 5.26543 loss)
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I0405 12:36:05.946686 26038 sgd_solver.cpp:105] Iteration 19152, lr = 1e-05
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I0405 12:36:11.254245 26038 solver.cpp:218] Iteration 19164 (2.26094 iter/s, 5.30752s/12 iters), loss = 5.27373
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I0405 12:36:11.254294 26038 solver.cpp:237] Train net output #0: loss = 5.27373 (* 1 = 5.27373 loss)
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I0405 12:36:11.254302 26038 sgd_solver.cpp:105] Iteration 19164, lr = 1e-05
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I0405 12:36:15.920751 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19176.caffemodel
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I0405 12:36:18.972784 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19176.solverstate
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I0405 12:36:21.291563 26038 solver.cpp:330] Iteration 19176, Testing net (#0)
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I0405 12:36:21.291585 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:36:22.879621 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:36:25.770001 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:36:25.770030 26038 solver.cpp:397] Test net output #1: loss = 5.28046 (* 1 = 5.28046 loss)
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I0405 12:36:25.910737 26038 solver.cpp:218] Iteration 19176 (0.818758 iter/s, 14.6563s/12 iters), loss = 5.27444
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I0405 12:36:25.910809 26038 solver.cpp:237] Train net output #0: loss = 5.27444 (* 1 = 5.27444 loss)
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I0405 12:36:25.910817 26038 sgd_solver.cpp:105] Iteration 19176, lr = 1e-05
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I0405 12:36:30.007735 26038 solver.cpp:218] Iteration 19188 (2.92905 iter/s, 4.09689s/12 iters), loss = 5.27751
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I0405 12:36:30.007776 26038 solver.cpp:237] Train net output #0: loss = 5.27751 (* 1 = 5.27751 loss)
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I0405 12:36:30.007782 26038 sgd_solver.cpp:105] Iteration 19188, lr = 1e-05
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I0405 12:36:35.142657 26038 solver.cpp:218] Iteration 19200 (2.33698 iter/s, 5.13483s/12 iters), loss = 5.26052
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I0405 12:36:35.142712 26038 solver.cpp:237] Train net output #0: loss = 5.26052 (* 1 = 5.26052 loss)
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I0405 12:36:35.142720 26038 sgd_solver.cpp:105] Iteration 19200, lr = 1e-05
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I0405 12:36:40.401465 26038 solver.cpp:218] Iteration 19212 (2.28193 iter/s, 5.25871s/12 iters), loss = 5.26608
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I0405 12:36:40.401602 26038 solver.cpp:237] Train net output #0: loss = 5.26608 (* 1 = 5.26608 loss)
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||
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I0405 12:36:40.401607 26038 sgd_solver.cpp:105] Iteration 19212, lr = 1e-05
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I0405 12:36:43.358114 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:36:45.868216 26038 solver.cpp:218] Iteration 19224 (2.19516 iter/s, 5.46657s/12 iters), loss = 5.2875
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I0405 12:36:45.868258 26038 solver.cpp:237] Train net output #0: loss = 5.2875 (* 1 = 5.2875 loss)
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I0405 12:36:45.868263 26038 sgd_solver.cpp:105] Iteration 19224, lr = 1e-05
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I0405 12:36:51.197552 26038 solver.cpp:218] Iteration 19236 (2.25173 iter/s, 5.32924s/12 iters), loss = 5.27671
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I0405 12:36:51.197599 26038 solver.cpp:237] Train net output #0: loss = 5.27671 (* 1 = 5.27671 loss)
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I0405 12:36:51.197607 26038 sgd_solver.cpp:105] Iteration 19236, lr = 1e-05
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I0405 12:36:56.572510 26038 solver.cpp:218] Iteration 19248 (2.23261 iter/s, 5.37486s/12 iters), loss = 5.26027
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I0405 12:36:56.572552 26038 solver.cpp:237] Train net output #0: loss = 5.26027 (* 1 = 5.26027 loss)
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I0405 12:36:56.572558 26038 sgd_solver.cpp:105] Iteration 19248, lr = 1e-05
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I0405 12:37:02.027349 26038 solver.cpp:218] Iteration 19260 (2.19991 iter/s, 5.45476s/12 iters), loss = 5.28867
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I0405 12:37:02.027384 26038 solver.cpp:237] Train net output #0: loss = 5.28867 (* 1 = 5.28867 loss)
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I0405 12:37:02.027390 26038 sgd_solver.cpp:105] Iteration 19260, lr = 1e-05
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I0405 12:37:07.281802 26038 solver.cpp:218] Iteration 19272 (2.28382 iter/s, 5.25437s/12 iters), loss = 5.28484
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I0405 12:37:07.281862 26038 solver.cpp:237] Train net output #0: loss = 5.28484 (* 1 = 5.28484 loss)
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I0405 12:37:07.281869 26038 sgd_solver.cpp:105] Iteration 19272, lr = 1e-05
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I0405 12:37:09.581373 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19278.caffemodel
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||
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I0405 12:37:12.521693 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19278.solverstate
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I0405 12:37:14.820963 26038 solver.cpp:330] Iteration 19278, Testing net (#0)
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||
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I0405 12:37:14.820982 26038 net.cpp:676] Ignoring source layer train-data
|
||
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I0405 12:37:16.264443 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:37:19.206292 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
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I0405 12:37:19.206341 26038 solver.cpp:397] Test net output #1: loss = 5.28008 (* 1 = 5.28008 loss)
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||
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I0405 12:37:21.285567 26038 solver.cpp:218] Iteration 19284 (0.856922 iter/s, 14.0036s/12 iters), loss = 5.28338
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I0405 12:37:21.285614 26038 solver.cpp:237] Train net output #0: loss = 5.28338 (* 1 = 5.28338 loss)
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I0405 12:37:21.285620 26038 sgd_solver.cpp:105] Iteration 19284, lr = 1e-05
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I0405 12:37:26.473393 26038 solver.cpp:218] Iteration 19296 (2.31315 iter/s, 5.18774s/12 iters), loss = 5.29015
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I0405 12:37:26.473434 26038 solver.cpp:237] Train net output #0: loss = 5.29015 (* 1 = 5.29015 loss)
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I0405 12:37:26.473440 26038 sgd_solver.cpp:105] Iteration 19296, lr = 1e-05
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I0405 12:37:31.728247 26038 solver.cpp:218] Iteration 19308 (2.28364 iter/s, 5.25476s/12 iters), loss = 5.27836
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I0405 12:37:31.728305 26038 solver.cpp:237] Train net output #0: loss = 5.27836 (* 1 = 5.27836 loss)
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I0405 12:37:31.728314 26038 sgd_solver.cpp:105] Iteration 19308, lr = 1e-05
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I0405 12:37:36.905838 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:37:37.100878 26038 solver.cpp:218] Iteration 19320 (2.23359 iter/s, 5.37253s/12 iters), loss = 5.2866
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I0405 12:37:37.100934 26038 solver.cpp:237] Train net output #0: loss = 5.2866 (* 1 = 5.2866 loss)
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I0405 12:37:37.100944 26038 sgd_solver.cpp:105] Iteration 19320, lr = 1e-05
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I0405 12:37:42.597462 26038 solver.cpp:218] Iteration 19332 (2.18322 iter/s, 5.49648s/12 iters), loss = 5.26613
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I0405 12:37:42.597627 26038 solver.cpp:237] Train net output #0: loss = 5.26613 (* 1 = 5.26613 loss)
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I0405 12:37:42.597636 26038 sgd_solver.cpp:105] Iteration 19332, lr = 1e-05
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I0405 12:37:47.906971 26038 solver.cpp:218] Iteration 19344 (2.26019 iter/s, 5.3093s/12 iters), loss = 5.28214
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I0405 12:37:47.907021 26038 solver.cpp:237] Train net output #0: loss = 5.28214 (* 1 = 5.28214 loss)
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I0405 12:37:47.907027 26038 sgd_solver.cpp:105] Iteration 19344, lr = 1e-05
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I0405 12:37:53.082020 26038 solver.cpp:218] Iteration 19356 (2.31886 iter/s, 5.17495s/12 iters), loss = 5.28212
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I0405 12:37:53.082060 26038 solver.cpp:237] Train net output #0: loss = 5.28212 (* 1 = 5.28212 loss)
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I0405 12:37:53.082067 26038 sgd_solver.cpp:105] Iteration 19356, lr = 1e-05
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I0405 12:37:58.350322 26038 solver.cpp:218] Iteration 19368 (2.27781 iter/s, 5.26821s/12 iters), loss = 5.27187
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I0405 12:37:58.350359 26038 solver.cpp:237] Train net output #0: loss = 5.27187 (* 1 = 5.27187 loss)
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I0405 12:37:58.350364 26038 sgd_solver.cpp:105] Iteration 19368, lr = 1e-05
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I0405 12:38:03.180851 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19380.caffemodel
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I0405 12:38:06.233603 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19380.solverstate
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I0405 12:38:08.553817 26038 solver.cpp:330] Iteration 19380, Testing net (#0)
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I0405 12:38:08.553835 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:38:10.067284 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:38:13.121954 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:38:13.122095 26038 solver.cpp:397] Test net output #1: loss = 5.28052 (* 1 = 5.28052 loss)
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I0405 12:38:13.262887 26038 solver.cpp:218] Iteration 19380 (0.804698 iter/s, 14.9124s/12 iters), loss = 5.26267
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I0405 12:38:13.262931 26038 solver.cpp:237] Train net output #0: loss = 5.26267 (* 1 = 5.26267 loss)
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I0405 12:38:13.262938 26038 sgd_solver.cpp:105] Iteration 19380, lr = 1e-05
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I0405 12:38:17.873937 26038 solver.cpp:218] Iteration 19392 (2.6025 iter/s, 4.61096s/12 iters), loss = 5.27681
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I0405 12:38:17.873981 26038 solver.cpp:237] Train net output #0: loss = 5.27681 (* 1 = 5.27681 loss)
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I0405 12:38:17.873987 26038 sgd_solver.cpp:105] Iteration 19392, lr = 1e-05
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I0405 12:38:23.263689 26038 solver.cpp:218] Iteration 19404 (2.22649 iter/s, 5.38965s/12 iters), loss = 5.27005
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I0405 12:38:23.263749 26038 solver.cpp:237] Train net output #0: loss = 5.27005 (* 1 = 5.27005 loss)
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I0405 12:38:23.263758 26038 sgd_solver.cpp:105] Iteration 19404, lr = 1e-05
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I0405 12:38:24.088701 26038 blocking_queue.cpp:49] Waiting for data
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I0405 12:38:28.643070 26038 solver.cpp:218] Iteration 19416 (2.23078 iter/s, 5.37928s/12 iters), loss = 5.25716
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I0405 12:38:28.643128 26038 solver.cpp:237] Train net output #0: loss = 5.25716 (* 1 = 5.25716 loss)
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I0405 12:38:28.643137 26038 sgd_solver.cpp:105] Iteration 19416, lr = 1e-05
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I0405 12:38:30.759346 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:38:34.007202 26038 solver.cpp:218] Iteration 19428 (2.23712 iter/s, 5.36403s/12 iters), loss = 5.28604
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I0405 12:38:34.007244 26038 solver.cpp:237] Train net output #0: loss = 5.28604 (* 1 = 5.28604 loss)
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I0405 12:38:34.007251 26038 sgd_solver.cpp:105] Iteration 19428, lr = 1e-05
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I0405 12:38:39.416942 26038 solver.cpp:218] Iteration 19440 (2.21826 iter/s, 5.40965s/12 iters), loss = 5.26238
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I0405 12:38:39.416985 26038 solver.cpp:237] Train net output #0: loss = 5.26238 (* 1 = 5.26238 loss)
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||
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I0405 12:38:39.416990 26038 sgd_solver.cpp:105] Iteration 19440, lr = 1e-05
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I0405 12:38:44.781867 26038 solver.cpp:218] Iteration 19452 (2.23679 iter/s, 5.36483s/12 iters), loss = 5.27676
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||
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I0405 12:38:44.782027 26038 solver.cpp:237] Train net output #0: loss = 5.27676 (* 1 = 5.27676 loss)
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||
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I0405 12:38:44.782035 26038 sgd_solver.cpp:105] Iteration 19452, lr = 1e-05
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I0405 12:38:50.164242 26038 solver.cpp:218] Iteration 19464 (2.22958 iter/s, 5.38217s/12 iters), loss = 5.27278
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||
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I0405 12:38:50.164296 26038 solver.cpp:237] Train net output #0: loss = 5.27278 (* 1 = 5.27278 loss)
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I0405 12:38:50.164305 26038 sgd_solver.cpp:105] Iteration 19464, lr = 1e-05
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I0405 12:38:55.602864 26038 solver.cpp:218] Iteration 19476 (2.20648 iter/s, 5.43852s/12 iters), loss = 5.2797
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I0405 12:38:55.602907 26038 solver.cpp:237] Train net output #0: loss = 5.2797 (* 1 = 5.2797 loss)
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I0405 12:38:55.602913 26038 sgd_solver.cpp:105] Iteration 19476, lr = 1e-05
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I0405 12:38:57.772833 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19482.caffemodel
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||
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I0405 12:39:01.370059 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19482.solverstate
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I0405 12:39:03.696254 26038 solver.cpp:330] Iteration 19482, Testing net (#0)
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I0405 12:39:03.696274 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:39:05.038517 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:39:08.099385 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:39:08.099424 26038 solver.cpp:397] Test net output #1: loss = 5.28023 (* 1 = 5.28023 loss)
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I0405 12:39:09.955282 26038 solver.cpp:218] Iteration 19488 (0.836104 iter/s, 14.3523s/12 iters), loss = 5.26216
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I0405 12:39:09.955324 26038 solver.cpp:237] Train net output #0: loss = 5.26216 (* 1 = 5.26216 loss)
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I0405 12:39:09.955329 26038 sgd_solver.cpp:105] Iteration 19488, lr = 1e-05
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I0405 12:39:15.193038 26038 solver.cpp:218] Iteration 19500 (2.2911 iter/s, 5.23766s/12 iters), loss = 5.27988
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I0405 12:39:15.193159 26038 solver.cpp:237] Train net output #0: loss = 5.27988 (* 1 = 5.27988 loss)
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I0405 12:39:15.193169 26038 sgd_solver.cpp:105] Iteration 19500, lr = 1e-05
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I0405 12:39:20.599978 26038 solver.cpp:218] Iteration 19512 (2.21944 iter/s, 5.40678s/12 iters), loss = 5.27919
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I0405 12:39:20.600023 26038 solver.cpp:237] Train net output #0: loss = 5.27919 (* 1 = 5.27919 loss)
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I0405 12:39:20.600028 26038 sgd_solver.cpp:105] Iteration 19512, lr = 1e-05
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I0405 12:39:25.058496 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:39:25.902844 26038 solver.cpp:218] Iteration 19524 (2.26297 iter/s, 5.30278s/12 iters), loss = 5.26996
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I0405 12:39:25.902889 26038 solver.cpp:237] Train net output #0: loss = 5.26996 (* 1 = 5.26996 loss)
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I0405 12:39:25.902894 26038 sgd_solver.cpp:105] Iteration 19524, lr = 1e-05
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I0405 12:39:31.281388 26038 solver.cpp:218] Iteration 19536 (2.23112 iter/s, 5.37845s/12 iters), loss = 5.2525
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I0405 12:39:31.281431 26038 solver.cpp:237] Train net output #0: loss = 5.2525 (* 1 = 5.2525 loss)
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I0405 12:39:31.281437 26038 sgd_solver.cpp:105] Iteration 19536, lr = 1e-05
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I0405 12:39:36.683374 26038 solver.cpp:218] Iteration 19548 (2.22144 iter/s, 5.4019s/12 iters), loss = 5.30087
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I0405 12:39:36.683413 26038 solver.cpp:237] Train net output #0: loss = 5.30087 (* 1 = 5.30087 loss)
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I0405 12:39:36.683418 26038 sgd_solver.cpp:105] Iteration 19548, lr = 1e-05
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I0405 12:39:42.154340 26038 solver.cpp:218] Iteration 19560 (2.19343 iter/s, 5.47088s/12 iters), loss = 5.29095
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I0405 12:39:42.154376 26038 solver.cpp:237] Train net output #0: loss = 5.29095 (* 1 = 5.29095 loss)
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I0405 12:39:42.154381 26038 sgd_solver.cpp:105] Iteration 19560, lr = 1e-05
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I0405 12:39:47.629014 26038 solver.cpp:218] Iteration 19572 (2.19194 iter/s, 5.47459s/12 iters), loss = 5.27504
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I0405 12:39:47.629135 26038 solver.cpp:237] Train net output #0: loss = 5.27504 (* 1 = 5.27504 loss)
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I0405 12:39:47.629142 26038 sgd_solver.cpp:105] Iteration 19572, lr = 1e-05
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I0405 12:39:52.250406 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19584.caffemodel
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||
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I0405 12:39:55.285885 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19584.solverstate
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I0405 12:39:57.594892 26038 solver.cpp:330] Iteration 19584, Testing net (#0)
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I0405 12:39:57.594910 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:39:58.904850 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:40:01.946456 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:40:01.946486 26038 solver.cpp:397] Test net output #1: loss = 5.28024 (* 1 = 5.28024 loss)
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I0405 12:40:02.089577 26038 solver.cpp:218] Iteration 19584 (0.829856 iter/s, 14.4603s/12 iters), loss = 5.27872
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I0405 12:40:02.089617 26038 solver.cpp:237] Train net output #0: loss = 5.27872 (* 1 = 5.27872 loss)
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I0405 12:40:02.089622 26038 sgd_solver.cpp:105] Iteration 19584, lr = 1e-05
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I0405 12:40:06.293988 26038 solver.cpp:218] Iteration 19596 (2.8542 iter/s, 4.20433s/12 iters), loss = 5.27609
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I0405 12:40:06.294037 26038 solver.cpp:237] Train net output #0: loss = 5.27609 (* 1 = 5.27609 loss)
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I0405 12:40:06.294044 26038 sgd_solver.cpp:105] Iteration 19596, lr = 1e-05
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I0405 12:40:11.759757 26038 solver.cpp:218] Iteration 19608 (2.19552 iter/s, 5.46567s/12 iters), loss = 5.28397
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I0405 12:40:11.759793 26038 solver.cpp:237] Train net output #0: loss = 5.28397 (* 1 = 5.28397 loss)
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I0405 12:40:11.759799 26038 sgd_solver.cpp:105] Iteration 19608, lr = 1e-05
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I0405 12:40:17.088734 26038 solver.cpp:218] Iteration 19620 (2.25188 iter/s, 5.32889s/12 iters), loss = 5.26896
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I0405 12:40:17.088789 26038 solver.cpp:237] Train net output #0: loss = 5.26896 (* 1 = 5.26896 loss)
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I0405 12:40:17.088798 26038 sgd_solver.cpp:105] Iteration 19620, lr = 1e-05
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I0405 12:40:18.533449 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:40:22.519812 26038 solver.cpp:218] Iteration 19632 (2.20955 iter/s, 5.43097s/12 iters), loss = 5.27612
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I0405 12:40:22.519865 26038 solver.cpp:237] Train net output #0: loss = 5.27612 (* 1 = 5.27612 loss)
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I0405 12:40:22.519872 26038 sgd_solver.cpp:105] Iteration 19632, lr = 1e-05
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I0405 12:40:27.905735 26038 solver.cpp:218] Iteration 19644 (2.22807 iter/s, 5.38583s/12 iters), loss = 5.26898
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I0405 12:40:27.905778 26038 solver.cpp:237] Train net output #0: loss = 5.26898 (* 1 = 5.26898 loss)
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||
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I0405 12:40:27.905784 26038 sgd_solver.cpp:105] Iteration 19644, lr = 1e-05
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I0405 12:40:33.131819 26038 solver.cpp:218] Iteration 19656 (2.29622 iter/s, 5.22599s/12 iters), loss = 5.26978
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I0405 12:40:33.131858 26038 solver.cpp:237] Train net output #0: loss = 5.26978 (* 1 = 5.26978 loss)
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||
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I0405 12:40:33.131863 26038 sgd_solver.cpp:105] Iteration 19656, lr = 1e-05
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I0405 12:40:38.427390 26038 solver.cpp:218] Iteration 19668 (2.26608 iter/s, 5.29548s/12 iters), loss = 5.25814
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I0405 12:40:38.427457 26038 solver.cpp:237] Train net output #0: loss = 5.25814 (* 1 = 5.25814 loss)
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||
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I0405 12:40:38.427466 26038 sgd_solver.cpp:105] Iteration 19668, lr = 1e-05
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I0405 12:40:43.751315 26038 solver.cpp:218] Iteration 19680 (2.25402 iter/s, 5.32381s/12 iters), loss = 5.28804
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I0405 12:40:43.751368 26038 solver.cpp:237] Train net output #0: loss = 5.28804 (* 1 = 5.28804 loss)
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||
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I0405 12:40:43.751375 26038 sgd_solver.cpp:105] Iteration 19680, lr = 1e-05
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||
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I0405 12:40:45.754498 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19686.caffemodel
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||
|
|
I0405 12:40:48.764747 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19686.solverstate
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||
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I0405 12:40:51.063694 26038 solver.cpp:330] Iteration 19686, Testing net (#0)
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||
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I0405 12:40:51.063715 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
|
I0405 12:40:52.296351 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
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|
I0405 12:40:55.338032 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 12:40:55.338068 26038 solver.cpp:397] Test net output #1: loss = 5.28043 (* 1 = 5.28043 loss)
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||
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I0405 12:40:57.226262 26038 solver.cpp:218] Iteration 19692 (0.890551 iter/s, 13.4748s/12 iters), loss = 5.27423
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||
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I0405 12:40:57.226302 26038 solver.cpp:237] Train net output #0: loss = 5.27423 (* 1 = 5.27423 loss)
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||
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I0405 12:40:57.226307 26038 sgd_solver.cpp:105] Iteration 19692, lr = 1e-05
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||
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I0405 12:41:02.497656 26038 solver.cpp:218] Iteration 19704 (2.27648 iter/s, 5.2713s/12 iters), loss = 5.28451
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I0405 12:41:02.497702 26038 solver.cpp:237] Train net output #0: loss = 5.28451 (* 1 = 5.28451 loss)
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||
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I0405 12:41:02.497707 26038 sgd_solver.cpp:105] Iteration 19704, lr = 1e-05
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||
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I0405 12:41:07.435276 26038 solver.cpp:218] Iteration 19716 (2.43037 iter/s, 4.93753s/12 iters), loss = 5.28079
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||
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I0405 12:41:07.435315 26038 solver.cpp:237] Train net output #0: loss = 5.28079 (* 1 = 5.28079 loss)
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||
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I0405 12:41:07.435321 26038 sgd_solver.cpp:105] Iteration 19716, lr = 1e-05
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||
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I0405 12:41:11.075407 26057 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:41:12.749606 26038 solver.cpp:218] Iteration 19728 (2.25808 iter/s, 5.31424s/12 iters), loss = 5.27404
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||
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I0405 12:41:12.749645 26038 solver.cpp:237] Train net output #0: loss = 5.27404 (* 1 = 5.27404 loss)
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I0405 12:41:12.749650 26038 sgd_solver.cpp:105] Iteration 19728, lr = 1e-05
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I0405 12:41:18.130453 26038 solver.cpp:218] Iteration 19740 (2.23017 iter/s, 5.38076s/12 iters), loss = 5.26419
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||
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I0405 12:41:18.130494 26038 solver.cpp:237] Train net output #0: loss = 5.26419 (* 1 = 5.26419 loss)
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||
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I0405 12:41:18.130499 26038 sgd_solver.cpp:105] Iteration 19740, lr = 1e-05
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||
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I0405 12:41:23.500214 26038 solver.cpp:218] Iteration 19752 (2.23477 iter/s, 5.36967s/12 iters), loss = 5.25812
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||
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|
I0405 12:41:23.500349 26038 solver.cpp:237] Train net output #0: loss = 5.25812 (* 1 = 5.25812 loss)
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||
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I0405 12:41:23.500356 26038 sgd_solver.cpp:105] Iteration 19752, lr = 1e-05
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||
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I0405 12:41:28.718384 26038 solver.cpp:218] Iteration 19764 (2.29973 iter/s, 5.21799s/12 iters), loss = 5.26777
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||
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|
I0405 12:41:28.718428 26038 solver.cpp:237] Train net output #0: loss = 5.26777 (* 1 = 5.26777 loss)
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||
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|
I0405 12:41:28.718436 26038 sgd_solver.cpp:105] Iteration 19764, lr = 1e-05
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||
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I0405 12:41:34.131561 26038 solver.cpp:218] Iteration 19776 (2.21685 iter/s, 5.41309s/12 iters), loss = 5.28934
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||
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I0405 12:41:34.131603 26038 solver.cpp:237] Train net output #0: loss = 5.28934 (* 1 = 5.28934 loss)
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||
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I0405 12:41:34.131608 26038 sgd_solver.cpp:105] Iteration 19776, lr = 1e-05
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||
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I0405 12:41:39.089020 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19788.caffemodel
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||
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|
I0405 12:41:42.164718 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19788.solverstate
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||
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I0405 12:41:44.475530 26038 solver.cpp:330] Iteration 19788, Testing net (#0)
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||
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I0405 12:41:44.475553 26038 net.cpp:676] Ignoring source layer train-data
|
||
|
|
I0405 12:41:45.672382 26101 data_layer.cpp:73] Restarting data prefetching from start.
|
||
|
|
I0405 12:41:48.774952 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
||
|
|
I0405 12:41:48.775002 26038 solver.cpp:397] Test net output #1: loss = 5.28047 (* 1 = 5.28047 loss)
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||
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I0405 12:41:48.916664 26038 solver.cpp:218] Iteration 19788 (0.811635 iter/s, 14.785s/12 iters), loss = 5.24409
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I0405 12:41:48.916716 26038 solver.cpp:237] Train net output #0: loss = 5.24409 (* 1 = 5.24409 loss)
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||
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I0405 12:41:48.916723 26038 sgd_solver.cpp:105] Iteration 19788, lr = 1e-05
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||
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I0405 12:41:53.464556 26038 solver.cpp:218] Iteration 19800 (2.63864 iter/s, 4.54779s/12 iters), loss = 5.27672
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||
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I0405 12:41:53.464607 26038 solver.cpp:237] Train net output #0: loss = 5.27672 (* 1 = 5.27672 loss)
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||
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I0405 12:41:53.464612 26038 sgd_solver.cpp:105] Iteration 19800, lr = 1e-05
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I0405 12:41:58.800344 26038 solver.cpp:218] Iteration 19812 (2.249 iter/s, 5.3357s/12 iters), loss = 5.27045
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I0405 12:41:58.800467 26038 solver.cpp:237] Train net output #0: loss = 5.27045 (* 1 = 5.27045 loss)
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I0405 12:41:58.800474 26038 sgd_solver.cpp:105] Iteration 19812, lr = 1e-05
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I0405 12:42:04.314625 26038 solver.cpp:218] Iteration 19824 (2.17623 iter/s, 5.51411s/12 iters), loss = 5.27799
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I0405 12:42:04.314666 26038 solver.cpp:237] Train net output #0: loss = 5.27799 (* 1 = 5.27799 loss)
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I0405 12:42:04.314671 26038 sgd_solver.cpp:105] Iteration 19824, lr = 1e-05
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I0405 12:42:04.972365 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:42:09.784596 26038 solver.cpp:218] Iteration 19836 (2.19383 iter/s, 5.46988s/12 iters), loss = 5.27115
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I0405 12:42:09.784648 26038 solver.cpp:237] Train net output #0: loss = 5.27115 (* 1 = 5.27115 loss)
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I0405 12:42:09.784657 26038 sgd_solver.cpp:105] Iteration 19836, lr = 1e-05
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I0405 12:42:14.946175 26038 solver.cpp:218] Iteration 19848 (2.32491 iter/s, 5.16148s/12 iters), loss = 5.26487
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I0405 12:42:14.946216 26038 solver.cpp:237] Train net output #0: loss = 5.26487 (* 1 = 5.26487 loss)
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I0405 12:42:14.946223 26038 sgd_solver.cpp:105] Iteration 19848, lr = 1e-05
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I0405 12:42:20.126132 26038 solver.cpp:218] Iteration 19860 (2.31666 iter/s, 5.17987s/12 iters), loss = 5.26672
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I0405 12:42:20.126178 26038 solver.cpp:237] Train net output #0: loss = 5.26672 (* 1 = 5.26672 loss)
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I0405 12:42:20.126184 26038 sgd_solver.cpp:105] Iteration 19860, lr = 1e-05
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I0405 12:42:25.491529 26038 solver.cpp:218] Iteration 19872 (2.23659 iter/s, 5.3653s/12 iters), loss = 5.27061
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I0405 12:42:25.491585 26038 solver.cpp:237] Train net output #0: loss = 5.27061 (* 1 = 5.27061 loss)
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I0405 12:42:25.491595 26038 sgd_solver.cpp:105] Iteration 19872, lr = 1e-05
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I0405 12:42:30.775851 26038 solver.cpp:218] Iteration 19884 (2.27091 iter/s, 5.28422s/12 iters), loss = 5.27521
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I0405 12:42:30.775980 26038 solver.cpp:237] Train net output #0: loss = 5.27521 (* 1 = 5.27521 loss)
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I0405 12:42:30.775987 26038 sgd_solver.cpp:105] Iteration 19884, lr = 1e-05
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I0405 12:42:33.070881 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19890.caffemodel
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I0405 12:42:36.045598 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19890.solverstate
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I0405 12:42:38.383361 26038 solver.cpp:330] Iteration 19890, Testing net (#0)
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I0405 12:42:38.383384 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:42:39.585454 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:42:42.755574 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:42:42.755611 26038 solver.cpp:397] Test net output #1: loss = 5.2807 (* 1 = 5.2807 loss)
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I0405 12:42:44.673945 26038 solver.cpp:218] Iteration 19896 (0.863442 iter/s, 13.8979s/12 iters), loss = 5.2744
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I0405 12:42:44.673988 26038 solver.cpp:237] Train net output #0: loss = 5.2744 (* 1 = 5.2744 loss)
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I0405 12:42:44.673993 26038 sgd_solver.cpp:105] Iteration 19896, lr = 1e-05
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I0405 12:42:49.985191 26038 solver.cpp:218] Iteration 19908 (2.2594 iter/s, 5.31115s/12 iters), loss = 5.26502
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I0405 12:42:49.985251 26038 solver.cpp:237] Train net output #0: loss = 5.26502 (* 1 = 5.26502 loss)
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I0405 12:42:49.985260 26038 sgd_solver.cpp:105] Iteration 19908, lr = 1e-05
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I0405 12:42:55.339231 26038 solver.cpp:218] Iteration 19920 (2.24134 iter/s, 5.35394s/12 iters), loss = 5.27423
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I0405 12:42:55.339275 26038 solver.cpp:237] Train net output #0: loss = 5.27423 (* 1 = 5.27423 loss)
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I0405 12:42:55.339282 26038 sgd_solver.cpp:105] Iteration 19920, lr = 1e-05
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I0405 12:42:58.271230 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:43:00.764075 26038 solver.cpp:218] Iteration 19932 (2.21208 iter/s, 5.42475s/12 iters), loss = 5.27867
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I0405 12:43:00.764115 26038 solver.cpp:237] Train net output #0: loss = 5.27867 (* 1 = 5.27867 loss)
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I0405 12:43:00.764122 26038 sgd_solver.cpp:105] Iteration 19932, lr = 1e-05
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I0405 12:43:06.136212 26038 solver.cpp:218] Iteration 19944 (2.23378 iter/s, 5.37205s/12 iters), loss = 5.28624
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I0405 12:43:06.136364 26038 solver.cpp:237] Train net output #0: loss = 5.28624 (* 1 = 5.28624 loss)
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I0405 12:43:06.136375 26038 sgd_solver.cpp:105] Iteration 19944, lr = 1e-05
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I0405 12:43:11.557792 26038 solver.cpp:218] Iteration 19956 (2.21346 iter/s, 5.42138s/12 iters), loss = 5.28528
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I0405 12:43:11.557834 26038 solver.cpp:237] Train net output #0: loss = 5.28528 (* 1 = 5.28528 loss)
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I0405 12:43:11.557840 26038 sgd_solver.cpp:105] Iteration 19956, lr = 1e-05
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I0405 12:43:16.700557 26038 solver.cpp:218] Iteration 19968 (2.33342 iter/s, 5.14267s/12 iters), loss = 5.2876
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I0405 12:43:16.700603 26038 solver.cpp:237] Train net output #0: loss = 5.2876 (* 1 = 5.2876 loss)
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I0405 12:43:16.700609 26038 sgd_solver.cpp:105] Iteration 19968, lr = 1e-05
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I0405 12:43:22.022686 26038 solver.cpp:218] Iteration 19980 (2.25478 iter/s, 5.32204s/12 iters), loss = 5.27728
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I0405 12:43:22.022740 26038 solver.cpp:237] Train net output #0: loss = 5.27728 (* 1 = 5.27728 loss)
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I0405 12:43:22.022749 26038 sgd_solver.cpp:105] Iteration 19980, lr = 1e-05
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I0405 12:43:26.864866 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19992.caffemodel
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I0405 12:43:30.151832 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19992.solverstate
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I0405 12:43:33.119494 26038 solver.cpp:330] Iteration 19992, Testing net (#0)
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I0405 12:43:33.119516 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:43:34.233584 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:43:37.459681 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:43:37.459815 26038 solver.cpp:397] Test net output #1: loss = 5.28036 (* 1 = 5.28036 loss)
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I0405 12:43:37.598814 26038 solver.cpp:218] Iteration 19992 (0.770418 iter/s, 15.576s/12 iters), loss = 5.27943
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I0405 12:43:37.598865 26038 solver.cpp:237] Train net output #0: loss = 5.27943 (* 1 = 5.27943 loss)
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I0405 12:43:37.598875 26038 sgd_solver.cpp:105] Iteration 19992, lr = 1e-05
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I0405 12:43:41.910604 26038 solver.cpp:218] Iteration 20004 (2.78313 iter/s, 4.3117s/12 iters), loss = 5.28999
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I0405 12:43:41.910655 26038 solver.cpp:237] Train net output #0: loss = 5.28999 (* 1 = 5.28999 loss)
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I0405 12:43:41.910667 26038 sgd_solver.cpp:105] Iteration 20004, lr = 1e-05
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I0405 12:43:47.092846 26038 solver.cpp:218] Iteration 20016 (2.31564 iter/s, 5.18214s/12 iters), loss = 5.27886
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I0405 12:43:47.092890 26038 solver.cpp:237] Train net output #0: loss = 5.27886 (* 1 = 5.27886 loss)
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I0405 12:43:47.092895 26038 sgd_solver.cpp:105] Iteration 20016, lr = 1e-05
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I0405 12:43:52.309167 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:43:52.476913 26038 solver.cpp:218] Iteration 20028 (2.22884 iter/s, 5.38397s/12 iters), loss = 5.27504
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I0405 12:43:52.476979 26038 solver.cpp:237] Train net output #0: loss = 5.27504 (* 1 = 5.27504 loss)
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I0405 12:43:52.476989 26038 sgd_solver.cpp:105] Iteration 20028, lr = 1e-05
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I0405 12:43:57.745963 26038 solver.cpp:218] Iteration 20040 (2.2775 iter/s, 5.26894s/12 iters), loss = 5.26114
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I0405 12:43:57.746008 26038 solver.cpp:237] Train net output #0: loss = 5.26114 (* 1 = 5.26114 loss)
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I0405 12:43:57.746016 26038 sgd_solver.cpp:105] Iteration 20040, lr = 1e-05
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I0405 12:44:03.043653 26038 solver.cpp:218] Iteration 20052 (2.26518 iter/s, 5.29759s/12 iters), loss = 5.27387
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I0405 12:44:03.043699 26038 solver.cpp:237] Train net output #0: loss = 5.27387 (* 1 = 5.27387 loss)
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I0405 12:44:03.043704 26038 sgd_solver.cpp:105] Iteration 20052, lr = 1e-05
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I0405 12:44:08.419564 26038 solver.cpp:218] Iteration 20064 (2.23222 iter/s, 5.37581s/12 iters), loss = 5.28864
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I0405 12:44:08.419688 26038 solver.cpp:237] Train net output #0: loss = 5.28864 (* 1 = 5.28864 loss)
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I0405 12:44:08.419696 26038 sgd_solver.cpp:105] Iteration 20064, lr = 1e-05
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I0405 12:44:13.667574 26038 solver.cpp:218] Iteration 20076 (2.28665 iter/s, 5.24784s/12 iters), loss = 5.26694
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I0405 12:44:13.667613 26038 solver.cpp:237] Train net output #0: loss = 5.26694 (* 1 = 5.26694 loss)
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I0405 12:44:13.667619 26038 sgd_solver.cpp:105] Iteration 20076, lr = 1e-05
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I0405 12:44:19.082861 26038 solver.cpp:218] Iteration 20088 (2.21599 iter/s, 5.4152s/12 iters), loss = 5.27947
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I0405 12:44:19.082911 26038 solver.cpp:237] Train net output #0: loss = 5.27947 (* 1 = 5.27947 loss)
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I0405 12:44:19.082921 26038 sgd_solver.cpp:105] Iteration 20088, lr = 1e-05
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I0405 12:44:21.066548 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20094.caffemodel
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I0405 12:44:24.108608 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20094.solverstate
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I0405 12:44:26.646656 26038 solver.cpp:330] Iteration 20094, Testing net (#0)
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I0405 12:44:26.646677 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:44:27.790249 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:44:30.632462 26038 blocking_queue.cpp:49] Waiting for data
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I0405 12:44:31.087960 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:44:31.087994 26038 solver.cpp:397] Test net output #1: loss = 5.28042 (* 1 = 5.28042 loss)
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I0405 12:44:33.048602 26038 solver.cpp:218] Iteration 20100 (0.859255 iter/s, 13.9656s/12 iters), loss = 5.27321
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I0405 12:44:33.048658 26038 solver.cpp:237] Train net output #0: loss = 5.27321 (* 1 = 5.27321 loss)
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I0405 12:44:33.048667 26038 sgd_solver.cpp:105] Iteration 20100, lr = 1e-05
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I0405 12:44:38.388173 26038 solver.cpp:218] Iteration 20112 (2.24741 iter/s, 5.33947s/12 iters), loss = 5.27682
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I0405 12:44:38.388218 26038 solver.cpp:237] Train net output #0: loss = 5.27682 (* 1 = 5.27682 loss)
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I0405 12:44:38.388224 26038 sgd_solver.cpp:105] Iteration 20112, lr = 1e-05
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I0405 12:44:43.750717 26038 solver.cpp:218] Iteration 20124 (2.23778 iter/s, 5.36245s/12 iters), loss = 5.26523
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I0405 12:44:43.750852 26038 solver.cpp:237] Train net output #0: loss = 5.26523 (* 1 = 5.26523 loss)
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I0405 12:44:43.750859 26038 sgd_solver.cpp:105] Iteration 20124, lr = 1e-05
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I0405 12:44:46.172027 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:44:49.446295 26038 solver.cpp:218] Iteration 20136 (2.10697 iter/s, 5.69539s/12 iters), loss = 5.27289
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I0405 12:44:49.446336 26038 solver.cpp:237] Train net output #0: loss = 5.27289 (* 1 = 5.27289 loss)
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I0405 12:44:49.446341 26038 sgd_solver.cpp:105] Iteration 20136, lr = 1e-05
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I0405 12:44:54.775009 26038 solver.cpp:218] Iteration 20148 (2.25199 iter/s, 5.32863s/12 iters), loss = 5.27622
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I0405 12:44:54.775048 26038 solver.cpp:237] Train net output #0: loss = 5.27622 (* 1 = 5.27622 loss)
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I0405 12:44:54.775053 26038 sgd_solver.cpp:105] Iteration 20148, lr = 1e-05
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I0405 12:45:00.114310 26038 solver.cpp:218] Iteration 20160 (2.24752 iter/s, 5.33921s/12 iters), loss = 5.2649
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I0405 12:45:00.114357 26038 solver.cpp:237] Train net output #0: loss = 5.2649 (* 1 = 5.2649 loss)
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I0405 12:45:00.114363 26038 sgd_solver.cpp:105] Iteration 20160, lr = 1e-05
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I0405 12:45:05.605906 26038 solver.cpp:218] Iteration 20172 (2.1852 iter/s, 5.4915s/12 iters), loss = 5.28717
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I0405 12:45:05.605948 26038 solver.cpp:237] Train net output #0: loss = 5.28717 (* 1 = 5.28717 loss)
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I0405 12:45:05.605954 26038 sgd_solver.cpp:105] Iteration 20172, lr = 1e-05
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I0405 12:45:10.807567 26038 solver.cpp:218] Iteration 20184 (2.30699 iter/s, 5.20157s/12 iters), loss = 5.26783
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I0405 12:45:10.807606 26038 solver.cpp:237] Train net output #0: loss = 5.26783 (* 1 = 5.26783 loss)
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I0405 12:45:10.807611 26038 sgd_solver.cpp:105] Iteration 20184, lr = 1e-05
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I0405 12:45:15.655848 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20196.caffemodel
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||
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I0405 12:45:18.730743 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20196.solverstate
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I0405 12:45:21.053910 26038 solver.cpp:330] Iteration 20196, Testing net (#0)
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||
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I0405 12:45:21.053930 26038 net.cpp:676] Ignoring source layer train-data
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||
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I0405 12:45:22.150380 26101 data_layer.cpp:73] Restarting data prefetching from start.
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||
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I0405 12:45:25.431905 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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||
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I0405 12:45:25.431943 26038 solver.cpp:397] Test net output #1: loss = 5.28058 (* 1 = 5.28058 loss)
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I0405 12:45:25.573797 26038 solver.cpp:218] Iteration 20196 (0.812673 iter/s, 14.7661s/12 iters), loss = 5.27725
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I0405 12:45:25.573848 26038 solver.cpp:237] Train net output #0: loss = 5.27725 (* 1 = 5.27725 loss)
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I0405 12:45:25.573854 26038 sgd_solver.cpp:105] Iteration 20196, lr = 1e-05
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I0405 12:45:30.092514 26038 solver.cpp:218] Iteration 20208 (2.65568 iter/s, 4.51862s/12 iters), loss = 5.25362
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I0405 12:45:30.092567 26038 solver.cpp:237] Train net output #0: loss = 5.25362 (* 1 = 5.25362 loss)
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I0405 12:45:30.092576 26038 sgd_solver.cpp:105] Iteration 20208, lr = 1e-05
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I0405 12:45:35.415241 26038 solver.cpp:218] Iteration 20220 (2.25453 iter/s, 5.32263s/12 iters), loss = 5.2751
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I0405 12:45:35.415294 26038 solver.cpp:237] Train net output #0: loss = 5.2751 (* 1 = 5.2751 loss)
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I0405 12:45:35.415303 26038 sgd_solver.cpp:105] Iteration 20220, lr = 1e-05
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I0405 12:45:40.114485 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:45:40.971601 26038 solver.cpp:218] Iteration 20232 (2.15973 iter/s, 5.55626s/12 iters), loss = 5.26549
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I0405 12:45:40.971642 26038 solver.cpp:237] Train net output #0: loss = 5.26549 (* 1 = 5.26549 loss)
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I0405 12:45:40.971647 26038 sgd_solver.cpp:105] Iteration 20232, lr = 1e-05
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I0405 12:45:46.287683 26038 solver.cpp:218] Iteration 20244 (2.25734 iter/s, 5.31599s/12 iters), loss = 5.26551
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I0405 12:45:46.287824 26038 solver.cpp:237] Train net output #0: loss = 5.26551 (* 1 = 5.26551 loss)
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I0405 12:45:46.287832 26038 sgd_solver.cpp:105] Iteration 20244, lr = 1e-05
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I0405 12:45:51.574730 26038 solver.cpp:218] Iteration 20256 (2.26978 iter/s, 5.28685s/12 iters), loss = 5.2876
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I0405 12:45:51.574788 26038 solver.cpp:237] Train net output #0: loss = 5.2876 (* 1 = 5.2876 loss)
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I0405 12:45:51.574796 26038 sgd_solver.cpp:105] Iteration 20256, lr = 1e-05
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I0405 12:45:56.746685 26038 solver.cpp:218] Iteration 20268 (2.32025 iter/s, 5.17185s/12 iters), loss = 5.26798
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I0405 12:45:56.746727 26038 solver.cpp:237] Train net output #0: loss = 5.26798 (* 1 = 5.26798 loss)
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I0405 12:45:56.746732 26038 sgd_solver.cpp:105] Iteration 20268, lr = 1e-05
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I0405 12:46:01.927014 26038 solver.cpp:218] Iteration 20280 (2.31649 iter/s, 5.18025s/12 iters), loss = 5.29229
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I0405 12:46:01.927059 26038 solver.cpp:237] Train net output #0: loss = 5.29229 (* 1 = 5.29229 loss)
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I0405 12:46:01.927067 26038 sgd_solver.cpp:105] Iteration 20280, lr = 1e-05
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I0405 12:46:07.244798 26038 solver.cpp:218] Iteration 20292 (2.25662 iter/s, 5.31769s/12 iters), loss = 5.24964
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I0405 12:46:07.244856 26038 solver.cpp:237] Train net output #0: loss = 5.24964 (* 1 = 5.24964 loss)
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I0405 12:46:07.244864 26038 sgd_solver.cpp:105] Iteration 20292, lr = 1e-05
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I0405 12:46:09.331866 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20298.caffemodel
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I0405 12:46:12.379015 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20298.solverstate
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I0405 12:46:14.696076 26038 solver.cpp:330] Iteration 20298, Testing net (#0)
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I0405 12:46:14.696094 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:46:15.701925 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:46:18.966948 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:46:18.967061 26038 solver.cpp:397] Test net output #1: loss = 5.28022 (* 1 = 5.28022 loss)
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I0405 12:46:20.917675 26038 solver.cpp:218] Iteration 20304 (0.87766 iter/s, 13.6727s/12 iters), loss = 5.27835
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I0405 12:46:20.917732 26038 solver.cpp:237] Train net output #0: loss = 5.27835 (* 1 = 5.27835 loss)
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I0405 12:46:20.917742 26038 sgd_solver.cpp:105] Iteration 20304, lr = 1e-05
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I0405 12:46:26.199136 26038 solver.cpp:218] Iteration 20316 (2.27214 iter/s, 5.28136s/12 iters), loss = 5.27377
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I0405 12:46:26.199172 26038 solver.cpp:237] Train net output #0: loss = 5.27377 (* 1 = 5.27377 loss)
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I0405 12:46:26.199177 26038 sgd_solver.cpp:105] Iteration 20316, lr = 1e-05
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I0405 12:46:31.252684 26038 solver.cpp:218] Iteration 20328 (2.37461 iter/s, 5.05346s/12 iters), loss = 5.28237
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I0405 12:46:31.252738 26038 solver.cpp:237] Train net output #0: loss = 5.28237 (* 1 = 5.28237 loss)
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I0405 12:46:31.252746 26038 sgd_solver.cpp:105] Iteration 20328, lr = 1e-05
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I0405 12:46:32.666491 26057 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:46:36.565099 26038 solver.cpp:218] Iteration 20340 (2.2589 iter/s, 5.31231s/12 iters), loss = 5.2864
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I0405 12:46:36.565145 26038 solver.cpp:237] Train net output #0: loss = 5.2864 (* 1 = 5.2864 loss)
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I0405 12:46:36.565152 26038 sgd_solver.cpp:105] Iteration 20340, lr = 1e-05
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I0405 12:46:41.858045 26038 solver.cpp:218] Iteration 20352 (2.26721 iter/s, 5.29285s/12 iters), loss = 5.26813
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I0405 12:46:41.858101 26038 solver.cpp:237] Train net output #0: loss = 5.26813 (* 1 = 5.26813 loss)
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I0405 12:46:41.858110 26038 sgd_solver.cpp:105] Iteration 20352, lr = 1e-05
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I0405 12:46:47.174535 26038 solver.cpp:218] Iteration 20364 (2.25717 iter/s, 5.31638s/12 iters), loss = 5.27068
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I0405 12:46:47.174593 26038 solver.cpp:237] Train net output #0: loss = 5.27068 (* 1 = 5.27068 loss)
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I0405 12:46:47.174602 26038 sgd_solver.cpp:105] Iteration 20364, lr = 1e-05
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I0405 12:46:52.345675 26038 solver.cpp:218] Iteration 20376 (2.32062 iter/s, 5.17104s/12 iters), loss = 5.28354
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I0405 12:46:52.345818 26038 solver.cpp:237] Train net output #0: loss = 5.28354 (* 1 = 5.28354 loss)
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I0405 12:46:52.345825 26038 sgd_solver.cpp:105] Iteration 20376, lr = 1e-05
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I0405 12:46:57.920754 26038 solver.cpp:218] Iteration 20388 (2.15251 iter/s, 5.57489s/12 iters), loss = 5.28096
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I0405 12:46:57.920791 26038 solver.cpp:237] Train net output #0: loss = 5.28096 (* 1 = 5.28096 loss)
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I0405 12:46:57.920796 26038 sgd_solver.cpp:105] Iteration 20388, lr = 1e-05
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I0405 12:47:02.779928 26038 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20400.caffemodel
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I0405 12:47:05.812800 26038 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20400.solverstate
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I0405 12:47:08.166180 26038 solver.cpp:310] Iteration 20400, loss = 5.26563
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I0405 12:47:08.166210 26038 solver.cpp:330] Iteration 20400, Testing net (#0)
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I0405 12:47:08.166215 26038 net.cpp:676] Ignoring source layer train-data
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I0405 12:47:09.179878 26101 data_layer.cpp:73] Restarting data prefetching from start.
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I0405 12:47:12.583894 26038 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0405 12:47:12.583936 26038 solver.cpp:397] Test net output #1: loss = 5.28059 (* 1 = 5.28059 loss)
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I0405 12:47:12.583943 26038 solver.cpp:315] Optimization Done.
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I0405 12:47:12.583946 26038 caffe.cpp:259] Optimization Done.
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