4867 lines
367 KiB
Plaintext
4867 lines
367 KiB
Plaintext
I0412 14:19:55.851986 20203 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210412-125840-4f61/solver.prototxt
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I0412 14:19:55.852174 20203 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
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W0412 14:19:55.852181 20203 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
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I0412 14:19:55.852253 20203 caffe.cpp:218] Using GPUs 3
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I0412 14:19:55.867364 20203 caffe.cpp:223] GPU 3: GeForce GTX 1080 Ti
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I0412 14:19:56.181569 20203 solver.cpp:44] Initializing solver from parameters:
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test_iter: 51
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test_interval: 102
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base_lr: 0.01
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display: 12
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max_iter: 10200
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lr_policy: "exp"
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gamma: 0.99980193
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momentum: 0.9
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weight_decay: 0.0001
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snapshot: 102
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snapshot_prefix: "snapshot"
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solver_mode: GPU
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device_id: 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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I0412 14:19:56.182649 20203 solver.cpp:87] Creating training net from net file: train_val.prototxt
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I0412 14:19:56.183364 20203 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
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I0412 14:19:56.183380 20203 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
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I0412 14:19:56.183549 20203 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-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
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}
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data_param {
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source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/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: 96
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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: "conv4.2"
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type: "Convolution"
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bottom: "conv4"
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top: "conv4.2"
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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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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: "relu4.2"
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type: "ReLU"
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bottom: "conv4.2"
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top: "conv4.2"
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}
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layer {
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name: "conv4.3"
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type: "Convolution"
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bottom: "conv4.2"
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top: "conv4.3"
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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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|
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: "relu4.3"
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type: "ReLU"
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bottom: "conv4.3"
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top: "conv4.3"
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}
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layer {
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name: "conv4.4"
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type: "Convolution"
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bottom: "conv4.3"
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top: "conv4.4"
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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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|
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: "relu4.4"
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|
type: "ReLU"
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|
bottom: "conv4.4"
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|
top: "conv4.4"
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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: "conv4.4"
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|
top: "conv5"
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|
param {
|
|
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 {
|
|
num_output: 256
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|
pad: 1
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|
kernel_size: 3
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|
group: 2
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|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
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|
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: "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 {
|
|
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 {
|
|
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 {
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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: "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 {
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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: 196
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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: "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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|
I0412 14:19:56.183648 20203 layer_factory.hpp:77] Creating layer train-data
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I0412 14:19:56.189752 20203 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
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I0412 14:19:56.189978 20203 net.cpp:84] Creating Layer train-data
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I0412 14:19:56.189990 20203 net.cpp:380] train-data -> data
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I0412 14:19:56.190009 20203 net.cpp:380] train-data -> label
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I0412 14:19:56.190021 20203 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
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I0412 14:19:56.195195 20203 data_layer.cpp:45] output data size: 128,3,227,227
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I0412 14:19:56.347721 20203 net.cpp:122] Setting up train-data
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|
I0412 14:19:56.347744 20203 net.cpp:129] Top shape: 128 3 227 227 (19787136)
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I0412 14:19:56.347751 20203 net.cpp:129] Top shape: 128 (128)
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|
I0412 14:19:56.347754 20203 net.cpp:137] Memory required for data: 79149056
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|
I0412 14:19:56.347764 20203 layer_factory.hpp:77] Creating layer conv1
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I0412 14:19:56.347784 20203 net.cpp:84] Creating Layer conv1
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I0412 14:19:56.347790 20203 net.cpp:406] conv1 <- data
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|
I0412 14:19:56.347802 20203 net.cpp:380] conv1 -> conv1
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I0412 14:19:57.059183 20203 net.cpp:122] Setting up conv1
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|
I0412 14:19:57.059213 20203 net.cpp:129] Top shape: 128 96 55 55 (37171200)
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|
I0412 14:19:57.059221 20203 net.cpp:137] Memory required for data: 227833856
|
|
I0412 14:19:57.059247 20203 layer_factory.hpp:77] Creating layer relu1
|
|
I0412 14:19:57.059259 20203 net.cpp:84] Creating Layer relu1
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|
I0412 14:19:57.059267 20203 net.cpp:406] relu1 <- conv1
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|
I0412 14:19:57.059274 20203 net.cpp:367] relu1 -> conv1 (in-place)
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|
I0412 14:19:57.059715 20203 net.cpp:122] Setting up relu1
|
|
I0412 14:19:57.059728 20203 net.cpp:129] Top shape: 128 96 55 55 (37171200)
|
|
I0412 14:19:57.059734 20203 net.cpp:137] Memory required for data: 376518656
|
|
I0412 14:19:57.059741 20203 layer_factory.hpp:77] Creating layer norm1
|
|
I0412 14:19:57.059752 20203 net.cpp:84] Creating Layer norm1
|
|
I0412 14:19:57.059758 20203 net.cpp:406] norm1 <- conv1
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|
I0412 14:19:57.059767 20203 net.cpp:380] norm1 -> norm1
|
|
I0412 14:19:57.060418 20203 net.cpp:122] Setting up norm1
|
|
I0412 14:19:57.060433 20203 net.cpp:129] Top shape: 128 96 55 55 (37171200)
|
|
I0412 14:19:57.060439 20203 net.cpp:137] Memory required for data: 525203456
|
|
I0412 14:19:57.060444 20203 layer_factory.hpp:77] Creating layer pool1
|
|
I0412 14:19:57.060456 20203 net.cpp:84] Creating Layer pool1
|
|
I0412 14:19:57.060461 20203 net.cpp:406] pool1 <- norm1
|
|
I0412 14:19:57.060469 20203 net.cpp:380] pool1 -> pool1
|
|
I0412 14:19:57.060528 20203 net.cpp:122] Setting up pool1
|
|
I0412 14:19:57.060539 20203 net.cpp:129] Top shape: 128 96 27 27 (8957952)
|
|
I0412 14:19:57.060544 20203 net.cpp:137] Memory required for data: 561035264
|
|
I0412 14:19:57.060549 20203 layer_factory.hpp:77] Creating layer conv2
|
|
I0412 14:19:57.060564 20203 net.cpp:84] Creating Layer conv2
|
|
I0412 14:19:57.060571 20203 net.cpp:406] conv2 <- pool1
|
|
I0412 14:19:57.060580 20203 net.cpp:380] conv2 -> conv2
|
|
I0412 14:19:57.084409 20203 net.cpp:122] Setting up conv2
|
|
I0412 14:19:57.084430 20203 net.cpp:129] Top shape: 128 256 27 27 (23887872)
|
|
I0412 14:19:57.084434 20203 net.cpp:137] Memory required for data: 656586752
|
|
I0412 14:19:57.084447 20203 layer_factory.hpp:77] Creating layer relu2
|
|
I0412 14:19:57.084457 20203 net.cpp:84] Creating Layer relu2
|
|
I0412 14:19:57.084461 20203 net.cpp:406] relu2 <- conv2
|
|
I0412 14:19:57.084468 20203 net.cpp:367] relu2 -> conv2 (in-place)
|
|
I0412 14:19:57.085386 20203 net.cpp:122] Setting up relu2
|
|
I0412 14:19:57.085397 20203 net.cpp:129] Top shape: 128 256 27 27 (23887872)
|
|
I0412 14:19:57.085400 20203 net.cpp:137] Memory required for data: 752138240
|
|
I0412 14:19:57.085404 20203 layer_factory.hpp:77] Creating layer norm2
|
|
I0412 14:19:57.085413 20203 net.cpp:84] Creating Layer norm2
|
|
I0412 14:19:57.085417 20203 net.cpp:406] norm2 <- conv2
|
|
I0412 14:19:57.085424 20203 net.cpp:380] norm2 -> norm2
|
|
I0412 14:19:57.085721 20203 net.cpp:122] Setting up norm2
|
|
I0412 14:19:57.085729 20203 net.cpp:129] Top shape: 128 256 27 27 (23887872)
|
|
I0412 14:19:57.085734 20203 net.cpp:137] Memory required for data: 847689728
|
|
I0412 14:19:57.085737 20203 layer_factory.hpp:77] Creating layer pool2
|
|
I0412 14:19:57.085747 20203 net.cpp:84] Creating Layer pool2
|
|
I0412 14:19:57.085750 20203 net.cpp:406] pool2 <- norm2
|
|
I0412 14:19:57.085757 20203 net.cpp:380] pool2 -> pool2
|
|
I0412 14:19:57.085785 20203 net.cpp:122] Setting up pool2
|
|
I0412 14:19:57.085791 20203 net.cpp:129] Top shape: 128 256 13 13 (5537792)
|
|
I0412 14:19:57.085794 20203 net.cpp:137] Memory required for data: 869840896
|
|
I0412 14:19:57.085798 20203 layer_factory.hpp:77] Creating layer conv3
|
|
I0412 14:19:57.085829 20203 net.cpp:84] Creating Layer conv3
|
|
I0412 14:19:57.085834 20203 net.cpp:406] conv3 <- pool2
|
|
I0412 14:19:57.085841 20203 net.cpp:380] conv3 -> conv3
|
|
I0412 14:19:57.097656 20203 net.cpp:122] Setting up conv3
|
|
I0412 14:19:57.097671 20203 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
|
I0412 14:19:57.097673 20203 net.cpp:137] Memory required for data: 903067648
|
|
I0412 14:19:57.097684 20203 layer_factory.hpp:77] Creating layer relu3
|
|
I0412 14:19:57.097692 20203 net.cpp:84] Creating Layer relu3
|
|
I0412 14:19:57.097697 20203 net.cpp:406] relu3 <- conv3
|
|
I0412 14:19:57.097702 20203 net.cpp:367] relu3 -> conv3 (in-place)
|
|
I0412 14:19:57.098191 20203 net.cpp:122] Setting up relu3
|
|
I0412 14:19:57.098201 20203 net.cpp:129] Top shape: 128 384 13 13 (8306688)
|
|
I0412 14:19:57.098204 20203 net.cpp:137] Memory required for data: 936294400
|
|
I0412 14:19:57.098208 20203 layer_factory.hpp:77] Creating layer conv4
|
|
I0412 14:19:57.098218 20203 net.cpp:84] Creating Layer conv4
|
|
I0412 14:19:57.098222 20203 net.cpp:406] conv4 <- conv3
|
|
I0412 14:19:57.098229 20203 net.cpp:380] conv4 -> conv4
|
|
I0412 14:19:57.103674 20203 net.cpp:122] Setting up conv4
|
|
I0412 14:19:57.103686 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
|
|
I0412 14:19:57.103690 20203 net.cpp:137] Memory required for data: 944601088
|
|
I0412 14:19:57.103698 20203 layer_factory.hpp:77] Creating layer relu4
|
|
I0412 14:19:57.103704 20203 net.cpp:84] Creating Layer relu4
|
|
I0412 14:19:57.103708 20203 net.cpp:406] relu4 <- conv4
|
|
I0412 14:19:57.103715 20203 net.cpp:367] relu4 -> conv4 (in-place)
|
|
I0412 14:19:57.104053 20203 net.cpp:122] Setting up relu4
|
|
I0412 14:19:57.104060 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
|
|
I0412 14:19:57.104064 20203 net.cpp:137] Memory required for data: 952907776
|
|
I0412 14:19:57.104068 20203 layer_factory.hpp:77] Creating layer conv4.2
|
|
I0412 14:19:57.104077 20203 net.cpp:84] Creating Layer conv4.2
|
|
I0412 14:19:57.104080 20203 net.cpp:406] conv4.2 <- conv4
|
|
I0412 14:19:57.104087 20203 net.cpp:380] conv4.2 -> conv4.2
|
|
I0412 14:19:57.108472 20203 net.cpp:122] Setting up conv4.2
|
|
I0412 14:19:57.108484 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
|
|
I0412 14:19:57.108489 20203 net.cpp:137] Memory required for data: 961214464
|
|
I0412 14:19:57.108500 20203 layer_factory.hpp:77] Creating layer relu4.2
|
|
I0412 14:19:57.108506 20203 net.cpp:84] Creating Layer relu4.2
|
|
I0412 14:19:57.108510 20203 net.cpp:406] relu4.2 <- conv4.2
|
|
I0412 14:19:57.108516 20203 net.cpp:367] relu4.2 -> conv4.2 (in-place)
|
|
I0412 14:19:57.108997 20203 net.cpp:122] Setting up relu4.2
|
|
I0412 14:19:57.109006 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
|
|
I0412 14:19:57.109010 20203 net.cpp:137] Memory required for data: 969521152
|
|
I0412 14:19:57.109014 20203 layer_factory.hpp:77] Creating layer conv4.3
|
|
I0412 14:19:57.109025 20203 net.cpp:84] Creating Layer conv4.3
|
|
I0412 14:19:57.109028 20203 net.cpp:406] conv4.3 <- conv4.2
|
|
I0412 14:19:57.109035 20203 net.cpp:380] conv4.3 -> conv4.3
|
|
I0412 14:19:57.112463 20203 net.cpp:122] Setting up conv4.3
|
|
I0412 14:19:57.112473 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
|
|
I0412 14:19:57.112478 20203 net.cpp:137] Memory required for data: 977827840
|
|
I0412 14:19:57.112484 20203 layer_factory.hpp:77] Creating layer relu4.3
|
|
I0412 14:19:57.112493 20203 net.cpp:84] Creating Layer relu4.3
|
|
I0412 14:19:57.112496 20203 net.cpp:406] relu4.3 <- conv4.3
|
|
I0412 14:19:57.112504 20203 net.cpp:367] relu4.3 -> conv4.3 (in-place)
|
|
I0412 14:19:57.112991 20203 net.cpp:122] Setting up relu4.3
|
|
I0412 14:19:57.113000 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
|
|
I0412 14:19:57.113003 20203 net.cpp:137] Memory required for data: 986134528
|
|
I0412 14:19:57.113008 20203 layer_factory.hpp:77] Creating layer conv4.4
|
|
I0412 14:19:57.113016 20203 net.cpp:84] Creating Layer conv4.4
|
|
I0412 14:19:57.113020 20203 net.cpp:406] conv4.4 <- conv4.3
|
|
I0412 14:19:57.113027 20203 net.cpp:380] conv4.4 -> conv4.4
|
|
I0412 14:19:57.118353 20203 net.cpp:122] Setting up conv4.4
|
|
I0412 14:19:57.118371 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
|
|
I0412 14:19:57.118376 20203 net.cpp:137] Memory required for data: 994441216
|
|
I0412 14:19:57.118407 20203 layer_factory.hpp:77] Creating layer relu4.4
|
|
I0412 14:19:57.118414 20203 net.cpp:84] Creating Layer relu4.4
|
|
I0412 14:19:57.118423 20203 net.cpp:406] relu4.4 <- conv4.4
|
|
I0412 14:19:57.118430 20203 net.cpp:367] relu4.4 -> conv4.4 (in-place)
|
|
I0412 14:19:57.119064 20203 net.cpp:122] Setting up relu4.4
|
|
I0412 14:19:57.119076 20203 net.cpp:129] Top shape: 128 96 13 13 (2076672)
|
|
I0412 14:19:57.119081 20203 net.cpp:137] Memory required for data: 1002747904
|
|
I0412 14:19:57.119086 20203 layer_factory.hpp:77] Creating layer conv5
|
|
I0412 14:19:57.119099 20203 net.cpp:84] Creating Layer conv5
|
|
I0412 14:19:57.119104 20203 net.cpp:406] conv5 <- conv4.4
|
|
I0412 14:19:57.119114 20203 net.cpp:380] conv5 -> conv5
|
|
I0412 14:19:57.125795 20203 net.cpp:122] Setting up conv5
|
|
I0412 14:19:57.125809 20203 net.cpp:129] Top shape: 128 256 13 13 (5537792)
|
|
I0412 14:19:57.125816 20203 net.cpp:137] Memory required for data: 1024899072
|
|
I0412 14:19:57.125826 20203 layer_factory.hpp:77] Creating layer relu5
|
|
I0412 14:19:57.125836 20203 net.cpp:84] Creating Layer relu5
|
|
I0412 14:19:57.125841 20203 net.cpp:406] relu5 <- conv5
|
|
I0412 14:19:57.125849 20203 net.cpp:367] relu5 -> conv5 (in-place)
|
|
I0412 14:19:57.126375 20203 net.cpp:122] Setting up relu5
|
|
I0412 14:19:57.126386 20203 net.cpp:129] Top shape: 128 256 13 13 (5537792)
|
|
I0412 14:19:57.126390 20203 net.cpp:137] Memory required for data: 1047050240
|
|
I0412 14:19:57.126394 20203 layer_factory.hpp:77] Creating layer pool5
|
|
I0412 14:19:57.126401 20203 net.cpp:84] Creating Layer pool5
|
|
I0412 14:19:57.126405 20203 net.cpp:406] pool5 <- conv5
|
|
I0412 14:19:57.126412 20203 net.cpp:380] pool5 -> pool5
|
|
I0412 14:19:57.126451 20203 net.cpp:122] Setting up pool5
|
|
I0412 14:19:57.126457 20203 net.cpp:129] Top shape: 128 256 6 6 (1179648)
|
|
I0412 14:19:57.126461 20203 net.cpp:137] Memory required for data: 1051768832
|
|
I0412 14:19:57.126464 20203 layer_factory.hpp:77] Creating layer fc6
|
|
I0412 14:19:57.126472 20203 net.cpp:84] Creating Layer fc6
|
|
I0412 14:19:57.126477 20203 net.cpp:406] fc6 <- pool5
|
|
I0412 14:19:57.126483 20203 net.cpp:380] fc6 -> fc6
|
|
I0412 14:19:57.557391 20203 net.cpp:122] Setting up fc6
|
|
I0412 14:19:57.557415 20203 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0412 14:19:57.557420 20203 net.cpp:137] Memory required for data: 1053865984
|
|
I0412 14:19:57.557432 20203 layer_factory.hpp:77] Creating layer relu6
|
|
I0412 14:19:57.557442 20203 net.cpp:84] Creating Layer relu6
|
|
I0412 14:19:57.557446 20203 net.cpp:406] relu6 <- fc6
|
|
I0412 14:19:57.557453 20203 net.cpp:367] relu6 -> fc6 (in-place)
|
|
I0412 14:19:57.558105 20203 net.cpp:122] Setting up relu6
|
|
I0412 14:19:57.558116 20203 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0412 14:19:57.558120 20203 net.cpp:137] Memory required for data: 1055963136
|
|
I0412 14:19:57.558125 20203 layer_factory.hpp:77] Creating layer drop6
|
|
I0412 14:19:57.558130 20203 net.cpp:84] Creating Layer drop6
|
|
I0412 14:19:57.558135 20203 net.cpp:406] drop6 <- fc6
|
|
I0412 14:19:57.558140 20203 net.cpp:367] drop6 -> fc6 (in-place)
|
|
I0412 14:19:57.558167 20203 net.cpp:122] Setting up drop6
|
|
I0412 14:19:57.558173 20203 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0412 14:19:57.558177 20203 net.cpp:137] Memory required for data: 1058060288
|
|
I0412 14:19:57.558180 20203 layer_factory.hpp:77] Creating layer fc7
|
|
I0412 14:19:57.558189 20203 net.cpp:84] Creating Layer fc7
|
|
I0412 14:19:57.558192 20203 net.cpp:406] fc7 <- fc6
|
|
I0412 14:19:57.558198 20203 net.cpp:380] fc7 -> fc7
|
|
I0412 14:19:57.737767 20203 net.cpp:122] Setting up fc7
|
|
I0412 14:19:57.737788 20203 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0412 14:19:57.737792 20203 net.cpp:137] Memory required for data: 1060157440
|
|
I0412 14:19:57.737802 20203 layer_factory.hpp:77] Creating layer relu7
|
|
I0412 14:19:57.737812 20203 net.cpp:84] Creating Layer relu7
|
|
I0412 14:19:57.737818 20203 net.cpp:406] relu7 <- fc7
|
|
I0412 14:19:57.737824 20203 net.cpp:367] relu7 -> fc7 (in-place)
|
|
I0412 14:19:57.738255 20203 net.cpp:122] Setting up relu7
|
|
I0412 14:19:57.738265 20203 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0412 14:19:57.738268 20203 net.cpp:137] Memory required for data: 1062254592
|
|
I0412 14:19:57.738291 20203 layer_factory.hpp:77] Creating layer drop7
|
|
I0412 14:19:57.738298 20203 net.cpp:84] Creating Layer drop7
|
|
I0412 14:19:57.738301 20203 net.cpp:406] drop7 <- fc7
|
|
I0412 14:19:57.738307 20203 net.cpp:367] drop7 -> fc7 (in-place)
|
|
I0412 14:19:57.738332 20203 net.cpp:122] Setting up drop7
|
|
I0412 14:19:57.738337 20203 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0412 14:19:57.738339 20203 net.cpp:137] Memory required for data: 1064351744
|
|
I0412 14:19:57.738343 20203 layer_factory.hpp:77] Creating layer fc8
|
|
I0412 14:19:57.738350 20203 net.cpp:84] Creating Layer fc8
|
|
I0412 14:19:57.738354 20203 net.cpp:406] fc8 <- fc7
|
|
I0412 14:19:57.738360 20203 net.cpp:380] fc8 -> fc8
|
|
I0412 14:19:57.746182 20203 net.cpp:122] Setting up fc8
|
|
I0412 14:19:57.746196 20203 net.cpp:129] Top shape: 128 196 (25088)
|
|
I0412 14:19:57.746198 20203 net.cpp:137] Memory required for data: 1064452096
|
|
I0412 14:19:57.746206 20203 layer_factory.hpp:77] Creating layer loss
|
|
I0412 14:19:57.746214 20203 net.cpp:84] Creating Layer loss
|
|
I0412 14:19:57.746218 20203 net.cpp:406] loss <- fc8
|
|
I0412 14:19:57.746223 20203 net.cpp:406] loss <- label
|
|
I0412 14:19:57.746230 20203 net.cpp:380] loss -> loss
|
|
I0412 14:19:57.746240 20203 layer_factory.hpp:77] Creating layer loss
|
|
I0412 14:19:57.747061 20203 net.cpp:122] Setting up loss
|
|
I0412 14:19:57.747071 20203 net.cpp:129] Top shape: (1)
|
|
I0412 14:19:57.747073 20203 net.cpp:132] with loss weight 1
|
|
I0412 14:19:57.747089 20203 net.cpp:137] Memory required for data: 1064452100
|
|
I0412 14:19:57.747094 20203 net.cpp:198] loss needs backward computation.
|
|
I0412 14:19:57.747102 20203 net.cpp:198] fc8 needs backward computation.
|
|
I0412 14:19:57.747105 20203 net.cpp:198] drop7 needs backward computation.
|
|
I0412 14:19:57.747108 20203 net.cpp:198] relu7 needs backward computation.
|
|
I0412 14:19:57.747112 20203 net.cpp:198] fc7 needs backward computation.
|
|
I0412 14:19:57.747115 20203 net.cpp:198] drop6 needs backward computation.
|
|
I0412 14:19:57.747119 20203 net.cpp:198] relu6 needs backward computation.
|
|
I0412 14:19:57.747123 20203 net.cpp:198] fc6 needs backward computation.
|
|
I0412 14:19:57.747126 20203 net.cpp:198] pool5 needs backward computation.
|
|
I0412 14:19:57.747130 20203 net.cpp:198] relu5 needs backward computation.
|
|
I0412 14:19:57.747134 20203 net.cpp:198] conv5 needs backward computation.
|
|
I0412 14:19:57.747138 20203 net.cpp:198] relu4.4 needs backward computation.
|
|
I0412 14:19:57.747141 20203 net.cpp:198] conv4.4 needs backward computation.
|
|
I0412 14:19:57.747145 20203 net.cpp:198] relu4.3 needs backward computation.
|
|
I0412 14:19:57.747149 20203 net.cpp:198] conv4.3 needs backward computation.
|
|
I0412 14:19:57.747153 20203 net.cpp:198] relu4.2 needs backward computation.
|
|
I0412 14:19:57.747156 20203 net.cpp:198] conv4.2 needs backward computation.
|
|
I0412 14:19:57.747160 20203 net.cpp:198] relu4 needs backward computation.
|
|
I0412 14:19:57.747164 20203 net.cpp:198] conv4 needs backward computation.
|
|
I0412 14:19:57.747167 20203 net.cpp:198] relu3 needs backward computation.
|
|
I0412 14:19:57.747172 20203 net.cpp:198] conv3 needs backward computation.
|
|
I0412 14:19:57.747177 20203 net.cpp:198] pool2 needs backward computation.
|
|
I0412 14:19:57.747180 20203 net.cpp:198] norm2 needs backward computation.
|
|
I0412 14:19:57.747185 20203 net.cpp:198] relu2 needs backward computation.
|
|
I0412 14:19:57.747189 20203 net.cpp:198] conv2 needs backward computation.
|
|
I0412 14:19:57.747192 20203 net.cpp:198] pool1 needs backward computation.
|
|
I0412 14:19:57.747195 20203 net.cpp:198] norm1 needs backward computation.
|
|
I0412 14:19:57.747200 20203 net.cpp:198] relu1 needs backward computation.
|
|
I0412 14:19:57.747202 20203 net.cpp:198] conv1 needs backward computation.
|
|
I0412 14:19:57.747206 20203 net.cpp:200] train-data does not need backward computation.
|
|
I0412 14:19:57.747210 20203 net.cpp:242] This network produces output loss
|
|
I0412 14:19:57.747226 20203 net.cpp:255] Network initialization done.
|
|
I0412 14:19:57.754376 20203 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
|
|
I0412 14:19:57.754411 20203 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
|
|
I0412 14:19:57.754595 20203 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-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
|
|
}
|
|
data_param {
|
|
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/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: 96
|
|
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: "conv4.2"
|
|
type: "Convolution"
|
|
bottom: "conv4"
|
|
top: "conv4.2"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 96
|
|
pad: 1
|
|
kernel_size: 3
|
|
group: 2
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu4.2"
|
|
type: "ReLU"
|
|
bottom: "conv4.2"
|
|
top: "conv4.2"
|
|
}
|
|
layer {
|
|
name: "conv4.3"
|
|
type: "Convolution"
|
|
bottom: "conv4.2"
|
|
top: "conv4.3"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 96
|
|
pad: 1
|
|
kernel_size: 3
|
|
group: 2
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu4.3"
|
|
type: "ReLU"
|
|
bottom: "conv4.3"
|
|
top: "conv4.3"
|
|
}
|
|
layer {
|
|
name: "conv4.4"
|
|
type: "Convolution"
|
|
bottom: "conv4.3"
|
|
top: "conv4.4"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 96
|
|
pad: 1
|
|
kernel_size: 3
|
|
group: 2
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu4.4"
|
|
type: "ReLU"
|
|
bottom: "conv4.4"
|
|
top: "conv4.4"
|
|
}
|
|
layer {
|
|
name: "conv5"
|
|
type: "Convolution"
|
|
bottom: "conv4.4"
|
|
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"
|
|
}
|
|
I0412 14:19:57.754696 20203 layer_factory.hpp:77] Creating layer val-data
|
|
I0412 14:19:57.756206 20203 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
|
|
I0412 14:19:57.756412 20203 net.cpp:84] Creating Layer val-data
|
|
I0412 14:19:57.756420 20203 net.cpp:380] val-data -> data
|
|
I0412 14:19:57.756431 20203 net.cpp:380] val-data -> label
|
|
I0412 14:19:57.756438 20203 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
|
|
I0412 14:19:57.760958 20203 data_layer.cpp:45] output data size: 32,3,227,227
|
|
I0412 14:19:57.800777 20203 net.cpp:122] Setting up val-data
|
|
I0412 14:19:57.800796 20203 net.cpp:129] Top shape: 32 3 227 227 (4946784)
|
|
I0412 14:19:57.800801 20203 net.cpp:129] Top shape: 32 (32)
|
|
I0412 14:19:57.800804 20203 net.cpp:137] Memory required for data: 19787264
|
|
I0412 14:19:57.800810 20203 layer_factory.hpp:77] Creating layer label_val-data_1_split
|
|
I0412 14:19:57.800822 20203 net.cpp:84] Creating Layer label_val-data_1_split
|
|
I0412 14:19:57.800827 20203 net.cpp:406] label_val-data_1_split <- label
|
|
I0412 14:19:57.800833 20203 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
|
|
I0412 14:19:57.800843 20203 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
|
|
I0412 14:19:57.800920 20203 net.cpp:122] Setting up label_val-data_1_split
|
|
I0412 14:19:57.800925 20203 net.cpp:129] Top shape: 32 (32)
|
|
I0412 14:19:57.800930 20203 net.cpp:129] Top shape: 32 (32)
|
|
I0412 14:19:57.800951 20203 net.cpp:137] Memory required for data: 19787520
|
|
I0412 14:19:57.800954 20203 layer_factory.hpp:77] Creating layer conv1
|
|
I0412 14:19:57.800966 20203 net.cpp:84] Creating Layer conv1
|
|
I0412 14:19:57.800969 20203 net.cpp:406] conv1 <- data
|
|
I0412 14:19:57.800976 20203 net.cpp:380] conv1 -> conv1
|
|
I0412 14:19:57.802868 20203 net.cpp:122] Setting up conv1
|
|
I0412 14:19:57.802881 20203 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
|
I0412 14:19:57.802884 20203 net.cpp:137] Memory required for data: 56958720
|
|
I0412 14:19:57.802894 20203 layer_factory.hpp:77] Creating layer relu1
|
|
I0412 14:19:57.802901 20203 net.cpp:84] Creating Layer relu1
|
|
I0412 14:19:57.802906 20203 net.cpp:406] relu1 <- conv1
|
|
I0412 14:19:57.802909 20203 net.cpp:367] relu1 -> conv1 (in-place)
|
|
I0412 14:19:57.803200 20203 net.cpp:122] Setting up relu1
|
|
I0412 14:19:57.803208 20203 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
|
I0412 14:19:57.803211 20203 net.cpp:137] Memory required for data: 94129920
|
|
I0412 14:19:57.803215 20203 layer_factory.hpp:77] Creating layer norm1
|
|
I0412 14:19:57.803223 20203 net.cpp:84] Creating Layer norm1
|
|
I0412 14:19:57.803227 20203 net.cpp:406] norm1 <- conv1
|
|
I0412 14:19:57.803232 20203 net.cpp:380] norm1 -> norm1
|
|
I0412 14:19:57.806309 20203 net.cpp:122] Setting up norm1
|
|
I0412 14:19:57.806321 20203 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
|
I0412 14:19:57.806325 20203 net.cpp:137] Memory required for data: 131301120
|
|
I0412 14:19:57.806329 20203 layer_factory.hpp:77] Creating layer pool1
|
|
I0412 14:19:57.806336 20203 net.cpp:84] Creating Layer pool1
|
|
I0412 14:19:57.806340 20203 net.cpp:406] pool1 <- norm1
|
|
I0412 14:19:57.806346 20203 net.cpp:380] pool1 -> pool1
|
|
I0412 14:19:57.806376 20203 net.cpp:122] Setting up pool1
|
|
I0412 14:19:57.806382 20203 net.cpp:129] Top shape: 32 96 27 27 (2239488)
|
|
I0412 14:19:57.806385 20203 net.cpp:137] Memory required for data: 140259072
|
|
I0412 14:19:57.806389 20203 layer_factory.hpp:77] Creating layer conv2
|
|
I0412 14:19:57.806398 20203 net.cpp:84] Creating Layer conv2
|
|
I0412 14:19:57.806401 20203 net.cpp:406] conv2 <- pool1
|
|
I0412 14:19:57.806406 20203 net.cpp:380] conv2 -> conv2
|
|
I0412 14:19:57.812628 20203 net.cpp:122] Setting up conv2
|
|
I0412 14:19:57.812642 20203 net.cpp:129] Top shape: 32 256 27 27 (5971968)
|
|
I0412 14:19:57.812646 20203 net.cpp:137] Memory required for data: 164146944
|
|
I0412 14:19:57.812656 20203 layer_factory.hpp:77] Creating layer relu2
|
|
I0412 14:19:57.812664 20203 net.cpp:84] Creating Layer relu2
|
|
I0412 14:19:57.812667 20203 net.cpp:406] relu2 <- conv2
|
|
I0412 14:19:57.812674 20203 net.cpp:367] relu2 -> conv2 (in-place)
|
|
I0412 14:19:57.813177 20203 net.cpp:122] Setting up relu2
|
|
I0412 14:19:57.813186 20203 net.cpp:129] Top shape: 32 256 27 27 (5971968)
|
|
I0412 14:19:57.813190 20203 net.cpp:137] Memory required for data: 188034816
|
|
I0412 14:19:57.813194 20203 layer_factory.hpp:77] Creating layer norm2
|
|
I0412 14:19:57.813205 20203 net.cpp:84] Creating Layer norm2
|
|
I0412 14:19:57.813208 20203 net.cpp:406] norm2 <- conv2
|
|
I0412 14:19:57.813215 20203 net.cpp:380] norm2 -> norm2
|
|
I0412 14:19:57.813580 20203 net.cpp:122] Setting up norm2
|
|
I0412 14:19:57.813589 20203 net.cpp:129] Top shape: 32 256 27 27 (5971968)
|
|
I0412 14:19:57.813592 20203 net.cpp:137] Memory required for data: 211922688
|
|
I0412 14:19:57.813596 20203 layer_factory.hpp:77] Creating layer pool2
|
|
I0412 14:19:57.813603 20203 net.cpp:84] Creating Layer pool2
|
|
I0412 14:19:57.813607 20203 net.cpp:406] pool2 <- norm2
|
|
I0412 14:19:57.813613 20203 net.cpp:380] pool2 -> pool2
|
|
I0412 14:19:57.813643 20203 net.cpp:122] Setting up pool2
|
|
I0412 14:19:57.813648 20203 net.cpp:129] Top shape: 32 256 13 13 (1384448)
|
|
I0412 14:19:57.813652 20203 net.cpp:137] Memory required for data: 217460480
|
|
I0412 14:19:57.813655 20203 layer_factory.hpp:77] Creating layer conv3
|
|
I0412 14:19:57.813666 20203 net.cpp:84] Creating Layer conv3
|
|
I0412 14:19:57.813670 20203 net.cpp:406] conv3 <- pool2
|
|
I0412 14:19:57.813675 20203 net.cpp:380] conv3 -> conv3
|
|
I0412 14:19:57.829823 20203 net.cpp:122] Setting up conv3
|
|
I0412 14:19:57.829846 20203 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
|
I0412 14:19:57.829870 20203 net.cpp:137] Memory required for data: 225767168
|
|
I0412 14:19:57.829885 20203 layer_factory.hpp:77] Creating layer relu3
|
|
I0412 14:19:57.829896 20203 net.cpp:84] Creating Layer relu3
|
|
I0412 14:19:57.829900 20203 net.cpp:406] relu3 <- conv3
|
|
I0412 14:19:57.829910 20203 net.cpp:367] relu3 -> conv3 (in-place)
|
|
I0412 14:19:57.830451 20203 net.cpp:122] Setting up relu3
|
|
I0412 14:19:57.830459 20203 net.cpp:129] Top shape: 32 384 13 13 (2076672)
|
|
I0412 14:19:57.830463 20203 net.cpp:137] Memory required for data: 234073856
|
|
I0412 14:19:57.830467 20203 layer_factory.hpp:77] Creating layer conv4
|
|
I0412 14:19:57.830479 20203 net.cpp:84] Creating Layer conv4
|
|
I0412 14:19:57.830483 20203 net.cpp:406] conv4 <- conv3
|
|
I0412 14:19:57.830490 20203 net.cpp:380] conv4 -> conv4
|
|
I0412 14:19:57.834941 20203 net.cpp:122] Setting up conv4
|
|
I0412 14:19:57.834954 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
|
|
I0412 14:19:57.834957 20203 net.cpp:137] Memory required for data: 236150528
|
|
I0412 14:19:57.834964 20203 layer_factory.hpp:77] Creating layer relu4
|
|
I0412 14:19:57.834971 20203 net.cpp:84] Creating Layer relu4
|
|
I0412 14:19:57.834975 20203 net.cpp:406] relu4 <- conv4
|
|
I0412 14:19:57.834982 20203 net.cpp:367] relu4 -> conv4 (in-place)
|
|
I0412 14:19:57.835481 20203 net.cpp:122] Setting up relu4
|
|
I0412 14:19:57.835490 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
|
|
I0412 14:19:57.835494 20203 net.cpp:137] Memory required for data: 238227200
|
|
I0412 14:19:57.835498 20203 layer_factory.hpp:77] Creating layer conv4.2
|
|
I0412 14:19:57.835508 20203 net.cpp:84] Creating Layer conv4.2
|
|
I0412 14:19:57.835512 20203 net.cpp:406] conv4.2 <- conv4
|
|
I0412 14:19:57.835520 20203 net.cpp:380] conv4.2 -> conv4.2
|
|
I0412 14:19:57.840180 20203 net.cpp:122] Setting up conv4.2
|
|
I0412 14:19:57.840196 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
|
|
I0412 14:19:57.840200 20203 net.cpp:137] Memory required for data: 240303872
|
|
I0412 14:19:57.840212 20203 layer_factory.hpp:77] Creating layer relu4.2
|
|
I0412 14:19:57.840220 20203 net.cpp:84] Creating Layer relu4.2
|
|
I0412 14:19:57.840224 20203 net.cpp:406] relu4.2 <- conv4.2
|
|
I0412 14:19:57.840230 20203 net.cpp:367] relu4.2 -> conv4.2 (in-place)
|
|
I0412 14:19:57.840734 20203 net.cpp:122] Setting up relu4.2
|
|
I0412 14:19:57.840744 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
|
|
I0412 14:19:57.840747 20203 net.cpp:137] Memory required for data: 242380544
|
|
I0412 14:19:57.840750 20203 layer_factory.hpp:77] Creating layer conv4.3
|
|
I0412 14:19:57.840765 20203 net.cpp:84] Creating Layer conv4.3
|
|
I0412 14:19:57.840770 20203 net.cpp:406] conv4.3 <- conv4.2
|
|
I0412 14:19:57.840775 20203 net.cpp:380] conv4.3 -> conv4.3
|
|
I0412 14:19:57.845449 20203 net.cpp:122] Setting up conv4.3
|
|
I0412 14:19:57.845466 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
|
|
I0412 14:19:57.845470 20203 net.cpp:137] Memory required for data: 244457216
|
|
I0412 14:19:57.845479 20203 layer_factory.hpp:77] Creating layer relu4.3
|
|
I0412 14:19:57.845487 20203 net.cpp:84] Creating Layer relu4.3
|
|
I0412 14:19:57.845492 20203 net.cpp:406] relu4.3 <- conv4.3
|
|
I0412 14:19:57.845499 20203 net.cpp:367] relu4.3 -> conv4.3 (in-place)
|
|
I0412 14:19:57.845846 20203 net.cpp:122] Setting up relu4.3
|
|
I0412 14:19:57.845856 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
|
|
I0412 14:19:57.845860 20203 net.cpp:137] Memory required for data: 246533888
|
|
I0412 14:19:57.845863 20203 layer_factory.hpp:77] Creating layer conv4.4
|
|
I0412 14:19:57.845875 20203 net.cpp:84] Creating Layer conv4.4
|
|
I0412 14:19:57.845880 20203 net.cpp:406] conv4.4 <- conv4.3
|
|
I0412 14:19:57.845886 20203 net.cpp:380] conv4.4 -> conv4.4
|
|
I0412 14:19:57.849527 20203 net.cpp:122] Setting up conv4.4
|
|
I0412 14:19:57.849540 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
|
|
I0412 14:19:57.849545 20203 net.cpp:137] Memory required for data: 248610560
|
|
I0412 14:19:57.849552 20203 layer_factory.hpp:77] Creating layer relu4.4
|
|
I0412 14:19:57.849558 20203 net.cpp:84] Creating Layer relu4.4
|
|
I0412 14:19:57.849562 20203 net.cpp:406] relu4.4 <- conv4.4
|
|
I0412 14:19:57.849568 20203 net.cpp:367] relu4.4 -> conv4.4 (in-place)
|
|
I0412 14:19:57.851155 20203 net.cpp:122] Setting up relu4.4
|
|
I0412 14:19:57.851184 20203 net.cpp:129] Top shape: 32 96 13 13 (519168)
|
|
I0412 14:19:57.851187 20203 net.cpp:137] Memory required for data: 250687232
|
|
I0412 14:19:57.851191 20203 layer_factory.hpp:77] Creating layer conv5
|
|
I0412 14:19:57.851203 20203 net.cpp:84] Creating Layer conv5
|
|
I0412 14:19:57.851207 20203 net.cpp:406] conv5 <- conv4.4
|
|
I0412 14:19:57.851214 20203 net.cpp:380] conv5 -> conv5
|
|
I0412 14:19:57.855340 20203 net.cpp:122] Setting up conv5
|
|
I0412 14:19:57.855350 20203 net.cpp:129] Top shape: 32 256 13 13 (1384448)
|
|
I0412 14:19:57.855355 20203 net.cpp:137] Memory required for data: 256225024
|
|
I0412 14:19:57.855361 20203 layer_factory.hpp:77] Creating layer relu5
|
|
I0412 14:19:57.855370 20203 net.cpp:84] Creating Layer relu5
|
|
I0412 14:19:57.855373 20203 net.cpp:406] relu5 <- conv5
|
|
I0412 14:19:57.855378 20203 net.cpp:367] relu5 -> conv5 (in-place)
|
|
I0412 14:19:57.855880 20203 net.cpp:122] Setting up relu5
|
|
I0412 14:19:57.855890 20203 net.cpp:129] Top shape: 32 256 13 13 (1384448)
|
|
I0412 14:19:57.855892 20203 net.cpp:137] Memory required for data: 261762816
|
|
I0412 14:19:57.855896 20203 layer_factory.hpp:77] Creating layer pool5
|
|
I0412 14:19:57.855903 20203 net.cpp:84] Creating Layer pool5
|
|
I0412 14:19:57.855907 20203 net.cpp:406] pool5 <- conv5
|
|
I0412 14:19:57.855913 20203 net.cpp:380] pool5 -> pool5
|
|
I0412 14:19:57.855957 20203 net.cpp:122] Setting up pool5
|
|
I0412 14:19:57.855963 20203 net.cpp:129] Top shape: 32 256 6 6 (294912)
|
|
I0412 14:19:57.855967 20203 net.cpp:137] Memory required for data: 262942464
|
|
I0412 14:19:57.855969 20203 layer_factory.hpp:77] Creating layer fc6
|
|
I0412 14:19:57.855976 20203 net.cpp:84] Creating Layer fc6
|
|
I0412 14:19:57.855980 20203 net.cpp:406] fc6 <- pool5
|
|
I0412 14:19:57.855986 20203 net.cpp:380] fc6 -> fc6
|
|
I0412 14:19:58.210417 20203 net.cpp:122] Setting up fc6
|
|
I0412 14:19:58.210438 20203 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0412 14:19:58.210443 20203 net.cpp:137] Memory required for data: 263466752
|
|
I0412 14:19:58.210458 20203 layer_factory.hpp:77] Creating layer relu6
|
|
I0412 14:19:58.210467 20203 net.cpp:84] Creating Layer relu6
|
|
I0412 14:19:58.210471 20203 net.cpp:406] relu6 <- fc6
|
|
I0412 14:19:58.210479 20203 net.cpp:367] relu6 -> fc6 (in-place)
|
|
I0412 14:19:58.210903 20203 net.cpp:122] Setting up relu6
|
|
I0412 14:19:58.210912 20203 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0412 14:19:58.210916 20203 net.cpp:137] Memory required for data: 263991040
|
|
I0412 14:19:58.210919 20203 layer_factory.hpp:77] Creating layer drop6
|
|
I0412 14:19:58.210925 20203 net.cpp:84] Creating Layer drop6
|
|
I0412 14:19:58.210929 20203 net.cpp:406] drop6 <- fc6
|
|
I0412 14:19:58.210935 20203 net.cpp:367] drop6 -> fc6 (in-place)
|
|
I0412 14:19:58.210958 20203 net.cpp:122] Setting up drop6
|
|
I0412 14:19:58.210964 20203 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0412 14:19:58.210968 20203 net.cpp:137] Memory required for data: 264515328
|
|
I0412 14:19:58.210970 20203 layer_factory.hpp:77] Creating layer fc7
|
|
I0412 14:19:58.210978 20203 net.cpp:84] Creating Layer fc7
|
|
I0412 14:19:58.210980 20203 net.cpp:406] fc7 <- fc6
|
|
I0412 14:19:58.210986 20203 net.cpp:380] fc7 -> fc7
|
|
I0412 14:19:58.371541 20203 net.cpp:122] Setting up fc7
|
|
I0412 14:19:58.371562 20203 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0412 14:19:58.371567 20203 net.cpp:137] Memory required for data: 265039616
|
|
I0412 14:19:58.371577 20203 layer_factory.hpp:77] Creating layer relu7
|
|
I0412 14:19:58.371585 20203 net.cpp:84] Creating Layer relu7
|
|
I0412 14:19:58.371590 20203 net.cpp:406] relu7 <- fc7
|
|
I0412 14:19:58.371598 20203 net.cpp:367] relu7 -> fc7 (in-place)
|
|
I0412 14:19:58.372228 20203 net.cpp:122] Setting up relu7
|
|
I0412 14:19:58.372238 20203 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0412 14:19:58.372241 20203 net.cpp:137] Memory required for data: 265563904
|
|
I0412 14:19:58.372246 20203 layer_factory.hpp:77] Creating layer drop7
|
|
I0412 14:19:58.372252 20203 net.cpp:84] Creating Layer drop7
|
|
I0412 14:19:58.372256 20203 net.cpp:406] drop7 <- fc7
|
|
I0412 14:19:58.372262 20203 net.cpp:367] drop7 -> fc7 (in-place)
|
|
I0412 14:19:58.372287 20203 net.cpp:122] Setting up drop7
|
|
I0412 14:19:58.372310 20203 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0412 14:19:58.372313 20203 net.cpp:137] Memory required for data: 266088192
|
|
I0412 14:19:58.372318 20203 layer_factory.hpp:77] Creating layer fc8
|
|
I0412 14:19:58.372325 20203 net.cpp:84] Creating Layer fc8
|
|
I0412 14:19:58.372328 20203 net.cpp:406] fc8 <- fc7
|
|
I0412 14:19:58.372335 20203 net.cpp:380] fc8 -> fc8
|
|
I0412 14:19:58.380040 20203 net.cpp:122] Setting up fc8
|
|
I0412 14:19:58.380053 20203 net.cpp:129] Top shape: 32 196 (6272)
|
|
I0412 14:19:58.380055 20203 net.cpp:137] Memory required for data: 266113280
|
|
I0412 14:19:58.380062 20203 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
|
|
I0412 14:19:58.380071 20203 net.cpp:84] Creating Layer fc8_fc8_0_split
|
|
I0412 14:19:58.380074 20203 net.cpp:406] fc8_fc8_0_split <- fc8
|
|
I0412 14:19:58.380080 20203 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
|
|
I0412 14:19:58.380087 20203 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
|
|
I0412 14:19:58.380121 20203 net.cpp:122] Setting up fc8_fc8_0_split
|
|
I0412 14:19:58.380125 20203 net.cpp:129] Top shape: 32 196 (6272)
|
|
I0412 14:19:58.380129 20203 net.cpp:129] Top shape: 32 196 (6272)
|
|
I0412 14:19:58.380132 20203 net.cpp:137] Memory required for data: 266163456
|
|
I0412 14:19:58.380136 20203 layer_factory.hpp:77] Creating layer accuracy
|
|
I0412 14:19:58.380143 20203 net.cpp:84] Creating Layer accuracy
|
|
I0412 14:19:58.380146 20203 net.cpp:406] accuracy <- fc8_fc8_0_split_0
|
|
I0412 14:19:58.380151 20203 net.cpp:406] accuracy <- label_val-data_1_split_0
|
|
I0412 14:19:58.380156 20203 net.cpp:380] accuracy -> accuracy
|
|
I0412 14:19:58.380162 20203 net.cpp:122] Setting up accuracy
|
|
I0412 14:19:58.380167 20203 net.cpp:129] Top shape: (1)
|
|
I0412 14:19:58.380169 20203 net.cpp:137] Memory required for data: 266163460
|
|
I0412 14:19:58.380172 20203 layer_factory.hpp:77] Creating layer loss
|
|
I0412 14:19:58.380183 20203 net.cpp:84] Creating Layer loss
|
|
I0412 14:19:58.380187 20203 net.cpp:406] loss <- fc8_fc8_0_split_1
|
|
I0412 14:19:58.380192 20203 net.cpp:406] loss <- label_val-data_1_split_1
|
|
I0412 14:19:58.380195 20203 net.cpp:380] loss -> loss
|
|
I0412 14:19:58.380203 20203 layer_factory.hpp:77] Creating layer loss
|
|
I0412 14:19:58.391234 20203 net.cpp:122] Setting up loss
|
|
I0412 14:19:58.391250 20203 net.cpp:129] Top shape: (1)
|
|
I0412 14:19:58.391253 20203 net.cpp:132] with loss weight 1
|
|
I0412 14:19:58.391264 20203 net.cpp:137] Memory required for data: 266163464
|
|
I0412 14:19:58.391270 20203 net.cpp:198] loss needs backward computation.
|
|
I0412 14:19:58.391276 20203 net.cpp:200] accuracy does not need backward computation.
|
|
I0412 14:19:58.391281 20203 net.cpp:198] fc8_fc8_0_split needs backward computation.
|
|
I0412 14:19:58.391285 20203 net.cpp:198] fc8 needs backward computation.
|
|
I0412 14:19:58.391289 20203 net.cpp:198] drop7 needs backward computation.
|
|
I0412 14:19:58.391292 20203 net.cpp:198] relu7 needs backward computation.
|
|
I0412 14:19:58.391297 20203 net.cpp:198] fc7 needs backward computation.
|
|
I0412 14:19:58.391301 20203 net.cpp:198] drop6 needs backward computation.
|
|
I0412 14:19:58.391305 20203 net.cpp:198] relu6 needs backward computation.
|
|
I0412 14:19:58.391309 20203 net.cpp:198] fc6 needs backward computation.
|
|
I0412 14:19:58.391314 20203 net.cpp:198] pool5 needs backward computation.
|
|
I0412 14:19:58.391319 20203 net.cpp:198] relu5 needs backward computation.
|
|
I0412 14:19:58.391321 20203 net.cpp:198] conv5 needs backward computation.
|
|
I0412 14:19:58.391325 20203 net.cpp:198] relu4.4 needs backward computation.
|
|
I0412 14:19:58.391330 20203 net.cpp:198] conv4.4 needs backward computation.
|
|
I0412 14:19:58.391333 20203 net.cpp:198] relu4.3 needs backward computation.
|
|
I0412 14:19:58.391337 20203 net.cpp:198] conv4.3 needs backward computation.
|
|
I0412 14:19:58.391341 20203 net.cpp:198] relu4.2 needs backward computation.
|
|
I0412 14:19:58.391347 20203 net.cpp:198] conv4.2 needs backward computation.
|
|
I0412 14:19:58.391351 20203 net.cpp:198] relu4 needs backward computation.
|
|
I0412 14:19:58.391355 20203 net.cpp:198] conv4 needs backward computation.
|
|
I0412 14:19:58.391360 20203 net.cpp:198] relu3 needs backward computation.
|
|
I0412 14:19:58.391381 20203 net.cpp:198] conv3 needs backward computation.
|
|
I0412 14:19:58.391386 20203 net.cpp:198] pool2 needs backward computation.
|
|
I0412 14:19:58.391391 20203 net.cpp:198] norm2 needs backward computation.
|
|
I0412 14:19:58.391394 20203 net.cpp:198] relu2 needs backward computation.
|
|
I0412 14:19:58.391398 20203 net.cpp:198] conv2 needs backward computation.
|
|
I0412 14:19:58.391402 20203 net.cpp:198] pool1 needs backward computation.
|
|
I0412 14:19:58.391407 20203 net.cpp:198] norm1 needs backward computation.
|
|
I0412 14:19:58.391410 20203 net.cpp:198] relu1 needs backward computation.
|
|
I0412 14:19:58.391415 20203 net.cpp:198] conv1 needs backward computation.
|
|
I0412 14:19:58.391419 20203 net.cpp:200] label_val-data_1_split does not need backward computation.
|
|
I0412 14:19:58.391423 20203 net.cpp:200] val-data does not need backward computation.
|
|
I0412 14:19:58.391427 20203 net.cpp:242] This network produces output accuracy
|
|
I0412 14:19:58.391431 20203 net.cpp:242] This network produces output loss
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|
I0412 14:19:58.391453 20203 net.cpp:255] Network initialization done.
|
|
I0412 14:19:58.391537 20203 solver.cpp:56] Solver scaffolding done.
|
|
I0412 14:19:58.392206 20203 caffe.cpp:248] Starting Optimization
|
|
I0412 14:19:58.392217 20203 solver.cpp:272] Solving
|
|
I0412 14:19:58.392221 20203 solver.cpp:273] Learning Rate Policy: exp
|
|
I0412 14:19:58.393481 20203 solver.cpp:330] Iteration 0, Testing net (#0)
|
|
I0412 14:19:58.393491 20203 net.cpp:676] Ignoring source layer train-data
|
|
I0412 14:19:58.489135 20203 blocking_queue.cpp:49] Waiting for data
|
|
I0412 14:20:02.709460 20208 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0412 14:20:02.754825 20203 solver.cpp:397] Test net output #0: accuracy = 0.00428922
|
|
I0412 14:20:02.754885 20203 solver.cpp:397] Test net output #1: loss = 5.28069 (* 1 = 5.28069 loss)
|
|
I0412 14:20:02.850173 20203 solver.cpp:218] Iteration 0 (8.94696e+35 iter/s, 4.45774s/12 iters), loss = 5.2814
|
|
I0412 14:20:02.852138 20203 solver.cpp:237] Train net output #0: loss = 5.2814 (* 1 = 5.2814 loss)
|
|
I0412 14:20:02.852159 20203 sgd_solver.cpp:105] Iteration 0, lr = 0.01
|
|
I0412 14:20:06.848487 20203 solver.cpp:218] Iteration 12 (3.00286 iter/s, 3.99618s/12 iters), loss = 5.27198
|
|
I0412 14:20:06.848541 20203 solver.cpp:237] Train net output #0: loss = 5.27198 (* 1 = 5.27198 loss)
|
|
I0412 14:20:06.848554 20203 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
|
|
I0412 14:20:11.608510 20203 solver.cpp:218] Iteration 24 (2.52113 iter/s, 4.75977s/12 iters), loss = 5.27799
|
|
I0412 14:20:11.608573 20203 solver.cpp:237] Train net output #0: loss = 5.27799 (* 1 = 5.27799 loss)
|
|
I0412 14:20:11.608590 20203 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
|
|
I0412 14:20:16.668282 20203 solver.cpp:218] Iteration 36 (2.37178 iter/s, 5.05949s/12 iters), loss = 5.2983
|
|
I0412 14:20:16.668331 20203 solver.cpp:237] Train net output #0: loss = 5.2983 (* 1 = 5.2983 loss)
|
|
I0412 14:20:16.668341 20203 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
|
|
I0412 14:20:21.390647 20203 solver.cpp:218] Iteration 48 (2.54123 iter/s, 4.72212s/12 iters), loss = 5.3027
|
|
I0412 14:20:21.390698 20203 solver.cpp:237] Train net output #0: loss = 5.3027 (* 1 = 5.3027 loss)
|
|
I0412 14:20:21.390712 20203 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
|
|
I0412 14:20:26.679602 20203 solver.cpp:218] Iteration 60 (2.26899 iter/s, 5.28869s/12 iters), loss = 5.29198
|
|
I0412 14:20:26.679792 20203 solver.cpp:237] Train net output #0: loss = 5.29198 (* 1 = 5.29198 loss)
|
|
I0412 14:20:26.679806 20203 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
|
|
I0412 14:20:31.789865 20203 solver.cpp:218] Iteration 72 (2.3484 iter/s, 5.10987s/12 iters), loss = 5.29752
|
|
I0412 14:20:31.789916 20203 solver.cpp:237] Train net output #0: loss = 5.29752 (* 1 = 5.29752 loss)
|
|
I0412 14:20:31.789928 20203 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
|
|
I0412 14:20:36.748898 20203 solver.cpp:218] Iteration 84 (2.41995 iter/s, 4.95878s/12 iters), loss = 5.29723
|
|
I0412 14:20:36.748940 20203 solver.cpp:237] Train net output #0: loss = 5.29723 (* 1 = 5.29723 loss)
|
|
I0412 14:20:36.748950 20203 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
|
|
I0412 14:20:41.569759 20203 solver.cpp:218] Iteration 96 (2.48931 iter/s, 4.82062s/12 iters), loss = 5.31386
|
|
I0412 14:20:41.569813 20203 solver.cpp:237] Train net output #0: loss = 5.31386 (* 1 = 5.31386 loss)
|
|
I0412 14:20:41.569826 20203 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
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I0412 14:20:43.730574 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
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I0412 14:20:46.783524 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
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I0412 14:20:49.834396 20203 solver.cpp:330] Iteration 102, Testing net (#0)
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I0412 14:20:54.319389 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:20:54.396864 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:20:54.396908 20203 solver.cpp:397] Test net output #1: loss = 5.29109 (* 1 = 5.29109 loss)
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I0412 14:20:56.282728 20203 solver.cpp:218] Iteration 108 (0.815641 iter/s, 14.7123s/12 iters), loss = 5.31561
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I0412 14:20:56.282790 20203 solver.cpp:237] Train net output #0: loss = 5.31561 (* 1 = 5.31561 loss)
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I0412 14:20:56.282805 20203 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
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I0412 14:21:01.116189 20203 solver.cpp:218] Iteration 120 (2.48282 iter/s, 4.8332s/12 iters), loss = 5.28988
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I0412 14:21:01.116323 20203 solver.cpp:237] Train net output #0: loss = 5.28988 (* 1 = 5.28988 loss)
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I0412 14:21:01.116333 20203 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
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I0412 14:21:06.081346 20203 solver.cpp:237] Train net output #0: loss = 5.24655 (* 1 = 5.24655 loss)
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I0412 14:21:06.081358 20203 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
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I0412 14:21:10.773360 20203 solver.cpp:237] Train net output #0: loss = 5.30975 (* 1 = 5.30975 loss)
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I0412 14:21:10.773372 20203 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
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I0412 14:21:15.683455 20203 solver.cpp:237] Train net output #0: loss = 5.26393 (* 1 = 5.26393 loss)
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I0412 14:21:15.683466 20203 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
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I0412 14:21:20.401487 20203 solver.cpp:218] Iteration 168 (2.54354 iter/s, 4.71784s/12 iters), loss = 5.27205
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I0412 14:21:20.401538 20203 solver.cpp:237] Train net output #0: loss = 5.27205 (* 1 = 5.27205 loss)
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I0412 14:21:20.401549 20203 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
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I0412 14:21:25.212123 20203 solver.cpp:218] Iteration 180 (2.4946 iter/s, 4.81038s/12 iters), loss = 5.26786
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I0412 14:21:25.212188 20203 solver.cpp:237] Train net output #0: loss = 5.26786 (* 1 = 5.26786 loss)
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I0412 14:21:25.212203 20203 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
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I0412 14:21:30.054700 20203 solver.cpp:237] Train net output #0: loss = 5.29224 (* 1 = 5.29224 loss)
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I0412 14:21:30.054714 20203 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
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I0412 14:21:34.290046 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
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I0412 14:21:37.403096 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
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I0412 14:21:39.857748 20203 solver.cpp:330] Iteration 204, Testing net (#0)
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I0412 14:21:39.857782 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:21:44.959813 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:21:45.092500 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:21:45.092531 20203 solver.cpp:397] Test net output #1: loss = 5.28901 (* 1 = 5.28901 loss)
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I0412 14:21:45.178843 20203 solver.cpp:218] Iteration 204 (0.793465 iter/s, 15.1235s/12 iters), loss = 5.27528
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I0412 14:21:45.178885 20203 solver.cpp:237] Train net output #0: loss = 5.27528 (* 1 = 5.27528 loss)
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I0412 14:21:49.302399 20203 solver.cpp:237] Train net output #0: loss = 5.28655 (* 1 = 5.28655 loss)
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I0412 14:21:49.302412 20203 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
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I0412 14:21:54.507877 20203 solver.cpp:237] Train net output #0: loss = 5.26184 (* 1 = 5.26184 loss)
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I0412 14:21:54.507885 20203 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
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I0412 14:21:59.322007 20203 solver.cpp:237] Train net output #0: loss = 5.30184 (* 1 = 5.30184 loss)
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I0412 14:21:59.322021 20203 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
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I0412 14:22:04.508108 20203 solver.cpp:237] Train net output #0: loss = 5.28593 (* 1 = 5.28593 loss)
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I0412 14:22:04.508122 20203 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
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I0412 14:22:09.300410 20203 solver.cpp:237] Train net output #0: loss = 5.27203 (* 1 = 5.27203 loss)
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I0412 14:22:09.300423 20203 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
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I0412 14:22:14.334714 20203 solver.cpp:218] Iteration 276 (2.38375 iter/s, 5.03409s/12 iters), loss = 5.29673
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I0412 14:22:14.334767 20203 solver.cpp:237] Train net output #0: loss = 5.29673 (* 1 = 5.29673 loss)
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I0412 14:22:14.334780 20203 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
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I0412 14:22:19.253551 20203 solver.cpp:218] Iteration 288 (2.43973 iter/s, 4.91858s/12 iters), loss = 5.29123
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I0412 14:22:19.253600 20203 solver.cpp:237] Train net output #0: loss = 5.29123 (* 1 = 5.29123 loss)
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I0412 14:22:19.253612 20203 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
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I0412 14:22:24.219045 20203 solver.cpp:237] Train net output #0: loss = 5.28905 (* 1 = 5.28905 loss)
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I0412 14:22:24.219058 20203 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
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I0412 14:22:26.432472 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
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I0412 14:22:30.999752 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
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I0412 14:22:33.413734 20203 solver.cpp:330] Iteration 306, Testing net (#0)
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I0412 14:22:37.711786 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:22:37.877427 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:22:37.877481 20203 solver.cpp:397] Test net output #1: loss = 5.28766 (* 1 = 5.28766 loss)
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I0412 14:22:39.743162 20203 solver.cpp:218] Iteration 312 (0.773022 iter/s, 15.5235s/12 iters), loss = 5.28861
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I0412 14:22:44.634596 20203 solver.cpp:237] Train net output #0: loss = 5.24988 (* 1 = 5.24988 loss)
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I0412 14:22:49.578934 20203 solver.cpp:237] Train net output #0: loss = 5.26598 (* 1 = 5.26598 loss)
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I0412 14:22:54.403733 20203 solver.cpp:237] Train net output #0: loss = 5.27186 (* 1 = 5.27186 loss)
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I0412 14:22:59.204525 20203 solver.cpp:237] Train net output #0: loss = 5.29809 (* 1 = 5.29809 loss)
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I0412 14:22:59.204537 20203 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
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I0412 14:23:04.021169 20203 solver.cpp:218] Iteration 372 (2.49147 iter/s, 4.81644s/12 iters), loss = 5.2789
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I0412 14:23:04.021221 20203 solver.cpp:237] Train net output #0: loss = 5.2789 (* 1 = 5.2789 loss)
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I0412 14:23:04.021234 20203 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
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I0412 14:23:09.750658 20203 solver.cpp:218] Iteration 384 (2.09453 iter/s, 5.7292s/12 iters), loss = 5.28752
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I0412 14:23:09.750830 20203 solver.cpp:237] Train net output #0: loss = 5.28752 (* 1 = 5.28752 loss)
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I0412 14:23:09.750846 20203 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
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I0412 14:23:14.568517 20203 solver.cpp:218] Iteration 396 (2.49093 iter/s, 4.81749s/12 iters), loss = 5.27361
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I0412 14:23:14.568560 20203 solver.cpp:237] Train net output #0: loss = 5.27361 (* 1 = 5.27361 loss)
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I0412 14:23:14.568569 20203 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
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I0412 14:23:17.685600 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:23:19.067333 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
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I0412 14:23:23.403877 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
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I0412 14:23:27.090049 20203 solver.cpp:330] Iteration 408, Testing net (#0)
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I0412 14:23:27.090078 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:23:31.392374 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:23:31.595954 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:23:31.595997 20203 solver.cpp:397] Test net output #1: loss = 5.28793 (* 1 = 5.28793 loss)
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I0412 14:23:31.682664 20203 solver.cpp:218] Iteration 408 (0.701204 iter/s, 17.1134s/12 iters), loss = 5.28664
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I0412 14:23:31.682710 20203 solver.cpp:237] Train net output #0: loss = 5.28664 (* 1 = 5.28664 loss)
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I0412 14:23:35.715170 20203 solver.cpp:237] Train net output #0: loss = 5.28368 (* 1 = 5.28368 loss)
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I0412 14:23:40.520817 20203 solver.cpp:237] Train net output #0: loss = 5.27348 (* 1 = 5.27348 loss)
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I0412 14:23:40.520829 20203 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
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I0412 14:23:45.631803 20203 solver.cpp:218] Iteration 444 (2.34798 iter/s, 5.11077s/12 iters), loss = 5.28971
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I0412 14:23:45.631846 20203 solver.cpp:237] Train net output #0: loss = 5.28971 (* 1 = 5.28971 loss)
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I0412 14:23:50.340682 20203 solver.cpp:218] Iteration 456 (2.54851 iter/s, 4.70863s/12 iters), loss = 5.2974
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I0412 14:23:50.340739 20203 solver.cpp:237] Train net output #0: loss = 5.2974 (* 1 = 5.2974 loss)
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I0412 14:23:50.340754 20203 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
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I0412 14:23:55.126292 20203 solver.cpp:218] Iteration 468 (2.50765 iter/s, 4.78536s/12 iters), loss = 5.29855
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I0412 14:23:55.126335 20203 solver.cpp:237] Train net output #0: loss = 5.29855 (* 1 = 5.29855 loss)
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I0412 14:23:55.126343 20203 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
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I0412 14:24:00.246696 20203 solver.cpp:218] Iteration 480 (2.34369 iter/s, 5.12014s/12 iters), loss = 5.27091
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I0412 14:24:00.246753 20203 solver.cpp:237] Train net output #0: loss = 5.27091 (* 1 = 5.27091 loss)
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I0412 14:24:00.246767 20203 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
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I0412 14:24:05.141503 20203 solver.cpp:218] Iteration 492 (2.45171 iter/s, 4.89455s/12 iters), loss = 5.29064
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I0412 14:24:05.141541 20203 solver.cpp:237] Train net output #0: loss = 5.29064 (* 1 = 5.29064 loss)
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I0412 14:24:05.141548 20203 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
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I0412 14:24:10.206341 20203 solver.cpp:218] Iteration 504 (2.3694 iter/s, 5.06457s/12 iters), loss = 5.27148
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I0412 14:24:10.206401 20203 solver.cpp:237] Train net output #0: loss = 5.27148 (* 1 = 5.27148 loss)
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I0412 14:24:10.206414 20203 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
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I0412 14:24:10.516408 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:24:12.361799 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
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I0412 14:24:15.419447 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
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I0412 14:24:18.180577 20203 solver.cpp:330] Iteration 510, Testing net (#0)
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I0412 14:24:18.180598 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:24:22.428063 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:24:22.666286 20203 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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I0412 14:24:22.666335 20203 solver.cpp:397] Test net output #1: loss = 5.28686 (* 1 = 5.28686 loss)
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I0412 14:24:24.449348 20203 solver.cpp:218] Iteration 516 (0.842556 iter/s, 14.2424s/12 iters), loss = 5.28003
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I0412 14:24:24.449400 20203 solver.cpp:237] Train net output #0: loss = 5.28003 (* 1 = 5.28003 loss)
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I0412 14:24:24.449412 20203 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
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I0412 14:24:29.460561 20203 solver.cpp:218] Iteration 528 (2.39476 iter/s, 5.01095s/12 iters), loss = 5.28201
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I0412 14:24:29.460609 20203 solver.cpp:237] Train net output #0: loss = 5.28201 (* 1 = 5.28201 loss)
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I0412 14:24:29.460621 20203 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
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I0412 14:24:34.709429 20203 solver.cpp:218] Iteration 540 (2.28633 iter/s, 5.24859s/12 iters), loss = 5.28039
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I0412 14:24:34.709470 20203 solver.cpp:237] Train net output #0: loss = 5.28039 (* 1 = 5.28039 loss)
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I0412 14:24:34.709479 20203 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
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I0412 14:24:39.549939 20203 solver.cpp:218] Iteration 552 (2.4792 iter/s, 4.84026s/12 iters), loss = 5.27511
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I0412 14:24:39.549988 20203 solver.cpp:237] Train net output #0: loss = 5.27511 (* 1 = 5.27511 loss)
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I0412 14:24:44.766876 20203 solver.cpp:218] Iteration 564 (2.30032 iter/s, 5.21667s/12 iters), loss = 5.26647
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I0412 14:24:44.770224 20203 solver.cpp:237] Train net output #0: loss = 5.26647 (* 1 = 5.26647 loss)
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I0412 14:24:44.770233 20203 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
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I0412 14:24:49.573875 20203 solver.cpp:218] Iteration 576 (2.49821 iter/s, 4.80344s/12 iters), loss = 5.28473
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I0412 14:24:49.573930 20203 solver.cpp:237] Train net output #0: loss = 5.28473 (* 1 = 5.28473 loss)
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I0412 14:24:49.573943 20203 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
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I0412 14:24:54.635025 20203 solver.cpp:218] Iteration 588 (2.37113 iter/s, 5.06088s/12 iters), loss = 5.27385
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I0412 14:24:54.635076 20203 solver.cpp:237] Train net output #0: loss = 5.27385 (* 1 = 5.27385 loss)
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I0412 14:24:54.635089 20203 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
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I0412 14:24:59.714468 20203 solver.cpp:218] Iteration 600 (2.36259 iter/s, 5.07917s/12 iters), loss = 5.2635
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I0412 14:24:59.714519 20203 solver.cpp:237] Train net output #0: loss = 5.2635 (* 1 = 5.2635 loss)
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I0412 14:24:59.714529 20203 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
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I0412 14:25:02.031126 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:25:04.113529 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
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I0412 14:25:07.814806 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
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I0412 14:25:10.505007 20203 solver.cpp:330] Iteration 612, Testing net (#0)
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I0412 14:25:10.505033 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:25:14.863081 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:25:15.149114 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:25:15.149152 20203 solver.cpp:397] Test net output #1: loss = 5.28639 (* 1 = 5.28639 loss)
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I0412 14:25:15.235476 20203 solver.cpp:218] Iteration 612 (0.77318 iter/s, 15.5203s/12 iters), loss = 5.27685
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I0412 14:25:15.235522 20203 solver.cpp:237] Train net output #0: loss = 5.27685 (* 1 = 5.27685 loss)
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I0412 14:25:15.235533 20203 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
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I0412 14:25:19.068136 20203 solver.cpp:218] Iteration 624 (3.13117 iter/s, 3.83244s/12 iters), loss = 5.29367
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I0412 14:25:19.068197 20203 solver.cpp:237] Train net output #0: loss = 5.29367 (* 1 = 5.29367 loss)
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I0412 14:25:19.068212 20203 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
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I0412 14:25:23.912273 20203 solver.cpp:237] Train net output #0: loss = 5.2848 (* 1 = 5.2848 loss)
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I0412 14:25:23.912286 20203 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
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I0412 14:25:28.903384 20203 solver.cpp:218] Iteration 648 (2.40438 iter/s, 4.9909s/12 iters), loss = 5.27838
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I0412 14:25:28.903429 20203 solver.cpp:237] Train net output #0: loss = 5.27838 (* 1 = 5.27838 loss)
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I0412 14:25:28.903439 20203 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
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I0412 14:25:34.228880 20203 solver.cpp:237] Train net output #0: loss = 5.27062 (* 1 = 5.27062 loss)
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I0412 14:25:34.228893 20203 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
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I0412 14:25:39.182802 20203 solver.cpp:218] Iteration 672 (2.42243 iter/s, 4.95371s/12 iters), loss = 5.28096
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I0412 14:25:39.182857 20203 solver.cpp:237] Train net output #0: loss = 5.28096 (* 1 = 5.28096 loss)
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I0412 14:25:39.182873 20203 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
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I0412 14:25:44.318264 20203 solver.cpp:218] Iteration 684 (2.33682 iter/s, 5.13519s/12 iters), loss = 5.28366
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I0412 14:25:44.318317 20203 solver.cpp:237] Train net output #0: loss = 5.28366 (* 1 = 5.28366 loss)
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I0412 14:25:44.318328 20203 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
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I0412 14:25:45.114223 20203 blocking_queue.cpp:49] Waiting for data
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I0412 14:25:49.197029 20203 solver.cpp:218] Iteration 696 (2.45977 iter/s, 4.8785s/12 iters), loss = 5.28247
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I0412 14:25:49.197079 20203 solver.cpp:237] Train net output #0: loss = 5.28247 (* 1 = 5.28247 loss)
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I0412 14:25:49.197091 20203 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
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I0412 14:25:53.637346 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:25:54.012367 20203 solver.cpp:218] Iteration 708 (2.49217 iter/s, 4.81507s/12 iters), loss = 5.26947
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I0412 14:25:54.012428 20203 solver.cpp:237] Train net output #0: loss = 5.26947 (* 1 = 5.26947 loss)
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I0412 14:25:54.012442 20203 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
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I0412 14:25:55.878196 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
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I0412 14:25:58.900919 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
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I0412 14:26:01.195298 20203 solver.cpp:330] Iteration 714, Testing net (#0)
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I0412 14:26:01.195319 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:26:05.458324 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:26:05.779220 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:26:05.779271 20203 solver.cpp:397] Test net output #1: loss = 5.28651 (* 1 = 5.28651 loss)
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I0412 14:26:07.620666 20203 solver.cpp:218] Iteration 720 (0.881855 iter/s, 13.6077s/12 iters), loss = 5.27261
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I0412 14:26:07.620719 20203 solver.cpp:237] Train net output #0: loss = 5.27261 (* 1 = 5.27261 loss)
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I0412 14:26:07.620731 20203 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
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I0412 14:26:12.236428 20203 solver.cpp:218] Iteration 732 (2.59993 iter/s, 4.61551s/12 iters), loss = 5.27904
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I0412 14:26:12.236480 20203 solver.cpp:237] Train net output #0: loss = 5.27904 (* 1 = 5.27904 loss)
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I0412 14:26:12.236492 20203 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
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I0412 14:26:17.061066 20203 solver.cpp:218] Iteration 744 (2.48737 iter/s, 4.82438s/12 iters), loss = 5.28501
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I0412 14:26:17.061245 20203 solver.cpp:237] Train net output #0: loss = 5.28501 (* 1 = 5.28501 loss)
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I0412 14:26:17.061261 20203 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
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I0412 14:26:21.925262 20203 solver.cpp:218] Iteration 756 (2.4672 iter/s, 4.86381s/12 iters), loss = 5.28091
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I0412 14:26:21.925309 20203 solver.cpp:237] Train net output #0: loss = 5.28091 (* 1 = 5.28091 loss)
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I0412 14:26:21.925318 20203 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
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I0412 14:26:26.757680 20203 solver.cpp:218] Iteration 768 (2.48336 iter/s, 4.83216s/12 iters), loss = 5.28356
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I0412 14:26:26.757736 20203 solver.cpp:237] Train net output #0: loss = 5.28356 (* 1 = 5.28356 loss)
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I0412 14:26:26.757748 20203 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
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I0412 14:26:31.507895 20203 solver.cpp:218] Iteration 780 (2.52634 iter/s, 4.74995s/12 iters), loss = 5.27987
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I0412 14:26:31.507966 20203 solver.cpp:237] Train net output #0: loss = 5.27987 (* 1 = 5.27987 loss)
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I0412 14:26:31.507982 20203 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
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I0412 14:26:36.513116 20203 solver.cpp:218] Iteration 792 (2.39763 iter/s, 5.00495s/12 iters), loss = 5.27612
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I0412 14:26:36.513159 20203 solver.cpp:237] Train net output #0: loss = 5.27612 (* 1 = 5.27612 loss)
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I0412 14:26:36.513166 20203 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
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I0412 14:26:41.347577 20203 solver.cpp:218] Iteration 804 (2.48231 iter/s, 4.83421s/12 iters), loss = 5.29568
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I0412 14:26:41.347623 20203 solver.cpp:237] Train net output #0: loss = 5.29568 (* 1 = 5.29568 loss)
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I0412 14:26:41.347633 20203 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
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I0412 14:26:42.981989 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:26:45.734251 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
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I0412 14:26:48.654001 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
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I0412 14:26:50.977082 20203 solver.cpp:330] Iteration 816, Testing net (#0)
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I0412 14:26:50.977108 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:26:55.215575 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:26:55.630540 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:26:55.630605 20203 solver.cpp:397] Test net output #1: loss = 5.28606 (* 1 = 5.28606 loss)
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I0412 14:26:55.716826 20203 solver.cpp:218] Iteration 816 (0.835154 iter/s, 14.3686s/12 iters), loss = 5.27491
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I0412 14:26:55.716876 20203 solver.cpp:237] Train net output #0: loss = 5.27491 (* 1 = 5.27491 loss)
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I0412 14:26:55.716884 20203 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
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I0412 14:26:59.770033 20203 solver.cpp:237] Train net output #0: loss = 5.29088 (* 1 = 5.29088 loss)
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I0412 14:26:59.770043 20203 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
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I0412 14:27:04.572038 20203 solver.cpp:218] Iteration 840 (2.49906 iter/s, 4.8018s/12 iters), loss = 5.23848
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I0412 14:27:04.572085 20203 solver.cpp:237] Train net output #0: loss = 5.23848 (* 1 = 5.23848 loss)
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I0412 14:27:04.572098 20203 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
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I0412 14:27:09.391803 20203 solver.cpp:218] Iteration 852 (2.48988 iter/s, 4.81951s/12 iters), loss = 5.3071
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I0412 14:27:09.391845 20203 solver.cpp:237] Train net output #0: loss = 5.3071 (* 1 = 5.3071 loss)
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I0412 14:27:14.223094 20203 solver.cpp:237] Train net output #0: loss = 5.26 (* 1 = 5.26 loss)
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I0412 14:27:14.223106 20203 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
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I0412 14:27:19.198803 20203 solver.cpp:237] Train net output #0: loss = 5.27649 (* 1 = 5.27649 loss)
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I0412 14:27:19.198824 20203 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
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I0412 14:27:24.185992 20203 solver.cpp:218] Iteration 888 (2.40626 iter/s, 4.98699s/12 iters), loss = 5.27045
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I0412 14:27:24.186043 20203 solver.cpp:237] Train net output #0: loss = 5.27045 (* 1 = 5.27045 loss)
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I0412 14:27:24.186053 20203 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
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I0412 14:27:29.067260 20203 solver.cpp:218] Iteration 900 (2.45851 iter/s, 4.88101s/12 iters), loss = 5.27611
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I0412 14:27:29.067304 20203 solver.cpp:237] Train net output #0: loss = 5.27611 (* 1 = 5.27611 loss)
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I0412 14:27:29.067314 20203 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
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I0412 14:27:33.966756 20203 solver.cpp:218] Iteration 912 (2.44936 iter/s, 4.89924s/12 iters), loss = 5.26233
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I0412 14:27:33.966796 20203 solver.cpp:237] Train net output #0: loss = 5.26233 (* 1 = 5.26233 loss)
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I0412 14:27:33.966804 20203 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
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I0412 14:27:36.077018 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
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I0412 14:27:39.053488 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
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I0412 14:27:41.367806 20203 solver.cpp:330] Iteration 918, Testing net (#0)
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I0412 14:27:41.367831 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:27:45.759191 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:27:46.161813 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:27:46.161845 20203 solver.cpp:397] Test net output #1: loss = 5.28633 (* 1 = 5.28633 loss)
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I0412 14:27:47.910627 20203 solver.cpp:237] Train net output #0: loss = 5.29051 (* 1 = 5.29051 loss)
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I0412 14:27:53.039029 20203 solver.cpp:237] Train net output #0: loss = 5.26703 (* 1 = 5.26703 loss)
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I0412 14:27:53.039042 20203 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
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I0412 14:27:57.675796 20203 solver.cpp:218] Iteration 948 (2.58812 iter/s, 4.63657s/12 iters), loss = 5.29046
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I0412 14:27:57.675837 20203 solver.cpp:237] Train net output #0: loss = 5.29046 (* 1 = 5.29046 loss)
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I0412 14:27:57.675848 20203 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
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I0412 14:28:02.650091 20203 solver.cpp:237] Train net output #0: loss = 5.26992 (* 1 = 5.26992 loss)
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I0412 14:28:08.047298 20203 solver.cpp:237] Train net output #0: loss = 5.27182 (* 1 = 5.27182 loss)
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I0412 14:28:08.047307 20203 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
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I0412 14:28:13.169148 20203 solver.cpp:218] Iteration 984 (2.34301 iter/s, 5.12163s/12 iters), loss = 5.29486
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I0412 14:28:13.169193 20203 solver.cpp:237] Train net output #0: loss = 5.29486 (* 1 = 5.29486 loss)
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I0412 14:28:13.169203 20203 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
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I0412 14:28:18.197569 20203 solver.cpp:218] Iteration 996 (2.38657 iter/s, 5.02815s/12 iters), loss = 5.27459
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I0412 14:28:18.197633 20203 solver.cpp:237] Train net output #0: loss = 5.27459 (* 1 = 5.27459 loss)
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I0412 14:28:18.197645 20203 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
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I0412 14:28:23.084079 20203 solver.cpp:218] Iteration 1008 (2.45588 iter/s, 4.88623s/12 iters), loss = 5.29068
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I0412 14:28:23.084233 20203 solver.cpp:237] Train net output #0: loss = 5.29068 (* 1 = 5.29068 loss)
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I0412 14:28:23.084247 20203 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
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I0412 14:28:24.121089 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:28:27.601756 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
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I0412 14:28:31.486924 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
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I0412 14:28:33.830968 20203 solver.cpp:330] Iteration 1020, Testing net (#0)
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I0412 14:28:33.830996 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:28:37.987815 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:28:38.420653 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:28:38.420703 20203 solver.cpp:397] Test net output #1: loss = 5.28596 (* 1 = 5.28596 loss)
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I0412 14:28:38.504825 20203 solver.cpp:218] Iteration 1020 (0.778212 iter/s, 15.42s/12 iters), loss = 5.28923
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I0412 14:28:38.504876 20203 solver.cpp:237] Train net output #0: loss = 5.28923 (* 1 = 5.28923 loss)
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I0412 14:28:42.591905 20203 solver.cpp:218] Iteration 1032 (2.93625 iter/s, 4.08685s/12 iters), loss = 5.24977
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I0412 14:28:42.591954 20203 solver.cpp:237] Train net output #0: loss = 5.24977 (* 1 = 5.24977 loss)
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I0412 14:28:42.591966 20203 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
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I0412 14:28:47.706737 20203 solver.cpp:218] Iteration 1044 (2.34624 iter/s, 5.11456s/12 iters), loss = 5.25973
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I0412 14:28:47.706776 20203 solver.cpp:237] Train net output #0: loss = 5.25973 (* 1 = 5.25973 loss)
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I0412 14:28:47.706785 20203 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
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I0412 14:28:52.841341 20203 solver.cpp:218] Iteration 1056 (2.3372 iter/s, 5.13434s/12 iters), loss = 5.26493
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I0412 14:28:52.841379 20203 solver.cpp:237] Train net output #0: loss = 5.26493 (* 1 = 5.26493 loss)
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I0412 14:28:57.838786 20203 solver.cpp:218] Iteration 1068 (2.40135 iter/s, 4.99718s/12 iters), loss = 5.29016
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I0412 14:28:57.838917 20203 solver.cpp:237] Train net output #0: loss = 5.29016 (* 1 = 5.29016 loss)
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I0412 14:28:57.838932 20203 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
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I0412 14:29:02.689520 20203 solver.cpp:218] Iteration 1080 (2.47402 iter/s, 4.8504s/12 iters), loss = 5.27217
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I0412 14:29:02.689568 20203 solver.cpp:237] Train net output #0: loss = 5.27217 (* 1 = 5.27217 loss)
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I0412 14:29:02.689579 20203 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
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I0412 14:29:07.485769 20203 solver.cpp:218] Iteration 1092 (2.50209 iter/s, 4.79599s/12 iters), loss = 5.283
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I0412 14:29:07.485819 20203 solver.cpp:237] Train net output #0: loss = 5.283 (* 1 = 5.283 loss)
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I0412 14:29:07.485832 20203 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
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I0412 14:29:12.297775 20203 solver.cpp:218] Iteration 1104 (2.4939 iter/s, 4.81175s/12 iters), loss = 5.27852
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I0412 14:29:12.297824 20203 solver.cpp:237] Train net output #0: loss = 5.27852 (* 1 = 5.27852 loss)
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I0412 14:29:12.297837 20203 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
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I0412 14:29:15.380581 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:29:17.116842 20203 solver.cpp:218] Iteration 1116 (2.49024 iter/s, 4.81881s/12 iters), loss = 5.27309
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I0412 14:29:17.116883 20203 solver.cpp:237] Train net output #0: loss = 5.27309 (* 1 = 5.27309 loss)
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I0412 14:29:17.116891 20203 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
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I0412 14:29:19.266387 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
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I0412 14:29:22.247874 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
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I0412 14:29:24.562741 20203 solver.cpp:330] Iteration 1122, Testing net (#0)
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I0412 14:29:24.562772 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:29:28.643496 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:29:29.121688 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:29:29.121724 20203 solver.cpp:397] Test net output #1: loss = 5.28629 (* 1 = 5.28629 loss)
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I0412 14:29:31.114357 20203 solver.cpp:218] Iteration 1128 (0.857334 iter/s, 13.9969s/12 iters), loss = 5.27015
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I0412 14:29:31.114403 20203 solver.cpp:237] Train net output #0: loss = 5.27015 (* 1 = 5.27015 loss)
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I0412 14:29:31.114411 20203 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
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I0412 14:29:36.382889 20203 solver.cpp:218] Iteration 1140 (2.27779 iter/s, 5.26825s/12 iters), loss = 5.27328
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I0412 14:29:36.382952 20203 solver.cpp:237] Train net output #0: loss = 5.27328 (* 1 = 5.27328 loss)
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I0412 14:29:36.382968 20203 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
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I0412 14:29:41.129192 20203 solver.cpp:218] Iteration 1152 (2.52843 iter/s, 4.74603s/12 iters), loss = 5.28068
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I0412 14:29:41.129256 20203 solver.cpp:237] Train net output #0: loss = 5.28068 (* 1 = 5.28068 loss)
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I0412 14:29:41.129272 20203 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
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I0412 14:29:45.789966 20203 solver.cpp:218] Iteration 1164 (2.57483 iter/s, 4.6605s/12 iters), loss = 5.27419
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I0412 14:29:45.790009 20203 solver.cpp:237] Train net output #0: loss = 5.27419 (* 1 = 5.27419 loss)
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I0412 14:29:45.790019 20203 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
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I0412 14:29:50.526134 20203 solver.cpp:237] Train net output #0: loss = 5.29779 (* 1 = 5.29779 loss)
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I0412 14:29:50.526154 20203 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
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I0412 14:29:55.356207 20203 solver.cpp:218] Iteration 1188 (2.48454 iter/s, 4.82987s/12 iters), loss = 5.2712
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I0412 14:29:55.356261 20203 solver.cpp:237] Train net output #0: loss = 5.2712 (* 1 = 5.2712 loss)
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I0412 14:29:55.356272 20203 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
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I0412 14:30:00.385579 20203 solver.cpp:218] Iteration 1200 (2.38611 iter/s, 5.0291s/12 iters), loss = 5.29137
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I0412 14:30:00.385702 20203 solver.cpp:237] Train net output #0: loss = 5.29137 (* 1 = 5.29137 loss)
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I0412 14:30:00.385715 20203 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
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I0412 14:30:05.306516 20203 solver.cpp:218] Iteration 1212 (2.43873 iter/s, 4.9206s/12 iters), loss = 5.26578
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I0412 14:30:05.306571 20203 solver.cpp:237] Train net output #0: loss = 5.26578 (* 1 = 5.26578 loss)
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I0412 14:30:05.306581 20203 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
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I0412 14:30:05.605654 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:30:09.654474 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
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I0412 14:30:12.658262 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
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I0412 14:30:14.975736 20203 solver.cpp:330] Iteration 1224, Testing net (#0)
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I0412 14:30:14.975764 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:30:18.829417 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:30:19.343155 20203 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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I0412 14:30:19.343204 20203 solver.cpp:397] Test net output #1: loss = 5.28628 (* 1 = 5.28628 loss)
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I0412 14:30:19.430436 20203 solver.cpp:218] Iteration 1224 (0.849661 iter/s, 14.1233s/12 iters), loss = 5.28775
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I0412 14:30:19.430498 20203 solver.cpp:237] Train net output #0: loss = 5.28775 (* 1 = 5.28775 loss)
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I0412 14:30:19.430510 20203 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
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I0412 14:30:23.764019 20203 solver.cpp:218] Iteration 1236 (2.76923 iter/s, 4.33333s/12 iters), loss = 5.27237
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I0412 14:30:23.764062 20203 solver.cpp:237] Train net output #0: loss = 5.27237 (* 1 = 5.27237 loss)
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I0412 14:30:23.764072 20203 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
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I0412 14:30:28.629045 20203 solver.cpp:218] Iteration 1248 (2.46671 iter/s, 4.86477s/12 iters), loss = 5.28905
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I0412 14:30:28.629094 20203 solver.cpp:237] Train net output #0: loss = 5.28905 (* 1 = 5.28905 loss)
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I0412 14:30:28.629107 20203 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
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I0412 14:30:33.481115 20203 solver.cpp:218] Iteration 1260 (2.47331 iter/s, 4.85181s/12 iters), loss = 5.27449
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I0412 14:30:33.481232 20203 solver.cpp:237] Train net output #0: loss = 5.27449 (* 1 = 5.27449 loss)
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I0412 14:30:33.481245 20203 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
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I0412 14:30:38.347671 20203 solver.cpp:218] Iteration 1272 (2.46598 iter/s, 4.86623s/12 iters), loss = 5.25238
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I0412 14:30:38.347721 20203 solver.cpp:237] Train net output #0: loss = 5.25238 (* 1 = 5.25238 loss)
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I0412 14:30:38.347733 20203 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
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I0412 14:30:43.255817 20203 solver.cpp:218] Iteration 1284 (2.44505 iter/s, 4.90788s/12 iters), loss = 5.28613
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I0412 14:30:43.255870 20203 solver.cpp:237] Train net output #0: loss = 5.28613 (* 1 = 5.28613 loss)
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I0412 14:30:43.255883 20203 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
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I0412 14:30:48.271441 20203 solver.cpp:218] Iteration 1296 (2.39265 iter/s, 5.01536s/12 iters), loss = 5.27272
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I0412 14:30:48.271486 20203 solver.cpp:237] Train net output #0: loss = 5.27272 (* 1 = 5.27272 loss)
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I0412 14:30:48.271497 20203 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
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I0412 14:30:53.157065 20203 solver.cpp:218] Iteration 1308 (2.45632 iter/s, 4.88537s/12 iters), loss = 5.25763
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I0412 14:30:53.157119 20203 solver.cpp:237] Train net output #0: loss = 5.25763 (* 1 = 5.25763 loss)
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I0412 14:30:53.157133 20203 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
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I0412 14:30:55.531392 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:30:57.906651 20203 solver.cpp:218] Iteration 1320 (2.52667 iter/s, 4.74933s/12 iters), loss = 5.28228
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I0412 14:30:57.906699 20203 solver.cpp:237] Train net output #0: loss = 5.28228 (* 1 = 5.28228 loss)
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I0412 14:30:57.906710 20203 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
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I0412 14:30:59.858455 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
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I0412 14:31:02.882943 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
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I0412 14:31:05.192968 20203 solver.cpp:330] Iteration 1326, Testing net (#0)
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I0412 14:31:05.193050 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:31:09.071890 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:31:09.629256 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:31:09.629307 20203 solver.cpp:397] Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
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I0412 14:31:11.461581 20203 solver.cpp:218] Iteration 1332 (0.885327 iter/s, 13.5543s/12 iters), loss = 5.29231
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I0412 14:31:11.461632 20203 solver.cpp:237] Train net output #0: loss = 5.29231 (* 1 = 5.29231 loss)
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I0412 14:31:11.461642 20203 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
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I0412 14:31:16.259505 20203 solver.cpp:218] Iteration 1344 (2.50122 iter/s, 4.79767s/12 iters), loss = 5.2909
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I0412 14:31:16.259554 20203 solver.cpp:237] Train net output #0: loss = 5.2909 (* 1 = 5.2909 loss)
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I0412 14:31:16.259567 20203 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
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I0412 14:31:21.290009 20203 solver.cpp:218] Iteration 1356 (2.38557 iter/s, 5.03024s/12 iters), loss = 5.27759
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I0412 14:31:21.290058 20203 solver.cpp:237] Train net output #0: loss = 5.27759 (* 1 = 5.27759 loss)
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I0412 14:31:21.290071 20203 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
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I0412 14:31:26.048923 20203 solver.cpp:218] Iteration 1368 (2.52172 iter/s, 4.75866s/12 iters), loss = 5.27604
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I0412 14:31:26.048970 20203 solver.cpp:237] Train net output #0: loss = 5.27604 (* 1 = 5.27604 loss)
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I0412 14:31:26.048982 20203 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
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I0412 14:31:27.249073 20203 blocking_queue.cpp:49] Waiting for data
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I0412 14:31:31.006644 20203 solver.cpp:218] Iteration 1380 (2.4206 iter/s, 4.95746s/12 iters), loss = 5.2784
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I0412 14:31:31.006695 20203 solver.cpp:237] Train net output #0: loss = 5.2784 (* 1 = 5.2784 loss)
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I0412 14:31:31.006706 20203 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
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I0412 14:31:36.090852 20203 solver.cpp:218] Iteration 1392 (2.36038 iter/s, 5.08393s/12 iters), loss = 5.27204
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I0412 14:31:36.091010 20203 solver.cpp:237] Train net output #0: loss = 5.27204 (* 1 = 5.27204 loss)
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I0412 14:31:36.091022 20203 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
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I0412 14:31:41.028558 20203 solver.cpp:218] Iteration 1404 (2.43046 iter/s, 4.93734s/12 iters), loss = 5.27922
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I0412 14:31:41.028609 20203 solver.cpp:237] Train net output #0: loss = 5.27922 (* 1 = 5.27922 loss)
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I0412 14:31:41.028620 20203 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
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I0412 14:31:45.389554 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:31:45.734103 20203 solver.cpp:218] Iteration 1416 (2.55032 iter/s, 4.70529s/12 iters), loss = 5.27104
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I0412 14:31:45.734146 20203 solver.cpp:237] Train net output #0: loss = 5.27104 (* 1 = 5.27104 loss)
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I0412 14:31:45.734155 20203 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
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I0412 14:31:50.153856 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
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I0412 14:31:54.681999 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
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I0412 14:31:58.329341 20203 solver.cpp:330] Iteration 1428, Testing net (#0)
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I0412 14:32:02.284226 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:32:02.876135 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:32:02.876201 20203 solver.cpp:397] Test net output #1: loss = 5.28578 (* 1 = 5.28578 loss)
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I0412 14:32:02.962880 20203 solver.cpp:218] Iteration 1428 (0.69654 iter/s, 17.228s/12 iters), loss = 5.27529
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I0412 14:32:02.962935 20203 solver.cpp:237] Train net output #0: loss = 5.27529 (* 1 = 5.27529 loss)
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I0412 14:32:07.152431 20203 solver.cpp:237] Train net output #0: loss = 5.28676 (* 1 = 5.28676 loss)
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I0412 14:32:07.152441 20203 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
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I0412 14:32:11.789225 20203 solver.cpp:218] Iteration 1452 (2.58811 iter/s, 4.63659s/12 iters), loss = 5.28042
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I0412 14:32:11.789279 20203 solver.cpp:237] Train net output #0: loss = 5.28042 (* 1 = 5.28042 loss)
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I0412 14:32:11.789292 20203 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
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I0412 14:32:16.618544 20203 solver.cpp:218] Iteration 1464 (2.48496 iter/s, 4.82906s/12 iters), loss = 5.28038
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I0412 14:32:16.618594 20203 solver.cpp:237] Train net output #0: loss = 5.28038 (* 1 = 5.28038 loss)
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I0412 14:32:16.618607 20203 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
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I0412 14:32:21.563048 20203 solver.cpp:218] Iteration 1476 (2.42707 iter/s, 4.94424s/12 iters), loss = 5.27934
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I0412 14:32:21.563108 20203 solver.cpp:237] Train net output #0: loss = 5.27934 (* 1 = 5.27934 loss)
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I0412 14:32:21.563125 20203 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
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I0412 14:32:26.286481 20203 solver.cpp:218] Iteration 1488 (2.54067 iter/s, 4.72317s/12 iters), loss = 5.25491
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I0412 14:32:26.286535 20203 solver.cpp:237] Train net output #0: loss = 5.25491 (* 1 = 5.25491 loss)
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I0412 14:32:26.286547 20203 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
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I0412 14:32:30.961520 20203 solver.cpp:218] Iteration 1500 (2.56697 iter/s, 4.67478s/12 iters), loss = 5.27325
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I0412 14:32:30.961571 20203 solver.cpp:237] Train net output #0: loss = 5.27325 (* 1 = 5.27325 loss)
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I0412 14:32:30.961585 20203 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
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I0412 14:32:35.818334 20203 solver.cpp:218] Iteration 1512 (2.47089 iter/s, 4.85655s/12 iters), loss = 5.29068
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I0412 14:32:35.818383 20203 solver.cpp:237] Train net output #0: loss = 5.29068 (* 1 = 5.29068 loss)
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I0412 14:32:35.818394 20203 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
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I0412 14:32:37.425197 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:32:40.377233 20203 solver.cpp:218] Iteration 1524 (2.63236 iter/s, 4.55865s/12 iters), loss = 5.27764
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I0412 14:32:40.377283 20203 solver.cpp:237] Train net output #0: loss = 5.27764 (* 1 = 5.27764 loss)
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I0412 14:32:40.377295 20203 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
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I0412 14:32:42.308516 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
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I0412 14:32:46.726109 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
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I0412 14:32:49.479759 20203 solver.cpp:330] Iteration 1530, Testing net (#0)
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I0412 14:32:49.479779 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:32:53.311547 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:32:53.949318 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:32:53.949374 20203 solver.cpp:397] Test net output #1: loss = 5.28582 (* 1 = 5.28582 loss)
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I0412 14:32:55.816053 20203 solver.cpp:218] Iteration 1536 (0.777297 iter/s, 15.4381s/12 iters), loss = 5.27983
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I0412 14:32:55.816107 20203 solver.cpp:237] Train net output #0: loss = 5.27983 (* 1 = 5.27983 loss)
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I0412 14:32:55.816119 20203 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
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I0412 14:33:00.606197 20203 solver.cpp:218] Iteration 1548 (2.50528 iter/s, 4.78988s/12 iters), loss = 5.23678
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I0412 14:33:00.606249 20203 solver.cpp:237] Train net output #0: loss = 5.23678 (* 1 = 5.23678 loss)
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I0412 14:33:00.606262 20203 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
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I0412 14:33:05.346994 20203 solver.cpp:218] Iteration 1560 (2.53136 iter/s, 4.74054s/12 iters), loss = 5.29021
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I0412 14:33:05.347040 20203 solver.cpp:237] Train net output #0: loss = 5.29021 (* 1 = 5.29021 loss)
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I0412 14:33:05.347052 20203 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
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I0412 14:33:10.176949 20203 solver.cpp:218] Iteration 1572 (2.48463 iter/s, 4.8297s/12 iters), loss = 5.25786
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I0412 14:33:10.177074 20203 solver.cpp:237] Train net output #0: loss = 5.25786 (* 1 = 5.25786 loss)
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I0412 14:33:10.177088 20203 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
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I0412 14:33:14.950119 20203 solver.cpp:218] Iteration 1584 (2.51423 iter/s, 4.77283s/12 iters), loss = 5.27093
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I0412 14:33:14.950173 20203 solver.cpp:237] Train net output #0: loss = 5.27093 (* 1 = 5.27093 loss)
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I0412 14:33:14.950184 20203 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
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I0412 14:33:19.822873 20203 solver.cpp:218] Iteration 1596 (2.46281 iter/s, 4.87248s/12 iters), loss = 5.26888
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I0412 14:33:19.822922 20203 solver.cpp:237] Train net output #0: loss = 5.26888 (* 1 = 5.26888 loss)
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I0412 14:33:19.822934 20203 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
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I0412 14:33:24.672657 20203 solver.cpp:218] Iteration 1608 (2.47447 iter/s, 4.84952s/12 iters), loss = 5.26657
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I0412 14:33:24.672708 20203 solver.cpp:237] Train net output #0: loss = 5.26657 (* 1 = 5.26657 loss)
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I0412 14:33:24.672721 20203 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
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I0412 14:33:28.400812 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:33:29.438808 20203 solver.cpp:218] Iteration 1620 (2.51789 iter/s, 4.76589s/12 iters), loss = 5.2565
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I0412 14:33:29.438858 20203 solver.cpp:237] Train net output #0: loss = 5.2565 (* 1 = 5.2565 loss)
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I0412 14:33:29.438869 20203 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
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I0412 14:33:33.796377 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
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I0412 14:33:37.637215 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
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I0412 14:33:40.060614 20203 solver.cpp:330] Iteration 1632, Testing net (#0)
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I0412 14:33:40.060640 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:33:44.128077 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:33:44.799063 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:33:44.799115 20203 solver.cpp:397] Test net output #1: loss = 5.28602 (* 1 = 5.28602 loss)
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I0412 14:33:44.885888 20203 solver.cpp:218] Iteration 1632 (0.776881 iter/s, 15.4464s/12 iters), loss = 5.29265
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I0412 14:33:44.885939 20203 solver.cpp:237] Train net output #0: loss = 5.29265 (* 1 = 5.29265 loss)
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I0412 14:33:49.394542 20203 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
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I0412 14:33:54.316150 20203 solver.cpp:237] Train net output #0: loss = 5.29628 (* 1 = 5.29628 loss)
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I0412 14:33:59.331315 20203 solver.cpp:237] Train net output #0: loss = 5.26641 (* 1 = 5.26641 loss)
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I0412 14:34:04.147681 20203 solver.cpp:237] Train net output #0: loss = 5.27869 (* 1 = 5.27869 loss)
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I0412 14:34:08.971542 20203 solver.cpp:237] Train net output #0: loss = 5.29304 (* 1 = 5.29304 loss)
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I0412 14:34:08.971550 20203 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
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I0412 14:34:13.766613 20203 solver.cpp:237] Train net output #0: loss = 5.27258 (* 1 = 5.27258 loss)
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I0412 14:34:13.766625 20203 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
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I0412 14:34:18.470150 20203 solver.cpp:237] Train net output #0: loss = 5.28701 (* 1 = 5.28701 loss)
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I0412 14:34:18.470160 20203 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
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I0412 14:34:23.339465 20203 solver.cpp:218] Iteration 1728 (2.46452 iter/s, 4.8691s/12 iters), loss = 5.28606
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I0412 14:34:23.339517 20203 solver.cpp:237] Train net output #0: loss = 5.28606 (* 1 = 5.28606 loss)
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I0412 14:34:23.339529 20203 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
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I0412 14:34:28.371884 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
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I0412 14:34:30.701493 20203 solver.cpp:330] Iteration 1734, Testing net (#0)
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I0412 14:34:30.701517 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:34:34.509230 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:34:35.223407 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:34:35.223453 20203 solver.cpp:397] Test net output #1: loss = 5.28587 (* 1 = 5.28587 loss)
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I0412 14:34:37.035908 20203 solver.cpp:218] Iteration 1740 (0.876179 iter/s, 13.6958s/12 iters), loss = 5.25667
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I0412 14:34:37.035948 20203 solver.cpp:237] Train net output #0: loss = 5.25667 (* 1 = 5.25667 loss)
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I0412 14:34:37.035955 20203 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
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I0412 14:34:41.833170 20203 solver.cpp:218] Iteration 1752 (2.50156 iter/s, 4.79701s/12 iters), loss = 5.27123
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I0412 14:34:41.833218 20203 solver.cpp:237] Train net output #0: loss = 5.27123 (* 1 = 5.27123 loss)
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I0412 14:34:41.833231 20203 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
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I0412 14:34:46.643076 20203 solver.cpp:218] Iteration 1764 (2.49499 iter/s, 4.80965s/12 iters), loss = 5.26372
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I0412 14:34:46.643126 20203 solver.cpp:237] Train net output #0: loss = 5.26372 (* 1 = 5.26372 loss)
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I0412 14:34:46.643137 20203 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
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I0412 14:34:51.496328 20203 solver.cpp:237] Train net output #0: loss = 5.27911 (* 1 = 5.27911 loss)
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I0412 14:34:56.451323 20203 solver.cpp:237] Train net output #0: loss = 5.27036 (* 1 = 5.27036 loss)
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I0412 14:34:56.451335 20203 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
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I0412 14:35:01.353652 20203 solver.cpp:237] Train net output #0: loss = 5.28332 (* 1 = 5.28332 loss)
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I0412 14:35:01.353659 20203 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
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I0412 14:35:06.310406 20203 solver.cpp:218] Iteration 1812 (2.42105 iter/s, 4.95653s/12 iters), loss = 5.26619
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I0412 14:35:06.310458 20203 solver.cpp:237] Train net output #0: loss = 5.26619 (* 1 = 5.26619 loss)
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I0412 14:35:06.310470 20203 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
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I0412 14:35:09.421825 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:35:11.131248 20203 solver.cpp:218] Iteration 1824 (2.48933 iter/s, 4.82058s/12 iters), loss = 5.27572
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I0412 14:35:11.131289 20203 solver.cpp:237] Train net output #0: loss = 5.27572 (* 1 = 5.27572 loss)
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I0412 14:35:11.131296 20203 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
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I0412 14:35:15.910704 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
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I0412 14:35:18.864962 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
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I0412 14:35:21.150072 20203 solver.cpp:330] Iteration 1836, Testing net (#0)
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I0412 14:35:24.835844 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:35:25.586643 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:35:25.586692 20203 solver.cpp:397] Test net output #1: loss = 5.28561 (* 1 = 5.28561 loss)
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I0412 14:35:25.673192 20203 solver.cpp:218] Iteration 1836 (0.825236 iter/s, 14.5413s/12 iters), loss = 5.28035
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I0412 14:35:29.852078 20203 solver.cpp:237] Train net output #0: loss = 5.27165 (* 1 = 5.27165 loss)
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I0412 14:35:34.608232 20203 solver.cpp:237] Train net output #0: loss = 5.28583 (* 1 = 5.28583 loss)
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I0412 14:35:39.225365 20203 solver.cpp:237] Train net output #0: loss = 5.27681 (* 1 = 5.27681 loss)
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I0412 14:35:53.531049 20203 solver.cpp:237] Train net output #0: loss = 5.28179 (* 1 = 5.28179 loss)
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I0412 14:36:03.305263 20203 solver.cpp:237] Train net output #0: loss = 5.28012 (* 1 = 5.28012 loss)
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I0412 14:36:15.266052 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:36:15.266098 20203 solver.cpp:397] Test net output #1: loss = 5.28591 (* 1 = 5.28591 loss)
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I0412 14:36:17.193881 20203 solver.cpp:237] Train net output #0: loss = 5.26979 (* 1 = 5.26979 loss)
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I0412 14:36:26.528281 20203 solver.cpp:237] Train net output #0: loss = 5.2725 (* 1 = 5.2725 loss)
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I0412 14:36:31.182446 20203 solver.cpp:237] Train net output #0: loss = 5.25428 (* 1 = 5.25428 loss)
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I0412 14:36:36.248565 20203 solver.cpp:237] Train net output #0: loss = 5.28647 (* 1 = 5.28647 loss)
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I0412 14:36:41.096276 20203 solver.cpp:237] Train net output #0: loss = 5.27591 (* 1 = 5.27591 loss)
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I0412 14:36:46.017612 20203 solver.cpp:218] Iteration 2016 (2.43847 iter/s, 4.92112s/12 iters), loss = 5.25972
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I0412 14:36:46.017665 20203 solver.cpp:237] Train net output #0: loss = 5.25972 (* 1 = 5.25972 loss)
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I0412 14:36:46.017678 20203 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
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I0412 14:36:48.474326 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:36:50.825543 20203 solver.cpp:218] Iteration 2028 (2.49601 iter/s, 4.80767s/12 iters), loss = 5.27786
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I0412 14:36:50.825598 20203 solver.cpp:237] Train net output #0: loss = 5.27786 (* 1 = 5.27786 loss)
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I0412 14:36:58.575744 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
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I0412 14:37:00.866569 20203 solver.cpp:330] Iteration 2040, Testing net (#0)
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I0412 14:37:00.866592 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:37:04.608561 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:37:05.437083 20203 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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I0412 14:37:05.437130 20203 solver.cpp:397] Test net output #1: loss = 5.28541 (* 1 = 5.28541 loss)
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I0412 14:37:05.523108 20203 solver.cpp:218] Iteration 2040 (0.816499 iter/s, 14.6969s/12 iters), loss = 5.28493
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I0412 14:37:05.523155 20203 solver.cpp:237] Train net output #0: loss = 5.28493 (* 1 = 5.28493 loss)
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I0412 14:37:05.523166 20203 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
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I0412 14:37:09.676340 20203 solver.cpp:237] Train net output #0: loss = 5.28612 (* 1 = 5.28612 loss)
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I0412 14:37:14.769814 20203 solver.cpp:218] Iteration 2064 (2.35606 iter/s, 5.09325s/12 iters), loss = 5.27713
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I0412 14:37:19.740200 20203 solver.cpp:218] Iteration 2076 (2.41442 iter/s, 4.97013s/12 iters), loss = 5.2802
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I0412 14:37:19.740242 20203 solver.cpp:237] Train net output #0: loss = 5.2802 (* 1 = 5.2802 loss)
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I0412 14:37:24.509021 20203 solver.cpp:237] Train net output #0: loss = 5.2714 (* 1 = 5.2714 loss)
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I0412 14:37:24.509034 20203 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
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I0412 14:37:29.046216 20203 solver.cpp:237] Train net output #0: loss = 5.27168 (* 1 = 5.27168 loss)
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I0412 14:37:29.046226 20203 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
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I0412 14:37:33.790176 20203 solver.cpp:218] Iteration 2112 (2.52964 iter/s, 4.74375s/12 iters), loss = 5.27879
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I0412 14:37:33.790227 20203 solver.cpp:237] Train net output #0: loss = 5.27879 (* 1 = 5.27879 loss)
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I0412 14:37:33.790241 20203 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
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I0412 14:37:38.342389 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:37:38.655593 20203 solver.cpp:218] Iteration 2124 (2.46652 iter/s, 4.86515s/12 iters), loss = 5.26005
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I0412 14:37:38.655647 20203 solver.cpp:237] Train net output #0: loss = 5.26005 (* 1 = 5.26005 loss)
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I0412 14:37:38.655660 20203 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
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I0412 14:37:43.460351 20203 solver.cpp:218] Iteration 2136 (2.49766 iter/s, 4.8045s/12 iters), loss = 5.27404
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I0412 14:37:43.460392 20203 solver.cpp:237] Train net output #0: loss = 5.27404 (* 1 = 5.27404 loss)
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I0412 14:37:45.571499 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
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I0412 14:37:51.160931 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
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I0412 14:37:58.725816 20203 solver.cpp:330] Iteration 2142, Testing net (#0)
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I0412 14:38:02.298296 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:38:03.162250 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:38:03.162307 20203 solver.cpp:397] Test net output #1: loss = 5.2857 (* 1 = 5.2857 loss)
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I0412 14:38:09.715518 20203 solver.cpp:237] Train net output #0: loss = 5.28948 (* 1 = 5.28948 loss)
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I0412 14:38:14.981814 20203 solver.cpp:237] Train net output #0: loss = 5.27638 (* 1 = 5.27638 loss)
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I0412 14:38:14.981823 20203 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
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I0412 14:38:20.175617 20203 solver.cpp:237] Train net output #0: loss = 5.27519 (* 1 = 5.27519 loss)
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I0412 14:38:25.056118 20203 solver.cpp:237] Train net output #0: loss = 5.25271 (* 1 = 5.25271 loss)
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I0412 14:38:25.056134 20203 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
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I0412 14:38:29.964897 20203 solver.cpp:237] Train net output #0: loss = 5.27565 (* 1 = 5.27565 loss)
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I0412 14:38:29.964905 20203 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
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I0412 14:38:34.673532 20203 solver.cpp:218] Iteration 2220 (2.54862 iter/s, 4.70843s/12 iters), loss = 5.28582
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I0412 14:38:34.673583 20203 solver.cpp:237] Train net output #0: loss = 5.28582 (* 1 = 5.28582 loss)
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I0412 14:38:34.673596 20203 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
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I0412 14:38:39.621718 20203 solver.cpp:218] Iteration 2232 (2.42526 iter/s, 4.94792s/12 iters), loss = 5.28663
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I0412 14:38:39.621757 20203 solver.cpp:237] Train net output #0: loss = 5.28663 (* 1 = 5.28663 loss)
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I0412 14:38:39.621765 20203 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
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I0412 14:38:43.952095 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
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I0412 14:38:47.013427 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
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I0412 14:38:49.316294 20203 solver.cpp:330] Iteration 2244, Testing net (#0)
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I0412 14:38:52.899271 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:38:53.811206 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:38:53.811259 20203 solver.cpp:397] Test net output #1: loss = 5.28555 (* 1 = 5.28555 loss)
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I0412 14:38:53.895818 20203 solver.cpp:218] Iteration 2244 (0.840721 iter/s, 14.2735s/12 iters), loss = 5.28259
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I0412 14:38:53.895869 20203 solver.cpp:237] Train net output #0: loss = 5.28259 (* 1 = 5.28259 loss)
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I0412 14:38:53.895881 20203 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
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I0412 14:38:58.043228 20203 solver.cpp:237] Train net output #0: loss = 5.24283 (* 1 = 5.24283 loss)
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I0412 14:38:58.043243 20203 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
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I0412 14:39:02.933843 20203 solver.cpp:218] Iteration 2268 (2.45378 iter/s, 4.89041s/12 iters), loss = 5.28452
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I0412 14:39:02.933881 20203 solver.cpp:237] Train net output #0: loss = 5.28452 (* 1 = 5.28452 loss)
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I0412 14:39:02.933890 20203 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
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I0412 14:39:07.765126 20203 solver.cpp:218] Iteration 2280 (2.48394 iter/s, 4.83103s/12 iters), loss = 5.2582
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I0412 14:39:07.765170 20203 solver.cpp:237] Train net output #0: loss = 5.2582 (* 1 = 5.2582 loss)
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I0412 14:39:07.765180 20203 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
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I0412 14:39:12.598917 20203 solver.cpp:237] Train net output #0: loss = 5.27781 (* 1 = 5.27781 loss)
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I0412 14:39:12.598927 20203 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
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I0412 14:39:17.713145 20203 solver.cpp:218] Iteration 2304 (2.3465 iter/s, 5.114s/12 iters), loss = 5.26953
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I0412 14:39:17.713286 20203 solver.cpp:237] Train net output #0: loss = 5.26953 (* 1 = 5.26953 loss)
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I0412 14:39:22.610473 20203 solver.cpp:218] Iteration 2316 (2.45049 iter/s, 4.89698s/12 iters), loss = 5.26379
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I0412 14:39:22.610530 20203 solver.cpp:237] Train net output #0: loss = 5.26379 (* 1 = 5.26379 loss)
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I0412 14:39:22.610543 20203 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
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I0412 14:39:26.409194 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:39:27.433624 20203 solver.cpp:218] Iteration 2328 (2.48814 iter/s, 4.82288s/12 iters), loss = 5.26062
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I0412 14:39:27.433673 20203 solver.cpp:237] Train net output #0: loss = 5.26062 (* 1 = 5.26062 loss)
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I0412 14:39:27.433686 20203 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
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I0412 14:39:32.645256 20203 solver.cpp:218] Iteration 2340 (2.30266 iter/s, 5.21136s/12 iters), loss = 5.29315
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I0412 14:39:32.645306 20203 solver.cpp:237] Train net output #0: loss = 5.29315 (* 1 = 5.29315 loss)
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I0412 14:39:32.645320 20203 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
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I0412 14:39:37.616854 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
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I0412 14:39:39.973153 20203 solver.cpp:330] Iteration 2346, Testing net (#0)
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I0412 14:39:43.459493 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:39:44.403970 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:39:44.404024 20203 solver.cpp:397] Test net output #1: loss = 5.28624 (* 1 = 5.28624 loss)
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I0412 14:39:46.395629 20203 solver.cpp:237] Train net output #0: loss = 5.26079 (* 1 = 5.26079 loss)
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I0412 14:39:51.310989 20203 solver.cpp:237] Train net output #0: loss = 5.30361 (* 1 = 5.30361 loss)
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I0412 14:39:56.197887 20203 solver.cpp:237] Train net output #0: loss = 5.26044 (* 1 = 5.26044 loss)
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I0412 14:40:01.093552 20203 solver.cpp:237] Train net output #0: loss = 5.27485 (* 1 = 5.27485 loss)
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I0412 14:40:05.957468 20203 solver.cpp:237] Train net output #0: loss = 5.28419 (* 1 = 5.28419 loss)
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I0412 14:40:10.825850 20203 solver.cpp:237] Train net output #0: loss = 5.27397 (* 1 = 5.27397 loss)
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I0412 14:40:20.487936 20203 solver.cpp:218] Iteration 2436 (2.46406 iter/s, 4.87002s/12 iters), loss = 5.27936
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I0412 14:40:29.047355 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
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I0412 14:40:31.353529 20203 solver.cpp:330] Iteration 2448, Testing net (#0)
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I0412 14:40:35.912473 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:40:35.912520 20203 solver.cpp:397] Test net output #1: loss = 5.28574 (* 1 = 5.28574 loss)
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I0412 14:40:49.960904 20203 solver.cpp:237] Train net output #0: loss = 5.27253 (* 1 = 5.27253 loss)
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I0412 14:40:55.014307 20203 solver.cpp:237] Train net output #0: loss = 5.27558 (* 1 = 5.27558 loss)
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I0412 14:40:59.853729 20203 solver.cpp:237] Train net output #0: loss = 5.28963 (* 1 = 5.28963 loss)
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I0412 14:40:59.853745 20203 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
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I0412 14:41:04.822504 20203 solver.cpp:218] Iteration 2520 (2.41519 iter/s, 4.96855s/12 iters), loss = 5.2816
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I0412 14:41:04.822559 20203 solver.cpp:237] Train net output #0: loss = 5.2816 (* 1 = 5.2816 loss)
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I0412 14:41:04.822572 20203 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
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I0412 14:41:07.968413 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:41:09.744685 20203 solver.cpp:218] Iteration 2532 (2.43808 iter/s, 4.92191s/12 iters), loss = 5.28331
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I0412 14:41:09.744737 20203 solver.cpp:237] Train net output #0: loss = 5.28331 (* 1 = 5.28331 loss)
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I0412 14:41:09.744750 20203 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
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I0412 14:41:14.579466 20203 solver.cpp:218] Iteration 2544 (2.48215 iter/s, 4.83452s/12 iters), loss = 5.27082
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I0412 14:41:14.579512 20203 solver.cpp:237] Train net output #0: loss = 5.27082 (* 1 = 5.27082 loss)
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I0412 14:41:14.579522 20203 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
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I0412 14:41:16.462687 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
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I0412 14:41:21.469627 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
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I0412 14:41:25.455065 20203 solver.cpp:330] Iteration 2550, Testing net (#0)
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I0412 14:41:28.996760 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:41:30.088969 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:41:30.089015 20203 solver.cpp:397] Test net output #1: loss = 5.28602 (* 1 = 5.28602 loss)
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I0412 14:41:31.812268 20203 solver.cpp:218] Iteration 2556 (0.696378 iter/s, 17.232s/12 iters), loss = 5.28323
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I0412 14:41:36.585675 20203 solver.cpp:218] Iteration 2568 (2.51406 iter/s, 4.77315s/12 iters), loss = 5.28542
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I0412 14:41:36.585717 20203 solver.cpp:237] Train net output #0: loss = 5.28542 (* 1 = 5.28542 loss)
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I0412 14:41:41.945900 20203 solver.cpp:218] Iteration 2580 (2.23883 iter/s, 5.35995s/12 iters), loss = 5.26985
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I0412 14:41:46.793969 20203 solver.cpp:218] Iteration 2592 (2.47535 iter/s, 4.8478s/12 iters), loss = 5.29046
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I0412 14:41:46.794020 20203 solver.cpp:237] Train net output #0: loss = 5.29046 (* 1 = 5.29046 loss)
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I0412 14:41:52.121465 20203 solver.cpp:237] Train net output #0: loss = 5.26128 (* 1 = 5.26128 loss)
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I0412 14:41:57.214807 20203 solver.cpp:218] Iteration 2616 (2.35612 iter/s, 5.09311s/12 iters), loss = 5.28052
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I0412 14:41:57.215567 20203 solver.cpp:237] Train net output #0: loss = 5.28052 (* 1 = 5.28052 loss)
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I0412 14:41:57.215580 20203 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
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I0412 14:42:02.113315 20203 solver.cpp:218] Iteration 2628 (2.45021 iter/s, 4.89753s/12 iters), loss = 5.28175
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I0412 14:42:02.113371 20203 solver.cpp:237] Train net output #0: loss = 5.28175 (* 1 = 5.28175 loss)
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I0412 14:42:02.113385 20203 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
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I0412 14:42:02.540132 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:42:07.015877 20203 solver.cpp:218] Iteration 2640 (2.44784 iter/s, 4.90229s/12 iters), loss = 5.28006
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I0412 14:42:07.015934 20203 solver.cpp:237] Train net output #0: loss = 5.28006 (* 1 = 5.28006 loss)
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I0412 14:42:07.015946 20203 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
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I0412 14:42:11.467200 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
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I0412 14:42:14.453658 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
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I0412 14:42:19.016959 20203 solver.cpp:330] Iteration 2652, Testing net (#0)
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I0412 14:42:19.016986 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:42:22.620229 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:42:23.685060 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:42:23.685101 20203 solver.cpp:397] Test net output #1: loss = 5.28602 (* 1 = 5.28602 loss)
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I0412 14:42:23.771387 20203 solver.cpp:218] Iteration 2652 (0.716215 iter/s, 16.7547s/12 iters), loss = 5.27352
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I0412 14:42:23.771435 20203 solver.cpp:237] Train net output #0: loss = 5.27352 (* 1 = 5.27352 loss)
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I0412 14:42:27.953744 20203 solver.cpp:218] Iteration 2664 (2.86936 iter/s, 4.18212s/12 iters), loss = 5.27857
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I0412 14:42:27.962060 20203 solver.cpp:237] Train net output #0: loss = 5.27857 (* 1 = 5.27857 loss)
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I0412 14:42:27.962080 20203 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
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I0412 14:42:38.144275 20203 solver.cpp:218] Iteration 2688 (2.26862 iter/s, 5.28955s/12 iters), loss = 5.26354
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I0412 14:42:38.144331 20203 solver.cpp:237] Train net output #0: loss = 5.26354 (* 1 = 5.26354 loss)
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I0412 14:42:43.043161 20203 solver.cpp:237] Train net output #0: loss = 5.2793 (* 1 = 5.2793 loss)
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I0412 14:42:43.043174 20203 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
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I0412 14:42:47.774251 20203 solver.cpp:237] Train net output #0: loss = 5.28654 (* 1 = 5.28654 loss)
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I0412 14:42:47.774260 20203 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
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I0412 14:42:52.573870 20203 solver.cpp:237] Train net output #0: loss = 5.25994 (* 1 = 5.25994 loss)
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I0412 14:42:52.573879 20203 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
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I0412 14:42:55.042593 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:42:57.315706 20203 solver.cpp:218] Iteration 2736 (2.53078 iter/s, 4.74163s/12 iters), loss = 5.28018
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I0412 14:42:57.315753 20203 solver.cpp:237] Train net output #0: loss = 5.28018 (* 1 = 5.28018 loss)
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I0412 14:42:57.315764 20203 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
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I0412 14:43:02.048789 20203 solver.cpp:237] Train net output #0: loss = 5.27416 (* 1 = 5.27416 loss)
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I0412 14:43:02.048801 20203 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
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I0412 14:43:04.112962 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
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I0412 14:43:10.269196 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
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I0412 14:43:16.773481 20203 solver.cpp:330] Iteration 2754, Testing net (#0)
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I0412 14:43:16.773509 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:43:19.858124 20203 blocking_queue.cpp:49] Waiting for data
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I0412 14:43:20.094673 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:43:21.204228 20203 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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I0412 14:43:21.204268 20203 solver.cpp:397] Test net output #1: loss = 5.28608 (* 1 = 5.28608 loss)
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I0412 14:43:23.108382 20203 solver.cpp:218] Iteration 2760 (0.569835 iter/s, 21.0587s/12 iters), loss = 5.27861
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I0412 14:43:23.108436 20203 solver.cpp:237] Train net output #0: loss = 5.27861 (* 1 = 5.27861 loss)
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I0412 14:43:23.108449 20203 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
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I0412 14:43:28.046559 20203 solver.cpp:237] Train net output #0: loss = 5.27721 (* 1 = 5.27721 loss)
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I0412 14:43:28.046571 20203 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
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I0412 14:43:33.310155 20203 solver.cpp:237] Train net output #0: loss = 5.27837 (* 1 = 5.27837 loss)
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I0412 14:43:33.310168 20203 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
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I0412 14:43:38.172679 20203 solver.cpp:237] Train net output #0: loss = 5.27287 (* 1 = 5.27287 loss)
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I0412 14:43:38.172691 20203 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
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I0412 14:43:43.298175 20203 solver.cpp:218] Iteration 2808 (2.34134 iter/s, 5.12527s/12 iters), loss = 5.26906
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I0412 14:43:43.298228 20203 solver.cpp:237] Train net output #0: loss = 5.26906 (* 1 = 5.26906 loss)
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I0412 14:43:43.298240 20203 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
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I0412 14:43:48.256090 20203 solver.cpp:218] Iteration 2820 (2.42051 iter/s, 4.95764s/12 iters), loss = 5.27697
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I0412 14:43:48.256163 20203 solver.cpp:237] Train net output #0: loss = 5.27697 (* 1 = 5.27697 loss)
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I0412 14:43:48.256182 20203 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
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I0412 14:43:52.870714 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:43:53.155030 20203 solver.cpp:218] Iteration 2832 (2.44965 iter/s, 4.89865s/12 iters), loss = 5.26313
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I0412 14:43:53.155081 20203 solver.cpp:237] Train net output #0: loss = 5.26313 (* 1 = 5.26313 loss)
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I0412 14:43:53.155092 20203 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
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I0412 14:43:58.072561 20203 solver.cpp:237] Train net output #0: loss = 5.27177 (* 1 = 5.27177 loss)
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I0412 14:43:58.072573 20203 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
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I0412 14:44:02.646754 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
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I0412 14:44:05.657517 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
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I0412 14:44:07.991075 20203 solver.cpp:330] Iteration 2856, Testing net (#0)
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I0412 14:44:11.251796 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:44:12.395519 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:44:12.395555 20203 solver.cpp:397] Test net output #1: loss = 5.2865 (* 1 = 5.2865 loss)
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I0412 14:44:12.481992 20203 solver.cpp:218] Iteration 2856 (0.832824 iter/s, 14.4088s/12 iters), loss = 5.28789
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I0412 14:44:12.482046 20203 solver.cpp:237] Train net output #0: loss = 5.28789 (* 1 = 5.28789 loss)
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I0412 14:44:17.139485 20203 solver.cpp:237] Train net output #0: loss = 5.28625 (* 1 = 5.28625 loss)
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I0412 14:44:17.139494 20203 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
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I0412 14:44:21.853979 20203 solver.cpp:237] Train net output #0: loss = 5.28181 (* 1 = 5.28181 loss)
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I0412 14:44:26.685734 20203 solver.cpp:237] Train net output #0: loss = 5.26879 (* 1 = 5.26879 loss)
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I0412 14:44:31.674096 20203 solver.cpp:237] Train net output #0: loss = 5.25809 (* 1 = 5.25809 loss)
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I0412 14:44:36.648751 20203 solver.cpp:237] Train net output #0: loss = 5.27228 (* 1 = 5.27228 loss)
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I0412 14:44:41.723099 20203 solver.cpp:237] Train net output #0: loss = 5.27566 (* 1 = 5.27566 loss)
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I0412 14:44:46.574877 20203 solver.cpp:237] Train net output #0: loss = 5.28488 (* 1 = 5.28488 loss)
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I0412 14:44:51.483851 20203 solver.cpp:237] Train net output #0: loss = 5.28018 (* 1 = 5.28018 loss)
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I0412 14:44:56.485188 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
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I0412 14:45:03.368870 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:45:03.368906 20203 solver.cpp:397] Test net output #1: loss = 5.28589 (* 1 = 5.28589 loss)
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I0412 14:45:10.483701 20203 solver.cpp:237] Train net output #0: loss = 5.28481 (* 1 = 5.28481 loss)
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I0412 14:45:20.564078 20203 solver.cpp:237] Train net output #0: loss = 5.2692 (* 1 = 5.2692 loss)
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I0412 14:45:25.494868 20203 solver.cpp:237] Train net output #0: loss = 5.27872 (* 1 = 5.27872 loss)
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I0412 14:45:25.494879 20203 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
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I0412 14:45:30.384071 20203 solver.cpp:237] Train net output #0: loss = 5.25896 (* 1 = 5.25896 loss)
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I0412 14:45:30.384084 20203 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
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I0412 14:45:34.777765 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:45:35.805379 20203 solver.cpp:218] Iteration 3036 (2.21359 iter/s, 5.42107s/12 iters), loss = 5.25528
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I0412 14:45:35.805421 20203 solver.cpp:237] Train net output #0: loss = 5.25528 (* 1 = 5.25528 loss)
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I0412 14:45:35.805430 20203 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
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I0412 14:45:40.600070 20203 solver.cpp:218] Iteration 3048 (2.50291 iter/s, 4.79443s/12 iters), loss = 5.29232
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I0412 14:45:40.600245 20203 solver.cpp:237] Train net output #0: loss = 5.29232 (* 1 = 5.29232 loss)
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I0412 14:45:40.600260 20203 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
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I0412 14:45:44.896458 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
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I0412 14:45:49.625411 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
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I0412 14:45:54.290982 20203 solver.cpp:330] Iteration 3060, Testing net (#0)
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I0412 14:45:54.291007 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:45:57.582274 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:45:58.966588 20203 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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I0412 14:45:58.966619 20203 solver.cpp:397] Test net output #1: loss = 5.28565 (* 1 = 5.28565 loss)
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I0412 14:45:59.053122 20203 solver.cpp:218] Iteration 3060 (0.650333 iter/s, 18.4521s/12 iters), loss = 5.26214
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I0412 14:46:08.309201 20203 solver.cpp:218] Iteration 3084 (2.34262 iter/s, 5.12248s/12 iters), loss = 5.27829
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I0412 14:46:08.309253 20203 solver.cpp:237] Train net output #0: loss = 5.27829 (* 1 = 5.27829 loss)
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I0412 14:46:13.458748 20203 solver.cpp:237] Train net output #0: loss = 5.27814 (* 1 = 5.27814 loss)
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I0412 14:46:18.644302 20203 solver.cpp:237] Train net output #0: loss = 5.27548 (* 1 = 5.27548 loss)
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I0412 14:46:18.644311 20203 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
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I0412 14:46:23.381666 20203 solver.cpp:237] Train net output #0: loss = 5.27098 (* 1 = 5.27098 loss)
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I0412 14:46:23.381676 20203 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
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I0412 14:46:28.160441 20203 solver.cpp:218] Iteration 3132 (2.51121 iter/s, 4.77858s/12 iters), loss = 5.28089
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I0412 14:46:28.160491 20203 solver.cpp:237] Train net output #0: loss = 5.28089 (* 1 = 5.28089 loss)
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I0412 14:46:28.160502 20203 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
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I0412 14:46:29.190994 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:46:32.896806 20203 solver.cpp:218] Iteration 3144 (2.53371 iter/s, 4.73613s/12 iters), loss = 5.27633
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I0412 14:46:32.896859 20203 solver.cpp:237] Train net output #0: loss = 5.27633 (* 1 = 5.27633 loss)
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I0412 14:46:32.896873 20203 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
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I0412 14:46:37.676002 20203 solver.cpp:218] Iteration 3156 (2.511 iter/s, 4.77896s/12 iters), loss = 5.25186
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I0412 14:46:37.676051 20203 solver.cpp:237] Train net output #0: loss = 5.25186 (* 1 = 5.25186 loss)
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I0412 14:46:37.676065 20203 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
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I0412 14:46:39.512681 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
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I0412 14:46:42.629886 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
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I0412 14:46:44.922453 20203 solver.cpp:330] Iteration 3162, Testing net (#0)
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I0412 14:46:44.922530 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:46:48.644763 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:46:49.980644 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:46:49.980693 20203 solver.cpp:397] Test net output #1: loss = 5.2858 (* 1 = 5.2858 loss)
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I0412 14:46:51.761515 20203 solver.cpp:218] Iteration 3168 (0.851973 iter/s, 14.085s/12 iters), loss = 5.2653
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I0412 14:46:51.761574 20203 solver.cpp:237] Train net output #0: loss = 5.2653 (* 1 = 5.2653 loss)
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I0412 14:46:56.581533 20203 solver.cpp:218] Iteration 3180 (2.48974 iter/s, 4.81978s/12 iters), loss = 5.27439
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I0412 14:46:56.581571 20203 solver.cpp:237] Train net output #0: loss = 5.27439 (* 1 = 5.27439 loss)
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I0412 14:47:01.329355 20203 solver.cpp:237] Train net output #0: loss = 5.28217 (* 1 = 5.28217 loss)
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I0412 14:47:06.015096 20203 solver.cpp:237] Train net output #0: loss = 5.26267 (* 1 = 5.26267 loss)
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I0412 14:47:06.015105 20203 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
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I0412 14:47:10.977689 20203 solver.cpp:237] Train net output #0: loss = 5.28925 (* 1 = 5.28925 loss)
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I0412 14:47:10.977699 20203 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
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I0412 14:47:15.925087 20203 solver.cpp:218] Iteration 3228 (2.42561 iter/s, 4.94721s/12 iters), loss = 5.2761
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I0412 14:47:15.925205 20203 solver.cpp:237] Train net output #0: loss = 5.2761 (* 1 = 5.2761 loss)
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I0412 14:47:15.925218 20203 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
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I0412 14:47:19.064049 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:47:20.727052 20203 solver.cpp:218] Iteration 3240 (2.49913 iter/s, 4.80166s/12 iters), loss = 5.28372
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I0412 14:47:20.727097 20203 solver.cpp:237] Train net output #0: loss = 5.28372 (* 1 = 5.28372 loss)
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I0412 14:47:20.727105 20203 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
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I0412 14:47:25.659598 20203 solver.cpp:237] Train net output #0: loss = 5.27204 (* 1 = 5.27204 loss)
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I0412 14:47:25.659610 20203 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
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I0412 14:47:30.133180 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
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I0412 14:47:33.212905 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
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I0412 14:47:35.547243 20203 solver.cpp:330] Iteration 3264, Testing net (#0)
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I0412 14:47:35.547266 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:47:38.643540 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:47:39.945683 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:47:39.945731 20203 solver.cpp:397] Test net output #1: loss = 5.28614 (* 1 = 5.28614 loss)
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I0412 14:47:40.030686 20203 solver.cpp:218] Iteration 3264 (0.83504 iter/s, 14.3706s/12 iters), loss = 5.2797
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I0412 14:47:40.030735 20203 solver.cpp:237] Train net output #0: loss = 5.2797 (* 1 = 5.2797 loss)
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I0412 14:47:40.030746 20203 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
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I0412 14:47:44.119509 20203 solver.cpp:218] Iteration 3276 (2.93498 iter/s, 4.08861s/12 iters), loss = 5.28559
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I0412 14:47:44.119562 20203 solver.cpp:237] Train net output #0: loss = 5.28559 (* 1 = 5.28559 loss)
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I0412 14:47:44.119571 20203 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
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I0412 14:47:48.904942 20203 solver.cpp:218] Iteration 3288 (2.50773 iter/s, 4.7852s/12 iters), loss = 5.2633
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I0412 14:47:48.905110 20203 solver.cpp:237] Train net output #0: loss = 5.2633 (* 1 = 5.2633 loss)
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I0412 14:47:48.905124 20203 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
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I0412 14:47:54.097626 20203 solver.cpp:218] Iteration 3300 (2.3111 iter/s, 5.19233s/12 iters), loss = 5.28588
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I0412 14:47:54.097667 20203 solver.cpp:237] Train net output #0: loss = 5.28588 (* 1 = 5.28588 loss)
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I0412 14:47:54.097676 20203 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
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I0412 14:47:59.129233 20203 solver.cpp:218] Iteration 3312 (2.38504 iter/s, 5.03137s/12 iters), loss = 5.25902
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I0412 14:47:59.129284 20203 solver.cpp:237] Train net output #0: loss = 5.25902 (* 1 = 5.25902 loss)
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I0412 14:47:59.129297 20203 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
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I0412 14:48:04.114305 20203 solver.cpp:218] Iteration 3324 (2.4073 iter/s, 4.98483s/12 iters), loss = 5.28377
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I0412 14:48:04.114359 20203 solver.cpp:237] Train net output #0: loss = 5.28377 (* 1 = 5.28377 loss)
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I0412 14:48:04.114373 20203 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
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I0412 14:48:08.986852 20203 solver.cpp:218] Iteration 3336 (2.4629 iter/s, 4.8723s/12 iters), loss = 5.27794
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I0412 14:48:08.986902 20203 solver.cpp:237] Train net output #0: loss = 5.27794 (* 1 = 5.27794 loss)
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I0412 14:48:08.986913 20203 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
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I0412 14:48:09.518952 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:48:13.943917 20203 solver.cpp:218] Iteration 3348 (2.42091 iter/s, 4.95682s/12 iters), loss = 5.27595
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I0412 14:48:13.943985 20203 solver.cpp:237] Train net output #0: loss = 5.27595 (* 1 = 5.27595 loss)
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I0412 14:48:13.944003 20203 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
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I0412 14:48:18.500420 20203 solver.cpp:218] Iteration 3360 (2.63374 iter/s, 4.55626s/12 iters), loss = 5.26283
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I0412 14:48:18.500474 20203 solver.cpp:237] Train net output #0: loss = 5.26283 (* 1 = 5.26283 loss)
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I0412 14:48:18.500485 20203 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
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I0412 14:48:20.370951 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
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I0412 14:48:27.654428 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
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I0412 14:48:31.370849 20203 solver.cpp:330] Iteration 3366, Testing net (#0)
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I0412 14:48:31.370874 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:48:34.453132 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:48:35.787911 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:48:35.787961 20203 solver.cpp:397] Test net output #1: loss = 5.28584 (* 1 = 5.28584 loss)
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I0412 14:48:37.566478 20203 solver.cpp:218] Iteration 3372 (0.629416 iter/s, 19.0653s/12 iters), loss = 5.28889
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I0412 14:48:37.566522 20203 solver.cpp:237] Train net output #0: loss = 5.28889 (* 1 = 5.28889 loss)
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I0412 14:48:42.596885 20203 solver.cpp:237] Train net output #0: loss = 5.26981 (* 1 = 5.26981 loss)
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I0412 14:48:42.596895 20203 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
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I0412 14:48:47.502203 20203 solver.cpp:218] Iteration 3396 (2.44642 iter/s, 4.90512s/12 iters), loss = 5.26163
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I0412 14:48:47.502257 20203 solver.cpp:237] Train net output #0: loss = 5.26163 (* 1 = 5.26163 loss)
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I0412 14:48:52.297374 20203 solver.cpp:218] Iteration 3408 (2.50264 iter/s, 4.79493s/12 iters), loss = 5.28948
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I0412 14:48:52.297544 20203 solver.cpp:237] Train net output #0: loss = 5.28948 (* 1 = 5.28948 loss)
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I0412 14:48:57.303764 20203 solver.cpp:237] Train net output #0: loss = 5.27865 (* 1 = 5.27865 loss)
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I0412 14:48:57.303776 20203 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
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I0412 14:49:02.317157 20203 solver.cpp:237] Train net output #0: loss = 5.26551 (* 1 = 5.26551 loss)
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I0412 14:49:04.806252 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:49:07.150910 20203 solver.cpp:218] Iteration 3444 (2.48264 iter/s, 4.83357s/12 iters), loss = 5.27926
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I0412 14:49:07.150950 20203 solver.cpp:237] Train net output #0: loss = 5.27926 (* 1 = 5.27926 loss)
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I0412 14:49:23.766247 20203 solver.cpp:330] Iteration 3468, Testing net (#0)
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I0412 14:49:24.222954 20203 blocking_queue.cpp:49] Waiting for data
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I0412 14:49:26.844592 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:49:28.290660 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:49:28.290709 20203 solver.cpp:397] Test net output #1: loss = 5.28602 (* 1 = 5.28602 loss)
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I0412 14:49:28.377236 20203 solver.cpp:218] Iteration 3468 (0.735002 iter/s, 16.3265s/12 iters), loss = 5.27398
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I0412 14:49:28.377285 20203 solver.cpp:237] Train net output #0: loss = 5.27398 (* 1 = 5.27398 loss)
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I0412 14:49:32.350142 20203 solver.cpp:237] Train net output #0: loss = 5.27698 (* 1 = 5.27698 loss)
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I0412 14:49:37.083169 20203 solver.cpp:237] Train net output #0: loss = 5.2851 (* 1 = 5.2851 loss)
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I0412 14:49:41.867945 20203 solver.cpp:237] Train net output #0: loss = 5.27257 (* 1 = 5.27257 loss)
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I0412 14:49:46.674185 20203 solver.cpp:237] Train net output #0: loss = 5.26227 (* 1 = 5.26227 loss)
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I0412 14:49:51.365073 20203 solver.cpp:237] Train net output #0: loss = 5.27153 (* 1 = 5.27153 loss)
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I0412 14:49:51.365084 20203 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
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I0412 14:49:55.929831 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:49:56.186518 20203 solver.cpp:218] Iteration 3540 (2.48898 iter/s, 4.82125s/12 iters), loss = 5.26048
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I0412 14:49:56.186565 20203 solver.cpp:237] Train net output #0: loss = 5.26048 (* 1 = 5.26048 loss)
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I0412 14:49:56.186576 20203 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
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I0412 14:50:00.937678 20203 solver.cpp:218] Iteration 3552 (2.52582 iter/s, 4.75093s/12 iters), loss = 5.26698
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I0412 14:50:00.937719 20203 solver.cpp:237] Train net output #0: loss = 5.26698 (* 1 = 5.26698 loss)
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I0412 14:50:00.937728 20203 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
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I0412 14:50:05.851613 20203 solver.cpp:218] Iteration 3564 (2.44215 iter/s, 4.9137s/12 iters), loss = 5.29531
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I0412 14:50:05.851657 20203 solver.cpp:237] Train net output #0: loss = 5.29531 (* 1 = 5.29531 loss)
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I0412 14:50:05.851666 20203 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
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I0412 14:50:07.787504 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
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I0412 14:50:12.998208 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
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I0412 14:50:15.339449 20203 solver.cpp:330] Iteration 3570, Testing net (#0)
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I0412 14:50:15.339471 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:50:18.430682 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:50:19.845716 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:50:19.845762 20203 solver.cpp:397] Test net output #1: loss = 5.2861 (* 1 = 5.2861 loss)
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I0412 14:50:21.589823 20203 solver.cpp:218] Iteration 3576 (0.762507 iter/s, 15.7376s/12 iters), loss = 5.28651
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I0412 14:50:21.589879 20203 solver.cpp:237] Train net output #0: loss = 5.28651 (* 1 = 5.28651 loss)
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I0412 14:50:26.199734 20203 solver.cpp:218] Iteration 3588 (2.60322 iter/s, 4.60967s/12 iters), loss = 5.27851
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I0412 14:50:26.199863 20203 solver.cpp:237] Train net output #0: loss = 5.27851 (* 1 = 5.27851 loss)
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I0412 14:50:26.199875 20203 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
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I0412 14:50:31.022326 20203 solver.cpp:218] Iteration 3600 (2.48846 iter/s, 4.82227s/12 iters), loss = 5.26795
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I0412 14:50:31.022387 20203 solver.cpp:237] Train net output #0: loss = 5.26795 (* 1 = 5.26795 loss)
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I0412 14:50:31.022397 20203 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
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I0412 14:50:36.265947 20203 solver.cpp:218] Iteration 3612 (2.28861 iter/s, 5.24335s/12 iters), loss = 5.24729
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I0412 14:50:36.266006 20203 solver.cpp:237] Train net output #0: loss = 5.24729 (* 1 = 5.24729 loss)
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I0412 14:50:36.266016 20203 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
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I0412 14:50:41.433216 20203 solver.cpp:218] Iteration 3624 (2.32243 iter/s, 5.167s/12 iters), loss = 5.27239
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I0412 14:50:41.433260 20203 solver.cpp:237] Train net output #0: loss = 5.27239 (* 1 = 5.27239 loss)
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I0412 14:50:41.433269 20203 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
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I0412 14:50:46.347738 20203 solver.cpp:218] Iteration 3636 (2.44186 iter/s, 4.91428s/12 iters), loss = 5.28231
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I0412 14:50:46.347793 20203 solver.cpp:237] Train net output #0: loss = 5.28231 (* 1 = 5.28231 loss)
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I0412 14:50:46.347806 20203 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
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I0412 14:50:48.216459 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:50:51.352028 20203 solver.cpp:218] Iteration 3648 (2.39807 iter/s, 5.00403s/12 iters), loss = 5.28548
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I0412 14:50:51.352083 20203 solver.cpp:237] Train net output #0: loss = 5.28548 (* 1 = 5.28548 loss)
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I0412 14:50:51.352097 20203 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
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I0412 14:50:56.176306 20203 solver.cpp:218] Iteration 3660 (2.48754 iter/s, 4.82404s/12 iters), loss = 5.27446
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I0412 14:50:56.176354 20203 solver.cpp:237] Train net output #0: loss = 5.27446 (* 1 = 5.27446 loss)
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I0412 14:50:56.176364 20203 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
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I0412 14:51:00.652572 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
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I0412 14:51:04.887688 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
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I0412 14:51:09.717370 20203 solver.cpp:330] Iteration 3672, Testing net (#0)
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I0412 14:51:09.717398 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:51:12.776877 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:51:14.238708 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:51:14.238756 20203 solver.cpp:397] Test net output #1: loss = 5.28578 (* 1 = 5.28578 loss)
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I0412 14:51:14.325443 20203 solver.cpp:218] Iteration 3672 (0.661216 iter/s, 18.1484s/12 iters), loss = 5.25133
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I0412 14:51:14.325491 20203 solver.cpp:237] Train net output #0: loss = 5.25133 (* 1 = 5.25133 loss)
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I0412 14:51:18.526245 20203 solver.cpp:218] Iteration 3684 (2.85675 iter/s, 4.20058s/12 iters), loss = 5.27481
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I0412 14:51:18.526302 20203 solver.cpp:237] Train net output #0: loss = 5.27481 (* 1 = 5.27481 loss)
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I0412 14:51:23.315081 20203 solver.cpp:218] Iteration 3696 (2.50596 iter/s, 4.78858s/12 iters), loss = 5.26046
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I0412 14:51:23.315150 20203 solver.cpp:237] Train net output #0: loss = 5.26046 (* 1 = 5.26046 loss)
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I0412 14:51:23.315167 20203 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
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I0412 14:51:28.175426 20203 solver.cpp:237] Train net output #0: loss = 5.2669 (* 1 = 5.2669 loss)
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I0412 14:51:28.175436 20203 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
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I0412 14:51:33.106629 20203 solver.cpp:237] Train net output #0: loss = 5.26677 (* 1 = 5.26677 loss)
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I0412 14:51:33.106642 20203 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
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I0412 14:51:38.220019 20203 solver.cpp:237] Train net output #0: loss = 5.25716 (* 1 = 5.25716 loss)
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I0412 14:51:38.220031 20203 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
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I0412 14:51:42.109666 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:51:42.996583 20203 solver.cpp:218] Iteration 3744 (2.51237 iter/s, 4.77637s/12 iters), loss = 5.25704
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I0412 14:51:42.996639 20203 solver.cpp:237] Train net output #0: loss = 5.25704 (* 1 = 5.25704 loss)
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I0412 14:51:42.996651 20203 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
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I0412 14:51:48.072458 20203 solver.cpp:218] Iteration 3756 (2.36425 iter/s, 5.07561s/12 iters), loss = 5.27789
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I0412 14:51:48.072507 20203 solver.cpp:237] Train net output #0: loss = 5.27789 (* 1 = 5.27789 loss)
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I0412 14:51:48.072520 20203 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
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I0412 14:51:52.896070 20203 solver.cpp:218] Iteration 3768 (2.48789 iter/s, 4.82336s/12 iters), loss = 5.26562
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I0412 14:51:52.896122 20203 solver.cpp:237] Train net output #0: loss = 5.26562 (* 1 = 5.26562 loss)
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I0412 14:51:52.896134 20203 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
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I0412 14:51:54.857054 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
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I0412 14:51:57.815507 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
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I0412 14:52:01.596892 20203 solver.cpp:330] Iteration 3774, Testing net (#0)
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I0412 14:52:01.596917 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:52:04.718255 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:52:06.262342 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:52:06.262383 20203 solver.cpp:397] Test net output #1: loss = 5.28645 (* 1 = 5.28645 loss)
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I0412 14:52:08.108395 20203 solver.cpp:218] Iteration 3780 (0.788867 iter/s, 15.2117s/12 iters), loss = 5.30854
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I0412 14:52:08.108440 20203 solver.cpp:237] Train net output #0: loss = 5.30854 (* 1 = 5.30854 loss)
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I0412 14:52:08.108449 20203 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
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I0412 14:52:12.971513 20203 solver.cpp:218] Iteration 3792 (2.46767 iter/s, 4.86288s/12 iters), loss = 5.27677
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I0412 14:52:12.971556 20203 solver.cpp:237] Train net output #0: loss = 5.27677 (* 1 = 5.27677 loss)
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I0412 14:52:12.971565 20203 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
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I0412 14:52:17.817625 20203 solver.cpp:218] Iteration 3804 (2.47634 iter/s, 4.84587s/12 iters), loss = 5.2699
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I0412 14:52:17.817672 20203 solver.cpp:237] Train net output #0: loss = 5.2699 (* 1 = 5.2699 loss)
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I0412 14:52:17.817682 20203 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
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I0412 14:52:22.670135 20203 solver.cpp:218] Iteration 3816 (2.47307 iter/s, 4.85227s/12 iters), loss = 5.27531
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I0412 14:52:22.670173 20203 solver.cpp:237] Train net output #0: loss = 5.27531 (* 1 = 5.27531 loss)
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I0412 14:52:22.670181 20203 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
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I0412 14:52:27.708207 20203 solver.cpp:218] Iteration 3828 (2.38198 iter/s, 5.03782s/12 iters), loss = 5.2623
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I0412 14:52:27.708266 20203 solver.cpp:237] Train net output #0: loss = 5.2623 (* 1 = 5.2623 loss)
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I0412 14:52:27.708279 20203 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
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I0412 14:52:32.528736 20203 solver.cpp:218] Iteration 3840 (2.48948 iter/s, 4.82028s/12 iters), loss = 5.27217
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I0412 14:52:32.528786 20203 solver.cpp:237] Train net output #0: loss = 5.27217 (* 1 = 5.27217 loss)
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I0412 14:52:32.528797 20203 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
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I0412 14:52:33.518456 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:52:37.237802 20203 solver.cpp:218] Iteration 3852 (2.54841 iter/s, 4.70882s/12 iters), loss = 5.27034
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I0412 14:52:37.237903 20203 solver.cpp:237] Train net output #0: loss = 5.27034 (* 1 = 5.27034 loss)
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I0412 14:52:37.237915 20203 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
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I0412 14:52:42.018863 20203 solver.cpp:218] Iteration 3864 (2.51006 iter/s, 4.78076s/12 iters), loss = 5.25783
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I0412 14:52:42.018916 20203 solver.cpp:237] Train net output #0: loss = 5.25783 (* 1 = 5.25783 loss)
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I0412 14:52:42.018929 20203 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
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I0412 14:52:46.349892 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
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I0412 14:52:49.328094 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
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I0412 14:52:53.485181 20203 solver.cpp:330] Iteration 3876, Testing net (#0)
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I0412 14:52:53.485205 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:52:56.784876 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:52:58.331938 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:52:58.331991 20203 solver.cpp:397] Test net output #1: loss = 5.28589 (* 1 = 5.28589 loss)
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I0412 14:52:58.418503 20203 solver.cpp:218] Iteration 3876 (0.731754 iter/s, 16.3989s/12 iters), loss = 5.27296
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I0412 14:52:58.418551 20203 solver.cpp:237] Train net output #0: loss = 5.27296 (* 1 = 5.27296 loss)
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I0412 14:52:58.418562 20203 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
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I0412 14:53:02.387152 20203 solver.cpp:218] Iteration 3888 (3.02386 iter/s, 3.96844s/12 iters), loss = 5.26848
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I0412 14:53:02.387203 20203 solver.cpp:237] Train net output #0: loss = 5.26848 (* 1 = 5.26848 loss)
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I0412 14:53:02.387217 20203 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
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I0412 14:53:07.043529 20203 solver.cpp:218] Iteration 3900 (2.57724 iter/s, 4.65614s/12 iters), loss = 5.27223
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I0412 14:53:07.043570 20203 solver.cpp:237] Train net output #0: loss = 5.27223 (* 1 = 5.27223 loss)
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I0412 14:53:07.043578 20203 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
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I0412 14:53:11.974048 20203 solver.cpp:237] Train net output #0: loss = 5.26066 (* 1 = 5.26066 loss)
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I0412 14:53:17.124815 20203 solver.cpp:237] Train net output #0: loss = 5.29326 (* 1 = 5.29326 loss)
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I0412 14:53:17.124830 20203 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
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I0412 14:53:21.915202 20203 solver.cpp:237] Train net output #0: loss = 5.27655 (* 1 = 5.27655 loss)
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I0412 14:53:21.915215 20203 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
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I0412 14:53:26.744109 20203 solver.cpp:218] Iteration 3948 (2.48513 iter/s, 4.82872s/12 iters), loss = 5.28535
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I0412 14:53:26.744151 20203 solver.cpp:237] Train net output #0: loss = 5.28535 (* 1 = 5.28535 loss)
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I0412 14:53:26.744160 20203 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
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I0412 14:53:31.424352 20203 solver.cpp:218] Iteration 3960 (2.5641 iter/s, 4.68001s/12 iters), loss = 5.27473
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I0412 14:53:31.424397 20203 solver.cpp:237] Train net output #0: loss = 5.27473 (* 1 = 5.27473 loss)
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I0412 14:53:36.245193 20203 solver.cpp:237] Train net output #0: loss = 5.27741 (* 1 = 5.27741 loss)
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I0412 14:53:36.245204 20203 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
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I0412 14:53:41.512162 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
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I0412 14:53:47.046149 20203 solver.cpp:330] Iteration 3978, Testing net (#0)
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I0412 14:53:49.940659 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:53:51.551798 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:53:51.551849 20203 solver.cpp:397] Test net output #1: loss = 5.28564 (* 1 = 5.28564 loss)
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I0412 14:53:53.499877 20203 solver.cpp:237] Train net output #0: loss = 5.27831 (* 1 = 5.27831 loss)
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I0412 14:53:58.714393 20203 solver.cpp:237] Train net output #0: loss = 5.26273 (* 1 = 5.26273 loss)
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I0412 14:54:03.494187 20203 solver.cpp:218] Iteration 4008 (2.51067 iter/s, 4.7796s/12 iters), loss = 5.28744
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I0412 14:54:03.494235 20203 solver.cpp:237] Train net output #0: loss = 5.28744 (* 1 = 5.28744 loss)
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I0412 14:54:08.568572 20203 solver.cpp:218] Iteration 4020 (2.36494 iter/s, 5.07412s/12 iters), loss = 5.2585
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I0412 14:54:08.568634 20203 solver.cpp:237] Train net output #0: loss = 5.2585 (* 1 = 5.2585 loss)
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I0412 14:54:13.484258 20203 solver.cpp:237] Train net output #0: loss = 5.27531 (* 1 = 5.27531 loss)
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I0412 14:54:13.484272 20203 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
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I0412 14:54:18.370357 20203 solver.cpp:218] Iteration 4044 (2.45605 iter/s, 4.88589s/12 iters), loss = 5.27071
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I0412 14:54:18.370529 20203 solver.cpp:237] Train net output #0: loss = 5.27071 (* 1 = 5.27071 loss)
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I0412 14:54:18.370543 20203 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
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I0412 14:54:18.858544 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:54:23.275230 20203 solver.cpp:218] Iteration 4056 (2.44673 iter/s, 4.9045s/12 iters), loss = 5.27854
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I0412 14:54:23.275281 20203 solver.cpp:237] Train net output #0: loss = 5.27854 (* 1 = 5.27854 loss)
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I0412 14:54:23.275292 20203 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
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I0412 14:54:28.126752 20203 solver.cpp:218] Iteration 4068 (2.47358 iter/s, 4.85127s/12 iters), loss = 5.27489
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I0412 14:54:28.126811 20203 solver.cpp:237] Train net output #0: loss = 5.27489 (* 1 = 5.27489 loss)
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I0412 14:54:28.126822 20203 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
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I0412 14:54:32.575544 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
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I0412 14:54:39.450810 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
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I0412 14:54:41.756865 20203 solver.cpp:330] Iteration 4080, Testing net (#0)
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I0412 14:54:41.756889 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:54:44.652065 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:54:46.302225 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:54:46.302275 20203 solver.cpp:397] Test net output #1: loss = 5.28612 (* 1 = 5.28612 loss)
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I0412 14:54:46.388861 20203 solver.cpp:218] Iteration 4080 (0.657126 iter/s, 18.2613s/12 iters), loss = 5.28855
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I0412 14:54:46.388914 20203 solver.cpp:237] Train net output #0: loss = 5.28855 (* 1 = 5.28855 loss)
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I0412 14:54:46.388926 20203 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
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I0412 14:54:50.541756 20203 solver.cpp:218] Iteration 4092 (2.88971 iter/s, 4.15266s/12 iters), loss = 5.2675
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I0412 14:54:50.541890 20203 solver.cpp:237] Train net output #0: loss = 5.2675 (* 1 = 5.2675 loss)
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I0412 14:54:50.541908 20203 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
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I0412 14:54:55.237552 20203 solver.cpp:218] Iteration 4104 (2.55565 iter/s, 4.69547s/12 iters), loss = 5.26509
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I0412 14:54:55.237597 20203 solver.cpp:237] Train net output #0: loss = 5.26509 (* 1 = 5.26509 loss)
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I0412 14:54:55.237608 20203 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
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I0412 14:55:00.017884 20203 solver.cpp:218] Iteration 4116 (2.51041 iter/s, 4.78009s/12 iters), loss = 5.29439
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I0412 14:55:00.017942 20203 solver.cpp:237] Train net output #0: loss = 5.29439 (* 1 = 5.29439 loss)
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I0412 14:55:04.723619 20203 solver.cpp:218] Iteration 4128 (2.55022 iter/s, 4.70548s/12 iters), loss = 5.26788
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I0412 14:55:04.723667 20203 solver.cpp:237] Train net output #0: loss = 5.26788 (* 1 = 5.26788 loss)
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I0412 14:55:04.723677 20203 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
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I0412 14:55:09.548082 20203 solver.cpp:218] Iteration 4140 (2.48745 iter/s, 4.82422s/12 iters), loss = 5.25592
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I0412 14:55:09.548125 20203 solver.cpp:237] Train net output #0: loss = 5.25592 (* 1 = 5.25592 loss)
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I0412 14:55:09.548135 20203 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
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I0412 14:55:12.014966 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:55:14.251574 20203 solver.cpp:218] Iteration 4152 (2.55143 iter/s, 4.70325s/12 iters), loss = 5.27164
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I0412 14:55:14.251619 20203 solver.cpp:237] Train net output #0: loss = 5.27164 (* 1 = 5.27164 loss)
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I0412 14:55:14.251629 20203 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
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I0412 14:55:15.884907 20203 blocking_queue.cpp:49] Waiting for data
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I0412 14:55:19.093286 20203 solver.cpp:218] Iteration 4164 (2.47859 iter/s, 4.84146s/12 iters), loss = 5.26667
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I0412 14:55:19.093333 20203 solver.cpp:237] Train net output #0: loss = 5.26667 (* 1 = 5.26667 loss)
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I0412 14:55:19.093341 20203 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
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I0412 14:55:23.765028 20203 solver.cpp:218] Iteration 4176 (2.56877 iter/s, 4.6715s/12 iters), loss = 5.26513
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I0412 14:55:23.765146 20203 solver.cpp:237] Train net output #0: loss = 5.26513 (* 1 = 5.26513 loss)
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I0412 14:55:23.765156 20203 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
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I0412 14:55:25.693063 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
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I0412 14:55:33.821027 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
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I0412 14:55:38.367170 20203 solver.cpp:330] Iteration 4182, Testing net (#0)
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I0412 14:55:38.367198 20203 net.cpp:676] Ignoring source layer train-data
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I0412 14:55:41.374095 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:55:43.079970 20203 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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I0412 14:55:43.080001 20203 solver.cpp:397] Test net output #1: loss = 5.28616 (* 1 = 5.28616 loss)
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I0412 14:55:44.969589 20203 solver.cpp:218] Iteration 4188 (0.565942 iter/s, 21.2036s/12 iters), loss = 5.2698
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I0412 14:55:44.969643 20203 solver.cpp:237] Train net output #0: loss = 5.2698 (* 1 = 5.2698 loss)
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I0412 14:55:44.969653 20203 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
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I0412 14:55:49.830840 20203 solver.cpp:218] Iteration 4200 (2.46863 iter/s, 4.86099s/12 iters), loss = 5.28446
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I0412 14:55:49.830883 20203 solver.cpp:237] Train net output #0: loss = 5.28446 (* 1 = 5.28446 loss)
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I0412 14:55:49.830891 20203 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
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I0412 14:55:54.660923 20203 solver.cpp:218] Iteration 4212 (2.48455 iter/s, 4.82984s/12 iters), loss = 5.26906
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I0412 14:55:54.665406 20203 solver.cpp:237] Train net output #0: loss = 5.26906 (* 1 = 5.26906 loss)
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I0412 14:55:54.665421 20203 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
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I0412 14:55:59.415242 20203 solver.cpp:237] Train net output #0: loss = 5.26589 (* 1 = 5.26589 loss)
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I0412 14:55:59.415254 20203 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
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I0412 14:56:04.409797 20203 solver.cpp:237] Train net output #0: loss = 5.27137 (* 1 = 5.27137 loss)
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I0412 14:56:04.409806 20203 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
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I0412 14:56:09.099552 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:56:09.338095 20203 solver.cpp:218] Iteration 4248 (2.43502 iter/s, 4.92809s/12 iters), loss = 5.24737
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I0412 14:56:09.338148 20203 solver.cpp:237] Train net output #0: loss = 5.24737 (* 1 = 5.24737 loss)
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I0412 14:56:09.338162 20203 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
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I0412 14:56:14.373040 20203 solver.cpp:218] Iteration 4260 (2.38347 iter/s, 5.03468s/12 iters), loss = 5.27006
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I0412 14:56:14.373090 20203 solver.cpp:237] Train net output #0: loss = 5.27006 (* 1 = 5.27006 loss)
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I0412 14:56:14.373103 20203 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
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I0412 14:56:19.271162 20203 solver.cpp:237] Train net output #0: loss = 5.29113 (* 1 = 5.29113 loss)
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I0412 14:56:23.763881 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
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I0412 14:56:27.934703 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
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I0412 14:56:30.272567 20203 solver.cpp:330] Iteration 4284, Testing net (#0)
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I0412 14:56:33.224823 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:56:34.975618 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:56:34.975652 20203 solver.cpp:397] Test net output #1: loss = 5.28617 (* 1 = 5.28617 loss)
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I0412 14:56:35.062312 20203 solver.cpp:218] Iteration 4284 (0.75995 iter/s, 15.7905s/12 iters), loss = 5.27539
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I0412 14:56:35.062353 20203 solver.cpp:237] Train net output #0: loss = 5.27539 (* 1 = 5.27539 loss)
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I0412 14:56:35.062361 20203 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
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I0412 14:56:39.138587 20203 solver.cpp:218] Iteration 4296 (2.94402 iter/s, 4.07606s/12 iters), loss = 5.27735
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I0412 14:56:39.138638 20203 solver.cpp:237] Train net output #0: loss = 5.27735 (* 1 = 5.27735 loss)
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I0412 14:56:39.138649 20203 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
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I0412 14:56:44.001407 20203 solver.cpp:218] Iteration 4308 (2.46783 iter/s, 4.86256s/12 iters), loss = 5.26227
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I0412 14:56:44.001462 20203 solver.cpp:237] Train net output #0: loss = 5.26227 (* 1 = 5.26227 loss)
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I0412 14:56:44.001474 20203 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
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I0412 14:56:48.738699 20203 solver.cpp:218] Iteration 4320 (2.53323 iter/s, 4.73703s/12 iters), loss = 5.24807
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I0412 14:56:48.738760 20203 solver.cpp:237] Train net output #0: loss = 5.24807 (* 1 = 5.24807 loss)
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I0412 14:56:48.738775 20203 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
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I0412 14:56:53.635293 20203 solver.cpp:237] Train net output #0: loss = 5.27875 (* 1 = 5.27875 loss)
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I0412 14:56:53.635308 20203 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
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I0412 14:56:58.906927 20203 solver.cpp:237] Train net output #0: loss = 5.2827 (* 1 = 5.2827 loss)
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I0412 14:56:58.906936 20203 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
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I0412 14:57:03.890797 20203 solver.cpp:218] Iteration 4356 (2.40787 iter/s, 4.98366s/12 iters), loss = 5.28727
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I0412 14:57:03.890849 20203 solver.cpp:237] Train net output #0: loss = 5.28727 (* 1 = 5.28727 loss)
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I0412 14:57:03.890861 20203 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
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I0412 14:57:08.881582 20203 solver.cpp:218] Iteration 4368 (2.40456 iter/s, 4.99052s/12 iters), loss = 5.27685
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I0412 14:57:08.881626 20203 solver.cpp:237] Train net output #0: loss = 5.27685 (* 1 = 5.27685 loss)
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I0412 14:57:08.881636 20203 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
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I0412 14:57:13.747750 20203 solver.cpp:218] Iteration 4380 (2.46613 iter/s, 4.86591s/12 iters), loss = 5.26276
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I0412 14:57:13.747808 20203 solver.cpp:237] Train net output #0: loss = 5.26276 (* 1 = 5.26276 loss)
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I0412 14:57:13.747820 20203 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
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I0412 14:57:20.656735 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
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I0412 14:57:24.118881 20203 solver.cpp:330] Iteration 4386, Testing net (#0)
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I0412 14:57:26.781018 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:57:28.553771 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:57:28.553823 20203 solver.cpp:397] Test net output #1: loss = 5.28616 (* 1 = 5.28616 loss)
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I0412 14:57:30.338500 20203 solver.cpp:218] Iteration 4392 (0.723326 iter/s, 16.59s/12 iters), loss = 5.27373
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I0412 14:57:30.338595 20203 solver.cpp:237] Train net output #0: loss = 5.27373 (* 1 = 5.27373 loss)
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I0412 14:57:35.149184 20203 solver.cpp:237] Train net output #0: loss = 5.26201 (* 1 = 5.26201 loss)
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I0412 14:57:40.010107 20203 solver.cpp:237] Train net output #0: loss = 5.26384 (* 1 = 5.26384 loss)
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I0412 14:57:44.855134 20203 solver.cpp:237] Train net output #0: loss = 5.2683 (* 1 = 5.2683 loss)
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I0412 14:57:49.757134 20203 solver.cpp:237] Train net output #0: loss = 5.263 (* 1 = 5.263 loss)
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I0412 14:57:49.757146 20203 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
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I0412 14:57:54.845731 20203 solver.cpp:218] Iteration 4452 (2.35831 iter/s, 5.08838s/12 iters), loss = 5.26056
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I0412 14:57:54.845783 20203 solver.cpp:237] Train net output #0: loss = 5.26056 (* 1 = 5.26056 loss)
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I0412 14:57:59.712570 20203 solver.cpp:237] Train net output #0: loss = 5.28329 (* 1 = 5.28329 loss)
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I0412 14:58:16.793843 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
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I0412 14:58:25.606089 20203 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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I0412 14:58:25.606137 20203 solver.cpp:397] Test net output #1: loss = 5.28581 (* 1 = 5.28581 loss)
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I0412 14:58:54.735006 20203 solver.cpp:218] Iteration 4560 (2.43473 iter/s, 4.92868s/12 iters), loss = 5.27822
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I0412 14:58:54.735080 20203 solver.cpp:237] Train net output #0: loss = 5.27822 (* 1 = 5.27822 loss)
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I0412 14:58:59.509791 20203 solver.cpp:237] Train net output #0: loss = 5.26273 (* 1 = 5.26273 loss)
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I0412 14:59:04.299011 20203 solver.cpp:237] Train net output #0: loss = 5.27048 (* 1 = 5.27048 loss)
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I0412 14:59:13.401685 20203 solver.cpp:330] Iteration 4590, Testing net (#0)
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I0412 14:59:16.067224 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 14:59:17.926069 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 14:59:17.926110 20203 solver.cpp:397] Test net output #1: loss = 5.28576 (* 1 = 5.28576 loss)
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I0412 14:59:24.620038 20203 solver.cpp:237] Train net output #0: loss = 5.27387 (* 1 = 5.27387 loss)
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I0412 14:59:24.620050 20203 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
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I0412 14:59:29.335048 20203 solver.cpp:218] Iteration 4620 (2.54517 iter/s, 4.71481s/12 iters), loss = 5.26328
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I0412 14:59:29.335103 20203 solver.cpp:237] Train net output #0: loss = 5.26328 (* 1 = 5.26328 loss)
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I0412 14:59:34.289921 20203 solver.cpp:237] Train net output #0: loss = 5.29502 (* 1 = 5.29502 loss)
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I0412 14:59:39.557014 20203 solver.cpp:237] Train net output #0: loss = 5.267 (* 1 = 5.267 loss)
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I0412 14:59:39.557027 20203 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
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I0412 14:59:44.573364 20203 solver.cpp:218] Iteration 4656 (2.39228 iter/s, 5.01614s/12 iters), loss = 5.28178
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I0412 14:59:44.573416 20203 solver.cpp:237] Train net output #0: loss = 5.28178 (* 1 = 5.28178 loss)
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I0412 14:59:44.573429 20203 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
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I0412 14:59:49.420660 20203 solver.cpp:218] Iteration 4668 (2.47574 iter/s, 4.84704s/12 iters), loss = 5.27092
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I0412 14:59:49.420712 20203 solver.cpp:237] Train net output #0: loss = 5.27092 (* 1 = 5.27092 loss)
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I0412 14:59:49.420725 20203 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
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I0412 14:59:54.140960 20203 solver.cpp:218] Iteration 4680 (2.54235 iter/s, 4.72005s/12 iters), loss = 5.27902
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I0412 14:59:54.141013 20203 solver.cpp:237] Train net output #0: loss = 5.27902 (* 1 = 5.27902 loss)
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I0412 14:59:54.141026 20203 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
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I0412 14:59:58.477815 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
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I0412 15:00:01.541262 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
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I0412 15:00:03.840772 20203 solver.cpp:330] Iteration 4692, Testing net (#0)
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I0412 15:00:06.452533 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:00:08.443712 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:00:08.443760 20203 solver.cpp:397] Test net output #1: loss = 5.28608 (* 1 = 5.28608 loss)
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I0412 15:00:12.597398 20203 solver.cpp:218] Iteration 4704 (2.95017 iter/s, 4.06756s/12 iters), loss = 5.26967
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I0412 15:00:12.597533 20203 solver.cpp:237] Train net output #0: loss = 5.26967 (* 1 = 5.26967 loss)
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I0412 15:00:17.412139 20203 solver.cpp:218] Iteration 4716 (2.49252 iter/s, 4.8144s/12 iters), loss = 5.28253
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I0412 15:00:17.412196 20203 solver.cpp:237] Train net output #0: loss = 5.28253 (* 1 = 5.28253 loss)
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I0412 15:00:17.412212 20203 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
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I0412 15:00:22.070240 20203 solver.cpp:218] Iteration 4728 (2.5763 iter/s, 4.65784s/12 iters), loss = 5.26835
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I0412 15:00:22.070294 20203 solver.cpp:237] Train net output #0: loss = 5.26835 (* 1 = 5.26835 loss)
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I0412 15:00:26.844702 20203 solver.cpp:237] Train net output #0: loss = 5.28019 (* 1 = 5.28019 loss)
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I0412 15:00:26.844714 20203 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
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I0412 15:00:31.633894 20203 solver.cpp:218] Iteration 4752 (2.50575 iter/s, 4.78899s/12 iters), loss = 5.28123
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I0412 15:00:31.633946 20203 solver.cpp:237] Train net output #0: loss = 5.28123 (* 1 = 5.28123 loss)
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I0412 15:00:31.633972 20203 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
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I0412 15:00:32.118827 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:00:36.385919 20203 solver.cpp:218] Iteration 4764 (2.52537 iter/s, 4.75177s/12 iters), loss = 5.28028
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I0412 15:00:36.385996 20203 solver.cpp:237] Train net output #0: loss = 5.28028 (* 1 = 5.28028 loss)
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I0412 15:00:36.386013 20203 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
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I0412 15:00:41.193233 20203 solver.cpp:218] Iteration 4776 (2.49634 iter/s, 4.80703s/12 iters), loss = 5.27
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I0412 15:00:41.193272 20203 solver.cpp:237] Train net output #0: loss = 5.27 (* 1 = 5.27 loss)
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I0412 15:00:41.193280 20203 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
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I0412 15:00:45.935868 20203 solver.cpp:218] Iteration 4788 (2.53037 iter/s, 4.74239s/12 iters), loss = 5.29238
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I0412 15:00:45.935973 20203 solver.cpp:237] Train net output #0: loss = 5.29238 (* 1 = 5.29238 loss)
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I0412 15:00:45.935984 20203 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
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I0412 15:00:47.791832 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
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I0412 15:00:50.842615 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
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I0412 15:00:53.411418 20203 solver.cpp:330] Iteration 4794, Testing net (#0)
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I0412 15:00:53.411445 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:00:55.876456 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:00:57.780153 20203 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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I0412 15:00:57.780186 20203 solver.cpp:397] Test net output #1: loss = 5.28524 (* 1 = 5.28524 loss)
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I0412 15:00:59.581398 20203 solver.cpp:218] Iteration 4800 (0.879452 iter/s, 13.6449s/12 iters), loss = 5.27439
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I0412 15:00:59.581466 20203 solver.cpp:237] Train net output #0: loss = 5.27439 (* 1 = 5.27439 loss)
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I0412 15:00:59.581483 20203 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
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I0412 15:01:04.562119 20203 solver.cpp:218] Iteration 4812 (2.40942 iter/s, 4.98044s/12 iters), loss = 5.26806
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I0412 15:01:04.562170 20203 solver.cpp:237] Train net output #0: loss = 5.26806 (* 1 = 5.26806 loss)
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I0412 15:01:04.562180 20203 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
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I0412 15:01:09.292068 20203 solver.cpp:218] Iteration 4824 (2.53716 iter/s, 4.72969s/12 iters), loss = 5.29218
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I0412 15:01:09.292116 20203 solver.cpp:237] Train net output #0: loss = 5.29218 (* 1 = 5.29218 loss)
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I0412 15:01:09.292126 20203 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
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I0412 15:01:14.360018 20203 solver.cpp:218] Iteration 4836 (2.36795 iter/s, 5.06768s/12 iters), loss = 5.26653
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I0412 15:01:14.360071 20203 solver.cpp:237] Train net output #0: loss = 5.26653 (* 1 = 5.26653 loss)
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I0412 15:01:14.360081 20203 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
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I0412 15:01:16.389457 20203 blocking_queue.cpp:49] Waiting for data
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I0412 15:01:19.303280 20203 solver.cpp:218] Iteration 4848 (2.42768 iter/s, 4.943s/12 iters), loss = 5.26711
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I0412 15:01:19.303330 20203 solver.cpp:237] Train net output #0: loss = 5.26711 (* 1 = 5.26711 loss)
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I0412 15:01:19.303342 20203 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
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I0412 15:01:21.750389 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:01:23.987020 20203 solver.cpp:218] Iteration 4860 (2.56219 iter/s, 4.68349s/12 iters), loss = 5.27117
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I0412 15:01:23.987074 20203 solver.cpp:237] Train net output #0: loss = 5.27117 (* 1 = 5.27117 loss)
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I0412 15:01:23.987087 20203 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
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I0412 15:01:28.944484 20203 solver.cpp:218] Iteration 4872 (2.42072 iter/s, 4.9572s/12 iters), loss = 5.26447
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I0412 15:01:28.944535 20203 solver.cpp:237] Train net output #0: loss = 5.26447 (* 1 = 5.26447 loss)
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I0412 15:01:28.944546 20203 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
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I0412 15:01:33.862982 20203 solver.cpp:218] Iteration 4884 (2.4399 iter/s, 4.91824s/12 iters), loss = 5.26722
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I0412 15:01:33.863032 20203 solver.cpp:237] Train net output #0: loss = 5.26722 (* 1 = 5.26722 loss)
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I0412 15:01:33.863044 20203 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
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I0412 15:01:38.225490 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
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I0412 15:01:41.237854 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
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I0412 15:01:44.710546 20203 solver.cpp:330] Iteration 4896, Testing net (#0)
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I0412 15:01:44.710573 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:01:47.361538 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:01:49.322149 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:01:49.322192 20203 solver.cpp:397] Test net output #1: loss = 5.28624 (* 1 = 5.28624 loss)
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I0412 15:01:49.408514 20203 solver.cpp:218] Iteration 4896 (0.77196 iter/s, 15.5448s/12 iters), loss = 5.27152
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I0412 15:01:49.408561 20203 solver.cpp:237] Train net output #0: loss = 5.27152 (* 1 = 5.27152 loss)
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I0412 15:01:49.408572 20203 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
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I0412 15:01:53.453673 20203 solver.cpp:218] Iteration 4908 (2.96667 iter/s, 4.04493s/12 iters), loss = 5.28305
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I0412 15:01:53.453716 20203 solver.cpp:237] Train net output #0: loss = 5.28305 (* 1 = 5.28305 loss)
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I0412 15:01:53.453724 20203 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
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I0412 15:01:58.543151 20203 solver.cpp:237] Train net output #0: loss = 5.27674 (* 1 = 5.27674 loss)
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I0412 15:02:03.786625 20203 solver.cpp:237] Train net output #0: loss = 5.26524 (* 1 = 5.26524 loss)
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I0412 15:02:08.709297 20203 solver.cpp:237] Train net output #0: loss = 5.26692 (* 1 = 5.26692 loss)
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I0412 15:02:13.403257 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:02:13.598959 20203 solver.cpp:218] Iteration 4956 (2.45427 iter/s, 4.88944s/12 iters), loss = 5.25214
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I0412 15:02:13.599009 20203 solver.cpp:237] Train net output #0: loss = 5.25214 (* 1 = 5.25214 loss)
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I0412 15:02:13.599017 20203 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
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I0412 15:02:18.891083 20203 solver.cpp:237] Train net output #0: loss = 5.26454 (* 1 = 5.26454 loss)
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I0412 15:02:23.872947 20203 solver.cpp:237] Train net output #0: loss = 5.29201 (* 1 = 5.29201 loss)
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I0412 15:02:28.804390 20203 solver.cpp:237] Train net output #0: loss = 5.28927 (* 1 = 5.28927 loss)
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I0412 15:02:42.673750 20203 solver.cpp:330] Iteration 4998, Testing net (#0)
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I0412 15:02:45.194792 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:02:47.449172 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:02:47.449199 20203 solver.cpp:397] Test net output #1: loss = 5.28597 (* 1 = 5.28597 loss)
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I0412 15:02:49.148684 20203 solver.cpp:218] Iteration 5004 (0.58987 iter/s, 20.3435s/12 iters), loss = 5.27782
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I0412 15:02:59.770999 20203 solver.cpp:237] Train net output #0: loss = 5.24916 (* 1 = 5.24916 loss)
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I0412 15:03:04.767936 20203 solver.cpp:237] Train net output #0: loss = 5.2857 (* 1 = 5.2857 loss)
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I0412 15:03:09.854171 20203 solver.cpp:237] Train net output #0: loss = 5.27292 (* 1 = 5.27292 loss)
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I0412 15:03:09.854182 20203 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
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I0412 15:03:14.864961 20203 solver.cpp:218] Iteration 5064 (2.39493 iter/s, 5.01057s/12 iters), loss = 5.28993
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I0412 15:03:14.865020 20203 solver.cpp:237] Train net output #0: loss = 5.28993 (* 1 = 5.28993 loss)
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I0412 15:03:14.865031 20203 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
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I0412 15:03:20.152359 20203 solver.cpp:218] Iteration 5076 (2.26967 iter/s, 5.28711s/12 iters), loss = 5.2772
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I0412 15:03:20.152537 20203 solver.cpp:237] Train net output #0: loss = 5.2772 (* 1 = 5.2772 loss)
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I0412 15:03:20.152549 20203 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
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I0412 15:03:25.029428 20203 solver.cpp:218] Iteration 5088 (2.46069 iter/s, 4.87668s/12 iters), loss = 5.25972
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I0412 15:03:25.029484 20203 solver.cpp:237] Train net output #0: loss = 5.25972 (* 1 = 5.25972 loss)
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I0412 15:03:25.029497 20203 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
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I0412 15:03:29.589427 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
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I0412 15:03:32.679253 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
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I0412 15:03:36.015799 20203 solver.cpp:330] Iteration 5100, Testing net (#0)
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I0412 15:03:36.015825 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:03:38.835237 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:03:40.911414 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:03:40.911453 20203 solver.cpp:397] Test net output #1: loss = 5.28577 (* 1 = 5.28577 loss)
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I0412 15:03:40.997565 20203 solver.cpp:218] Iteration 5100 (0.751531 iter/s, 15.9674s/12 iters), loss = 5.26709
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I0412 15:03:40.997617 20203 solver.cpp:237] Train net output #0: loss = 5.26709 (* 1 = 5.26709 loss)
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I0412 15:03:45.363376 20203 solver.cpp:237] Train net output #0: loss = 5.26074 (* 1 = 5.26074 loss)
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I0412 15:03:45.363385 20203 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
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I0412 15:03:50.332114 20203 solver.cpp:218] Iteration 5124 (2.41521 iter/s, 4.96852s/12 iters), loss = 5.27339
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I0412 15:03:50.332258 20203 solver.cpp:237] Train net output #0: loss = 5.27339 (* 1 = 5.27339 loss)
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I0412 15:03:55.449498 20203 solver.cpp:218] Iteration 5136 (2.34512 iter/s, 5.11702s/12 iters), loss = 5.26781
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I0412 15:03:55.449551 20203 solver.cpp:237] Train net output #0: loss = 5.26781 (* 1 = 5.26781 loss)
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I0412 15:04:01.050966 20203 solver.cpp:237] Train net output #0: loss = 5.26418 (* 1 = 5.26418 loss)
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I0412 15:04:01.050978 20203 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
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I0412 15:04:05.369153 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:04:06.278863 20203 solver.cpp:218] Iteration 5160 (2.29548 iter/s, 5.22768s/12 iters), loss = 5.25651
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I0412 15:04:06.278901 20203 solver.cpp:237] Train net output #0: loss = 5.25651 (* 1 = 5.25651 loss)
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I0412 15:04:06.278909 20203 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
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I0412 15:04:11.498035 20203 solver.cpp:218] Iteration 5172 (2.29933 iter/s, 5.2189s/12 iters), loss = 5.27884
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I0412 15:04:11.498082 20203 solver.cpp:237] Train net output #0: loss = 5.27884 (* 1 = 5.27884 loss)
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I0412 15:04:11.498091 20203 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
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I0412 15:04:17.306514 20203 solver.cpp:218] Iteration 5184 (2.06605 iter/s, 5.80818s/12 iters), loss = 5.26932
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I0412 15:04:17.306562 20203 solver.cpp:237] Train net output #0: loss = 5.26932 (* 1 = 5.26932 loss)
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I0412 15:04:17.306572 20203 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
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I0412 15:04:23.110273 20203 solver.cpp:218] Iteration 5196 (2.06773 iter/s, 5.80346s/12 iters), loss = 5.30902
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I0412 15:04:23.110394 20203 solver.cpp:237] Train net output #0: loss = 5.30902 (* 1 = 5.30902 loss)
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I0412 15:04:23.110404 20203 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
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I0412 15:04:25.165647 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
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I0412 15:04:28.315901 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
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I0412 15:04:30.757428 20203 solver.cpp:330] Iteration 5202, Testing net (#0)
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I0412 15:04:30.757457 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:04:33.149019 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:04:35.467224 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:04:35.467262 20203 solver.cpp:397] Test net output #1: loss = 5.28581 (* 1 = 5.28581 loss)
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I0412 15:04:37.281471 20203 solver.cpp:218] Iteration 5208 (0.84683 iter/s, 14.1705s/12 iters), loss = 5.27637
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I0412 15:04:37.281512 20203 solver.cpp:237] Train net output #0: loss = 5.27637 (* 1 = 5.27637 loss)
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I0412 15:04:42.789005 20203 solver.cpp:218] Iteration 5220 (2.17895 iter/s, 5.50725s/12 iters), loss = 5.2749
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I0412 15:04:42.789047 20203 solver.cpp:237] Train net output #0: loss = 5.2749 (* 1 = 5.2749 loss)
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I0412 15:04:42.789057 20203 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
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I0412 15:04:48.159888 20203 solver.cpp:218] Iteration 5232 (2.23438 iter/s, 5.37061s/12 iters), loss = 5.2765
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I0412 15:04:48.159930 20203 solver.cpp:237] Train net output #0: loss = 5.2765 (* 1 = 5.2765 loss)
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I0412 15:04:48.159938 20203 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
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I0412 15:04:53.618305 20203 solver.cpp:237] Train net output #0: loss = 5.27276 (* 1 = 5.27276 loss)
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I0412 15:04:59.507452 20203 solver.cpp:237] Train net output #0: loss = 5.26236 (* 1 = 5.26236 loss)
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I0412 15:04:59.507462 20203 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
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I0412 15:05:00.948544 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:05:05.181013 20203 solver.cpp:218] Iteration 5268 (2.11517 iter/s, 5.67332s/12 iters), loss = 5.27716
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I0412 15:05:05.181074 20203 solver.cpp:237] Train net output #0: loss = 5.27716 (* 1 = 5.27716 loss)
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I0412 15:05:05.181085 20203 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
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I0412 15:05:10.225071 20203 solver.cpp:218] Iteration 5280 (2.37917 iter/s, 5.04378s/12 iters), loss = 5.26403
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I0412 15:05:10.225112 20203 solver.cpp:237] Train net output #0: loss = 5.26403 (* 1 = 5.26403 loss)
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I0412 15:05:10.225121 20203 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
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I0412 15:05:15.608934 20203 solver.cpp:237] Train net output #0: loss = 5.27839 (* 1 = 5.27839 loss)
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I0412 15:05:15.608943 20203 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
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I0412 15:05:20.105391 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
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I0412 15:05:23.170347 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
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I0412 15:05:25.576344 20203 solver.cpp:330] Iteration 5304, Testing net (#0)
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I0412 15:05:25.576413 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:05:28.213443 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:05:30.446103 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:05:30.446158 20203 solver.cpp:397] Test net output #1: loss = 5.28552 (* 1 = 5.28552 loss)
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I0412 15:05:30.532860 20203 solver.cpp:218] Iteration 5304 (0.804112 iter/s, 14.9233s/12 iters), loss = 5.27606
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I0412 15:05:30.532922 20203 solver.cpp:237] Train net output #0: loss = 5.27606 (* 1 = 5.27606 loss)
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I0412 15:05:30.532932 20203 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
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I0412 15:05:35.006239 20203 solver.cpp:237] Train net output #0: loss = 5.27015 (* 1 = 5.27015 loss)
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I0412 15:05:35.006250 20203 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
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I0412 15:05:40.552201 20203 solver.cpp:218] Iteration 5328 (2.16383 iter/s, 5.54572s/12 iters), loss = 5.26171
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I0412 15:05:40.552251 20203 solver.cpp:237] Train net output #0: loss = 5.26171 (* 1 = 5.26171 loss)
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I0412 15:05:40.552263 20203 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
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I0412 15:05:45.669826 20203 solver.cpp:218] Iteration 5340 (2.34496 iter/s, 5.11735s/12 iters), loss = 5.30371
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I0412 15:05:45.669888 20203 solver.cpp:237] Train net output #0: loss = 5.30371 (* 1 = 5.30371 loss)
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I0412 15:05:45.669900 20203 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
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I0412 15:05:50.844767 20203 solver.cpp:237] Train net output #0: loss = 5.27734 (* 1 = 5.27734 loss)
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I0412 15:05:50.844777 20203 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
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I0412 15:05:54.330857 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:05:55.966794 20203 solver.cpp:218] Iteration 5364 (2.34292 iter/s, 5.1218s/12 iters), loss = 5.27548
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I0412 15:05:55.966989 20203 solver.cpp:237] Train net output #0: loss = 5.27548 (* 1 = 5.27548 loss)
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I0412 15:05:55.967010 20203 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
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I0412 15:06:01.249897 20203 solver.cpp:218] Iteration 5376 (2.27157 iter/s, 5.28269s/12 iters), loss = 5.26503
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I0412 15:06:01.249977 20203 solver.cpp:237] Train net output #0: loss = 5.26503 (* 1 = 5.26503 loss)
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I0412 15:06:01.249990 20203 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
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I0412 15:06:06.717947 20203 solver.cpp:218] Iteration 5388 (2.19469 iter/s, 5.46775s/12 iters), loss = 5.2692
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I0412 15:06:06.718041 20203 solver.cpp:237] Train net output #0: loss = 5.2692 (* 1 = 5.2692 loss)
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I0412 15:06:06.718055 20203 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
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I0412 15:06:11.752352 20203 solver.cpp:237] Train net output #0: loss = 5.27035 (* 1 = 5.27035 loss)
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I0412 15:06:11.752364 20203 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
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I0412 15:06:17.129055 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
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I0412 15:06:19.408448 20203 solver.cpp:330] Iteration 5406, Testing net (#0)
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I0412 15:06:19.408468 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:06:21.958220 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:06:24.432535 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:06:24.432572 20203 solver.cpp:397] Test net output #1: loss = 5.28571 (* 1 = 5.28571 loss)
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I0412 15:06:26.791669 20203 solver.cpp:218] Iteration 5412 (0.797942 iter/s, 15.0387s/12 iters), loss = 5.26686
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I0412 15:06:26.791774 20203 solver.cpp:237] Train net output #0: loss = 5.26686 (* 1 = 5.26686 loss)
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I0412 15:06:32.200733 20203 solver.cpp:237] Train net output #0: loss = 5.27893 (* 1 = 5.27893 loss)
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I0412 15:06:32.200745 20203 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
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I0412 15:06:38.303429 20203 solver.cpp:237] Train net output #0: loss = 5.26305 (* 1 = 5.26305 loss)
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I0412 15:06:43.276842 20203 solver.cpp:237] Train net output #0: loss = 5.27688 (* 1 = 5.27688 loss)
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I0412 15:06:48.910643 20203 solver.cpp:237] Train net output #0: loss = 5.27875 (* 1 = 5.27875 loss)
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I0412 15:06:48.910653 20203 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
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I0412 15:06:49.641880 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:06:54.589007 20203 solver.cpp:218] Iteration 5472 (2.11338 iter/s, 5.67812s/12 iters), loss = 5.27766
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I0412 15:06:54.589063 20203 solver.cpp:237] Train net output #0: loss = 5.27766 (* 1 = 5.27766 loss)
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I0412 15:06:54.589076 20203 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
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I0412 15:06:59.702879 20203 solver.cpp:237] Train net output #0: loss = 5.27404 (* 1 = 5.27404 loss)
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I0412 15:06:59.702894 20203 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
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I0412 15:07:04.730983 20203 solver.cpp:237] Train net output #0: loss = 5.29195 (* 1 = 5.29195 loss)
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I0412 15:07:09.359189 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
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I0412 15:07:15.407721 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
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I0412 15:07:18.463213 20203 solver.cpp:330] Iteration 5508, Testing net (#0)
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I0412 15:07:18.463238 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:07:20.721006 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:07:23.206770 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:07:23.206800 20203 solver.cpp:397] Test net output #1: loss = 5.28651 (* 1 = 5.28651 loss)
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I0412 15:07:23.293555 20203 solver.cpp:237] Train net output #0: loss = 5.27538 (* 1 = 5.27538 loss)
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I0412 15:07:27.743201 20203 solver.cpp:237] Train net output #0: loss = 5.27501 (* 1 = 5.27501 loss)
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I0412 15:07:33.317771 20203 solver.cpp:237] Train net output #0: loss = 5.29159 (* 1 = 5.29159 loss)
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I0412 15:07:48.180132 20203 solver.cpp:218] Iteration 5568 (2.44512 iter/s, 4.90774s/12 iters), loss = 5.2793
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I0412 15:07:48.180187 20203 solver.cpp:237] Train net output #0: loss = 5.2793 (* 1 = 5.2793 loss)
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I0412 15:07:53.058111 20203 solver.cpp:237] Train net output #0: loss = 5.25813 (* 1 = 5.25813 loss)
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I0412 15:07:53.058125 20203 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
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I0412 15:07:57.909505 20203 solver.cpp:237] Train net output #0: loss = 5.27008 (* 1 = 5.27008 loss)
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I0412 15:08:02.785151 20203 solver.cpp:237] Train net output #0: loss = 5.26127 (* 1 = 5.26127 loss)
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I0412 15:08:08.385978 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
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I0412 15:08:10.699860 20203 solver.cpp:330] Iteration 5610, Testing net (#0)
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I0412 15:08:10.699890 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:08:13.099514 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:08:15.384557 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:08:15.384598 20203 solver.cpp:397] Test net output #1: loss = 5.28594 (* 1 = 5.28594 loss)
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I0412 15:08:17.159121 20203 solver.cpp:218] Iteration 5616 (0.834877 iter/s, 14.3734s/12 iters), loss = 5.29069
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I0412 15:08:22.994683 20203 solver.cpp:237] Train net output #0: loss = 5.27412 (* 1 = 5.27412 loss)
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I0412 15:08:22.994693 20203 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
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I0412 15:08:27.984640 20203 solver.cpp:237] Train net output #0: loss = 5.26035 (* 1 = 5.26035 loss)
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I0412 15:08:27.984652 20203 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
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I0412 15:08:33.300987 20203 solver.cpp:237] Train net output #0: loss = 5.26852 (* 1 = 5.26852 loss)
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I0412 15:08:33.300998 20203 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
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I0412 15:08:38.412521 20203 solver.cpp:218] Iteration 5664 (2.34773 iter/s, 5.11132s/12 iters), loss = 5.25082
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I0412 15:08:38.412572 20203 solver.cpp:237] Train net output #0: loss = 5.25082 (* 1 = 5.25082 loss)
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I0412 15:08:38.412582 20203 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
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I0412 15:08:43.553787 20203 solver.cpp:218] Iteration 5676 (2.33418 iter/s, 5.14099s/12 iters), loss = 5.26528
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I0412 15:08:43.553839 20203 solver.cpp:237] Train net output #0: loss = 5.26528 (* 1 = 5.26528 loss)
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I0412 15:08:43.553851 20203 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
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I0412 15:08:48.392977 20203 solver.cpp:218] Iteration 5688 (2.47989 iter/s, 4.83893s/12 iters), loss = 5.29343
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I0412 15:08:48.393028 20203 solver.cpp:237] Train net output #0: loss = 5.29343 (* 1 = 5.29343 loss)
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I0412 15:08:48.393038 20203 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
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I0412 15:08:53.364964 20203 solver.cpp:218] Iteration 5700 (2.41365 iter/s, 4.97171s/12 iters), loss = 5.2857
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I0412 15:08:53.365018 20203 solver.cpp:237] Train net output #0: loss = 5.2857 (* 1 = 5.2857 loss)
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I0412 15:08:53.365029 20203 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
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I0412 15:08:57.733935 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
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I0412 15:09:00.705591 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
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I0412 15:09:02.993194 20203 solver.cpp:330] Iteration 5712, Testing net (#0)
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I0412 15:09:02.993216 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:09:05.279351 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:09:07.638283 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:09:07.638317 20203 solver.cpp:397] Test net output #1: loss = 5.28586 (* 1 = 5.28586 loss)
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I0412 15:09:07.724763 20203 solver.cpp:218] Iteration 5712 (0.835704 iter/s, 14.3591s/12 iters), loss = 5.27599
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I0412 15:09:07.724804 20203 solver.cpp:237] Train net output #0: loss = 5.27599 (* 1 = 5.27599 loss)
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I0412 15:09:07.724812 20203 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
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I0412 15:09:12.038497 20203 solver.cpp:218] Iteration 5724 (2.78196 iter/s, 4.3135s/12 iters), loss = 5.27002
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I0412 15:09:12.038553 20203 solver.cpp:237] Train net output #0: loss = 5.27002 (* 1 = 5.27002 loss)
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I0412 15:09:12.038563 20203 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
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I0412 15:09:17.408769 20203 solver.cpp:218] Iteration 5736 (2.23465 iter/s, 5.36998s/12 iters), loss = 5.24681
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I0412 15:09:17.408824 20203 solver.cpp:237] Train net output #0: loss = 5.24681 (* 1 = 5.24681 loss)
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I0412 15:09:17.408836 20203 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
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I0412 15:09:23.447253 20203 solver.cpp:218] Iteration 5748 (1.98736 iter/s, 6.03817s/12 iters), loss = 5.27554
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I0412 15:09:23.447297 20203 solver.cpp:237] Train net output #0: loss = 5.27554 (* 1 = 5.27554 loss)
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I0412 15:09:23.447306 20203 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
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I0412 15:09:28.744179 20203 solver.cpp:218] Iteration 5760 (2.26558 iter/s, 5.29665s/12 iters), loss = 5.26676
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I0412 15:09:28.744240 20203 solver.cpp:237] Train net output #0: loss = 5.26676 (* 1 = 5.26676 loss)
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I0412 15:09:28.744252 20203 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
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I0412 15:09:30.881094 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:09:33.766916 20203 solver.cpp:218] Iteration 5772 (2.38927 iter/s, 5.02246s/12 iters), loss = 5.29131
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I0412 15:09:33.766966 20203 solver.cpp:237] Train net output #0: loss = 5.29131 (* 1 = 5.29131 loss)
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I0412 15:09:33.766978 20203 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
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I0412 15:09:38.726817 20203 solver.cpp:218] Iteration 5784 (2.41953 iter/s, 4.95964s/12 iters), loss = 5.27278
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I0412 15:09:38.729938 20203 solver.cpp:237] Train net output #0: loss = 5.27278 (* 1 = 5.27278 loss)
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I0412 15:09:38.729949 20203 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
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I0412 15:09:43.672003 20203 solver.cpp:218] Iteration 5796 (2.42824 iter/s, 4.94185s/12 iters), loss = 5.26662
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I0412 15:09:43.672060 20203 solver.cpp:237] Train net output #0: loss = 5.26662 (* 1 = 5.26662 loss)
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I0412 15:09:43.672070 20203 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
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I0412 15:09:48.795228 20203 solver.cpp:237] Train net output #0: loss = 5.26453 (* 1 = 5.26453 loss)
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I0412 15:09:48.795241 20203 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
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I0412 15:09:51.475117 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
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I0412 15:09:55.882344 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
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I0412 15:10:00.256659 20203 solver.cpp:330] Iteration 5814, Testing net (#0)
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I0412 15:10:00.256685 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:10:02.514200 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:10:04.946825 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:10:04.946864 20203 solver.cpp:397] Test net output #1: loss = 5.28588 (* 1 = 5.28588 loss)
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I0412 15:10:06.705030 20203 solver.cpp:218] Iteration 5820 (0.670052 iter/s, 17.9091s/12 iters), loss = 5.27597
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I0412 15:10:06.705073 20203 solver.cpp:237] Train net output #0: loss = 5.27597 (* 1 = 5.27597 loss)
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I0412 15:10:06.705081 20203 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
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I0412 15:10:12.215778 20203 solver.cpp:237] Train net output #0: loss = 5.27642 (* 1 = 5.27642 loss)
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I0412 15:10:12.215788 20203 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
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I0412 15:10:17.341519 20203 solver.cpp:218] Iteration 5844 (2.34123 iter/s, 5.12552s/12 iters), loss = 5.26281
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I0412 15:10:17.341574 20203 solver.cpp:237] Train net output #0: loss = 5.26281 (* 1 = 5.26281 loss)
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I0412 15:10:17.341586 20203 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
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I0412 15:10:22.270220 20203 solver.cpp:218] Iteration 5856 (2.43485 iter/s, 4.92843s/12 iters), loss = 5.26164
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I0412 15:10:22.270272 20203 solver.cpp:237] Train net output #0: loss = 5.26164 (* 1 = 5.26164 loss)
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I0412 15:10:26.239890 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:10:27.050088 20203 solver.cpp:218] Iteration 5868 (2.51066 iter/s, 4.77961s/12 iters), loss = 5.25292
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I0412 15:10:27.050138 20203 solver.cpp:237] Train net output #0: loss = 5.25292 (* 1 = 5.25292 loss)
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I0412 15:10:27.050150 20203 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
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I0412 15:10:31.852205 20203 solver.cpp:218] Iteration 5880 (2.49903 iter/s, 4.80186s/12 iters), loss = 5.27897
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I0412 15:10:31.852265 20203 solver.cpp:237] Train net output #0: loss = 5.27897 (* 1 = 5.27897 loss)
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I0412 15:10:31.852278 20203 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
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I0412 15:10:36.817564 20203 solver.cpp:218] Iteration 5892 (2.41688 iter/s, 4.96507s/12 iters), loss = 5.27429
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I0412 15:10:36.817629 20203 solver.cpp:237] Train net output #0: loss = 5.27429 (* 1 = 5.27429 loss)
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I0412 15:10:36.817641 20203 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
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I0412 15:10:42.873823 20203 solver.cpp:218] Iteration 5904 (1.98153 iter/s, 6.05592s/12 iters), loss = 5.30286
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I0412 15:10:42.876641 20203 solver.cpp:237] Train net output #0: loss = 5.30286 (* 1 = 5.30286 loss)
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I0412 15:10:42.876660 20203 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
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I0412 15:10:51.424916 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
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I0412 15:10:55.629880 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
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I0412 15:10:58.788185 20203 solver.cpp:330] Iteration 5916, Testing net (#0)
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I0412 15:10:58.788210 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:11:02.889050 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:11:07.237462 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:11:07.237499 20203 solver.cpp:397] Test net output #1: loss = 5.2859 (* 1 = 5.2859 loss)
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I0412 15:11:07.354224 20203 solver.cpp:218] Iteration 5916 (0.490265 iter/s, 24.4766s/12 iters), loss = 5.26934
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I0412 15:11:07.354276 20203 solver.cpp:237] Train net output #0: loss = 5.26934 (* 1 = 5.26934 loss)
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I0412 15:11:12.424994 20203 solver.cpp:218] Iteration 5928 (2.36663 iter/s, 5.07049s/12 iters), loss = 5.26841
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I0412 15:11:12.425047 20203 solver.cpp:237] Train net output #0: loss = 5.26841 (* 1 = 5.26841 loss)
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I0412 15:11:12.425060 20203 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
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I0412 15:11:18.995956 20203 solver.cpp:218] Iteration 5940 (1.82631 iter/s, 6.57062s/12 iters), loss = 5.28297
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I0412 15:11:18.996115 20203 solver.cpp:237] Train net output #0: loss = 5.28297 (* 1 = 5.28297 loss)
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I0412 15:11:18.996129 20203 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
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I0412 15:11:25.407430 20203 solver.cpp:237] Train net output #0: loss = 5.27539 (* 1 = 5.27539 loss)
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I0412 15:11:31.913076 20203 solver.cpp:237] Train net output #0: loss = 5.26104 (* 1 = 5.26104 loss)
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I0412 15:11:31.913087 20203 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
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I0412 15:11:33.537186 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:11:38.177057 20203 solver.cpp:218] Iteration 5976 (1.9158 iter/s, 6.26371s/12 iters), loss = 5.27749
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I0412 15:11:38.177112 20203 solver.cpp:237] Train net output #0: loss = 5.27749 (* 1 = 5.27749 loss)
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I0412 15:11:38.177124 20203 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
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I0412 15:11:44.562265 20203 solver.cpp:237] Train net output #0: loss = 5.26529 (* 1 = 5.26529 loss)
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I0412 15:11:44.562278 20203 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
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I0412 15:11:51.130182 20203 solver.cpp:237] Train net output #0: loss = 5.27904 (* 1 = 5.27904 loss)
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I0412 15:11:57.262110 20203 solver.cpp:237] Train net output #0: loss = 5.27328 (* 1 = 5.27328 loss)
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I0412 15:12:03.511188 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
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I0412 15:12:06.355477 20203 solver.cpp:330] Iteration 6018, Testing net (#0)
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I0412 15:12:06.355499 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:12:11.423851 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:12:11.423900 20203 solver.cpp:397] Test net output #1: loss = 5.28585 (* 1 = 5.28585 loss)
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I0412 15:12:13.168936 20203 solver.cpp:218] Iteration 6024 (0.754425 iter/s, 15.9062s/12 iters), loss = 5.26337
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I0412 15:12:13.168989 20203 solver.cpp:237] Train net output #0: loss = 5.26337 (* 1 = 5.26337 loss)
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I0412 15:12:17.929566 20203 solver.cpp:237] Train net output #0: loss = 5.26211 (* 1 = 5.26211 loss)
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I0412 15:12:23.013556 20203 solver.cpp:237] Train net output #0: loss = 5.30267 (* 1 = 5.30267 loss)
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I0412 15:12:27.942862 20203 solver.cpp:237] Train net output #0: loss = 5.27998 (* 1 = 5.27998 loss)
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I0412 15:12:32.837276 20203 solver.cpp:218] Iteration 6072 (2.45188 iter/s, 4.89419s/12 iters), loss = 5.27621
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I0412 15:12:32.837357 20203 solver.cpp:237] Train net output #0: loss = 5.27621 (* 1 = 5.27621 loss)
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I0412 15:12:37.868167 20203 solver.cpp:237] Train net output #0: loss = 5.25428 (* 1 = 5.25428 loss)
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I0412 15:12:37.868180 20203 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
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I0412 15:12:42.742688 20203 solver.cpp:218] Iteration 6096 (2.46189 iter/s, 4.87431s/12 iters), loss = 5.26106
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I0412 15:12:42.742745 20203 solver.cpp:237] Train net output #0: loss = 5.26106 (* 1 = 5.26106 loss)
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I0412 15:12:42.742758 20203 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
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I0412 15:12:47.594183 20203 solver.cpp:218] Iteration 6108 (2.4736 iter/s, 4.85123s/12 iters), loss = 5.2757
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I0412 15:12:47.594238 20203 solver.cpp:237] Train net output #0: loss = 5.2757 (* 1 = 5.2757 loss)
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I0412 15:12:47.594250 20203 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
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I0412 15:12:52.057535 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
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I0412 15:13:03.137260 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
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I0412 15:13:07.030314 20203 solver.cpp:330] Iteration 6120, Testing net (#0)
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I0412 15:13:07.030342 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:13:09.240321 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:13:11.829012 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:13:11.829066 20203 solver.cpp:397] Test net output #1: loss = 5.28584 (* 1 = 5.28584 loss)
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I0412 15:13:11.915617 20203 solver.cpp:218] Iteration 6120 (0.493414 iter/s, 24.3204s/12 iters), loss = 5.26584
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I0412 15:13:11.915681 20203 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
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I0412 15:13:16.251510 20203 solver.cpp:237] Train net output #0: loss = 5.27315 (* 1 = 5.27315 loss)
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I0412 15:13:16.251523 20203 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
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I0412 15:13:20.876045 20203 solver.cpp:218] Iteration 6144 (2.59497 iter/s, 4.62433s/12 iters), loss = 5.27318
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I0412 15:13:20.876101 20203 solver.cpp:237] Train net output #0: loss = 5.27318 (* 1 = 5.27318 loss)
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I0412 15:13:25.732228 20203 solver.cpp:218] Iteration 6156 (2.47122 iter/s, 4.8559s/12 iters), loss = 5.2791
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I0412 15:13:25.732283 20203 solver.cpp:237] Train net output #0: loss = 5.2791 (* 1 = 5.2791 loss)
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I0412 15:13:25.732296 20203 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
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I0412 15:13:30.565315 20203 solver.cpp:237] Train net output #0: loss = 5.28819 (* 1 = 5.28819 loss)
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I0412 15:13:30.565330 20203 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
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I0412 15:13:31.148136 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:13:35.511445 20203 solver.cpp:218] Iteration 6180 (2.42625 iter/s, 4.94591s/12 iters), loss = 5.28258
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I0412 15:13:35.511574 20203 solver.cpp:237] Train net output #0: loss = 5.28258 (* 1 = 5.28258 loss)
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I0412 15:13:35.511590 20203 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
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I0412 15:13:40.476969 20203 solver.cpp:218] Iteration 6192 (2.41683 iter/s, 4.96518s/12 iters), loss = 5.26933
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I0412 15:13:40.477020 20203 solver.cpp:237] Train net output #0: loss = 5.26933 (* 1 = 5.26933 loss)
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I0412 15:13:40.477030 20203 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
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I0412 15:13:45.623039 20203 solver.cpp:218] Iteration 6204 (2.33201 iter/s, 5.14579s/12 iters), loss = 5.28742
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I0412 15:13:45.623095 20203 solver.cpp:237] Train net output #0: loss = 5.28742 (* 1 = 5.28742 loss)
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I0412 15:13:45.623108 20203 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
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I0412 15:13:50.878185 20203 solver.cpp:218] Iteration 6216 (2.2836 iter/s, 5.25486s/12 iters), loss = 5.27879
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I0412 15:13:50.878238 20203 solver.cpp:237] Train net output #0: loss = 5.27879 (* 1 = 5.27879 loss)
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I0412 15:13:50.878250 20203 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
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I0412 15:13:53.087788 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
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I0412 15:13:56.052088 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
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I0412 15:13:58.334937 20203 solver.cpp:330] Iteration 6222, Testing net (#0)
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I0412 15:13:58.334959 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:14:00.526413 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:14:01.874378 20203 blocking_queue.cpp:49] Waiting for data
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I0412 15:14:03.202276 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:14:03.202312 20203 solver.cpp:397] Test net output #1: loss = 5.28585 (* 1 = 5.28585 loss)
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I0412 15:14:05.057821 20203 solver.cpp:218] Iteration 6228 (0.846323 iter/s, 14.179s/12 iters), loss = 5.27481
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I0412 15:14:05.057885 20203 solver.cpp:237] Train net output #0: loss = 5.27481 (* 1 = 5.27481 loss)
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I0412 15:14:05.057901 20203 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
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I0412 15:14:10.107360 20203 solver.cpp:218] Iteration 6240 (2.37659 iter/s, 5.04925s/12 iters), loss = 5.28006
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I0412 15:14:10.107484 20203 solver.cpp:237] Train net output #0: loss = 5.28006 (* 1 = 5.28006 loss)
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I0412 15:14:10.107496 20203 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
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I0412 15:14:15.176575 20203 solver.cpp:218] Iteration 6252 (2.36739 iter/s, 5.06887s/12 iters), loss = 5.25641
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I0412 15:14:15.176626 20203 solver.cpp:237] Train net output #0: loss = 5.25641 (* 1 = 5.25641 loss)
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I0412 15:14:15.176637 20203 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
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I0412 15:14:20.357657 20203 solver.cpp:218] Iteration 6264 (2.31624 iter/s, 5.18081s/12 iters), loss = 5.26748
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I0412 15:14:20.357707 20203 solver.cpp:237] Train net output #0: loss = 5.26748 (* 1 = 5.26748 loss)
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I0412 15:14:20.357719 20203 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
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I0412 15:14:23.155205 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:14:25.264369 20203 solver.cpp:218] Iteration 6276 (2.44576 iter/s, 4.90645s/12 iters), loss = 5.27923
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I0412 15:14:25.264425 20203 solver.cpp:237] Train net output #0: loss = 5.27923 (* 1 = 5.27923 loss)
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I0412 15:14:30.250985 20203 solver.cpp:237] Train net output #0: loss = 5.26172 (* 1 = 5.26172 loss)
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I0412 15:14:30.250998 20203 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
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I0412 15:14:35.493786 20203 solver.cpp:218] Iteration 6300 (2.28895 iter/s, 5.24257s/12 iters), loss = 5.27286
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I0412 15:14:35.493847 20203 solver.cpp:237] Train net output #0: loss = 5.27286 (* 1 = 5.27286 loss)
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I0412 15:14:35.493863 20203 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
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I0412 15:14:40.373884 20203 solver.cpp:237] Train net output #0: loss = 5.26009 (* 1 = 5.26009 loss)
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I0412 15:14:40.373898 20203 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
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I0412 15:14:44.808774 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
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I0412 15:14:47.829231 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
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I0412 15:14:50.136231 20203 solver.cpp:330] Iteration 6324, Testing net (#0)
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I0412 15:14:50.136260 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:14:52.106772 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:14:54.597656 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:14:54.597707 20203 solver.cpp:397] Test net output #1: loss = 5.28604 (* 1 = 5.28604 loss)
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I0412 15:14:54.684273 20203 solver.cpp:218] Iteration 6324 (0.838586 iter/s, 14.3098s/12 iters), loss = 5.29723
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I0412 15:14:54.684314 20203 solver.cpp:237] Train net output #0: loss = 5.29723 (* 1 = 5.29723 loss)
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I0412 15:14:54.684322 20203 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
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I0412 15:14:59.129735 20203 solver.cpp:237] Train net output #0: loss = 5.27196 (* 1 = 5.27196 loss)
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I0412 15:14:59.129745 20203 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
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I0412 15:15:03.914705 20203 solver.cpp:218] Iteration 6348 (2.50796 iter/s, 4.78476s/12 iters), loss = 5.26092
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I0412 15:15:03.914752 20203 solver.cpp:237] Train net output #0: loss = 5.26092 (* 1 = 5.26092 loss)
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I0412 15:15:03.914763 20203 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
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I0412 15:15:08.853554 20203 solver.cpp:218] Iteration 6360 (2.42985 iter/s, 4.93858s/12 iters), loss = 5.2661
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I0412 15:15:08.853605 20203 solver.cpp:237] Train net output #0: loss = 5.2661 (* 1 = 5.2661 loss)
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I0412 15:15:08.853615 20203 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
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I0412 15:15:13.686445 20203 solver.cpp:218] Iteration 6372 (2.48312 iter/s, 4.83263s/12 iters), loss = 5.2527
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I0412 15:15:13.686497 20203 solver.cpp:237] Train net output #0: loss = 5.2527 (* 1 = 5.2527 loss)
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I0412 15:15:13.686509 20203 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
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I0412 15:15:18.532565 20203 solver.cpp:218] Iteration 6384 (2.47634 iter/s, 4.84586s/12 iters), loss = 5.26777
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I0412 15:15:18.532606 20203 solver.cpp:237] Train net output #0: loss = 5.26777 (* 1 = 5.26777 loss)
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I0412 15:15:18.532614 20203 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
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I0412 15:15:23.672086 20203 solver.cpp:237] Train net output #0: loss = 5.29545 (* 1 = 5.29545 loss)
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I0412 15:15:23.672094 20203 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
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I0412 15:15:28.917548 20203 solver.cpp:237] Train net output #0: loss = 5.28598 (* 1 = 5.28598 loss)
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I0412 15:15:28.917558 20203 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
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I0412 15:15:33.723065 20203 solver.cpp:237] Train net output #0: loss = 5.28046 (* 1 = 5.28046 loss)
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I0412 15:15:33.723080 20203 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
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I0412 15:15:35.687567 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
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I0412 15:15:38.679883 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
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I0412 15:15:40.967046 20203 solver.cpp:330] Iteration 6426, Testing net (#0)
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I0412 15:15:40.967072 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:15:42.975662 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:15:45.597362 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:15:45.597491 20203 solver.cpp:397] Test net output #1: loss = 5.28593 (* 1 = 5.28593 loss)
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I0412 15:15:47.429556 20203 solver.cpp:218] Iteration 6432 (0.875534 iter/s, 13.7059s/12 iters), loss = 5.26904
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I0412 15:15:47.429600 20203 solver.cpp:237] Train net output #0: loss = 5.26904 (* 1 = 5.26904 loss)
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I0412 15:15:52.174752 20203 solver.cpp:237] Train net output #0: loss = 5.24683 (* 1 = 5.24683 loss)
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I0412 15:15:56.893028 20203 solver.cpp:237] Train net output #0: loss = 5.27312 (* 1 = 5.27312 loss)
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I0412 15:16:01.608693 20203 solver.cpp:237] Train net output #0: loss = 5.26983 (* 1 = 5.26983 loss)
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I0412 15:16:06.579228 20203 solver.cpp:218] Iteration 6480 (2.41433 iter/s, 4.97031s/12 iters), loss = 5.28773
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I0412 15:16:06.579277 20203 solver.cpp:237] Train net output #0: loss = 5.28773 (* 1 = 5.28773 loss)
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I0412 15:16:11.716926 20203 solver.cpp:237] Train net output #0: loss = 5.27215 (* 1 = 5.27215 loss)
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I0412 15:16:16.743528 20203 solver.cpp:237] Train net output #0: loss = 5.27113 (* 1 = 5.27113 loss)
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I0412 15:16:21.462642 20203 solver.cpp:237] Train net output #0: loss = 5.26386 (* 1 = 5.26386 loss)
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I0412 15:16:29.075585 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
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I0412 15:16:31.403976 20203 solver.cpp:330] Iteration 6528, Testing net (#0)
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I0412 15:16:35.954200 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:16:35.954249 20203 solver.cpp:397] Test net output #1: loss = 5.28599 (* 1 = 5.28599 loss)
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I0412 15:16:36.040585 20203 solver.cpp:218] Iteration 6528 (0.823196 iter/s, 14.5773s/12 iters), loss = 5.27058
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I0412 15:16:40.080178 20203 solver.cpp:237] Train net output #0: loss = 5.2722 (* 1 = 5.2722 loss)
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I0412 15:16:59.583376 20203 solver.cpp:237] Train net output #0: loss = 5.28269 (* 1 = 5.28269 loss)
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I0412 15:16:59.583389 20203 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
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I0412 15:17:04.138916 20203 solver.cpp:237] Train net output #0: loss = 5.27304 (* 1 = 5.27304 loss)
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I0412 15:17:04.138929 20203 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
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I0412 15:17:09.021381 20203 solver.cpp:218] Iteration 6612 (2.45788 iter/s, 4.88225s/12 iters), loss = 5.30456
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I0412 15:17:09.021425 20203 solver.cpp:237] Train net output #0: loss = 5.30456 (* 1 = 5.30456 loss)
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I0412 15:17:13.965463 20203 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
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I0412 15:17:20.525290 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
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I0412 15:17:24.314333 20203 solver.cpp:330] Iteration 6630, Testing net (#0)
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I0412 15:17:26.180444 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:17:28.786491 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:17:28.786520 20203 solver.cpp:397] Test net output #1: loss = 5.28602 (* 1 = 5.28602 loss)
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I0412 15:17:30.766371 20203 solver.cpp:218] Iteration 6636 (0.714276 iter/s, 16.8002s/12 iters), loss = 5.27259
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I0412 15:17:35.700028 20203 solver.cpp:237] Train net output #0: loss = 5.27591 (* 1 = 5.27591 loss)
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I0412 15:17:35.700038 20203 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
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I0412 15:17:40.811573 20203 solver.cpp:218] Iteration 6660 (2.34773 iter/s, 5.11132s/12 iters), loss = 5.27999
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I0412 15:17:40.811621 20203 solver.cpp:237] Train net output #0: loss = 5.27999 (* 1 = 5.27999 loss)
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I0412 15:17:45.738405 20203 solver.cpp:237] Train net output #0: loss = 5.26383 (* 1 = 5.26383 loss)
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I0412 15:17:45.738415 20203 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
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I0412 15:17:50.536304 20203 solver.cpp:218] Iteration 6684 (2.5012 iter/s, 4.79769s/12 iters), loss = 5.27217
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I0412 15:17:50.536399 20203 solver.cpp:237] Train net output #0: loss = 5.27217 (* 1 = 5.27217 loss)
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I0412 15:17:55.469942 20203 solver.cpp:218] Iteration 6696 (2.43244 iter/s, 4.93333s/12 iters), loss = 5.26558
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I0412 15:17:55.470012 20203 solver.cpp:237] Train net output #0: loss = 5.26558 (* 1 = 5.26558 loss)
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I0412 15:17:55.470029 20203 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
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I0412 15:18:00.346585 20203 solver.cpp:218] Iteration 6708 (2.46085 iter/s, 4.87636s/12 iters), loss = 5.27295
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I0412 15:18:00.346643 20203 solver.cpp:237] Train net output #0: loss = 5.27295 (* 1 = 5.27295 loss)
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I0412 15:18:00.346657 20203 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
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I0412 15:18:05.114842 20203 solver.cpp:218] Iteration 6720 (2.51678 iter/s, 4.76799s/12 iters), loss = 5.27062
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I0412 15:18:05.114894 20203 solver.cpp:237] Train net output #0: loss = 5.27062 (* 1 = 5.27062 loss)
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I0412 15:18:05.114905 20203 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
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I0412 15:18:09.709233 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
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I0412 15:18:12.695565 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
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I0412 15:18:14.988339 20203 solver.cpp:330] Iteration 6732, Testing net (#0)
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I0412 15:18:16.853916 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:18:19.527776 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:18:19.527815 20203 solver.cpp:397] Test net output #1: loss = 5.28573 (* 1 = 5.28573 loss)
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I0412 15:18:19.614166 20203 solver.cpp:218] Iteration 6732 (0.827663 iter/s, 14.4987s/12 iters), loss = 5.27075
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I0412 15:18:19.614220 20203 solver.cpp:237] Train net output #0: loss = 5.27075 (* 1 = 5.27075 loss)
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I0412 15:18:19.614231 20203 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
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I0412 15:18:23.796676 20203 solver.cpp:218] Iteration 6744 (2.86925 iter/s, 4.18227s/12 iters), loss = 5.26518
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I0412 15:18:23.796790 20203 solver.cpp:237] Train net output #0: loss = 5.26518 (* 1 = 5.26518 loss)
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I0412 15:18:23.796800 20203 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
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I0412 15:18:28.865465 20203 solver.cpp:218] Iteration 6756 (2.36759 iter/s, 5.06846s/12 iters), loss = 5.28919
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I0412 15:18:28.865505 20203 solver.cpp:237] Train net output #0: loss = 5.28919 (* 1 = 5.28919 loss)
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I0412 15:18:34.171104 20203 solver.cpp:218] Iteration 6768 (2.26186 iter/s, 5.30537s/12 iters), loss = 5.2726
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I0412 15:18:34.171146 20203 solver.cpp:237] Train net output #0: loss = 5.2726 (* 1 = 5.2726 loss)
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I0412 15:18:34.171155 20203 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
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I0412 15:18:39.101259 20203 solver.cpp:218] Iteration 6780 (2.43413 iter/s, 4.9299s/12 iters), loss = 5.27815
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I0412 15:18:39.101305 20203 solver.cpp:237] Train net output #0: loss = 5.27815 (* 1 = 5.27815 loss)
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I0412 15:18:44.173755 20203 solver.cpp:218] Iteration 6792 (2.36582 iter/s, 5.07223s/12 iters), loss = 5.25734
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I0412 15:18:44.173817 20203 solver.cpp:237] Train net output #0: loss = 5.25734 (* 1 = 5.25734 loss)
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I0412 15:18:44.173835 20203 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
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I0412 15:18:49.016947 20203 solver.cpp:237] Train net output #0: loss = 5.26586 (* 1 = 5.26586 loss)
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I0412 15:18:49.016960 20203 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
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I0412 15:18:54.165926 20203 solver.cpp:218] Iteration 6816 (2.33066 iter/s, 5.14876s/12 iters), loss = 5.28059
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I0412 15:18:54.166040 20203 solver.cpp:237] Train net output #0: loss = 5.28059 (* 1 = 5.28059 loss)
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I0412 15:18:54.166055 20203 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
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I0412 15:18:59.201215 20203 solver.cpp:237] Train net output #0: loss = 5.26984 (* 1 = 5.26984 loss)
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I0412 15:18:59.201228 20203 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
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I0412 15:19:01.145642 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
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I0412 15:19:08.688565 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
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I0412 15:19:14.451493 20203 solver.cpp:330] Iteration 6834, Testing net (#0)
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I0412 15:19:14.451514 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:19:16.353924 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:19:19.298652 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:19:19.298702 20203 solver.cpp:397] Test net output #1: loss = 5.28626 (* 1 = 5.28626 loss)
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I0412 15:19:21.288164 20203 solver.cpp:218] Iteration 6840 (0.54333 iter/s, 22.086s/12 iters), loss = 5.27379
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I0412 15:19:21.288210 20203 solver.cpp:237] Train net output #0: loss = 5.27379 (* 1 = 5.27379 loss)
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I0412 15:19:21.288219 20203 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
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I0412 15:19:26.224005 20203 solver.cpp:237] Train net output #0: loss = 5.27443 (* 1 = 5.27443 loss)
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I0412 15:19:26.224020 20203 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
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I0412 15:19:31.445181 20203 solver.cpp:218] Iteration 6864 (2.29843 iter/s, 5.22095s/12 iters), loss = 5.27451
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I0412 15:19:31.445225 20203 solver.cpp:237] Train net output #0: loss = 5.27451 (* 1 = 5.27451 loss)
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I0412 15:19:31.445235 20203 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
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I0412 15:19:36.634001 20203 solver.cpp:218] Iteration 6876 (2.31279 iter/s, 5.18855s/12 iters), loss = 5.28536
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I0412 15:19:36.634043 20203 solver.cpp:237] Train net output #0: loss = 5.28536 (* 1 = 5.28536 loss)
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I0412 15:19:36.634052 20203 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
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I0412 15:19:37.310127 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:19:41.766739 20203 solver.cpp:218] Iteration 6888 (2.33806 iter/s, 5.13246s/12 iters), loss = 5.28041
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I0412 15:19:41.766793 20203 solver.cpp:237] Train net output #0: loss = 5.28041 (* 1 = 5.28041 loss)
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I0412 15:19:41.766803 20203 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
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I0412 15:19:46.786130 20203 solver.cpp:218] Iteration 6900 (2.39086 iter/s, 5.01912s/12 iters), loss = 5.26838
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I0412 15:19:46.786186 20203 solver.cpp:237] Train net output #0: loss = 5.26838 (* 1 = 5.26838 loss)
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I0412 15:19:46.786199 20203 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
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I0412 15:19:51.526223 20203 solver.cpp:218] Iteration 6912 (2.53174 iter/s, 4.73983s/12 iters), loss = 5.28349
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I0412 15:19:51.526285 20203 solver.cpp:237] Train net output #0: loss = 5.28349 (* 1 = 5.28349 loss)
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I0412 15:19:51.526299 20203 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
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I0412 15:19:56.466756 20203 solver.cpp:218] Iteration 6924 (2.42902 iter/s, 4.94026s/12 iters), loss = 5.27887
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I0412 15:19:56.466827 20203 solver.cpp:237] Train net output #0: loss = 5.27887 (* 1 = 5.27887 loss)
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I0412 15:19:56.466838 20203 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
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I0412 15:20:01.082650 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
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I0412 15:20:04.340893 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
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I0412 15:20:09.051261 20203 solver.cpp:330] Iteration 6936, Testing net (#0)
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I0412 15:20:09.051291 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:20:09.730937 20203 blocking_queue.cpp:49] Waiting for data
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I0412 15:20:10.825984 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:20:13.546314 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:20:13.546355 20203 solver.cpp:397] Test net output #1: loss = 5.28607 (* 1 = 5.28607 loss)
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I0412 15:20:13.633729 20203 solver.cpp:218] Iteration 6936 (0.699049 iter/s, 17.1662s/12 iters), loss = 5.28635
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I0412 15:20:13.633785 20203 solver.cpp:237] Train net output #0: loss = 5.28635 (* 1 = 5.28635 loss)
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I0412 15:20:13.633798 20203 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
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I0412 15:20:17.614562 20203 solver.cpp:237] Train net output #0: loss = 5.27672 (* 1 = 5.27672 loss)
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I0412 15:20:17.614571 20203 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
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I0412 15:20:22.467316 20203 solver.cpp:237] Train net output #0: loss = 5.26537 (* 1 = 5.26537 loss)
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I0412 15:20:27.439916 20203 solver.cpp:237] Train net output #0: loss = 5.26719 (* 1 = 5.26719 loss)
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I0412 15:20:31.877635 20203 solver.cpp:218] Iteration 6984 (2.70421 iter/s, 4.43753s/12 iters), loss = 5.27756
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I0412 15:20:31.877677 20203 solver.cpp:237] Train net output #0: loss = 5.27756 (* 1 = 5.27756 loss)
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I0412 15:20:36.882846 20203 solver.cpp:237] Train net output #0: loss = 5.25855 (* 1 = 5.25855 loss)
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I0412 15:20:41.555128 20203 solver.cpp:237] Train net output #0: loss = 5.25928 (* 1 = 5.25928 loss)
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I0412 15:20:46.500197 20203 solver.cpp:237] Train net output #0: loss = 5.25792 (* 1 = 5.25792 loss)
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I0412 15:20:51.363934 20203 solver.cpp:237] Train net output #0: loss = 5.30613 (* 1 = 5.30613 loss)
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I0412 15:20:51.363945 20203 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
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I0412 15:20:59.086354 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
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I0412 15:21:02.318563 20203 solver.cpp:330] Iteration 7038, Testing net (#0)
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I0412 15:21:04.068678 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:21:06.881011 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:21:06.881049 20203 solver.cpp:397] Test net output #1: loss = 5.28582 (* 1 = 5.28582 loss)
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I0412 15:21:13.964186 20203 solver.cpp:237] Train net output #0: loss = 5.27084 (* 1 = 5.27084 loss)
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I0412 15:21:18.752712 20203 solver.cpp:237] Train net output #0: loss = 5.27059 (* 1 = 5.27059 loss)
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I0412 15:21:28.791203 20203 solver.cpp:237] Train net output #0: loss = 5.26595 (* 1 = 5.26595 loss)
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I0412 15:21:33.952080 20203 solver.cpp:237] Train net output #0: loss = 5.29585 (* 1 = 5.29585 loss)
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I0412 15:21:38.991907 20203 solver.cpp:218] Iteration 7116 (2.38113 iter/s, 5.03963s/12 iters), loss = 5.28198
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I0412 15:21:38.991947 20203 solver.cpp:237] Train net output #0: loss = 5.28198 (* 1 = 5.28198 loss)
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I0412 15:21:43.789316 20203 solver.cpp:237] Train net output #0: loss = 5.27304 (* 1 = 5.27304 loss)
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I0412 15:21:48.393666 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
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I0412 15:21:54.136540 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
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I0412 15:21:58.029908 20203 solver.cpp:330] Iteration 7140, Testing net (#0)
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I0412 15:21:58.029937 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:21:59.679991 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:22:02.685871 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:22:02.685909 20203 solver.cpp:397] Test net output #1: loss = 5.28604 (* 1 = 5.28604 loss)
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I0412 15:22:07.285292 20203 solver.cpp:237] Train net output #0: loss = 5.24827 (* 1 = 5.24827 loss)
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I0412 15:22:12.164276 20203 solver.cpp:237] Train net output #0: loss = 5.27281 (* 1 = 5.27281 loss)
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I0412 15:22:17.244985 20203 solver.cpp:237] Train net output #0: loss = 5.25898 (* 1 = 5.25898 loss)
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I0412 15:22:17.244994 20203 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
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I0412 15:22:22.260440 20203 solver.cpp:218] Iteration 7188 (2.3927 iter/s, 5.01525s/12 iters), loss = 5.27401
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I0412 15:22:22.260478 20203 solver.cpp:237] Train net output #0: loss = 5.27401 (* 1 = 5.27401 loss)
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I0412 15:22:27.144338 20203 solver.cpp:237] Train net output #0: loss = 5.27461 (* 1 = 5.27461 loss)
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I0412 15:22:27.144351 20203 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
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I0412 15:22:32.123286 20203 solver.cpp:218] Iteration 7212 (2.41025 iter/s, 4.97874s/12 iters), loss = 5.27992
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I0412 15:22:32.123347 20203 solver.cpp:237] Train net output #0: loss = 5.27992 (* 1 = 5.27992 loss)
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I0412 15:22:32.123359 20203 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
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I0412 15:22:37.158792 20203 solver.cpp:218] Iteration 7224 (2.3832 iter/s, 5.03524s/12 iters), loss = 5.26648
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I0412 15:22:37.158834 20203 solver.cpp:237] Train net output #0: loss = 5.26648 (* 1 = 5.26648 loss)
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I0412 15:22:37.158844 20203 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
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I0412 15:22:41.901510 20203 solver.cpp:218] Iteration 7236 (2.53032 iter/s, 4.74248s/12 iters), loss = 5.27598
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I0412 15:22:41.906646 20203 solver.cpp:237] Train net output #0: loss = 5.27598 (* 1 = 5.27598 loss)
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I0412 15:22:41.906661 20203 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
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I0412 15:22:43.769623 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
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I0412 15:22:46.980473 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
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I0412 15:22:50.691936 20203 solver.cpp:330] Iteration 7242, Testing net (#0)
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I0412 15:22:50.691958 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:22:52.336665 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:22:55.246462 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:22:55.246507 20203 solver.cpp:397] Test net output #1: loss = 5.28578 (* 1 = 5.28578 loss)
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I0412 15:22:57.209103 20203 solver.cpp:218] Iteration 7248 (0.784218 iter/s, 15.3019s/12 iters), loss = 5.27139
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I0412 15:22:57.209154 20203 solver.cpp:237] Train net output #0: loss = 5.27139 (* 1 = 5.27139 loss)
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I0412 15:23:02.111008 20203 solver.cpp:237] Train net output #0: loss = 5.27242 (* 1 = 5.27242 loss)
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I0412 15:23:02.111018 20203 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
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I0412 15:23:07.260687 20203 solver.cpp:237] Train net output #0: loss = 5.25625 (* 1 = 5.25625 loss)
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I0412 15:23:07.260696 20203 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
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I0412 15:23:12.026916 20203 solver.cpp:218] Iteration 7284 (2.51782 iter/s, 4.76603s/12 iters), loss = 5.25884
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I0412 15:23:12.027029 20203 solver.cpp:237] Train net output #0: loss = 5.25884 (* 1 = 5.25884 loss)
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I0412 15:23:16.948856 20203 solver.cpp:218] Iteration 7296 (2.43822 iter/s, 4.92163s/12 iters), loss = 5.28673
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I0412 15:23:16.948912 20203 solver.cpp:237] Train net output #0: loss = 5.28673 (* 1 = 5.28673 loss)
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I0412 15:23:21.598446 20203 solver.cpp:218] Iteration 7308 (2.58101 iter/s, 4.64934s/12 iters), loss = 5.28278
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I0412 15:23:21.598505 20203 solver.cpp:237] Train net output #0: loss = 5.28278 (* 1 = 5.28278 loss)
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I0412 15:23:26.366905 20203 solver.cpp:218] Iteration 7320 (2.51667 iter/s, 4.7682s/12 iters), loss = 5.29413
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I0412 15:23:26.366963 20203 solver.cpp:237] Train net output #0: loss = 5.29413 (* 1 = 5.29413 loss)
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I0412 15:23:26.366976 20203 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
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I0412 15:23:31.306993 20203 solver.cpp:218] Iteration 7332 (2.42924 iter/s, 4.93982s/12 iters), loss = 5.2685
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I0412 15:23:31.307046 20203 solver.cpp:237] Train net output #0: loss = 5.2685 (* 1 = 5.2685 loss)
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I0412 15:23:31.307057 20203 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
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I0412 15:23:35.648049 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
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I0412 15:23:38.678447 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
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I0412 15:23:42.202976 20203 solver.cpp:330] Iteration 7344, Testing net (#0)
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I0412 15:23:42.203078 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:23:43.836886 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:23:46.745597 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:23:46.745663 20203 solver.cpp:397] Test net output #1: loss = 5.28648 (* 1 = 5.28648 loss)
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I0412 15:23:46.832314 20203 solver.cpp:218] Iteration 7344 (0.772964 iter/s, 15.5247s/12 iters), loss = 5.27464
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I0412 15:23:46.832376 20203 solver.cpp:237] Train net output #0: loss = 5.27464 (* 1 = 5.27464 loss)
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I0412 15:23:46.832387 20203 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
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I0412 15:23:50.609112 20203 solver.cpp:218] Iteration 7356 (3.17748 iter/s, 3.77657s/12 iters), loss = 5.28521
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I0412 15:23:50.609163 20203 solver.cpp:237] Train net output #0: loss = 5.28521 (* 1 = 5.28521 loss)
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I0412 15:23:50.609175 20203 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
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I0412 15:23:55.763861 20203 solver.cpp:218] Iteration 7368 (2.32807 iter/s, 5.15448s/12 iters), loss = 5.27921
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I0412 15:23:55.763918 20203 solver.cpp:237] Train net output #0: loss = 5.27921 (* 1 = 5.27921 loss)
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I0412 15:23:55.763931 20203 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
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I0412 15:24:00.404268 20203 solver.cpp:218] Iteration 7380 (2.58612 iter/s, 4.64016s/12 iters), loss = 5.26507
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I0412 15:24:00.404320 20203 solver.cpp:237] Train net output #0: loss = 5.26507 (* 1 = 5.26507 loss)
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I0412 15:24:00.404330 20203 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
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I0412 15:24:01.732424 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:24:05.238771 20203 solver.cpp:218] Iteration 7392 (2.48229 iter/s, 4.83425s/12 iters), loss = 5.27383
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I0412 15:24:05.238817 20203 solver.cpp:237] Train net output #0: loss = 5.27383 (* 1 = 5.27383 loss)
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I0412 15:24:05.238828 20203 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
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I0412 15:24:10.091218 20203 solver.cpp:218] Iteration 7404 (2.4731 iter/s, 4.8522s/12 iters), loss = 5.27209
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I0412 15:24:10.091257 20203 solver.cpp:237] Train net output #0: loss = 5.27209 (* 1 = 5.27209 loss)
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I0412 15:24:10.091266 20203 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
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I0412 15:24:15.152302 20203 solver.cpp:218] Iteration 7416 (2.37115 iter/s, 5.06084s/12 iters), loss = 5.26952
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I0412 15:24:15.152431 20203 solver.cpp:237] Train net output #0: loss = 5.26952 (* 1 = 5.26952 loss)
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I0412 15:24:15.152441 20203 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
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I0412 15:24:19.913354 20203 solver.cpp:218] Iteration 7428 (2.52062 iter/s, 4.76073s/12 iters), loss = 5.27948
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I0412 15:24:19.913398 20203 solver.cpp:237] Train net output #0: loss = 5.27948 (* 1 = 5.27948 loss)
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I0412 15:24:19.913408 20203 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
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I0412 15:24:24.829066 20203 solver.cpp:218] Iteration 7440 (2.44128 iter/s, 4.91546s/12 iters), loss = 5.26439
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I0412 15:24:24.829119 20203 solver.cpp:237] Train net output #0: loss = 5.26439 (* 1 = 5.26439 loss)
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I0412 15:24:24.829133 20203 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
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I0412 15:24:26.777163 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
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I0412 15:24:29.888963 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
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I0412 15:24:32.309526 20203 solver.cpp:330] Iteration 7446, Testing net (#0)
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I0412 15:24:32.309547 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:24:33.801976 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:24:36.930061 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:24:36.930109 20203 solver.cpp:397] Test net output #1: loss = 5.28593 (* 1 = 5.28593 loss)
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I0412 15:24:38.757683 20203 solver.cpp:218] Iteration 7452 (0.861573 iter/s, 13.928s/12 iters), loss = 5.26301
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I0412 15:24:38.757741 20203 solver.cpp:237] Train net output #0: loss = 5.26301 (* 1 = 5.26301 loss)
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I0412 15:24:38.757755 20203 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
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I0412 15:24:43.598531 20203 solver.cpp:218] Iteration 7464 (2.47904 iter/s, 4.84059s/12 iters), loss = 5.28348
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I0412 15:24:43.598572 20203 solver.cpp:237] Train net output #0: loss = 5.28348 (* 1 = 5.28348 loss)
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I0412 15:24:43.598582 20203 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
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I0412 15:24:48.558331 20203 solver.cpp:218] Iteration 7476 (2.41957 iter/s, 4.95955s/12 iters), loss = 5.27815
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I0412 15:24:48.558483 20203 solver.cpp:237] Train net output #0: loss = 5.27815 (* 1 = 5.27815 loss)
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I0412 15:24:48.558497 20203 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
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I0412 15:24:51.899344 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:24:53.377063 20203 solver.cpp:218] Iteration 7488 (2.49046 iter/s, 4.81838s/12 iters), loss = 5.27258
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I0412 15:24:53.377115 20203 solver.cpp:237] Train net output #0: loss = 5.27258 (* 1 = 5.27258 loss)
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I0412 15:24:58.192462 20203 solver.cpp:237] Train net output #0: loss = 5.25784 (* 1 = 5.25784 loss)
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I0412 15:24:58.192471 20203 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
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I0412 15:25:02.953418 20203 solver.cpp:237] Train net output #0: loss = 5.26093 (* 1 = 5.26093 loss)
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I0412 15:25:02.953433 20203 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
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I0412 15:25:08.070164 20203 solver.cpp:237] Train net output #0: loss = 5.27204 (* 1 = 5.27204 loss)
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I0412 15:25:08.070173 20203 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
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I0412 15:25:12.984009 20203 solver.cpp:237] Train net output #0: loss = 5.26028 (* 1 = 5.26028 loss)
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I0412 15:25:12.984019 20203 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
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I0412 15:25:17.416427 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
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I0412 15:25:20.427570 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
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I0412 15:25:22.734510 20203 solver.cpp:330] Iteration 7548, Testing net (#0)
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I0412 15:25:22.734537 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:25:24.184075 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:25:27.144192 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:25:27.144243 20203 solver.cpp:397] Test net output #1: loss = 5.28602 (* 1 = 5.28602 loss)
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I0412 15:25:27.230922 20203 solver.cpp:218] Iteration 7548 (0.842321 iter/s, 14.2463s/12 iters), loss = 5.27983
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I0412 15:25:27.230970 20203 solver.cpp:237] Train net output #0: loss = 5.27983 (* 1 = 5.27983 loss)
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I0412 15:25:31.504456 20203 solver.cpp:237] Train net output #0: loss = 5.2711 (* 1 = 5.2711 loss)
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I0412 15:25:36.717738 20203 solver.cpp:237] Train net output #0: loss = 5.28055 (* 1 = 5.28055 loss)
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I0412 15:25:46.559960 20203 solver.cpp:237] Train net output #0: loss = 5.27866 (* 1 = 5.27866 loss)
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I0412 15:25:51.340612 20203 solver.cpp:237] Train net output #0: loss = 5.26217 (* 1 = 5.26217 loss)
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I0412 15:25:51.340624 20203 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
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I0412 15:25:56.209631 20203 solver.cpp:218] Iteration 7620 (2.46466 iter/s, 4.86882s/12 iters), loss = 5.27488
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I0412 15:25:56.209681 20203 solver.cpp:237] Train net output #0: loss = 5.27488 (* 1 = 5.27488 loss)
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I0412 15:25:56.209692 20203 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
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I0412 15:25:58.612236 20203 blocking_queue.cpp:49] Waiting for data
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I0412 15:26:01.021366 20203 solver.cpp:218] Iteration 7632 (2.49403 iter/s, 4.81149s/12 iters), loss = 5.27833
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I0412 15:26:01.021395 20203 solver.cpp:237] Train net output #0: loss = 5.27833 (* 1 = 5.27833 loss)
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I0412 15:26:01.021404 20203 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
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I0412 15:26:05.703943 20203 solver.cpp:237] Train net output #0: loss = 5.28349 (* 1 = 5.28349 loss)
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I0412 15:26:05.703954 20203 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
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I0412 15:26:07.529872 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
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I0412 15:26:10.592854 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
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I0412 15:26:12.872681 20203 solver.cpp:330] Iteration 7650, Testing net (#0)
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I0412 15:26:12.872704 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:26:14.431280 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:26:17.592679 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:26:17.592715 20203 solver.cpp:397] Test net output #1: loss = 5.28626 (* 1 = 5.28626 loss)
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I0412 15:26:19.603857 20203 solver.cpp:218] Iteration 7656 (0.86335 iter/s, 13.8994s/12 iters), loss = 5.2753
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I0412 15:26:24.785980 20203 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
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I0412 15:26:29.627094 20203 solver.cpp:218] Iteration 7680 (2.47885 iter/s, 4.84095s/12 iters), loss = 5.26028
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I0412 15:26:29.627136 20203 solver.cpp:237] Train net output #0: loss = 5.26028 (* 1 = 5.26028 loss)
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I0412 15:26:29.627146 20203 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
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I0412 15:26:32.349359 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:26:34.457772 20203 solver.cpp:218] Iteration 7692 (2.48425 iter/s, 4.83043s/12 iters), loss = 5.2697
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I0412 15:26:34.457816 20203 solver.cpp:237] Train net output #0: loss = 5.2697 (* 1 = 5.2697 loss)
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I0412 15:26:39.309653 20203 solver.cpp:237] Train net output #0: loss = 5.25373 (* 1 = 5.25373 loss)
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I0412 15:26:39.309660 20203 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
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I0412 15:26:43.973358 20203 solver.cpp:218] Iteration 7716 (2.57317 iter/s, 4.66351s/12 iters), loss = 5.25624
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I0412 15:26:43.973412 20203 solver.cpp:237] Train net output #0: loss = 5.25624 (* 1 = 5.25624 loss)
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I0412 15:26:43.973428 20203 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
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I0412 15:26:48.737704 20203 solver.cpp:218] Iteration 7728 (2.51884 iter/s, 4.76409s/12 iters), loss = 5.25984
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I0412 15:26:48.737762 20203 solver.cpp:237] Train net output #0: loss = 5.25984 (* 1 = 5.25984 loss)
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I0412 15:26:48.737779 20203 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
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I0412 15:26:53.783044 20203 solver.cpp:218] Iteration 7740 (2.37856 iter/s, 5.04508s/12 iters), loss = 5.29924
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I0412 15:26:53.783087 20203 solver.cpp:237] Train net output #0: loss = 5.29924 (* 1 = 5.29924 loss)
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I0412 15:26:53.783097 20203 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
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I0412 15:26:58.224647 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
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I0412 15:27:01.487565 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
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I0412 15:27:05.994937 20203 solver.cpp:330] Iteration 7752, Testing net (#0)
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I0412 15:27:05.994964 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:27:07.371959 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:27:10.499015 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:27:10.499084 20203 solver.cpp:397] Test net output #1: loss = 5.28598 (* 1 = 5.28598 loss)
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I0412 15:27:10.586448 20203 solver.cpp:218] Iteration 7752 (0.714173 iter/s, 16.8027s/12 iters), loss = 5.27397
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I0412 15:27:10.586505 20203 solver.cpp:237] Train net output #0: loss = 5.27397 (* 1 = 5.27397 loss)
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I0412 15:27:14.778576 20203 solver.cpp:237] Train net output #0: loss = 5.2773 (* 1 = 5.2773 loss)
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I0412 15:27:14.778587 20203 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
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I0412 15:27:19.666931 20203 solver.cpp:218] Iteration 7776 (2.45492 iter/s, 4.88815s/12 iters), loss = 5.27137
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I0412 15:27:19.666985 20203 solver.cpp:237] Train net output #0: loss = 5.27137 (* 1 = 5.27137 loss)
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I0412 15:27:19.666998 20203 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
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I0412 15:27:24.572911 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:27:24.589131 20203 solver.cpp:218] Iteration 7788 (2.43807 iter/s, 4.92193s/12 iters), loss = 5.2459
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I0412 15:27:24.589200 20203 solver.cpp:237] Train net output #0: loss = 5.2459 (* 1 = 5.2459 loss)
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I0412 15:27:24.589217 20203 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
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I0412 15:27:29.628073 20203 solver.cpp:218] Iteration 7800 (2.38158 iter/s, 5.03866s/12 iters), loss = 5.26702
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I0412 15:27:29.628190 20203 solver.cpp:237] Train net output #0: loss = 5.26702 (* 1 = 5.26702 loss)
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I0412 15:27:29.628202 20203 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
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I0412 15:27:34.889263 20203 solver.cpp:218] Iteration 7812 (2.28099 iter/s, 5.26086s/12 iters), loss = 5.29488
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I0412 15:27:34.889302 20203 solver.cpp:237] Train net output #0: loss = 5.29488 (* 1 = 5.29488 loss)
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I0412 15:27:39.765229 20203 solver.cpp:218] Iteration 7824 (2.46118 iter/s, 4.87572s/12 iters), loss = 5.27232
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I0412 15:27:39.765275 20203 solver.cpp:237] Train net output #0: loss = 5.27232 (* 1 = 5.27232 loss)
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I0412 15:27:39.765286 20203 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
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I0412 15:27:44.594676 20203 solver.cpp:218] Iteration 7836 (2.48489 iter/s, 4.82919s/12 iters), loss = 5.27469
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I0412 15:27:44.594727 20203 solver.cpp:237] Train net output #0: loss = 5.27469 (* 1 = 5.27469 loss)
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I0412 15:27:44.594738 20203 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
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I0412 15:27:49.403061 20203 solver.cpp:218] Iteration 7848 (2.49578 iter/s, 4.80813s/12 iters), loss = 5.26066
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I0412 15:27:49.403134 20203 solver.cpp:237] Train net output #0: loss = 5.26066 (* 1 = 5.26066 loss)
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I0412 15:27:49.403152 20203 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
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I0412 15:27:51.398577 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
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I0412 15:27:55.997890 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
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I0412 15:28:00.172044 20203 solver.cpp:330] Iteration 7854, Testing net (#0)
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I0412 15:28:00.172122 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:28:01.533481 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:28:04.616704 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:28:04.616757 20203 solver.cpp:397] Test net output #1: loss = 5.28591 (* 1 = 5.28591 loss)
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I0412 15:28:06.289708 20203 solver.cpp:218] Iteration 7860 (0.710652 iter/s, 16.8859s/12 iters), loss = 5.24535
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I0412 15:28:06.289767 20203 solver.cpp:237] Train net output #0: loss = 5.24535 (* 1 = 5.24535 loss)
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I0412 15:28:06.289779 20203 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
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I0412 15:28:11.140014 20203 solver.cpp:218] Iteration 7872 (2.4742 iter/s, 4.85005s/12 iters), loss = 5.26984
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I0412 15:28:11.140058 20203 solver.cpp:237] Train net output #0: loss = 5.26984 (* 1 = 5.26984 loss)
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I0412 15:28:11.140066 20203 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
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I0412 15:28:16.251185 20203 solver.cpp:218] Iteration 7884 (2.34792 iter/s, 5.11091s/12 iters), loss = 5.26037
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I0412 15:28:16.251240 20203 solver.cpp:237] Train net output #0: loss = 5.26037 (* 1 = 5.26037 loss)
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I0412 15:28:16.251255 20203 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
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I0412 15:28:18.213865 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:28:21.166421 20203 solver.cpp:218] Iteration 7896 (2.44152 iter/s, 4.91497s/12 iters), loss = 5.2722
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I0412 15:28:21.166482 20203 solver.cpp:237] Train net output #0: loss = 5.2722 (* 1 = 5.2722 loss)
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I0412 15:28:21.166493 20203 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
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I0412 15:28:26.242022 20203 solver.cpp:218] Iteration 7908 (2.36438 iter/s, 5.07534s/12 iters), loss = 5.27271
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I0412 15:28:26.242074 20203 solver.cpp:237] Train net output #0: loss = 5.27271 (* 1 = 5.27271 loss)
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I0412 15:28:26.242087 20203 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
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I0412 15:28:31.073557 20203 solver.cpp:218] Iteration 7920 (2.48382 iter/s, 4.83128s/12 iters), loss = 5.28665
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I0412 15:28:31.073710 20203 solver.cpp:237] Train net output #0: loss = 5.28665 (* 1 = 5.28665 loss)
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I0412 15:28:31.073724 20203 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
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I0412 15:28:36.165166 20203 solver.cpp:218] Iteration 7932 (2.35699 iter/s, 5.09124s/12 iters), loss = 5.26871
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I0412 15:28:36.165226 20203 solver.cpp:237] Train net output #0: loss = 5.26871 (* 1 = 5.26871 loss)
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I0412 15:28:36.165242 20203 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
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I0412 15:28:41.147238 20203 solver.cpp:218] Iteration 7944 (2.40877 iter/s, 4.9818s/12 iters), loss = 5.27035
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I0412 15:28:41.147281 20203 solver.cpp:237] Train net output #0: loss = 5.27035 (* 1 = 5.27035 loss)
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I0412 15:28:41.147290 20203 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
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I0412 15:28:45.595288 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
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I0412 15:28:49.420202 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
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I0412 15:28:51.724900 20203 solver.cpp:330] Iteration 7956, Testing net (#0)
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I0412 15:28:51.724920 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:28:53.070358 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:28:56.193133 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:28:56.193189 20203 solver.cpp:397] Test net output #1: loss = 5.28644 (* 1 = 5.28644 loss)
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I0412 15:28:56.279801 20203 solver.cpp:218] Iteration 7956 (0.793027 iter/s, 15.1319s/12 iters), loss = 5.27586
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I0412 15:28:56.279870 20203 solver.cpp:237] Train net output #0: loss = 5.27586 (* 1 = 5.27586 loss)
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I0412 15:28:56.279886 20203 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
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I0412 15:29:00.521121 20203 solver.cpp:218] Iteration 7968 (2.82947 iter/s, 4.24107s/12 iters), loss = 5.27508
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I0412 15:29:00.521167 20203 solver.cpp:237] Train net output #0: loss = 5.27508 (* 1 = 5.27508 loss)
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I0412 15:29:00.521175 20203 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
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I0412 15:29:05.363358 20203 solver.cpp:218] Iteration 7980 (2.47832 iter/s, 4.84199s/12 iters), loss = 5.2579
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I0412 15:29:05.363492 20203 solver.cpp:237] Train net output #0: loss = 5.2579 (* 1 = 5.2579 loss)
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I0412 15:29:05.363502 20203 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
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I0412 15:29:09.552873 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:29:10.262848 20203 solver.cpp:218] Iteration 7992 (2.4494 iter/s, 4.89915s/12 iters), loss = 5.25757
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I0412 15:29:10.262892 20203 solver.cpp:237] Train net output #0: loss = 5.25757 (* 1 = 5.25757 loss)
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I0412 15:29:15.138486 20203 solver.cpp:237] Train net output #0: loss = 5.27874 (* 1 = 5.27874 loss)
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I0412 15:29:15.138494 20203 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
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I0412 15:29:20.146723 20203 solver.cpp:237] Train net output #0: loss = 5.27812 (* 1 = 5.27812 loss)
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I0412 15:29:20.146736 20203 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
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I0412 15:29:24.800264 20203 solver.cpp:237] Train net output #0: loss = 5.29416 (* 1 = 5.29416 loss)
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I0412 15:29:24.800276 20203 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
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I0412 15:29:29.701719 20203 solver.cpp:237] Train net output #0: loss = 5.26554 (* 1 = 5.26554 loss)
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I0412 15:29:29.701727 20203 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
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I0412 15:29:34.649844 20203 solver.cpp:237] Train net output #0: loss = 5.28053 (* 1 = 5.28053 loss)
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I0412 15:29:34.649853 20203 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
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I0412 15:29:36.665815 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
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I0412 15:29:43.189147 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
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I0412 15:29:46.364116 20203 solver.cpp:330] Iteration 8058, Testing net (#0)
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I0412 15:29:46.364143 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:29:47.781121 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:29:51.213224 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:29:51.213265 20203 solver.cpp:397] Test net output #1: loss = 5.28637 (* 1 = 5.28637 loss)
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I0412 15:29:53.001890 20203 solver.cpp:218] Iteration 8064 (0.653905 iter/s, 18.3513s/12 iters), loss = 5.27781
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I0412 15:29:53.001940 20203 solver.cpp:237] Train net output #0: loss = 5.27781 (* 1 = 5.27781 loss)
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I0412 15:29:58.102317 20203 solver.cpp:237] Train net output #0: loss = 5.27321 (* 1 = 5.27321 loss)
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I0412 15:30:03.117262 20203 solver.cpp:237] Train net output #0: loss = 5.26334 (* 1 = 5.26334 loss)
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I0412 15:30:08.070446 20203 solver.cpp:237] Train net output #0: loss = 5.26411 (* 1 = 5.26411 loss)
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I0412 15:30:12.955591 20203 solver.cpp:237] Train net output #0: loss = 5.26587 (* 1 = 5.26587 loss)
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I0412 15:30:17.843205 20203 solver.cpp:237] Train net output #0: loss = 5.27273 (* 1 = 5.27273 loss)
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I0412 15:30:17.843219 20203 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
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I0412 15:30:22.670099 20203 solver.cpp:218] Iteration 8136 (2.48618 iter/s, 4.82668s/12 iters), loss = 5.28503
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I0412 15:30:22.670150 20203 solver.cpp:237] Train net output #0: loss = 5.28503 (* 1 = 5.28503 loss)
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I0412 15:30:22.670161 20203 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
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I0412 15:30:27.601545 20203 solver.cpp:218] Iteration 8148 (2.43349 iter/s, 4.93119s/12 iters), loss = 5.25097
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I0412 15:30:27.601595 20203 solver.cpp:237] Train net output #0: loss = 5.25097 (* 1 = 5.25097 loss)
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I0412 15:30:32.190500 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
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I0412 15:30:36.424566 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
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I0412 15:30:40.116905 20203 solver.cpp:330] Iteration 8160, Testing net (#0)
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I0412 15:30:40.117022 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:30:41.387616 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:30:44.633303 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:30:44.633353 20203 solver.cpp:397] Test net output #1: loss = 5.28623 (* 1 = 5.28623 loss)
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I0412 15:30:44.720098 20203 solver.cpp:218] Iteration 8160 (0.701025 iter/s, 17.1178s/12 iters), loss = 5.26621
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I0412 15:30:48.657764 20203 solver.cpp:237] Train net output #0: loss = 5.2843 (* 1 = 5.2843 loss)
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I0412 15:30:53.505920 20203 solver.cpp:237] Train net output #0: loss = 5.27292 (* 1 = 5.27292 loss)
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I0412 15:30:56.974884 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:30:58.364113 20203 solver.cpp:218] Iteration 8196 (2.47016 iter/s, 4.85799s/12 iters), loss = 5.27504
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I0412 15:30:58.364163 20203 solver.cpp:237] Train net output #0: loss = 5.27504 (* 1 = 5.27504 loss)
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I0412 15:31:03.509696 20203 solver.cpp:218] Iteration 8208 (2.33222 iter/s, 5.14531s/12 iters), loss = 5.25898
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I0412 15:31:03.509749 20203 solver.cpp:237] Train net output #0: loss = 5.25898 (* 1 = 5.25898 loss)
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I0412 15:31:08.389695 20203 solver.cpp:237] Train net output #0: loss = 5.26223 (* 1 = 5.26223 loss)
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I0412 15:31:13.456418 20203 solver.cpp:218] Iteration 8232 (2.3685 iter/s, 5.06651s/12 iters), loss = 5.27131
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I0412 15:31:13.456496 20203 solver.cpp:237] Train net output #0: loss = 5.27131 (* 1 = 5.27131 loss)
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I0412 15:31:13.456506 20203 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
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I0412 15:31:18.252421 20203 solver.cpp:218] Iteration 8244 (2.50223 iter/s, 4.79572s/12 iters), loss = 5.25447
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I0412 15:31:18.252467 20203 solver.cpp:237] Train net output #0: loss = 5.25447 (* 1 = 5.25447 loss)
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I0412 15:31:18.252478 20203 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
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I0412 15:31:22.939213 20203 solver.cpp:218] Iteration 8256 (2.56052 iter/s, 4.68654s/12 iters), loss = 5.26917
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I0412 15:31:22.939270 20203 solver.cpp:237] Train net output #0: loss = 5.26917 (* 1 = 5.26917 loss)
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I0412 15:31:24.904070 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
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I0412 15:31:27.929849 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
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I0412 15:31:30.228148 20203 solver.cpp:330] Iteration 8262, Testing net (#0)
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I0412 15:31:30.228173 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:31:31.455454 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:31:34.845175 20203 solver.cpp:397] Test net output #0: accuracy = 0.00612745
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I0412 15:31:34.845227 20203 solver.cpp:397] Test net output #1: loss = 5.28626 (* 1 = 5.28626 loss)
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I0412 15:31:36.490883 20203 solver.cpp:218] Iteration 8268 (0.885539 iter/s, 13.5511s/12 iters), loss = 5.27932
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I0412 15:31:36.490926 20203 solver.cpp:237] Train net output #0: loss = 5.27932 (* 1 = 5.27932 loss)
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I0412 15:31:41.468592 20203 solver.cpp:237] Train net output #0: loss = 5.28372 (* 1 = 5.28372 loss)
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I0412 15:31:41.468605 20203 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
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I0412 15:31:46.378732 20203 solver.cpp:218] Iteration 8292 (2.44403 iter/s, 4.90993s/12 iters), loss = 5.29387
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I0412 15:31:46.378890 20203 solver.cpp:237] Train net output #0: loss = 5.29387 (* 1 = 5.29387 loss)
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I0412 15:31:46.378903 20203 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
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I0412 15:31:51.257050 20203 solver.cpp:218] Iteration 8304 (2.46005 iter/s, 4.87795s/12 iters), loss = 5.27946
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I0412 15:31:51.257093 20203 solver.cpp:237] Train net output #0: loss = 5.27946 (* 1 = 5.27946 loss)
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I0412 15:31:56.086194 20203 solver.cpp:218] Iteration 8316 (2.48504 iter/s, 4.82889s/12 iters), loss = 5.2742
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I0412 15:31:56.086249 20203 solver.cpp:237] Train net output #0: loss = 5.2742 (* 1 = 5.2742 loss)
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I0412 15:31:56.086262 20203 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
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I0412 15:32:00.718389 20203 solver.cpp:218] Iteration 8328 (2.59072 iter/s, 4.63193s/12 iters), loss = 5.28222
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I0412 15:32:00.718444 20203 solver.cpp:237] Train net output #0: loss = 5.28222 (* 1 = 5.28222 loss)
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I0412 15:32:05.498267 20203 solver.cpp:218] Iteration 8340 (2.51066 iter/s, 4.77962s/12 iters), loss = 5.27299
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I0412 15:32:05.498309 20203 solver.cpp:237] Train net output #0: loss = 5.27299 (* 1 = 5.27299 loss)
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I0412 15:32:05.498318 20203 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
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I0412 15:32:10.242534 20203 solver.cpp:218] Iteration 8352 (2.5295 iter/s, 4.74402s/12 iters), loss = 5.28568
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I0412 15:32:10.242578 20203 solver.cpp:237] Train net output #0: loss = 5.28568 (* 1 = 5.28568 loss)
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I0412 15:32:10.242586 20203 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
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I0412 15:32:14.787986 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
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I0412 15:32:18.406054 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
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I0412 15:32:21.938247 20203 solver.cpp:330] Iteration 8364, Testing net (#0)
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I0412 15:32:21.938275 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:32:23.009761 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:32:26.294385 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:32:26.294445 20203 solver.cpp:397] Test net output #1: loss = 5.28601 (* 1 = 5.28601 loss)
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I0412 15:32:26.380964 20203 solver.cpp:218] Iteration 8364 (0.743599 iter/s, 16.1377s/12 iters), loss = 5.2667
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I0412 15:32:26.381012 20203 solver.cpp:237] Train net output #0: loss = 5.2667 (* 1 = 5.2667 loss)
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I0412 15:32:26.381023 20203 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
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I0412 15:32:30.752957 20203 solver.cpp:218] Iteration 8376 (2.74489 iter/s, 4.37175s/12 iters), loss = 5.26808
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I0412 15:32:30.752998 20203 solver.cpp:237] Train net output #0: loss = 5.26808 (* 1 = 5.26808 loss)
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I0412 15:32:30.753008 20203 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
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I0412 15:32:35.622118 20203 solver.cpp:218] Iteration 8388 (2.46462 iter/s, 4.86891s/12 iters), loss = 5.26135
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I0412 15:32:35.622157 20203 solver.cpp:237] Train net output #0: loss = 5.26135 (* 1 = 5.26135 loss)
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I0412 15:32:35.622165 20203 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
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I0412 15:32:38.273592 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:32:40.358208 20203 solver.cpp:218] Iteration 8400 (2.53387 iter/s, 4.73585s/12 iters), loss = 5.26284
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I0412 15:32:40.358254 20203 solver.cpp:237] Train net output #0: loss = 5.26284 (* 1 = 5.26284 loss)
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I0412 15:32:40.358265 20203 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
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I0412 15:32:45.132910 20203 solver.cpp:218] Iteration 8412 (2.51338 iter/s, 4.77445s/12 iters), loss = 5.24944
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I0412 15:32:45.132952 20203 solver.cpp:237] Train net output #0: loss = 5.24944 (* 1 = 5.24944 loss)
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I0412 15:32:45.132962 20203 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
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I0412 15:32:50.062273 20203 solver.cpp:218] Iteration 8424 (2.43452 iter/s, 4.92911s/12 iters), loss = 5.25725
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I0412 15:32:50.062381 20203 solver.cpp:237] Train net output #0: loss = 5.25725 (* 1 = 5.25725 loss)
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I0412 15:32:50.062391 20203 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
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I0412 15:32:54.860025 20203 solver.cpp:218] Iteration 8436 (2.50134 iter/s, 4.79744s/12 iters), loss = 5.25387
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I0412 15:32:54.860078 20203 solver.cpp:237] Train net output #0: loss = 5.25387 (* 1 = 5.25387 loss)
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I0412 15:32:54.860093 20203 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
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I0412 15:32:59.479420 20203 solver.cpp:218] Iteration 8448 (2.59788 iter/s, 4.61914s/12 iters), loss = 5.29134
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I0412 15:32:59.479470 20203 solver.cpp:237] Train net output #0: loss = 5.29134 (* 1 = 5.29134 loss)
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I0412 15:32:59.479481 20203 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
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I0412 15:33:04.318562 20203 solver.cpp:218] Iteration 8460 (2.47991 iter/s, 4.83888s/12 iters), loss = 5.27586
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I0412 15:33:04.318620 20203 solver.cpp:237] Train net output #0: loss = 5.27586 (* 1 = 5.27586 loss)
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I0412 15:33:04.318634 20203 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
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I0412 15:33:06.245391 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
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I0412 15:33:09.206806 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
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I0412 15:33:12.582526 20203 solver.cpp:330] Iteration 8466, Testing net (#0)
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I0412 15:33:12.582551 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:33:13.614732 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:33:17.048444 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:33:17.048496 20203 solver.cpp:397] Test net output #1: loss = 5.28604 (* 1 = 5.28604 loss)
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I0412 15:33:19.035228 20203 solver.cpp:218] Iteration 8472 (0.815439 iter/s, 14.716s/12 iters), loss = 5.27412
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I0412 15:33:19.035275 20203 solver.cpp:237] Train net output #0: loss = 5.27412 (* 1 = 5.27412 loss)
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I0412 15:33:19.035284 20203 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
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I0412 15:33:23.973507 20203 solver.cpp:218] Iteration 8484 (2.43012 iter/s, 4.93802s/12 iters), loss = 5.26998
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I0412 15:33:23.973603 20203 solver.cpp:237] Train net output #0: loss = 5.26998 (* 1 = 5.26998 loss)
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I0412 15:33:23.973613 20203 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
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I0412 15:33:28.763748 20203 solver.cpp:218] Iteration 8496 (2.50525 iter/s, 4.78993s/12 iters), loss = 5.25916
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I0412 15:33:28.763800 20203 solver.cpp:237] Train net output #0: loss = 5.25916 (* 1 = 5.25916 loss)
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I0412 15:33:28.763813 20203 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
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I0412 15:33:28.766341 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:33:33.770190 20203 solver.cpp:218] Iteration 8508 (2.39704 iter/s, 5.00617s/12 iters), loss = 5.27625
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I0412 15:33:33.770234 20203 solver.cpp:237] Train net output #0: loss = 5.27625 (* 1 = 5.27625 loss)
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I0412 15:33:33.770243 20203 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
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I0412 15:33:38.517611 20203 solver.cpp:218] Iteration 8520 (2.52782 iter/s, 4.74717s/12 iters), loss = 5.29513
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I0412 15:33:38.517663 20203 solver.cpp:237] Train net output #0: loss = 5.29513 (* 1 = 5.29513 loss)
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I0412 15:33:38.517676 20203 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
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I0412 15:33:43.649036 20203 solver.cpp:218] Iteration 8532 (2.33866 iter/s, 5.13115s/12 iters), loss = 5.27167
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I0412 15:33:43.649086 20203 solver.cpp:237] Train net output #0: loss = 5.27167 (* 1 = 5.27167 loss)
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I0412 15:33:43.649098 20203 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
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I0412 15:33:48.430992 20203 solver.cpp:218] Iteration 8544 (2.50957 iter/s, 4.7817s/12 iters), loss = 5.27323
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I0412 15:33:48.431046 20203 solver.cpp:237] Train net output #0: loss = 5.27323 (* 1 = 5.27323 loss)
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I0412 15:33:48.431057 20203 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
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I0412 15:33:53.551559 20203 solver.cpp:237] Train net output #0: loss = 5.26213 (* 1 = 5.26213 loss)
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I0412 15:33:53.551573 20203 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
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I0412 15:33:57.949862 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
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I0412 15:34:04.425137 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
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I0412 15:34:08.550441 20203 solver.cpp:330] Iteration 8568, Testing net (#0)
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I0412 15:34:09.676143 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:34:13.256289 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:34:13.256335 20203 solver.cpp:397] Test net output #1: loss = 5.28607 (* 1 = 5.28607 loss)
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I0412 15:34:13.343042 20203 solver.cpp:218] Iteration 8568 (0.606346 iter/s, 19.7907s/12 iters), loss = 5.24684
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I0412 15:34:13.343086 20203 solver.cpp:237] Train net output #0: loss = 5.24684 (* 1 = 5.24684 loss)
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I0412 15:34:13.343096 20203 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
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I0412 15:34:17.618911 20203 solver.cpp:237] Train net output #0: loss = 5.26182 (* 1 = 5.26182 loss)
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I0412 15:34:17.618922 20203 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
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I0412 15:34:22.363056 20203 solver.cpp:237] Train net output #0: loss = 5.24997 (* 1 = 5.24997 loss)
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I0412 15:34:27.245273 20203 solver.cpp:218] Iteration 8604 (2.45801 iter/s, 4.882s/12 iters), loss = 5.27126
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I0412 15:34:27.245327 20203 solver.cpp:237] Train net output #0: loss = 5.27126 (* 1 = 5.27126 loss)
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I0412 15:34:32.360210 20203 solver.cpp:237] Train net output #0: loss = 5.26403 (* 1 = 5.26403 loss)
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I0412 15:34:37.478962 20203 solver.cpp:237] Train net output #0: loss = 5.28819 (* 1 = 5.28819 loss)
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I0412 15:34:37.478976 20203 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
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I0412 15:34:42.141993 20203 solver.cpp:218] Iteration 8640 (2.57355 iter/s, 4.66283s/12 iters), loss = 5.26465
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I0412 15:34:42.142038 20203 solver.cpp:237] Train net output #0: loss = 5.26465 (* 1 = 5.26465 loss)
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I0412 15:34:42.142048 20203 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
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I0412 15:34:47.088464 20203 solver.cpp:218] Iteration 8652 (2.4261 iter/s, 4.94622s/12 iters), loss = 5.26606
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I0412 15:34:47.088500 20203 solver.cpp:237] Train net output #0: loss = 5.26606 (* 1 = 5.26606 loss)
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I0412 15:34:47.088508 20203 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
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I0412 15:34:52.274624 20203 solver.cpp:218] Iteration 8664 (2.31397 iter/s, 5.1859s/12 iters), loss = 5.27519
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I0412 15:34:52.274670 20203 solver.cpp:237] Train net output #0: loss = 5.27519 (* 1 = 5.27519 loss)
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I0412 15:34:52.274682 20203 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
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I0412 15:34:57.221943 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
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I0412 15:34:59.538867 20203 solver.cpp:330] Iteration 8670, Testing net (#0)
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I0412 15:34:59.538894 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:35:00.529345 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:35:04.033752 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:35:04.033931 20203 solver.cpp:397] Test net output #1: loss = 5.28632 (* 1 = 5.28632 loss)
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I0412 15:35:05.815874 20203 solver.cpp:218] Iteration 8676 (0.886221 iter/s, 13.5406s/12 iters), loss = 5.27559
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I0412 15:35:05.815928 20203 solver.cpp:237] Train net output #0: loss = 5.27559 (* 1 = 5.27559 loss)
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I0412 15:35:10.577709 20203 solver.cpp:237] Train net output #0: loss = 5.26459 (* 1 = 5.26459 loss)
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I0412 15:35:10.577720 20203 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
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I0412 15:35:15.705168 20203 solver.cpp:218] Iteration 8700 (2.34044 iter/s, 5.12724s/12 iters), loss = 5.26724
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I0412 15:35:15.705217 20203 solver.cpp:237] Train net output #0: loss = 5.26724 (* 1 = 5.26724 loss)
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I0412 15:35:15.705230 20203 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
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I0412 15:35:20.707196 20203 solver.cpp:218] Iteration 8712 (2.39915 iter/s, 5.00177s/12 iters), loss = 5.28004
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I0412 15:35:20.707247 20203 solver.cpp:237] Train net output #0: loss = 5.28004 (* 1 = 5.28004 loss)
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I0412 15:35:20.707257 20203 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
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I0412 15:35:25.370041 20203 solver.cpp:218] Iteration 8724 (2.57368 iter/s, 4.66259s/12 iters), loss = 5.2823
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I0412 15:35:25.370093 20203 solver.cpp:237] Train net output #0: loss = 5.2823 (* 1 = 5.2823 loss)
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I0412 15:35:25.370105 20203 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
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I0412 15:35:30.057395 20203 solver.cpp:218] Iteration 8736 (2.56022 iter/s, 4.6871s/12 iters), loss = 5.29202
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I0412 15:35:30.057436 20203 solver.cpp:237] Train net output #0: loss = 5.29202 (* 1 = 5.29202 loss)
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I0412 15:35:30.057446 20203 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
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I0412 15:35:34.972498 20203 solver.cpp:218] Iteration 8748 (2.44158 iter/s, 4.91485s/12 iters), loss = 5.26968
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I0412 15:35:34.972568 20203 solver.cpp:237] Train net output #0: loss = 5.26968 (* 1 = 5.26968 loss)
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I0412 15:35:34.972576 20203 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
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I0412 15:35:39.936558 20203 solver.cpp:218] Iteration 8760 (2.41752 iter/s, 4.96377s/12 iters), loss = 5.27703
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I0412 15:35:39.936607 20203 solver.cpp:237] Train net output #0: loss = 5.27703 (* 1 = 5.27703 loss)
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I0412 15:35:39.936617 20203 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
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I0412 15:35:44.471428 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
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I0412 15:35:47.509896 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
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I0412 15:35:49.804253 20203 solver.cpp:330] Iteration 8772, Testing net (#0)
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I0412 15:35:49.804280 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:35:50.805224 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:35:54.324822 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:35:54.324872 20203 solver.cpp:397] Test net output #1: loss = 5.28633 (* 1 = 5.28633 loss)
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I0412 15:35:54.411244 20203 solver.cpp:218] Iteration 8772 (0.829071 iter/s, 14.474s/12 iters), loss = 5.2774
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I0412 15:35:58.591804 20203 solver.cpp:237] Train net output #0: loss = 5.27447 (* 1 = 5.27447 loss)
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I0412 15:35:58.591816 20203 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
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I0412 15:36:03.398241 20203 solver.cpp:218] Iteration 8796 (2.49676 iter/s, 4.80623s/12 iters), loss = 5.25981
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I0412 15:36:03.398288 20203 solver.cpp:237] Train net output #0: loss = 5.25981 (* 1 = 5.25981 loss)
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I0412 15:36:03.398298 20203 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
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I0412 15:36:04.694408 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:36:08.093032 20203 solver.cpp:218] Iteration 8808 (2.55616 iter/s, 4.69454s/12 iters), loss = 5.26536
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I0412 15:36:08.093190 20203 solver.cpp:237] Train net output #0: loss = 5.26536 (* 1 = 5.26536 loss)
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I0412 15:36:08.093204 20203 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
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I0412 15:36:12.913491 20203 solver.cpp:218] Iteration 8820 (2.48958 iter/s, 4.8201s/12 iters), loss = 5.265
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I0412 15:36:12.913539 20203 solver.cpp:237] Train net output #0: loss = 5.265 (* 1 = 5.265 loss)
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I0412 15:36:17.982472 20203 solver.cpp:218] Iteration 8832 (2.36746 iter/s, 5.06871s/12 iters), loss = 5.26538
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I0412 15:36:17.982515 20203 solver.cpp:237] Train net output #0: loss = 5.26538 (* 1 = 5.26538 loss)
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I0412 15:36:22.765110 20203 solver.cpp:218] Iteration 8844 (2.50921 iter/s, 4.78238s/12 iters), loss = 5.29729
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I0412 15:36:22.765156 20203 solver.cpp:237] Train net output #0: loss = 5.29729 (* 1 = 5.29729 loss)
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I0412 15:36:22.765166 20203 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
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I0412 15:36:27.662655 20203 solver.cpp:218] Iteration 8856 (2.45034 iter/s, 4.89728s/12 iters), loss = 5.2579
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I0412 15:36:27.662725 20203 solver.cpp:237] Train net output #0: loss = 5.2579 (* 1 = 5.2579 loss)
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I0412 15:36:27.662742 20203 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
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I0412 15:36:32.581202 20203 solver.cpp:218] Iteration 8868 (2.43988 iter/s, 4.91827s/12 iters), loss = 5.25932
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I0412 15:36:32.581243 20203 solver.cpp:237] Train net output #0: loss = 5.25932 (* 1 = 5.25932 loss)
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I0412 15:36:32.581254 20203 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
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I0412 15:36:34.682504 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
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I0412 15:36:37.649307 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
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I0412 15:36:39.966547 20203 solver.cpp:330] Iteration 8874, Testing net (#0)
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I0412 15:36:40.969674 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:36:44.709307 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:36:44.709352 20203 solver.cpp:397] Test net output #1: loss = 5.28609 (* 1 = 5.28609 loss)
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I0412 15:36:46.646832 20203 solver.cpp:218] Iteration 8880 (0.853182 iter/s, 14.065s/12 iters), loss = 5.27805
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I0412 15:36:46.646888 20203 solver.cpp:237] Train net output #0: loss = 5.27805 (* 1 = 5.27805 loss)
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I0412 15:36:46.646900 20203 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
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I0412 15:36:51.541565 20203 solver.cpp:218] Iteration 8892 (2.45175 iter/s, 4.89446s/12 iters), loss = 5.27673
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I0412 15:36:51.541617 20203 solver.cpp:237] Train net output #0: loss = 5.27673 (* 1 = 5.27673 loss)
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I0412 15:36:51.541630 20203 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
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I0412 15:36:54.863018 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:36:56.264587 20203 solver.cpp:218] Iteration 8904 (2.54089 iter/s, 4.72276s/12 iters), loss = 5.27711
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I0412 15:36:56.264642 20203 solver.cpp:237] Train net output #0: loss = 5.27711 (* 1 = 5.27711 loss)
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I0412 15:36:56.264652 20203 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
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I0412 15:37:00.999433 20203 solver.cpp:218] Iteration 8916 (2.53454 iter/s, 4.73458s/12 iters), loss = 5.26748
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I0412 15:37:00.999491 20203 solver.cpp:237] Train net output #0: loss = 5.26748 (* 1 = 5.26748 loss)
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I0412 15:37:00.999505 20203 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
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I0412 15:37:05.579375 20203 solver.cpp:218] Iteration 8928 (2.62027 iter/s, 4.57968s/12 iters), loss = 5.25884
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I0412 15:37:05.579428 20203 solver.cpp:237] Train net output #0: loss = 5.25884 (* 1 = 5.25884 loss)
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I0412 15:37:05.579439 20203 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
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I0412 15:37:10.144542 20203 solver.cpp:218] Iteration 8940 (2.62874 iter/s, 4.56492s/12 iters), loss = 5.2647
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I0412 15:37:10.144676 20203 solver.cpp:237] Train net output #0: loss = 5.2647 (* 1 = 5.2647 loss)
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I0412 15:37:10.144687 20203 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
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I0412 15:37:14.980597 20203 solver.cpp:218] Iteration 8952 (2.48154 iter/s, 4.83571s/12 iters), loss = 5.25694
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I0412 15:37:14.980639 20203 solver.cpp:237] Train net output #0: loss = 5.25694 (* 1 = 5.25694 loss)
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I0412 15:37:14.980649 20203 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
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I0412 15:37:19.825624 20203 solver.cpp:218] Iteration 8964 (2.4769 iter/s, 4.84477s/12 iters), loss = 5.27773
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I0412 15:37:19.825685 20203 solver.cpp:237] Train net output #0: loss = 5.27773 (* 1 = 5.27773 loss)
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I0412 15:37:19.825700 20203 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
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I0412 15:37:24.107930 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
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I0412 15:37:28.482040 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
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I0412 15:37:31.594421 20203 solver.cpp:330] Iteration 8976, Testing net (#0)
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I0412 15:37:31.594449 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:37:32.545554 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:37:36.053890 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:37:36.053937 20203 solver.cpp:397] Test net output #1: loss = 5.28619 (* 1 = 5.28619 loss)
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I0412 15:37:36.140065 20203 solver.cpp:218] Iteration 8976 (0.735578 iter/s, 16.3137s/12 iters), loss = 5.27676
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I0412 15:37:36.140115 20203 solver.cpp:237] Train net output #0: loss = 5.27676 (* 1 = 5.27676 loss)
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I0412 15:37:36.140125 20203 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
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I0412 15:37:40.579262 20203 solver.cpp:218] Iteration 8988 (2.70334 iter/s, 4.43895s/12 iters), loss = 5.28436
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I0412 15:37:40.579392 20203 solver.cpp:237] Train net output #0: loss = 5.28436 (* 1 = 5.28436 loss)
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I0412 15:37:40.579406 20203 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
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I0412 15:37:43.690866 20203 blocking_queue.cpp:49] Waiting for data
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I0412 15:37:45.393451 20203 solver.cpp:218] Iteration 9000 (2.49281 iter/s, 4.81385s/12 iters), loss = 5.28965
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I0412 15:37:45.393501 20203 solver.cpp:237] Train net output #0: loss = 5.28965 (* 1 = 5.28965 loss)
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I0412 15:37:45.393513 20203 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
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I0412 15:37:46.054184 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:37:50.202512 20203 solver.cpp:218] Iteration 9012 (2.49543 iter/s, 4.80879s/12 iters), loss = 5.28532
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I0412 15:37:50.202569 20203 solver.cpp:237] Train net output #0: loss = 5.28532 (* 1 = 5.28532 loss)
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I0412 15:37:50.202584 20203 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
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I0412 15:37:55.127367 20203 solver.cpp:218] Iteration 9024 (2.43675 iter/s, 4.92459s/12 iters), loss = 5.26774
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I0412 15:37:55.127410 20203 solver.cpp:237] Train net output #0: loss = 5.26774 (* 1 = 5.26774 loss)
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I0412 15:37:55.127419 20203 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
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I0412 15:38:00.024955 20203 solver.cpp:218] Iteration 9036 (2.45031 iter/s, 4.89734s/12 iters), loss = 5.27078
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I0412 15:38:00.025002 20203 solver.cpp:237] Train net output #0: loss = 5.27078 (* 1 = 5.27078 loss)
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I0412 15:38:00.025012 20203 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
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I0412 15:38:04.854529 20203 solver.cpp:218] Iteration 9048 (2.48482 iter/s, 4.82932s/12 iters), loss = 5.27458
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I0412 15:38:04.854573 20203 solver.cpp:237] Train net output #0: loss = 5.27458 (* 1 = 5.27458 loss)
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I0412 15:38:04.854583 20203 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
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I0412 15:38:09.793200 20203 solver.cpp:218] Iteration 9060 (2.42993 iter/s, 4.93841s/12 iters), loss = 5.28836
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I0412 15:38:09.793257 20203 solver.cpp:237] Train net output #0: loss = 5.28836 (* 1 = 5.28836 loss)
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I0412 15:38:09.793270 20203 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
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I0412 15:38:14.546180 20203 solver.cpp:218] Iteration 9072 (2.52487 iter/s, 4.75272s/12 iters), loss = 5.26691
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I0412 15:38:14.546298 20203 solver.cpp:237] Train net output #0: loss = 5.26691 (* 1 = 5.26691 loss)
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I0412 15:38:14.546306 20203 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
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I0412 15:38:16.609436 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
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I0412 15:38:27.333056 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
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I0412 15:38:34.075868 20203 solver.cpp:330] Iteration 9078, Testing net (#0)
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I0412 15:38:34.075898 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:38:35.033079 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:38:38.569547 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:38:38.569595 20203 solver.cpp:397] Test net output #1: loss = 5.28643 (* 1 = 5.28643 loss)
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I0412 15:38:40.148409 20203 solver.cpp:218] Iteration 9084 (0.468731 iter/s, 25.6011s/12 iters), loss = 5.25392
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I0412 15:38:40.148463 20203 solver.cpp:237] Train net output #0: loss = 5.25392 (* 1 = 5.25392 loss)
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I0412 15:38:44.904762 20203 solver.cpp:218] Iteration 9096 (2.52308 iter/s, 4.7561s/12 iters), loss = 5.2639
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I0412 15:38:44.904844 20203 solver.cpp:237] Train net output #0: loss = 5.2639 (* 1 = 5.2639 loss)
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I0412 15:38:44.904855 20203 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
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I0412 15:38:47.712978 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:38:49.754688 20203 solver.cpp:218] Iteration 9108 (2.47441 iter/s, 4.84964s/12 iters), loss = 5.2596
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I0412 15:38:49.754732 20203 solver.cpp:237] Train net output #0: loss = 5.2596 (* 1 = 5.2596 loss)
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I0412 15:38:49.754743 20203 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
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I0412 15:38:54.622622 20203 solver.cpp:218] Iteration 9120 (2.46524 iter/s, 4.86768s/12 iters), loss = 5.25133
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I0412 15:38:54.622670 20203 solver.cpp:237] Train net output #0: loss = 5.25133 (* 1 = 5.25133 loss)
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I0412 15:38:59.382162 20203 solver.cpp:218] Iteration 9132 (2.52139 iter/s, 4.75929s/12 iters), loss = 5.25209
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I0412 15:38:59.382212 20203 solver.cpp:237] Train net output #0: loss = 5.25209 (* 1 = 5.25209 loss)
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I0412 15:38:59.382225 20203 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
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I0412 15:39:04.078800 20203 solver.cpp:218] Iteration 9144 (2.55515 iter/s, 4.69639s/12 iters), loss = 5.25975
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I0412 15:39:04.078837 20203 solver.cpp:237] Train net output #0: loss = 5.25975 (* 1 = 5.25975 loss)
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I0412 15:39:04.078845 20203 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
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I0412 15:39:08.769277 20203 solver.cpp:218] Iteration 9156 (2.55851 iter/s, 4.69023s/12 iters), loss = 5.2868
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I0412 15:39:08.769331 20203 solver.cpp:237] Train net output #0: loss = 5.2868 (* 1 = 5.2868 loss)
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I0412 15:39:08.769345 20203 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
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I0412 15:39:13.884618 20203 solver.cpp:218] Iteration 9168 (2.34601 iter/s, 5.11506s/12 iters), loss = 5.27301
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I0412 15:39:13.884661 20203 solver.cpp:237] Train net output #0: loss = 5.27301 (* 1 = 5.27301 loss)
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I0412 15:39:13.884670 20203 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
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I0412 15:39:18.312557 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
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I0412 15:39:21.322760 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
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I0412 15:39:24.496577 20203 solver.cpp:330] Iteration 9180, Testing net (#0)
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I0412 15:39:24.496605 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:39:25.364827 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:39:28.998241 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:39:28.998283 20203 solver.cpp:397] Test net output #1: loss = 5.28668 (* 1 = 5.28668 loss)
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I0412 15:39:29.084628 20203 solver.cpp:218] Iteration 9180 (0.789508 iter/s, 15.1993s/12 iters), loss = 5.27212
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I0412 15:39:29.086153 20203 solver.cpp:237] Train net output #0: loss = 5.27212 (* 1 = 5.27212 loss)
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I0412 15:39:33.024816 20203 solver.cpp:237] Train net output #0: loss = 5.27496 (* 1 = 5.27496 loss)
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I0412 15:39:33.024824 20203 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
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I0412 15:39:37.779979 20203 solver.cpp:218] Iteration 9204 (2.52368 iter/s, 4.75496s/12 iters), loss = 5.26664
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I0412 15:39:37.780026 20203 solver.cpp:237] Train net output #0: loss = 5.26664 (* 1 = 5.26664 loss)
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I0412 15:39:37.780036 20203 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
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I0412 15:39:37.841830 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:39:42.560645 20203 solver.cpp:218] Iteration 9216 (2.51025 iter/s, 4.7804s/12 iters), loss = 5.27821
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I0412 15:39:42.560696 20203 solver.cpp:237] Train net output #0: loss = 5.27821 (* 1 = 5.27821 loss)
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I0412 15:39:42.560708 20203 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
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I0412 15:39:47.419960 20203 solver.cpp:218] Iteration 9228 (2.46962 iter/s, 4.85906s/12 iters), loss = 5.28463
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I0412 15:39:47.420006 20203 solver.cpp:237] Train net output #0: loss = 5.28463 (* 1 = 5.28463 loss)
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I0412 15:39:47.420017 20203 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
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I0412 15:39:52.107452 20203 solver.cpp:218] Iteration 9240 (2.56014 iter/s, 4.68724s/12 iters), loss = 5.26042
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I0412 15:39:52.107648 20203 solver.cpp:237] Train net output #0: loss = 5.26042 (* 1 = 5.26042 loss)
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I0412 15:39:52.107659 20203 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
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I0412 15:39:56.958048 20203 solver.cpp:218] Iteration 9252 (2.47413 iter/s, 4.85019s/12 iters), loss = 5.27391
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I0412 15:39:56.958104 20203 solver.cpp:237] Train net output #0: loss = 5.27391 (* 1 = 5.27391 loss)
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I0412 15:39:56.958117 20203 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
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I0412 15:40:01.711480 20203 solver.cpp:218] Iteration 9264 (2.52463 iter/s, 4.75317s/12 iters), loss = 5.26001
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I0412 15:40:01.711532 20203 solver.cpp:237] Train net output #0: loss = 5.26001 (* 1 = 5.26001 loss)
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I0412 15:40:01.711544 20203 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
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I0412 15:40:06.389189 20203 solver.cpp:218] Iteration 9276 (2.5655 iter/s, 4.67746s/12 iters), loss = 5.24845
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I0412 15:40:06.389230 20203 solver.cpp:237] Train net output #0: loss = 5.24845 (* 1 = 5.24845 loss)
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I0412 15:40:06.389240 20203 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
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I0412 15:40:08.329900 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
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I0412 15:40:12.255445 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
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I0412 15:40:15.439523 20203 solver.cpp:330] Iteration 9282, Testing net (#0)
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I0412 15:40:15.439549 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:40:16.240686 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:40:19.865128 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:40:19.865171 20203 solver.cpp:397] Test net output #1: loss = 5.28618 (* 1 = 5.28618 loss)
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I0412 15:40:21.581295 20203 solver.cpp:218] Iteration 9288 (0.789919 iter/s, 15.1914s/12 iters), loss = 5.2657
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I0412 15:40:21.581349 20203 solver.cpp:237] Train net output #0: loss = 5.2657 (* 1 = 5.2657 loss)
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I0412 15:40:21.581360 20203 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
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I0412 15:40:26.377923 20203 solver.cpp:218] Iteration 9300 (2.50189 iter/s, 4.79637s/12 iters), loss = 5.25138
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I0412 15:40:26.378087 20203 solver.cpp:237] Train net output #0: loss = 5.25138 (* 1 = 5.25138 loss)
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I0412 15:40:26.378103 20203 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
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I0412 15:40:28.499367 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:40:31.078605 20203 solver.cpp:218] Iteration 9312 (2.55302 iter/s, 4.70032s/12 iters), loss = 5.27112
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I0412 15:40:31.078657 20203 solver.cpp:237] Train net output #0: loss = 5.27112 (* 1 = 5.27112 loss)
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I0412 15:40:31.078670 20203 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
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I0412 15:40:35.930605 20203 solver.cpp:218] Iteration 9324 (2.47334 iter/s, 4.85174s/12 iters), loss = 5.28037
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I0412 15:40:35.930647 20203 solver.cpp:237] Train net output #0: loss = 5.28037 (* 1 = 5.28037 loss)
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I0412 15:40:35.930655 20203 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
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I0412 15:40:40.742200 20203 solver.cpp:218] Iteration 9336 (2.49411 iter/s, 4.81134s/12 iters), loss = 5.28342
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I0412 15:40:40.742254 20203 solver.cpp:237] Train net output #0: loss = 5.28342 (* 1 = 5.28342 loss)
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I0412 15:40:40.742267 20203 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
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I0412 15:40:45.426623 20203 solver.cpp:218] Iteration 9348 (2.56182 iter/s, 4.68416s/12 iters), loss = 5.26963
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I0412 15:40:45.426678 20203 solver.cpp:237] Train net output #0: loss = 5.26963 (* 1 = 5.26963 loss)
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I0412 15:40:45.426689 20203 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
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I0412 15:40:50.269573 20203 solver.cpp:218] Iteration 9360 (2.47796 iter/s, 4.84269s/12 iters), loss = 5.27046
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I0412 15:40:50.269615 20203 solver.cpp:237] Train net output #0: loss = 5.27046 (* 1 = 5.27046 loss)
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I0412 15:40:50.269625 20203 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
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I0412 15:40:55.259543 20203 solver.cpp:218] Iteration 9372 (2.40495 iter/s, 4.98971s/12 iters), loss = 5.27356
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I0412 15:40:55.259596 20203 solver.cpp:237] Train net output #0: loss = 5.27356 (* 1 = 5.27356 loss)
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I0412 15:40:55.259609 20203 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
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I0412 15:40:59.436190 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
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I0412 15:41:03.260964 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
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I0412 15:41:08.495152 20203 solver.cpp:330] Iteration 9384, Testing net (#0)
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I0412 15:41:08.495178 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:41:09.271287 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:41:12.935277 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:41:12.935328 20203 solver.cpp:397] Test net output #1: loss = 5.28619 (* 1 = 5.28619 loss)
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I0412 15:41:13.021574 20203 solver.cpp:218] Iteration 9384 (0.675628 iter/s, 17.7612s/12 iters), loss = 5.2775
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I0412 15:41:13.021631 20203 solver.cpp:237] Train net output #0: loss = 5.2775 (* 1 = 5.2775 loss)
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I0412 15:41:13.021643 20203 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
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I0412 15:41:17.201077 20203 solver.cpp:218] Iteration 9396 (2.87132 iter/s, 4.17926s/12 iters), loss = 5.26747
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I0412 15:41:17.201124 20203 solver.cpp:237] Train net output #0: loss = 5.26747 (* 1 = 5.26747 loss)
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I0412 15:41:17.201134 20203 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
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I0412 15:41:21.432715 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:41:22.085342 20203 solver.cpp:218] Iteration 9408 (2.457 iter/s, 4.88401s/12 iters), loss = 5.27144
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I0412 15:41:22.085388 20203 solver.cpp:237] Train net output #0: loss = 5.27144 (* 1 = 5.27144 loss)
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I0412 15:41:22.085397 20203 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
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I0412 15:41:26.962070 20203 solver.cpp:218] Iteration 9420 (2.46079 iter/s, 4.87648s/12 iters), loss = 5.27622
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I0412 15:41:26.962118 20203 solver.cpp:237] Train net output #0: loss = 5.27622 (* 1 = 5.27622 loss)
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I0412 15:41:26.962128 20203 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
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I0412 15:41:31.764657 20203 solver.cpp:218] Iteration 9432 (2.49879 iter/s, 4.80233s/12 iters), loss = 5.28189
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I0412 15:41:31.764786 20203 solver.cpp:237] Train net output #0: loss = 5.28189 (* 1 = 5.28189 loss)
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I0412 15:41:31.764799 20203 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
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I0412 15:41:36.444387 20203 solver.cpp:218] Iteration 9444 (2.56443 iter/s, 4.6794s/12 iters), loss = 5.2884
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I0412 15:41:36.444444 20203 solver.cpp:237] Train net output #0: loss = 5.2884 (* 1 = 5.2884 loss)
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I0412 15:41:36.444458 20203 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
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I0412 15:41:41.095502 20203 solver.cpp:218] Iteration 9456 (2.58017 iter/s, 4.65086s/12 iters), loss = 5.26404
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I0412 15:41:41.095549 20203 solver.cpp:237] Train net output #0: loss = 5.26404 (* 1 = 5.26404 loss)
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I0412 15:41:41.095561 20203 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
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I0412 15:41:45.975881 20203 solver.cpp:218] Iteration 9468 (2.45895 iter/s, 4.88012s/12 iters), loss = 5.27535
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I0412 15:41:45.975932 20203 solver.cpp:237] Train net output #0: loss = 5.27535 (* 1 = 5.27535 loss)
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I0412 15:41:45.975945 20203 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
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I0412 15:41:50.915442 20203 solver.cpp:218] Iteration 9480 (2.4295 iter/s, 4.9393s/12 iters), loss = 5.27597
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I0412 15:41:50.915498 20203 solver.cpp:237] Train net output #0: loss = 5.27597 (* 1 = 5.27597 loss)
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I0412 15:41:50.915510 20203 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
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I0412 15:41:52.786736 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
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I0412 15:41:57.502929 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
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I0412 15:42:01.163645 20203 solver.cpp:330] Iteration 9486, Testing net (#0)
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I0412 15:42:01.163671 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:42:01.878803 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:42:05.595094 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:42:05.595144 20203 solver.cpp:397] Test net output #1: loss = 5.28572 (* 1 = 5.28572 loss)
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I0412 15:42:07.465207 20203 solver.cpp:218] Iteration 9492 (0.725118 iter/s, 16.549s/12 iters), loss = 5.26883
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I0412 15:42:07.465252 20203 solver.cpp:237] Train net output #0: loss = 5.26883 (* 1 = 5.26883 loss)
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I0412 15:42:07.465261 20203 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
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I0412 15:42:12.328616 20203 solver.cpp:218] Iteration 9504 (2.46754 iter/s, 4.86315s/12 iters), loss = 5.262
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I0412 15:42:12.328665 20203 solver.cpp:237] Train net output #0: loss = 5.262 (* 1 = 5.262 loss)
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I0412 15:42:12.328673 20203 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
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I0412 15:42:13.752477 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:42:17.303251 20203 solver.cpp:218] Iteration 9516 (2.41237 iter/s, 4.97437s/12 iters), loss = 5.26309
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I0412 15:42:17.303303 20203 solver.cpp:237] Train net output #0: loss = 5.26309 (* 1 = 5.26309 loss)
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I0412 15:42:17.303315 20203 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
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I0412 15:42:22.278666 20203 solver.cpp:218] Iteration 9528 (2.41199 iter/s, 4.97514s/12 iters), loss = 5.26606
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I0412 15:42:22.278734 20203 solver.cpp:237] Train net output #0: loss = 5.26606 (* 1 = 5.26606 loss)
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I0412 15:42:22.278751 20203 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
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I0412 15:42:27.099793 20203 solver.cpp:218] Iteration 9540 (2.48918 iter/s, 4.82086s/12 iters), loss = 5.24732
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I0412 15:42:27.099836 20203 solver.cpp:237] Train net output #0: loss = 5.24732 (* 1 = 5.24732 loss)
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I0412 15:42:27.099845 20203 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
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I0412 15:42:32.020284 20203 solver.cpp:218] Iteration 9552 (2.43891 iter/s, 4.92023s/12 iters), loss = 5.29814
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I0412 15:42:32.020416 20203 solver.cpp:237] Train net output #0: loss = 5.29814 (* 1 = 5.29814 loss)
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I0412 15:42:32.020424 20203 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
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I0412 15:42:36.844597 20203 solver.cpp:218] Iteration 9564 (2.48758 iter/s, 4.82397s/12 iters), loss = 5.26019
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I0412 15:42:36.844663 20203 solver.cpp:237] Train net output #0: loss = 5.26019 (* 1 = 5.26019 loss)
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I0412 15:42:36.844676 20203 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
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I0412 15:42:41.707489 20203 solver.cpp:218] Iteration 9576 (2.4678 iter/s, 4.86262s/12 iters), loss = 5.26352
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I0412 15:42:41.707535 20203 solver.cpp:237] Train net output #0: loss = 5.26352 (* 1 = 5.26352 loss)
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I0412 15:42:41.707545 20203 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
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I0412 15:42:46.282269 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
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I0412 15:42:53.667389 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
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I0412 15:42:58.544469 20203 solver.cpp:330] Iteration 9588, Testing net (#0)
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I0412 15:42:58.544494 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:42:59.192852 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:43:02.940796 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:43:02.940955 20203 solver.cpp:397] Test net output #1: loss = 5.28592 (* 1 = 5.28592 loss)
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I0412 15:43:03.027034 20203 solver.cpp:218] Iteration 9588 (0.562888 iter/s, 21.3186s/12 iters), loss = 5.27456
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I0412 15:43:03.027093 20203 solver.cpp:237] Train net output #0: loss = 5.27456 (* 1 = 5.27456 loss)
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I0412 15:43:03.027107 20203 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
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I0412 15:43:07.110119 20203 solver.cpp:218] Iteration 9600 (2.93913 iter/s, 4.08285s/12 iters), loss = 5.27407
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I0412 15:43:07.110173 20203 solver.cpp:237] Train net output #0: loss = 5.27407 (* 1 = 5.27407 loss)
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I0412 15:43:07.110188 20203 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
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I0412 15:43:10.591666 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:43:11.897214 20203 solver.cpp:218] Iteration 9612 (2.50688 iter/s, 4.78683s/12 iters), loss = 5.27317
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I0412 15:43:11.897262 20203 solver.cpp:237] Train net output #0: loss = 5.27317 (* 1 = 5.27317 loss)
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I0412 15:43:11.897274 20203 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
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I0412 15:43:16.754258 20203 solver.cpp:218] Iteration 9624 (2.47077 iter/s, 4.85679s/12 iters), loss = 5.26776
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I0412 15:43:16.754312 20203 solver.cpp:237] Train net output #0: loss = 5.26776 (* 1 = 5.26776 loss)
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I0412 15:43:16.754324 20203 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
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I0412 15:43:21.473009 20203 solver.cpp:218] Iteration 9636 (2.54318 iter/s, 4.7185s/12 iters), loss = 5.25605
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I0412 15:43:21.473050 20203 solver.cpp:237] Train net output #0: loss = 5.25605 (* 1 = 5.25605 loss)
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I0412 15:43:21.473059 20203 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
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I0412 15:43:26.252184 20203 solver.cpp:218] Iteration 9648 (2.51103 iter/s, 4.77892s/12 iters), loss = 5.26528
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I0412 15:43:26.252241 20203 solver.cpp:237] Train net output #0: loss = 5.26528 (* 1 = 5.26528 loss)
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I0412 15:43:26.252256 20203 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
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I0412 15:43:31.117851 20203 solver.cpp:218] Iteration 9660 (2.46639 iter/s, 4.8654s/12 iters), loss = 5.25527
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I0412 15:43:31.117897 20203 solver.cpp:237] Train net output #0: loss = 5.25527 (* 1 = 5.25527 loss)
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I0412 15:43:31.117908 20203 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
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I0412 15:43:35.917219 20203 solver.cpp:218] Iteration 9672 (2.50046 iter/s, 4.79912s/12 iters), loss = 5.27395
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I0412 15:43:35.917366 20203 solver.cpp:237] Train net output #0: loss = 5.27395 (* 1 = 5.27395 loss)
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I0412 15:43:35.917378 20203 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
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I0412 15:43:40.773049 20203 solver.cpp:218] Iteration 9684 (2.47144 iter/s, 4.85547s/12 iters), loss = 5.29045
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I0412 15:43:40.773099 20203 solver.cpp:237] Train net output #0: loss = 5.29045 (* 1 = 5.29045 loss)
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I0412 15:43:40.773110 20203 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
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I0412 15:43:42.720353 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
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I0412 15:43:45.690094 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
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I0412 15:43:49.930917 20203 solver.cpp:330] Iteration 9690, Testing net (#0)
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I0412 15:43:49.930943 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:43:50.660697 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:43:53.498392 20203 blocking_queue.cpp:49] Waiting for data
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I0412 15:43:54.548525 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:43:54.548563 20203 solver.cpp:397] Test net output #1: loss = 5.28612 (* 1 = 5.28612 loss)
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I0412 15:43:56.379498 20203 solver.cpp:218] Iteration 9696 (0.768947 iter/s, 15.6058s/12 iters), loss = 5.28464
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I0412 15:43:56.379560 20203 solver.cpp:237] Train net output #0: loss = 5.28464 (* 1 = 5.28464 loss)
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I0412 15:43:56.379575 20203 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
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I0412 15:44:01.190232 20203 solver.cpp:218] Iteration 9708 (2.49456 iter/s, 4.81047s/12 iters), loss = 5.28731
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I0412 15:44:01.190292 20203 solver.cpp:237] Train net output #0: loss = 5.28731 (* 1 = 5.28731 loss)
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I0412 15:44:01.190305 20203 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
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I0412 15:44:01.881536 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:44:05.950599 20203 solver.cpp:218] Iteration 9720 (2.52095 iter/s, 4.76011s/12 iters), loss = 5.28671
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I0412 15:44:05.950667 20203 solver.cpp:237] Train net output #0: loss = 5.28671 (* 1 = 5.28671 loss)
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I0412 15:44:05.950676 20203 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
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I0412 15:44:10.540201 20203 solver.cpp:218] Iteration 9732 (2.61476 iter/s, 4.58933s/12 iters), loss = 5.26464
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I0412 15:44:10.540254 20203 solver.cpp:237] Train net output #0: loss = 5.26464 (* 1 = 5.26464 loss)
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I0412 15:44:10.540267 20203 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
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I0412 15:44:15.464294 20203 solver.cpp:218] Iteration 9744 (2.43713 iter/s, 4.92383s/12 iters), loss = 5.26843
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I0412 15:44:15.464351 20203 solver.cpp:237] Train net output #0: loss = 5.26843 (* 1 = 5.26843 loss)
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I0412 15:44:15.464365 20203 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
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I0412 15:44:20.221170 20203 solver.cpp:218] Iteration 9756 (2.5228 iter/s, 4.75661s/12 iters), loss = 5.27298
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I0412 15:44:20.221228 20203 solver.cpp:237] Train net output #0: loss = 5.27298 (* 1 = 5.27298 loss)
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I0412 15:44:20.221242 20203 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
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I0412 15:44:25.249290 20203 solver.cpp:218] Iteration 9768 (2.38671 iter/s, 5.02785s/12 iters), loss = 5.28843
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I0412 15:44:25.249341 20203 solver.cpp:237] Train net output #0: loss = 5.28843 (* 1 = 5.28843 loss)
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I0412 15:44:25.249354 20203 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
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I0412 15:44:30.082895 20203 solver.cpp:218] Iteration 9780 (2.48275 iter/s, 4.83334s/12 iters), loss = 5.26947
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I0412 15:44:30.082953 20203 solver.cpp:237] Train net output #0: loss = 5.26947 (* 1 = 5.26947 loss)
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I0412 15:44:30.082970 20203 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
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I0412 15:44:34.764575 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
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I0412 15:44:37.860040 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
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I0412 15:44:40.212739 20203 solver.cpp:330] Iteration 9792, Testing net (#0)
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I0412 15:44:40.212765 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:44:40.782757 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:44:44.630259 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:44:44.630312 20203 solver.cpp:397] Test net output #1: loss = 5.28592 (* 1 = 5.28592 loss)
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I0412 15:44:44.716943 20203 solver.cpp:218] Iteration 9792 (0.820043 iter/s, 14.6334s/12 iters), loss = 5.25462
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I0412 15:44:48.630312 20203 solver.cpp:237] Train net output #0: loss = 5.27171 (* 1 = 5.27171 loss)
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I0412 15:44:48.630322 20203 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
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I0412 15:44:51.571174 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:44:53.551941 20203 solver.cpp:218] Iteration 9816 (2.43833 iter/s, 4.92141s/12 iters), loss = 5.26084
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I0412 15:44:53.552001 20203 solver.cpp:237] Train net output #0: loss = 5.26084 (* 1 = 5.26084 loss)
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I0412 15:44:53.552014 20203 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
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I0412 15:44:58.042928 20203 solver.cpp:218] Iteration 9828 (2.67217 iter/s, 4.49074s/12 iters), loss = 5.25629
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I0412 15:44:58.042970 20203 solver.cpp:237] Train net output #0: loss = 5.25629 (* 1 = 5.25629 loss)
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I0412 15:44:58.042979 20203 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
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I0412 15:45:02.845257 20203 solver.cpp:218] Iteration 9840 (2.49892 iter/s, 4.80208s/12 iters), loss = 5.24957
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I0412 15:45:02.845302 20203 solver.cpp:237] Train net output #0: loss = 5.24957 (* 1 = 5.24957 loss)
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I0412 15:45:02.845312 20203 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
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I0412 15:45:07.517685 20203 solver.cpp:218] Iteration 9852 (2.56839 iter/s, 4.67218s/12 iters), loss = 5.2702
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I0412 15:45:07.517727 20203 solver.cpp:237] Train net output #0: loss = 5.2702 (* 1 = 5.2702 loss)
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I0412 15:45:12.339273 20203 solver.cpp:218] Iteration 9864 (2.48894 iter/s, 4.82134s/12 iters), loss = 5.28898
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I0412 15:45:12.339381 20203 solver.cpp:237] Train net output #0: loss = 5.28898 (* 1 = 5.28898 loss)
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I0412 15:45:12.339393 20203 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
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I0412 15:45:17.120802 20203 solver.cpp:218] Iteration 9876 (2.50982 iter/s, 4.78122s/12 iters), loss = 5.27031
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I0412 15:45:17.120852 20203 solver.cpp:237] Train net output #0: loss = 5.27031 (* 1 = 5.27031 loss)
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I0412 15:45:17.120863 20203 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
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I0412 15:45:21.711014 20203 solver.cpp:218] Iteration 9888 (2.6144 iter/s, 4.58996s/12 iters), loss = 5.2793
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I0412 15:45:21.711064 20203 solver.cpp:237] Train net output #0: loss = 5.2793 (* 1 = 5.2793 loss)
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I0412 15:45:21.711078 20203 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
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I0412 15:45:23.549007 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
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I0412 15:45:28.412320 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
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I0412 15:45:31.135496 20203 solver.cpp:330] Iteration 9894, Testing net (#0)
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I0412 15:45:31.135524 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:45:31.685168 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:45:35.564921 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:45:35.564967 20203 solver.cpp:397] Test net output #1: loss = 5.28619 (* 1 = 5.28619 loss)
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I0412 15:45:37.482803 20203 solver.cpp:218] Iteration 9900 (0.760886 iter/s, 15.7711s/12 iters), loss = 5.28127
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I0412 15:45:37.482851 20203 solver.cpp:237] Train net output #0: loss = 5.28127 (* 1 = 5.28127 loss)
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I0412 15:45:37.482862 20203 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
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I0412 15:45:42.263310 20203 solver.cpp:218] Iteration 9912 (2.51033 iter/s, 4.78025s/12 iters), loss = 5.25773
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I0412 15:45:42.263361 20203 solver.cpp:237] Train net output #0: loss = 5.25773 (* 1 = 5.25773 loss)
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I0412 15:45:42.263375 20203 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
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I0412 15:45:42.322026 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:45:46.989665 20203 solver.cpp:218] Iteration 9924 (2.53909 iter/s, 4.7261s/12 iters), loss = 5.27642
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I0412 15:45:46.989820 20203 solver.cpp:237] Train net output #0: loss = 5.27642 (* 1 = 5.27642 loss)
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I0412 15:45:46.989835 20203 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
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I0412 15:45:52.200415 20203 solver.cpp:218] Iteration 9936 (2.3031 iter/s, 5.21038s/12 iters), loss = 5.28919
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I0412 15:45:52.200471 20203 solver.cpp:237] Train net output #0: loss = 5.28919 (* 1 = 5.28919 loss)
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I0412 15:45:52.200484 20203 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
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I0412 15:45:57.135111 20203 solver.cpp:218] Iteration 9948 (2.43189 iter/s, 4.93443s/12 iters), loss = 5.2615
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I0412 15:45:57.135159 20203 solver.cpp:237] Train net output #0: loss = 5.2615 (* 1 = 5.2615 loss)
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I0412 15:45:57.135172 20203 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
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I0412 15:46:02.045898 20203 solver.cpp:218] Iteration 9960 (2.44373 iter/s, 4.91052s/12 iters), loss = 5.27054
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I0412 15:46:02.045969 20203 solver.cpp:237] Train net output #0: loss = 5.27054 (* 1 = 5.27054 loss)
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I0412 15:46:02.045985 20203 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
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I0412 15:46:06.795205 20203 solver.cpp:218] Iteration 9972 (2.52682 iter/s, 4.74904s/12 iters), loss = 5.26548
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I0412 15:46:06.795251 20203 solver.cpp:237] Train net output #0: loss = 5.26548 (* 1 = 5.26548 loss)
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I0412 15:46:06.795264 20203 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
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I0412 15:46:11.692808 20203 solver.cpp:218] Iteration 9984 (2.45031 iter/s, 4.89735s/12 iters), loss = 5.24676
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I0412 15:46:11.692857 20203 solver.cpp:237] Train net output #0: loss = 5.24676 (* 1 = 5.24676 loss)
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I0412 15:46:11.692867 20203 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
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I0412 15:46:15.958686 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
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I0412 15:46:20.687606 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
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I0412 15:46:23.423405 20203 solver.cpp:330] Iteration 9996, Testing net (#0)
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I0412 15:46:23.423431 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:46:23.882230 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:46:27.774756 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:46:27.774807 20203 solver.cpp:397] Test net output #1: loss = 5.28659 (* 1 = 5.28659 loss)
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I0412 15:46:27.859943 20203 solver.cpp:218] Iteration 9996 (0.742279 iter/s, 16.1664s/12 iters), loss = 5.26978
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I0412 15:46:27.859994 20203 solver.cpp:237] Train net output #0: loss = 5.26978 (* 1 = 5.26978 loss)
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I0412 15:46:27.860005 20203 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
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I0412 15:46:31.877318 20203 solver.cpp:218] Iteration 10008 (2.98719 iter/s, 4.01715s/12 iters), loss = 5.24806
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I0412 15:46:31.877368 20203 solver.cpp:237] Train net output #0: loss = 5.24806 (* 1 = 5.24806 loss)
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I0412 15:46:31.877379 20203 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
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I0412 15:46:33.972394 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:46:36.597298 20203 solver.cpp:218] Iteration 10020 (2.54252 iter/s, 4.71972s/12 iters), loss = 5.26702
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I0412 15:46:36.597355 20203 solver.cpp:237] Train net output #0: loss = 5.26702 (* 1 = 5.26702 loss)
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I0412 15:46:36.597369 20203 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
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I0412 15:46:41.411087 20203 solver.cpp:218] Iteration 10032 (2.49298 iter/s, 4.81353s/12 iters), loss = 5.27722
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I0412 15:46:41.411129 20203 solver.cpp:237] Train net output #0: loss = 5.27722 (* 1 = 5.27722 loss)
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I0412 15:46:41.411137 20203 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
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I0412 15:46:46.493856 20203 solver.cpp:218] Iteration 10044 (2.36104 iter/s, 5.0825s/12 iters), loss = 5.2859
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I0412 15:46:46.493908 20203 solver.cpp:237] Train net output #0: loss = 5.2859 (* 1 = 5.2859 loss)
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I0412 15:46:46.493921 20203 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
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I0412 15:46:51.321216 20203 solver.cpp:218] Iteration 10056 (2.48596 iter/s, 4.8271s/12 iters), loss = 5.27758
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I0412 15:46:51.321374 20203 solver.cpp:237] Train net output #0: loss = 5.27758 (* 1 = 5.27758 loss)
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I0412 15:46:51.321385 20203 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
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I0412 15:46:56.216876 20203 solver.cpp:218] Iteration 10068 (2.45134 iter/s, 4.89529s/12 iters), loss = 5.27808
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I0412 15:46:56.216934 20203 solver.cpp:237] Train net output #0: loss = 5.27808 (* 1 = 5.27808 loss)
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I0412 15:46:56.216946 20203 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
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I0412 15:47:01.092988 20203 solver.cpp:218] Iteration 10080 (2.46111 iter/s, 4.87584s/12 iters), loss = 5.2633
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I0412 15:47:01.093042 20203 solver.cpp:237] Train net output #0: loss = 5.2633 (* 1 = 5.2633 loss)
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I0412 15:47:01.093053 20203 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
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I0412 15:47:06.103775 20203 solver.cpp:218] Iteration 10092 (2.39496 iter/s, 5.01052s/12 iters), loss = 5.27904
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I0412 15:47:06.103829 20203 solver.cpp:237] Train net output #0: loss = 5.27904 (* 1 = 5.27904 loss)
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I0412 15:47:06.103842 20203 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
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I0412 15:47:08.086088 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
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I0412 15:47:19.575757 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
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I0412 15:47:31.936216 20203 solver.cpp:330] Iteration 10098, Testing net (#0)
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I0412 15:47:31.936273 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:47:32.412896 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:47:36.365536 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:47:36.365576 20203 solver.cpp:397] Test net output #1: loss = 5.2863 (* 1 = 5.2863 loss)
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I0412 15:47:38.130440 20203 solver.cpp:218] Iteration 10104 (0.374704 iter/s, 32.0253s/12 iters), loss = 5.26893
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I0412 15:47:38.130502 20203 solver.cpp:237] Train net output #0: loss = 5.26893 (* 1 = 5.26893 loss)
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I0412 15:47:38.130515 20203 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
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I0412 15:47:42.194747 20207 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:47:42.767423 20203 solver.cpp:218] Iteration 10116 (2.58804 iter/s, 4.63672s/12 iters), loss = 5.26199
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I0412 15:47:42.767486 20203 solver.cpp:237] Train net output #0: loss = 5.26199 (* 1 = 5.26199 loss)
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I0412 15:47:42.767500 20203 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
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I0412 15:47:47.594655 20203 solver.cpp:218] Iteration 10128 (2.48604 iter/s, 4.82696s/12 iters), loss = 5.27257
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I0412 15:47:47.594708 20203 solver.cpp:237] Train net output #0: loss = 5.27257 (* 1 = 5.27257 loss)
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I0412 15:47:47.594722 20203 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
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I0412 15:47:52.280985 20203 solver.cpp:218] Iteration 10140 (2.56078 iter/s, 4.68608s/12 iters), loss = 5.28286
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I0412 15:47:52.281026 20203 solver.cpp:237] Train net output #0: loss = 5.28286 (* 1 = 5.28286 loss)
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I0412 15:47:52.281034 20203 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
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I0412 15:47:57.104593 20203 solver.cpp:218] Iteration 10152 (2.48789 iter/s, 4.82336s/12 iters), loss = 5.27932
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I0412 15:47:57.104636 20203 solver.cpp:237] Train net output #0: loss = 5.27932 (* 1 = 5.27932 loss)
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I0412 15:47:57.104647 20203 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
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I0412 15:48:01.756955 20203 solver.cpp:218] Iteration 10164 (2.57947 iter/s, 4.65212s/12 iters), loss = 5.26393
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I0412 15:48:01.757009 20203 solver.cpp:237] Train net output #0: loss = 5.26393 (* 1 = 5.26393 loss)
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I0412 15:48:01.757022 20203 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
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I0412 15:48:06.566059 20203 solver.cpp:218] Iteration 10176 (2.4954 iter/s, 4.80885s/12 iters), loss = 5.27173
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I0412 15:48:06.566156 20203 solver.cpp:237] Train net output #0: loss = 5.27173 (* 1 = 5.27173 loss)
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I0412 15:48:06.566164 20203 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
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I0412 15:48:11.118269 20203 solver.cpp:218] Iteration 10188 (2.63625 iter/s, 4.55191s/12 iters), loss = 5.27859
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I0412 15:48:11.118331 20203 solver.cpp:237] Train net output #0: loss = 5.27859 (* 1 = 5.27859 loss)
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I0412 15:48:11.118347 20203 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
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I0412 15:48:15.393011 20203 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
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I0412 15:48:18.434180 20203 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
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I0412 15:48:24.292459 20203 solver.cpp:310] Iteration 10200, loss = 5.26314
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I0412 15:48:24.292495 20203 solver.cpp:330] Iteration 10200, Testing net (#0)
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I0412 15:48:24.292505 20203 net.cpp:676] Ignoring source layer train-data
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I0412 15:48:24.710184 20208 data_layer.cpp:73] Restarting data prefetching from start.
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I0412 15:48:28.723512 20203 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0412 15:48:28.723559 20203 solver.cpp:397] Test net output #1: loss = 5.28573 (* 1 = 5.28573 loss)
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I0412 15:48:28.723572 20203 solver.cpp:315] Optimization Done.
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I0412 15:48:28.723579 20203 caffe.cpp:259] Optimization Done.
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