2181 lines
132 KiB
Plaintext
2181 lines
132 KiB
Plaintext
I0419 14:01:32.474609 5641 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-140131-bb21/solver.prototxt
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I0419 14:01:32.474752 5641 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
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W0419 14:01:32.474758 5641 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
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I0419 14:01:32.474822 5641 caffe.cpp:218] Using GPUs 3
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I0419 14:01:32.517134 5641 caffe.cpp:223] GPU 3: GeForce RTX 2080
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I0419 14:01:32.875255 5641 solver.cpp:44] Initializing solver from parameters:
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test_iter: 51
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test_interval: 203
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base_lr: 0.01
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display: 25
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max_iter: 6090
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lr_policy: "exp"
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gamma: 0.9996683
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momentum: 0.9
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weight_decay: 0.0001
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snapshot: 203
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snapshot_prefix: "snapshot"
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solver_mode: GPU
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device_id: 3
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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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I0419 14:01:32.876848 5641 solver.cpp:87] Creating training net from net file: train_val.prototxt
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I0419 14:01:32.877802 5641 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
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I0419 14:01:32.877817 5641 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
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I0419 14:01:32.877959 5641 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-AMB-2/digits/jobs/20210419-135836-fd84/mean.binaryproto"
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}
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data_param {
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source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/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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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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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: "relu4"
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type: "ReLU"
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bottom: "conv4"
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top: "conv4"
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}
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layer {
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name: "conv5"
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type: "Convolution"
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bottom: "conv4"
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top: "conv5"
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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 {
|
|
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: 1
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|
kernel_size: 3
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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: "relu5"
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type: "ReLU"
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bottom: "conv5"
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top: "conv5"
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}
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layer {
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name: "pool5"
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type: "Pooling"
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bottom: "conv5"
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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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stride: 2
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}
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}
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layer {
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name: "fc6"
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type: "InnerProduct"
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bottom: "pool5"
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top: "fc6"
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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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inner_product_param {
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|
num_output: 4096
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weight_filler {
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|
type: "gaussian"
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|
std: 0.005
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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: "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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|
name: "drop6"
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type: "Dropout"
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bottom: "fc6"
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top: "fc6"
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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: "fc7"
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type: "InnerProduct"
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|
bottom: "fc6"
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|
top: "fc7"
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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 {
|
|
lr_mult: 2
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|
decay_mult: 0
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|
}
|
|
inner_product_param {
|
|
num_output: 4096
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.005
|
|
}
|
|
bias_filler {
|
|
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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|
name: "relu7"
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|
type: "ReLU"
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|
bottom: "fc7"
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top: "fc7"
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|
}
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|
layer {
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|
name: "drop7"
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|
type: "Dropout"
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|
bottom: "fc7"
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|
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"
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|
bottom: "fc7"
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|
top: "fc8"
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|
param {
|
|
lr_mult: 1
|
|
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
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|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
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|
}
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|
}
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|
}
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|
layer {
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|
name: "loss"
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|
type: "SoftmaxWithLoss"
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|
bottom: "fc8"
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|
bottom: "label"
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|
top: "loss"
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|
}
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I0419 14:01:32.878058 5641 layer_factory.hpp:77] Creating layer train-data
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I0419 14:01:32.882405 5641 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/train_db
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I0419 14:01:32.883975 5641 net.cpp:84] Creating Layer train-data
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I0419 14:01:32.883994 5641 net.cpp:380] train-data -> data
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I0419 14:01:32.884021 5641 net.cpp:380] train-data -> label
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I0419 14:01:32.884034 5641 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/mean.binaryproto
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I0419 14:01:32.889397 5641 data_layer.cpp:45] output data size: 128,3,227,227
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I0419 14:01:33.037503 5641 net.cpp:122] Setting up train-data
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I0419 14:01:33.037528 5641 net.cpp:129] Top shape: 128 3 227 227 (19787136)
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I0419 14:01:33.037533 5641 net.cpp:129] Top shape: 128 (128)
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I0419 14:01:33.037534 5641 net.cpp:137] Memory required for data: 79149056
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I0419 14:01:33.037544 5641 layer_factory.hpp:77] Creating layer conv1
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I0419 14:01:33.037586 5641 net.cpp:84] Creating Layer conv1
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I0419 14:01:33.037592 5641 net.cpp:406] conv1 <- data
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I0419 14:01:33.037606 5641 net.cpp:380] conv1 -> conv1
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I0419 14:01:33.930706 5641 net.cpp:122] Setting up conv1
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I0419 14:01:33.930727 5641 net.cpp:129] Top shape: 128 96 55 55 (37171200)
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I0419 14:01:33.930730 5641 net.cpp:137] Memory required for data: 227833856
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I0419 14:01:33.930750 5641 layer_factory.hpp:77] Creating layer relu1
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I0419 14:01:33.930760 5641 net.cpp:84] Creating Layer relu1
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I0419 14:01:33.930764 5641 net.cpp:406] relu1 <- conv1
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I0419 14:01:33.930770 5641 net.cpp:367] relu1 -> conv1 (in-place)
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I0419 14:01:33.931092 5641 net.cpp:122] Setting up relu1
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I0419 14:01:33.931102 5641 net.cpp:129] Top shape: 128 96 55 55 (37171200)
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I0419 14:01:33.931104 5641 net.cpp:137] Memory required for data: 376518656
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I0419 14:01:33.931107 5641 layer_factory.hpp:77] Creating layer norm1
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I0419 14:01:33.931118 5641 net.cpp:84] Creating Layer norm1
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I0419 14:01:33.931121 5641 net.cpp:406] norm1 <- conv1
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I0419 14:01:33.931147 5641 net.cpp:380] norm1 -> norm1
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I0419 14:01:33.931666 5641 net.cpp:122] Setting up norm1
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I0419 14:01:33.931676 5641 net.cpp:129] Top shape: 128 96 55 55 (37171200)
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I0419 14:01:33.931679 5641 net.cpp:137] Memory required for data: 525203456
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I0419 14:01:33.931682 5641 layer_factory.hpp:77] Creating layer pool1
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I0419 14:01:33.931690 5641 net.cpp:84] Creating Layer pool1
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I0419 14:01:33.931694 5641 net.cpp:406] pool1 <- norm1
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|
I0419 14:01:33.931699 5641 net.cpp:380] pool1 -> pool1
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I0419 14:01:33.931731 5641 net.cpp:122] Setting up pool1
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|
I0419 14:01:33.931737 5641 net.cpp:129] Top shape: 128 96 27 27 (8957952)
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I0419 14:01:33.931740 5641 net.cpp:137] Memory required for data: 561035264
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|
I0419 14:01:33.931743 5641 layer_factory.hpp:77] Creating layer conv2
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I0419 14:01:33.931753 5641 net.cpp:84] Creating Layer conv2
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|
I0419 14:01:33.931756 5641 net.cpp:406] conv2 <- pool1
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|
I0419 14:01:33.931761 5641 net.cpp:380] conv2 -> conv2
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|
I0419 14:01:33.939317 5641 net.cpp:122] Setting up conv2
|
|
I0419 14:01:33.939333 5641 net.cpp:129] Top shape: 128 256 27 27 (23887872)
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|
I0419 14:01:33.939337 5641 net.cpp:137] Memory required for data: 656586752
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|
I0419 14:01:33.939347 5641 layer_factory.hpp:77] Creating layer relu2
|
|
I0419 14:01:33.939354 5641 net.cpp:84] Creating Layer relu2
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|
I0419 14:01:33.939358 5641 net.cpp:406] relu2 <- conv2
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|
I0419 14:01:33.939363 5641 net.cpp:367] relu2 -> conv2 (in-place)
|
|
I0419 14:01:33.939851 5641 net.cpp:122] Setting up relu2
|
|
I0419 14:01:33.939860 5641 net.cpp:129] Top shape: 128 256 27 27 (23887872)
|
|
I0419 14:01:33.939863 5641 net.cpp:137] Memory required for data: 752138240
|
|
I0419 14:01:33.939867 5641 layer_factory.hpp:77] Creating layer norm2
|
|
I0419 14:01:33.939873 5641 net.cpp:84] Creating Layer norm2
|
|
I0419 14:01:33.939877 5641 net.cpp:406] norm2 <- conv2
|
|
I0419 14:01:33.939882 5641 net.cpp:380] norm2 -> norm2
|
|
I0419 14:01:33.940204 5641 net.cpp:122] Setting up norm2
|
|
I0419 14:01:33.940213 5641 net.cpp:129] Top shape: 128 256 27 27 (23887872)
|
|
I0419 14:01:33.940217 5641 net.cpp:137] Memory required for data: 847689728
|
|
I0419 14:01:33.940219 5641 layer_factory.hpp:77] Creating layer pool2
|
|
I0419 14:01:33.940227 5641 net.cpp:84] Creating Layer pool2
|
|
I0419 14:01:33.940230 5641 net.cpp:406] pool2 <- norm2
|
|
I0419 14:01:33.940235 5641 net.cpp:380] pool2 -> pool2
|
|
I0419 14:01:33.940259 5641 net.cpp:122] Setting up pool2
|
|
I0419 14:01:33.940264 5641 net.cpp:129] Top shape: 128 256 13 13 (5537792)
|
|
I0419 14:01:33.940268 5641 net.cpp:137] Memory required for data: 869840896
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|
I0419 14:01:33.940270 5641 layer_factory.hpp:77] Creating layer conv3
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|
I0419 14:01:33.940279 5641 net.cpp:84] Creating Layer conv3
|
|
I0419 14:01:33.940282 5641 net.cpp:406] conv3 <- pool2
|
|
I0419 14:01:33.940287 5641 net.cpp:380] conv3 -> conv3
|
|
I0419 14:01:33.950843 5641 net.cpp:122] Setting up conv3
|
|
I0419 14:01:33.950860 5641 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
|
I0419 14:01:33.950863 5641 net.cpp:137] Memory required for data: 903067648
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|
I0419 14:01:33.950873 5641 layer_factory.hpp:77] Creating layer relu3
|
|
I0419 14:01:33.950883 5641 net.cpp:84] Creating Layer relu3
|
|
I0419 14:01:33.950887 5641 net.cpp:406] relu3 <- conv3
|
|
I0419 14:01:33.950892 5641 net.cpp:367] relu3 -> conv3 (in-place)
|
|
I0419 14:01:33.951483 5641 net.cpp:122] Setting up relu3
|
|
I0419 14:01:33.951493 5641 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
|
I0419 14:01:33.951496 5641 net.cpp:137] Memory required for data: 936294400
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|
I0419 14:01:33.951499 5641 layer_factory.hpp:77] Creating layer conv4
|
|
I0419 14:01:33.951511 5641 net.cpp:84] Creating Layer conv4
|
|
I0419 14:01:33.951514 5641 net.cpp:406] conv4 <- conv3
|
|
I0419 14:01:33.951520 5641 net.cpp:380] conv4 -> conv4
|
|
I0419 14:01:33.962769 5641 net.cpp:122] Setting up conv4
|
|
I0419 14:01:33.962785 5641 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
|
I0419 14:01:33.962788 5641 net.cpp:137] Memory required for data: 969521152
|
|
I0419 14:01:33.962796 5641 layer_factory.hpp:77] Creating layer relu4
|
|
I0419 14:01:33.962805 5641 net.cpp:84] Creating Layer relu4
|
|
I0419 14:01:33.962827 5641 net.cpp:406] relu4 <- conv4
|
|
I0419 14:01:33.962833 5641 net.cpp:367] relu4 -> conv4 (in-place)
|
|
I0419 14:01:33.963376 5641 net.cpp:122] Setting up relu4
|
|
I0419 14:01:33.963385 5641 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
|
I0419 14:01:33.963388 5641 net.cpp:137] Memory required for data: 1002747904
|
|
I0419 14:01:33.963392 5641 layer_factory.hpp:77] Creating layer conv5
|
|
I0419 14:01:33.963402 5641 net.cpp:84] Creating Layer conv5
|
|
I0419 14:01:33.963407 5641 net.cpp:406] conv5 <- conv4
|
|
I0419 14:01:33.963414 5641 net.cpp:380] conv5 -> conv5
|
|
I0419 14:01:33.972692 5641 net.cpp:122] Setting up conv5
|
|
I0419 14:01:33.972708 5641 net.cpp:129] Top shape: 128 256 13 13 (5537792)
|
|
I0419 14:01:33.972712 5641 net.cpp:137] Memory required for data: 1024899072
|
|
I0419 14:01:33.972723 5641 layer_factory.hpp:77] Creating layer relu5
|
|
I0419 14:01:33.972733 5641 net.cpp:84] Creating Layer relu5
|
|
I0419 14:01:33.972735 5641 net.cpp:406] relu5 <- conv5
|
|
I0419 14:01:33.972741 5641 net.cpp:367] relu5 -> conv5 (in-place)
|
|
I0419 14:01:33.973284 5641 net.cpp:122] Setting up relu5
|
|
I0419 14:01:33.973294 5641 net.cpp:129] Top shape: 128 256 13 13 (5537792)
|
|
I0419 14:01:33.973296 5641 net.cpp:137] Memory required for data: 1047050240
|
|
I0419 14:01:33.973299 5641 layer_factory.hpp:77] Creating layer pool5
|
|
I0419 14:01:33.973307 5641 net.cpp:84] Creating Layer pool5
|
|
I0419 14:01:33.973311 5641 net.cpp:406] pool5 <- conv5
|
|
I0419 14:01:33.973316 5641 net.cpp:380] pool5 -> pool5
|
|
I0419 14:01:33.973352 5641 net.cpp:122] Setting up pool5
|
|
I0419 14:01:33.973358 5641 net.cpp:129] Top shape: 128 256 6 6 (1179648)
|
|
I0419 14:01:33.973361 5641 net.cpp:137] Memory required for data: 1051768832
|
|
I0419 14:01:33.973364 5641 layer_factory.hpp:77] Creating layer fc6
|
|
I0419 14:01:33.973373 5641 net.cpp:84] Creating Layer fc6
|
|
I0419 14:01:33.973376 5641 net.cpp:406] fc6 <- pool5
|
|
I0419 14:01:33.973385 5641 net.cpp:380] fc6 -> fc6
|
|
I0419 14:01:34.330973 5641 net.cpp:122] Setting up fc6
|
|
I0419 14:01:34.330996 5641 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0419 14:01:34.330998 5641 net.cpp:137] Memory required for data: 1053865984
|
|
I0419 14:01:34.331008 5641 layer_factory.hpp:77] Creating layer relu6
|
|
I0419 14:01:34.331017 5641 net.cpp:84] Creating Layer relu6
|
|
I0419 14:01:34.331022 5641 net.cpp:406] relu6 <- fc6
|
|
I0419 14:01:34.331027 5641 net.cpp:367] relu6 -> fc6 (in-place)
|
|
I0419 14:01:34.331807 5641 net.cpp:122] Setting up relu6
|
|
I0419 14:01:34.331816 5641 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0419 14:01:34.331820 5641 net.cpp:137] Memory required for data: 1055963136
|
|
I0419 14:01:34.331822 5641 layer_factory.hpp:77] Creating layer drop6
|
|
I0419 14:01:34.331828 5641 net.cpp:84] Creating Layer drop6
|
|
I0419 14:01:34.331832 5641 net.cpp:406] drop6 <- fc6
|
|
I0419 14:01:34.331838 5641 net.cpp:367] drop6 -> fc6 (in-place)
|
|
I0419 14:01:34.331864 5641 net.cpp:122] Setting up drop6
|
|
I0419 14:01:34.331871 5641 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0419 14:01:34.331872 5641 net.cpp:137] Memory required for data: 1058060288
|
|
I0419 14:01:34.331876 5641 layer_factory.hpp:77] Creating layer fc7
|
|
I0419 14:01:34.331884 5641 net.cpp:84] Creating Layer fc7
|
|
I0419 14:01:34.331887 5641 net.cpp:406] fc7 <- fc6
|
|
I0419 14:01:34.331893 5641 net.cpp:380] fc7 -> fc7
|
|
I0419 14:01:34.491063 5641 net.cpp:122] Setting up fc7
|
|
I0419 14:01:34.491086 5641 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0419 14:01:34.491089 5641 net.cpp:137] Memory required for data: 1060157440
|
|
I0419 14:01:34.491098 5641 layer_factory.hpp:77] Creating layer relu7
|
|
I0419 14:01:34.491113 5641 net.cpp:84] Creating Layer relu7
|
|
I0419 14:01:34.491118 5641 net.cpp:406] relu7 <- fc7
|
|
I0419 14:01:34.491123 5641 net.cpp:367] relu7 -> fc7 (in-place)
|
|
I0419 14:01:34.491621 5641 net.cpp:122] Setting up relu7
|
|
I0419 14:01:34.491629 5641 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0419 14:01:34.491632 5641 net.cpp:137] Memory required for data: 1062254592
|
|
I0419 14:01:34.491636 5641 layer_factory.hpp:77] Creating layer drop7
|
|
I0419 14:01:34.491643 5641 net.cpp:84] Creating Layer drop7
|
|
I0419 14:01:34.491668 5641 net.cpp:406] drop7 <- fc7
|
|
I0419 14:01:34.491673 5641 net.cpp:367] drop7 -> fc7 (in-place)
|
|
I0419 14:01:34.491698 5641 net.cpp:122] Setting up drop7
|
|
I0419 14:01:34.491704 5641 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0419 14:01:34.491708 5641 net.cpp:137] Memory required for data: 1064351744
|
|
I0419 14:01:34.491710 5641 layer_factory.hpp:77] Creating layer fc8
|
|
I0419 14:01:34.491716 5641 net.cpp:84] Creating Layer fc8
|
|
I0419 14:01:34.491719 5641 net.cpp:406] fc8 <- fc7
|
|
I0419 14:01:34.491725 5641 net.cpp:380] fc8 -> fc8
|
|
I0419 14:01:34.499543 5641 net.cpp:122] Setting up fc8
|
|
I0419 14:01:34.499552 5641 net.cpp:129] Top shape: 128 196 (25088)
|
|
I0419 14:01:34.499555 5641 net.cpp:137] Memory required for data: 1064452096
|
|
I0419 14:01:34.499562 5641 layer_factory.hpp:77] Creating layer loss
|
|
I0419 14:01:34.499567 5641 net.cpp:84] Creating Layer loss
|
|
I0419 14:01:34.499570 5641 net.cpp:406] loss <- fc8
|
|
I0419 14:01:34.499574 5641 net.cpp:406] loss <- label
|
|
I0419 14:01:34.499583 5641 net.cpp:380] loss -> loss
|
|
I0419 14:01:34.499593 5641 layer_factory.hpp:77] Creating layer loss
|
|
I0419 14:01:34.500245 5641 net.cpp:122] Setting up loss
|
|
I0419 14:01:34.500254 5641 net.cpp:129] Top shape: (1)
|
|
I0419 14:01:34.500257 5641 net.cpp:132] with loss weight 1
|
|
I0419 14:01:34.500274 5641 net.cpp:137] Memory required for data: 1064452100
|
|
I0419 14:01:34.500278 5641 net.cpp:198] loss needs backward computation.
|
|
I0419 14:01:34.500284 5641 net.cpp:198] fc8 needs backward computation.
|
|
I0419 14:01:34.500288 5641 net.cpp:198] drop7 needs backward computation.
|
|
I0419 14:01:34.500290 5641 net.cpp:198] relu7 needs backward computation.
|
|
I0419 14:01:34.500293 5641 net.cpp:198] fc7 needs backward computation.
|
|
I0419 14:01:34.500295 5641 net.cpp:198] drop6 needs backward computation.
|
|
I0419 14:01:34.500298 5641 net.cpp:198] relu6 needs backward computation.
|
|
I0419 14:01:34.500301 5641 net.cpp:198] fc6 needs backward computation.
|
|
I0419 14:01:34.500304 5641 net.cpp:198] pool5 needs backward computation.
|
|
I0419 14:01:34.500308 5641 net.cpp:198] relu5 needs backward computation.
|
|
I0419 14:01:34.500310 5641 net.cpp:198] conv5 needs backward computation.
|
|
I0419 14:01:34.500313 5641 net.cpp:198] relu4 needs backward computation.
|
|
I0419 14:01:34.500316 5641 net.cpp:198] conv4 needs backward computation.
|
|
I0419 14:01:34.500319 5641 net.cpp:198] relu3 needs backward computation.
|
|
I0419 14:01:34.500321 5641 net.cpp:198] conv3 needs backward computation.
|
|
I0419 14:01:34.500324 5641 net.cpp:198] pool2 needs backward computation.
|
|
I0419 14:01:34.500329 5641 net.cpp:198] norm2 needs backward computation.
|
|
I0419 14:01:34.500330 5641 net.cpp:198] relu2 needs backward computation.
|
|
I0419 14:01:34.500334 5641 net.cpp:198] conv2 needs backward computation.
|
|
I0419 14:01:34.500336 5641 net.cpp:198] pool1 needs backward computation.
|
|
I0419 14:01:34.500339 5641 net.cpp:198] norm1 needs backward computation.
|
|
I0419 14:01:34.500342 5641 net.cpp:198] relu1 needs backward computation.
|
|
I0419 14:01:34.500345 5641 net.cpp:198] conv1 needs backward computation.
|
|
I0419 14:01:34.500349 5641 net.cpp:200] train-data does not need backward computation.
|
|
I0419 14:01:34.500351 5641 net.cpp:242] This network produces output loss
|
|
I0419 14:01:34.500365 5641 net.cpp:255] Network initialization done.
|
|
I0419 14:01:34.564695 5641 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
|
|
I0419 14:01:34.564777 5641 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
|
|
I0419 14:01:34.565129 5641 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-AMB-2/digits/jobs/20210419-135836-fd84/mean.binaryproto"
|
|
}
|
|
data_param {
|
|
source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/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"
|
|
}
|
|
I0419 14:01:34.565382 5641 layer_factory.hpp:77] Creating layer val-data
|
|
I0419 14:01:35.063510 5641 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/val_db
|
|
I0419 14:01:35.109279 5641 net.cpp:84] Creating Layer val-data
|
|
I0419 14:01:35.109324 5641 net.cpp:380] val-data -> data
|
|
I0419 14:01:35.109351 5641 net.cpp:380] val-data -> label
|
|
I0419 14:01:35.109370 5641 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-135836-fd84/mean.binaryproto
|
|
I0419 14:01:35.133947 5641 data_layer.cpp:45] output data size: 32,3,227,227
|
|
I0419 14:01:35.177662 5641 net.cpp:122] Setting up val-data
|
|
I0419 14:01:35.177685 5641 net.cpp:129] Top shape: 32 3 227 227 (4946784)
|
|
I0419 14:01:35.177690 5641 net.cpp:129] Top shape: 32 (32)
|
|
I0419 14:01:35.177692 5641 net.cpp:137] Memory required for data: 19787264
|
|
I0419 14:01:35.177698 5641 layer_factory.hpp:77] Creating layer label_val-data_1_split
|
|
I0419 14:01:35.177709 5641 net.cpp:84] Creating Layer label_val-data_1_split
|
|
I0419 14:01:35.177714 5641 net.cpp:406] label_val-data_1_split <- label
|
|
I0419 14:01:35.177721 5641 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
|
|
I0419 14:01:35.177731 5641 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
|
|
I0419 14:01:35.177772 5641 net.cpp:122] Setting up label_val-data_1_split
|
|
I0419 14:01:35.177778 5641 net.cpp:129] Top shape: 32 (32)
|
|
I0419 14:01:35.177781 5641 net.cpp:129] Top shape: 32 (32)
|
|
I0419 14:01:35.177783 5641 net.cpp:137] Memory required for data: 19787520
|
|
I0419 14:01:35.177786 5641 layer_factory.hpp:77] Creating layer conv1
|
|
I0419 14:01:35.177798 5641 net.cpp:84] Creating Layer conv1
|
|
I0419 14:01:35.177801 5641 net.cpp:406] conv1 <- data
|
|
I0419 14:01:35.177806 5641 net.cpp:380] conv1 -> conv1
|
|
I0419 14:01:35.180999 5641 net.cpp:122] Setting up conv1
|
|
I0419 14:01:35.181010 5641 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
|
I0419 14:01:35.181013 5641 net.cpp:137] Memory required for data: 56958720
|
|
I0419 14:01:35.181022 5641 layer_factory.hpp:77] Creating layer relu1
|
|
I0419 14:01:35.181030 5641 net.cpp:84] Creating Layer relu1
|
|
I0419 14:01:35.181032 5641 net.cpp:406] relu1 <- conv1
|
|
I0419 14:01:35.181037 5641 net.cpp:367] relu1 -> conv1 (in-place)
|
|
I0419 14:01:35.181360 5641 net.cpp:122] Setting up relu1
|
|
I0419 14:01:35.181370 5641 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
|
I0419 14:01:35.181372 5641 net.cpp:137] Memory required for data: 94129920
|
|
I0419 14:01:35.181375 5641 layer_factory.hpp:77] Creating layer norm1
|
|
I0419 14:01:35.181385 5641 net.cpp:84] Creating Layer norm1
|
|
I0419 14:01:35.181388 5641 net.cpp:406] norm1 <- conv1
|
|
I0419 14:01:35.181393 5641 net.cpp:380] norm1 -> norm1
|
|
I0419 14:01:35.181910 5641 net.cpp:122] Setting up norm1
|
|
I0419 14:01:35.181919 5641 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
|
I0419 14:01:35.181922 5641 net.cpp:137] Memory required for data: 131301120
|
|
I0419 14:01:35.181926 5641 layer_factory.hpp:77] Creating layer pool1
|
|
I0419 14:01:35.181932 5641 net.cpp:84] Creating Layer pool1
|
|
I0419 14:01:35.181936 5641 net.cpp:406] pool1 <- norm1
|
|
I0419 14:01:35.181941 5641 net.cpp:380] pool1 -> pool1
|
|
I0419 14:01:35.181965 5641 net.cpp:122] Setting up pool1
|
|
I0419 14:01:35.181972 5641 net.cpp:129] Top shape: 32 96 27 27 (2239488)
|
|
I0419 14:01:35.181974 5641 net.cpp:137] Memory required for data: 140259072
|
|
I0419 14:01:35.181977 5641 layer_factory.hpp:77] Creating layer conv2
|
|
I0419 14:01:35.181984 5641 net.cpp:84] Creating Layer conv2
|
|
I0419 14:01:35.181988 5641 net.cpp:406] conv2 <- pool1
|
|
I0419 14:01:35.182013 5641 net.cpp:380] conv2 -> conv2
|
|
I0419 14:01:35.191722 5641 net.cpp:122] Setting up conv2
|
|
I0419 14:01:35.191736 5641 net.cpp:129] Top shape: 32 256 27 27 (5971968)
|
|
I0419 14:01:35.191740 5641 net.cpp:137] Memory required for data: 164146944
|
|
I0419 14:01:35.191751 5641 layer_factory.hpp:77] Creating layer relu2
|
|
I0419 14:01:35.191757 5641 net.cpp:84] Creating Layer relu2
|
|
I0419 14:01:35.191761 5641 net.cpp:406] relu2 <- conv2
|
|
I0419 14:01:35.191768 5641 net.cpp:367] relu2 -> conv2 (in-place)
|
|
I0419 14:01:35.192343 5641 net.cpp:122] Setting up relu2
|
|
I0419 14:01:35.192354 5641 net.cpp:129] Top shape: 32 256 27 27 (5971968)
|
|
I0419 14:01:35.192358 5641 net.cpp:137] Memory required for data: 188034816
|
|
I0419 14:01:35.192361 5641 layer_factory.hpp:77] Creating layer norm2
|
|
I0419 14:01:35.192373 5641 net.cpp:84] Creating Layer norm2
|
|
I0419 14:01:35.192375 5641 net.cpp:406] norm2 <- conv2
|
|
I0419 14:01:35.192381 5641 net.cpp:380] norm2 -> norm2
|
|
I0419 14:01:35.193140 5641 net.cpp:122] Setting up norm2
|
|
I0419 14:01:35.193150 5641 net.cpp:129] Top shape: 32 256 27 27 (5971968)
|
|
I0419 14:01:35.193152 5641 net.cpp:137] Memory required for data: 211922688
|
|
I0419 14:01:35.193156 5641 layer_factory.hpp:77] Creating layer pool2
|
|
I0419 14:01:35.193161 5641 net.cpp:84] Creating Layer pool2
|
|
I0419 14:01:35.193164 5641 net.cpp:406] pool2 <- norm2
|
|
I0419 14:01:35.193171 5641 net.cpp:380] pool2 -> pool2
|
|
I0419 14:01:35.193202 5641 net.cpp:122] Setting up pool2
|
|
I0419 14:01:35.193207 5641 net.cpp:129] Top shape: 32 256 13 13 (1384448)
|
|
I0419 14:01:35.193209 5641 net.cpp:137] Memory required for data: 217460480
|
|
I0419 14:01:35.193212 5641 layer_factory.hpp:77] Creating layer conv3
|
|
I0419 14:01:35.193222 5641 net.cpp:84] Creating Layer conv3
|
|
I0419 14:01:35.193225 5641 net.cpp:406] conv3 <- pool2
|
|
I0419 14:01:35.193233 5641 net.cpp:380] conv3 -> conv3
|
|
I0419 14:01:35.205010 5641 net.cpp:122] Setting up conv3
|
|
I0419 14:01:35.205029 5641 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
|
I0419 14:01:35.205031 5641 net.cpp:137] Memory required for data: 225767168
|
|
I0419 14:01:35.205042 5641 layer_factory.hpp:77] Creating layer relu3
|
|
I0419 14:01:35.205050 5641 net.cpp:84] Creating Layer relu3
|
|
I0419 14:01:35.205054 5641 net.cpp:406] relu3 <- conv3
|
|
I0419 14:01:35.205061 5641 net.cpp:367] relu3 -> conv3 (in-place)
|
|
I0419 14:01:35.205639 5641 net.cpp:122] Setting up relu3
|
|
I0419 14:01:35.205648 5641 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
|
I0419 14:01:35.205651 5641 net.cpp:137] Memory required for data: 234073856
|
|
I0419 14:01:35.205654 5641 layer_factory.hpp:77] Creating layer conv4
|
|
I0419 14:01:35.205667 5641 net.cpp:84] Creating Layer conv4
|
|
I0419 14:01:35.205670 5641 net.cpp:406] conv4 <- conv3
|
|
I0419 14:01:35.205677 5641 net.cpp:380] conv4 -> conv4
|
|
I0419 14:01:35.215924 5641 net.cpp:122] Setting up conv4
|
|
I0419 14:01:35.215940 5641 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
|
I0419 14:01:35.215945 5641 net.cpp:137] Memory required for data: 242380544
|
|
I0419 14:01:35.215951 5641 layer_factory.hpp:77] Creating layer relu4
|
|
I0419 14:01:35.215960 5641 net.cpp:84] Creating Layer relu4
|
|
I0419 14:01:35.215963 5641 net.cpp:406] relu4 <- conv4
|
|
I0419 14:01:35.215968 5641 net.cpp:367] relu4 -> conv4 (in-place)
|
|
I0419 14:01:35.216344 5641 net.cpp:122] Setting up relu4
|
|
I0419 14:01:35.216357 5641 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
|
I0419 14:01:35.216361 5641 net.cpp:137] Memory required for data: 250687232
|
|
I0419 14:01:35.216363 5641 layer_factory.hpp:77] Creating layer conv5
|
|
I0419 14:01:35.216374 5641 net.cpp:84] Creating Layer conv5
|
|
I0419 14:01:35.216378 5641 net.cpp:406] conv5 <- conv4
|
|
I0419 14:01:35.216384 5641 net.cpp:380] conv5 -> conv5
|
|
I0419 14:01:35.226244 5641 net.cpp:122] Setting up conv5
|
|
I0419 14:01:35.226260 5641 net.cpp:129] Top shape: 32 256 13 13 (1384448)
|
|
I0419 14:01:35.226264 5641 net.cpp:137] Memory required for data: 256225024
|
|
I0419 14:01:35.226277 5641 layer_factory.hpp:77] Creating layer relu5
|
|
I0419 14:01:35.226285 5641 net.cpp:84] Creating Layer relu5
|
|
I0419 14:01:35.226289 5641 net.cpp:406] relu5 <- conv5
|
|
I0419 14:01:35.226315 5641 net.cpp:367] relu5 -> conv5 (in-place)
|
|
I0419 14:01:35.226878 5641 net.cpp:122] Setting up relu5
|
|
I0419 14:01:35.226888 5641 net.cpp:129] Top shape: 32 256 13 13 (1384448)
|
|
I0419 14:01:35.226891 5641 net.cpp:137] Memory required for data: 261762816
|
|
I0419 14:01:35.226894 5641 layer_factory.hpp:77] Creating layer pool5
|
|
I0419 14:01:35.226907 5641 net.cpp:84] Creating Layer pool5
|
|
I0419 14:01:35.226910 5641 net.cpp:406] pool5 <- conv5
|
|
I0419 14:01:35.226915 5641 net.cpp:380] pool5 -> pool5
|
|
I0419 14:01:35.226953 5641 net.cpp:122] Setting up pool5
|
|
I0419 14:01:35.226959 5641 net.cpp:129] Top shape: 32 256 6 6 (294912)
|
|
I0419 14:01:35.226963 5641 net.cpp:137] Memory required for data: 262942464
|
|
I0419 14:01:35.226965 5641 layer_factory.hpp:77] Creating layer fc6
|
|
I0419 14:01:35.226972 5641 net.cpp:84] Creating Layer fc6
|
|
I0419 14:01:35.226975 5641 net.cpp:406] fc6 <- pool5
|
|
I0419 14:01:35.226981 5641 net.cpp:380] fc6 -> fc6
|
|
I0419 14:01:35.585745 5641 net.cpp:122] Setting up fc6
|
|
I0419 14:01:35.585767 5641 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0419 14:01:35.585769 5641 net.cpp:137] Memory required for data: 263466752
|
|
I0419 14:01:35.585779 5641 layer_factory.hpp:77] Creating layer relu6
|
|
I0419 14:01:35.585788 5641 net.cpp:84] Creating Layer relu6
|
|
I0419 14:01:35.585791 5641 net.cpp:406] relu6 <- fc6
|
|
I0419 14:01:35.585799 5641 net.cpp:367] relu6 -> fc6 (in-place)
|
|
I0419 14:01:35.586575 5641 net.cpp:122] Setting up relu6
|
|
I0419 14:01:35.586586 5641 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0419 14:01:35.586589 5641 net.cpp:137] Memory required for data: 263991040
|
|
I0419 14:01:35.586592 5641 layer_factory.hpp:77] Creating layer drop6
|
|
I0419 14:01:35.586599 5641 net.cpp:84] Creating Layer drop6
|
|
I0419 14:01:35.586602 5641 net.cpp:406] drop6 <- fc6
|
|
I0419 14:01:35.586607 5641 net.cpp:367] drop6 -> fc6 (in-place)
|
|
I0419 14:01:35.586632 5641 net.cpp:122] Setting up drop6
|
|
I0419 14:01:35.586637 5641 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0419 14:01:35.586639 5641 net.cpp:137] Memory required for data: 264515328
|
|
I0419 14:01:35.586642 5641 layer_factory.hpp:77] Creating layer fc7
|
|
I0419 14:01:35.586650 5641 net.cpp:84] Creating Layer fc7
|
|
I0419 14:01:35.586653 5641 net.cpp:406] fc7 <- fc6
|
|
I0419 14:01:35.586658 5641 net.cpp:380] fc7 -> fc7
|
|
I0419 14:01:35.746621 5641 net.cpp:122] Setting up fc7
|
|
I0419 14:01:35.746640 5641 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0419 14:01:35.746644 5641 net.cpp:137] Memory required for data: 265039616
|
|
I0419 14:01:35.746654 5641 layer_factory.hpp:77] Creating layer relu7
|
|
I0419 14:01:35.746661 5641 net.cpp:84] Creating Layer relu7
|
|
I0419 14:01:35.746665 5641 net.cpp:406] relu7 <- fc7
|
|
I0419 14:01:35.746673 5641 net.cpp:367] relu7 -> fc7 (in-place)
|
|
I0419 14:01:35.747174 5641 net.cpp:122] Setting up relu7
|
|
I0419 14:01:35.747184 5641 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0419 14:01:35.747186 5641 net.cpp:137] Memory required for data: 265563904
|
|
I0419 14:01:35.747189 5641 layer_factory.hpp:77] Creating layer drop7
|
|
I0419 14:01:35.747195 5641 net.cpp:84] Creating Layer drop7
|
|
I0419 14:01:35.747198 5641 net.cpp:406] drop7 <- fc7
|
|
I0419 14:01:35.747205 5641 net.cpp:367] drop7 -> fc7 (in-place)
|
|
I0419 14:01:35.747227 5641 net.cpp:122] Setting up drop7
|
|
I0419 14:01:35.747232 5641 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0419 14:01:35.747236 5641 net.cpp:137] Memory required for data: 266088192
|
|
I0419 14:01:35.747238 5641 layer_factory.hpp:77] Creating layer fc8
|
|
I0419 14:01:35.747246 5641 net.cpp:84] Creating Layer fc8
|
|
I0419 14:01:35.747249 5641 net.cpp:406] fc8 <- fc7
|
|
I0419 14:01:35.747256 5641 net.cpp:380] fc8 -> fc8
|
|
I0419 14:01:35.755070 5641 net.cpp:122] Setting up fc8
|
|
I0419 14:01:35.755080 5641 net.cpp:129] Top shape: 32 196 (6272)
|
|
I0419 14:01:35.755084 5641 net.cpp:137] Memory required for data: 266113280
|
|
I0419 14:01:35.755089 5641 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
|
|
I0419 14:01:35.755097 5641 net.cpp:84] Creating Layer fc8_fc8_0_split
|
|
I0419 14:01:35.755100 5641 net.cpp:406] fc8_fc8_0_split <- fc8
|
|
I0419 14:01:35.755125 5641 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
|
|
I0419 14:01:35.755131 5641 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
|
|
I0419 14:01:35.755160 5641 net.cpp:122] Setting up fc8_fc8_0_split
|
|
I0419 14:01:35.755167 5641 net.cpp:129] Top shape: 32 196 (6272)
|
|
I0419 14:01:35.755169 5641 net.cpp:129] Top shape: 32 196 (6272)
|
|
I0419 14:01:35.755172 5641 net.cpp:137] Memory required for data: 266163456
|
|
I0419 14:01:35.755174 5641 layer_factory.hpp:77] Creating layer accuracy
|
|
I0419 14:01:35.755182 5641 net.cpp:84] Creating Layer accuracy
|
|
I0419 14:01:35.755184 5641 net.cpp:406] accuracy <- fc8_fc8_0_split_0
|
|
I0419 14:01:35.755189 5641 net.cpp:406] accuracy <- label_val-data_1_split_0
|
|
I0419 14:01:35.755194 5641 net.cpp:380] accuracy -> accuracy
|
|
I0419 14:01:35.755201 5641 net.cpp:122] Setting up accuracy
|
|
I0419 14:01:35.755205 5641 net.cpp:129] Top shape: (1)
|
|
I0419 14:01:35.755208 5641 net.cpp:137] Memory required for data: 266163460
|
|
I0419 14:01:35.755210 5641 layer_factory.hpp:77] Creating layer loss
|
|
I0419 14:01:35.755215 5641 net.cpp:84] Creating Layer loss
|
|
I0419 14:01:35.755218 5641 net.cpp:406] loss <- fc8_fc8_0_split_1
|
|
I0419 14:01:35.755223 5641 net.cpp:406] loss <- label_val-data_1_split_1
|
|
I0419 14:01:35.755226 5641 net.cpp:380] loss -> loss
|
|
I0419 14:01:35.755234 5641 layer_factory.hpp:77] Creating layer loss
|
|
I0419 14:01:35.755898 5641 net.cpp:122] Setting up loss
|
|
I0419 14:01:35.755908 5641 net.cpp:129] Top shape: (1)
|
|
I0419 14:01:35.755910 5641 net.cpp:132] with loss weight 1
|
|
I0419 14:01:35.755920 5641 net.cpp:137] Memory required for data: 266163464
|
|
I0419 14:01:35.755923 5641 net.cpp:198] loss needs backward computation.
|
|
I0419 14:01:35.755928 5641 net.cpp:200] accuracy does not need backward computation.
|
|
I0419 14:01:35.755931 5641 net.cpp:198] fc8_fc8_0_split needs backward computation.
|
|
I0419 14:01:35.755934 5641 net.cpp:198] fc8 needs backward computation.
|
|
I0419 14:01:35.755937 5641 net.cpp:198] drop7 needs backward computation.
|
|
I0419 14:01:35.755940 5641 net.cpp:198] relu7 needs backward computation.
|
|
I0419 14:01:35.755942 5641 net.cpp:198] fc7 needs backward computation.
|
|
I0419 14:01:35.755945 5641 net.cpp:198] drop6 needs backward computation.
|
|
I0419 14:01:35.755949 5641 net.cpp:198] relu6 needs backward computation.
|
|
I0419 14:01:35.755950 5641 net.cpp:198] fc6 needs backward computation.
|
|
I0419 14:01:35.755954 5641 net.cpp:198] pool5 needs backward computation.
|
|
I0419 14:01:35.755959 5641 net.cpp:198] relu5 needs backward computation.
|
|
I0419 14:01:35.755960 5641 net.cpp:198] conv5 needs backward computation.
|
|
I0419 14:01:35.755964 5641 net.cpp:198] relu4 needs backward computation.
|
|
I0419 14:01:35.755966 5641 net.cpp:198] conv4 needs backward computation.
|
|
I0419 14:01:35.755970 5641 net.cpp:198] relu3 needs backward computation.
|
|
I0419 14:01:35.755972 5641 net.cpp:198] conv3 needs backward computation.
|
|
I0419 14:01:35.755975 5641 net.cpp:198] pool2 needs backward computation.
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I0419 14:01:35.755978 5641 net.cpp:198] norm2 needs backward computation.
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I0419 14:01:35.755981 5641 net.cpp:198] relu2 needs backward computation.
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I0419 14:01:35.755985 5641 net.cpp:198] conv2 needs backward computation.
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I0419 14:01:35.755986 5641 net.cpp:198] pool1 needs backward computation.
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I0419 14:01:35.755991 5641 net.cpp:198] norm1 needs backward computation.
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I0419 14:01:35.755995 5641 net.cpp:198] relu1 needs backward computation.
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I0419 14:01:35.755997 5641 net.cpp:198] conv1 needs backward computation.
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I0419 14:01:35.756000 5641 net.cpp:200] label_val-data_1_split does not need backward computation.
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I0419 14:01:35.756004 5641 net.cpp:200] val-data does not need backward computation.
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I0419 14:01:35.756006 5641 net.cpp:242] This network produces output accuracy
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I0419 14:01:35.756009 5641 net.cpp:242] This network produces output loss
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I0419 14:01:35.756026 5641 net.cpp:255] Network initialization done.
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I0419 14:01:35.756094 5641 solver.cpp:56] Solver scaffolding done.
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I0419 14:01:35.756435 5641 caffe.cpp:248] Starting Optimization
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I0419 14:01:35.756443 5641 solver.cpp:272] Solving
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I0419 14:01:35.756456 5641 solver.cpp:273] Learning Rate Policy: exp
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I0419 14:01:35.758036 5641 solver.cpp:330] Iteration 0, Testing net (#0)
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I0419 14:01:35.758046 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:01:35.845803 5641 blocking_queue.cpp:49] Waiting for data
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I0419 14:01:40.160600 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:01:40.206907 5641 solver.cpp:397] Test net output #0: accuracy = 0.00367647
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I0419 14:01:40.206941 5641 solver.cpp:397] Test net output #1: loss = 5.28393 (* 1 = 5.28393 loss)
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I0419 14:01:40.304884 5641 solver.cpp:218] Iteration 0 (0 iter/s, 4.54841s/25 iters), loss = 5.28968
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I0419 14:01:40.306488 5641 solver.cpp:237] Train net output #0: loss = 5.28968 (* 1 = 5.28968 loss)
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I0419 14:01:40.306510 5641 sgd_solver.cpp:105] Iteration 0, lr = 0.01
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I0419 14:01:49.542680 5641 solver.cpp:218] Iteration 25 (2.70674 iter/s, 9.23621s/25 iters), loss = 5.2735
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I0419 14:01:49.542722 5641 solver.cpp:237] Train net output #0: loss = 5.2735 (* 1 = 5.2735 loss)
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I0419 14:01:49.542732 5641 sgd_solver.cpp:105] Iteration 25, lr = 0.0099174
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I0419 14:01:59.785262 5641 solver.cpp:218] Iteration 50 (2.4408 iter/s, 10.2426s/25 iters), loss = 5.28654
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I0419 14:01:59.785310 5641 solver.cpp:237] Train net output #0: loss = 5.28654 (* 1 = 5.28654 loss)
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I0419 14:01:59.785318 5641 sgd_solver.cpp:105] Iteration 50, lr = 0.00983549
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I0419 14:02:10.080400 5641 solver.cpp:218] Iteration 75 (2.42834 iter/s, 10.2951s/25 iters), loss = 5.29125
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I0419 14:02:10.080485 5641 solver.cpp:237] Train net output #0: loss = 5.29125 (* 1 = 5.29125 loss)
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I0419 14:02:10.080494 5641 sgd_solver.cpp:105] Iteration 75, lr = 0.00975425
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I0419 14:02:20.273496 5641 solver.cpp:218] Iteration 100 (2.45266 iter/s, 10.193s/25 iters), loss = 5.28158
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I0419 14:02:20.273551 5641 solver.cpp:237] Train net output #0: loss = 5.28158 (* 1 = 5.28158 loss)
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I0419 14:02:20.273566 5641 sgd_solver.cpp:105] Iteration 100, lr = 0.00967369
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I0419 14:02:30.645865 5641 solver.cpp:218] Iteration 125 (2.41026 iter/s, 10.3723s/25 iters), loss = 5.29012
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I0419 14:02:30.645917 5641 solver.cpp:237] Train net output #0: loss = 5.29012 (* 1 = 5.29012 loss)
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I0419 14:02:30.645928 5641 sgd_solver.cpp:105] Iteration 125, lr = 0.00959379
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I0419 14:02:41.416363 5641 solver.cpp:218] Iteration 150 (2.32116 iter/s, 10.7705s/25 iters), loss = 5.263
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I0419 14:02:41.416481 5641 solver.cpp:237] Train net output #0: loss = 5.263 (* 1 = 5.263 loss)
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I0419 14:02:41.416492 5641 sgd_solver.cpp:105] Iteration 150, lr = 0.00951455
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I0419 14:02:51.896564 5641 solver.cpp:218] Iteration 175 (2.38547 iter/s, 10.4801s/25 iters), loss = 5.13014
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I0419 14:02:51.896612 5641 solver.cpp:237] Train net output #0: loss = 5.13014 (* 1 = 5.13014 loss)
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I0419 14:02:51.896621 5641 sgd_solver.cpp:105] Iteration 175, lr = 0.00943596
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I0419 14:03:02.154103 5641 solver.cpp:218] Iteration 200 (2.43724 iter/s, 10.2575s/25 iters), loss = 5.19255
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I0419 14:03:02.154160 5641 solver.cpp:237] Train net output #0: loss = 5.19255 (* 1 = 5.19255 loss)
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I0419 14:03:02.154173 5641 sgd_solver.cpp:105] Iteration 200, lr = 0.00935802
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I0419 14:03:02.654548 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:03:02.913359 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_203.caffemodel
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I0419 14:03:08.035128 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_203.solverstate
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I0419 14:03:12.451395 5641 solver.cpp:330] Iteration 203, Testing net (#0)
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I0419 14:03:12.451508 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:03:17.159303 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:03:17.248688 5641 solver.cpp:397] Test net output #0: accuracy = 0.0110294
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I0419 14:03:17.248737 5641 solver.cpp:397] Test net output #1: loss = 5.18412 (* 1 = 5.18412 loss)
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I0419 14:03:25.623641 5641 solver.cpp:218] Iteration 225 (1.06521 iter/s, 23.4696s/25 iters), loss = 5.18133
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I0419 14:03:25.623684 5641 solver.cpp:237] Train net output #0: loss = 5.18133 (* 1 = 5.18133 loss)
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I0419 14:03:25.623692 5641 sgd_solver.cpp:105] Iteration 225, lr = 0.00928073
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I0419 14:03:36.096823 5641 solver.cpp:218] Iteration 250 (2.38705 iter/s, 10.4732s/25 iters), loss = 5.11332
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I0419 14:03:36.096863 5641 solver.cpp:237] Train net output #0: loss = 5.11332 (* 1 = 5.11332 loss)
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I0419 14:03:36.096871 5641 sgd_solver.cpp:105] Iteration 250, lr = 0.00920408
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I0419 14:03:46.378137 5641 solver.cpp:218] Iteration 275 (2.4316 iter/s, 10.2813s/25 iters), loss = 5.11181
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I0419 14:03:46.378346 5641 solver.cpp:237] Train net output #0: loss = 5.11181 (* 1 = 5.11181 loss)
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I0419 14:03:46.378371 5641 sgd_solver.cpp:105] Iteration 275, lr = 0.00912805
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I0419 14:03:56.683835 5641 solver.cpp:218] Iteration 300 (2.42588 iter/s, 10.3055s/25 iters), loss = 5.19241
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I0419 14:03:56.683872 5641 solver.cpp:237] Train net output #0: loss = 5.19241 (* 1 = 5.19241 loss)
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I0419 14:03:56.683881 5641 sgd_solver.cpp:105] Iteration 300, lr = 0.00905266
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I0419 14:04:06.915755 5641 solver.cpp:218] Iteration 325 (2.44334 iter/s, 10.2319s/25 iters), loss = 5.1512
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I0419 14:04:06.915797 5641 solver.cpp:237] Train net output #0: loss = 5.1512 (* 1 = 5.1512 loss)
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I0419 14:04:06.915807 5641 sgd_solver.cpp:105] Iteration 325, lr = 0.00897789
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I0419 14:04:17.201691 5641 solver.cpp:218] Iteration 350 (2.43051 iter/s, 10.2859s/25 iters), loss = 5.09956
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I0419 14:04:17.201812 5641 solver.cpp:237] Train net output #0: loss = 5.09956 (* 1 = 5.09956 loss)
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I0419 14:04:17.201822 5641 sgd_solver.cpp:105] Iteration 350, lr = 0.00890374
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I0419 14:04:27.564157 5641 solver.cpp:218] Iteration 375 (2.41257 iter/s, 10.3624s/25 iters), loss = 5.13256
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I0419 14:04:27.564198 5641 solver.cpp:237] Train net output #0: loss = 5.13256 (* 1 = 5.13256 loss)
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I0419 14:04:27.564206 5641 sgd_solver.cpp:105] Iteration 375, lr = 0.00883019
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I0419 14:04:37.836354 5641 solver.cpp:218] Iteration 400 (2.43376 iter/s, 10.2722s/25 iters), loss = 5.13526
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I0419 14:04:37.836390 5641 solver.cpp:237] Train net output #0: loss = 5.13526 (* 1 = 5.13526 loss)
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I0419 14:04:37.836398 5641 sgd_solver.cpp:105] Iteration 400, lr = 0.00875726
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I0419 14:04:39.252063 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:04:39.823882 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_406.caffemodel
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I0419 14:04:47.300324 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_406.solverstate
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I0419 14:04:50.778381 5641 solver.cpp:330] Iteration 406, Testing net (#0)
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I0419 14:04:50.778403 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:04:55.136124 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:04:55.266973 5641 solver.cpp:397] Test net output #0: accuracy = 0.0189951
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I0419 14:04:55.267009 5641 solver.cpp:397] Test net output #1: loss = 5.1041 (* 1 = 5.1041 loss)
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I0419 14:05:02.377279 5641 solver.cpp:218] Iteration 425 (1.0187 iter/s, 24.541s/25 iters), loss = 5.04969
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I0419 14:05:02.377322 5641 solver.cpp:237] Train net output #0: loss = 5.04969 (* 1 = 5.04969 loss)
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I0419 14:05:02.377331 5641 sgd_solver.cpp:105] Iteration 425, lr = 0.00868493
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I0419 14:05:12.636615 5641 solver.cpp:218] Iteration 450 (2.43681 iter/s, 10.2593s/25 iters), loss = 5.02885
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I0419 14:05:12.636662 5641 solver.cpp:237] Train net output #0: loss = 5.02885 (* 1 = 5.02885 loss)
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I0419 14:05:12.636672 5641 sgd_solver.cpp:105] Iteration 450, lr = 0.0086132
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I0419 14:05:22.901185 5641 solver.cpp:218] Iteration 475 (2.43557 iter/s, 10.2646s/25 iters), loss = 5.06222
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I0419 14:05:22.901326 5641 solver.cpp:237] Train net output #0: loss = 5.06222 (* 1 = 5.06222 loss)
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I0419 14:05:22.901336 5641 sgd_solver.cpp:105] Iteration 475, lr = 0.00854205
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I0419 14:05:33.246012 5641 solver.cpp:218] Iteration 500 (2.41669 iter/s, 10.3447s/25 iters), loss = 5.03832
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I0419 14:05:33.246059 5641 solver.cpp:237] Train net output #0: loss = 5.03832 (* 1 = 5.03832 loss)
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I0419 14:05:33.246068 5641 sgd_solver.cpp:105] Iteration 500, lr = 0.0084715
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I0419 14:05:43.723551 5641 solver.cpp:218] Iteration 525 (2.38606 iter/s, 10.4775s/25 iters), loss = 5.16302
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I0419 14:05:43.723592 5641 solver.cpp:237] Train net output #0: loss = 5.16302 (* 1 = 5.16302 loss)
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I0419 14:05:43.723603 5641 sgd_solver.cpp:105] Iteration 525, lr = 0.00840153
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I0419 14:05:54.045506 5641 solver.cpp:218] Iteration 550 (2.42203 iter/s, 10.3219s/25 iters), loss = 4.9856
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I0419 14:05:54.045672 5641 solver.cpp:237] Train net output #0: loss = 4.9856 (* 1 = 4.9856 loss)
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I0419 14:05:54.045683 5641 sgd_solver.cpp:105] Iteration 550, lr = 0.00833214
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I0419 14:06:04.334699 5641 solver.cpp:218] Iteration 575 (2.42976 iter/s, 10.2891s/25 iters), loss = 4.96575
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I0419 14:06:04.334739 5641 solver.cpp:237] Train net output #0: loss = 4.96575 (* 1 = 4.96575 loss)
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I0419 14:06:04.334748 5641 sgd_solver.cpp:105] Iteration 575, lr = 0.00826332
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I0419 14:06:14.663206 5641 solver.cpp:218] Iteration 600 (2.42049 iter/s, 10.3285s/25 iters), loss = 5.09066
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I0419 14:06:14.663254 5641 solver.cpp:237] Train net output #0: loss = 5.09066 (* 1 = 5.09066 loss)
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I0419 14:06:14.663262 5641 sgd_solver.cpp:105] Iteration 600, lr = 0.00819506
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I0419 14:06:16.993516 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:06:17.878571 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_609.caffemodel
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I0419 14:06:21.833431 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_609.solverstate
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I0419 14:06:24.721303 5641 solver.cpp:330] Iteration 609, Testing net (#0)
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I0419 14:06:24.721374 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:06:29.460523 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:06:29.642154 5641 solver.cpp:397] Test net output #0: accuracy = 0.028799
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I0419 14:06:29.642201 5641 solver.cpp:397] Test net output #1: loss = 4.99972 (* 1 = 4.99972 loss)
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I0419 14:06:35.552500 5641 solver.cpp:218] Iteration 625 (1.19678 iter/s, 20.8893s/25 iters), loss = 4.95463
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I0419 14:06:35.552543 5641 solver.cpp:237] Train net output #0: loss = 4.95463 (* 1 = 4.95463 loss)
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I0419 14:06:35.552552 5641 sgd_solver.cpp:105] Iteration 625, lr = 0.00812738
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I0419 14:06:45.739406 5641 solver.cpp:218] Iteration 650 (2.45413 iter/s, 10.1869s/25 iters), loss = 4.90559
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I0419 14:06:45.739451 5641 solver.cpp:237] Train net output #0: loss = 4.90559 (* 1 = 4.90559 loss)
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I0419 14:06:45.739460 5641 sgd_solver.cpp:105] Iteration 650, lr = 0.00806025
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I0419 14:06:55.972401 5641 solver.cpp:218] Iteration 675 (2.44308 iter/s, 10.233s/25 iters), loss = 4.91618
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I0419 14:06:55.972512 5641 solver.cpp:237] Train net output #0: loss = 4.91618 (* 1 = 4.91618 loss)
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I0419 14:06:55.972522 5641 sgd_solver.cpp:105] Iteration 675, lr = 0.00799367
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I0419 14:07:06.244796 5641 solver.cpp:218] Iteration 700 (2.43373 iter/s, 10.2723s/25 iters), loss = 4.96497
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I0419 14:07:06.244838 5641 solver.cpp:237] Train net output #0: loss = 4.96497 (* 1 = 4.96497 loss)
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I0419 14:07:06.244848 5641 sgd_solver.cpp:105] Iteration 700, lr = 0.00792765
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I0419 14:07:16.550117 5641 solver.cpp:218] Iteration 725 (2.42593 iter/s, 10.3053s/25 iters), loss = 4.96663
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I0419 14:07:16.550155 5641 solver.cpp:237] Train net output #0: loss = 4.96663 (* 1 = 4.96663 loss)
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I0419 14:07:16.550163 5641 sgd_solver.cpp:105] Iteration 725, lr = 0.00786217
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I0419 14:07:26.893802 5641 solver.cpp:218] Iteration 750 (2.41694 iter/s, 10.3437s/25 iters), loss = 4.84795
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I0419 14:07:26.893926 5641 solver.cpp:237] Train net output #0: loss = 4.84795 (* 1 = 4.84795 loss)
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I0419 14:07:26.893937 5641 sgd_solver.cpp:105] Iteration 750, lr = 0.00779723
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I0419 14:07:37.163556 5641 solver.cpp:218] Iteration 775 (2.43436 iter/s, 10.2697s/25 iters), loss = 4.88064
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I0419 14:07:37.163597 5641 solver.cpp:237] Train net output #0: loss = 4.88064 (* 1 = 4.88064 loss)
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I0419 14:07:37.163606 5641 sgd_solver.cpp:105] Iteration 775, lr = 0.00773283
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I0419 14:07:47.454977 5641 solver.cpp:218] Iteration 800 (2.42921 iter/s, 10.2914s/25 iters), loss = 4.86067
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I0419 14:07:47.455005 5641 solver.cpp:237] Train net output #0: loss = 4.86067 (* 1 = 4.86067 loss)
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I0419 14:07:47.455013 5641 sgd_solver.cpp:105] Iteration 800, lr = 0.00766896
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I0419 14:07:50.837666 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:07:51.934983 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_812.caffemodel
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I0419 14:07:55.934711 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_812.solverstate
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I0419 14:07:59.243669 5641 solver.cpp:330] Iteration 812, Testing net (#0)
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I0419 14:07:59.243799 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:08:00.259982 5641 blocking_queue.cpp:49] Waiting for data
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I0419 14:08:03.811772 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:08:04.044229 5641 solver.cpp:397] Test net output #0: accuracy = 0.0330882
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I0419 14:08:04.044278 5641 solver.cpp:397] Test net output #1: loss = 4.85421 (* 1 = 4.85421 loss)
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I0419 14:08:08.684974 5641 solver.cpp:218] Iteration 825 (1.17758 iter/s, 21.23s/25 iters), loss = 4.76147
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I0419 14:08:08.685021 5641 solver.cpp:237] Train net output #0: loss = 4.76147 (* 1 = 4.76147 loss)
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I0419 14:08:08.685030 5641 sgd_solver.cpp:105] Iteration 825, lr = 0.00760562
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I0419 14:08:18.927225 5641 solver.cpp:218] Iteration 850 (2.44087 iter/s, 10.2422s/25 iters), loss = 4.74056
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I0419 14:08:18.927263 5641 solver.cpp:237] Train net output #0: loss = 4.74056 (* 1 = 4.74056 loss)
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I0419 14:08:18.927273 5641 sgd_solver.cpp:105] Iteration 850, lr = 0.0075428
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I0419 14:08:29.192045 5641 solver.cpp:218] Iteration 875 (2.43551 iter/s, 10.2648s/25 iters), loss = 4.76719
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I0419 14:08:29.192086 5641 solver.cpp:237] Train net output #0: loss = 4.76719 (* 1 = 4.76719 loss)
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I0419 14:08:29.192096 5641 sgd_solver.cpp:105] Iteration 875, lr = 0.0074805
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I0419 14:08:39.449246 5641 solver.cpp:218] Iteration 900 (2.43731 iter/s, 10.2572s/25 iters), loss = 4.81611
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I0419 14:08:39.449368 5641 solver.cpp:237] Train net output #0: loss = 4.81611 (* 1 = 4.81611 loss)
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I0419 14:08:39.449376 5641 sgd_solver.cpp:105] Iteration 900, lr = 0.00741871
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I0419 14:08:49.923041 5641 solver.cpp:218] Iteration 925 (2.38693 iter/s, 10.4737s/25 iters), loss = 4.69867
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I0419 14:08:49.923086 5641 solver.cpp:237] Train net output #0: loss = 4.69867 (* 1 = 4.69867 loss)
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I0419 14:08:49.923095 5641 sgd_solver.cpp:105] Iteration 925, lr = 0.00735744
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I0419 14:09:00.176379 5641 solver.cpp:218] Iteration 950 (2.43823 iter/s, 10.2533s/25 iters), loss = 4.76767
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I0419 14:09:00.176419 5641 solver.cpp:237] Train net output #0: loss = 4.76767 (* 1 = 4.76767 loss)
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I0419 14:09:00.176429 5641 sgd_solver.cpp:105] Iteration 950, lr = 0.00729667
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I0419 14:09:12.608666 5641 solver.cpp:218] Iteration 975 (2.01089 iter/s, 12.4323s/25 iters), loss = 4.9079
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I0419 14:09:12.608781 5641 solver.cpp:237] Train net output #0: loss = 4.9079 (* 1 = 4.9079 loss)
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I0419 14:09:12.608793 5641 sgd_solver.cpp:105] Iteration 975, lr = 0.0072364
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I0419 14:09:26.677714 5641 solver.cpp:218] Iteration 1000 (1.77696 iter/s, 14.069s/25 iters), loss = 4.74583
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I0419 14:09:26.677772 5641 solver.cpp:237] Train net output #0: loss = 4.74583 (* 1 = 4.74583 loss)
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I0419 14:09:26.677783 5641 sgd_solver.cpp:105] Iteration 1000, lr = 0.00717663
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I0419 14:09:31.987418 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:09:33.675572 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1015.caffemodel
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I0419 14:09:38.122453 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1015.solverstate
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I0419 14:09:45.204486 5641 solver.cpp:330] Iteration 1015, Testing net (#0)
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I0419 14:09:45.204627 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:09:50.543468 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:09:50.841886 5641 solver.cpp:397] Test net output #0: accuracy = 0.0520833
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I0419 14:09:50.841925 5641 solver.cpp:397] Test net output #1: loss = 4.62428 (* 1 = 4.62428 loss)
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I0419 14:09:54.890720 5641 solver.cpp:218] Iteration 1025 (0.886115 iter/s, 28.2131s/25 iters), loss = 4.58336
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I0419 14:09:54.890776 5641 solver.cpp:237] Train net output #0: loss = 4.58336 (* 1 = 4.58336 loss)
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I0419 14:09:54.890789 5641 sgd_solver.cpp:105] Iteration 1025, lr = 0.00711736
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I0419 14:10:06.457566 5641 solver.cpp:218] Iteration 1050 (2.16135 iter/s, 11.5668s/25 iters), loss = 4.6238
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I0419 14:10:06.457626 5641 solver.cpp:237] Train net output #0: loss = 4.6238 (* 1 = 4.6238 loss)
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I0419 14:10:06.457638 5641 sgd_solver.cpp:105] Iteration 1050, lr = 0.00705857
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I0419 14:10:18.729419 5641 solver.cpp:218] Iteration 1075 (2.03719 iter/s, 12.2718s/25 iters), loss = 4.35653
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I0419 14:10:18.729547 5641 solver.cpp:237] Train net output #0: loss = 4.35653 (* 1 = 4.35653 loss)
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I0419 14:10:18.729557 5641 sgd_solver.cpp:105] Iteration 1075, lr = 0.00700027
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I0419 14:10:30.697918 5641 solver.cpp:218] Iteration 1100 (2.08883 iter/s, 11.9684s/25 iters), loss = 4.39964
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I0419 14:10:30.697963 5641 solver.cpp:237] Train net output #0: loss = 4.39964 (* 1 = 4.39964 loss)
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I0419 14:10:30.697973 5641 sgd_solver.cpp:105] Iteration 1100, lr = 0.00694245
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I0419 14:10:42.547474 5641 solver.cpp:218] Iteration 1125 (2.10979 iter/s, 11.8495s/25 iters), loss = 4.47212
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I0419 14:10:42.547533 5641 solver.cpp:237] Train net output #0: loss = 4.47212 (* 1 = 4.47212 loss)
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I0419 14:10:42.547544 5641 sgd_solver.cpp:105] Iteration 1125, lr = 0.00688511
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I0419 14:10:54.980530 5641 solver.cpp:218] Iteration 1150 (2.01077 iter/s, 12.433s/25 iters), loss = 4.3589
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I0419 14:10:54.980636 5641 solver.cpp:237] Train net output #0: loss = 4.3589 (* 1 = 4.3589 loss)
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I0419 14:10:54.980646 5641 sgd_solver.cpp:105] Iteration 1150, lr = 0.00682824
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I0419 14:11:07.119390 5641 solver.cpp:218] Iteration 1175 (2.05952 iter/s, 12.1388s/25 iters), loss = 4.3002
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I0419 14:11:07.119447 5641 solver.cpp:237] Train net output #0: loss = 4.3002 (* 1 = 4.3002 loss)
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I0419 14:11:07.119459 5641 sgd_solver.cpp:105] Iteration 1175, lr = 0.00677184
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I0419 14:11:19.635769 5641 solver.cpp:218] Iteration 1200 (1.99739 iter/s, 12.5163s/25 iters), loss = 4.34189
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I0419 14:11:19.635897 5641 solver.cpp:237] Train net output #0: loss = 4.34189 (* 1 = 4.34189 loss)
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I0419 14:11:19.635912 5641 sgd_solver.cpp:105] Iteration 1200, lr = 0.00671591
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I0419 14:11:26.002862 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:11:27.963625 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1218.caffemodel
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I0419 14:11:31.431193 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1218.solverstate
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I0419 14:11:34.092985 5641 solver.cpp:330] Iteration 1218, Testing net (#0)
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I0419 14:11:34.093005 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:11:38.973973 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:11:39.361166 5641 solver.cpp:397] Test net output #0: accuracy = 0.0778186
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I0419 14:11:39.361202 5641 solver.cpp:397] Test net output #1: loss = 4.36642 (* 1 = 4.36642 loss)
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I0419 14:11:41.908663 5641 solver.cpp:218] Iteration 1225 (1.12244 iter/s, 22.2728s/25 iters), loss = 4.24339
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I0419 14:11:41.908717 5641 solver.cpp:237] Train net output #0: loss = 4.24339 (* 1 = 4.24339 loss)
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I0419 14:11:41.908726 5641 sgd_solver.cpp:105] Iteration 1225, lr = 0.00666044
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I0419 14:11:54.460340 5641 solver.cpp:218] Iteration 1250 (1.99177 iter/s, 12.5516s/25 iters), loss = 4.35648
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I0419 14:11:54.460391 5641 solver.cpp:237] Train net output #0: loss = 4.35648 (* 1 = 4.35648 loss)
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I0419 14:11:54.460399 5641 sgd_solver.cpp:105] Iteration 1250, lr = 0.00660543
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I0419 14:12:06.468503 5641 solver.cpp:218] Iteration 1275 (2.08192 iter/s, 12.0081s/25 iters), loss = 4.18069
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I0419 14:12:06.468631 5641 solver.cpp:237] Train net output #0: loss = 4.18069 (* 1 = 4.18069 loss)
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I0419 14:12:06.468641 5641 sgd_solver.cpp:105] Iteration 1275, lr = 0.00655087
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I0419 14:12:16.749181 5641 solver.cpp:218] Iteration 1300 (2.43177 iter/s, 10.2806s/25 iters), loss = 4.15964
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I0419 14:12:16.749218 5641 solver.cpp:237] Train net output #0: loss = 4.15964 (* 1 = 4.15964 loss)
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I0419 14:12:16.749228 5641 sgd_solver.cpp:105] Iteration 1300, lr = 0.00649676
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I0419 14:12:27.036585 5641 solver.cpp:218] Iteration 1325 (2.43016 iter/s, 10.2874s/25 iters), loss = 4.26866
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I0419 14:12:27.036625 5641 solver.cpp:237] Train net output #0: loss = 4.26866 (* 1 = 4.26866 loss)
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I0419 14:12:27.036633 5641 sgd_solver.cpp:105] Iteration 1325, lr = 0.0064431
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I0419 14:12:37.313050 5641 solver.cpp:218] Iteration 1350 (2.43275 iter/s, 10.2764s/25 iters), loss = 4.39093
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I0419 14:12:37.313184 5641 solver.cpp:237] Train net output #0: loss = 4.39093 (* 1 = 4.39093 loss)
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I0419 14:12:37.313194 5641 sgd_solver.cpp:105] Iteration 1350, lr = 0.00638988
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I0419 14:12:47.611085 5641 solver.cpp:218] Iteration 1375 (2.42767 iter/s, 10.2979s/25 iters), loss = 4.00225
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I0419 14:12:47.611137 5641 solver.cpp:237] Train net output #0: loss = 4.00225 (* 1 = 4.00225 loss)
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I0419 14:12:47.611148 5641 sgd_solver.cpp:105] Iteration 1375, lr = 0.00633711
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I0419 14:12:57.867422 5641 solver.cpp:218] Iteration 1400 (2.43752 iter/s, 10.2563s/25 iters), loss = 4.02475
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I0419 14:12:57.867462 5641 solver.cpp:237] Train net output #0: loss = 4.02475 (* 1 = 4.02475 loss)
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I0419 14:12:57.867471 5641 sgd_solver.cpp:105] Iteration 1400, lr = 0.00628476
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I0419 14:13:04.095816 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:13:06.034847 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1421.caffemodel
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I0419 14:13:09.815518 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1421.solverstate
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I0419 14:13:12.408000 5641 solver.cpp:330] Iteration 1421, Testing net (#0)
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I0419 14:13:12.408020 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:13:16.845903 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:13:17.207988 5641 solver.cpp:397] Test net output #0: accuracy = 0.113358
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I0419 14:13:17.208029 5641 solver.cpp:397] Test net output #1: loss = 4.0395 (* 1 = 4.0395 loss)
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I0419 14:13:18.186266 5641 solver.cpp:218] Iteration 1425 (1.23038 iter/s, 20.3189s/25 iters), loss = 3.71325
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I0419 14:13:18.186309 5641 solver.cpp:237] Train net output #0: loss = 3.71325 (* 1 = 3.71325 loss)
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I0419 14:13:18.186318 5641 sgd_solver.cpp:105] Iteration 1425, lr = 0.00623285
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I0419 14:13:28.429260 5641 solver.cpp:218] Iteration 1450 (2.4407 iter/s, 10.243s/25 iters), loss = 3.93161
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I0419 14:13:28.429302 5641 solver.cpp:237] Train net output #0: loss = 3.93161 (* 1 = 3.93161 loss)
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I0419 14:13:28.429311 5641 sgd_solver.cpp:105] Iteration 1450, lr = 0.00618137
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I0419 14:13:38.687557 5641 solver.cpp:218] Iteration 1475 (2.43706 iter/s, 10.2583s/25 iters), loss = 3.59354
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I0419 14:13:38.687608 5641 solver.cpp:237] Train net output #0: loss = 3.59354 (* 1 = 3.59354 loss)
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I0419 14:13:38.687619 5641 sgd_solver.cpp:105] Iteration 1475, lr = 0.00613032
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I0419 14:13:48.909845 5641 solver.cpp:218] Iteration 1500 (2.44564 iter/s, 10.2223s/25 iters), loss = 3.81874
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I0419 14:13:48.909963 5641 solver.cpp:237] Train net output #0: loss = 3.81874 (* 1 = 3.81874 loss)
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I0419 14:13:48.909973 5641 sgd_solver.cpp:105] Iteration 1500, lr = 0.00607968
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I0419 14:13:59.175431 5641 solver.cpp:218] Iteration 1525 (2.43534 iter/s, 10.2655s/25 iters), loss = 3.93031
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I0419 14:13:59.175473 5641 solver.cpp:237] Train net output #0: loss = 3.93031 (* 1 = 3.93031 loss)
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I0419 14:13:59.175482 5641 sgd_solver.cpp:105] Iteration 1525, lr = 0.00602947
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I0419 14:14:09.433513 5641 solver.cpp:218] Iteration 1550 (2.43711 iter/s, 10.2581s/25 iters), loss = 3.7464
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I0419 14:14:09.433555 5641 solver.cpp:237] Train net output #0: loss = 3.7464 (* 1 = 3.7464 loss)
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I0419 14:14:09.433563 5641 sgd_solver.cpp:105] Iteration 1550, lr = 0.00597967
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I0419 14:14:19.708779 5641 solver.cpp:218] Iteration 1575 (2.43303 iter/s, 10.2753s/25 iters), loss = 3.74985
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I0419 14:14:19.708910 5641 solver.cpp:237] Train net output #0: loss = 3.74985 (* 1 = 3.74985 loss)
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I0419 14:14:19.708920 5641 sgd_solver.cpp:105] Iteration 1575, lr = 0.00593028
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I0419 14:14:29.980648 5641 solver.cpp:218] Iteration 1600 (2.43386 iter/s, 10.2718s/25 iters), loss = 3.85836
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I0419 14:14:29.980685 5641 solver.cpp:237] Train net output #0: loss = 3.85836 (* 1 = 3.85836 loss)
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I0419 14:14:29.980693 5641 sgd_solver.cpp:105] Iteration 1600, lr = 0.0058813
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I0419 14:14:36.891788 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:14:39.082839 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1624.caffemodel
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I0419 14:14:44.000028 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1624.solverstate
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I0419 14:14:48.624176 5641 solver.cpp:330] Iteration 1624, Testing net (#0)
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I0419 14:14:48.624200 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:14:50.419579 5641 blocking_queue.cpp:49] Waiting for data
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I0419 14:14:53.011854 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:14:53.411294 5641 solver.cpp:397] Test net output #0: accuracy = 0.155025
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I0419 14:14:53.411340 5641 solver.cpp:397] Test net output #1: loss = 3.72833 (* 1 = 3.72833 loss)
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I0419 14:14:53.596061 5641 solver.cpp:218] Iteration 1625 (1.05863 iter/s, 23.6155s/25 iters), loss = 3.74921
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I0419 14:14:53.597586 5641 solver.cpp:237] Train net output #0: loss = 3.74921 (* 1 = 3.74921 loss)
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I0419 14:14:53.597600 5641 sgd_solver.cpp:105] Iteration 1625, lr = 0.00583272
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I0419 14:15:02.916477 5641 solver.cpp:218] Iteration 1650 (2.68271 iter/s, 9.31892s/25 iters), loss = 3.53211
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I0419 14:15:02.916508 5641 solver.cpp:237] Train net output #0: loss = 3.53211 (* 1 = 3.53211 loss)
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I0419 14:15:02.916517 5641 sgd_solver.cpp:105] Iteration 1650, lr = 0.00578454
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I0419 14:15:12.715358 5641 solver.cpp:218] Iteration 1675 (2.55131 iter/s, 9.79887s/25 iters), loss = 3.62825
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I0419 14:15:12.715389 5641 solver.cpp:237] Train net output #0: loss = 3.62825 (* 1 = 3.62825 loss)
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I0419 14:15:12.715396 5641 sgd_solver.cpp:105] Iteration 1675, lr = 0.00573677
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I0419 14:15:22.480072 5641 solver.cpp:218] Iteration 1700 (2.56024 iter/s, 9.76471s/25 iters), loss = 3.53001
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I0419 14:15:22.480180 5641 solver.cpp:237] Train net output #0: loss = 3.53001 (* 1 = 3.53001 loss)
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I0419 14:15:22.480190 5641 sgd_solver.cpp:105] Iteration 1700, lr = 0.00568938
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I0419 14:15:32.424620 5641 solver.cpp:218] Iteration 1725 (2.51396 iter/s, 9.94447s/25 iters), loss = 3.29863
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I0419 14:15:32.424654 5641 solver.cpp:237] Train net output #0: loss = 3.29863 (* 1 = 3.29863 loss)
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I0419 14:15:32.424661 5641 sgd_solver.cpp:105] Iteration 1725, lr = 0.00564239
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I0419 14:15:42.495066 5641 solver.cpp:218] Iteration 1750 (2.48251 iter/s, 10.0704s/25 iters), loss = 3.48977
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I0419 14:15:42.495100 5641 solver.cpp:237] Train net output #0: loss = 3.48977 (* 1 = 3.48977 loss)
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I0419 14:15:42.495107 5641 sgd_solver.cpp:105] Iteration 1750, lr = 0.00559579
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I0419 14:15:52.542850 5641 solver.cpp:218] Iteration 1775 (2.48811 iter/s, 10.0478s/25 iters), loss = 3.28971
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I0419 14:15:52.542966 5641 solver.cpp:237] Train net output #0: loss = 3.28971 (* 1 = 3.28971 loss)
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I0419 14:15:52.542975 5641 sgd_solver.cpp:105] Iteration 1775, lr = 0.00554957
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I0419 14:16:02.375272 5641 solver.cpp:218] Iteration 1800 (2.54263 iter/s, 9.83233s/25 iters), loss = 3.25936
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I0419 14:16:02.375303 5641 solver.cpp:237] Train net output #0: loss = 3.25936 (* 1 = 3.25936 loss)
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I0419 14:16:02.375311 5641 sgd_solver.cpp:105] Iteration 1800, lr = 0.00550373
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I0419 14:16:10.111662 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:16:12.244972 5641 solver.cpp:218] Iteration 1825 (2.53301 iter/s, 9.86969s/25 iters), loss = 3.45339
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I0419 14:16:12.245002 5641 solver.cpp:237] Train net output #0: loss = 3.45339 (* 1 = 3.45339 loss)
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I0419 14:16:12.245008 5641 sgd_solver.cpp:105] Iteration 1825, lr = 0.00545827
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I0419 14:16:12.584921 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1827.caffemodel
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I0419 14:16:15.687897 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1827.solverstate
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I0419 14:16:20.917524 5641 solver.cpp:330] Iteration 1827, Testing net (#0)
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I0419 14:16:20.917548 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:16:24.969094 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:16:25.364053 5641 solver.cpp:397] Test net output #0: accuracy = 0.184436
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I0419 14:16:25.364091 5641 solver.cpp:397] Test net output #1: loss = 3.5789 (* 1 = 3.5789 loss)
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I0419 14:16:33.733165 5641 solver.cpp:218] Iteration 1850 (1.16343 iter/s, 21.4882s/25 iters), loss = 3.11673
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I0419 14:16:33.733201 5641 solver.cpp:237] Train net output #0: loss = 3.11673 (* 1 = 3.11673 loss)
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I0419 14:16:33.733208 5641 sgd_solver.cpp:105] Iteration 1850, lr = 0.00541319
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I0419 14:16:43.519465 5641 solver.cpp:218] Iteration 1875 (2.55459 iter/s, 9.78629s/25 iters), loss = 3.22515
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I0419 14:16:43.519495 5641 solver.cpp:237] Train net output #0: loss = 3.22515 (* 1 = 3.22515 loss)
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I0419 14:16:43.519505 5641 sgd_solver.cpp:105] Iteration 1875, lr = 0.00536848
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I0419 14:16:53.338224 5641 solver.cpp:218] Iteration 1900 (2.54615 iter/s, 9.81875s/25 iters), loss = 2.79268
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I0419 14:16:53.338259 5641 solver.cpp:237] Train net output #0: loss = 2.79268 (* 1 = 2.79268 loss)
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I0419 14:16:53.338268 5641 sgd_solver.cpp:105] Iteration 1900, lr = 0.00532414
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I0419 14:17:03.391485 5641 solver.cpp:218] Iteration 1925 (2.48676 iter/s, 10.0533s/25 iters), loss = 2.95102
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I0419 14:17:03.391605 5641 solver.cpp:237] Train net output #0: loss = 2.95102 (* 1 = 2.95102 loss)
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I0419 14:17:03.391614 5641 sgd_solver.cpp:105] Iteration 1925, lr = 0.00528016
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I0419 14:17:13.422617 5641 solver.cpp:218] Iteration 1950 (2.49226 iter/s, 10.031s/25 iters), loss = 3.69669
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I0419 14:17:13.422650 5641 solver.cpp:237] Train net output #0: loss = 3.69669 (* 1 = 3.69669 loss)
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I0419 14:17:13.422658 5641 sgd_solver.cpp:105] Iteration 1950, lr = 0.00523655
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I0419 14:17:23.452436 5641 solver.cpp:218] Iteration 1975 (2.49257 iter/s, 10.0298s/25 iters), loss = 3.35295
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I0419 14:17:23.452471 5641 solver.cpp:237] Train net output #0: loss = 3.35295 (* 1 = 3.35295 loss)
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I0419 14:17:23.452478 5641 sgd_solver.cpp:105] Iteration 1975, lr = 0.0051933
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I0419 14:17:33.498407 5641 solver.cpp:218] Iteration 2000 (2.48856 iter/s, 10.046s/25 iters), loss = 2.87868
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I0419 14:17:33.498489 5641 solver.cpp:237] Train net output #0: loss = 2.87868 (* 1 = 2.87868 loss)
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I0419 14:17:33.498498 5641 sgd_solver.cpp:105] Iteration 2000, lr = 0.0051504
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I0419 14:17:42.192467 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:17:43.326037 5641 solver.cpp:218] Iteration 2025 (2.54386 iter/s, 9.82757s/25 iters), loss = 2.98912
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I0419 14:17:43.326071 5641 solver.cpp:237] Train net output #0: loss = 2.98912 (* 1 = 2.98912 loss)
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I0419 14:17:43.326078 5641 sgd_solver.cpp:105] Iteration 2025, lr = 0.00510786
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I0419 14:17:44.854931 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2030.caffemodel
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I0419 14:17:48.542573 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2030.solverstate
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I0419 14:17:52.492229 5641 solver.cpp:330] Iteration 2030, Testing net (#0)
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I0419 14:17:52.492247 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:17:56.783181 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:17:57.265926 5641 solver.cpp:397] Test net output #0: accuracy = 0.22549
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I0419 14:17:57.265971 5641 solver.cpp:397] Test net output #1: loss = 3.32883 (* 1 = 3.32883 loss)
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I0419 14:18:04.641139 5641 solver.cpp:218] Iteration 2050 (1.17288 iter/s, 21.3151s/25 iters), loss = 3.13583
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I0419 14:18:04.641283 5641 solver.cpp:237] Train net output #0: loss = 3.13583 (* 1 = 3.13583 loss)
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I0419 14:18:04.641294 5641 sgd_solver.cpp:105] Iteration 2050, lr = 0.00506568
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I0419 14:18:14.700824 5641 solver.cpp:218] Iteration 2075 (2.4852 iter/s, 10.0596s/25 iters), loss = 3.11361
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I0419 14:18:14.700860 5641 solver.cpp:237] Train net output #0: loss = 3.11361 (* 1 = 3.11361 loss)
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I0419 14:18:14.700867 5641 sgd_solver.cpp:105] Iteration 2075, lr = 0.00502384
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I0419 14:18:24.766062 5641 solver.cpp:218] Iteration 2100 (2.4838 iter/s, 10.0652s/25 iters), loss = 2.6712
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I0419 14:18:24.766094 5641 solver.cpp:237] Train net output #0: loss = 2.6712 (* 1 = 2.6712 loss)
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I0419 14:18:24.766101 5641 sgd_solver.cpp:105] Iteration 2100, lr = 0.00498234
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I0419 14:18:34.909193 5641 solver.cpp:218] Iteration 2125 (2.46473 iter/s, 10.1431s/25 iters), loss = 2.76979
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I0419 14:18:34.909329 5641 solver.cpp:237] Train net output #0: loss = 2.76979 (* 1 = 2.76979 loss)
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I0419 14:18:34.909339 5641 sgd_solver.cpp:105] Iteration 2125, lr = 0.00494119
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I0419 14:18:45.150557 5641 solver.cpp:218] Iteration 2150 (2.44111 iter/s, 10.2413s/25 iters), loss = 2.96564
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I0419 14:18:45.150596 5641 solver.cpp:237] Train net output #0: loss = 2.96564 (* 1 = 2.96564 loss)
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I0419 14:18:45.150605 5641 sgd_solver.cpp:105] Iteration 2150, lr = 0.00490038
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I0419 14:18:55.287472 5641 solver.cpp:218] Iteration 2175 (2.46624 iter/s, 10.1369s/25 iters), loss = 2.55917
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I0419 14:18:55.287510 5641 solver.cpp:237] Train net output #0: loss = 2.55917 (* 1 = 2.55917 loss)
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I0419 14:18:55.287518 5641 sgd_solver.cpp:105] Iteration 2175, lr = 0.0048599
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I0419 14:19:05.503497 5641 solver.cpp:218] Iteration 2200 (2.44714 iter/s, 10.216s/25 iters), loss = 2.79901
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I0419 14:19:05.503607 5641 solver.cpp:237] Train net output #0: loss = 2.79901 (* 1 = 2.79901 loss)
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I0419 14:19:05.503615 5641 sgd_solver.cpp:105] Iteration 2200, lr = 0.00481976
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I0419 14:19:15.452318 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:19:15.721666 5641 solver.cpp:218] Iteration 2225 (2.44664 iter/s, 10.2181s/25 iters), loss = 2.63563
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I0419 14:19:15.721704 5641 solver.cpp:237] Train net output #0: loss = 2.63563 (* 1 = 2.63563 loss)
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I0419 14:19:15.721711 5641 sgd_solver.cpp:105] Iteration 2225, lr = 0.00477995
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I0419 14:19:18.508772 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2233.caffemodel
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I0419 14:19:25.431241 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2233.solverstate
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I0419 14:19:31.323683 5641 solver.cpp:330] Iteration 2233, Testing net (#0)
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I0419 14:19:31.323702 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:19:35.417866 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:19:35.911478 5641 solver.cpp:397] Test net output #0: accuracy = 0.278799
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I0419 14:19:35.911638 5641 solver.cpp:397] Test net output #1: loss = 3.11168 (* 1 = 3.11168 loss)
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I0419 14:19:42.222187 5641 solver.cpp:218] Iteration 2250 (0.943376 iter/s, 26.5006s/25 iters), loss = 2.36173
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I0419 14:19:42.222230 5641 solver.cpp:237] Train net output #0: loss = 2.36173 (* 1 = 2.36173 loss)
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I0419 14:19:42.222239 5641 sgd_solver.cpp:105] Iteration 2250, lr = 0.00474047
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I0419 14:19:52.374325 5641 solver.cpp:218] Iteration 2275 (2.46254 iter/s, 10.1521s/25 iters), loss = 2.42782
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I0419 14:19:52.374382 5641 solver.cpp:237] Train net output #0: loss = 2.42782 (* 1 = 2.42782 loss)
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I0419 14:19:52.374392 5641 sgd_solver.cpp:105] Iteration 2275, lr = 0.00470132
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I0419 14:20:02.493752 5641 solver.cpp:218] Iteration 2300 (2.47051 iter/s, 10.1194s/25 iters), loss = 2.53739
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I0419 14:20:02.493798 5641 solver.cpp:237] Train net output #0: loss = 2.53739 (* 1 = 2.53739 loss)
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I0419 14:20:02.493808 5641 sgd_solver.cpp:105] Iteration 2300, lr = 0.00466249
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I0419 14:20:12.699811 5641 solver.cpp:218] Iteration 2325 (2.44953 iter/s, 10.206s/25 iters), loss = 2.54374
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I0419 14:20:12.699971 5641 solver.cpp:237] Train net output #0: loss = 2.54374 (* 1 = 2.54374 loss)
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I0419 14:20:12.699981 5641 sgd_solver.cpp:105] Iteration 2325, lr = 0.00462398
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I0419 14:20:22.935232 5641 solver.cpp:218] Iteration 2350 (2.44253 iter/s, 10.2353s/25 iters), loss = 2.63239
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I0419 14:20:22.935271 5641 solver.cpp:237] Train net output #0: loss = 2.63239 (* 1 = 2.63239 loss)
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I0419 14:20:22.935281 5641 sgd_solver.cpp:105] Iteration 2350, lr = 0.00458578
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I0419 14:20:33.141217 5641 solver.cpp:218] Iteration 2375 (2.44955 iter/s, 10.206s/25 iters), loss = 2.54957
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I0419 14:20:33.141260 5641 solver.cpp:237] Train net output #0: loss = 2.54957 (* 1 = 2.54957 loss)
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I0419 14:20:33.141268 5641 sgd_solver.cpp:105] Iteration 2375, lr = 0.00454791
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I0419 14:20:43.304523 5641 solver.cpp:218] Iteration 2400 (2.45983 iter/s, 10.1633s/25 iters), loss = 2.50655
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I0419 14:20:43.304699 5641 solver.cpp:237] Train net output #0: loss = 2.50655 (* 1 = 2.50655 loss)
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I0419 14:20:43.304710 5641 sgd_solver.cpp:105] Iteration 2400, lr = 0.00451034
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I0419 14:20:53.518246 5641 solver.cpp:218] Iteration 2425 (2.44772 iter/s, 10.2136s/25 iters), loss = 2.34169
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I0419 14:20:53.518291 5641 solver.cpp:237] Train net output #0: loss = 2.34169 (* 1 = 2.34169 loss)
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I0419 14:20:53.518299 5641 sgd_solver.cpp:105] Iteration 2425, lr = 0.00447309
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I0419 14:20:54.152041 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:20:57.523703 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2436.caffemodel
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I0419 14:21:01.819643 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2436.solverstate
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I0419 14:21:04.937570 5641 solver.cpp:330] Iteration 2436, Testing net (#0)
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I0419 14:21:04.937598 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:21:07.532016 5641 blocking_queue.cpp:49] Waiting for data
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I0419 14:21:09.135675 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:21:09.707363 5641 solver.cpp:397] Test net output #0: accuracy = 0.289216
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I0419 14:21:09.707410 5641 solver.cpp:397] Test net output #1: loss = 3.06754 (* 1 = 3.06754 loss)
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I0419 14:21:14.779211 5641 solver.cpp:218] Iteration 2450 (1.17586 iter/s, 21.261s/25 iters), loss = 2.22959
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I0419 14:21:14.779337 5641 solver.cpp:237] Train net output #0: loss = 2.22959 (* 1 = 2.22959 loss)
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I0419 14:21:14.779347 5641 sgd_solver.cpp:105] Iteration 2450, lr = 0.00443614
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I0419 14:21:25.041440 5641 solver.cpp:218] Iteration 2475 (2.43614 iter/s, 10.2621s/25 iters), loss = 2.21876
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I0419 14:21:25.041482 5641 solver.cpp:237] Train net output #0: loss = 2.21876 (* 1 = 2.21876 loss)
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I0419 14:21:25.041491 5641 sgd_solver.cpp:105] Iteration 2475, lr = 0.0043995
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I0419 14:21:35.192113 5641 solver.cpp:218] Iteration 2500 (2.46289 iter/s, 10.1507s/25 iters), loss = 2.17826
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I0419 14:21:35.192152 5641 solver.cpp:237] Train net output #0: loss = 2.17826 (* 1 = 2.17826 loss)
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I0419 14:21:35.192159 5641 sgd_solver.cpp:105] Iteration 2500, lr = 0.00436317
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I0419 14:21:45.389387 5641 solver.cpp:218] Iteration 2525 (2.45164 iter/s, 10.1973s/25 iters), loss = 2.12903
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I0419 14:21:45.389513 5641 solver.cpp:237] Train net output #0: loss = 2.12903 (* 1 = 2.12903 loss)
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I0419 14:21:45.389523 5641 sgd_solver.cpp:105] Iteration 2525, lr = 0.00432713
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I0419 14:21:55.626343 5641 solver.cpp:218] Iteration 2550 (2.44216 iter/s, 10.2368s/25 iters), loss = 2.00262
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I0419 14:21:55.626389 5641 solver.cpp:237] Train net output #0: loss = 2.00262 (* 1 = 2.00262 loss)
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I0419 14:21:55.626399 5641 sgd_solver.cpp:105] Iteration 2550, lr = 0.00429139
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I0419 14:22:05.793372 5641 solver.cpp:218] Iteration 2575 (2.45893 iter/s, 10.167s/25 iters), loss = 2.40184
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I0419 14:22:05.793411 5641 solver.cpp:237] Train net output #0: loss = 2.40184 (* 1 = 2.40184 loss)
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I0419 14:22:05.793419 5641 sgd_solver.cpp:105] Iteration 2575, lr = 0.00425594
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I0419 14:22:18.591523 5641 solver.cpp:218] Iteration 2600 (1.95416 iter/s, 12.7932s/25 iters), loss = 2.26601
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I0419 14:22:18.604359 5641 solver.cpp:237] Train net output #0: loss = 2.26601 (* 1 = 2.26601 loss)
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I0419 14:22:18.604379 5641 sgd_solver.cpp:105] Iteration 2600, lr = 0.00422079
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I0419 14:22:31.821003 5641 solver.cpp:218] Iteration 2625 (1.89155 iter/s, 13.2167s/25 iters), loss = 2.16769
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I0419 14:22:31.821060 5641 solver.cpp:237] Train net output #0: loss = 2.16769 (* 1 = 2.16769 loss)
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I0419 14:22:31.821072 5641 sgd_solver.cpp:105] Iteration 2625, lr = 0.00418593
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I0419 14:22:34.012193 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:22:38.738700 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2639.caffemodel
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I0419 14:22:47.011075 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2639.solverstate
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I0419 14:22:51.821808 5641 solver.cpp:330] Iteration 2639, Testing net (#0)
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I0419 14:22:51.821907 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:22:56.556540 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:22:57.290704 5641 solver.cpp:397] Test net output #0: accuracy = 0.318627
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I0419 14:22:57.290746 5641 solver.cpp:397] Test net output #1: loss = 2.8945 (* 1 = 2.8945 loss)
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I0419 14:23:01.936854 5641 solver.cpp:218] Iteration 2650 (0.830127 iter/s, 30.1159s/25 iters), loss = 2.29006
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I0419 14:23:01.936909 5641 solver.cpp:237] Train net output #0: loss = 2.29006 (* 1 = 2.29006 loss)
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I0419 14:23:01.936925 5641 sgd_solver.cpp:105] Iteration 2650, lr = 0.00415135
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I0419 14:23:14.447846 5641 solver.cpp:218] Iteration 2675 (1.99825 iter/s, 12.511s/25 iters), loss = 1.76573
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I0419 14:23:14.447919 5641 solver.cpp:237] Train net output #0: loss = 1.76573 (* 1 = 1.76573 loss)
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I0419 14:23:14.447933 5641 sgd_solver.cpp:105] Iteration 2675, lr = 0.00411707
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I0419 14:23:27.086788 5641 solver.cpp:218] Iteration 2700 (1.97802 iter/s, 12.6389s/25 iters), loss = 1.73395
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I0419 14:23:27.086890 5641 solver.cpp:237] Train net output #0: loss = 1.73395 (* 1 = 1.73395 loss)
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I0419 14:23:27.086899 5641 sgd_solver.cpp:105] Iteration 2700, lr = 0.00408306
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I0419 14:23:37.260416 5641 solver.cpp:218] Iteration 2725 (2.45735 iter/s, 10.1736s/25 iters), loss = 2.00441
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I0419 14:23:37.260455 5641 solver.cpp:237] Train net output #0: loss = 2.00441 (* 1 = 2.00441 loss)
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I0419 14:23:37.260463 5641 sgd_solver.cpp:105] Iteration 2725, lr = 0.00404934
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I0419 14:23:47.515369 5641 solver.cpp:218] Iteration 2750 (2.43785 iter/s, 10.2549s/25 iters), loss = 1.77419
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I0419 14:23:47.515413 5641 solver.cpp:237] Train net output #0: loss = 1.77419 (* 1 = 1.77419 loss)
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I0419 14:23:47.515422 5641 sgd_solver.cpp:105] Iteration 2750, lr = 0.00401589
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I0419 14:23:57.701210 5641 solver.cpp:218] Iteration 2775 (2.45439 iter/s, 10.1858s/25 iters), loss = 1.78277
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I0419 14:23:57.701323 5641 solver.cpp:237] Train net output #0: loss = 1.78277 (* 1 = 1.78277 loss)
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I0419 14:23:57.701333 5641 sgd_solver.cpp:105] Iteration 2775, lr = 0.00398272
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I0419 14:24:07.856420 5641 solver.cpp:218] Iteration 2800 (2.46181 iter/s, 10.1551s/25 iters), loss = 1.75782
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I0419 14:24:07.856456 5641 solver.cpp:237] Train net output #0: loss = 1.75782 (* 1 = 1.75782 loss)
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I0419 14:24:07.856465 5641 sgd_solver.cpp:105] Iteration 2800, lr = 0.00394983
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I0419 14:24:18.105621 5641 solver.cpp:218] Iteration 2825 (2.43922 iter/s, 10.2492s/25 iters), loss = 1.82542
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I0419 14:24:18.105667 5641 solver.cpp:237] Train net output #0: loss = 1.82542 (* 1 = 1.82542 loss)
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I0419 14:24:18.105675 5641 sgd_solver.cpp:105] Iteration 2825, lr = 0.0039172
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I0419 14:24:20.664542 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:24:24.615979 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2842.caffemodel
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I0419 14:24:27.683416 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2842.solverstate
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I0419 14:24:32.177199 5641 solver.cpp:330] Iteration 2842, Testing net (#0)
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I0419 14:24:32.177279 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:24:36.181674 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:24:36.796478 5641 solver.cpp:397] Test net output #0: accuracy = 0.341912
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I0419 14:24:36.796526 5641 solver.cpp:397] Test net output #1: loss = 2.86469 (* 1 = 2.86469 loss)
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I0419 14:24:39.446346 5641 solver.cpp:218] Iteration 2850 (1.17147 iter/s, 21.3407s/25 iters), loss = 1.9536
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I0419 14:24:39.446398 5641 solver.cpp:237] Train net output #0: loss = 1.9536 (* 1 = 1.9536 loss)
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I0419 14:24:39.446408 5641 sgd_solver.cpp:105] Iteration 2850, lr = 0.00388485
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I0419 14:24:49.826619 5641 solver.cpp:218] Iteration 2875 (2.40842 iter/s, 10.3802s/25 iters), loss = 1.86112
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I0419 14:24:49.826659 5641 solver.cpp:237] Train net output #0: loss = 1.86112 (* 1 = 1.86112 loss)
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I0419 14:24:49.826668 5641 sgd_solver.cpp:105] Iteration 2875, lr = 0.00385276
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I0419 14:25:00.041225 5641 solver.cpp:218] Iteration 2900 (2.44748 iter/s, 10.2146s/25 iters), loss = 1.7595
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I0419 14:25:00.041265 5641 solver.cpp:237] Train net output #0: loss = 1.7595 (* 1 = 1.7595 loss)
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I0419 14:25:00.041275 5641 sgd_solver.cpp:105] Iteration 2900, lr = 0.00382094
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I0419 14:25:10.263217 5641 solver.cpp:218] Iteration 2925 (2.44571 iter/s, 10.222s/25 iters), loss = 1.91377
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I0419 14:25:10.263362 5641 solver.cpp:237] Train net output #0: loss = 1.91377 (* 1 = 1.91377 loss)
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I0419 14:25:10.263372 5641 sgd_solver.cpp:105] Iteration 2925, lr = 0.00378938
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I0419 14:25:20.573980 5641 solver.cpp:218] Iteration 2950 (2.42468 iter/s, 10.3107s/25 iters), loss = 1.92524
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I0419 14:25:20.574020 5641 solver.cpp:237] Train net output #0: loss = 1.92524 (* 1 = 1.92524 loss)
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I0419 14:25:20.574030 5641 sgd_solver.cpp:105] Iteration 2950, lr = 0.00375808
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I0419 14:25:30.801142 5641 solver.cpp:218] Iteration 2975 (2.44448 iter/s, 10.2271s/25 iters), loss = 1.92762
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I0419 14:25:30.801206 5641 solver.cpp:237] Train net output #0: loss = 1.92762 (* 1 = 1.92762 loss)
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I0419 14:25:30.801219 5641 sgd_solver.cpp:105] Iteration 2975, lr = 0.00372704
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I0419 14:25:40.984552 5641 solver.cpp:218] Iteration 3000 (2.45498 iter/s, 10.1834s/25 iters), loss = 1.46283
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I0419 14:25:40.984648 5641 solver.cpp:237] Train net output #0: loss = 1.46283 (* 1 = 1.46283 loss)
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I0419 14:25:40.984658 5641 sgd_solver.cpp:105] Iteration 3000, lr = 0.00369626
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I0419 14:25:51.207800 5641 solver.cpp:218] Iteration 3025 (2.44542 iter/s, 10.2232s/25 iters), loss = 1.69898
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I0419 14:25:51.207840 5641 solver.cpp:237] Train net output #0: loss = 1.69898 (* 1 = 1.69898 loss)
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I0419 14:25:51.207849 5641 sgd_solver.cpp:105] Iteration 3025, lr = 0.00366573
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I0419 14:25:54.682435 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:25:58.908695 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3045.caffemodel
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I0419 14:26:02.672448 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3045.solverstate
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I0419 14:26:06.396268 5641 solver.cpp:330] Iteration 3045, Testing net (#0)
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I0419 14:26:06.396289 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:26:10.487376 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:26:11.196235 5641 solver.cpp:397] Test net output #0: accuracy = 0.364583
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I0419 14:26:11.196393 5641 solver.cpp:397] Test net output #1: loss = 2.72717 (* 1 = 2.72717 loss)
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I0419 14:26:12.621870 5641 solver.cpp:218] Iteration 3050 (1.16746 iter/s, 21.4141s/25 iters), loss = 1.71845
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I0419 14:26:12.621912 5641 solver.cpp:237] Train net output #0: loss = 1.71845 (* 1 = 1.71845 loss)
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I0419 14:26:12.621920 5641 sgd_solver.cpp:105] Iteration 3050, lr = 0.00363545
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I0419 14:26:22.959616 5641 solver.cpp:218] Iteration 3075 (2.41833 iter/s, 10.3377s/25 iters), loss = 1.46681
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I0419 14:26:22.959659 5641 solver.cpp:237] Train net output #0: loss = 1.46681 (* 1 = 1.46681 loss)
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I0419 14:26:22.959668 5641 sgd_solver.cpp:105] Iteration 3075, lr = 0.00360542
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I0419 14:26:33.246506 5641 solver.cpp:218] Iteration 3100 (2.43028 iter/s, 10.2869s/25 iters), loss = 1.31161
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I0419 14:26:33.246546 5641 solver.cpp:237] Train net output #0: loss = 1.31161 (* 1 = 1.31161 loss)
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I0419 14:26:33.246554 5641 sgd_solver.cpp:105] Iteration 3100, lr = 0.00357564
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I0419 14:26:43.506893 5641 solver.cpp:218] Iteration 3125 (2.43656 iter/s, 10.2604s/25 iters), loss = 1.43628
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I0419 14:26:43.507017 5641 solver.cpp:237] Train net output #0: loss = 1.43628 (* 1 = 1.43628 loss)
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I0419 14:26:43.507026 5641 sgd_solver.cpp:105] Iteration 3125, lr = 0.00354611
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I0419 14:26:53.715255 5641 solver.cpp:218] Iteration 3150 (2.449 iter/s, 10.2083s/25 iters), loss = 1.43696
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I0419 14:26:53.715299 5641 solver.cpp:237] Train net output #0: loss = 1.43696 (* 1 = 1.43696 loss)
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I0419 14:26:53.715308 5641 sgd_solver.cpp:105] Iteration 3150, lr = 0.00351682
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I0419 14:27:03.864729 5641 solver.cpp:218] Iteration 3175 (2.46319 iter/s, 10.1495s/25 iters), loss = 1.28869
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I0419 14:27:03.864778 5641 solver.cpp:237] Train net output #0: loss = 1.28869 (* 1 = 1.28869 loss)
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I0419 14:27:03.864787 5641 sgd_solver.cpp:105] Iteration 3175, lr = 0.00348777
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I0419 14:27:14.087602 5641 solver.cpp:218] Iteration 3200 (2.4455 iter/s, 10.2228s/25 iters), loss = 1.35698
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I0419 14:27:14.087713 5641 solver.cpp:237] Train net output #0: loss = 1.35698 (* 1 = 1.35698 loss)
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I0419 14:27:14.087723 5641 sgd_solver.cpp:105] Iteration 3200, lr = 0.00345897
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I0419 14:27:24.365356 5641 solver.cpp:218] Iteration 3225 (2.43246 iter/s, 10.2777s/25 iters), loss = 1.23517
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I0419 14:27:24.365401 5641 solver.cpp:237] Train net output #0: loss = 1.23517 (* 1 = 1.23517 loss)
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I0419 14:27:24.365409 5641 sgd_solver.cpp:105] Iteration 3225, lr = 0.0034304
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I0419 14:27:28.760087 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:27:33.332242 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3248.caffemodel
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I0419 14:27:36.390766 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3248.solverstate
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I0419 14:27:39.613477 5641 solver.cpp:330] Iteration 3248, Testing net (#0)
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I0419 14:27:39.613493 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:27:43.025583 5641 blocking_queue.cpp:49] Waiting for data
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I0419 14:27:43.667726 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:27:44.413271 5641 solver.cpp:397] Test net output #0: accuracy = 0.376226
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I0419 14:27:44.413403 5641 solver.cpp:397] Test net output #1: loss = 2.754 (* 1 = 2.754 loss)
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I0419 14:27:44.685653 5641 solver.cpp:218] Iteration 3250 (1.2303 iter/s, 20.3203s/25 iters), loss = 0.985497
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I0419 14:27:44.685693 5641 solver.cpp:237] Train net output #0: loss = 0.985497 (* 1 = 0.985497 loss)
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I0419 14:27:44.685701 5641 sgd_solver.cpp:105] Iteration 3250, lr = 0.00340206
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I0419 14:27:54.990331 5641 solver.cpp:218] Iteration 3275 (2.42609 iter/s, 10.3047s/25 iters), loss = 1.48273
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I0419 14:27:54.990382 5641 solver.cpp:237] Train net output #0: loss = 1.48273 (* 1 = 1.48273 loss)
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I0419 14:27:54.990392 5641 sgd_solver.cpp:105] Iteration 3275, lr = 0.00337396
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I0419 14:28:05.406405 5641 solver.cpp:218] Iteration 3300 (2.40014 iter/s, 10.4161s/25 iters), loss = 1.171
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I0419 14:28:05.406447 5641 solver.cpp:237] Train net output #0: loss = 1.171 (* 1 = 1.171 loss)
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I0419 14:28:05.406456 5641 sgd_solver.cpp:105] Iteration 3300, lr = 0.0033461
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I0419 14:28:15.602285 5641 solver.cpp:218] Iteration 3325 (2.45197 iter/s, 10.1959s/25 iters), loss = 1.38755
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I0419 14:28:15.602386 5641 solver.cpp:237] Train net output #0: loss = 1.38755 (* 1 = 1.38755 loss)
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I0419 14:28:15.602396 5641 sgd_solver.cpp:105] Iteration 3325, lr = 0.00331846
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I0419 14:28:26.006083 5641 solver.cpp:218] Iteration 3350 (2.40299 iter/s, 10.4037s/25 iters), loss = 1.14105
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I0419 14:28:26.006131 5641 solver.cpp:237] Train net output #0: loss = 1.14105 (* 1 = 1.14105 loss)
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I0419 14:28:26.006140 5641 sgd_solver.cpp:105] Iteration 3350, lr = 0.00329105
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I0419 14:28:36.407260 5641 solver.cpp:218] Iteration 3375 (2.40358 iter/s, 10.4012s/25 iters), loss = 1.308
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I0419 14:28:36.407302 5641 solver.cpp:237] Train net output #0: loss = 1.308 (* 1 = 1.308 loss)
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I0419 14:28:36.407310 5641 sgd_solver.cpp:105] Iteration 3375, lr = 0.00326387
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I0419 14:28:46.642566 5641 solver.cpp:218] Iteration 3400 (2.44253 iter/s, 10.2353s/25 iters), loss = 1.31028
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I0419 14:28:46.642727 5641 solver.cpp:237] Train net output #0: loss = 1.31028 (* 1 = 1.31028 loss)
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I0419 14:28:46.642737 5641 sgd_solver.cpp:105] Iteration 3400, lr = 0.00323691
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I0419 14:28:56.931830 5641 solver.cpp:218] Iteration 3425 (2.42975 iter/s, 10.2891s/25 iters), loss = 0.90968
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I0419 14:28:56.931875 5641 solver.cpp:237] Train net output #0: loss = 0.90968 (* 1 = 0.90968 loss)
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I0419 14:28:56.931883 5641 sgd_solver.cpp:105] Iteration 3425, lr = 0.00321017
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I0419 14:29:02.335434 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:29:07.174417 5641 solver.cpp:218] Iteration 3450 (2.44079 iter/s, 10.2426s/25 iters), loss = 1.22888
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I0419 14:29:07.174461 5641 solver.cpp:237] Train net output #0: loss = 1.22888 (* 1 = 1.22888 loss)
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I0419 14:29:07.174470 5641 sgd_solver.cpp:105] Iteration 3450, lr = 0.00318366
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I0419 14:29:07.174612 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3451.caffemodel
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I0419 14:29:15.099121 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3451.solverstate
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I0419 14:29:20.551133 5641 solver.cpp:330] Iteration 3451, Testing net (#0)
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I0419 14:29:20.551220 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:29:24.354084 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:29:25.075884 5641 solver.cpp:397] Test net output #0: accuracy = 0.373162
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I0419 14:29:25.075918 5641 solver.cpp:397] Test net output #1: loss = 2.74269 (* 1 = 2.74269 loss)
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I0419 14:29:34.183874 5641 solver.cpp:218] Iteration 3475 (0.9256 iter/s, 27.0095s/25 iters), loss = 1.05523
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I0419 14:29:34.183914 5641 solver.cpp:237] Train net output #0: loss = 1.05523 (* 1 = 1.05523 loss)
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I0419 14:29:34.183924 5641 sgd_solver.cpp:105] Iteration 3475, lr = 0.00315736
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I0419 14:29:44.208995 5641 solver.cpp:218] Iteration 3500 (2.49374 iter/s, 10.0251s/25 iters), loss = 1.04507
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I0419 14:29:44.209036 5641 solver.cpp:237] Train net output #0: loss = 1.04507 (* 1 = 1.04507 loss)
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I0419 14:29:44.209044 5641 sgd_solver.cpp:105] Iteration 3500, lr = 0.00313128
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I0419 14:29:54.421937 5641 solver.cpp:218] Iteration 3525 (2.44788 iter/s, 10.2129s/25 iters), loss = 1.14588
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I0419 14:29:54.422034 5641 solver.cpp:237] Train net output #0: loss = 1.14588 (* 1 = 1.14588 loss)
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I0419 14:29:54.422044 5641 sgd_solver.cpp:105] Iteration 3525, lr = 0.00310542
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I0419 14:30:04.629186 5641 solver.cpp:218] Iteration 3550 (2.44926 iter/s, 10.2072s/25 iters), loss = 0.763028
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I0419 14:30:04.629236 5641 solver.cpp:237] Train net output #0: loss = 0.763028 (* 1 = 0.763028 loss)
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I0419 14:30:04.629245 5641 sgd_solver.cpp:105] Iteration 3550, lr = 0.00307977
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I0419 14:30:14.831068 5641 solver.cpp:218] Iteration 3575 (2.45053 iter/s, 10.2019s/25 iters), loss = 1.1386
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I0419 14:30:14.831115 5641 solver.cpp:237] Train net output #0: loss = 1.1386 (* 1 = 1.1386 loss)
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I0419 14:30:14.831122 5641 sgd_solver.cpp:105] Iteration 3575, lr = 0.00305433
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I0419 14:30:25.050068 5641 solver.cpp:218] Iteration 3600 (2.44643 iter/s, 10.219s/25 iters), loss = 0.97737
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I0419 14:30:25.050184 5641 solver.cpp:237] Train net output #0: loss = 0.97737 (* 1 = 0.97737 loss)
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I0419 14:30:25.050194 5641 sgd_solver.cpp:105] Iteration 3600, lr = 0.00302911
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I0419 14:30:35.305599 5641 solver.cpp:218] Iteration 3625 (2.43773 iter/s, 10.2554s/25 iters), loss = 0.906844
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I0419 14:30:35.305647 5641 solver.cpp:237] Train net output #0: loss = 0.906844 (* 1 = 0.906844 loss)
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I0419 14:30:35.305656 5641 sgd_solver.cpp:105] Iteration 3625, lr = 0.00300409
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I0419 14:30:41.603281 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:30:45.521833 5641 solver.cpp:218] Iteration 3650 (2.44709 iter/s, 10.2162s/25 iters), loss = 0.859877
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I0419 14:30:45.521878 5641 solver.cpp:237] Train net output #0: loss = 0.859877 (* 1 = 0.859877 loss)
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I0419 14:30:45.521888 5641 sgd_solver.cpp:105] Iteration 3650, lr = 0.00297927
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I0419 14:30:46.683755 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3654.caffemodel
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I0419 14:30:49.751343 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3654.solverstate
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I0419 14:30:52.112924 5641 solver.cpp:330] Iteration 3654, Testing net (#0)
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I0419 14:30:52.112947 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:30:55.901258 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:30:56.716610 5641 solver.cpp:397] Test net output #0: accuracy = 0.392157
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I0419 14:30:56.716657 5641 solver.cpp:397] Test net output #1: loss = 2.73453 (* 1 = 2.73453 loss)
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I0419 14:31:04.664886 5641 solver.cpp:218] Iteration 3675 (1.30596 iter/s, 19.1431s/25 iters), loss = 1.11373
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I0419 14:31:04.664949 5641 solver.cpp:237] Train net output #0: loss = 1.11373 (* 1 = 1.11373 loss)
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I0419 14:31:04.664964 5641 sgd_solver.cpp:105] Iteration 3675, lr = 0.00295467
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I0419 14:31:14.876871 5641 solver.cpp:218] Iteration 3700 (2.44811 iter/s, 10.212s/25 iters), loss = 0.850574
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I0419 14:31:14.876912 5641 solver.cpp:237] Train net output #0: loss = 0.850574 (* 1 = 0.850574 loss)
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I0419 14:31:14.876921 5641 sgd_solver.cpp:105] Iteration 3700, lr = 0.00293026
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I0419 14:31:25.029376 5641 solver.cpp:218] Iteration 3725 (2.46245 iter/s, 10.1525s/25 iters), loss = 0.726597
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I0419 14:31:25.029425 5641 solver.cpp:237] Train net output #0: loss = 0.726597 (* 1 = 0.726597 loss)
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I0419 14:31:25.029434 5641 sgd_solver.cpp:105] Iteration 3725, lr = 0.00290606
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I0419 14:31:35.255102 5641 solver.cpp:218] Iteration 3750 (2.44482 iter/s, 10.2257s/25 iters), loss = 0.726657
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I0419 14:31:35.255226 5641 solver.cpp:237] Train net output #0: loss = 0.726657 (* 1 = 0.726657 loss)
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I0419 14:31:35.255236 5641 sgd_solver.cpp:105] Iteration 3750, lr = 0.00288206
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I0419 14:31:45.471721 5641 solver.cpp:218] Iteration 3775 (2.44702 iter/s, 10.2165s/25 iters), loss = 1.04373
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I0419 14:31:45.471760 5641 solver.cpp:237] Train net output #0: loss = 1.04373 (* 1 = 1.04373 loss)
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I0419 14:31:45.471769 5641 sgd_solver.cpp:105] Iteration 3775, lr = 0.00285825
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I0419 14:31:55.694582 5641 solver.cpp:218] Iteration 3800 (2.4455 iter/s, 10.2229s/25 iters), loss = 0.556887
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I0419 14:31:55.694625 5641 solver.cpp:237] Train net output #0: loss = 0.556887 (* 1 = 0.556887 loss)
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I0419 14:31:55.694633 5641 sgd_solver.cpp:105] Iteration 3800, lr = 0.00283464
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I0419 14:32:05.997402 5641 solver.cpp:218] Iteration 3825 (2.42652 iter/s, 10.3028s/25 iters), loss = 0.901688
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I0419 14:32:05.997519 5641 solver.cpp:237] Train net output #0: loss = 0.901688 (* 1 = 0.901688 loss)
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I0419 14:32:05.997529 5641 sgd_solver.cpp:105] Iteration 3825, lr = 0.00281123
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I0419 14:32:13.261265 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:32:16.272601 5641 solver.cpp:218] Iteration 3850 (2.43306 iter/s, 10.2751s/25 iters), loss = 0.904654
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I0419 14:32:16.272642 5641 solver.cpp:237] Train net output #0: loss = 0.904654 (* 1 = 0.904654 loss)
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I0419 14:32:16.272652 5641 sgd_solver.cpp:105] Iteration 3850, lr = 0.00278801
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I0419 14:32:18.645606 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3857.caffemodel
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I0419 14:32:22.535179 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3857.solverstate
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I0419 14:32:27.712337 5641 solver.cpp:330] Iteration 3857, Testing net (#0)
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I0419 14:32:27.712357 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:32:31.658828 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:32:32.527655 5641 solver.cpp:397] Test net output #0: accuracy = 0.417892
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I0419 14:32:32.527704 5641 solver.cpp:397] Test net output #1: loss = 2.73795 (* 1 = 2.73795 loss)
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I0419 14:32:39.255193 5641 solver.cpp:218] Iteration 3875 (1.08778 iter/s, 22.9826s/25 iters), loss = 0.530852
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I0419 14:32:39.255347 5641 solver.cpp:237] Train net output #0: loss = 0.530852 (* 1 = 0.530852 loss)
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I0419 14:32:39.255357 5641 sgd_solver.cpp:105] Iteration 3875, lr = 0.00276498
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I0419 14:32:49.517469 5641 solver.cpp:218] Iteration 3900 (2.43614 iter/s, 10.2621s/25 iters), loss = 0.775794
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I0419 14:32:49.517511 5641 solver.cpp:237] Train net output #0: loss = 0.775794 (* 1 = 0.775794 loss)
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I0419 14:32:49.517520 5641 sgd_solver.cpp:105] Iteration 3900, lr = 0.00274215
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I0419 14:32:59.761452 5641 solver.cpp:218] Iteration 3925 (2.44046 iter/s, 10.244s/25 iters), loss = 0.764237
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I0419 14:32:59.761507 5641 solver.cpp:237] Train net output #0: loss = 0.764237 (* 1 = 0.764237 loss)
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I0419 14:32:59.761516 5641 sgd_solver.cpp:105] Iteration 3925, lr = 0.0027195
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I0419 14:33:09.994838 5641 solver.cpp:218] Iteration 3950 (2.44299 iter/s, 10.2334s/25 iters), loss = 0.65273
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I0419 14:33:09.995004 5641 solver.cpp:237] Train net output #0: loss = 0.65273 (* 1 = 0.65273 loss)
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I0419 14:33:09.995015 5641 sgd_solver.cpp:105] Iteration 3950, lr = 0.00269704
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I0419 14:33:20.194157 5641 solver.cpp:218] Iteration 3975 (2.45118 iter/s, 10.1992s/25 iters), loss = 0.764568
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I0419 14:33:20.194208 5641 solver.cpp:237] Train net output #0: loss = 0.764568 (* 1 = 0.764568 loss)
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I0419 14:33:20.194217 5641 sgd_solver.cpp:105] Iteration 3975, lr = 0.00267476
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I0419 14:33:30.636826 5641 solver.cpp:218] Iteration 4000 (2.39403 iter/s, 10.4426s/25 iters), loss = 0.658535
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I0419 14:33:30.636864 5641 solver.cpp:237] Train net output #0: loss = 0.658535 (* 1 = 0.658535 loss)
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I0419 14:33:30.636873 5641 sgd_solver.cpp:105] Iteration 4000, lr = 0.00265267
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I0419 14:33:40.855167 5641 solver.cpp:218] Iteration 4025 (2.44658 iter/s, 10.2183s/25 iters), loss = 0.562619
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I0419 14:33:40.855286 5641 solver.cpp:237] Train net output #0: loss = 0.562619 (* 1 = 0.562619 loss)
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I0419 14:33:40.855295 5641 sgd_solver.cpp:105] Iteration 4025, lr = 0.00263076
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I0419 14:33:49.121002 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:33:51.086438 5641 solver.cpp:218] Iteration 4050 (2.44351 iter/s, 10.2312s/25 iters), loss = 0.630477
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I0419 14:33:51.086483 5641 solver.cpp:237] Train net output #0: loss = 0.630477 (* 1 = 0.630477 loss)
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I0419 14:33:51.086493 5641 sgd_solver.cpp:105] Iteration 4050, lr = 0.00260903
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I0419 14:33:54.688628 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4060.caffemodel
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I0419 14:34:01.584273 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4060.solverstate
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I0419 14:34:06.774752 5641 solver.cpp:330] Iteration 4060, Testing net (#0)
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I0419 14:34:06.774778 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:34:10.631599 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:34:10.964532 5641 blocking_queue.cpp:49] Waiting for data
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I0419 14:34:11.554489 5641 solver.cpp:397] Test net output #0: accuracy = 0.424632
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I0419 14:34:11.554544 5641 solver.cpp:397] Test net output #1: loss = 2.70381 (* 1 = 2.70381 loss)
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I0419 14:34:17.004354 5641 solver.cpp:218] Iteration 4075 (0.964582 iter/s, 25.918s/25 iters), loss = 0.579757
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I0419 14:34:17.004402 5641 solver.cpp:237] Train net output #0: loss = 0.579757 (* 1 = 0.579757 loss)
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I0419 14:34:17.004412 5641 sgd_solver.cpp:105] Iteration 4075, lr = 0.00258748
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I0419 14:34:27.240958 5641 solver.cpp:218] Iteration 4100 (2.44222 iter/s, 10.2366s/25 iters), loss = 0.601687
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I0419 14:34:27.241000 5641 solver.cpp:237] Train net output #0: loss = 0.601687 (* 1 = 0.601687 loss)
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I0419 14:34:27.241009 5641 sgd_solver.cpp:105] Iteration 4100, lr = 0.00256611
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I0419 14:34:37.452534 5641 solver.cpp:218] Iteration 4125 (2.44821 iter/s, 10.2116s/25 iters), loss = 0.515617
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I0419 14:34:37.452575 5641 solver.cpp:237] Train net output #0: loss = 0.515617 (* 1 = 0.515617 loss)
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I0419 14:34:37.452584 5641 sgd_solver.cpp:105] Iteration 4125, lr = 0.00254491
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I0419 14:34:47.631350 5641 solver.cpp:218] Iteration 4150 (2.45608 iter/s, 10.1788s/25 iters), loss = 0.662964
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I0419 14:34:47.631469 5641 solver.cpp:237] Train net output #0: loss = 0.662964 (* 1 = 0.662964 loss)
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I0419 14:34:47.631479 5641 sgd_solver.cpp:105] Iteration 4150, lr = 0.00252389
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I0419 14:34:57.853241 5641 solver.cpp:218] Iteration 4175 (2.44575 iter/s, 10.2218s/25 iters), loss = 0.560824
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I0419 14:34:57.853283 5641 solver.cpp:237] Train net output #0: loss = 0.560824 (* 1 = 0.560824 loss)
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I0419 14:34:57.853292 5641 sgd_solver.cpp:105] Iteration 4175, lr = 0.00250305
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I0419 14:35:08.166456 5641 solver.cpp:218] Iteration 4200 (2.42408 iter/s, 10.3132s/25 iters), loss = 0.757143
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I0419 14:35:08.166517 5641 solver.cpp:237] Train net output #0: loss = 0.757143 (* 1 = 0.757143 loss)
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I0419 14:35:08.166525 5641 sgd_solver.cpp:105] Iteration 4200, lr = 0.00248237
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I0419 14:35:18.438527 5641 solver.cpp:218] Iteration 4225 (2.43379 iter/s, 10.272s/25 iters), loss = 0.431391
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I0419 14:35:18.438655 5641 solver.cpp:237] Train net output #0: loss = 0.431391 (* 1 = 0.431391 loss)
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I0419 14:35:18.438666 5641 sgd_solver.cpp:105] Iteration 4225, lr = 0.00246187
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I0419 14:35:27.623004 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:35:28.675992 5641 solver.cpp:218] Iteration 4250 (2.44204 iter/s, 10.2374s/25 iters), loss = 0.569325
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I0419 14:35:28.676052 5641 solver.cpp:237] Train net output #0: loss = 0.569325 (* 1 = 0.569325 loss)
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I0419 14:35:28.676066 5641 sgd_solver.cpp:105] Iteration 4250, lr = 0.00244153
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I0419 14:35:33.538461 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4263.caffemodel
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I0419 14:35:37.614526 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4263.solverstate
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I0419 14:35:39.975512 5641 solver.cpp:330] Iteration 4263, Testing net (#0)
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I0419 14:35:39.975533 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:35:43.814136 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:35:44.773416 5641 solver.cpp:397] Test net output #0: accuracy = 0.43076
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I0419 14:35:44.773452 5641 solver.cpp:397] Test net output #1: loss = 2.69404 (* 1 = 2.69404 loss)
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I0419 14:35:49.109501 5641 solver.cpp:218] Iteration 4275 (1.22348 iter/s, 20.4335s/25 iters), loss = 0.512272
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I0419 14:35:49.109623 5641 solver.cpp:237] Train net output #0: loss = 0.512272 (* 1 = 0.512272 loss)
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I0419 14:35:49.109633 5641 sgd_solver.cpp:105] Iteration 4275, lr = 0.00242137
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I0419 14:35:59.348222 5641 solver.cpp:218] Iteration 4300 (2.44173 iter/s, 10.2386s/25 iters), loss = 0.638096
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I0419 14:35:59.348263 5641 solver.cpp:237] Train net output #0: loss = 0.638096 (* 1 = 0.638096 loss)
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I0419 14:35:59.348269 5641 sgd_solver.cpp:105] Iteration 4300, lr = 0.00240137
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I0419 14:36:09.517463 5641 solver.cpp:218] Iteration 4325 (2.4584 iter/s, 10.1692s/25 iters), loss = 0.563371
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I0419 14:36:09.517504 5641 solver.cpp:237] Train net output #0: loss = 0.563371 (* 1 = 0.563371 loss)
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I0419 14:36:09.517513 5641 sgd_solver.cpp:105] Iteration 4325, lr = 0.00238154
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I0419 14:36:19.939704 5641 solver.cpp:218] Iteration 4350 (2.39872 iter/s, 10.4222s/25 iters), loss = 0.470349
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I0419 14:36:19.939805 5641 solver.cpp:237] Train net output #0: loss = 0.470349 (* 1 = 0.470349 loss)
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I0419 14:36:19.939815 5641 sgd_solver.cpp:105] Iteration 4350, lr = 0.00236186
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I0419 14:36:30.220465 5641 solver.cpp:218] Iteration 4375 (2.43174 iter/s, 10.2807s/25 iters), loss = 0.529345
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I0419 14:36:30.220506 5641 solver.cpp:237] Train net output #0: loss = 0.529345 (* 1 = 0.529345 loss)
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I0419 14:36:30.220515 5641 sgd_solver.cpp:105] Iteration 4375, lr = 0.00234236
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I0419 14:36:40.451691 5641 solver.cpp:218] Iteration 4400 (2.4435 iter/s, 10.2312s/25 iters), loss = 0.513042
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I0419 14:36:40.451740 5641 solver.cpp:237] Train net output #0: loss = 0.513042 (* 1 = 0.513042 loss)
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I0419 14:36:40.451750 5641 sgd_solver.cpp:105] Iteration 4400, lr = 0.00232301
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I0419 14:36:50.594061 5641 solver.cpp:218] Iteration 4425 (2.46491 iter/s, 10.1423s/25 iters), loss = 0.454299
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I0419 14:36:50.594152 5641 solver.cpp:237] Train net output #0: loss = 0.454299 (* 1 = 0.454299 loss)
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I0419 14:36:50.594162 5641 sgd_solver.cpp:105] Iteration 4425, lr = 0.00230382
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I0419 14:37:00.876405 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:37:01.007230 5641 solver.cpp:218] Iteration 4450 (2.40082 iter/s, 10.4131s/25 iters), loss = 0.662408
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I0419 14:37:01.007272 5641 solver.cpp:237] Train net output #0: loss = 0.662408 (* 1 = 0.662408 loss)
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I0419 14:37:01.007280 5641 sgd_solver.cpp:105] Iteration 4450, lr = 0.00228479
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I0419 14:37:07.202983 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4466.caffemodel
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I0419 14:37:10.272317 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4466.solverstate
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I0419 14:37:14.186977 5641 solver.cpp:330] Iteration 4466, Testing net (#0)
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I0419 14:37:14.187000 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:37:17.974148 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:37:18.984681 5641 solver.cpp:397] Test net output #0: accuracy = 0.449142
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I0419 14:37:18.984726 5641 solver.cpp:397] Test net output #1: loss = 2.72253 (* 1 = 2.72253 loss)
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I0419 14:37:22.027653 5641 solver.cpp:218] Iteration 4475 (1.18932 iter/s, 21.0205s/25 iters), loss = 0.350469
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I0419 14:37:22.027817 5641 solver.cpp:237] Train net output #0: loss = 0.350469 (* 1 = 0.350469 loss)
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I0419 14:37:22.027827 5641 sgd_solver.cpp:105] Iteration 4475, lr = 0.00226592
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I0419 14:37:32.265102 5641 solver.cpp:218] Iteration 4500 (2.44205 iter/s, 10.2373s/25 iters), loss = 0.408643
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I0419 14:37:32.265144 5641 solver.cpp:237] Train net output #0: loss = 0.408643 (* 1 = 0.408643 loss)
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I0419 14:37:32.265153 5641 sgd_solver.cpp:105] Iteration 4500, lr = 0.00224721
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I0419 14:37:42.493573 5641 solver.cpp:218] Iteration 4525 (2.44416 iter/s, 10.2285s/25 iters), loss = 0.203843
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I0419 14:37:42.493613 5641 solver.cpp:237] Train net output #0: loss = 0.203843 (* 1 = 0.203843 loss)
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I0419 14:37:42.493621 5641 sgd_solver.cpp:105] Iteration 4525, lr = 0.00222865
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I0419 14:37:52.762887 5641 solver.cpp:218] Iteration 4550 (2.43444 iter/s, 10.2693s/25 iters), loss = 0.613631
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I0419 14:37:52.763013 5641 solver.cpp:237] Train net output #0: loss = 0.613631 (* 1 = 0.613631 loss)
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I0419 14:37:52.763022 5641 sgd_solver.cpp:105] Iteration 4550, lr = 0.00221024
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I0419 14:38:03.004679 5641 solver.cpp:218] Iteration 4575 (2.441 iter/s, 10.2417s/25 iters), loss = 0.564088
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I0419 14:38:03.004750 5641 solver.cpp:237] Train net output #0: loss = 0.564088 (* 1 = 0.564088 loss)
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I0419 14:38:03.004765 5641 sgd_solver.cpp:105] Iteration 4575, lr = 0.00219198
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I0419 14:38:13.177558 5641 solver.cpp:218] Iteration 4600 (2.45752 iter/s, 10.1728s/25 iters), loss = 0.33355
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I0419 14:38:13.177598 5641 solver.cpp:237] Train net output #0: loss = 0.33355 (* 1 = 0.33355 loss)
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I0419 14:38:13.177606 5641 sgd_solver.cpp:105] Iteration 4600, lr = 0.00217388
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I0419 14:38:23.248816 5641 solver.cpp:218] Iteration 4625 (2.48231 iter/s, 10.0712s/25 iters), loss = 0.448197
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I0419 14:38:23.248908 5641 solver.cpp:237] Train net output #0: loss = 0.448197 (* 1 = 0.448197 loss)
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I0419 14:38:23.248917 5641 sgd_solver.cpp:105] Iteration 4625, lr = 0.00215592
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I0419 14:38:33.496027 5641 solver.cpp:218] Iteration 4650 (2.4397 iter/s, 10.2471s/25 iters), loss = 0.267619
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I0419 14:38:33.496074 5641 solver.cpp:237] Train net output #0: loss = 0.267619 (* 1 = 0.267619 loss)
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I0419 14:38:33.496083 5641 sgd_solver.cpp:105] Iteration 4650, lr = 0.00213812
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I0419 14:38:34.309540 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:38:40.741173 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4669.caffemodel
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I0419 14:38:47.360610 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4669.solverstate
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I0419 14:38:52.371317 5641 solver.cpp:330] Iteration 4669, Testing net (#0)
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I0419 14:38:52.371341 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:38:56.150502 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:38:57.210040 5641 solver.cpp:397] Test net output #0: accuracy = 0.457108
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I0419 14:38:57.210081 5641 solver.cpp:397] Test net output #1: loss = 2.73347 (* 1 = 2.73347 loss)
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I0419 14:38:59.023247 5641 solver.cpp:218] Iteration 4675 (0.979345 iter/s, 25.5273s/25 iters), loss = 0.374063
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I0419 14:38:59.023288 5641 solver.cpp:237] Train net output #0: loss = 0.374063 (* 1 = 0.374063 loss)
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I0419 14:38:59.023296 5641 sgd_solver.cpp:105] Iteration 4675, lr = 0.00212046
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I0419 14:39:09.318501 5641 solver.cpp:218] Iteration 4700 (2.42831 iter/s, 10.2952s/25 iters), loss = 0.508026
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I0419 14:39:09.318538 5641 solver.cpp:237] Train net output #0: loss = 0.508026 (* 1 = 0.508026 loss)
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I0419 14:39:09.318547 5641 sgd_solver.cpp:105] Iteration 4700, lr = 0.00210294
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I0419 14:39:19.600538 5641 solver.cpp:218] Iteration 4725 (2.43143 iter/s, 10.282s/25 iters), loss = 0.34877
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I0419 14:39:19.600580 5641 solver.cpp:237] Train net output #0: loss = 0.34877 (* 1 = 0.34877 loss)
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I0419 14:39:19.600589 5641 sgd_solver.cpp:105] Iteration 4725, lr = 0.00208557
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I0419 14:39:29.736452 5641 solver.cpp:218] Iteration 4750 (2.46648 iter/s, 10.1359s/25 iters), loss = 0.24267
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I0419 14:39:29.736595 5641 solver.cpp:237] Train net output #0: loss = 0.24267 (* 1 = 0.24267 loss)
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I0419 14:39:29.736605 5641 sgd_solver.cpp:105] Iteration 4750, lr = 0.00206835
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I0419 14:39:40.084869 5641 solver.cpp:218] Iteration 4775 (2.41585 iter/s, 10.3483s/25 iters), loss = 0.334032
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I0419 14:39:40.084909 5641 solver.cpp:237] Train net output #0: loss = 0.334032 (* 1 = 0.334032 loss)
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I0419 14:39:40.084918 5641 sgd_solver.cpp:105] Iteration 4775, lr = 0.00205126
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I0419 14:39:50.267042 5641 solver.cpp:218] Iteration 4800 (2.45528 iter/s, 10.1821s/25 iters), loss = 0.439225
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I0419 14:39:50.267105 5641 solver.cpp:237] Train net output #0: loss = 0.439225 (* 1 = 0.439225 loss)
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I0419 14:39:50.267119 5641 sgd_solver.cpp:105] Iteration 4800, lr = 0.00203432
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I0419 14:40:00.490568 5641 solver.cpp:218] Iteration 4825 (2.44535 iter/s, 10.2235s/25 iters), loss = 0.31071
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I0419 14:40:00.490687 5641 solver.cpp:237] Train net output #0: loss = 0.31071 (* 1 = 0.31071 loss)
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I0419 14:40:00.490698 5641 sgd_solver.cpp:105] Iteration 4825, lr = 0.00201752
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I0419 14:40:10.993149 5641 solver.cpp:218] Iteration 4850 (2.38039 iter/s, 10.5025s/25 iters), loss = 0.194017
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I0419 14:40:10.993189 5641 solver.cpp:237] Train net output #0: loss = 0.194017 (* 1 = 0.194017 loss)
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I0419 14:40:10.993197 5641 sgd_solver.cpp:105] Iteration 4850, lr = 0.00200085
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I0419 14:40:12.798501 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:40:19.664297 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4872.caffemodel
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I0419 14:40:24.879587 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4872.solverstate
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I0419 14:40:27.232568 5641 solver.cpp:330] Iteration 4872, Testing net (#0)
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I0419 14:40:27.232592 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:40:30.949120 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:40:32.036024 5641 solver.cpp:397] Test net output #0: accuracy = 0.470588
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I0419 14:40:32.036065 5641 solver.cpp:397] Test net output #1: loss = 2.60758 (* 1 = 2.60758 loss)
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I0419 14:40:32.642908 5641 solver.cpp:218] Iteration 4875 (1.15474 iter/s, 21.6498s/25 iters), loss = 0.453405
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I0419 14:40:32.642953 5641 solver.cpp:237] Train net output #0: loss = 0.453405 (* 1 = 0.453405 loss)
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I0419 14:40:32.642963 5641 sgd_solver.cpp:105] Iteration 4875, lr = 0.00198433
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I0419 14:40:32.989015 5641 blocking_queue.cpp:49] Waiting for data
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I0419 14:40:42.849282 5641 solver.cpp:218] Iteration 4900 (2.44945 iter/s, 10.2064s/25 iters), loss = 0.269448
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I0419 14:40:42.849325 5641 solver.cpp:237] Train net output #0: loss = 0.269448 (* 1 = 0.269448 loss)
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I0419 14:40:42.849334 5641 sgd_solver.cpp:105] Iteration 4900, lr = 0.00196794
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I0419 14:40:52.995492 5641 solver.cpp:218] Iteration 4925 (2.46398 iter/s, 10.1462s/25 iters), loss = 0.325479
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I0419 14:40:52.995535 5641 solver.cpp:237] Train net output #0: loss = 0.325479 (* 1 = 0.325479 loss)
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I0419 14:40:52.995543 5641 sgd_solver.cpp:105] Iteration 4925, lr = 0.00195168
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I0419 14:41:03.295034 5641 solver.cpp:218] Iteration 4950 (2.4273 iter/s, 10.2995s/25 iters), loss = 0.404693
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I0419 14:41:03.295164 5641 solver.cpp:237] Train net output #0: loss = 0.404693 (* 1 = 0.404693 loss)
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I0419 14:41:03.295174 5641 sgd_solver.cpp:105] Iteration 4950, lr = 0.00193556
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I0419 14:41:13.717434 5641 solver.cpp:218] Iteration 4975 (2.3987 iter/s, 10.4223s/25 iters), loss = 0.396123
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I0419 14:41:13.717475 5641 solver.cpp:237] Train net output #0: loss = 0.396123 (* 1 = 0.396123 loss)
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I0419 14:41:13.717483 5641 sgd_solver.cpp:105] Iteration 4975, lr = 0.00191958
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I0419 14:41:23.964408 5641 solver.cpp:218] Iteration 5000 (2.43975 iter/s, 10.247s/25 iters), loss = 0.22273
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I0419 14:41:23.964450 5641 solver.cpp:237] Train net output #0: loss = 0.22273 (* 1 = 0.22273 loss)
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I0419 14:41:23.964459 5641 sgd_solver.cpp:105] Iteration 5000, lr = 0.00190372
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I0419 14:41:34.082320 5641 solver.cpp:218] Iteration 5025 (2.47087 iter/s, 10.1179s/25 iters), loss = 0.270844
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I0419 14:41:34.082422 5641 solver.cpp:237] Train net output #0: loss = 0.270844 (* 1 = 0.270844 loss)
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I0419 14:41:34.082433 5641 sgd_solver.cpp:105] Iteration 5025, lr = 0.001888
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I0419 14:41:44.319321 5641 solver.cpp:218] Iteration 5050 (2.44214 iter/s, 10.2369s/25 iters), loss = 0.401348
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I0419 14:41:44.319363 5641 solver.cpp:237] Train net output #0: loss = 0.401348 (* 1 = 0.401348 loss)
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I0419 14:41:44.319372 5641 sgd_solver.cpp:105] Iteration 5050, lr = 0.0018724
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I0419 14:41:47.003199 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:41:54.069538 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5075.caffemodel
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I0419 14:41:57.154803 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5075.solverstate
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I0419 14:42:02.311205 5641 solver.cpp:330] Iteration 5075, Testing net (#0)
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I0419 14:42:02.311225 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:42:05.801754 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:42:06.946496 5641 solver.cpp:397] Test net output #0: accuracy = 0.459559
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I0419 14:42:06.946542 5641 solver.cpp:397] Test net output #1: loss = 2.72999 (* 1 = 2.72999 loss)
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I0419 14:42:07.043119 5641 solver.cpp:218] Iteration 5075 (1.10017 iter/s, 22.7238s/25 iters), loss = 0.276953
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I0419 14:42:07.043162 5641 solver.cpp:237] Train net output #0: loss = 0.276953 (* 1 = 0.276953 loss)
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I0419 14:42:07.043171 5641 sgd_solver.cpp:105] Iteration 5075, lr = 0.00185694
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I0419 14:42:16.453083 5641 solver.cpp:218] Iteration 5100 (2.65676 iter/s, 9.40995s/25 iters), loss = 0.321424
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I0419 14:42:16.453125 5641 solver.cpp:237] Train net output #0: loss = 0.321424 (* 1 = 0.321424 loss)
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I0419 14:42:16.453133 5641 sgd_solver.cpp:105] Iteration 5100, lr = 0.0018416
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I0419 14:42:26.723574 5641 solver.cpp:218] Iteration 5125 (2.43416 iter/s, 10.2705s/25 iters), loss = 0.304902
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I0419 14:42:26.723615 5641 solver.cpp:237] Train net output #0: loss = 0.304902 (* 1 = 0.304902 loss)
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I0419 14:42:26.723624 5641 sgd_solver.cpp:105] Iteration 5125, lr = 0.00182639
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I0419 14:42:36.953509 5641 solver.cpp:218] Iteration 5150 (2.44381 iter/s, 10.2299s/25 iters), loss = 0.253327
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I0419 14:42:36.953624 5641 solver.cpp:237] Train net output #0: loss = 0.253327 (* 1 = 0.253327 loss)
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I0419 14:42:36.953634 5641 sgd_solver.cpp:105] Iteration 5150, lr = 0.0018113
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I0419 14:42:47.102084 5641 solver.cpp:218] Iteration 5175 (2.46342 iter/s, 10.1485s/25 iters), loss = 0.268665
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I0419 14:42:47.102126 5641 solver.cpp:237] Train net output #0: loss = 0.268665 (* 1 = 0.268665 loss)
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I0419 14:42:47.102135 5641 sgd_solver.cpp:105] Iteration 5175, lr = 0.00179634
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I0419 14:42:57.323961 5641 solver.cpp:218] Iteration 5200 (2.44574 iter/s, 10.2219s/25 iters), loss = 0.265666
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I0419 14:42:57.324004 5641 solver.cpp:237] Train net output #0: loss = 0.265666 (* 1 = 0.265666 loss)
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I0419 14:42:57.324013 5641 sgd_solver.cpp:105] Iteration 5200, lr = 0.00178151
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I0419 14:43:07.439918 5641 solver.cpp:218] Iteration 5225 (2.47135 iter/s, 10.1159s/25 iters), loss = 0.470726
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I0419 14:43:07.440085 5641 solver.cpp:237] Train net output #0: loss = 0.470726 (* 1 = 0.470726 loss)
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I0419 14:43:07.440097 5641 sgd_solver.cpp:105] Iteration 5225, lr = 0.00176679
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I0419 14:43:17.638957 5641 solver.cpp:218] Iteration 5250 (2.45124 iter/s, 10.1989s/25 iters), loss = 0.173605
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I0419 14:43:17.638999 5641 solver.cpp:237] Train net output #0: loss = 0.173606 (* 1 = 0.173606 loss)
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I0419 14:43:17.639008 5641 sgd_solver.cpp:105] Iteration 5250, lr = 0.0017522
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I0419 14:43:21.327953 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:43:27.835289 5641 solver.cpp:218] Iteration 5275 (2.45187 iter/s, 10.1963s/25 iters), loss = 0.178291
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I0419 14:43:27.835330 5641 solver.cpp:237] Train net output #0: loss = 0.178291 (* 1 = 0.178291 loss)
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I0419 14:43:27.835338 5641 sgd_solver.cpp:105] Iteration 5275, lr = 0.00173773
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I0419 14:43:28.607610 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5278.caffemodel
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I0419 14:43:34.369074 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5278.solverstate
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I0419 14:43:38.727876 5641 solver.cpp:330] Iteration 5278, Testing net (#0)
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I0419 14:43:38.727982 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:43:42.370247 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:43:43.546171 5641 solver.cpp:397] Test net output #0: accuracy = 0.463848
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I0419 14:43:43.546202 5641 solver.cpp:397] Test net output #1: loss = 2.77806 (* 1 = 2.77806 loss)
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I0419 14:43:51.832051 5641 solver.cpp:218] Iteration 5300 (1.0418 iter/s, 23.9968s/25 iters), loss = 0.354999
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I0419 14:43:51.832093 5641 solver.cpp:237] Train net output #0: loss = 0.354999 (* 1 = 0.354999 loss)
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I0419 14:43:51.832103 5641 sgd_solver.cpp:105] Iteration 5300, lr = 0.00172337
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I0419 14:44:02.043135 5641 solver.cpp:218] Iteration 5325 (2.44832 iter/s, 10.2111s/25 iters), loss = 0.173672
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I0419 14:44:02.043179 5641 solver.cpp:237] Train net output #0: loss = 0.173672 (* 1 = 0.173672 loss)
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I0419 14:44:02.043187 5641 sgd_solver.cpp:105] Iteration 5325, lr = 0.00170914
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I0419 14:44:12.319061 5641 solver.cpp:218] Iteration 5350 (2.43288 iter/s, 10.2759s/25 iters), loss = 0.220941
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I0419 14:44:12.319186 5641 solver.cpp:237] Train net output #0: loss = 0.220941 (* 1 = 0.220941 loss)
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I0419 14:44:12.319195 5641 sgd_solver.cpp:105] Iteration 5350, lr = 0.00169502
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I0419 14:44:22.551235 5641 solver.cpp:218] Iteration 5375 (2.4433 iter/s, 10.2321s/25 iters), loss = 0.128467
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I0419 14:44:22.551280 5641 solver.cpp:237] Train net output #0: loss = 0.128467 (* 1 = 0.128467 loss)
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I0419 14:44:22.551290 5641 sgd_solver.cpp:105] Iteration 5375, lr = 0.00168102
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I0419 14:44:32.695631 5641 solver.cpp:218] Iteration 5400 (2.46442 iter/s, 10.1444s/25 iters), loss = 0.22859
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I0419 14:44:32.695670 5641 solver.cpp:237] Train net output #0: loss = 0.22859 (* 1 = 0.22859 loss)
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I0419 14:44:32.695678 5641 sgd_solver.cpp:105] Iteration 5400, lr = 0.00166714
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I0419 14:44:42.923986 5641 solver.cpp:218] Iteration 5425 (2.44419 iter/s, 10.2283s/25 iters), loss = 0.174306
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I0419 14:44:42.924118 5641 solver.cpp:237] Train net output #0: loss = 0.174306 (* 1 = 0.174306 loss)
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I0419 14:44:42.924127 5641 sgd_solver.cpp:105] Iteration 5425, lr = 0.00165337
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I0419 14:44:53.149219 5641 solver.cpp:218] Iteration 5450 (2.44495 iter/s, 10.2251s/25 iters), loss = 0.263581
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I0419 14:44:53.149257 5641 solver.cpp:237] Train net output #0: loss = 0.263581 (* 1 = 0.263581 loss)
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I0419 14:44:53.149266 5641 sgd_solver.cpp:105] Iteration 5450, lr = 0.00163971
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I0419 14:44:57.751217 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:45:03.307222 5641 solver.cpp:218] Iteration 5475 (2.46111 iter/s, 10.158s/25 iters), loss = 0.203212
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I0419 14:45:03.307262 5641 solver.cpp:237] Train net output #0: loss = 0.203212 (* 1 = 0.203212 loss)
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I0419 14:45:03.307271 5641 sgd_solver.cpp:105] Iteration 5475, lr = 0.00162617
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I0419 14:45:05.284641 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5481.caffemodel
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I0419 14:45:10.004534 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5481.solverstate
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I0419 14:45:13.146553 5641 solver.cpp:330] Iteration 5481, Testing net (#0)
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I0419 14:45:13.146706 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:45:16.694645 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:45:17.918606 5641 solver.cpp:397] Test net output #0: accuracy = 0.46201
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I0419 14:45:17.918642 5641 solver.cpp:397] Test net output #1: loss = 2.83155 (* 1 = 2.83155 loss)
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I0419 14:45:25.381101 5641 solver.cpp:218] Iteration 5500 (1.13256 iter/s, 22.0739s/25 iters), loss = 0.18516
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I0419 14:45:25.381143 5641 solver.cpp:237] Train net output #0: loss = 0.18516 (* 1 = 0.18516 loss)
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I0419 14:45:25.381151 5641 sgd_solver.cpp:105] Iteration 5500, lr = 0.00161274
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I0419 14:45:35.553614 5641 solver.cpp:218] Iteration 5525 (2.45761 iter/s, 10.1725s/25 iters), loss = 0.217688
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I0419 14:45:35.553665 5641 solver.cpp:237] Train net output #0: loss = 0.217688 (* 1 = 0.217688 loss)
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I0419 14:45:35.553674 5641 sgd_solver.cpp:105] Iteration 5525, lr = 0.00159942
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I0419 14:45:45.714970 5641 solver.cpp:218] Iteration 5550 (2.46031 iter/s, 10.1613s/25 iters), loss = 0.20394
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I0419 14:45:45.715085 5641 solver.cpp:237] Train net output #0: loss = 0.20394 (* 1 = 0.20394 loss)
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I0419 14:45:45.715096 5641 sgd_solver.cpp:105] Iteration 5550, lr = 0.00158621
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I0419 14:45:55.987085 5641 solver.cpp:218] Iteration 5575 (2.43379 iter/s, 10.272s/25 iters), loss = 0.212294
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I0419 14:45:55.987131 5641 solver.cpp:237] Train net output #0: loss = 0.212294 (* 1 = 0.212294 loss)
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I0419 14:45:55.987140 5641 sgd_solver.cpp:105] Iteration 5575, lr = 0.00157311
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I0419 14:46:06.399782 5641 solver.cpp:218] Iteration 5600 (2.40092 iter/s, 10.4127s/25 iters), loss = 0.203
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I0419 14:46:06.399829 5641 solver.cpp:237] Train net output #0: loss = 0.203 (* 1 = 0.203 loss)
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I0419 14:46:06.399837 5641 sgd_solver.cpp:105] Iteration 5600, lr = 0.00156011
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I0419 14:46:16.632541 5641 solver.cpp:218] Iteration 5625 (2.44314 iter/s, 10.2327s/25 iters), loss = 0.232603
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I0419 14:46:16.632661 5641 solver.cpp:237] Train net output #0: loss = 0.232604 (* 1 = 0.232604 loss)
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I0419 14:46:16.632671 5641 sgd_solver.cpp:105] Iteration 5625, lr = 0.00154723
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I0419 14:46:26.902263 5641 solver.cpp:218] Iteration 5650 (2.43436 iter/s, 10.2696s/25 iters), loss = 0.214364
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I0419 14:46:26.902312 5641 solver.cpp:237] Train net output #0: loss = 0.214364 (* 1 = 0.214364 loss)
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I0419 14:46:26.902320 5641 sgd_solver.cpp:105] Iteration 5650, lr = 0.00153445
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I0419 14:46:32.418609 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:46:37.014101 5641 solver.cpp:218] Iteration 5675 (2.47235 iter/s, 10.1118s/25 iters), loss = 0.328744
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I0419 14:46:37.014143 5641 solver.cpp:237] Train net output #0: loss = 0.328744 (* 1 = 0.328744 loss)
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I0419 14:46:37.014153 5641 sgd_solver.cpp:105] Iteration 5675, lr = 0.00152177
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I0419 14:46:40.206171 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5684.caffemodel
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I0419 14:46:45.609608 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5684.solverstate
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I0419 14:46:49.506906 5641 solver.cpp:330] Iteration 5684, Testing net (#0)
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I0419 14:46:49.506964 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:46:53.028721 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:46:54.301589 5641 solver.cpp:397] Test net output #0: accuracy = 0.454657
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I0419 14:46:54.301635 5641 solver.cpp:397] Test net output #1: loss = 2.81063 (* 1 = 2.81063 loss)
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I0419 14:46:58.505877 5641 blocking_queue.cpp:49] Waiting for data
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I0419 14:47:00.190644 5641 solver.cpp:218] Iteration 5700 (1.07867 iter/s, 23.1766s/25 iters), loss = 0.21221
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I0419 14:47:00.190690 5641 solver.cpp:237] Train net output #0: loss = 0.21221 (* 1 = 0.21221 loss)
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I0419 14:47:00.190699 5641 sgd_solver.cpp:105] Iteration 5700, lr = 0.0015092
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I0419 14:47:10.475116 5641 solver.cpp:218] Iteration 5725 (2.43085 iter/s, 10.2845s/25 iters), loss = 0.14684
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I0419 14:47:10.475157 5641 solver.cpp:237] Train net output #0: loss = 0.14684 (* 1 = 0.14684 loss)
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I0419 14:47:10.475167 5641 sgd_solver.cpp:105] Iteration 5725, lr = 0.00149674
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I0419 14:47:20.776544 5641 solver.cpp:218] Iteration 5750 (2.42685 iter/s, 10.3014s/25 iters), loss = 0.234267
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I0419 14:47:20.776705 5641 solver.cpp:237] Train net output #0: loss = 0.234267 (* 1 = 0.234267 loss)
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I0419 14:47:20.776715 5641 sgd_solver.cpp:105] Iteration 5750, lr = 0.00148438
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I0419 14:47:31.063251 5641 solver.cpp:218] Iteration 5775 (2.43035 iter/s, 10.2866s/25 iters), loss = 0.200757
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I0419 14:47:31.063299 5641 solver.cpp:237] Train net output #0: loss = 0.200757 (* 1 = 0.200757 loss)
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I0419 14:47:31.063308 5641 sgd_solver.cpp:105] Iteration 5775, lr = 0.00147212
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I0419 14:47:41.236389 5641 solver.cpp:218] Iteration 5800 (2.45746 iter/s, 10.1731s/25 iters), loss = 0.198618
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I0419 14:47:41.236438 5641 solver.cpp:237] Train net output #0: loss = 0.198618 (* 1 = 0.198618 loss)
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I0419 14:47:41.236446 5641 sgd_solver.cpp:105] Iteration 5800, lr = 0.00145996
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I0419 14:47:51.503260 5641 solver.cpp:218] Iteration 5825 (2.43502 iter/s, 10.2669s/25 iters), loss = 0.194516
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I0419 14:47:51.503356 5641 solver.cpp:237] Train net output #0: loss = 0.194516 (* 1 = 0.194516 loss)
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I0419 14:47:51.503366 5641 sgd_solver.cpp:105] Iteration 5825, lr = 0.0014479
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I0419 14:48:01.802124 5641 solver.cpp:218] Iteration 5850 (2.42747 iter/s, 10.2988s/25 iters), loss = 0.135043
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I0419 14:48:01.802165 5641 solver.cpp:237] Train net output #0: loss = 0.135043 (* 1 = 0.135043 loss)
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I0419 14:48:01.802175 5641 sgd_solver.cpp:105] Iteration 5850, lr = 0.00143594
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I0419 14:48:08.407303 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:48:12.097473 5641 solver.cpp:218] Iteration 5875 (2.42828 iter/s, 10.2953s/25 iters), loss = 0.084661
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I0419 14:48:12.097515 5641 solver.cpp:237] Train net output #0: loss = 0.0846611 (* 1 = 0.0846611 loss)
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I0419 14:48:12.097524 5641 sgd_solver.cpp:105] Iteration 5875, lr = 0.00142408
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I0419 14:48:16.542558 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5887.caffemodel
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I0419 14:48:22.829888 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5887.solverstate
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I0419 14:48:27.559689 5641 solver.cpp:330] Iteration 5887, Testing net (#0)
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I0419 14:48:27.559708 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:48:31.015259 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:48:32.330834 5641 solver.cpp:397] Test net output #0: accuracy = 0.451593
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I0419 14:48:32.330874 5641 solver.cpp:397] Test net output #1: loss = 2.85278 (* 1 = 2.85278 loss)
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I0419 14:48:37.071161 5641 solver.cpp:218] Iteration 5900 (1.00105 iter/s, 24.9737s/25 iters), loss = 0.127019
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I0419 14:48:37.071197 5641 solver.cpp:237] Train net output #0: loss = 0.127019 (* 1 = 0.127019 loss)
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I0419 14:48:37.071204 5641 sgd_solver.cpp:105] Iteration 5900, lr = 0.00141232
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I0419 14:48:47.362195 5641 solver.cpp:218] Iteration 5925 (2.4293 iter/s, 10.291s/25 iters), loss = 0.0723131
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I0419 14:48:47.362246 5641 solver.cpp:237] Train net output #0: loss = 0.0723132 (* 1 = 0.0723132 loss)
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I0419 14:48:47.362254 5641 sgd_solver.cpp:105] Iteration 5925, lr = 0.00140065
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I0419 14:48:57.473963 5641 solver.cpp:218] Iteration 5950 (2.47237 iter/s, 10.1117s/25 iters), loss = 0.127304
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I0419 14:48:57.474061 5641 solver.cpp:237] Train net output #0: loss = 0.127304 (* 1 = 0.127304 loss)
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I0419 14:48:57.474071 5641 sgd_solver.cpp:105] Iteration 5950, lr = 0.00138908
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I0419 14:49:07.707334 5641 solver.cpp:218] Iteration 5975 (2.443 iter/s, 10.2333s/25 iters), loss = 0.157204
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I0419 14:49:07.707382 5641 solver.cpp:237] Train net output #0: loss = 0.157204 (* 1 = 0.157204 loss)
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I0419 14:49:07.707391 5641 sgd_solver.cpp:105] Iteration 5975, lr = 0.00137761
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I0419 14:49:17.954186 5641 solver.cpp:218] Iteration 6000 (2.43978 iter/s, 10.2468s/25 iters), loss = 0.142721
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I0419 14:49:17.954234 5641 solver.cpp:237] Train net output #0: loss = 0.142721 (* 1 = 0.142721 loss)
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I0419 14:49:17.954243 5641 sgd_solver.cpp:105] Iteration 6000, lr = 0.00136623
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I0419 14:49:28.131871 5641 solver.cpp:218] Iteration 6025 (2.45636 iter/s, 10.1777s/25 iters), loss = 0.270546
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I0419 14:49:28.132035 5641 solver.cpp:237] Train net output #0: loss = 0.270546 (* 1 = 0.270546 loss)
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I0419 14:49:28.132045 5641 sgd_solver.cpp:105] Iteration 6025, lr = 0.00135495
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I0419 14:49:38.425176 5641 solver.cpp:218] Iteration 6050 (2.42879 iter/s, 10.2932s/25 iters), loss = 0.153748
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I0419 14:49:38.425220 5641 solver.cpp:237] Train net output #0: loss = 0.153748 (* 1 = 0.153748 loss)
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I0419 14:49:38.425228 5641 sgd_solver.cpp:105] Iteration 6050, lr = 0.00134376
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I0419 14:49:45.908378 5649 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:49:48.691574 5641 solver.cpp:218] Iteration 6075 (2.43513 iter/s, 10.2664s/25 iters), loss = 0.259049
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I0419 14:49:48.691617 5641 solver.cpp:237] Train net output #0: loss = 0.259049 (* 1 = 0.259049 loss)
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I0419 14:49:48.691625 5641 sgd_solver.cpp:105] Iteration 6075, lr = 0.00133266
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I0419 14:49:54.345464 5641 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6090.caffemodel
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I0419 14:49:59.159415 5641 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6090.solverstate
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I0419 14:50:02.553045 5641 solver.cpp:330] Iteration 6090, Testing net (#0)
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I0419 14:50:02.553066 5641 net.cpp:676] Ignoring source layer train-data
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I0419 14:50:06.012302 5662 data_layer.cpp:73] Restarting data prefetching from start.
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I0419 14:50:07.363533 5641 solver.cpp:397] Test net output #0: accuracy = 0.476103
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I0419 14:50:07.363575 5641 solver.cpp:397] Test net output #1: loss = 2.85155 (* 1 = 2.85155 loss)
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I0419 14:50:07.363584 5641 solver.cpp:315] Optimization Done.
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I0419 14:50:07.363590 5641 caffe.cpp:259] Optimization Done.
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