4741 lines
365 KiB
Plaintext
4741 lines
365 KiB
Plaintext
I0428 13:17:00.901860 11373 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210428-115411-68aa/solver.prototxt
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I0428 13:17:00.902019 11373 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
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W0428 13:17:00.902025 11373 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
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I0428 13:17:00.902086 11373 caffe.cpp:218] Using GPUs 3
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I0428 13:17:00.990882 11373 caffe.cpp:223] GPU 3: GeForce GTX 1080 Ti
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I0428 13:17:01.421993 11373 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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I0428 13:17:01.626667 11373 solver.cpp:87] Creating training net from net file: train_val.prototxt
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I0428 13:17:01.628357 11373 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
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I0428 13:17:01.628377 11373 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
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I0428 13:17:01.628571 11373 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-3/digits/jobs/20210421-230320-902c/mean.binaryproto"
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}
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data_param {
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source: "/mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210421-230320-902c/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: "conv1.5"
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type: "Convolution"
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bottom: "pool1"
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top: "conv1.5"
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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: 176
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kernel_size: 5
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stride: 1
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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: "relu1.5"
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type: "ReLU"
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bottom: "conv1.5"
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top: "conv1.5"
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}
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layer {
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name: "norm1.5"
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type: "LRN"
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bottom: "conv1.5"
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top: "norm1.5"
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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: "pool1.5"
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type: "Pooling"
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bottom: "norm1.5"
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top: "pool1.5"
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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: "pool1.5"
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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 {
|
|
num_output: 384
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|
pad: 1
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|
kernel_size: 3
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|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
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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 {
|
|
num_output: 384
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|
pad: 1
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|
kernel_size: 3
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|
group: 2
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|
weight_filler {
|
|
type: "gaussian"
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|
std: 0.01
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|
}
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|
bias_filler {
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|
type: "constant"
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|
value: 0.1
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}
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}
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}
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layer {
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|
name: "relu4"
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|
type: "ReLU"
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bottom: "conv4"
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top: "conv4"
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|
}
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|
layer {
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|
name: "conv5"
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|
type: "Convolution"
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|
bottom: "conv4"
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|
top: "conv5"
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|
param {
|
|
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"
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|
std: 0.01
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|
}
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|
bias_filler {
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|
type: "constant"
|
|
value: 0.1
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|
}
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|
}
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|
}
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|
layer {
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|
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"
|
|
bottom: "conv5"
|
|
top: "pool5"
|
|
pooling_param {
|
|
pool: MAX
|
|
kernel_size: 3
|
|
stride: 2
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|
}
|
|
}
|
|
layer {
|
|
name: "fc6"
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|
type: "InnerProduct"
|
|
bottom: "pool5"
|
|
top: "fc6"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
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|
}
|
|
param {
|
|
lr_mult: 2
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|
decay_mult: 0
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|
}
|
|
inner_product_param {
|
|
num_output: 4096
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|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.005
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu6"
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|
type: "ReLU"
|
|
bottom: "fc6"
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|
top: "fc6"
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|
}
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|
layer {
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|
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
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|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.005
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu7"
|
|
type: "ReLU"
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|
bottom: "fc7"
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|
top: "fc7"
|
|
}
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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 {
|
|
dropout_ratio: 0.5
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|
}
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|
}
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|
layer {
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|
name: "fc8"
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|
type: "InnerProduct"
|
|
bottom: "fc7"
|
|
top: "fc8"
|
|
param {
|
|
lr_mult: 1
|
|
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
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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"
|
|
top: "loss"
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|
}
|
|
I0428 13:17:01.628684 11373 layer_factory.hpp:77] Creating layer train-data
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I0428 13:17:01.679536 11373 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210421-230320-902c/train_db
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I0428 13:17:01.702476 11373 net.cpp:84] Creating Layer train-data
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|
I0428 13:17:01.702504 11373 net.cpp:380] train-data -> data
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|
I0428 13:17:01.702530 11373 net.cpp:380] train-data -> label
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I0428 13:17:01.702546 11373 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210421-230320-902c/mean.binaryproto
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I0428 13:17:01.779839 11373 data_layer.cpp:45] output data size: 128,3,227,227
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I0428 13:17:01.985886 11373 net.cpp:122] Setting up train-data
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|
I0428 13:17:01.985908 11373 net.cpp:129] Top shape: 128 3 227 227 (19787136)
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I0428 13:17:01.985914 11373 net.cpp:129] Top shape: 128 (128)
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|
I0428 13:17:01.985918 11373 net.cpp:137] Memory required for data: 79149056
|
|
I0428 13:17:01.985929 11373 layer_factory.hpp:77] Creating layer conv1
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|
I0428 13:17:01.985950 11373 net.cpp:84] Creating Layer conv1
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|
I0428 13:17:01.985956 11373 net.cpp:406] conv1 <- data
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|
I0428 13:17:01.985970 11373 net.cpp:380] conv1 -> conv1
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I0428 13:17:02.964372 11373 net.cpp:122] Setting up conv1
|
|
I0428 13:17:02.964416 11373 net.cpp:129] Top shape: 128 96 55 55 (37171200)
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|
I0428 13:17:02.964421 11373 net.cpp:137] Memory required for data: 227833856
|
|
I0428 13:17:02.964442 11373 layer_factory.hpp:77] Creating layer relu1
|
|
I0428 13:17:02.964452 11373 net.cpp:84] Creating Layer relu1
|
|
I0428 13:17:02.964457 11373 net.cpp:406] relu1 <- conv1
|
|
I0428 13:17:02.964464 11373 net.cpp:367] relu1 -> conv1 (in-place)
|
|
I0428 13:17:02.964776 11373 net.cpp:122] Setting up relu1
|
|
I0428 13:17:02.964785 11373 net.cpp:129] Top shape: 128 96 55 55 (37171200)
|
|
I0428 13:17:02.964792 11373 net.cpp:137] Memory required for data: 376518656
|
|
I0428 13:17:02.964797 11373 layer_factory.hpp:77] Creating layer norm1
|
|
I0428 13:17:02.964807 11373 net.cpp:84] Creating Layer norm1
|
|
I0428 13:17:02.964810 11373 net.cpp:406] norm1 <- conv1
|
|
I0428 13:17:02.964816 11373 net.cpp:380] norm1 -> norm1
|
|
I0428 13:17:02.966630 11373 net.cpp:122] Setting up norm1
|
|
I0428 13:17:02.966640 11373 net.cpp:129] Top shape: 128 96 55 55 (37171200)
|
|
I0428 13:17:02.966645 11373 net.cpp:137] Memory required for data: 525203456
|
|
I0428 13:17:02.966650 11373 layer_factory.hpp:77] Creating layer pool1
|
|
I0428 13:17:02.966658 11373 net.cpp:84] Creating Layer pool1
|
|
I0428 13:17:02.966662 11373 net.cpp:406] pool1 <- norm1
|
|
I0428 13:17:02.966668 11373 net.cpp:380] pool1 -> pool1
|
|
I0428 13:17:02.966706 11373 net.cpp:122] Setting up pool1
|
|
I0428 13:17:02.966713 11373 net.cpp:129] Top shape: 128 96 27 27 (8957952)
|
|
I0428 13:17:02.966717 11373 net.cpp:137] Memory required for data: 561035264
|
|
I0428 13:17:02.966722 11373 layer_factory.hpp:77] Creating layer conv1.5
|
|
I0428 13:17:02.966732 11373 net.cpp:84] Creating Layer conv1.5
|
|
I0428 13:17:02.966735 11373 net.cpp:406] conv1.5 <- pool1
|
|
I0428 13:17:02.966742 11373 net.cpp:380] conv1.5 -> conv1.5
|
|
I0428 13:17:02.972527 11373 net.cpp:122] Setting up conv1.5
|
|
I0428 13:17:02.972539 11373 net.cpp:129] Top shape: 128 176 23 23 (11917312)
|
|
I0428 13:17:02.972543 11373 net.cpp:137] Memory required for data: 608704512
|
|
I0428 13:17:02.972553 11373 layer_factory.hpp:77] Creating layer relu1.5
|
|
I0428 13:17:02.972563 11373 net.cpp:84] Creating Layer relu1.5
|
|
I0428 13:17:02.972568 11373 net.cpp:406] relu1.5 <- conv1.5
|
|
I0428 13:17:02.972573 11373 net.cpp:367] relu1.5 -> conv1.5 (in-place)
|
|
I0428 13:17:02.972914 11373 net.cpp:122] Setting up relu1.5
|
|
I0428 13:17:02.972924 11373 net.cpp:129] Top shape: 128 176 23 23 (11917312)
|
|
I0428 13:17:02.972927 11373 net.cpp:137] Memory required for data: 656373760
|
|
I0428 13:17:02.972931 11373 layer_factory.hpp:77] Creating layer norm1.5
|
|
I0428 13:17:02.972939 11373 net.cpp:84] Creating Layer norm1.5
|
|
I0428 13:17:02.972944 11373 net.cpp:406] norm1.5 <- conv1.5
|
|
I0428 13:17:02.972950 11373 net.cpp:380] norm1.5 -> norm1.5
|
|
I0428 13:17:02.974581 11373 net.cpp:122] Setting up norm1.5
|
|
I0428 13:17:02.974592 11373 net.cpp:129] Top shape: 128 176 23 23 (11917312)
|
|
I0428 13:17:02.974597 11373 net.cpp:137] Memory required for data: 704043008
|
|
I0428 13:17:02.974601 11373 layer_factory.hpp:77] Creating layer pool1.5
|
|
I0428 13:17:02.974611 11373 net.cpp:84] Creating Layer pool1.5
|
|
I0428 13:17:02.974617 11373 net.cpp:406] pool1.5 <- norm1.5
|
|
I0428 13:17:02.974622 11373 net.cpp:380] pool1.5 -> pool1.5
|
|
I0428 13:17:02.974653 11373 net.cpp:122] Setting up pool1.5
|
|
I0428 13:17:02.974658 11373 net.cpp:129] Top shape: 128 176 11 11 (2725888)
|
|
I0428 13:17:02.974663 11373 net.cpp:137] Memory required for data: 714946560
|
|
I0428 13:17:02.974666 11373 layer_factory.hpp:77] Creating layer conv2
|
|
I0428 13:17:02.974676 11373 net.cpp:84] Creating Layer conv2
|
|
I0428 13:17:02.974680 11373 net.cpp:406] conv2 <- pool1.5
|
|
I0428 13:17:02.974687 11373 net.cpp:380] conv2 -> conv2
|
|
I0428 13:17:02.983048 11373 net.cpp:122] Setting up conv2
|
|
I0428 13:17:02.983065 11373 net.cpp:129] Top shape: 128 256 11 11 (3964928)
|
|
I0428 13:17:02.983069 11373 net.cpp:137] Memory required for data: 730806272
|
|
I0428 13:17:02.983083 11373 layer_factory.hpp:77] Creating layer relu2
|
|
I0428 13:17:02.983093 11373 net.cpp:84] Creating Layer relu2
|
|
I0428 13:17:02.983096 11373 net.cpp:406] relu2 <- conv2
|
|
I0428 13:17:02.983103 11373 net.cpp:367] relu2 -> conv2 (in-place)
|
|
I0428 13:17:02.983619 11373 net.cpp:122] Setting up relu2
|
|
I0428 13:17:02.983628 11373 net.cpp:129] Top shape: 128 256 11 11 (3964928)
|
|
I0428 13:17:02.983633 11373 net.cpp:137] Memory required for data: 746665984
|
|
I0428 13:17:02.983637 11373 layer_factory.hpp:77] Creating layer norm2
|
|
I0428 13:17:02.983646 11373 net.cpp:84] Creating Layer norm2
|
|
I0428 13:17:02.983651 11373 net.cpp:406] norm2 <- conv2
|
|
I0428 13:17:02.983657 11373 net.cpp:380] norm2 -> norm2
|
|
I0428 13:17:02.984019 11373 net.cpp:122] Setting up norm2
|
|
I0428 13:17:02.984027 11373 net.cpp:129] Top shape: 128 256 11 11 (3964928)
|
|
I0428 13:17:02.984031 11373 net.cpp:137] Memory required for data: 762525696
|
|
I0428 13:17:02.984035 11373 layer_factory.hpp:77] Creating layer pool2
|
|
I0428 13:17:02.984043 11373 net.cpp:84] Creating Layer pool2
|
|
I0428 13:17:02.984047 11373 net.cpp:406] pool2 <- norm2
|
|
I0428 13:17:02.984052 11373 net.cpp:380] pool2 -> pool2
|
|
I0428 13:17:02.984083 11373 net.cpp:122] Setting up pool2
|
|
I0428 13:17:02.984088 11373 net.cpp:129] Top shape: 128 256 5 5 (819200)
|
|
I0428 13:17:02.984092 11373 net.cpp:137] Memory required for data: 765802496
|
|
I0428 13:17:02.984097 11373 layer_factory.hpp:77] Creating layer conv3
|
|
I0428 13:17:02.984107 11373 net.cpp:84] Creating Layer conv3
|
|
I0428 13:17:02.984112 11373 net.cpp:406] conv3 <- pool2
|
|
I0428 13:17:02.984118 11373 net.cpp:380] conv3 -> conv3
|
|
I0428 13:17:02.995426 11373 net.cpp:122] Setting up conv3
|
|
I0428 13:17:02.995447 11373 net.cpp:129] Top shape: 128 384 5 5 (1228800)
|
|
I0428 13:17:02.995452 11373 net.cpp:137] Memory required for data: 770717696
|
|
I0428 13:17:02.995462 11373 layer_factory.hpp:77] Creating layer relu3
|
|
I0428 13:17:02.995472 11373 net.cpp:84] Creating Layer relu3
|
|
I0428 13:17:02.995477 11373 net.cpp:406] relu3 <- conv3
|
|
I0428 13:17:02.995483 11373 net.cpp:367] relu3 -> conv3 (in-place)
|
|
I0428 13:17:02.995981 11373 net.cpp:122] Setting up relu3
|
|
I0428 13:17:02.995991 11373 net.cpp:129] Top shape: 128 384 5 5 (1228800)
|
|
I0428 13:17:02.995995 11373 net.cpp:137] Memory required for data: 775632896
|
|
I0428 13:17:02.996001 11373 layer_factory.hpp:77] Creating layer conv4
|
|
I0428 13:17:02.996013 11373 net.cpp:84] Creating Layer conv4
|
|
I0428 13:17:02.996017 11373 net.cpp:406] conv4 <- conv3
|
|
I0428 13:17:02.996026 11373 net.cpp:380] conv4 -> conv4
|
|
I0428 13:17:03.005424 11373 net.cpp:122] Setting up conv4
|
|
I0428 13:17:03.005442 11373 net.cpp:129] Top shape: 128 384 5 5 (1228800)
|
|
I0428 13:17:03.005446 11373 net.cpp:137] Memory required for data: 780548096
|
|
I0428 13:17:03.005460 11373 layer_factory.hpp:77] Creating layer relu4
|
|
I0428 13:17:03.005471 11373 net.cpp:84] Creating Layer relu4
|
|
I0428 13:17:03.005476 11373 net.cpp:406] relu4 <- conv4
|
|
I0428 13:17:03.005482 11373 net.cpp:367] relu4 -> conv4 (in-place)
|
|
I0428 13:17:03.005973 11373 net.cpp:122] Setting up relu4
|
|
I0428 13:17:03.005985 11373 net.cpp:129] Top shape: 128 384 5 5 (1228800)
|
|
I0428 13:17:03.005988 11373 net.cpp:137] Memory required for data: 785463296
|
|
I0428 13:17:03.005992 11373 layer_factory.hpp:77] Creating layer conv5
|
|
I0428 13:17:03.006003 11373 net.cpp:84] Creating Layer conv5
|
|
I0428 13:17:03.006007 11373 net.cpp:406] conv5 <- conv4
|
|
I0428 13:17:03.006013 11373 net.cpp:380] conv5 -> conv5
|
|
I0428 13:17:03.020987 11373 net.cpp:122] Setting up conv5
|
|
I0428 13:17:03.021006 11373 net.cpp:129] Top shape: 128 256 5 5 (819200)
|
|
I0428 13:17:03.021010 11373 net.cpp:137] Memory required for data: 788740096
|
|
I0428 13:17:03.021021 11373 layer_factory.hpp:77] Creating layer relu5
|
|
I0428 13:17:03.021032 11373 net.cpp:84] Creating Layer relu5
|
|
I0428 13:17:03.021037 11373 net.cpp:406] relu5 <- conv5
|
|
I0428 13:17:03.021044 11373 net.cpp:367] relu5 -> conv5 (in-place)
|
|
I0428 13:17:03.022289 11373 net.cpp:122] Setting up relu5
|
|
I0428 13:17:03.022300 11373 net.cpp:129] Top shape: 128 256 5 5 (819200)
|
|
I0428 13:17:03.022303 11373 net.cpp:137] Memory required for data: 792016896
|
|
I0428 13:17:03.022307 11373 layer_factory.hpp:77] Creating layer pool5
|
|
I0428 13:17:03.022315 11373 net.cpp:84] Creating Layer pool5
|
|
I0428 13:17:03.022318 11373 net.cpp:406] pool5 <- conv5
|
|
I0428 13:17:03.022325 11373 net.cpp:380] pool5 -> pool5
|
|
I0428 13:17:03.022384 11373 net.cpp:122] Setting up pool5
|
|
I0428 13:17:03.022392 11373 net.cpp:129] Top shape: 128 256 2 2 (131072)
|
|
I0428 13:17:03.022394 11373 net.cpp:137] Memory required for data: 792541184
|
|
I0428 13:17:03.022398 11373 layer_factory.hpp:77] Creating layer fc6
|
|
I0428 13:17:03.022406 11373 net.cpp:84] Creating Layer fc6
|
|
I0428 13:17:03.022410 11373 net.cpp:406] fc6 <- pool5
|
|
I0428 13:17:03.022415 11373 net.cpp:380] fc6 -> fc6
|
|
I0428 13:17:03.062209 11373 net.cpp:122] Setting up fc6
|
|
I0428 13:17:03.062232 11373 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0428 13:17:03.062237 11373 net.cpp:137] Memory required for data: 794638336
|
|
I0428 13:17:03.062247 11373 layer_factory.hpp:77] Creating layer relu6
|
|
I0428 13:17:03.062256 11373 net.cpp:84] Creating Layer relu6
|
|
I0428 13:17:03.062260 11373 net.cpp:406] relu6 <- fc6
|
|
I0428 13:17:03.062268 11373 net.cpp:367] relu6 -> fc6 (in-place)
|
|
I0428 13:17:03.062880 11373 net.cpp:122] Setting up relu6
|
|
I0428 13:17:03.062889 11373 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0428 13:17:03.062896 11373 net.cpp:137] Memory required for data: 796735488
|
|
I0428 13:17:03.062901 11373 layer_factory.hpp:77] Creating layer drop6
|
|
I0428 13:17:03.062911 11373 net.cpp:84] Creating Layer drop6
|
|
I0428 13:17:03.062914 11373 net.cpp:406] drop6 <- fc6
|
|
I0428 13:17:03.062922 11373 net.cpp:367] drop6 -> fc6 (in-place)
|
|
I0428 13:17:03.062950 11373 net.cpp:122] Setting up drop6
|
|
I0428 13:17:03.062958 11373 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0428 13:17:03.062961 11373 net.cpp:137] Memory required for data: 798832640
|
|
I0428 13:17:03.062965 11373 layer_factory.hpp:77] Creating layer fc7
|
|
I0428 13:17:03.062974 11373 net.cpp:84] Creating Layer fc7
|
|
I0428 13:17:03.062978 11373 net.cpp:406] fc7 <- fc6
|
|
I0428 13:17:03.062985 11373 net.cpp:380] fc7 -> fc7
|
|
I0428 13:17:03.232481 11373 net.cpp:122] Setting up fc7
|
|
I0428 13:17:03.232527 11373 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0428 13:17:03.232532 11373 net.cpp:137] Memory required for data: 800929792
|
|
I0428 13:17:03.232542 11373 layer_factory.hpp:77] Creating layer relu7
|
|
I0428 13:17:03.232551 11373 net.cpp:84] Creating Layer relu7
|
|
I0428 13:17:03.232558 11373 net.cpp:406] relu7 <- fc7
|
|
I0428 13:17:03.232566 11373 net.cpp:367] relu7 -> fc7 (in-place)
|
|
I0428 13:17:03.232981 11373 net.cpp:122] Setting up relu7
|
|
I0428 13:17:03.232990 11373 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0428 13:17:03.232995 11373 net.cpp:137] Memory required for data: 803026944
|
|
I0428 13:17:03.233000 11373 layer_factory.hpp:77] Creating layer drop7
|
|
I0428 13:17:03.233006 11373 net.cpp:84] Creating Layer drop7
|
|
I0428 13:17:03.233011 11373 net.cpp:406] drop7 <- fc7
|
|
I0428 13:17:03.233016 11373 net.cpp:367] drop7 -> fc7 (in-place)
|
|
I0428 13:17:03.233042 11373 net.cpp:122] Setting up drop7
|
|
I0428 13:17:03.233047 11373 net.cpp:129] Top shape: 128 4096 (524288)
|
|
I0428 13:17:03.233050 11373 net.cpp:137] Memory required for data: 805124096
|
|
I0428 13:17:03.233054 11373 layer_factory.hpp:77] Creating layer fc8
|
|
I0428 13:17:03.233062 11373 net.cpp:84] Creating Layer fc8
|
|
I0428 13:17:03.233067 11373 net.cpp:406] fc8 <- fc7
|
|
I0428 13:17:03.233072 11373 net.cpp:380] fc8 -> fc8
|
|
I0428 13:17:03.246526 11373 net.cpp:122] Setting up fc8
|
|
I0428 13:17:03.246541 11373 net.cpp:129] Top shape: 128 196 (25088)
|
|
I0428 13:17:03.246546 11373 net.cpp:137] Memory required for data: 805224448
|
|
I0428 13:17:03.246560 11373 layer_factory.hpp:77] Creating layer loss
|
|
I0428 13:17:03.246570 11373 net.cpp:84] Creating Layer loss
|
|
I0428 13:17:03.246574 11373 net.cpp:406] loss <- fc8
|
|
I0428 13:17:03.246580 11373 net.cpp:406] loss <- label
|
|
I0428 13:17:03.246588 11373 net.cpp:380] loss -> loss
|
|
I0428 13:17:03.246599 11373 layer_factory.hpp:77] Creating layer loss
|
|
I0428 13:17:03.249460 11373 net.cpp:122] Setting up loss
|
|
I0428 13:17:03.249471 11373 net.cpp:129] Top shape: (1)
|
|
I0428 13:17:03.249475 11373 net.cpp:132] with loss weight 1
|
|
I0428 13:17:03.249493 11373 net.cpp:137] Memory required for data: 805224452
|
|
I0428 13:17:03.249498 11373 net.cpp:198] loss needs backward computation.
|
|
I0428 13:17:03.249506 11373 net.cpp:198] fc8 needs backward computation.
|
|
I0428 13:17:03.249527 11373 net.cpp:198] drop7 needs backward computation.
|
|
I0428 13:17:03.249532 11373 net.cpp:198] relu7 needs backward computation.
|
|
I0428 13:17:03.249536 11373 net.cpp:198] fc7 needs backward computation.
|
|
I0428 13:17:03.249541 11373 net.cpp:198] drop6 needs backward computation.
|
|
I0428 13:17:03.249543 11373 net.cpp:198] relu6 needs backward computation.
|
|
I0428 13:17:03.249547 11373 net.cpp:198] fc6 needs backward computation.
|
|
I0428 13:17:03.249552 11373 net.cpp:198] pool5 needs backward computation.
|
|
I0428 13:17:03.249557 11373 net.cpp:198] relu5 needs backward computation.
|
|
I0428 13:17:03.249560 11373 net.cpp:198] conv5 needs backward computation.
|
|
I0428 13:17:03.249564 11373 net.cpp:198] relu4 needs backward computation.
|
|
I0428 13:17:03.249568 11373 net.cpp:198] conv4 needs backward computation.
|
|
I0428 13:17:03.249572 11373 net.cpp:198] relu3 needs backward computation.
|
|
I0428 13:17:03.249577 11373 net.cpp:198] conv3 needs backward computation.
|
|
I0428 13:17:03.249580 11373 net.cpp:198] pool2 needs backward computation.
|
|
I0428 13:17:03.249585 11373 net.cpp:198] norm2 needs backward computation.
|
|
I0428 13:17:03.249588 11373 net.cpp:198] relu2 needs backward computation.
|
|
I0428 13:17:03.249593 11373 net.cpp:198] conv2 needs backward computation.
|
|
I0428 13:17:03.249596 11373 net.cpp:198] pool1.5 needs backward computation.
|
|
I0428 13:17:03.249600 11373 net.cpp:198] norm1.5 needs backward computation.
|
|
I0428 13:17:03.249604 11373 net.cpp:198] relu1.5 needs backward computation.
|
|
I0428 13:17:03.249608 11373 net.cpp:198] conv1.5 needs backward computation.
|
|
I0428 13:17:03.249612 11373 net.cpp:198] pool1 needs backward computation.
|
|
I0428 13:17:03.249617 11373 net.cpp:198] norm1 needs backward computation.
|
|
I0428 13:17:03.249622 11373 net.cpp:198] relu1 needs backward computation.
|
|
I0428 13:17:03.249626 11373 net.cpp:198] conv1 needs backward computation.
|
|
I0428 13:17:03.249631 11373 net.cpp:200] train-data does not need backward computation.
|
|
I0428 13:17:03.249636 11373 net.cpp:242] This network produces output loss
|
|
I0428 13:17:03.249651 11373 net.cpp:255] Network initialization done.
|
|
I0428 13:17:03.250797 11373 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
|
|
I0428 13:17:03.250836 11373 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
|
|
I0428 13:17:03.251009 11373 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-3/digits/jobs/20210421-230320-902c/mean.binaryproto"
|
|
}
|
|
data_param {
|
|
source: "/mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210421-230320-902c/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: "conv1.5"
|
|
type: "Convolution"
|
|
bottom: "pool1"
|
|
top: "conv1.5"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 176
|
|
kernel_size: 5
|
|
stride: 1
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu1.5"
|
|
type: "ReLU"
|
|
bottom: "conv1.5"
|
|
top: "conv1.5"
|
|
}
|
|
layer {
|
|
name: "norm1.5"
|
|
type: "LRN"
|
|
bottom: "conv1.5"
|
|
top: "norm1.5"
|
|
lrn_param {
|
|
local_size: 5
|
|
alpha: 0.0001
|
|
beta: 0.75
|
|
}
|
|
}
|
|
layer {
|
|
name: "pool1.5"
|
|
type: "Pooling"
|
|
bottom: "norm1.5"
|
|
top: "pool1.5"
|
|
pooling_param {
|
|
pool: MAX
|
|
kernel_size: 3
|
|
stride: 2
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv2"
|
|
type: "Convolution"
|
|
bottom: "pool1.5"
|
|
top: "conv2"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
pad: 2
|
|
kernel_size: 5
|
|
group: 2
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu2"
|
|
type: "ReLU"
|
|
bottom: "conv2"
|
|
top: "conv2"
|
|
}
|
|
layer {
|
|
name: "norm2"
|
|
type: "LRN"
|
|
bottom: "conv2"
|
|
top: "norm2"
|
|
lrn_param {
|
|
local_size: 5
|
|
alpha: 0.0001
|
|
beta: 0.75
|
|
}
|
|
}
|
|
layer {
|
|
name: "pool2"
|
|
type: "Pooling"
|
|
bottom: "norm2"
|
|
top: "pool2"
|
|
pooling_param {
|
|
pool: MAX
|
|
kernel_size: 3
|
|
stride: 2
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv3"
|
|
type: "Convolution"
|
|
bottom: "pool2"
|
|
top: "conv3"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 384
|
|
pad: 1
|
|
kernel_size: 3
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu3"
|
|
type: "ReLU"
|
|
bottom: "conv3"
|
|
top: "conv3"
|
|
}
|
|
layer {
|
|
name: "conv4"
|
|
type: "Convolution"
|
|
bottom: "conv3"
|
|
top: "conv4"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 384
|
|
pad: 1
|
|
kernel_size: 3
|
|
group: 2
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu4"
|
|
type: "ReLU"
|
|
bottom: "conv4"
|
|
top: "conv4"
|
|
}
|
|
layer {
|
|
name: "conv5"
|
|
type: "Convolution"
|
|
bottom: "conv4"
|
|
top: "conv5"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
pad: 1
|
|
kernel_size: 3
|
|
group: 2
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu5"
|
|
type: "ReLU"
|
|
bottom: "conv5"
|
|
top: "conv5"
|
|
}
|
|
layer {
|
|
name: "pool5"
|
|
type: "Pooling"
|
|
bottom: "conv5"
|
|
top: "pool5"
|
|
pooling_param {
|
|
pool: MAX
|
|
kernel_size: 3
|
|
stride: 2
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc6"
|
|
type: "InnerProduct"
|
|
bottom: "pool5"
|
|
top: "fc6"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
inner_product_param {
|
|
num_output: 4096
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.005
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu6"
|
|
type: "ReLU"
|
|
bottom: "fc6"
|
|
top: "fc6"
|
|
}
|
|
layer {
|
|
name: "drop6"
|
|
type: "Dropout"
|
|
bottom: "fc6"
|
|
top: "fc6"
|
|
dropout_param {
|
|
dropout_ratio: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc7"
|
|
type: "InnerProduct"
|
|
bottom: "fc6"
|
|
top: "fc7"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
inner_product_param {
|
|
num_output: 4096
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.005
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.1
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "relu7"
|
|
type: "ReLU"
|
|
bottom: "fc7"
|
|
top: "fc7"
|
|
}
|
|
layer {
|
|
name: "drop7"
|
|
type: "Dropout"
|
|
bottom: "fc7"
|
|
top: "fc7"
|
|
dropout_param {
|
|
dropout_ratio: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc8"
|
|
type: "InnerProduct"
|
|
bottom: "fc7"
|
|
top: "fc8"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
inner_product_param {
|
|
num_output: 196
|
|
weight_filler {
|
|
type: "gaussian"
|
|
std: 0.01
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "accuracy"
|
|
type: "Accuracy"
|
|
bottom: "fc8"
|
|
bottom: "label"
|
|
top: "accuracy"
|
|
include {
|
|
phase: TEST
|
|
}
|
|
}
|
|
layer {
|
|
name: "loss"
|
|
type: "SoftmaxWithLoss"
|
|
bottom: "fc8"
|
|
bottom: "label"
|
|
top: "loss"
|
|
}
|
|
I0428 13:17:03.251114 11373 layer_factory.hpp:77] Creating layer val-data
|
|
I0428 13:17:03.290225 11373 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210421-230320-902c/val_db
|
|
I0428 13:17:03.309953 11373 net.cpp:84] Creating Layer val-data
|
|
I0428 13:17:03.309979 11373 net.cpp:380] val-data -> data
|
|
I0428 13:17:03.309993 11373 net.cpp:380] val-data -> label
|
|
I0428 13:17:03.310001 11373 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-3/digits/jobs/20210421-230320-902c/mean.binaryproto
|
|
I0428 13:17:03.317636 11373 data_layer.cpp:45] output data size: 32,3,227,227
|
|
I0428 13:17:03.474905 11373 net.cpp:122] Setting up val-data
|
|
I0428 13:17:03.474931 11373 net.cpp:129] Top shape: 32 3 227 227 (4946784)
|
|
I0428 13:17:03.474938 11373 net.cpp:129] Top shape: 32 (32)
|
|
I0428 13:17:03.474943 11373 net.cpp:137] Memory required for data: 19787264
|
|
I0428 13:17:03.474952 11373 layer_factory.hpp:77] Creating layer label_val-data_1_split
|
|
I0428 13:17:03.474969 11373 net.cpp:84] Creating Layer label_val-data_1_split
|
|
I0428 13:17:03.474975 11373 net.cpp:406] label_val-data_1_split <- label
|
|
I0428 13:17:03.474985 11373 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
|
|
I0428 13:17:03.474998 11373 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
|
|
I0428 13:17:03.475067 11373 net.cpp:122] Setting up label_val-data_1_split
|
|
I0428 13:17:03.475076 11373 net.cpp:129] Top shape: 32 (32)
|
|
I0428 13:17:03.475082 11373 net.cpp:129] Top shape: 32 (32)
|
|
I0428 13:17:03.475086 11373 net.cpp:137] Memory required for data: 19787520
|
|
I0428 13:17:03.475091 11373 layer_factory.hpp:77] Creating layer conv1
|
|
I0428 13:17:03.475111 11373 net.cpp:84] Creating Layer conv1
|
|
I0428 13:17:03.475116 11373 net.cpp:406] conv1 <- data
|
|
I0428 13:17:03.475126 11373 net.cpp:380] conv1 -> conv1
|
|
I0428 13:17:03.589087 11373 net.cpp:122] Setting up conv1
|
|
I0428 13:17:03.589112 11373 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
|
I0428 13:17:03.589118 11373 net.cpp:137] Memory required for data: 56958720
|
|
I0428 13:17:03.589136 11373 layer_factory.hpp:77] Creating layer relu1
|
|
I0428 13:17:03.589148 11373 net.cpp:84] Creating Layer relu1
|
|
I0428 13:17:03.589154 11373 net.cpp:406] relu1 <- conv1
|
|
I0428 13:17:03.589164 11373 net.cpp:367] relu1 -> conv1 (in-place)
|
|
I0428 13:17:03.589607 11373 net.cpp:122] Setting up relu1
|
|
I0428 13:17:03.589620 11373 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
|
I0428 13:17:03.589625 11373 net.cpp:137] Memory required for data: 94129920
|
|
I0428 13:17:03.589632 11373 layer_factory.hpp:77] Creating layer norm1
|
|
I0428 13:17:03.589643 11373 net.cpp:84] Creating Layer norm1
|
|
I0428 13:17:03.589650 11373 net.cpp:406] norm1 <- conv1
|
|
I0428 13:17:03.589661 11373 net.cpp:380] norm1 -> norm1
|
|
I0428 13:17:03.628386 11373 net.cpp:122] Setting up norm1
|
|
I0428 13:17:03.628412 11373 net.cpp:129] Top shape: 32 96 55 55 (9292800)
|
|
I0428 13:17:03.628417 11373 net.cpp:137] Memory required for data: 131301120
|
|
I0428 13:17:03.628425 11373 layer_factory.hpp:77] Creating layer pool1
|
|
I0428 13:17:03.628439 11373 net.cpp:84] Creating Layer pool1
|
|
I0428 13:17:03.628446 11373 net.cpp:406] pool1 <- norm1
|
|
I0428 13:17:03.628458 11373 net.cpp:380] pool1 -> pool1
|
|
I0428 13:17:03.628549 11373 net.cpp:122] Setting up pool1
|
|
I0428 13:17:03.628559 11373 net.cpp:129] Top shape: 32 96 27 27 (2239488)
|
|
I0428 13:17:03.628564 11373 net.cpp:137] Memory required for data: 140259072
|
|
I0428 13:17:03.628571 11373 layer_factory.hpp:77] Creating layer conv1.5
|
|
I0428 13:17:03.628585 11373 net.cpp:84] Creating Layer conv1.5
|
|
I0428 13:17:03.628592 11373 net.cpp:406] conv1.5 <- pool1
|
|
I0428 13:17:03.628600 11373 net.cpp:380] conv1.5 -> conv1.5
|
|
I0428 13:17:03.638517 11373 net.cpp:122] Setting up conv1.5
|
|
I0428 13:17:03.638537 11373 net.cpp:129] Top shape: 32 176 23 23 (2979328)
|
|
I0428 13:17:03.638543 11373 net.cpp:137] Memory required for data: 152176384
|
|
I0428 13:17:03.638558 11373 layer_factory.hpp:77] Creating layer relu1.5
|
|
I0428 13:17:03.638572 11373 net.cpp:84] Creating Layer relu1.5
|
|
I0428 13:17:03.638607 11373 net.cpp:406] relu1.5 <- conv1.5
|
|
I0428 13:17:03.638617 11373 net.cpp:367] relu1.5 -> conv1.5 (in-place)
|
|
I0428 13:17:03.639147 11373 net.cpp:122] Setting up relu1.5
|
|
I0428 13:17:03.639158 11373 net.cpp:129] Top shape: 32 176 23 23 (2979328)
|
|
I0428 13:17:03.639163 11373 net.cpp:137] Memory required for data: 164093696
|
|
I0428 13:17:03.639169 11373 layer_factory.hpp:77] Creating layer norm1.5
|
|
I0428 13:17:03.639184 11373 net.cpp:84] Creating Layer norm1.5
|
|
I0428 13:17:03.639191 11373 net.cpp:406] norm1.5 <- conv1.5
|
|
I0428 13:17:03.639201 11373 net.cpp:380] norm1.5 -> norm1.5
|
|
I0428 13:17:03.639938 11373 net.cpp:122] Setting up norm1.5
|
|
I0428 13:17:03.639952 11373 net.cpp:129] Top shape: 32 176 23 23 (2979328)
|
|
I0428 13:17:03.639957 11373 net.cpp:137] Memory required for data: 176011008
|
|
I0428 13:17:03.639963 11373 layer_factory.hpp:77] Creating layer pool1.5
|
|
I0428 13:17:03.639972 11373 net.cpp:84] Creating Layer pool1.5
|
|
I0428 13:17:03.639978 11373 net.cpp:406] pool1.5 <- norm1.5
|
|
I0428 13:17:03.639987 11373 net.cpp:380] pool1.5 -> pool1.5
|
|
I0428 13:17:03.640035 11373 net.cpp:122] Setting up pool1.5
|
|
I0428 13:17:03.640045 11373 net.cpp:129] Top shape: 32 176 11 11 (681472)
|
|
I0428 13:17:03.640050 11373 net.cpp:137] Memory required for data: 178736896
|
|
I0428 13:17:03.640056 11373 layer_factory.hpp:77] Creating layer conv2
|
|
I0428 13:17:03.640070 11373 net.cpp:84] Creating Layer conv2
|
|
I0428 13:17:03.640075 11373 net.cpp:406] conv2 <- pool1.5
|
|
I0428 13:17:03.640084 11373 net.cpp:380] conv2 -> conv2
|
|
I0428 13:17:03.732378 11373 net.cpp:122] Setting up conv2
|
|
I0428 13:17:03.732403 11373 net.cpp:129] Top shape: 32 256 11 11 (991232)
|
|
I0428 13:17:03.732407 11373 net.cpp:137] Memory required for data: 182701824
|
|
I0428 13:17:03.732429 11373 layer_factory.hpp:77] Creating layer relu2
|
|
I0428 13:17:03.732441 11373 net.cpp:84] Creating Layer relu2
|
|
I0428 13:17:03.732448 11373 net.cpp:406] relu2 <- conv2
|
|
I0428 13:17:03.732457 11373 net.cpp:367] relu2 -> conv2 (in-place)
|
|
I0428 13:17:03.733309 11373 net.cpp:122] Setting up relu2
|
|
I0428 13:17:03.733322 11373 net.cpp:129] Top shape: 32 256 11 11 (991232)
|
|
I0428 13:17:03.733327 11373 net.cpp:137] Memory required for data: 186666752
|
|
I0428 13:17:03.733333 11373 layer_factory.hpp:77] Creating layer norm2
|
|
I0428 13:17:03.733345 11373 net.cpp:84] Creating Layer norm2
|
|
I0428 13:17:03.733351 11373 net.cpp:406] norm2 <- conv2
|
|
I0428 13:17:03.733361 11373 net.cpp:380] norm2 -> norm2
|
|
I0428 13:17:03.733909 11373 net.cpp:122] Setting up norm2
|
|
I0428 13:17:03.733922 11373 net.cpp:129] Top shape: 32 256 11 11 (991232)
|
|
I0428 13:17:03.733927 11373 net.cpp:137] Memory required for data: 190631680
|
|
I0428 13:17:03.733932 11373 layer_factory.hpp:77] Creating layer pool2
|
|
I0428 13:17:03.733942 11373 net.cpp:84] Creating Layer pool2
|
|
I0428 13:17:03.733947 11373 net.cpp:406] pool2 <- norm2
|
|
I0428 13:17:03.733956 11373 net.cpp:380] pool2 -> pool2
|
|
I0428 13:17:03.734001 11373 net.cpp:122] Setting up pool2
|
|
I0428 13:17:03.734011 11373 net.cpp:129] Top shape: 32 256 5 5 (204800)
|
|
I0428 13:17:03.734016 11373 net.cpp:137] Memory required for data: 191450880
|
|
I0428 13:17:03.734021 11373 layer_factory.hpp:77] Creating layer conv3
|
|
I0428 13:17:03.734036 11373 net.cpp:84] Creating Layer conv3
|
|
I0428 13:17:03.734041 11373 net.cpp:406] conv3 <- pool2
|
|
I0428 13:17:03.734052 11373 net.cpp:380] conv3 -> conv3
|
|
I0428 13:17:03.756932 11373 net.cpp:122] Setting up conv3
|
|
I0428 13:17:03.756959 11373 net.cpp:129] Top shape: 32 384 5 5 (307200)
|
|
I0428 13:17:03.756964 11373 net.cpp:137] Memory required for data: 192679680
|
|
I0428 13:17:03.756978 11373 layer_factory.hpp:77] Creating layer relu3
|
|
I0428 13:17:03.756990 11373 net.cpp:84] Creating Layer relu3
|
|
I0428 13:17:03.756997 11373 net.cpp:406] relu3 <- conv3
|
|
I0428 13:17:03.757006 11373 net.cpp:367] relu3 -> conv3 (in-place)
|
|
I0428 13:17:03.757688 11373 net.cpp:122] Setting up relu3
|
|
I0428 13:17:03.757701 11373 net.cpp:129] Top shape: 32 384 5 5 (307200)
|
|
I0428 13:17:03.757706 11373 net.cpp:137] Memory required for data: 193908480
|
|
I0428 13:17:03.757712 11373 layer_factory.hpp:77] Creating layer conv4
|
|
I0428 13:17:03.757761 11373 net.cpp:84] Creating Layer conv4
|
|
I0428 13:17:03.757767 11373 net.cpp:406] conv4 <- conv3
|
|
I0428 13:17:03.757776 11373 net.cpp:380] conv4 -> conv4
|
|
I0428 13:17:03.773646 11373 net.cpp:122] Setting up conv4
|
|
I0428 13:17:03.773674 11373 net.cpp:129] Top shape: 32 384 5 5 (307200)
|
|
I0428 13:17:03.773680 11373 net.cpp:137] Memory required for data: 195137280
|
|
I0428 13:17:03.773699 11373 layer_factory.hpp:77] Creating layer relu4
|
|
I0428 13:17:03.773712 11373 net.cpp:84] Creating Layer relu4
|
|
I0428 13:17:03.773718 11373 net.cpp:406] relu4 <- conv4
|
|
I0428 13:17:03.773730 11373 net.cpp:367] relu4 -> conv4 (in-place)
|
|
I0428 13:17:03.774492 11373 net.cpp:122] Setting up relu4
|
|
I0428 13:17:03.774507 11373 net.cpp:129] Top shape: 32 384 5 5 (307200)
|
|
I0428 13:17:03.774514 11373 net.cpp:137] Memory required for data: 196366080
|
|
I0428 13:17:03.774521 11373 layer_factory.hpp:77] Creating layer conv5
|
|
I0428 13:17:03.774538 11373 net.cpp:84] Creating Layer conv5
|
|
I0428 13:17:03.774545 11373 net.cpp:406] conv5 <- conv4
|
|
I0428 13:17:03.774556 11373 net.cpp:380] conv5 -> conv5
|
|
I0428 13:17:03.788188 11373 net.cpp:122] Setting up conv5
|
|
I0428 13:17:03.788211 11373 net.cpp:129] Top shape: 32 256 5 5 (204800)
|
|
I0428 13:17:03.788215 11373 net.cpp:137] Memory required for data: 197185280
|
|
I0428 13:17:03.788225 11373 layer_factory.hpp:77] Creating layer relu5
|
|
I0428 13:17:03.788235 11373 net.cpp:84] Creating Layer relu5
|
|
I0428 13:17:03.788240 11373 net.cpp:406] relu5 <- conv5
|
|
I0428 13:17:03.788247 11373 net.cpp:367] relu5 -> conv5 (in-place)
|
|
I0428 13:17:03.788777 11373 net.cpp:122] Setting up relu5
|
|
I0428 13:17:03.788787 11373 net.cpp:129] Top shape: 32 256 5 5 (204800)
|
|
I0428 13:17:03.788791 11373 net.cpp:137] Memory required for data: 198004480
|
|
I0428 13:17:03.788795 11373 layer_factory.hpp:77] Creating layer pool5
|
|
I0428 13:17:03.788802 11373 net.cpp:84] Creating Layer pool5
|
|
I0428 13:17:03.788806 11373 net.cpp:406] pool5 <- conv5
|
|
I0428 13:17:03.788813 11373 net.cpp:380] pool5 -> pool5
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|
I0428 13:17:03.788859 11373 net.cpp:122] Setting up pool5
|
|
I0428 13:17:03.788866 11373 net.cpp:129] Top shape: 32 256 2 2 (32768)
|
|
I0428 13:17:03.788869 11373 net.cpp:137] Memory required for data: 198135552
|
|
I0428 13:17:03.788873 11373 layer_factory.hpp:77] Creating layer fc6
|
|
I0428 13:17:03.788882 11373 net.cpp:84] Creating Layer fc6
|
|
I0428 13:17:03.788885 11373 net.cpp:406] fc6 <- pool5
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|
I0428 13:17:03.788892 11373 net.cpp:380] fc6 -> fc6
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|
I0428 13:17:03.836680 11373 net.cpp:122] Setting up fc6
|
|
I0428 13:17:03.836701 11373 net.cpp:129] Top shape: 32 4096 (131072)
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|
I0428 13:17:03.836706 11373 net.cpp:137] Memory required for data: 198659840
|
|
I0428 13:17:03.836715 11373 layer_factory.hpp:77] Creating layer relu6
|
|
I0428 13:17:03.836724 11373 net.cpp:84] Creating Layer relu6
|
|
I0428 13:17:03.836730 11373 net.cpp:406] relu6 <- fc6
|
|
I0428 13:17:03.836738 11373 net.cpp:367] relu6 -> fc6 (in-place)
|
|
I0428 13:17:03.837430 11373 net.cpp:122] Setting up relu6
|
|
I0428 13:17:03.837438 11373 net.cpp:129] Top shape: 32 4096 (131072)
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|
I0428 13:17:03.837443 11373 net.cpp:137] Memory required for data: 199184128
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|
I0428 13:17:03.837446 11373 layer_factory.hpp:77] Creating layer drop6
|
|
I0428 13:17:03.837455 11373 net.cpp:84] Creating Layer drop6
|
|
I0428 13:17:03.837458 11373 net.cpp:406] drop6 <- fc6
|
|
I0428 13:17:03.837466 11373 net.cpp:367] drop6 -> fc6 (in-place)
|
|
I0428 13:17:03.837491 11373 net.cpp:122] Setting up drop6
|
|
I0428 13:17:03.837496 11373 net.cpp:129] Top shape: 32 4096 (131072)
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|
I0428 13:17:03.837500 11373 net.cpp:137] Memory required for data: 199708416
|
|
I0428 13:17:03.837503 11373 layer_factory.hpp:77] Creating layer fc7
|
|
I0428 13:17:03.837512 11373 net.cpp:84] Creating Layer fc7
|
|
I0428 13:17:03.837517 11373 net.cpp:406] fc7 <- fc6
|
|
I0428 13:17:03.837522 11373 net.cpp:380] fc7 -> fc7
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|
I0428 13:17:04.036653 11373 net.cpp:122] Setting up fc7
|
|
I0428 13:17:04.036672 11373 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0428 13:17:04.036677 11373 net.cpp:137] Memory required for data: 200232704
|
|
I0428 13:17:04.036687 11373 layer_factory.hpp:77] Creating layer relu7
|
|
I0428 13:17:04.036697 11373 net.cpp:84] Creating Layer relu7
|
|
I0428 13:17:04.036721 11373 net.cpp:406] relu7 <- fc7
|
|
I0428 13:17:04.036731 11373 net.cpp:367] relu7 -> fc7 (in-place)
|
|
I0428 13:17:04.037153 11373 net.cpp:122] Setting up relu7
|
|
I0428 13:17:04.037163 11373 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0428 13:17:04.037168 11373 net.cpp:137] Memory required for data: 200756992
|
|
I0428 13:17:04.037171 11373 layer_factory.hpp:77] Creating layer drop7
|
|
I0428 13:17:04.037179 11373 net.cpp:84] Creating Layer drop7
|
|
I0428 13:17:04.037184 11373 net.cpp:406] drop7 <- fc7
|
|
I0428 13:17:04.037191 11373 net.cpp:367] drop7 -> fc7 (in-place)
|
|
I0428 13:17:04.037216 11373 net.cpp:122] Setting up drop7
|
|
I0428 13:17:04.037221 11373 net.cpp:129] Top shape: 32 4096 (131072)
|
|
I0428 13:17:04.037225 11373 net.cpp:137] Memory required for data: 201281280
|
|
I0428 13:17:04.037230 11373 layer_factory.hpp:77] Creating layer fc8
|
|
I0428 13:17:04.037238 11373 net.cpp:84] Creating Layer fc8
|
|
I0428 13:17:04.037242 11373 net.cpp:406] fc8 <- fc7
|
|
I0428 13:17:04.037248 11373 net.cpp:380] fc8 -> fc8
|
|
I0428 13:17:04.045235 11373 net.cpp:122] Setting up fc8
|
|
I0428 13:17:04.045248 11373 net.cpp:129] Top shape: 32 196 (6272)
|
|
I0428 13:17:04.045253 11373 net.cpp:137] Memory required for data: 201306368
|
|
I0428 13:17:04.045266 11373 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
|
|
I0428 13:17:04.045274 11373 net.cpp:84] Creating Layer fc8_fc8_0_split
|
|
I0428 13:17:04.045279 11373 net.cpp:406] fc8_fc8_0_split <- fc8
|
|
I0428 13:17:04.045286 11373 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
|
|
I0428 13:17:04.045295 11373 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
|
|
I0428 13:17:04.045333 11373 net.cpp:122] Setting up fc8_fc8_0_split
|
|
I0428 13:17:04.045338 11373 net.cpp:129] Top shape: 32 196 (6272)
|
|
I0428 13:17:04.045342 11373 net.cpp:129] Top shape: 32 196 (6272)
|
|
I0428 13:17:04.045346 11373 net.cpp:137] Memory required for data: 201356544
|
|
I0428 13:17:04.045349 11373 layer_factory.hpp:77] Creating layer accuracy
|
|
I0428 13:17:04.045357 11373 net.cpp:84] Creating Layer accuracy
|
|
I0428 13:17:04.045361 11373 net.cpp:406] accuracy <- fc8_fc8_0_split_0
|
|
I0428 13:17:04.045367 11373 net.cpp:406] accuracy <- label_val-data_1_split_0
|
|
I0428 13:17:04.045372 11373 net.cpp:380] accuracy -> accuracy
|
|
I0428 13:17:04.045379 11373 net.cpp:122] Setting up accuracy
|
|
I0428 13:17:04.045384 11373 net.cpp:129] Top shape: (1)
|
|
I0428 13:17:04.045388 11373 net.cpp:137] Memory required for data: 201356548
|
|
I0428 13:17:04.045392 11373 layer_factory.hpp:77] Creating layer loss
|
|
I0428 13:17:04.045403 11373 net.cpp:84] Creating Layer loss
|
|
I0428 13:17:04.045408 11373 net.cpp:406] loss <- fc8_fc8_0_split_1
|
|
I0428 13:17:04.045413 11373 net.cpp:406] loss <- label_val-data_1_split_1
|
|
I0428 13:17:04.045418 11373 net.cpp:380] loss -> loss
|
|
I0428 13:17:04.045425 11373 layer_factory.hpp:77] Creating layer loss
|
|
I0428 13:17:04.051158 11373 net.cpp:122] Setting up loss
|
|
I0428 13:17:04.051169 11373 net.cpp:129] Top shape: (1)
|
|
I0428 13:17:04.051173 11373 net.cpp:132] with loss weight 1
|
|
I0428 13:17:04.051183 11373 net.cpp:137] Memory required for data: 201356552
|
|
I0428 13:17:04.051188 11373 net.cpp:198] loss needs backward computation.
|
|
I0428 13:17:04.051193 11373 net.cpp:200] accuracy does not need backward computation.
|
|
I0428 13:17:04.051198 11373 net.cpp:198] fc8_fc8_0_split needs backward computation.
|
|
I0428 13:17:04.051203 11373 net.cpp:198] fc8 needs backward computation.
|
|
I0428 13:17:04.051206 11373 net.cpp:198] drop7 needs backward computation.
|
|
I0428 13:17:04.051210 11373 net.cpp:198] relu7 needs backward computation.
|
|
I0428 13:17:04.051213 11373 net.cpp:198] fc7 needs backward computation.
|
|
I0428 13:17:04.051219 11373 net.cpp:198] drop6 needs backward computation.
|
|
I0428 13:17:04.051223 11373 net.cpp:198] relu6 needs backward computation.
|
|
I0428 13:17:04.051227 11373 net.cpp:198] fc6 needs backward computation.
|
|
I0428 13:17:04.051234 11373 net.cpp:198] pool5 needs backward computation.
|
|
I0428 13:17:04.051237 11373 net.cpp:198] relu5 needs backward computation.
|
|
I0428 13:17:04.051241 11373 net.cpp:198] conv5 needs backward computation.
|
|
I0428 13:17:04.051245 11373 net.cpp:198] relu4 needs backward computation.
|
|
I0428 13:17:04.051266 11373 net.cpp:198] conv4 needs backward computation.
|
|
I0428 13:17:04.051270 11373 net.cpp:198] relu3 needs backward computation.
|
|
I0428 13:17:04.051275 11373 net.cpp:198] conv3 needs backward computation.
|
|
I0428 13:17:04.051278 11373 net.cpp:198] pool2 needs backward computation.
|
|
I0428 13:17:04.051281 11373 net.cpp:198] norm2 needs backward computation.
|
|
I0428 13:17:04.051286 11373 net.cpp:198] relu2 needs backward computation.
|
|
I0428 13:17:04.051290 11373 net.cpp:198] conv2 needs backward computation.
|
|
I0428 13:17:04.051293 11373 net.cpp:198] pool1.5 needs backward computation.
|
|
I0428 13:17:04.051297 11373 net.cpp:198] norm1.5 needs backward computation.
|
|
I0428 13:17:04.051301 11373 net.cpp:198] relu1.5 needs backward computation.
|
|
I0428 13:17:04.051306 11373 net.cpp:198] conv1.5 needs backward computation.
|
|
I0428 13:17:04.051308 11373 net.cpp:198] pool1 needs backward computation.
|
|
I0428 13:17:04.051312 11373 net.cpp:198] norm1 needs backward computation.
|
|
I0428 13:17:04.051316 11373 net.cpp:198] relu1 needs backward computation.
|
|
I0428 13:17:04.051321 11373 net.cpp:198] conv1 needs backward computation.
|
|
I0428 13:17:04.051324 11373 net.cpp:200] label_val-data_1_split does not need backward computation.
|
|
I0428 13:17:04.051329 11373 net.cpp:200] val-data does not need backward computation.
|
|
I0428 13:17:04.051334 11373 net.cpp:242] This network produces output accuracy
|
|
I0428 13:17:04.051338 11373 net.cpp:242] This network produces output loss
|
|
I0428 13:17:04.051358 11373 net.cpp:255] Network initialization done.
|
|
I0428 13:17:04.051473 11373 solver.cpp:56] Solver scaffolding done.
|
|
I0428 13:17:04.051996 11373 caffe.cpp:248] Starting Optimization
|
|
I0428 13:17:04.052006 11373 solver.cpp:272] Solving
|
|
I0428 13:17:04.052011 11373 solver.cpp:273] Learning Rate Policy: exp
|
|
I0428 13:17:04.057855 11373 solver.cpp:330] Iteration 0, Testing net (#0)
|
|
I0428 13:17:04.057866 11373 net.cpp:676] Ignoring source layer train-data
|
|
I0428 13:17:04.111104 11373 blocking_queue.cpp:49] Waiting for data
|
|
I0428 13:17:10.024549 11427 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 13:17:10.068169 11373 solver.cpp:397] Test net output #0: accuracy = 0.00857843
|
|
I0428 13:17:10.068202 11373 solver.cpp:397] Test net output #1: loss = 5.28129 (* 1 = 5.28129 loss)
|
|
I0428 13:17:10.149349 11373 solver.cpp:218] Iteration 0 (1.22097e+37 iter/s, 6.09705s/12 iters), loss = 5.27508
|
|
I0428 13:17:10.149392 11373 solver.cpp:237] Train net output #0: loss = 5.27508 (* 1 = 5.27508 loss)
|
|
I0428 13:17:10.149415 11373 sgd_solver.cpp:105] Iteration 0, lr = 0.01
|
|
I0428 13:17:15.842056 11373 solver.cpp:218] Iteration 12 (2.10807 iter/s, 5.69241s/12 iters), loss = 5.27219
|
|
I0428 13:17:15.842099 11373 solver.cpp:237] Train net output #0: loss = 5.27219 (* 1 = 5.27219 loss)
|
|
I0428 13:17:15.842108 11373 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
|
|
I0428 13:17:22.277156 11373 solver.cpp:218] Iteration 24 (1.86487 iter/s, 6.43478s/12 iters), loss = 5.26834
|
|
I0428 13:17:22.277201 11373 solver.cpp:237] Train net output #0: loss = 5.26834 (* 1 = 5.26834 loss)
|
|
I0428 13:17:22.277210 11373 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
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|
I0428 13:17:28.041640 11373 solver.cpp:218] Iteration 36 (2.08182 iter/s, 5.76419s/12 iters), loss = 5.31232
|
|
I0428 13:17:28.047670 11373 solver.cpp:237] Train net output #0: loss = 5.31232 (* 1 = 5.31232 loss)
|
|
I0428 13:17:28.047688 11373 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
|
|
I0428 13:17:38.544667 11373 solver.cpp:218] Iteration 48 (1.14323 iter/s, 10.4966s/12 iters), loss = 5.27441
|
|
I0428 13:17:38.544740 11373 solver.cpp:237] Train net output #0: loss = 5.27441 (* 1 = 5.27441 loss)
|
|
I0428 13:17:38.544749 11373 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
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|
I0428 13:17:43.665076 11373 solver.cpp:218] Iteration 60 (2.3437 iter/s, 5.12011s/12 iters), loss = 5.30342
|
|
I0428 13:17:43.665117 11373 solver.cpp:237] Train net output #0: loss = 5.30342 (* 1 = 5.30342 loss)
|
|
I0428 13:17:43.665125 11373 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
|
|
I0428 13:17:48.917183 11373 solver.cpp:218] Iteration 72 (2.28491 iter/s, 5.25184s/12 iters), loss = 5.27519
|
|
I0428 13:17:48.917227 11373 solver.cpp:237] Train net output #0: loss = 5.27519 (* 1 = 5.27519 loss)
|
|
I0428 13:17:48.917237 11373 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
|
|
I0428 13:17:54.162674 11373 solver.cpp:218] Iteration 84 (2.2878 iter/s, 5.24522s/12 iters), loss = 5.30966
|
|
I0428 13:17:54.162712 11373 solver.cpp:237] Train net output #0: loss = 5.30966 (* 1 = 5.30966 loss)
|
|
I0428 13:17:54.162720 11373 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
|
|
I0428 13:18:02.901893 11373 solver.cpp:218] Iteration 96 (1.37318 iter/s, 8.73881s/12 iters), loss = 5.29896
|
|
I0428 13:18:02.901934 11373 solver.cpp:237] Train net output #0: loss = 5.29896 (* 1 = 5.29896 loss)
|
|
I0428 13:18:02.901943 11373 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
|
|
I0428 13:18:05.687115 11401 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 13:18:05.988389 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
|
|
I0428 13:18:09.500555 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
|
|
I0428 13:18:11.801779 11373 solver.cpp:330] Iteration 102, Testing net (#0)
|
|
I0428 13:18:11.801798 11373 net.cpp:676] Ignoring source layer train-data
|
|
I0428 13:18:16.094358 11427 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 13:18:16.172827 11373 solver.cpp:397] Test net output #0: accuracy = 0.00245098
|
|
I0428 13:18:16.172859 11373 solver.cpp:397] Test net output #1: loss = 5.29063 (* 1 = 5.29063 loss)
|
|
I0428 13:18:17.953936 11373 solver.cpp:218] Iteration 108 (0.79727 iter/s, 15.0514s/12 iters), loss = 5.28888
|
|
I0428 13:18:17.953989 11373 solver.cpp:237] Train net output #0: loss = 5.28888 (* 1 = 5.28888 loss)
|
|
I0428 13:18:17.954001 11373 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
|
|
I0428 13:18:22.677121 11373 solver.cpp:218] Iteration 120 (2.5408 iter/s, 4.72293s/12 iters), loss = 5.28766
|
|
I0428 13:18:22.677161 11373 solver.cpp:237] Train net output #0: loss = 5.28766 (* 1 = 5.28766 loss)
|
|
I0428 13:18:22.677171 11373 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
|
|
I0428 13:18:27.355937 11373 solver.cpp:218] Iteration 132 (2.56489 iter/s, 4.67857s/12 iters), loss = 5.26567
|
|
I0428 13:18:27.355978 11373 solver.cpp:237] Train net output #0: loss = 5.26567 (* 1 = 5.26567 loss)
|
|
I0428 13:18:27.355988 11373 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
|
|
I0428 13:18:32.144799 11373 solver.cpp:218] Iteration 144 (2.50594 iter/s, 4.78861s/12 iters), loss = 5.28829
|
|
I0428 13:18:32.144837 11373 solver.cpp:237] Train net output #0: loss = 5.28829 (* 1 = 5.28829 loss)
|
|
I0428 13:18:32.144847 11373 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
|
|
I0428 13:18:36.854207 11373 solver.cpp:218] Iteration 156 (2.54822 iter/s, 4.70916s/12 iters), loss = 5.3021
|
|
I0428 13:18:36.854244 11373 solver.cpp:237] Train net output #0: loss = 5.3021 (* 1 = 5.3021 loss)
|
|
I0428 13:18:36.854254 11373 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
|
|
I0428 13:18:41.627709 11373 solver.cpp:218] Iteration 168 (2.51401 iter/s, 4.77325s/12 iters), loss = 5.28898
|
|
I0428 13:18:41.627807 11373 solver.cpp:237] Train net output #0: loss = 5.28898 (* 1 = 5.28898 loss)
|
|
I0428 13:18:41.627816 11373 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
|
|
I0428 13:18:46.333209 11373 solver.cpp:218] Iteration 180 (2.55037 iter/s, 4.70519s/12 iters), loss = 5.28584
|
|
I0428 13:18:46.333256 11373 solver.cpp:237] Train net output #0: loss = 5.28584 (* 1 = 5.28584 loss)
|
|
I0428 13:18:46.333266 11373 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
|
|
I0428 13:18:51.140205 11373 solver.cpp:218] Iteration 192 (2.4965 iter/s, 4.80673s/12 iters), loss = 5.28289
|
|
I0428 13:18:51.140247 11373 solver.cpp:237] Train net output #0: loss = 5.28289 (* 1 = 5.28289 loss)
|
|
I0428 13:18:51.140255 11373 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
|
|
I0428 13:18:54.965975 11401 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 13:18:55.614225 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
|
|
I0428 13:18:58.571651 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
|
|
I0428 13:18:59.927807 11373 solver.cpp:330] Iteration 204, Testing net (#0)
|
|
I0428 13:18:59.927831 11373 net.cpp:676] Ignoring source layer train-data
|
|
I0428 13:19:04.233593 11427 data_layer.cpp:73] Restarting data prefetching from start.
|
|
I0428 13:19:04.356990 11373 solver.cpp:397] Test net output #0: accuracy = 0.00551471
|
|
I0428 13:19:04.357031 11373 solver.cpp:397] Test net output #1: loss = 5.28762 (* 1 = 5.28762 loss)
|
|
I0428 13:19:04.435726 11373 solver.cpp:218] Iteration 204 (0.902601 iter/s, 13.2949s/12 iters), loss = 5.26395
|
|
I0428 13:19:04.435787 11373 solver.cpp:237] Train net output #0: loss = 5.26395 (* 1 = 5.26395 loss)
|
|
I0428 13:19:04.435799 11373 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
|
|
I0428 13:19:08.607481 11373 solver.cpp:218] Iteration 216 (2.87666 iter/s, 4.17151s/12 iters), loss = 5.28774
|
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I0428 13:19:08.607530 11373 solver.cpp:237] Train net output #0: loss = 5.28774 (* 1 = 5.28774 loss)
|
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I0428 13:19:08.607542 11373 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
|
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I0428 13:19:13.416364 11373 solver.cpp:218] Iteration 228 (2.49552 iter/s, 4.80861s/12 iters), loss = 5.26397
|
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I0428 13:19:13.416581 11373 solver.cpp:237] Train net output #0: loss = 5.26397 (* 1 = 5.26397 loss)
|
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I0428 13:19:13.416602 11373 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
|
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I0428 13:19:18.217216 11373 solver.cpp:218] Iteration 240 (2.49978 iter/s, 4.80043s/12 iters), loss = 5.27728
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I0428 13:19:18.217258 11373 solver.cpp:237] Train net output #0: loss = 5.27728 (* 1 = 5.27728 loss)
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I0428 13:19:18.217267 11373 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
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I0428 13:19:22.997457 11373 solver.cpp:218] Iteration 252 (2.51047 iter/s, 4.77998s/12 iters), loss = 5.28117
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I0428 13:19:22.997514 11373 solver.cpp:237] Train net output #0: loss = 5.28117 (* 1 = 5.28117 loss)
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I0428 13:19:22.997526 11373 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
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I0428 13:19:27.733182 11373 solver.cpp:218] Iteration 264 (2.53407 iter/s, 4.73546s/12 iters), loss = 5.28039
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I0428 13:19:27.733234 11373 solver.cpp:237] Train net output #0: loss = 5.28039 (* 1 = 5.28039 loss)
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I0428 13:19:27.733247 11373 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
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I0428 13:19:32.477766 11373 solver.cpp:218] Iteration 276 (2.52934 iter/s, 4.74432s/12 iters), loss = 5.27699
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I0428 13:19:32.477820 11373 solver.cpp:237] Train net output #0: loss = 5.27699 (* 1 = 5.27699 loss)
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I0428 13:19:32.477833 11373 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
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I0428 13:19:37.211321 11373 solver.cpp:218] Iteration 288 (2.53523 iter/s, 4.73329s/12 iters), loss = 5.30523
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I0428 13:19:37.211375 11373 solver.cpp:237] Train net output #0: loss = 5.30523 (* 1 = 5.30523 loss)
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I0428 13:19:37.211388 11373 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
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I0428 13:19:41.951737 11373 solver.cpp:218] Iteration 300 (2.53156 iter/s, 4.74015s/12 iters), loss = 5.29043
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I0428 13:19:41.951792 11373 solver.cpp:237] Train net output #0: loss = 5.29043 (* 1 = 5.29043 loss)
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I0428 13:19:41.951803 11373 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
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I0428 13:19:42.895670 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:19:43.892112 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
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I0428 13:19:48.664014 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
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I0428 13:19:49.995617 11373 solver.cpp:330] Iteration 306, Testing net (#0)
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I0428 13:19:49.995636 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:19:54.168992 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:19:54.325451 11373 solver.cpp:397] Test net output #0: accuracy = 0.00551471
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I0428 13:19:54.325481 11373 solver.cpp:397] Test net output #1: loss = 5.2788 (* 1 = 5.2788 loss)
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I0428 13:19:55.981968 11373 solver.cpp:218] Iteration 312 (0.855335 iter/s, 14.0296s/12 iters), loss = 5.264
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I0428 13:19:55.982012 11373 solver.cpp:237] Train net output #0: loss = 5.264 (* 1 = 5.264 loss)
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I0428 13:19:55.982021 11373 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
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I0428 13:20:00.857983 11373 solver.cpp:218] Iteration 324 (2.46116 iter/s, 4.87575s/12 iters), loss = 5.27519
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I0428 13:20:00.858026 11373 solver.cpp:237] Train net output #0: loss = 5.27519 (* 1 = 5.27519 loss)
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I0428 13:20:00.858034 11373 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
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I0428 13:20:05.596647 11373 solver.cpp:218] Iteration 336 (2.5325 iter/s, 4.73841s/12 iters), loss = 5.2562
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I0428 13:20:05.596701 11373 solver.cpp:237] Train net output #0: loss = 5.2562 (* 1 = 5.2562 loss)
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I0428 13:20:05.596712 11373 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
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I0428 13:20:10.266120 11373 solver.cpp:218] Iteration 348 (2.57003 iter/s, 4.66921s/12 iters), loss = 5.21133
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I0428 13:20:10.266162 11373 solver.cpp:237] Train net output #0: loss = 5.21133 (* 1 = 5.21133 loss)
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I0428 13:20:10.266171 11373 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
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I0428 13:20:15.066000 11373 solver.cpp:218] Iteration 360 (2.5002 iter/s, 4.79962s/12 iters), loss = 5.16829
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I0428 13:20:15.066532 11373 solver.cpp:237] Train net output #0: loss = 5.16829 (* 1 = 5.16829 loss)
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I0428 13:20:15.066545 11373 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
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I0428 13:20:19.693867 11373 solver.cpp:218] Iteration 372 (2.5934 iter/s, 4.62713s/12 iters), loss = 5.24623
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I0428 13:20:19.693913 11373 solver.cpp:237] Train net output #0: loss = 5.24623 (* 1 = 5.24623 loss)
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I0428 13:20:19.693922 11373 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
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I0428 13:20:24.423794 11373 solver.cpp:218] Iteration 384 (2.53717 iter/s, 4.72967s/12 iters), loss = 5.19889
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I0428 13:20:24.423833 11373 solver.cpp:237] Train net output #0: loss = 5.19889 (* 1 = 5.19889 loss)
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I0428 13:20:24.423842 11373 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
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I0428 13:20:29.150851 11373 solver.cpp:218] Iteration 396 (2.53871 iter/s, 4.72681s/12 iters), loss = 5.1605
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I0428 13:20:29.150892 11373 solver.cpp:237] Train net output #0: loss = 5.1605 (* 1 = 5.1605 loss)
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I0428 13:20:29.150900 11373 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
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I0428 13:20:32.116286 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:20:33.470628 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
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I0428 13:20:38.353621 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
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I0428 13:20:42.544695 11373 solver.cpp:330] Iteration 408, Testing net (#0)
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I0428 13:20:42.544716 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:20:46.609949 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:20:46.812100 11373 solver.cpp:397] Test net output #0: accuracy = 0.00980392
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I0428 13:20:46.812135 11373 solver.cpp:397] Test net output #1: loss = 5.1729 (* 1 = 5.1729 loss)
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I0428 13:20:46.890933 11373 solver.cpp:218] Iteration 408 (0.676465 iter/s, 17.7393s/12 iters), loss = 5.24176
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I0428 13:20:46.890995 11373 solver.cpp:237] Train net output #0: loss = 5.24176 (* 1 = 5.24176 loss)
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I0428 13:20:46.891006 11373 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
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I0428 13:20:50.934857 11373 solver.cpp:218] Iteration 420 (2.96759 iter/s, 4.04368s/12 iters), loss = 5.1095
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I0428 13:20:50.934900 11373 solver.cpp:237] Train net output #0: loss = 5.1095 (* 1 = 5.1095 loss)
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I0428 13:20:50.934911 11373 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
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I0428 13:20:55.695487 11373 solver.cpp:218] Iteration 432 (2.52081 iter/s, 4.76037s/12 iters), loss = 5.06021
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I0428 13:20:55.695535 11373 solver.cpp:237] Train net output #0: loss = 5.06021 (* 1 = 5.06021 loss)
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I0428 13:20:55.695546 11373 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
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I0428 13:21:00.287437 11373 solver.cpp:218] Iteration 444 (2.61341 iter/s, 4.5917s/12 iters), loss = 5.20223
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I0428 13:21:00.287484 11373 solver.cpp:237] Train net output #0: loss = 5.20223 (* 1 = 5.20223 loss)
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I0428 13:21:00.287493 11373 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
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I0428 13:21:05.093039 11373 solver.cpp:218] Iteration 456 (2.49722 iter/s, 4.80534s/12 iters), loss = 5.15537
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I0428 13:21:05.093081 11373 solver.cpp:237] Train net output #0: loss = 5.15537 (* 1 = 5.15537 loss)
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I0428 13:21:05.093091 11373 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
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I0428 13:21:09.743561 11373 solver.cpp:218] Iteration 468 (2.5805 iter/s, 4.65027s/12 iters), loss = 5.16909
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I0428 13:21:09.743600 11373 solver.cpp:237] Train net output #0: loss = 5.16909 (* 1 = 5.16909 loss)
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I0428 13:21:09.743611 11373 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
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I0428 13:21:14.556772 11373 solver.cpp:218] Iteration 480 (2.49328 iter/s, 4.81295s/12 iters), loss = 5.13167
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I0428 13:21:14.556844 11373 solver.cpp:237] Train net output #0: loss = 5.13167 (* 1 = 5.13167 loss)
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I0428 13:21:14.556860 11373 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
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I0428 13:21:19.328416 11373 solver.cpp:218] Iteration 492 (2.515 iter/s, 4.77137s/12 iters), loss = 5.17781
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I0428 13:21:19.329053 11373 solver.cpp:237] Train net output #0: loss = 5.17781 (* 1 = 5.17781 loss)
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I0428 13:21:19.329064 11373 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
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I0428 13:21:24.102145 11373 solver.cpp:218] Iteration 504 (2.51421 iter/s, 4.77287s/12 iters), loss = 5.18619
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I0428 13:21:24.102201 11373 solver.cpp:237] Train net output #0: loss = 5.18619 (* 1 = 5.18619 loss)
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I0428 13:21:24.102214 11373 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
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I0428 13:21:24.353929 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:21:26.048116 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
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I0428 13:21:28.703711 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
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I0428 13:21:29.691582 11373 solver.cpp:330] Iteration 510, Testing net (#0)
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I0428 13:21:29.691606 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:21:33.832849 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:21:34.069981 11373 solver.cpp:397] Test net output #0: accuracy = 0.00919118
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I0428 13:21:34.070015 11373 solver.cpp:397] Test net output #1: loss = 5.15354 (* 1 = 5.15354 loss)
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I0428 13:21:35.738003 11373 solver.cpp:218] Iteration 516 (1.03134 iter/s, 11.6353s/12 iters), loss = 5.08709
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I0428 13:21:35.738062 11373 solver.cpp:237] Train net output #0: loss = 5.08709 (* 1 = 5.08709 loss)
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I0428 13:21:35.738073 11373 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
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I0428 13:21:40.448709 11373 solver.cpp:218] Iteration 528 (2.54753 iter/s, 4.71044s/12 iters), loss = 5.12035
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I0428 13:21:40.448750 11373 solver.cpp:237] Train net output #0: loss = 5.12035 (* 1 = 5.12035 loss)
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I0428 13:21:40.448760 11373 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
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I0428 13:21:45.309121 11373 solver.cpp:218] Iteration 540 (2.46906 iter/s, 4.86016s/12 iters), loss = 5.12117
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I0428 13:21:45.309163 11373 solver.cpp:237] Train net output #0: loss = 5.12117 (* 1 = 5.12117 loss)
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I0428 13:21:45.309173 11373 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
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I0428 13:21:50.071393 11373 solver.cpp:218] Iteration 552 (2.51994 iter/s, 4.76201s/12 iters), loss = 5.18737
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I0428 13:21:50.071530 11373 solver.cpp:237] Train net output #0: loss = 5.18737 (* 1 = 5.18737 loss)
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I0428 13:21:50.071543 11373 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
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I0428 13:21:54.839185 11373 solver.cpp:218] Iteration 564 (2.51707 iter/s, 4.76745s/12 iters), loss = 5.15016
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I0428 13:21:54.839224 11373 solver.cpp:237] Train net output #0: loss = 5.15016 (* 1 = 5.15016 loss)
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I0428 13:21:54.839233 11373 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
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I0428 13:21:59.528587 11373 solver.cpp:218] Iteration 576 (2.5591 iter/s, 4.68915s/12 iters), loss = 5.15475
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I0428 13:21:59.528637 11373 solver.cpp:237] Train net output #0: loss = 5.15475 (* 1 = 5.15475 loss)
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I0428 13:21:59.528650 11373 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
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I0428 13:22:04.335408 11373 solver.cpp:218] Iteration 588 (2.49659 iter/s, 4.80655s/12 iters), loss = 5.0982
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I0428 13:22:04.335467 11373 solver.cpp:237] Train net output #0: loss = 5.0982 (* 1 = 5.0982 loss)
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I0428 13:22:04.335480 11373 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
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I0428 13:22:09.075700 11373 solver.cpp:218] Iteration 600 (2.53163 iter/s, 4.74002s/12 iters), loss = 5.21068
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I0428 13:22:09.075743 11373 solver.cpp:237] Train net output #0: loss = 5.21068 (* 1 = 5.21068 loss)
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I0428 13:22:09.075754 11373 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
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I0428 13:22:11.394992 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:22:13.486464 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
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I0428 13:22:16.527585 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
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I0428 13:22:21.500499 11373 solver.cpp:330] Iteration 612, Testing net (#0)
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I0428 13:22:21.500594 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:22:25.542457 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:22:25.825783 11373 solver.cpp:397] Test net output #0: accuracy = 0.00857843
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I0428 13:22:25.825834 11373 solver.cpp:397] Test net output #1: loss = 5.13657 (* 1 = 5.13657 loss)
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I0428 13:22:25.904994 11373 solver.cpp:218] Iteration 612 (0.713075 iter/s, 16.8285s/12 iters), loss = 5.14043
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I0428 13:22:25.905061 11373 solver.cpp:237] Train net output #0: loss = 5.14043 (* 1 = 5.14043 loss)
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I0428 13:22:25.905081 11373 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
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I0428 13:22:29.980437 11373 solver.cpp:218] Iteration 624 (2.94464 iter/s, 4.0752s/12 iters), loss = 5.24554
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I0428 13:22:29.980476 11373 solver.cpp:237] Train net output #0: loss = 5.24554 (* 1 = 5.24554 loss)
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I0428 13:22:29.980515 11373 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
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I0428 13:22:34.737741 11373 solver.cpp:218] Iteration 636 (2.52257 iter/s, 4.75705s/12 iters), loss = 5.23932
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I0428 13:22:34.737782 11373 solver.cpp:237] Train net output #0: loss = 5.23932 (* 1 = 5.23932 loss)
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I0428 13:22:34.737792 11373 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
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I0428 13:22:39.432993 11373 solver.cpp:218] Iteration 648 (2.55591 iter/s, 4.695s/12 iters), loss = 5.20384
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I0428 13:22:39.433046 11373 solver.cpp:237] Train net output #0: loss = 5.20384 (* 1 = 5.20384 loss)
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I0428 13:22:39.433055 11373 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
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I0428 13:22:44.214581 11373 solver.cpp:218] Iteration 660 (2.50977 iter/s, 4.78132s/12 iters), loss = 5.12183
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I0428 13:22:44.214620 11373 solver.cpp:237] Train net output #0: loss = 5.12183 (* 1 = 5.12183 loss)
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I0428 13:22:44.214629 11373 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
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I0428 13:22:49.063163 11373 solver.cpp:218] Iteration 672 (2.47508 iter/s, 4.84833s/12 iters), loss = 5.11294
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I0428 13:22:49.063202 11373 solver.cpp:237] Train net output #0: loss = 5.11294 (* 1 = 5.11294 loss)
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I0428 13:22:49.063210 11373 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
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I0428 13:22:53.806128 11373 solver.cpp:218] Iteration 684 (2.5302 iter/s, 4.74271s/12 iters), loss = 5.11243
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I0428 13:22:53.806229 11373 solver.cpp:237] Train net output #0: loss = 5.11243 (* 1 = 5.11243 loss)
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I0428 13:22:53.806238 11373 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
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I0428 13:22:54.552284 11373 blocking_queue.cpp:49] Waiting for data
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I0428 13:22:58.500233 11373 solver.cpp:218] Iteration 696 (2.55657 iter/s, 4.69379s/12 iters), loss = 5.13265
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I0428 13:22:58.500290 11373 solver.cpp:237] Train net output #0: loss = 5.13265 (* 1 = 5.13265 loss)
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I0428 13:22:58.500303 11373 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
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I0428 13:23:02.840621 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:23:03.204779 11373 solver.cpp:218] Iteration 708 (2.55086 iter/s, 4.70429s/12 iters), loss = 5.10291
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I0428 13:23:03.204818 11373 solver.cpp:237] Train net output #0: loss = 5.10291 (* 1 = 5.10291 loss)
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I0428 13:23:03.204826 11373 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
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I0428 13:23:05.182174 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
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I0428 13:23:07.896224 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
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I0428 13:23:12.901965 11373 solver.cpp:330] Iteration 714, Testing net (#0)
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I0428 13:23:12.901985 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:23:16.918998 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:23:17.246031 11373 solver.cpp:397] Test net output #0: accuracy = 0.0110294
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I0428 13:23:17.246068 11373 solver.cpp:397] Test net output #1: loss = 5.12091 (* 1 = 5.12091 loss)
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I0428 13:23:18.859813 11373 solver.cpp:218] Iteration 720 (0.766561 iter/s, 15.6543s/12 iters), loss = 5.13747
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I0428 13:23:18.859861 11373 solver.cpp:237] Train net output #0: loss = 5.13747 (* 1 = 5.13747 loss)
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I0428 13:23:18.859874 11373 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
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I0428 13:23:23.678867 11373 solver.cpp:218] Iteration 732 (2.49025 iter/s, 4.8188s/12 iters), loss = 5.15259
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I0428 13:23:23.678902 11373 solver.cpp:237] Train net output #0: loss = 5.15259 (* 1 = 5.15259 loss)
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I0428 13:23:23.678910 11373 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
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I0428 13:23:28.370218 11373 solver.cpp:218] Iteration 744 (2.55803 iter/s, 4.69111s/12 iters), loss = 5.08382
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I0428 13:23:28.399838 11373 solver.cpp:237] Train net output #0: loss = 5.08382 (* 1 = 5.08382 loss)
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I0428 13:23:28.399852 11373 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
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I0428 13:23:33.124742 11373 solver.cpp:218] Iteration 756 (2.53984 iter/s, 4.72471s/12 iters), loss = 5.11278
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I0428 13:23:33.124783 11373 solver.cpp:237] Train net output #0: loss = 5.11278 (* 1 = 5.11278 loss)
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I0428 13:23:33.124791 11373 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
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I0428 13:23:37.788049 11373 solver.cpp:218] Iteration 768 (2.57342 iter/s, 4.66305s/12 iters), loss = 5.13917
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I0428 13:23:37.788101 11373 solver.cpp:237] Train net output #0: loss = 5.13917 (* 1 = 5.13917 loss)
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I0428 13:23:37.788113 11373 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
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I0428 13:23:42.476136 11373 solver.cpp:218] Iteration 780 (2.55982 iter/s, 4.68783s/12 iters), loss = 5.0533
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I0428 13:23:42.476177 11373 solver.cpp:237] Train net output #0: loss = 5.0533 (* 1 = 5.0533 loss)
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I0428 13:23:42.476186 11373 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
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I0428 13:23:47.221324 11373 solver.cpp:218] Iteration 792 (2.52901 iter/s, 4.74494s/12 iters), loss = 5.04662
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I0428 13:23:47.221369 11373 solver.cpp:237] Train net output #0: loss = 5.04662 (* 1 = 5.04662 loss)
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I0428 13:23:47.221381 11373 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
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I0428 13:23:52.028666 11373 solver.cpp:218] Iteration 804 (2.49631 iter/s, 4.80709s/12 iters), loss = 5.12475
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I0428 13:23:52.028699 11373 solver.cpp:237] Train net output #0: loss = 5.12475 (* 1 = 5.12475 loss)
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I0428 13:23:52.028708 11373 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
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I0428 13:23:53.686563 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:23:56.319736 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
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I0428 13:23:58.615172 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
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I0428 13:23:59.644448 11373 solver.cpp:330] Iteration 816, Testing net (#0)
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I0428 13:23:59.644474 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:24:03.679015 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:24:04.030041 11373 solver.cpp:397] Test net output #0: accuracy = 0.0116422
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I0428 13:24:04.030071 11373 solver.cpp:397] Test net output #1: loss = 5.07178 (* 1 = 5.07178 loss)
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I0428 13:24:04.108918 11373 solver.cpp:218] Iteration 816 (0.993402 iter/s, 12.0797s/12 iters), loss = 5.02392
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I0428 13:24:04.108964 11373 solver.cpp:237] Train net output #0: loss = 5.02392 (* 1 = 5.02392 loss)
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I0428 13:24:04.108974 11373 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
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I0428 13:24:08.138636 11373 solver.cpp:218] Iteration 828 (2.97804 iter/s, 4.02949s/12 iters), loss = 5.0765
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I0428 13:24:08.138675 11373 solver.cpp:237] Train net output #0: loss = 5.0765 (* 1 = 5.0765 loss)
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I0428 13:24:08.138684 11373 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
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I0428 13:24:12.870760 11373 solver.cpp:218] Iteration 840 (2.53599 iter/s, 4.73188s/12 iters), loss = 5.01143
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I0428 13:24:12.870805 11373 solver.cpp:237] Train net output #0: loss = 5.01143 (* 1 = 5.01143 loss)
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I0428 13:24:12.870815 11373 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
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I0428 13:24:17.698350 11373 solver.cpp:218] Iteration 852 (2.48585 iter/s, 4.82733s/12 iters), loss = 5.04168
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I0428 13:24:17.698395 11373 solver.cpp:237] Train net output #0: loss = 5.04168 (* 1 = 5.04168 loss)
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I0428 13:24:17.698402 11373 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
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I0428 13:24:22.522650 11373 solver.cpp:218] Iteration 864 (2.48754 iter/s, 4.82405s/12 iters), loss = 5.16549
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I0428 13:24:22.522691 11373 solver.cpp:237] Train net output #0: loss = 5.16549 (* 1 = 5.16549 loss)
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I0428 13:24:22.522698 11373 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
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I0428 13:24:27.335513 11373 solver.cpp:218] Iteration 876 (2.49345 iter/s, 4.81261s/12 iters), loss = 5.01365
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I0428 13:24:27.335556 11373 solver.cpp:237] Train net output #0: loss = 5.01365 (* 1 = 5.01365 loss)
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I0428 13:24:27.335566 11373 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
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I0428 13:24:32.050611 11373 solver.cpp:218] Iteration 888 (2.54515 iter/s, 4.71484s/12 iters), loss = 5.00945
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I0428 13:24:32.050806 11373 solver.cpp:237] Train net output #0: loss = 5.00945 (* 1 = 5.00945 loss)
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I0428 13:24:32.050818 11373 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
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I0428 13:24:36.768082 11373 solver.cpp:218] Iteration 900 (2.54395 iter/s, 4.71708s/12 iters), loss = 5.01505
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I0428 13:24:36.768117 11373 solver.cpp:237] Train net output #0: loss = 5.01505 (* 1 = 5.01505 loss)
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I0428 13:24:36.768126 11373 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
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I0428 13:24:40.580251 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:24:41.653250 11373 solver.cpp:218] Iteration 912 (2.45654 iter/s, 4.88491s/12 iters), loss = 5.0138
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I0428 13:24:41.653296 11373 solver.cpp:237] Train net output #0: loss = 5.0138 (* 1 = 5.0138 loss)
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I0428 13:24:41.653304 11373 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
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I0428 13:24:43.615067 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
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I0428 13:24:47.623579 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
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I0428 13:24:52.909610 11373 solver.cpp:330] Iteration 918, Testing net (#0)
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I0428 13:24:52.909628 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:24:56.824316 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:24:57.224818 11373 solver.cpp:397] Test net output #0: accuracy = 0.00980392
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I0428 13:24:57.224846 11373 solver.cpp:397] Test net output #1: loss = 5.04821 (* 1 = 5.04821 loss)
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I0428 13:24:59.014408 11373 solver.cpp:218] Iteration 924 (0.691229 iter/s, 17.3604s/12 iters), loss = 5.00162
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I0428 13:24:59.014461 11373 solver.cpp:237] Train net output #0: loss = 5.00162 (* 1 = 5.00162 loss)
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I0428 13:24:59.014474 11373 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
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I0428 13:25:03.696833 11373 solver.cpp:218] Iteration 936 (2.56291 iter/s, 4.68217s/12 iters), loss = 4.98451
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I0428 13:25:03.696950 11373 solver.cpp:237] Train net output #0: loss = 4.98451 (* 1 = 4.98451 loss)
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I0428 13:25:03.696959 11373 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
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I0428 13:25:08.415138 11373 solver.cpp:218] Iteration 948 (2.54347 iter/s, 4.71797s/12 iters), loss = 5.05817
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I0428 13:25:08.415199 11373 solver.cpp:237] Train net output #0: loss = 5.05817 (* 1 = 5.05817 loss)
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I0428 13:25:08.415210 11373 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
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I0428 13:25:13.088598 11373 solver.cpp:218] Iteration 960 (2.56784 iter/s, 4.6732s/12 iters), loss = 5.14622
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I0428 13:25:13.088641 11373 solver.cpp:237] Train net output #0: loss = 5.14622 (* 1 = 5.14622 loss)
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I0428 13:25:13.088650 11373 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
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I0428 13:25:17.850769 11373 solver.cpp:218] Iteration 972 (2.51999 iter/s, 4.76192s/12 iters), loss = 5.00183
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I0428 13:25:17.850812 11373 solver.cpp:237] Train net output #0: loss = 5.00183 (* 1 = 5.00183 loss)
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I0428 13:25:17.850821 11373 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
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I0428 13:25:22.709991 11373 solver.cpp:218] Iteration 984 (2.46966 iter/s, 4.85896s/12 iters), loss = 5.03799
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I0428 13:25:22.710036 11373 solver.cpp:237] Train net output #0: loss = 5.03799 (* 1 = 5.03799 loss)
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I0428 13:25:22.710047 11373 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
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I0428 13:25:27.581916 11373 solver.cpp:218] Iteration 996 (2.46322 iter/s, 4.87167s/12 iters), loss = 5.01964
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I0428 13:25:27.581957 11373 solver.cpp:237] Train net output #0: loss = 5.01964 (* 1 = 5.01964 loss)
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I0428 13:25:27.581965 11373 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
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I0428 13:25:32.357035 11373 solver.cpp:218] Iteration 1008 (2.51316 iter/s, 4.77486s/12 iters), loss = 5.0875
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I0428 13:25:32.357075 11373 solver.cpp:237] Train net output #0: loss = 5.0875 (* 1 = 5.0875 loss)
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I0428 13:25:32.357084 11373 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
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I0428 13:25:33.332031 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:25:36.659157 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
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I0428 13:25:40.120955 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
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I0428 13:25:47.010913 11373 solver.cpp:330] Iteration 1020, Testing net (#0)
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I0428 13:25:47.010932 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:25:50.929039 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:25:51.357901 11373 solver.cpp:397] Test net output #0: accuracy = 0.0171569
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I0428 13:25:51.357942 11373 solver.cpp:397] Test net output #1: loss = 5.03126 (* 1 = 5.03126 loss)
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I0428 13:25:51.436688 11373 solver.cpp:218] Iteration 1020 (0.62897 iter/s, 19.0788s/12 iters), loss = 5.12591
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I0428 13:25:51.436733 11373 solver.cpp:237] Train net output #0: loss = 5.12591 (* 1 = 5.12591 loss)
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I0428 13:25:51.436740 11373 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
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I0428 13:25:55.431170 11373 solver.cpp:218] Iteration 1032 (3.00431 iter/s, 3.99426s/12 iters), loss = 5.01725
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I0428 13:25:55.431218 11373 solver.cpp:237] Train net output #0: loss = 5.01725 (* 1 = 5.01725 loss)
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I0428 13:25:55.431229 11373 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
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I0428 13:26:00.183089 11373 solver.cpp:218] Iteration 1044 (2.52543 iter/s, 4.75167s/12 iters), loss = 5.03663
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I0428 13:26:00.183130 11373 solver.cpp:237] Train net output #0: loss = 5.03663 (* 1 = 5.03663 loss)
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I0428 13:26:00.183140 11373 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
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I0428 13:26:04.828665 11373 solver.cpp:218] Iteration 1056 (2.58324 iter/s, 4.64533s/12 iters), loss = 4.92871
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I0428 13:26:04.828706 11373 solver.cpp:237] Train net output #0: loss = 4.92871 (* 1 = 4.92871 loss)
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I0428 13:26:04.828714 11373 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
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I0428 13:26:09.780361 11373 solver.cpp:218] Iteration 1068 (2.42354 iter/s, 4.95143s/12 iters), loss = 4.93764
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I0428 13:26:09.780517 11373 solver.cpp:237] Train net output #0: loss = 4.93764 (* 1 = 4.93764 loss)
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I0428 13:26:09.780531 11373 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
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I0428 13:26:14.529330 11373 solver.cpp:218] Iteration 1080 (2.52705 iter/s, 4.74863s/12 iters), loss = 5.03854
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I0428 13:26:14.529371 11373 solver.cpp:237] Train net output #0: loss = 5.03854 (* 1 = 5.03854 loss)
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I0428 13:26:14.529381 11373 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
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I0428 13:26:19.382992 11373 solver.cpp:218] Iteration 1092 (2.47249 iter/s, 4.8534s/12 iters), loss = 5.00068
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I0428 13:26:19.383035 11373 solver.cpp:237] Train net output #0: loss = 5.00068 (* 1 = 5.00068 loss)
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I0428 13:26:19.383044 11373 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
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I0428 13:26:24.119666 11373 solver.cpp:218] Iteration 1104 (2.53356 iter/s, 4.73642s/12 iters), loss = 4.87311
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I0428 13:26:24.119719 11373 solver.cpp:237] Train net output #0: loss = 4.87311 (* 1 = 4.87311 loss)
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I0428 13:26:24.119730 11373 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
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I0428 13:26:27.175670 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:26:28.923437 11373 solver.cpp:218] Iteration 1116 (2.49817 iter/s, 4.80351s/12 iters), loss = 5.05521
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I0428 13:26:28.923480 11373 solver.cpp:237] Train net output #0: loss = 5.05521 (* 1 = 5.05521 loss)
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I0428 13:26:28.923489 11373 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
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I0428 13:26:30.918675 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
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I0428 13:26:34.002121 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
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I0428 13:26:38.374171 11373 solver.cpp:330] Iteration 1122, Testing net (#0)
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I0428 13:26:38.374197 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:26:42.419159 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:26:42.905232 11373 solver.cpp:397] Test net output #0: accuracy = 0.0208333
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I0428 13:26:42.905272 11373 solver.cpp:397] Test net output #1: loss = 4.92651 (* 1 = 4.92651 loss)
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I0428 13:26:44.654991 11373 solver.cpp:218] Iteration 1128 (0.762832 iter/s, 15.7308s/12 iters), loss = 4.83625
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I0428 13:26:44.655043 11373 solver.cpp:237] Train net output #0: loss = 4.83625 (* 1 = 4.83625 loss)
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I0428 13:26:44.655055 11373 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
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I0428 13:26:49.510664 11373 solver.cpp:218] Iteration 1140 (2.47147 iter/s, 4.85541s/12 iters), loss = 4.87402
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I0428 13:26:49.510710 11373 solver.cpp:237] Train net output #0: loss = 4.87402 (* 1 = 4.87402 loss)
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I0428 13:26:49.510720 11373 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
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I0428 13:26:54.387297 11373 solver.cpp:218] Iteration 1152 (2.46084 iter/s, 4.87637s/12 iters), loss = 4.93913
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I0428 13:26:54.387344 11373 solver.cpp:237] Train net output #0: loss = 4.93913 (* 1 = 4.93913 loss)
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I0428 13:26:54.387352 11373 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
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I0428 13:26:59.361395 11373 solver.cpp:218] Iteration 1164 (2.41263 iter/s, 4.97383s/12 iters), loss = 4.89002
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I0428 13:26:59.361438 11373 solver.cpp:237] Train net output #0: loss = 4.89002 (* 1 = 4.89002 loss)
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I0428 13:26:59.361446 11373 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
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I0428 13:27:03.965981 11373 solver.cpp:218] Iteration 1176 (2.60624 iter/s, 4.60433s/12 iters), loss = 4.89473
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I0428 13:27:03.966035 11373 solver.cpp:237] Train net output #0: loss = 4.89473 (* 1 = 4.89473 loss)
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I0428 13:27:03.966046 11373 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
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I0428 13:27:08.625908 11373 solver.cpp:218] Iteration 1188 (2.57529 iter/s, 4.65967s/12 iters), loss = 4.95833
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I0428 13:27:08.625954 11373 solver.cpp:237] Train net output #0: loss = 4.95833 (* 1 = 4.95833 loss)
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I0428 13:27:08.625963 11373 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
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I0428 13:27:13.370545 11373 solver.cpp:218] Iteration 1200 (2.52931 iter/s, 4.74438s/12 iters), loss = 4.97957
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I0428 13:27:13.370661 11373 solver.cpp:237] Train net output #0: loss = 4.97957 (* 1 = 4.97957 loss)
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I0428 13:27:13.370676 11373 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
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I0428 13:27:18.234655 11373 solver.cpp:218] Iteration 1212 (2.46721 iter/s, 4.86378s/12 iters), loss = 4.96435
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I0428 13:27:18.234704 11373 solver.cpp:237] Train net output #0: loss = 4.96435 (* 1 = 4.96435 loss)
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I0428 13:27:18.234715 11373 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
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I0428 13:27:18.552001 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:27:22.529922 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
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I0428 13:27:25.948403 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
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I0428 13:27:28.910694 11373 solver.cpp:330] Iteration 1224, Testing net (#0)
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I0428 13:27:28.910722 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:27:32.750034 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:27:33.325909 11373 solver.cpp:397] Test net output #0: accuracy = 0.0251225
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I0428 13:27:33.325949 11373 solver.cpp:397] Test net output #1: loss = 4.86067 (* 1 = 4.86067 loss)
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I0428 13:27:33.405043 11373 solver.cpp:218] Iteration 1224 (0.79105 iter/s, 15.1697s/12 iters), loss = 4.73411
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I0428 13:27:33.405099 11373 solver.cpp:237] Train net output #0: loss = 4.73411 (* 1 = 4.73411 loss)
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I0428 13:27:33.405112 11373 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
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I0428 13:27:37.320960 11373 solver.cpp:218] Iteration 1236 (3.06459 iter/s, 3.91569s/12 iters), loss = 4.7402
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I0428 13:27:37.321012 11373 solver.cpp:237] Train net output #0: loss = 4.7402 (* 1 = 4.7402 loss)
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I0428 13:27:37.321023 11373 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
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I0428 13:27:42.017093 11373 solver.cpp:218] Iteration 1248 (2.55543 iter/s, 4.69588s/12 iters), loss = 4.808
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I0428 13:27:42.017134 11373 solver.cpp:237] Train net output #0: loss = 4.808 (* 1 = 4.808 loss)
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I0428 13:27:42.017143 11373 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
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I0428 13:27:46.711019 11373 solver.cpp:218] Iteration 1260 (2.55663 iter/s, 4.69367s/12 iters), loss = 4.94018
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I0428 13:27:46.711146 11373 solver.cpp:237] Train net output #0: loss = 4.94018 (* 1 = 4.94018 loss)
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I0428 13:27:46.711156 11373 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
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I0428 13:27:51.404927 11373 solver.cpp:218] Iteration 1272 (2.55669 iter/s, 4.69358s/12 iters), loss = 4.9063
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I0428 13:27:51.404964 11373 solver.cpp:237] Train net output #0: loss = 4.9063 (* 1 = 4.9063 loss)
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I0428 13:27:51.404973 11373 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
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I0428 13:27:56.168102 11373 solver.cpp:218] Iteration 1284 (2.51946 iter/s, 4.76293s/12 iters), loss = 4.76975
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I0428 13:27:56.168138 11373 solver.cpp:237] Train net output #0: loss = 4.76975 (* 1 = 4.76975 loss)
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I0428 13:27:56.168148 11373 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
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I0428 13:28:01.102962 11373 solver.cpp:218] Iteration 1296 (2.43181 iter/s, 4.9346s/12 iters), loss = 4.82736
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I0428 13:28:01.103010 11373 solver.cpp:237] Train net output #0: loss = 4.82736 (* 1 = 4.82736 loss)
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I0428 13:28:01.103018 11373 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
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I0428 13:28:05.824350 11373 solver.cpp:218] Iteration 1308 (2.54176 iter/s, 4.72114s/12 iters), loss = 4.92497
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I0428 13:28:05.824381 11373 solver.cpp:237] Train net output #0: loss = 4.92497 (* 1 = 4.92497 loss)
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I0428 13:28:05.824389 11373 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
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I0428 13:28:08.161927 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:28:10.546305 11373 solver.cpp:218] Iteration 1320 (2.54145 iter/s, 4.72172s/12 iters), loss = 4.87989
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I0428 13:28:10.546346 11373 solver.cpp:237] Train net output #0: loss = 4.87989 (* 1 = 4.87989 loss)
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I0428 13:28:10.546355 11373 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
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I0428 13:28:12.453495 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
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I0428 13:28:15.343150 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
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I0428 13:28:17.897662 11373 solver.cpp:330] Iteration 1326, Testing net (#0)
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I0428 13:28:17.897775 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:28:21.661907 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:28:22.212075 11373 solver.cpp:397] Test net output #0: accuracy = 0.0367647
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I0428 13:28:22.212105 11373 solver.cpp:397] Test net output #1: loss = 4.74228 (* 1 = 4.74228 loss)
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I0428 13:28:23.941440 11373 solver.cpp:218] Iteration 1332 (0.895888 iter/s, 13.3945s/12 iters), loss = 4.83878
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I0428 13:28:23.941486 11373 solver.cpp:237] Train net output #0: loss = 4.83878 (* 1 = 4.83878 loss)
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I0428 13:28:23.941494 11373 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
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I0428 13:28:28.773944 11373 solver.cpp:218] Iteration 1344 (2.48332 iter/s, 4.83225s/12 iters), loss = 4.92993
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I0428 13:28:28.773989 11373 solver.cpp:237] Train net output #0: loss = 4.92993 (* 1 = 4.92993 loss)
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I0428 13:28:28.773998 11373 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
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I0428 13:28:33.447419 11373 solver.cpp:218] Iteration 1356 (2.56782 iter/s, 4.67323s/12 iters), loss = 4.77427
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I0428 13:28:33.447463 11373 solver.cpp:237] Train net output #0: loss = 4.77427 (* 1 = 4.77427 loss)
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I0428 13:28:33.447472 11373 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
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I0428 13:28:38.223377 11373 solver.cpp:218] Iteration 1368 (2.51272 iter/s, 4.77571s/12 iters), loss = 4.69994
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I0428 13:28:38.223417 11373 solver.cpp:237] Train net output #0: loss = 4.69994 (* 1 = 4.69994 loss)
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I0428 13:28:38.223426 11373 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
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I0428 13:28:39.357251 11373 blocking_queue.cpp:49] Waiting for data
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I0428 13:28:42.968894 11373 solver.cpp:218] Iteration 1380 (2.52883 iter/s, 4.74527s/12 iters), loss = 4.67364
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I0428 13:28:42.968932 11373 solver.cpp:237] Train net output #0: loss = 4.67364 (* 1 = 4.67364 loss)
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I0428 13:28:42.968941 11373 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
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I0428 13:28:47.653481 11373 solver.cpp:218] Iteration 1392 (2.56173 iter/s, 4.68434s/12 iters), loss = 4.67373
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I0428 13:28:47.653527 11373 solver.cpp:237] Train net output #0: loss = 4.67373 (* 1 = 4.67373 loss)
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I0428 13:28:47.653537 11373 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
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I0428 13:28:52.442932 11373 solver.cpp:218] Iteration 1404 (2.50564 iter/s, 4.7892s/12 iters), loss = 4.59106
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I0428 13:28:52.443290 11373 solver.cpp:237] Train net output #0: loss = 4.59106 (* 1 = 4.59106 loss)
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I0428 13:28:52.443300 11373 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
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I0428 13:28:56.806731 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:28:57.142904 11373 solver.cpp:218] Iteration 1416 (2.55351 iter/s, 4.69941s/12 iters), loss = 4.71781
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I0428 13:28:57.142946 11373 solver.cpp:237] Train net output #0: loss = 4.71781 (* 1 = 4.71781 loss)
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I0428 13:28:57.142956 11373 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
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I0428 13:29:01.500049 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
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I0428 13:29:07.580960 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
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I0428 13:29:09.208964 11373 solver.cpp:330] Iteration 1428, Testing net (#0)
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I0428 13:29:09.208986 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:29:12.974050 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:29:13.559219 11373 solver.cpp:397] Test net output #0: accuracy = 0.0386029
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I0428 13:29:13.559295 11373 solver.cpp:397] Test net output #1: loss = 4.61206 (* 1 = 4.61206 loss)
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I0428 13:29:13.638118 11373 solver.cpp:218] Iteration 1428 (0.727516 iter/s, 16.4945s/12 iters), loss = 4.67238
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I0428 13:29:13.638161 11373 solver.cpp:237] Train net output #0: loss = 4.67238 (* 1 = 4.67238 loss)
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I0428 13:29:13.638170 11373 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
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I0428 13:29:17.534198 11373 solver.cpp:218] Iteration 1440 (3.0802 iter/s, 3.89586s/12 iters), loss = 4.65504
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I0428 13:29:17.534248 11373 solver.cpp:237] Train net output #0: loss = 4.65504 (* 1 = 4.65504 loss)
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I0428 13:29:17.534261 11373 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
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I0428 13:29:22.272825 11373 solver.cpp:218] Iteration 1452 (2.53252 iter/s, 4.73837s/12 iters), loss = 4.61786
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I0428 13:29:22.272877 11373 solver.cpp:237] Train net output #0: loss = 4.61786 (* 1 = 4.61786 loss)
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I0428 13:29:22.272888 11373 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
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I0428 13:29:27.018421 11373 solver.cpp:218] Iteration 1464 (2.5288 iter/s, 4.74533s/12 iters), loss = 4.63027
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I0428 13:29:27.018580 11373 solver.cpp:237] Train net output #0: loss = 4.63027 (* 1 = 4.63027 loss)
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I0428 13:29:27.018592 11373 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
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I0428 13:29:31.796121 11373 solver.cpp:218] Iteration 1476 (2.51186 iter/s, 4.77734s/12 iters), loss = 4.75459
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I0428 13:29:31.796160 11373 solver.cpp:237] Train net output #0: loss = 4.75459 (* 1 = 4.75459 loss)
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I0428 13:29:31.796169 11373 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
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I0428 13:29:36.610976 11373 solver.cpp:218] Iteration 1488 (2.49242 iter/s, 4.8146s/12 iters), loss = 4.57777
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I0428 13:29:36.611032 11373 solver.cpp:237] Train net output #0: loss = 4.57777 (* 1 = 4.57777 loss)
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I0428 13:29:36.611043 11373 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
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I0428 13:29:41.306726 11373 solver.cpp:218] Iteration 1500 (2.55564 iter/s, 4.69549s/12 iters), loss = 4.48798
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I0428 13:29:41.306767 11373 solver.cpp:237] Train net output #0: loss = 4.48798 (* 1 = 4.48798 loss)
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I0428 13:29:41.306777 11373 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
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I0428 13:29:46.085871 11373 solver.cpp:218] Iteration 1512 (2.51104 iter/s, 4.77889s/12 iters), loss = 4.46817
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I0428 13:29:46.085927 11373 solver.cpp:237] Train net output #0: loss = 4.46817 (* 1 = 4.46817 loss)
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I0428 13:29:46.085938 11373 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
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I0428 13:29:47.774631 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:29:50.816000 11373 solver.cpp:218] Iteration 1524 (2.53707 iter/s, 4.72987s/12 iters), loss = 4.34017
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I0428 13:29:50.816061 11373 solver.cpp:237] Train net output #0: loss = 4.34017 (* 1 = 4.34017 loss)
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I0428 13:29:50.816073 11373 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
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I0428 13:29:52.752749 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
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I0428 13:29:56.926667 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
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I0428 13:30:00.930362 11373 solver.cpp:330] Iteration 1530, Testing net (#0)
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I0428 13:30:00.930436 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:30:04.628885 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:30:05.261270 11373 solver.cpp:397] Test net output #0: accuracy = 0.0594363
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I0428 13:30:05.261310 11373 solver.cpp:397] Test net output #1: loss = 4.54014 (* 1 = 4.54014 loss)
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I0428 13:30:06.906416 11373 solver.cpp:218] Iteration 1536 (0.745819 iter/s, 16.0897s/12 iters), loss = 4.42923
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I0428 13:30:06.906483 11373 solver.cpp:237] Train net output #0: loss = 4.42923 (* 1 = 4.42923 loss)
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I0428 13:30:06.906497 11373 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
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I0428 13:30:11.702534 11373 solver.cpp:218] Iteration 1548 (2.50217 iter/s, 4.79583s/12 iters), loss = 4.40219
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I0428 13:30:11.702591 11373 solver.cpp:237] Train net output #0: loss = 4.40219 (* 1 = 4.40219 loss)
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I0428 13:30:11.702603 11373 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
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I0428 13:30:16.412096 11373 solver.cpp:218] Iteration 1560 (2.54815 iter/s, 4.7093s/12 iters), loss = 4.23232
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I0428 13:30:16.412148 11373 solver.cpp:237] Train net output #0: loss = 4.23232 (* 1 = 4.23232 loss)
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I0428 13:30:16.412159 11373 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
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I0428 13:30:21.187739 11373 solver.cpp:218] Iteration 1572 (2.51289 iter/s, 4.77539s/12 iters), loss = 4.35249
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I0428 13:30:21.187788 11373 solver.cpp:237] Train net output #0: loss = 4.35249 (* 1 = 4.35249 loss)
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I0428 13:30:21.187799 11373 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
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I0428 13:30:25.908051 11373 solver.cpp:218] Iteration 1584 (2.54234 iter/s, 4.72006s/12 iters), loss = 4.28097
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I0428 13:30:25.908115 11373 solver.cpp:237] Train net output #0: loss = 4.28097 (* 1 = 4.28097 loss)
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I0428 13:30:25.908128 11373 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
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I0428 13:30:30.701354 11373 solver.cpp:218] Iteration 1596 (2.50363 iter/s, 4.79304s/12 iters), loss = 4.2686
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I0428 13:30:30.701390 11373 solver.cpp:237] Train net output #0: loss = 4.2686 (* 1 = 4.2686 loss)
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I0428 13:30:30.701398 11373 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
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I0428 13:30:35.562412 11373 solver.cpp:218] Iteration 1608 (2.46873 iter/s, 4.8608s/12 iters), loss = 4.45788
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I0428 13:30:35.562597 11373 solver.cpp:237] Train net output #0: loss = 4.45788 (* 1 = 4.45788 loss)
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I0428 13:30:35.562616 11373 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
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I0428 13:30:39.273417 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:30:40.337813 11373 solver.cpp:218] Iteration 1620 (2.51308 iter/s, 4.77502s/12 iters), loss = 4.39352
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I0428 13:30:40.337852 11373 solver.cpp:237] Train net output #0: loss = 4.39352 (* 1 = 4.39352 loss)
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I0428 13:30:40.337862 11373 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
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I0428 13:30:44.572398 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
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I0428 13:30:46.492805 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
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I0428 13:30:49.586717 11373 solver.cpp:330] Iteration 1632, Testing net (#0)
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I0428 13:30:49.586742 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:30:53.357892 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:30:54.020185 11373 solver.cpp:397] Test net output #0: accuracy = 0.057598
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I0428 13:30:54.020221 11373 solver.cpp:397] Test net output #1: loss = 4.38481 (* 1 = 4.38481 loss)
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I0428 13:30:54.099074 11373 solver.cpp:218] Iteration 1632 (0.872051 iter/s, 13.7607s/12 iters), loss = 4.41461
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I0428 13:30:54.099113 11373 solver.cpp:237] Train net output #0: loss = 4.41461 (* 1 = 4.41461 loss)
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I0428 13:30:54.099123 11373 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
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I0428 13:30:58.126561 11373 solver.cpp:218] Iteration 1644 (2.97969 iter/s, 4.02727s/12 iters), loss = 4.08251
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I0428 13:30:58.126601 11373 solver.cpp:237] Train net output #0: loss = 4.08251 (* 1 = 4.08251 loss)
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I0428 13:30:58.126610 11373 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
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I0428 13:31:02.885311 11373 solver.cpp:218] Iteration 1656 (2.5218 iter/s, 4.7585s/12 iters), loss = 4.35712
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I0428 13:31:02.885357 11373 solver.cpp:237] Train net output #0: loss = 4.35712 (* 1 = 4.35712 loss)
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I0428 13:31:02.885365 11373 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
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I0428 13:31:07.703804 11373 solver.cpp:218] Iteration 1668 (2.49054 iter/s, 4.81824s/12 iters), loss = 4.41581
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I0428 13:31:07.703899 11373 solver.cpp:237] Train net output #0: loss = 4.41581 (* 1 = 4.41581 loss)
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I0428 13:31:07.703909 11373 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
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I0428 13:31:12.510557 11373 solver.cpp:218] Iteration 1680 (2.49664 iter/s, 4.80645s/12 iters), loss = 4.28096
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I0428 13:31:12.510596 11373 solver.cpp:237] Train net output #0: loss = 4.28096 (* 1 = 4.28096 loss)
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I0428 13:31:12.510603 11373 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
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I0428 13:31:17.240662 11373 solver.cpp:218] Iteration 1692 (2.53707 iter/s, 4.72986s/12 iters), loss = 4.26092
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I0428 13:31:17.240703 11373 solver.cpp:237] Train net output #0: loss = 4.26092 (* 1 = 4.26092 loss)
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I0428 13:31:17.240712 11373 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
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I0428 13:31:21.974256 11373 solver.cpp:218] Iteration 1704 (2.5352 iter/s, 4.73335s/12 iters), loss = 4.17022
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I0428 13:31:21.974294 11373 solver.cpp:237] Train net output #0: loss = 4.17022 (* 1 = 4.17022 loss)
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I0428 13:31:21.974303 11373 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
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I0428 13:31:26.711201 11373 solver.cpp:218] Iteration 1716 (2.53341 iter/s, 4.7367s/12 iters), loss = 4.46214
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I0428 13:31:26.711249 11373 solver.cpp:237] Train net output #0: loss = 4.46214 (* 1 = 4.46214 loss)
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I0428 13:31:26.711261 11373 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
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I0428 13:31:27.730778 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:31:31.541055 11373 solver.cpp:218] Iteration 1728 (2.48468 iter/s, 4.8296s/12 iters), loss = 4.25146
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I0428 13:31:31.541102 11373 solver.cpp:237] Train net output #0: loss = 4.25146 (* 1 = 4.25146 loss)
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I0428 13:31:31.541110 11373 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
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I0428 13:31:33.484988 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
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I0428 13:31:36.210404 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
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I0428 13:31:41.547338 11373 solver.cpp:330] Iteration 1734, Testing net (#0)
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I0428 13:31:41.547446 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:31:45.186570 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:31:45.954301 11373 solver.cpp:397] Test net output #0: accuracy = 0.0612745
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I0428 13:31:45.954331 11373 solver.cpp:397] Test net output #1: loss = 4.23175 (* 1 = 4.23175 loss)
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I0428 13:31:47.684729 11373 solver.cpp:218] Iteration 1740 (0.743358 iter/s, 16.143s/12 iters), loss = 4.22266
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I0428 13:31:47.684770 11373 solver.cpp:237] Train net output #0: loss = 4.22266 (* 1 = 4.22266 loss)
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I0428 13:31:47.684778 11373 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
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I0428 13:31:52.476523 11373 solver.cpp:218] Iteration 1752 (2.50442 iter/s, 4.79153s/12 iters), loss = 4.4174
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I0428 13:31:52.476572 11373 solver.cpp:237] Train net output #0: loss = 4.4174 (* 1 = 4.4174 loss)
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I0428 13:31:52.476584 11373 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
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I0428 13:31:57.223548 11373 solver.cpp:218] Iteration 1764 (2.52803 iter/s, 4.74677s/12 iters), loss = 4.10818
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I0428 13:31:57.223587 11373 solver.cpp:237] Train net output #0: loss = 4.10818 (* 1 = 4.10818 loss)
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I0428 13:31:57.223595 11373 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
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I0428 13:32:01.952841 11373 solver.cpp:218] Iteration 1776 (2.53751 iter/s, 4.72905s/12 iters), loss = 4.07563
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I0428 13:32:01.952881 11373 solver.cpp:237] Train net output #0: loss = 4.07563 (* 1 = 4.07563 loss)
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I0428 13:32:01.952889 11373 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
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I0428 13:32:06.670300 11373 solver.cpp:218] Iteration 1788 (2.54388 iter/s, 4.71721s/12 iters), loss = 4.17411
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I0428 13:32:06.670359 11373 solver.cpp:237] Train net output #0: loss = 4.17411 (* 1 = 4.17411 loss)
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I0428 13:32:06.670372 11373 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
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I0428 13:32:11.470471 11373 solver.cpp:218] Iteration 1800 (2.50005 iter/s, 4.79991s/12 iters), loss = 4.19936
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I0428 13:32:11.470510 11373 solver.cpp:237] Train net output #0: loss = 4.19936 (* 1 = 4.19936 loss)
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I0428 13:32:11.470518 11373 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
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I0428 13:32:16.219472 11373 solver.cpp:218] Iteration 1812 (2.52698 iter/s, 4.74875s/12 iters), loss = 3.93631
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I0428 13:32:16.219597 11373 solver.cpp:237] Train net output #0: loss = 3.93631 (* 1 = 3.93631 loss)
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I0428 13:32:16.219609 11373 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
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I0428 13:32:19.315325 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:32:21.035055 11373 solver.cpp:218] Iteration 1824 (2.49208 iter/s, 4.81526s/12 iters), loss = 4.20929
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I0428 13:32:21.035094 11373 solver.cpp:237] Train net output #0: loss = 4.20929 (* 1 = 4.20929 loss)
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I0428 13:32:21.035102 11373 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
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I0428 13:32:25.336083 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
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I0428 13:32:26.550820 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
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I0428 13:32:27.565963 11373 solver.cpp:330] Iteration 1836, Testing net (#0)
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I0428 13:32:27.565984 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:32:31.231225 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:32:31.975466 11373 solver.cpp:397] Test net output #0: accuracy = 0.0833333
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I0428 13:32:31.975497 11373 solver.cpp:397] Test net output #1: loss = 4.04347 (* 1 = 4.04347 loss)
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I0428 13:32:32.054386 11373 solver.cpp:218] Iteration 1836 (1.08904 iter/s, 11.0188s/12 iters), loss = 3.84376
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I0428 13:32:32.054425 11373 solver.cpp:237] Train net output #0: loss = 3.84376 (* 1 = 3.84376 loss)
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I0428 13:32:32.054435 11373 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
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I0428 13:32:36.034864 11373 solver.cpp:218] Iteration 1848 (3.01487 iter/s, 3.98027s/12 iters), loss = 3.87689
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I0428 13:32:36.034905 11373 solver.cpp:237] Train net output #0: loss = 3.87689 (* 1 = 3.87689 loss)
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I0428 13:32:36.034914 11373 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
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I0428 13:32:40.882589 11373 solver.cpp:218] Iteration 1860 (2.47552 iter/s, 4.84747s/12 iters), loss = 4.07721
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I0428 13:32:40.882652 11373 solver.cpp:237] Train net output #0: loss = 4.07721 (* 1 = 4.07721 loss)
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I0428 13:32:40.882664 11373 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
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I0428 13:32:45.628266 11373 solver.cpp:218] Iteration 1872 (2.52876 iter/s, 4.74541s/12 iters), loss = 3.92475
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I0428 13:32:45.628321 11373 solver.cpp:237] Train net output #0: loss = 3.92475 (* 1 = 3.92475 loss)
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I0428 13:32:45.628334 11373 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
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I0428 13:32:50.368019 11373 solver.cpp:218] Iteration 1884 (2.53192 iter/s, 4.73949s/12 iters), loss = 4.14419
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I0428 13:32:50.368321 11373 solver.cpp:237] Train net output #0: loss = 4.14419 (* 1 = 4.14419 loss)
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I0428 13:32:50.368335 11373 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
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I0428 13:32:55.218510 11373 solver.cpp:218] Iteration 1896 (2.47424 iter/s, 4.84998s/12 iters), loss = 4.00236
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I0428 13:32:55.218556 11373 solver.cpp:237] Train net output #0: loss = 4.00236 (* 1 = 4.00236 loss)
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I0428 13:32:55.218565 11373 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
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I0428 13:32:59.973570 11373 solver.cpp:218] Iteration 1908 (2.52376 iter/s, 4.7548s/12 iters), loss = 4.08045
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I0428 13:32:59.973623 11373 solver.cpp:237] Train net output #0: loss = 4.08045 (* 1 = 4.08045 loss)
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I0428 13:32:59.973635 11373 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
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I0428 13:33:04.738508 11373 solver.cpp:218] Iteration 1920 (2.51853 iter/s, 4.76468s/12 iters), loss = 4.01649
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I0428 13:33:04.738551 11373 solver.cpp:237] Train net output #0: loss = 4.01649 (* 1 = 4.01649 loss)
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I0428 13:33:04.738561 11373 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
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I0428 13:33:05.053290 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:33:09.424808 11373 solver.cpp:218] Iteration 1932 (2.56079 iter/s, 4.68605s/12 iters), loss = 3.68669
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I0428 13:33:09.424855 11373 solver.cpp:237] Train net output #0: loss = 3.68669 (* 1 = 3.68669 loss)
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I0428 13:33:09.424866 11373 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
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I0428 13:33:11.331738 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
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I0428 13:33:18.610541 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
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I0428 13:33:23.170636 11373 solver.cpp:330] Iteration 1938, Testing net (#0)
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I0428 13:33:23.170713 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:33:26.786491 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:33:27.562810 11373 solver.cpp:397] Test net output #0: accuracy = 0.0839461
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I0428 13:33:27.562840 11373 solver.cpp:397] Test net output #1: loss = 3.99157 (* 1 = 3.99157 loss)
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I0428 13:33:29.304071 11373 solver.cpp:218] Iteration 1944 (0.60367 iter/s, 19.8784s/12 iters), loss = 3.77562
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I0428 13:33:29.304111 11373 solver.cpp:237] Train net output #0: loss = 3.77562 (* 1 = 3.77562 loss)
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I0428 13:33:29.304121 11373 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
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I0428 13:33:33.991874 11373 solver.cpp:218] Iteration 1956 (2.55997 iter/s, 4.68756s/12 iters), loss = 4.1207
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I0428 13:33:33.991920 11373 solver.cpp:237] Train net output #0: loss = 4.1207 (* 1 = 4.1207 loss)
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I0428 13:33:33.991930 11373 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
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I0428 13:33:38.738593 11373 solver.cpp:218] Iteration 1968 (2.5282 iter/s, 4.74647s/12 iters), loss = 3.75269
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I0428 13:33:38.738634 11373 solver.cpp:237] Train net output #0: loss = 3.75269 (* 1 = 3.75269 loss)
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I0428 13:33:38.738643 11373 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
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I0428 13:33:43.509974 11373 solver.cpp:218] Iteration 1980 (2.51513 iter/s, 4.77113s/12 iters), loss = 3.94759
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I0428 13:33:43.510015 11373 solver.cpp:237] Train net output #0: loss = 3.94759 (* 1 = 3.94759 loss)
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I0428 13:33:43.510022 11373 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
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I0428 13:33:48.236392 11373 solver.cpp:218] Iteration 1992 (2.53905 iter/s, 4.72618s/12 iters), loss = 3.91258
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I0428 13:33:48.236425 11373 solver.cpp:237] Train net output #0: loss = 3.91258 (* 1 = 3.91258 loss)
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I0428 13:33:48.236433 11373 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
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I0428 13:33:52.995419 11373 solver.cpp:218] Iteration 2004 (2.52165 iter/s, 4.75879s/12 iters), loss = 3.86342
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I0428 13:33:52.995456 11373 solver.cpp:237] Train net output #0: loss = 3.86342 (* 1 = 3.86342 loss)
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I0428 13:33:52.995465 11373 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
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I0428 13:33:57.745999 11373 solver.cpp:218] Iteration 2016 (2.52614 iter/s, 4.75033s/12 iters), loss = 3.99458
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I0428 13:33:57.746701 11373 solver.cpp:237] Train net output #0: loss = 3.99458 (* 1 = 3.99458 loss)
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I0428 13:33:57.746714 11373 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
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I0428 13:34:00.184089 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:34:02.624133 11373 solver.cpp:218] Iteration 2028 (2.46042 iter/s, 4.87723s/12 iters), loss = 3.86666
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I0428 13:34:02.624179 11373 solver.cpp:237] Train net output #0: loss = 3.86666 (* 1 = 3.86666 loss)
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I0428 13:34:02.624189 11373 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
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I0428 13:34:06.979648 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
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I0428 13:34:09.750555 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
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I0428 13:34:10.752543 11373 solver.cpp:330] Iteration 2040, Testing net (#0)
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I0428 13:34:10.752569 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:34:14.245486 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:34:15.063251 11373 solver.cpp:397] Test net output #0: accuracy = 0.0814951
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I0428 13:34:15.063289 11373 solver.cpp:397] Test net output #1: loss = 3.96648 (* 1 = 3.96648 loss)
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I0428 13:34:15.142391 11373 solver.cpp:218] Iteration 2040 (0.958643 iter/s, 12.5177s/12 iters), loss = 4.02491
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I0428 13:34:15.142431 11373 solver.cpp:237] Train net output #0: loss = 4.02491 (* 1 = 4.02491 loss)
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I0428 13:34:15.142441 11373 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
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I0428 13:34:19.147403 11373 solver.cpp:218] Iteration 2052 (2.99641 iter/s, 4.00479s/12 iters), loss = 3.8872
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I0428 13:34:19.147444 11373 solver.cpp:237] Train net output #0: loss = 3.8872 (* 1 = 3.8872 loss)
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I0428 13:34:19.147454 11373 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
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I0428 13:34:20.667889 11373 blocking_queue.cpp:49] Waiting for data
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I0428 13:34:23.969496 11373 solver.cpp:218] Iteration 2064 (2.48867 iter/s, 4.82185s/12 iters), loss = 3.78608
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I0428 13:34:23.969534 11373 solver.cpp:237] Train net output #0: loss = 3.78608 (* 1 = 3.78608 loss)
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I0428 13:34:23.969542 11373 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
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I0428 13:34:28.719219 11373 solver.cpp:218] Iteration 2076 (2.5266 iter/s, 4.74947s/12 iters), loss = 3.73905
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I0428 13:34:28.719343 11373 solver.cpp:237] Train net output #0: loss = 3.73905 (* 1 = 3.73905 loss)
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I0428 13:34:28.719358 11373 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
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I0428 13:34:33.648000 11373 solver.cpp:218] Iteration 2088 (2.43485 iter/s, 4.92844s/12 iters), loss = 3.67483
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I0428 13:34:33.648054 11373 solver.cpp:237] Train net output #0: loss = 3.67483 (* 1 = 3.67483 loss)
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I0428 13:34:33.648066 11373 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
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I0428 13:34:38.383917 11373 solver.cpp:218] Iteration 2100 (2.53396 iter/s, 4.73566s/12 iters), loss = 3.74619
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I0428 13:34:38.383957 11373 solver.cpp:237] Train net output #0: loss = 3.74619 (* 1 = 3.74619 loss)
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I0428 13:34:38.383966 11373 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
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I0428 13:34:43.078115 11373 solver.cpp:218] Iteration 2112 (2.55648 iter/s, 4.69395s/12 iters), loss = 3.4628
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I0428 13:34:43.078157 11373 solver.cpp:237] Train net output #0: loss = 3.4628 (* 1 = 3.4628 loss)
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I0428 13:34:43.078166 11373 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
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I0428 13:34:47.509559 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:34:47.816996 11373 solver.cpp:218] Iteration 2124 (2.53238 iter/s, 4.73863s/12 iters), loss = 3.58772
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I0428 13:34:47.817039 11373 solver.cpp:237] Train net output #0: loss = 3.58772 (* 1 = 3.58772 loss)
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I0428 13:34:47.817049 11373 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
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I0428 13:34:52.543527 11373 solver.cpp:218] Iteration 2136 (2.53899 iter/s, 4.72628s/12 iters), loss = 3.61411
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I0428 13:34:52.543578 11373 solver.cpp:237] Train net output #0: loss = 3.61411 (* 1 = 3.61411 loss)
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I0428 13:34:52.543591 11373 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
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I0428 13:34:54.495849 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
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I0428 13:34:56.097002 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
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I0428 13:35:00.593123 11373 solver.cpp:330] Iteration 2142, Testing net (#0)
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I0428 13:35:00.593219 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:35:04.083293 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:35:04.937443 11373 solver.cpp:397] Test net output #0: accuracy = 0.0968137
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I0428 13:35:04.937469 11373 solver.cpp:397] Test net output #1: loss = 3.89031 (* 1 = 3.89031 loss)
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I0428 13:35:06.690248 11373 solver.cpp:218] Iteration 2148 (0.848291 iter/s, 14.1461s/12 iters), loss = 3.65347
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I0428 13:35:06.690289 11373 solver.cpp:237] Train net output #0: loss = 3.65347 (* 1 = 3.65347 loss)
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I0428 13:35:06.690297 11373 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
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I0428 13:35:11.395661 11373 solver.cpp:218] Iteration 2160 (2.55039 iter/s, 4.70516s/12 iters), loss = 3.67302
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I0428 13:35:11.395704 11373 solver.cpp:237] Train net output #0: loss = 3.67302 (* 1 = 3.67302 loss)
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I0428 13:35:11.395714 11373 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
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I0428 13:35:16.218755 11373 solver.cpp:218] Iteration 2172 (2.48816 iter/s, 4.82284s/12 iters), loss = 3.64174
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I0428 13:35:16.218791 11373 solver.cpp:237] Train net output #0: loss = 3.64174 (* 1 = 3.64174 loss)
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I0428 13:35:16.218801 11373 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
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I0428 13:35:21.036826 11373 solver.cpp:218] Iteration 2184 (2.49075 iter/s, 4.81782s/12 iters), loss = 3.48333
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I0428 13:35:21.036880 11373 solver.cpp:237] Train net output #0: loss = 3.48333 (* 1 = 3.48333 loss)
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I0428 13:35:21.036892 11373 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
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I0428 13:35:25.822525 11373 solver.cpp:218] Iteration 2196 (2.50761 iter/s, 4.78544s/12 iters), loss = 3.6826
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I0428 13:35:25.822568 11373 solver.cpp:237] Train net output #0: loss = 3.6826 (* 1 = 3.6826 loss)
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I0428 13:35:25.822578 11373 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
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I0428 13:35:30.554170 11373 solver.cpp:218] Iteration 2208 (2.53625 iter/s, 4.7314s/12 iters), loss = 3.51399
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I0428 13:35:30.554211 11373 solver.cpp:237] Train net output #0: loss = 3.51399 (* 1 = 3.51399 loss)
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I0428 13:35:30.554219 11373 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
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I0428 13:35:35.235651 11373 solver.cpp:218] Iteration 2220 (2.56342 iter/s, 4.68124s/12 iters), loss = 3.38736
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I0428 13:35:35.235756 11373 solver.cpp:237] Train net output #0: loss = 3.38736 (* 1 = 3.38736 loss)
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I0428 13:35:35.235765 11373 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
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I0428 13:35:36.955886 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:35:39.926837 11373 solver.cpp:218] Iteration 2232 (2.55816 iter/s, 4.69088s/12 iters), loss = 3.28206
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I0428 13:35:39.926878 11373 solver.cpp:237] Train net output #0: loss = 3.28206 (* 1 = 3.28206 loss)
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I0428 13:35:39.926887 11373 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
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I0428 13:35:44.190762 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
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I0428 13:35:48.178675 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
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I0428 13:35:53.947374 11373 solver.cpp:330] Iteration 2244, Testing net (#0)
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I0428 13:35:53.947394 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:35:57.437852 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:35:58.338248 11373 solver.cpp:397] Test net output #0: accuracy = 0.122549
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I0428 13:35:58.338295 11373 solver.cpp:397] Test net output #1: loss = 3.70254 (* 1 = 3.70254 loss)
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I0428 13:35:58.415977 11373 solver.cpp:218] Iteration 2244 (0.649058 iter/s, 18.4883s/12 iters), loss = 3.37055
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I0428 13:35:58.416025 11373 solver.cpp:237] Train net output #0: loss = 3.37055 (* 1 = 3.37055 loss)
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I0428 13:35:58.416034 11373 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
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I0428 13:36:02.496853 11373 solver.cpp:218] Iteration 2256 (2.94071 iter/s, 4.08065s/12 iters), loss = 3.47634
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I0428 13:36:02.496896 11373 solver.cpp:237] Train net output #0: loss = 3.47634 (* 1 = 3.47634 loss)
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I0428 13:36:02.496904 11373 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
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I0428 13:36:07.270233 11373 solver.cpp:218] Iteration 2268 (2.51407 iter/s, 4.77313s/12 iters), loss = 3.33374
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I0428 13:36:07.270359 11373 solver.cpp:237] Train net output #0: loss = 3.33374 (* 1 = 3.33374 loss)
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I0428 13:36:07.270367 11373 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
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I0428 13:36:12.069923 11373 solver.cpp:218] Iteration 2280 (2.50034 iter/s, 4.79935s/12 iters), loss = 3.39587
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I0428 13:36:12.069968 11373 solver.cpp:237] Train net output #0: loss = 3.39587 (* 1 = 3.39587 loss)
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I0428 13:36:12.069977 11373 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
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I0428 13:36:16.789757 11373 solver.cpp:218] Iteration 2292 (2.5426 iter/s, 4.71959s/12 iters), loss = 3.27348
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I0428 13:36:16.789803 11373 solver.cpp:237] Train net output #0: loss = 3.27348 (* 1 = 3.27348 loss)
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I0428 13:36:16.789811 11373 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
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I0428 13:36:21.581769 11373 solver.cpp:218] Iteration 2304 (2.5043 iter/s, 4.79175s/12 iters), loss = 3.52503
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I0428 13:36:21.581825 11373 solver.cpp:237] Train net output #0: loss = 3.52503 (* 1 = 3.52503 loss)
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I0428 13:36:21.581840 11373 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
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I0428 13:36:26.523988 11373 solver.cpp:218] Iteration 2316 (2.42819 iter/s, 4.94195s/12 iters), loss = 3.39497
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I0428 13:36:26.524040 11373 solver.cpp:237] Train net output #0: loss = 3.39497 (* 1 = 3.39497 loss)
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I0428 13:36:26.524052 11373 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
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I0428 13:36:30.238243 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:36:31.233268 11373 solver.cpp:218] Iteration 2328 (2.5483 iter/s, 4.70903s/12 iters), loss = 3.25704
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I0428 13:36:31.233310 11373 solver.cpp:237] Train net output #0: loss = 3.25704 (* 1 = 3.25704 loss)
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I0428 13:36:31.233321 11373 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
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I0428 13:36:35.921785 11373 solver.cpp:218] Iteration 2340 (2.55958 iter/s, 4.68826s/12 iters), loss = 3.51774
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I0428 13:36:35.921854 11373 solver.cpp:237] Train net output #0: loss = 3.51774 (* 1 = 3.51774 loss)
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I0428 13:36:35.921873 11373 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
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I0428 13:36:37.838085 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
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I0428 13:36:41.329355 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
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I0428 13:36:44.364786 11373 solver.cpp:330] Iteration 2346, Testing net (#0)
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I0428 13:36:44.364814 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:36:47.717947 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:36:48.670123 11373 solver.cpp:397] Test net output #0: accuracy = 0.152574
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I0428 13:36:48.670181 11373 solver.cpp:397] Test net output #1: loss = 3.58292 (* 1 = 3.58292 loss)
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I0428 13:36:50.357383 11373 solver.cpp:218] Iteration 2352 (0.831316 iter/s, 14.4349s/12 iters), loss = 3.33079
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I0428 13:36:50.357424 11373 solver.cpp:237] Train net output #0: loss = 3.33079 (* 1 = 3.33079 loss)
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I0428 13:36:50.357434 11373 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
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I0428 13:36:54.991629 11373 solver.cpp:218] Iteration 2364 (2.58955 iter/s, 4.634s/12 iters), loss = 3.36762
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I0428 13:36:54.991669 11373 solver.cpp:237] Train net output #0: loss = 3.36762 (* 1 = 3.36762 loss)
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I0428 13:36:54.991679 11373 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
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I0428 13:36:59.709700 11373 solver.cpp:218] Iteration 2376 (2.54355 iter/s, 4.71782s/12 iters), loss = 3.26149
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I0428 13:36:59.709739 11373 solver.cpp:237] Train net output #0: loss = 3.26149 (* 1 = 3.26149 loss)
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I0428 13:36:59.709748 11373 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
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I0428 13:37:04.391650 11373 solver.cpp:218] Iteration 2388 (2.56317 iter/s, 4.6817s/12 iters), loss = 3.25378
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I0428 13:37:04.391695 11373 solver.cpp:237] Train net output #0: loss = 3.25378 (* 1 = 3.25378 loss)
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I0428 13:37:04.391705 11373 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
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I0428 13:37:09.158918 11373 solver.cpp:218] Iteration 2400 (2.5173 iter/s, 4.76701s/12 iters), loss = 3.11984
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I0428 13:37:09.159054 11373 solver.cpp:237] Train net output #0: loss = 3.11984 (* 1 = 3.11984 loss)
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I0428 13:37:09.159065 11373 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
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I0428 13:37:13.913761 11373 solver.cpp:218] Iteration 2412 (2.52392 iter/s, 4.7545s/12 iters), loss = 3.40112
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I0428 13:37:13.913806 11373 solver.cpp:237] Train net output #0: loss = 3.40112 (* 1 = 3.40112 loss)
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I0428 13:37:13.913815 11373 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
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I0428 13:37:18.809147 11373 solver.cpp:218] Iteration 2424 (2.45142 iter/s, 4.89512s/12 iters), loss = 3.12353
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I0428 13:37:18.809203 11373 solver.cpp:237] Train net output #0: loss = 3.12353 (* 1 = 3.12353 loss)
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I0428 13:37:18.809217 11373 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
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I0428 13:37:19.853420 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:37:23.727690 11373 solver.cpp:218] Iteration 2436 (2.43988 iter/s, 4.91827s/12 iters), loss = 3.4897
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I0428 13:37:23.727739 11373 solver.cpp:237] Train net output #0: loss = 3.4897 (* 1 = 3.4897 loss)
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I0428 13:37:23.727748 11373 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
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I0428 13:37:28.126672 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
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I0428 13:37:29.358011 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
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I0428 13:37:30.347693 11373 solver.cpp:330] Iteration 2448, Testing net (#0)
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I0428 13:37:30.347718 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:37:33.897276 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:37:34.866092 11373 solver.cpp:397] Test net output #0: accuracy = 0.170343
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I0428 13:37:34.866127 11373 solver.cpp:397] Test net output #1: loss = 3.44516 (* 1 = 3.44516 loss)
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I0428 13:37:34.944630 11373 solver.cpp:218] Iteration 2448 (1.06986 iter/s, 11.2164s/12 iters), loss = 3.2625
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I0428 13:37:34.944672 11373 solver.cpp:237] Train net output #0: loss = 3.2625 (* 1 = 3.2625 loss)
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I0428 13:37:34.944681 11373 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
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I0428 13:37:39.001446 11373 solver.cpp:218] Iteration 2460 (2.95815 iter/s, 4.05659s/12 iters), loss = 3.17363
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I0428 13:37:39.001492 11373 solver.cpp:237] Train net output #0: loss = 3.17363 (* 1 = 3.17363 loss)
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I0428 13:37:39.001500 11373 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
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I0428 13:37:43.712185 11373 solver.cpp:218] Iteration 2472 (2.5475 iter/s, 4.71049s/12 iters), loss = 3.32455
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I0428 13:37:43.712705 11373 solver.cpp:237] Train net output #0: loss = 3.32455 (* 1 = 3.32455 loss)
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I0428 13:37:43.712714 11373 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
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I0428 13:37:48.456506 11373 solver.cpp:218] Iteration 2484 (2.52973 iter/s, 4.74358s/12 iters), loss = 3.32704
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I0428 13:37:48.456548 11373 solver.cpp:237] Train net output #0: loss = 3.32704 (* 1 = 3.32704 loss)
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I0428 13:37:48.456557 11373 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
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I0428 13:37:53.191175 11373 solver.cpp:218] Iteration 2496 (2.53463 iter/s, 4.73443s/12 iters), loss = 3.40708
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I0428 13:37:53.191212 11373 solver.cpp:237] Train net output #0: loss = 3.40708 (* 1 = 3.40708 loss)
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I0428 13:37:53.191220 11373 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
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I0428 13:37:57.974896 11373 solver.cpp:218] Iteration 2508 (2.50864 iter/s, 4.78347s/12 iters), loss = 3.03213
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I0428 13:37:57.974954 11373 solver.cpp:237] Train net output #0: loss = 3.03213 (* 1 = 3.03213 loss)
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I0428 13:37:57.974967 11373 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
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I0428 13:38:02.681196 11373 solver.cpp:218] Iteration 2520 (2.54991 iter/s, 4.70604s/12 iters), loss = 3.06565
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I0428 13:38:02.681241 11373 solver.cpp:237] Train net output #0: loss = 3.06565 (* 1 = 3.06565 loss)
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I0428 13:38:02.681252 11373 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
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I0428 13:38:05.746397 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:38:07.434764 11373 solver.cpp:218] Iteration 2532 (2.52455 iter/s, 4.75332s/12 iters), loss = 3.25757
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I0428 13:38:07.434810 11373 solver.cpp:237] Train net output #0: loss = 3.25757 (* 1 = 3.25757 loss)
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I0428 13:38:07.434820 11373 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
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I0428 13:38:12.186878 11373 solver.cpp:218] Iteration 2544 (2.52533 iter/s, 4.75186s/12 iters), loss = 2.93816
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I0428 13:38:12.186928 11373 solver.cpp:237] Train net output #0: loss = 2.93816 (* 1 = 2.93816 loss)
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I0428 13:38:12.186942 11373 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
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I0428 13:38:14.227341 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
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I0428 13:38:25.186350 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
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I0428 13:38:27.813917 11373 solver.cpp:330] Iteration 2550, Testing net (#0)
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I0428 13:38:27.813937 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:38:31.177016 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:38:32.215380 11373 solver.cpp:397] Test net output #0: accuracy = 0.190564
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I0428 13:38:32.215418 11373 solver.cpp:397] Test net output #1: loss = 3.32994 (* 1 = 3.32994 loss)
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I0428 13:38:33.932842 11373 solver.cpp:218] Iteration 2556 (0.55185 iter/s, 21.745s/12 iters), loss = 2.96082
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I0428 13:38:33.932885 11373 solver.cpp:237] Train net output #0: loss = 2.96082 (* 1 = 2.96082 loss)
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I0428 13:38:33.932894 11373 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
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I0428 13:38:38.650609 11373 solver.cpp:218] Iteration 2568 (2.54371 iter/s, 4.71752s/12 iters), loss = 3.25713
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I0428 13:38:38.650648 11373 solver.cpp:237] Train net output #0: loss = 3.25713 (* 1 = 3.25713 loss)
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I0428 13:38:38.650657 11373 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
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I0428 13:38:43.290292 11373 solver.cpp:218] Iteration 2580 (2.58652 iter/s, 4.63944s/12 iters), loss = 2.98992
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I0428 13:38:43.290335 11373 solver.cpp:237] Train net output #0: loss = 2.98992 (* 1 = 2.98992 loss)
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I0428 13:38:43.290343 11373 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
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I0428 13:38:48.101971 11373 solver.cpp:218] Iteration 2592 (2.49406 iter/s, 4.81143s/12 iters), loss = 3.25405
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I0428 13:38:48.102095 11373 solver.cpp:237] Train net output #0: loss = 3.25405 (* 1 = 3.25405 loss)
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I0428 13:38:48.102105 11373 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
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I0428 13:38:52.796605 11373 solver.cpp:218] Iteration 2604 (2.55629 iter/s, 4.6943s/12 iters), loss = 3.07086
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I0428 13:38:52.796659 11373 solver.cpp:237] Train net output #0: loss = 3.07086 (* 1 = 3.07086 loss)
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I0428 13:38:52.796671 11373 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
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I0428 13:38:57.602128 11373 solver.cpp:218] Iteration 2616 (2.49726 iter/s, 4.80526s/12 iters), loss = 3.08753
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I0428 13:38:57.602172 11373 solver.cpp:237] Train net output #0: loss = 3.08753 (* 1 = 3.08753 loss)
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I0428 13:38:57.602181 11373 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
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I0428 13:39:02.354025 11373 solver.cpp:218] Iteration 2628 (2.52544 iter/s, 4.75165s/12 iters), loss = 3.11364
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I0428 13:39:02.354075 11373 solver.cpp:237] Train net output #0: loss = 3.11364 (* 1 = 3.11364 loss)
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I0428 13:39:02.354089 11373 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
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I0428 13:39:02.736624 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:39:07.055799 11373 solver.cpp:218] Iteration 2640 (2.55237 iter/s, 4.70152s/12 iters), loss = 2.92013
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I0428 13:39:07.055861 11373 solver.cpp:237] Train net output #0: loss = 2.92013 (* 1 = 2.92013 loss)
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I0428 13:39:07.055873 11373 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
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I0428 13:39:11.362624 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
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I0428 13:39:16.833597 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
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I0428 13:39:17.940312 11373 solver.cpp:330] Iteration 2652, Testing net (#0)
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I0428 13:39:17.940333 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:39:21.200457 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:39:22.247505 11373 solver.cpp:397] Test net output #0: accuracy = 0.19424
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I0428 13:39:22.247535 11373 solver.cpp:397] Test net output #1: loss = 3.24572 (* 1 = 3.24572 loss)
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I0428 13:39:22.326437 11373 solver.cpp:218] Iteration 2652 (0.785857 iter/s, 15.27s/12 iters), loss = 2.87213
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I0428 13:39:22.326478 11373 solver.cpp:237] Train net output #0: loss = 2.87213 (* 1 = 2.87213 loss)
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I0428 13:39:22.326488 11373 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
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I0428 13:39:26.310034 11373 solver.cpp:218] Iteration 2664 (3.01251 iter/s, 3.98338s/12 iters), loss = 3.14833
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I0428 13:39:26.310081 11373 solver.cpp:237] Train net output #0: loss = 3.14833 (* 1 = 3.14833 loss)
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I0428 13:39:26.310091 11373 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
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I0428 13:39:31.001572 11373 solver.cpp:218] Iteration 2676 (2.55793 iter/s, 4.69129s/12 iters), loss = 2.88322
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I0428 13:39:31.001613 11373 solver.cpp:237] Train net output #0: loss = 2.88322 (* 1 = 2.88322 loss)
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I0428 13:39:31.001622 11373 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
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I0428 13:39:35.734901 11373 solver.cpp:218] Iteration 2688 (2.53535 iter/s, 4.73308s/12 iters), loss = 2.99357
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I0428 13:39:35.734946 11373 solver.cpp:237] Train net output #0: loss = 2.99357 (* 1 = 2.99357 loss)
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I0428 13:39:35.734954 11373 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
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I0428 13:39:40.471820 11373 solver.cpp:218] Iteration 2700 (2.53342 iter/s, 4.73667s/12 iters), loss = 2.99214
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I0428 13:39:40.471864 11373 solver.cpp:237] Train net output #0: loss = 2.99214 (* 1 = 2.99214 loss)
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I0428 13:39:40.471873 11373 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
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I0428 13:39:45.145530 11373 solver.cpp:218] Iteration 2712 (2.56769 iter/s, 4.67346s/12 iters), loss = 2.94618
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I0428 13:39:45.145577 11373 solver.cpp:237] Train net output #0: loss = 2.94618 (* 1 = 2.94618 loss)
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I0428 13:39:45.145587 11373 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
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I0428 13:39:49.893410 11373 solver.cpp:218] Iteration 2724 (2.52758 iter/s, 4.74762s/12 iters), loss = 2.87048
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I0428 13:39:49.893453 11373 solver.cpp:237] Train net output #0: loss = 2.87048 (* 1 = 2.87048 loss)
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I0428 13:39:49.893462 11373 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
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I0428 13:39:52.326519 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:39:54.628286 11373 solver.cpp:218] Iteration 2736 (2.53452 iter/s, 4.73463s/12 iters), loss = 2.68825
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I0428 13:39:54.628324 11373 solver.cpp:237] Train net output #0: loss = 2.68825 (* 1 = 2.68825 loss)
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I0428 13:39:54.628332 11373 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
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I0428 13:39:59.415184 11373 solver.cpp:218] Iteration 2748 (2.50698 iter/s, 4.78664s/12 iters), loss = 2.95322
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I0428 13:39:59.415238 11373 solver.cpp:237] Train net output #0: loss = 2.95322 (* 1 = 2.95322 loss)
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I0428 13:39:59.415251 11373 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
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I0428 13:40:01.351621 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
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I0428 13:40:07.225700 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
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I0428 13:40:10.939672 11373 solver.cpp:330] Iteration 2754, Testing net (#0)
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I0428 13:40:10.939697 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:40:14.004640 11373 blocking_queue.cpp:49] Waiting for data
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I0428 13:40:14.243965 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:40:15.333034 11373 solver.cpp:397] Test net output #0: accuracy = 0.223039
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I0428 13:40:15.333063 11373 solver.cpp:397] Test net output #1: loss = 3.18698 (* 1 = 3.18698 loss)
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I0428 13:40:17.039994 11373 solver.cpp:218] Iteration 2760 (0.680888 iter/s, 17.624s/12 iters), loss = 2.67638
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I0428 13:40:17.040060 11373 solver.cpp:237] Train net output #0: loss = 2.67638 (* 1 = 2.67638 loss)
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I0428 13:40:17.040071 11373 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
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I0428 13:40:21.770921 11373 solver.cpp:218] Iteration 2772 (2.53664 iter/s, 4.73066s/12 iters), loss = 2.86184
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I0428 13:40:21.770962 11373 solver.cpp:237] Train net output #0: loss = 2.86184 (* 1 = 2.86184 loss)
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I0428 13:40:21.770970 11373 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
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I0428 13:40:26.531584 11373 solver.cpp:218] Iteration 2784 (2.52079 iter/s, 4.76041s/12 iters), loss = 2.84422
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I0428 13:40:26.532112 11373 solver.cpp:237] Train net output #0: loss = 2.84422 (* 1 = 2.84422 loss)
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I0428 13:40:26.532126 11373 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
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I0428 13:40:31.383008 11373 solver.cpp:218] Iteration 2796 (2.47388 iter/s, 4.85068s/12 iters), loss = 2.86212
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I0428 13:40:31.383065 11373 solver.cpp:237] Train net output #0: loss = 2.86212 (* 1 = 2.86212 loss)
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I0428 13:40:31.383076 11373 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
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I0428 13:40:36.118777 11373 solver.cpp:218] Iteration 2808 (2.53405 iter/s, 4.73551s/12 iters), loss = 2.88516
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I0428 13:40:36.118834 11373 solver.cpp:237] Train net output #0: loss = 2.88516 (* 1 = 2.88516 loss)
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I0428 13:40:36.118846 11373 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
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I0428 13:40:40.856015 11373 solver.cpp:218] Iteration 2820 (2.53326 iter/s, 4.73698s/12 iters), loss = 2.67097
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I0428 13:40:40.856057 11373 solver.cpp:237] Train net output #0: loss = 2.67097 (* 1 = 2.67097 loss)
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I0428 13:40:40.856065 11373 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
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I0428 13:40:45.392318 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:40:45.670316 11373 solver.cpp:218] Iteration 2832 (2.4927 iter/s, 4.81405s/12 iters), loss = 2.79611
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I0428 13:40:45.670356 11373 solver.cpp:237] Train net output #0: loss = 2.79611 (* 1 = 2.79611 loss)
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I0428 13:40:45.670365 11373 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
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I0428 13:40:50.420648 11373 solver.cpp:218] Iteration 2844 (2.52627 iter/s, 4.75008s/12 iters), loss = 2.81702
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I0428 13:40:50.420696 11373 solver.cpp:237] Train net output #0: loss = 2.81702 (* 1 = 2.81702 loss)
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I0428 13:40:50.420707 11373 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
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I0428 13:40:54.687620 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
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I0428 13:41:00.074434 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
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I0428 13:41:02.395706 11373 solver.cpp:330] Iteration 2856, Testing net (#0)
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I0428 13:41:02.395727 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:41:05.781746 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:41:06.910455 11373 solver.cpp:397] Test net output #0: accuracy = 0.210784
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I0428 13:41:06.910486 11373 solver.cpp:397] Test net output #1: loss = 3.21039 (* 1 = 3.21039 loss)
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I0428 13:41:06.988940 11373 solver.cpp:218] Iteration 2856 (0.724307 iter/s, 16.5676s/12 iters), loss = 2.89289
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I0428 13:41:06.988981 11373 solver.cpp:237] Train net output #0: loss = 2.89289 (* 1 = 2.89289 loss)
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I0428 13:41:06.988989 11373 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
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I0428 13:41:11.019279 11373 solver.cpp:218] Iteration 2868 (2.97758 iter/s, 4.03012s/12 iters), loss = 2.6832
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I0428 13:41:11.019320 11373 solver.cpp:237] Train net output #0: loss = 2.6832 (* 1 = 2.6832 loss)
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I0428 13:41:11.019327 11373 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
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I0428 13:41:15.723188 11373 solver.cpp:218] Iteration 2880 (2.5512 iter/s, 4.70366s/12 iters), loss = 2.96806
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I0428 13:41:15.723235 11373 solver.cpp:237] Train net output #0: loss = 2.96806 (* 1 = 2.96806 loss)
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I0428 13:41:15.723246 11373 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
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I0428 13:41:20.461987 11373 solver.cpp:218] Iteration 2892 (2.53242 iter/s, 4.73855s/12 iters), loss = 2.90024
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I0428 13:41:20.462028 11373 solver.cpp:237] Train net output #0: loss = 2.90024 (* 1 = 2.90024 loss)
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I0428 13:41:20.462036 11373 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
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I0428 13:41:25.213316 11373 solver.cpp:218] Iteration 2904 (2.52574 iter/s, 4.75108s/12 iters), loss = 2.72377
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I0428 13:41:25.213372 11373 solver.cpp:237] Train net output #0: loss = 2.72377 (* 1 = 2.72377 loss)
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I0428 13:41:25.213384 11373 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
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I0428 13:41:29.912809 11373 solver.cpp:218] Iteration 2916 (2.55361 iter/s, 4.69924s/12 iters), loss = 2.63025
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I0428 13:41:29.912850 11373 solver.cpp:237] Train net output #0: loss = 2.63025 (* 1 = 2.63025 loss)
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I0428 13:41:29.912858 11373 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
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I0428 13:41:34.644433 11373 solver.cpp:218] Iteration 2928 (2.53626 iter/s, 4.73138s/12 iters), loss = 2.386
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I0428 13:41:34.644584 11373 solver.cpp:237] Train net output #0: loss = 2.386 (* 1 = 2.386 loss)
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I0428 13:41:34.644593 11373 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
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I0428 13:41:36.341866 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:41:39.320297 11373 solver.cpp:218] Iteration 2940 (2.56656 iter/s, 4.67551s/12 iters), loss = 2.41233
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I0428 13:41:39.320338 11373 solver.cpp:237] Train net output #0: loss = 2.41233 (* 1 = 2.41233 loss)
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I0428 13:41:39.320348 11373 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
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I0428 13:41:44.121541 11373 solver.cpp:218] Iteration 2952 (2.49948 iter/s, 4.801s/12 iters), loss = 2.3741
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I0428 13:41:44.121577 11373 solver.cpp:237] Train net output #0: loss = 2.3741 (* 1 = 2.3741 loss)
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I0428 13:41:44.121585 11373 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
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I0428 13:41:46.015149 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
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I0428 13:41:47.249234 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
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I0428 13:41:48.241353 11373 solver.cpp:330] Iteration 2958, Testing net (#0)
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I0428 13:41:48.241375 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:41:51.366745 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:41:52.645619 11373 solver.cpp:397] Test net output #0: accuracy = 0.237745
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I0428 13:41:52.645646 11373 solver.cpp:397] Test net output #1: loss = 3.12616 (* 1 = 3.12616 loss)
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I0428 13:41:54.304198 11373 solver.cpp:218] Iteration 2964 (1.17853 iter/s, 10.1822s/12 iters), loss = 2.85835
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I0428 13:41:54.304240 11373 solver.cpp:237] Train net output #0: loss = 2.85835 (* 1 = 2.85835 loss)
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I0428 13:41:54.304250 11373 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
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I0428 13:41:58.913341 11373 solver.cpp:218] Iteration 2976 (2.60366 iter/s, 4.6089s/12 iters), loss = 2.36919
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I0428 13:41:58.913379 11373 solver.cpp:237] Train net output #0: loss = 2.36919 (* 1 = 2.36919 loss)
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I0428 13:41:58.913388 11373 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
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I0428 13:42:03.666401 11373 solver.cpp:218] Iteration 2988 (2.52482 iter/s, 4.75281s/12 iters), loss = 2.61471
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I0428 13:42:03.666460 11373 solver.cpp:237] Train net output #0: loss = 2.61471 (* 1 = 2.61471 loss)
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I0428 13:42:03.666471 11373 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
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I0428 13:42:08.275380 11373 solver.cpp:218] Iteration 3000 (2.60375 iter/s, 4.60873s/12 iters), loss = 2.75375
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I0428 13:42:08.275516 11373 solver.cpp:237] Train net output #0: loss = 2.75375 (* 1 = 2.75375 loss)
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I0428 13:42:08.275525 11373 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
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I0428 13:42:13.070190 11373 solver.cpp:218] Iteration 3012 (2.50288 iter/s, 4.79447s/12 iters), loss = 2.68808
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I0428 13:42:13.070225 11373 solver.cpp:237] Train net output #0: loss = 2.68808 (* 1 = 2.68808 loss)
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I0428 13:42:13.070233 11373 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
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I0428 13:42:17.956701 11373 solver.cpp:218] Iteration 3024 (2.45586 iter/s, 4.88627s/12 iters), loss = 2.49742
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I0428 13:42:17.956744 11373 solver.cpp:237] Train net output #0: loss = 2.49742 (* 1 = 2.49742 loss)
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I0428 13:42:17.956753 11373 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
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I0428 13:42:21.788431 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:42:22.778918 11373 solver.cpp:218] Iteration 3036 (2.48862 iter/s, 4.82196s/12 iters), loss = 2.56678
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I0428 13:42:22.778964 11373 solver.cpp:237] Train net output #0: loss = 2.56678 (* 1 = 2.56678 loss)
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I0428 13:42:22.778975 11373 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
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I0428 13:42:27.523216 11373 solver.cpp:218] Iteration 3048 (2.52949 iter/s, 4.74404s/12 iters), loss = 2.7477
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I0428 13:42:27.523268 11373 solver.cpp:237] Train net output #0: loss = 2.7477 (* 1 = 2.7477 loss)
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I0428 13:42:27.523281 11373 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
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I0428 13:42:31.904392 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
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I0428 13:42:33.392100 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
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I0428 13:42:35.378067 11373 solver.cpp:330] Iteration 3060, Testing net (#0)
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I0428 13:42:35.378089 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:42:38.499671 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:42:39.698732 11373 solver.cpp:397] Test net output #0: accuracy = 0.280637
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I0428 13:42:39.698776 11373 solver.cpp:397] Test net output #1: loss = 2.92656 (* 1 = 2.92656 loss)
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I0428 13:42:39.777794 11373 solver.cpp:218] Iteration 3060 (0.97927 iter/s, 12.254s/12 iters), loss = 2.41508
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I0428 13:42:39.777844 11373 solver.cpp:237] Train net output #0: loss = 2.41508 (* 1 = 2.41508 loss)
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I0428 13:42:39.777854 11373 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
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I0428 13:42:43.810190 11373 solver.cpp:218] Iteration 3072 (2.97606 iter/s, 4.03217s/12 iters), loss = 3.06193
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I0428 13:42:43.810230 11373 solver.cpp:237] Train net output #0: loss = 3.06193 (* 1 = 3.06193 loss)
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I0428 13:42:43.810238 11373 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
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I0428 13:42:48.529419 11373 solver.cpp:218] Iteration 3084 (2.54292 iter/s, 4.71898s/12 iters), loss = 2.73003
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I0428 13:42:48.529467 11373 solver.cpp:237] Train net output #0: loss = 2.73003 (* 1 = 2.73003 loss)
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I0428 13:42:48.529477 11373 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
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I0428 13:42:53.213671 11373 solver.cpp:218] Iteration 3096 (2.56191 iter/s, 4.684s/12 iters), loss = 2.45291
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I0428 13:42:53.213716 11373 solver.cpp:237] Train net output #0: loss = 2.45291 (* 1 = 2.45291 loss)
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I0428 13:42:53.213724 11373 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
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I0428 13:42:58.001014 11373 solver.cpp:218] Iteration 3108 (2.50674 iter/s, 4.78709s/12 iters), loss = 2.32105
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I0428 13:42:58.001051 11373 solver.cpp:237] Train net output #0: loss = 2.32105 (* 1 = 2.32105 loss)
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I0428 13:42:58.001060 11373 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
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I0428 13:43:02.644258 11373 solver.cpp:218] Iteration 3120 (2.58453 iter/s, 4.643s/12 iters), loss = 2.4508
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I0428 13:43:02.644318 11373 solver.cpp:237] Train net output #0: loss = 2.4508 (* 1 = 2.4508 loss)
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I0428 13:43:02.644331 11373 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
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I0428 13:43:07.391244 11373 solver.cpp:218] Iteration 3132 (2.52806 iter/s, 4.74672s/12 iters), loss = 2.13281
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I0428 13:43:07.391304 11373 solver.cpp:237] Train net output #0: loss = 2.13281 (* 1 = 2.13281 loss)
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I0428 13:43:07.391315 11373 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
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I0428 13:43:08.445318 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:43:12.115705 11373 solver.cpp:218] Iteration 3144 (2.54011 iter/s, 4.7242s/12 iters), loss = 2.40034
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I0428 13:43:12.115876 11373 solver.cpp:237] Train net output #0: loss = 2.40034 (* 1 = 2.40034 loss)
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I0428 13:43:12.115890 11373 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
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I0428 13:43:16.860913 11373 solver.cpp:218] Iteration 3156 (2.52907 iter/s, 4.74483s/12 iters), loss = 2.65332
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I0428 13:43:16.860966 11373 solver.cpp:237] Train net output #0: loss = 2.65332 (* 1 = 2.65332 loss)
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I0428 13:43:16.860978 11373 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
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I0428 13:43:18.810439 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
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I0428 13:43:22.223220 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
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I0428 13:43:23.236024 11373 solver.cpp:330] Iteration 3162, Testing net (#0)
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I0428 13:43:23.236049 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:43:26.399008 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:43:27.662487 11373 solver.cpp:397] Test net output #0: accuracy = 0.260417
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I0428 13:43:27.662518 11373 solver.cpp:397] Test net output #1: loss = 3.0198 (* 1 = 3.0198 loss)
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I0428 13:43:29.502635 11373 solver.cpp:218] Iteration 3168 (0.94928 iter/s, 12.6412s/12 iters), loss = 2.49979
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I0428 13:43:29.502681 11373 solver.cpp:237] Train net output #0: loss = 2.49979 (* 1 = 2.49979 loss)
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I0428 13:43:29.502691 11373 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
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I0428 13:43:34.282529 11373 solver.cpp:218] Iteration 3180 (2.51065 iter/s, 4.77964s/12 iters), loss = 2.20238
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I0428 13:43:34.282574 11373 solver.cpp:237] Train net output #0: loss = 2.20238 (* 1 = 2.20238 loss)
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I0428 13:43:34.282584 11373 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
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I0428 13:43:38.935278 11373 solver.cpp:218] Iteration 3192 (2.57925 iter/s, 4.65251s/12 iters), loss = 2.47376
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I0428 13:43:38.935317 11373 solver.cpp:237] Train net output #0: loss = 2.47376 (* 1 = 2.47376 loss)
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I0428 13:43:38.935326 11373 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
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I0428 13:43:43.798616 11373 solver.cpp:218] Iteration 3204 (2.46756 iter/s, 4.8631s/12 iters), loss = 2.6401
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I0428 13:43:43.798717 11373 solver.cpp:237] Train net output #0: loss = 2.6401 (* 1 = 2.6401 loss)
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I0428 13:43:43.798727 11373 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
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I0428 13:43:48.468816 11373 solver.cpp:218] Iteration 3216 (2.56965 iter/s, 4.66989s/12 iters), loss = 2.46198
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I0428 13:43:48.468874 11373 solver.cpp:237] Train net output #0: loss = 2.46198 (* 1 = 2.46198 loss)
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I0428 13:43:48.468888 11373 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
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I0428 13:43:53.132864 11373 solver.cpp:218] Iteration 3228 (2.57301 iter/s, 4.66379s/12 iters), loss = 2.28078
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I0428 13:43:53.132916 11373 solver.cpp:237] Train net output #0: loss = 2.28078 (* 1 = 2.28078 loss)
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I0428 13:43:53.132930 11373 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
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I0428 13:43:56.197741 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:43:57.854504 11373 solver.cpp:218] Iteration 3240 (2.54163 iter/s, 4.72138s/12 iters), loss = 2.60133
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I0428 13:43:57.854549 11373 solver.cpp:237] Train net output #0: loss = 2.60133 (* 1 = 2.60133 loss)
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I0428 13:43:57.854559 11373 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
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I0428 13:44:02.595254 11373 solver.cpp:218] Iteration 3252 (2.53138 iter/s, 4.74049s/12 iters), loss = 2.59796
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I0428 13:44:02.595305 11373 solver.cpp:237] Train net output #0: loss = 2.59796 (* 1 = 2.59796 loss)
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I0428 13:44:02.595317 11373 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
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I0428 13:44:07.007644 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
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I0428 13:44:09.208168 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
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I0428 13:44:13.895215 11373 solver.cpp:330] Iteration 3264, Testing net (#0)
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I0428 13:44:13.895437 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:44:16.887769 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:44:18.205240 11373 solver.cpp:397] Test net output #0: accuracy = 0.300858
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I0428 13:44:18.205276 11373 solver.cpp:397] Test net output #1: loss = 2.78455 (* 1 = 2.78455 loss)
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I0428 13:44:18.284049 11373 solver.cpp:218] Iteration 3264 (0.764911 iter/s, 15.6881s/12 iters), loss = 2.0771
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I0428 13:44:18.284106 11373 solver.cpp:237] Train net output #0: loss = 2.0771 (* 1 = 2.0771 loss)
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I0428 13:44:18.284118 11373 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
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I0428 13:44:22.266420 11373 solver.cpp:218] Iteration 3276 (3.01345 iter/s, 3.98214s/12 iters), loss = 2.58727
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I0428 13:44:22.266469 11373 solver.cpp:237] Train net output #0: loss = 2.58727 (* 1 = 2.58727 loss)
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I0428 13:44:22.266480 11373 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
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I0428 13:44:26.974357 11373 solver.cpp:218] Iteration 3288 (2.54902 iter/s, 4.70768s/12 iters), loss = 2.37099
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I0428 13:44:26.974408 11373 solver.cpp:237] Train net output #0: loss = 2.37099 (* 1 = 2.37099 loss)
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I0428 13:44:26.974421 11373 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
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I0428 13:44:31.715134 11373 solver.cpp:218] Iteration 3300 (2.53137 iter/s, 4.74052s/12 iters), loss = 2.48384
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I0428 13:44:31.715190 11373 solver.cpp:237] Train net output #0: loss = 2.48384 (* 1 = 2.48384 loss)
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I0428 13:44:31.715202 11373 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
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I0428 13:44:36.462942 11373 solver.cpp:218] Iteration 3312 (2.52762 iter/s, 4.74756s/12 iters), loss = 2.31629
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I0428 13:44:36.462972 11373 solver.cpp:237] Train net output #0: loss = 2.31629 (* 1 = 2.31629 loss)
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I0428 13:44:36.462980 11373 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
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I0428 13:44:41.298390 11373 solver.cpp:218] Iteration 3324 (2.4818 iter/s, 4.8352s/12 iters), loss = 2.2666
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I0428 13:44:41.298446 11373 solver.cpp:237] Train net output #0: loss = 2.2666 (* 1 = 2.2666 loss)
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I0428 13:44:41.298460 11373 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
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I0428 13:44:46.016535 11373 solver.cpp:218] Iteration 3336 (2.54352 iter/s, 4.71787s/12 iters), loss = 2.06497
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I0428 13:44:46.016678 11373 solver.cpp:237] Train net output #0: loss = 2.06497 (* 1 = 2.06497 loss)
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I0428 13:44:46.016691 11373 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
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I0428 13:44:46.464064 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:44:50.756881 11373 solver.cpp:218] Iteration 3348 (2.53174 iter/s, 4.73983s/12 iters), loss = 2.40344
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I0428 13:44:50.756925 11373 solver.cpp:237] Train net output #0: loss = 2.40344 (* 1 = 2.40344 loss)
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I0428 13:44:50.756934 11373 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
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I0428 13:44:55.419787 11373 solver.cpp:218] Iteration 3360 (2.57364 iter/s, 4.66266s/12 iters), loss = 2.34873
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I0428 13:44:55.419828 11373 solver.cpp:237] Train net output #0: loss = 2.34873 (* 1 = 2.34873 loss)
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I0428 13:44:55.419840 11373 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
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I0428 13:44:57.335409 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
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I0428 13:45:03.221626 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
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I0428 13:45:08.161648 11373 solver.cpp:330] Iteration 3366, Testing net (#0)
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I0428 13:45:08.161669 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:45:11.153370 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:45:12.468655 11373 solver.cpp:397] Test net output #0: accuracy = 0.289216
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I0428 13:45:12.468698 11373 solver.cpp:397] Test net output #1: loss = 2.92834 (* 1 = 2.92834 loss)
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I0428 13:45:14.211819 11373 solver.cpp:218] Iteration 3372 (0.638596 iter/s, 18.7912s/12 iters), loss = 2.24268
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I0428 13:45:14.211882 11373 solver.cpp:237] Train net output #0: loss = 2.24268 (* 1 = 2.24268 loss)
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I0428 13:45:14.211895 11373 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
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I0428 13:45:18.957670 11373 solver.cpp:218] Iteration 3384 (2.52867 iter/s, 4.74558s/12 iters), loss = 2.70453
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I0428 13:45:18.957840 11373 solver.cpp:237] Train net output #0: loss = 2.70453 (* 1 = 2.70453 loss)
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I0428 13:45:18.957850 11373 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
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I0428 13:45:23.729204 11373 solver.cpp:218] Iteration 3396 (2.51511 iter/s, 4.77115s/12 iters), loss = 2.01887
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I0428 13:45:23.729270 11373 solver.cpp:237] Train net output #0: loss = 2.01887 (* 1 = 2.01887 loss)
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I0428 13:45:23.729282 11373 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
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I0428 13:45:28.466060 11373 solver.cpp:218] Iteration 3408 (2.53347 iter/s, 4.73659s/12 iters), loss = 1.94127
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I0428 13:45:28.466114 11373 solver.cpp:237] Train net output #0: loss = 1.94127 (* 1 = 1.94127 loss)
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I0428 13:45:28.466126 11373 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
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I0428 13:45:33.195649 11373 solver.cpp:218] Iteration 3420 (2.53736 iter/s, 4.72933s/12 iters), loss = 2.22617
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I0428 13:45:33.195713 11373 solver.cpp:237] Train net output #0: loss = 2.22617 (* 1 = 2.22617 loss)
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I0428 13:45:33.195724 11373 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
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I0428 13:45:37.945245 11373 solver.cpp:218] Iteration 3432 (2.52667 iter/s, 4.74933s/12 iters), loss = 2.30219
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I0428 13:45:37.945298 11373 solver.cpp:237] Train net output #0: loss = 2.30219 (* 1 = 2.30219 loss)
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I0428 13:45:37.945310 11373 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
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I0428 13:45:40.409570 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:45:42.680605 11373 solver.cpp:218] Iteration 3444 (2.53427 iter/s, 4.7351s/12 iters), loss = 2.02813
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I0428 13:45:42.680665 11373 solver.cpp:237] Train net output #0: loss = 2.02813 (* 1 = 2.02813 loss)
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I0428 13:45:42.680677 11373 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
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I0428 13:45:47.414712 11373 solver.cpp:218] Iteration 3456 (2.53494 iter/s, 4.73384s/12 iters), loss = 2.35844
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I0428 13:45:47.414770 11373 solver.cpp:237] Train net output #0: loss = 2.35844 (* 1 = 2.35844 loss)
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I0428 13:45:47.414783 11373 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
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I0428 13:45:51.724352 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
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I0428 13:45:54.900679 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
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I0428 13:45:58.672968 11373 solver.cpp:330] Iteration 3468, Testing net (#0)
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I0428 13:45:58.672989 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:45:59.074556 11373 blocking_queue.cpp:49] Waiting for data
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I0428 13:46:01.810942 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:46:03.190264 11373 solver.cpp:397] Test net output #0: accuracy = 0.287377
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I0428 13:46:03.190294 11373 solver.cpp:397] Test net output #1: loss = 2.93003 (* 1 = 2.93003 loss)
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I0428 13:46:03.269228 11373 solver.cpp:218] Iteration 3468 (0.756916 iter/s, 15.8538s/12 iters), loss = 2.21389
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I0428 13:46:03.269279 11373 solver.cpp:237] Train net output #0: loss = 2.21389 (* 1 = 2.21389 loss)
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I0428 13:46:03.269287 11373 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
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I0428 13:46:07.288841 11373 solver.cpp:218] Iteration 3480 (2.98553 iter/s, 4.01939s/12 iters), loss = 2.17217
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I0428 13:46:07.288877 11373 solver.cpp:237] Train net output #0: loss = 2.17217 (* 1 = 2.17217 loss)
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I0428 13:46:07.288887 11373 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
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I0428 13:46:11.911303 11373 solver.cpp:218] Iteration 3492 (2.59615 iter/s, 4.62223s/12 iters), loss = 2.09105
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I0428 13:46:11.911342 11373 solver.cpp:237] Train net output #0: loss = 2.09105 (* 1 = 2.09105 loss)
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I0428 13:46:11.911350 11373 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
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I0428 13:46:16.559295 11373 solver.cpp:218] Iteration 3504 (2.58189 iter/s, 4.64775s/12 iters), loss = 2.01995
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I0428 13:46:16.559346 11373 solver.cpp:237] Train net output #0: loss = 2.01995 (* 1 = 2.01995 loss)
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I0428 13:46:16.559358 11373 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
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I0428 13:46:21.224659 11373 solver.cpp:218] Iteration 3516 (2.57228 iter/s, 4.66512s/12 iters), loss = 2.14597
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I0428 13:46:21.224699 11373 solver.cpp:237] Train net output #0: loss = 2.14597 (* 1 = 2.14597 loss)
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I0428 13:46:21.224709 11373 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
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I0428 13:46:25.903673 11373 solver.cpp:218] Iteration 3528 (2.56477 iter/s, 4.67878s/12 iters), loss = 1.92631
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I0428 13:46:25.903784 11373 solver.cpp:237] Train net output #0: loss = 1.92631 (* 1 = 1.92631 loss)
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I0428 13:46:25.903792 11373 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
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I0428 13:46:30.449064 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:46:30.696991 11373 solver.cpp:218] Iteration 3540 (2.50365 iter/s, 4.793s/12 iters), loss = 2.16654
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I0428 13:46:30.697046 11373 solver.cpp:237] Train net output #0: loss = 2.16654 (* 1 = 2.16654 loss)
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I0428 13:46:30.697057 11373 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
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I0428 13:46:35.343297 11373 solver.cpp:218] Iteration 3552 (2.58284 iter/s, 4.64605s/12 iters), loss = 2.18986
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I0428 13:46:35.343343 11373 solver.cpp:237] Train net output #0: loss = 2.18986 (* 1 = 2.18986 loss)
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I0428 13:46:35.343355 11373 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
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I0428 13:46:40.089579 11373 solver.cpp:218] Iteration 3564 (2.52843 iter/s, 4.74603s/12 iters), loss = 2.03284
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I0428 13:46:40.089622 11373 solver.cpp:237] Train net output #0: loss = 2.03284 (* 1 = 2.03284 loss)
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I0428 13:46:40.089632 11373 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
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I0428 13:46:42.068471 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
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I0428 13:46:43.331349 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
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I0428 13:46:44.331799 11373 solver.cpp:330] Iteration 3570, Testing net (#0)
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I0428 13:46:44.331817 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:46:47.287196 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:46:48.684515 11373 solver.cpp:397] Test net output #0: accuracy = 0.305147
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I0428 13:46:48.684556 11373 solver.cpp:397] Test net output #1: loss = 2.7756 (* 1 = 2.7756 loss)
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I0428 13:46:50.382465 11373 solver.cpp:218] Iteration 3576 (1.16591 iter/s, 10.2924s/12 iters), loss = 1.70504
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I0428 13:46:50.382521 11373 solver.cpp:237] Train net output #0: loss = 1.70504 (* 1 = 1.70504 loss)
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I0428 13:46:50.382534 11373 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
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I0428 13:46:55.253386 11373 solver.cpp:218] Iteration 3588 (2.46374 iter/s, 4.87065s/12 iters), loss = 2.33992
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I0428 13:46:55.253446 11373 solver.cpp:237] Train net output #0: loss = 2.33992 (* 1 = 2.33992 loss)
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I0428 13:46:55.253458 11373 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
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I0428 13:47:00.170645 11373 solver.cpp:218] Iteration 3600 (2.44052 iter/s, 4.91699s/12 iters), loss = 2.08974
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I0428 13:47:00.192356 11373 solver.cpp:237] Train net output #0: loss = 2.08974 (* 1 = 2.08974 loss)
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I0428 13:47:00.192369 11373 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
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I0428 13:47:04.882761 11373 solver.cpp:218] Iteration 3612 (2.55852 iter/s, 4.69021s/12 iters), loss = 1.9748
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I0428 13:47:04.882799 11373 solver.cpp:237] Train net output #0: loss = 1.9748 (* 1 = 1.9748 loss)
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I0428 13:47:04.882808 11373 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
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I0428 13:47:09.677582 11373 solver.cpp:218] Iteration 3624 (2.50283 iter/s, 4.79458s/12 iters), loss = 2.10522
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I0428 13:47:09.677623 11373 solver.cpp:237] Train net output #0: loss = 2.10522 (* 1 = 2.10522 loss)
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I0428 13:47:09.677631 11373 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
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I0428 13:47:14.441560 11373 solver.cpp:218] Iteration 3636 (2.51903 iter/s, 4.76373s/12 iters), loss = 1.81675
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I0428 13:47:14.441603 11373 solver.cpp:237] Train net output #0: loss = 1.81675 (* 1 = 1.81675 loss)
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I0428 13:47:14.441613 11373 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
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I0428 13:47:16.213685 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:47:19.164880 11373 solver.cpp:218] Iteration 3648 (2.54072 iter/s, 4.72307s/12 iters), loss = 1.94071
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I0428 13:47:19.164928 11373 solver.cpp:237] Train net output #0: loss = 1.94071 (* 1 = 1.94071 loss)
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I0428 13:47:19.164939 11373 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
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I0428 13:47:23.830533 11373 solver.cpp:218] Iteration 3660 (2.57212 iter/s, 4.66541s/12 iters), loss = 1.9799
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I0428 13:47:23.830571 11373 solver.cpp:237] Train net output #0: loss = 1.9799 (* 1 = 1.9799 loss)
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I0428 13:47:23.830580 11373 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
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I0428 13:47:28.057054 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
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I0428 13:47:38.177173 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
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I0428 13:47:41.616900 11373 solver.cpp:330] Iteration 3672, Testing net (#0)
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I0428 13:47:41.616920 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:47:44.461664 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:47:45.903765 11373 solver.cpp:397] Test net output #0: accuracy = 0.292279
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I0428 13:47:45.903803 11373 solver.cpp:397] Test net output #1: loss = 2.93609 (* 1 = 2.93609 loss)
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I0428 13:47:45.982764 11373 solver.cpp:218] Iteration 3672 (0.541729 iter/s, 22.1513s/12 iters), loss = 2.49703
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I0428 13:47:45.982821 11373 solver.cpp:237] Train net output #0: loss = 2.49703 (* 1 = 2.49703 loss)
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I0428 13:47:45.982836 11373 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
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I0428 13:47:49.888132 11373 solver.cpp:218] Iteration 3684 (3.07287 iter/s, 3.90514s/12 iters), loss = 1.74773
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I0428 13:47:49.888180 11373 solver.cpp:237] Train net output #0: loss = 1.74773 (* 1 = 1.74773 loss)
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I0428 13:47:49.888190 11373 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
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I0428 13:47:54.605165 11373 solver.cpp:218] Iteration 3696 (2.54411 iter/s, 4.71678s/12 iters), loss = 1.96092
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I0428 13:47:54.605208 11373 solver.cpp:237] Train net output #0: loss = 1.96092 (* 1 = 1.96092 loss)
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I0428 13:47:54.605219 11373 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
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I0428 13:47:59.286547 11373 solver.cpp:218] Iteration 3708 (2.56348 iter/s, 4.68113s/12 iters), loss = 2.04738
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I0428 13:47:59.286595 11373 solver.cpp:237] Train net output #0: loss = 2.04738 (* 1 = 2.04738 loss)
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I0428 13:47:59.286607 11373 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
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I0428 13:48:04.047988 11373 solver.cpp:218] Iteration 3720 (2.52038 iter/s, 4.76119s/12 iters), loss = 2.15907
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I0428 13:48:04.048032 11373 solver.cpp:237] Train net output #0: loss = 2.15907 (* 1 = 2.15907 loss)
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I0428 13:48:04.048043 11373 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
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I0428 13:48:08.809026 11373 solver.cpp:218] Iteration 3732 (2.52059 iter/s, 4.76079s/12 iters), loss = 1.89187
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I0428 13:48:08.809134 11373 solver.cpp:237] Train net output #0: loss = 1.89187 (* 1 = 1.89187 loss)
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I0428 13:48:08.809144 11373 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
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I0428 13:48:12.656015 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:48:13.597959 11373 solver.cpp:218] Iteration 3744 (2.50594 iter/s, 4.78863s/12 iters), loss = 1.95197
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I0428 13:48:13.597997 11373 solver.cpp:237] Train net output #0: loss = 1.95197 (* 1 = 1.95197 loss)
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I0428 13:48:13.598006 11373 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
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I0428 13:48:18.372215 11373 solver.cpp:218] Iteration 3756 (2.51361 iter/s, 4.77401s/12 iters), loss = 2.2912
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I0428 13:48:18.372263 11373 solver.cpp:237] Train net output #0: loss = 2.2912 (* 1 = 2.2912 loss)
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I0428 13:48:18.372274 11373 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
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I0428 13:48:23.071398 11373 solver.cpp:218] Iteration 3768 (2.55377 iter/s, 4.69894s/12 iters), loss = 1.77249
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I0428 13:48:23.071434 11373 solver.cpp:237] Train net output #0: loss = 1.77249 (* 1 = 1.77249 loss)
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I0428 13:48:23.071442 11373 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
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I0428 13:48:25.015259 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
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I0428 13:48:30.278151 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
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I0428 13:48:34.866673 11373 solver.cpp:330] Iteration 3774, Testing net (#0)
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I0428 13:48:34.866698 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:48:37.666690 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:48:39.143544 11373 solver.cpp:397] Test net output #0: accuracy = 0.321691
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I0428 13:48:39.143941 11373 solver.cpp:397] Test net output #1: loss = 2.7264 (* 1 = 2.7264 loss)
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I0428 13:48:40.940266 11373 solver.cpp:218] Iteration 3780 (0.671588 iter/s, 17.8681s/12 iters), loss = 1.65992
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I0428 13:48:40.940320 11373 solver.cpp:237] Train net output #0: loss = 1.65992 (* 1 = 1.65992 loss)
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I0428 13:48:40.940332 11373 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
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I0428 13:48:45.629673 11373 solver.cpp:218] Iteration 3792 (2.5591 iter/s, 4.68915s/12 iters), loss = 1.82809
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I0428 13:48:45.629722 11373 solver.cpp:237] Train net output #0: loss = 1.82809 (* 1 = 1.82809 loss)
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I0428 13:48:45.629734 11373 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
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I0428 13:48:50.387545 11373 solver.cpp:218] Iteration 3804 (2.52227 iter/s, 4.75762s/12 iters), loss = 1.72788
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I0428 13:48:50.387588 11373 solver.cpp:237] Train net output #0: loss = 1.72788 (* 1 = 1.72788 loss)
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I0428 13:48:50.387598 11373 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
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I0428 13:48:55.173920 11373 solver.cpp:218] Iteration 3816 (2.50725 iter/s, 4.78612s/12 iters), loss = 1.54262
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I0428 13:48:55.173975 11373 solver.cpp:237] Train net output #0: loss = 1.54262 (* 1 = 1.54262 loss)
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I0428 13:48:55.173986 11373 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
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I0428 13:48:59.952075 11373 solver.cpp:218] Iteration 3828 (2.51157 iter/s, 4.77789s/12 iters), loss = 1.89975
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I0428 13:48:59.952116 11373 solver.cpp:237] Train net output #0: loss = 1.89975 (* 1 = 1.89975 loss)
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I0428 13:48:59.952124 11373 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
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I0428 13:49:04.700179 11373 solver.cpp:218] Iteration 3840 (2.52746 iter/s, 4.74785s/12 iters), loss = 1.6414
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I0428 13:49:04.700228 11373 solver.cpp:237] Train net output #0: loss = 1.6414 (* 1 = 1.6414 loss)
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I0428 13:49:04.700237 11373 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
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I0428 13:49:05.804369 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:49:09.387758 11373 solver.cpp:218] Iteration 3852 (2.5601 iter/s, 4.68732s/12 iters), loss = 1.83488
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I0428 13:49:09.387867 11373 solver.cpp:237] Train net output #0: loss = 1.83488 (* 1 = 1.83488 loss)
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I0428 13:49:09.387876 11373 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
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I0428 13:49:14.224221 11373 solver.cpp:218] Iteration 3864 (2.48132 iter/s, 4.83614s/12 iters), loss = 1.9794
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I0428 13:49:14.224272 11373 solver.cpp:237] Train net output #0: loss = 1.9794 (* 1 = 1.9794 loss)
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I0428 13:49:14.224283 11373 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
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I0428 13:49:18.611936 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
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I0428 13:49:21.001217 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
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I0428 13:49:24.453111 11373 solver.cpp:330] Iteration 3876, Testing net (#0)
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I0428 13:49:24.453135 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:49:27.364560 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:49:28.885051 11373 solver.cpp:397] Test net output #0: accuracy = 0.321691
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I0428 13:49:28.885079 11373 solver.cpp:397] Test net output #1: loss = 2.72087 (* 1 = 2.72087 loss)
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I0428 13:49:28.963820 11373 solver.cpp:218] Iteration 3876 (0.81417 iter/s, 14.7389s/12 iters), loss = 1.92348
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I0428 13:49:28.963863 11373 solver.cpp:237] Train net output #0: loss = 1.92348 (* 1 = 1.92348 loss)
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I0428 13:49:28.963873 11373 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
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I0428 13:49:32.934756 11373 solver.cpp:218] Iteration 3888 (3.02213 iter/s, 3.97071s/12 iters), loss = 1.85387
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I0428 13:49:32.934795 11373 solver.cpp:237] Train net output #0: loss = 1.85387 (* 1 = 1.85387 loss)
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I0428 13:49:32.934804 11373 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
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I0428 13:49:37.647377 11373 solver.cpp:218] Iteration 3900 (2.54648 iter/s, 4.71238s/12 iters), loss = 1.76525
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I0428 13:49:37.647408 11373 solver.cpp:237] Train net output #0: loss = 1.76525 (* 1 = 1.76525 loss)
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I0428 13:49:37.647416 11373 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
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I0428 13:49:42.334178 11373 solver.cpp:218] Iteration 3912 (2.56051 iter/s, 4.68656s/12 iters), loss = 1.97843
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I0428 13:49:42.334309 11373 solver.cpp:237] Train net output #0: loss = 1.97843 (* 1 = 1.97843 loss)
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I0428 13:49:42.334321 11373 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
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I0428 13:49:47.062227 11373 solver.cpp:218] Iteration 3924 (2.53823 iter/s, 4.7277s/12 iters), loss = 1.67269
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I0428 13:49:47.062276 11373 solver.cpp:237] Train net output #0: loss = 1.67269 (* 1 = 1.67269 loss)
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I0428 13:49:47.062284 11373 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
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I0428 13:49:51.787997 11373 solver.cpp:218] Iteration 3936 (2.53941 iter/s, 4.72551s/12 iters), loss = 1.61966
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I0428 13:49:51.788051 11373 solver.cpp:237] Train net output #0: loss = 1.61966 (* 1 = 1.61966 loss)
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I0428 13:49:51.788062 11373 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
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I0428 13:49:54.993083 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:49:56.542412 11373 solver.cpp:218] Iteration 3948 (2.52411 iter/s, 4.75415s/12 iters), loss = 1.95363
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I0428 13:49:56.542456 11373 solver.cpp:237] Train net output #0: loss = 1.95363 (* 1 = 1.95363 loss)
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I0428 13:49:56.542465 11373 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
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I0428 13:50:01.214115 11373 solver.cpp:218] Iteration 3960 (2.56879 iter/s, 4.67146s/12 iters), loss = 1.62628
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I0428 13:50:01.214155 11373 solver.cpp:237] Train net output #0: loss = 1.62628 (* 1 = 1.62628 loss)
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I0428 13:50:01.214165 11373 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
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I0428 13:50:05.911473 11373 solver.cpp:218] Iteration 3972 (2.55476 iter/s, 4.69712s/12 iters), loss = 1.86049
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I0428 13:50:05.911509 11373 solver.cpp:237] Train net output #0: loss = 1.86049 (* 1 = 1.86049 loss)
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I0428 13:50:05.911517 11373 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
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I0428 13:50:07.841336 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
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I0428 13:50:17.196410 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
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I0428 13:50:28.817690 11373 solver.cpp:330] Iteration 3978, Testing net (#0)
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I0428 13:50:28.817718 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:50:31.583838 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:50:33.244264 11373 solver.cpp:397] Test net output #0: accuracy = 0.358456
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I0428 13:50:33.244305 11373 solver.cpp:397] Test net output #1: loss = 2.66318 (* 1 = 2.66318 loss)
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I0428 13:50:35.018241 11373 solver.cpp:218] Iteration 3984 (0.412293 iter/s, 29.1055s/12 iters), loss = 1.75675
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I0428 13:50:35.018282 11373 solver.cpp:237] Train net output #0: loss = 1.75675 (* 1 = 1.75675 loss)
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I0428 13:50:35.018290 11373 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
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I0428 13:50:39.961660 11373 solver.cpp:218] Iteration 3996 (2.4276 iter/s, 4.94316s/12 iters), loss = 1.87103
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I0428 13:50:39.961700 11373 solver.cpp:237] Train net output #0: loss = 1.87103 (* 1 = 1.87103 loss)
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I0428 13:50:39.961710 11373 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
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I0428 13:50:44.704011 11373 solver.cpp:218] Iteration 4008 (2.53052 iter/s, 4.7421s/12 iters), loss = 1.98207
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I0428 13:50:44.704059 11373 solver.cpp:237] Train net output #0: loss = 1.98207 (* 1 = 1.98207 loss)
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I0428 13:50:44.704068 11373 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
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I0428 13:50:49.652848 11373 solver.cpp:218] Iteration 4020 (2.42494 iter/s, 4.94857s/12 iters), loss = 1.77336
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I0428 13:50:49.656522 11373 solver.cpp:237] Train net output #0: loss = 1.77336 (* 1 = 1.77336 loss)
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I0428 13:50:49.656535 11373 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
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I0428 13:50:54.398651 11373 solver.cpp:218] Iteration 4032 (2.5306 iter/s, 4.74195s/12 iters), loss = 1.89527
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I0428 13:50:54.398691 11373 solver.cpp:237] Train net output #0: loss = 1.89527 (* 1 = 1.89527 loss)
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I0428 13:50:54.398701 11373 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
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I0428 13:50:59.180418 11373 solver.cpp:218] Iteration 4044 (2.50966 iter/s, 4.78152s/12 iters), loss = 1.66074
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I0428 13:50:59.180461 11373 solver.cpp:237] Train net output #0: loss = 1.66074 (* 1 = 1.66074 loss)
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I0428 13:50:59.180469 11373 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
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I0428 13:50:59.657274 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:51:03.988708 11373 solver.cpp:218] Iteration 4056 (2.49582 iter/s, 4.80804s/12 iters), loss = 1.74209
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I0428 13:51:03.988750 11373 solver.cpp:237] Train net output #0: loss = 1.74209 (* 1 = 1.74209 loss)
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I0428 13:51:03.988759 11373 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
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I0428 13:51:08.755712 11373 solver.cpp:218] Iteration 4068 (2.51744 iter/s, 4.76675s/12 iters), loss = 1.74539
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I0428 13:51:08.755769 11373 solver.cpp:237] Train net output #0: loss = 1.74539 (* 1 = 1.74539 loss)
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I0428 13:51:08.755780 11373 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
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I0428 13:51:13.017730 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
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I0428 13:51:16.348201 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
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I0428 13:51:27.355162 11373 solver.cpp:330] Iteration 4080, Testing net (#0)
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I0428 13:51:27.355250 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:51:30.042121 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:51:31.630270 11373 solver.cpp:397] Test net output #0: accuracy = 0.352328
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I0428 13:51:31.630311 11373 solver.cpp:397] Test net output #1: loss = 2.73274 (* 1 = 2.73274 loss)
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I0428 13:51:31.709219 11373 solver.cpp:218] Iteration 4080 (0.522819 iter/s, 22.9525s/12 iters), loss = 2.02191
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I0428 13:51:31.709261 11373 solver.cpp:237] Train net output #0: loss = 2.02191 (* 1 = 2.02191 loss)
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I0428 13:51:31.709270 11373 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
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I0428 13:51:35.654019 11373 solver.cpp:218] Iteration 4092 (3.04214 iter/s, 3.94459s/12 iters), loss = 1.63447
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I0428 13:51:35.654053 11373 solver.cpp:237] Train net output #0: loss = 1.63447 (* 1 = 1.63447 loss)
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I0428 13:51:35.654062 11373 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
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I0428 13:51:40.381631 11373 solver.cpp:218] Iteration 4104 (2.53841 iter/s, 4.72737s/12 iters), loss = 1.45043
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I0428 13:51:40.381670 11373 solver.cpp:237] Train net output #0: loss = 1.45043 (* 1 = 1.45043 loss)
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I0428 13:51:40.381680 11373 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
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I0428 13:51:45.144378 11373 solver.cpp:218] Iteration 4116 (2.51968 iter/s, 4.7625s/12 iters), loss = 1.72993
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I0428 13:51:45.144419 11373 solver.cpp:237] Train net output #0: loss = 1.72993 (* 1 = 1.72993 loss)
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I0428 13:51:45.144426 11373 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
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I0428 13:51:49.853649 11373 solver.cpp:218] Iteration 4128 (2.5483 iter/s, 4.70902s/12 iters), loss = 1.67404
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I0428 13:51:49.853691 11373 solver.cpp:237] Train net output #0: loss = 1.67404 (* 1 = 1.67404 loss)
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I0428 13:51:49.853700 11373 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
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I0428 13:51:54.597054 11373 solver.cpp:218] Iteration 4140 (2.52996 iter/s, 4.74315s/12 iters), loss = 1.7278
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I0428 13:51:54.597096 11373 solver.cpp:237] Train net output #0: loss = 1.7278 (* 1 = 1.7278 loss)
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I0428 13:51:54.597106 11373 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
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I0428 13:51:57.123081 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:51:59.389590 11373 solver.cpp:218] Iteration 4152 (2.50403 iter/s, 4.79228s/12 iters), loss = 1.58887
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I0428 13:51:59.389729 11373 solver.cpp:237] Train net output #0: loss = 1.58887 (* 1 = 1.58887 loss)
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I0428 13:51:59.389739 11373 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
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I0428 13:52:01.003979 11373 blocking_queue.cpp:49] Waiting for data
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I0428 13:52:04.322707 11373 solver.cpp:218] Iteration 4164 (2.43271 iter/s, 4.93277s/12 iters), loss = 1.67429
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I0428 13:52:04.322753 11373 solver.cpp:237] Train net output #0: loss = 1.67429 (* 1 = 1.67429 loss)
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I0428 13:52:04.322764 11373 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
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I0428 13:52:09.057902 11373 solver.cpp:218] Iteration 4176 (2.53435 iter/s, 4.73494s/12 iters), loss = 1.59351
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I0428 13:52:09.057952 11373 solver.cpp:237] Train net output #0: loss = 1.59351 (* 1 = 1.59351 loss)
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I0428 13:52:09.057965 11373 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
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I0428 13:52:10.976236 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
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I0428 13:52:12.219861 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
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I0428 13:52:15.311159 11373 solver.cpp:330] Iteration 4182, Testing net (#0)
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I0428 13:52:15.311180 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:52:18.104163 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:52:19.766691 11373 solver.cpp:397] Test net output #0: accuracy = 0.353554
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I0428 13:52:19.766722 11373 solver.cpp:397] Test net output #1: loss = 2.71572 (* 1 = 2.71572 loss)
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I0428 13:52:21.529165 11373 solver.cpp:218] Iteration 4188 (0.962256 iter/s, 12.4707s/12 iters), loss = 1.56977
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I0428 13:52:21.529209 11373 solver.cpp:237] Train net output #0: loss = 1.56977 (* 1 = 1.56977 loss)
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I0428 13:52:21.529217 11373 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
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I0428 13:52:26.386171 11373 solver.cpp:218] Iteration 4200 (2.47079 iter/s, 4.85675s/12 iters), loss = 1.49452
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I0428 13:52:26.386211 11373 solver.cpp:237] Train net output #0: loss = 1.49452 (* 1 = 1.49452 loss)
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I0428 13:52:26.386220 11373 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
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I0428 13:52:31.306450 11373 solver.cpp:218] Iteration 4212 (2.43901 iter/s, 4.92002s/12 iters), loss = 1.61248
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I0428 13:52:31.306545 11373 solver.cpp:237] Train net output #0: loss = 1.61248 (* 1 = 1.61248 loss)
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I0428 13:52:31.306555 11373 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
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I0428 13:52:36.052970 11373 solver.cpp:218] Iteration 4224 (2.52833 iter/s, 4.74621s/12 iters), loss = 1.82929
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I0428 13:52:36.053027 11373 solver.cpp:237] Train net output #0: loss = 1.82929 (* 1 = 1.82929 loss)
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I0428 13:52:36.053040 11373 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
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I0428 13:52:40.750502 11373 solver.cpp:218] Iteration 4236 (2.55467 iter/s, 4.69727s/12 iters), loss = 1.65615
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I0428 13:52:40.750542 11373 solver.cpp:237] Train net output #0: loss = 1.65615 (* 1 = 1.65615 loss)
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I0428 13:52:40.750551 11373 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
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I0428 13:52:45.075042 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:52:45.291029 11373 solver.cpp:218] Iteration 4248 (2.643 iter/s, 4.54029s/12 iters), loss = 1.47038
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I0428 13:52:45.291071 11373 solver.cpp:237] Train net output #0: loss = 1.47038 (* 1 = 1.47038 loss)
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I0428 13:52:45.291082 11373 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
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I0428 13:52:50.018389 11373 solver.cpp:218] Iteration 4260 (2.53855 iter/s, 4.72712s/12 iters), loss = 1.62415
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I0428 13:52:50.018424 11373 solver.cpp:237] Train net output #0: loss = 1.62415 (* 1 = 1.62415 loss)
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I0428 13:52:50.018431 11373 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
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I0428 13:52:54.781476 11373 solver.cpp:218] Iteration 4272 (2.5195 iter/s, 4.76285s/12 iters), loss = 1.74606
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I0428 13:52:54.781518 11373 solver.cpp:237] Train net output #0: loss = 1.74606 (* 1 = 1.74606 loss)
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I0428 13:52:54.781527 11373 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
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I0428 13:52:59.087074 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
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I0428 13:53:00.329922 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
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I0428 13:53:01.329649 11373 solver.cpp:330] Iteration 4284, Testing net (#0)
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I0428 13:53:01.329751 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:53:03.988440 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:53:05.658205 11373 solver.cpp:397] Test net output #0: accuracy = 0.387255
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I0428 13:53:05.658233 11373 solver.cpp:397] Test net output #1: loss = 2.57438 (* 1 = 2.57438 loss)
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I0428 13:53:05.737120 11373 solver.cpp:218] Iteration 4284 (1.09538 iter/s, 10.9551s/12 iters), loss = 1.33821
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I0428 13:53:05.737169 11373 solver.cpp:237] Train net output #0: loss = 1.33821 (* 1 = 1.33821 loss)
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I0428 13:53:05.737179 11373 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
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I0428 13:53:09.629344 11373 solver.cpp:218] Iteration 4296 (3.08324 iter/s, 3.892s/12 iters), loss = 1.62401
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I0428 13:53:09.629382 11373 solver.cpp:237] Train net output #0: loss = 1.62401 (* 1 = 1.62401 loss)
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I0428 13:53:09.629392 11373 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
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I0428 13:53:14.385066 11373 solver.cpp:218] Iteration 4308 (2.52341 iter/s, 4.75548s/12 iters), loss = 1.52513
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I0428 13:53:14.385105 11373 solver.cpp:237] Train net output #0: loss = 1.52513 (* 1 = 1.52513 loss)
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I0428 13:53:14.385113 11373 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
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I0428 13:53:19.131709 11373 solver.cpp:218] Iteration 4320 (2.52824 iter/s, 4.74639s/12 iters), loss = 1.54359
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I0428 13:53:19.131765 11373 solver.cpp:237] Train net output #0: loss = 1.54359 (* 1 = 1.54359 loss)
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I0428 13:53:19.131778 11373 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
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I0428 13:53:23.880524 11373 solver.cpp:218] Iteration 4332 (2.52717 iter/s, 4.7484s/12 iters), loss = 1.48964
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I0428 13:53:23.880569 11373 solver.cpp:237] Train net output #0: loss = 1.48964 (* 1 = 1.48964 loss)
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I0428 13:53:23.880591 11373 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
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I0428 13:53:28.640177 11373 solver.cpp:218] Iteration 4344 (2.52132 iter/s, 4.75941s/12 iters), loss = 1.54492
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I0428 13:53:28.640216 11373 solver.cpp:237] Train net output #0: loss = 1.54492 (* 1 = 1.54492 loss)
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I0428 13:53:28.640224 11373 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
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I0428 13:53:30.469993 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:53:33.359936 11373 solver.cpp:218] Iteration 4356 (2.54264 iter/s, 4.71951s/12 iters), loss = 1.2784
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I0428 13:53:33.360080 11373 solver.cpp:237] Train net output #0: loss = 1.2784 (* 1 = 1.2784 loss)
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I0428 13:53:33.360090 11373 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
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I0428 13:53:38.055719 11373 solver.cpp:218] Iteration 4368 (2.55569 iter/s, 4.69541s/12 iters), loss = 1.58566
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I0428 13:53:38.055778 11373 solver.cpp:237] Train net output #0: loss = 1.58566 (* 1 = 1.58566 loss)
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I0428 13:53:38.055790 11373 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
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I0428 13:53:42.806713 11373 solver.cpp:218] Iteration 4380 (2.52593 iter/s, 4.75073s/12 iters), loss = 1.55667
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I0428 13:53:42.806772 11373 solver.cpp:237] Train net output #0: loss = 1.55667 (* 1 = 1.55667 loss)
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I0428 13:53:42.806785 11373 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
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I0428 13:53:44.751405 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
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I0428 13:53:47.340319 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
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I0428 13:53:50.204026 11373 solver.cpp:330] Iteration 4386, Testing net (#0)
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I0428 13:53:50.204049 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:53:53.199281 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:53:55.183845 11373 solver.cpp:397] Test net output #0: accuracy = 0.379902
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I0428 13:53:55.183872 11373 solver.cpp:397] Test net output #1: loss = 2.64446 (* 1 = 2.64446 loss)
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I0428 13:53:56.863587 11373 solver.cpp:218] Iteration 4392 (0.853713 iter/s, 14.0562s/12 iters), loss = 1.25386
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I0428 13:53:56.863629 11373 solver.cpp:237] Train net output #0: loss = 1.25386 (* 1 = 1.25386 loss)
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I0428 13:53:56.863639 11373 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
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I0428 13:54:01.623834 11373 solver.cpp:218] Iteration 4404 (2.52101 iter/s, 4.76s/12 iters), loss = 1.49516
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I0428 13:54:01.623874 11373 solver.cpp:237] Train net output #0: loss = 1.49516 (* 1 = 1.49516 loss)
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I0428 13:54:01.623883 11373 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
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I0428 13:54:06.553552 11373 solver.cpp:218] Iteration 4416 (2.43539 iter/s, 4.92733s/12 iters), loss = 1.41561
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I0428 13:54:06.553733 11373 solver.cpp:237] Train net output #0: loss = 1.41561 (* 1 = 1.41561 loss)
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I0428 13:54:06.553751 11373 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
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I0428 13:54:11.253350 11373 solver.cpp:218] Iteration 4428 (2.55463 iter/s, 4.69736s/12 iters), loss = 1.37535
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I0428 13:54:11.253394 11373 solver.cpp:237] Train net output #0: loss = 1.37535 (* 1 = 1.37535 loss)
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I0428 13:54:11.253403 11373 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
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I0428 13:54:16.040688 11373 solver.cpp:218] Iteration 4440 (2.50674 iter/s, 4.78709s/12 iters), loss = 1.44686
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I0428 13:54:16.040727 11373 solver.cpp:237] Train net output #0: loss = 1.44686 (* 1 = 1.44686 loss)
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I0428 13:54:16.040736 11373 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
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I0428 13:54:20.055955 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:54:21.132670 11373 solver.cpp:218] Iteration 4452 (2.35677 iter/s, 5.09172s/12 iters), loss = 1.21874
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I0428 13:54:21.132721 11373 solver.cpp:237] Train net output #0: loss = 1.21874 (* 1 = 1.21874 loss)
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I0428 13:54:21.132735 11373 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
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I0428 13:54:25.983409 11373 solver.cpp:218] Iteration 4464 (2.47399 iter/s, 4.85046s/12 iters), loss = 1.5586
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I0428 13:54:25.983448 11373 solver.cpp:237] Train net output #0: loss = 1.5586 (* 1 = 1.5586 loss)
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I0428 13:54:25.983456 11373 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
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I0428 13:54:30.773674 11373 solver.cpp:218] Iteration 4476 (2.50636 iter/s, 4.78782s/12 iters), loss = 1.37701
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I0428 13:54:30.773711 11373 solver.cpp:237] Train net output #0: loss = 1.37701 (* 1 = 1.37701 loss)
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I0428 13:54:30.773720 11373 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
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I0428 13:54:35.158759 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
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I0428 13:54:36.667294 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
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I0428 13:54:39.075174 11373 solver.cpp:330] Iteration 4488, Testing net (#0)
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I0428 13:54:39.075199 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:54:42.004563 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:54:43.984123 11373 solver.cpp:397] Test net output #0: accuracy = 0.362745
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I0428 13:54:43.984150 11373 solver.cpp:397] Test net output #1: loss = 2.66403 (* 1 = 2.66403 loss)
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I0428 13:54:44.148671 11373 solver.cpp:218] Iteration 4488 (0.897384 iter/s, 13.3722s/12 iters), loss = 1.368
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I0428 13:54:44.150377 11373 solver.cpp:237] Train net output #0: loss = 1.368 (* 1 = 1.368 loss)
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I0428 13:54:44.150388 11373 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
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I0428 13:54:48.373637 11373 solver.cpp:218] Iteration 4500 (2.84153 iter/s, 4.22307s/12 iters), loss = 1.66781
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I0428 13:54:48.373697 11373 solver.cpp:237] Train net output #0: loss = 1.66781 (* 1 = 1.66781 loss)
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I0428 13:54:48.373710 11373 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
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I0428 13:54:53.166987 11373 solver.cpp:218] Iteration 4512 (2.50475 iter/s, 4.7909s/12 iters), loss = 1.37659
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I0428 13:54:53.167032 11373 solver.cpp:237] Train net output #0: loss = 1.37659 (* 1 = 1.37659 loss)
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I0428 13:54:53.167042 11373 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
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I0428 13:54:58.102367 11373 solver.cpp:218] Iteration 4524 (2.43264 iter/s, 4.93292s/12 iters), loss = 1.24177
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I0428 13:54:58.102409 11373 solver.cpp:237] Train net output #0: loss = 1.24177 (* 1 = 1.24177 loss)
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I0428 13:54:58.102421 11373 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
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I0428 13:55:03.036293 11373 solver.cpp:218] Iteration 4536 (2.43226 iter/s, 4.93368s/12 iters), loss = 1.42265
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I0428 13:55:03.036330 11373 solver.cpp:237] Train net output #0: loss = 1.42265 (* 1 = 1.42265 loss)
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I0428 13:55:03.036339 11373 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
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I0428 13:55:07.913789 11373 solver.cpp:218] Iteration 4548 (2.4604 iter/s, 4.87725s/12 iters), loss = 1.51403
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I0428 13:55:07.913913 11373 solver.cpp:237] Train net output #0: loss = 1.51403 (* 1 = 1.51403 loss)
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I0428 13:55:07.913923 11373 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
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I0428 13:55:09.196519 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:55:12.857882 11373 solver.cpp:218] Iteration 4560 (2.4273 iter/s, 4.94376s/12 iters), loss = 1.14009
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I0428 13:55:12.857923 11373 solver.cpp:237] Train net output #0: loss = 1.14009 (* 1 = 1.14009 loss)
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I0428 13:55:12.857933 11373 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
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I0428 13:55:17.851835 11373 solver.cpp:218] Iteration 4572 (2.40303 iter/s, 4.9937s/12 iters), loss = 1.40401
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I0428 13:55:17.851877 11373 solver.cpp:237] Train net output #0: loss = 1.40401 (* 1 = 1.40401 loss)
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I0428 13:55:17.851886 11373 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
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I0428 13:55:23.061422 11373 solver.cpp:218] Iteration 4584 (2.30455 iter/s, 5.2071s/12 iters), loss = 1.33717
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I0428 13:55:23.061465 11373 solver.cpp:237] Train net output #0: loss = 1.33717 (* 1 = 1.33717 loss)
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I0428 13:55:23.061475 11373 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
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I0428 13:55:25.205757 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
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I0428 13:55:27.260866 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
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I0428 13:55:29.679201 11373 solver.cpp:330] Iteration 4590, Testing net (#0)
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I0428 13:55:29.679222 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:55:32.550631 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:55:34.577986 11373 solver.cpp:397] Test net output #0: accuracy = 0.403186
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I0428 13:55:34.578018 11373 solver.cpp:397] Test net output #1: loss = 2.5758 (* 1 = 2.5758 loss)
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I0428 13:55:36.398859 11373 solver.cpp:218] Iteration 4596 (0.899761 iter/s, 13.3369s/12 iters), loss = 1.68379
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I0428 13:55:36.398905 11373 solver.cpp:237] Train net output #0: loss = 1.68379 (* 1 = 1.68379 loss)
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I0428 13:55:36.398914 11373 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
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I0428 13:55:41.301160 11373 solver.cpp:218] Iteration 4608 (2.44795 iter/s, 4.90205s/12 iters), loss = 1.40562
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I0428 13:55:41.310328 11373 solver.cpp:237] Train net output #0: loss = 1.40562 (* 1 = 1.40562 loss)
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I0428 13:55:41.310345 11373 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
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I0428 13:55:46.274106 11373 solver.cpp:218] Iteration 4620 (2.41761 iter/s, 4.96357s/12 iters), loss = 1.41808
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I0428 13:55:46.274149 11373 solver.cpp:237] Train net output #0: loss = 1.41808 (* 1 = 1.41808 loss)
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I0428 13:55:46.274159 11373 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
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I0428 13:55:51.200065 11373 solver.cpp:218] Iteration 4632 (2.4373 iter/s, 4.92348s/12 iters), loss = 1.30146
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I0428 13:55:51.200114 11373 solver.cpp:237] Train net output #0: loss = 1.30146 (* 1 = 1.30146 loss)
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I0428 13:55:51.200124 11373 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
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I0428 13:55:56.284241 11373 solver.cpp:218] Iteration 4644 (2.3604 iter/s, 5.08389s/12 iters), loss = 1.22335
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I0428 13:55:56.284291 11373 solver.cpp:237] Train net output #0: loss = 1.22335 (* 1 = 1.22335 loss)
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I0428 13:55:56.284299 11373 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
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I0428 13:55:59.559132 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:56:01.206225 11373 solver.cpp:218] Iteration 4656 (2.43817 iter/s, 4.92173s/12 iters), loss = 1.31347
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I0428 13:56:01.206262 11373 solver.cpp:237] Train net output #0: loss = 1.31347 (* 1 = 1.31347 loss)
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I0428 13:56:01.206270 11373 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
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I0428 13:56:06.148089 11373 solver.cpp:218] Iteration 4668 (2.42835 iter/s, 4.94163s/12 iters), loss = 1.4993
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I0428 13:56:06.148126 11373 solver.cpp:237] Train net output #0: loss = 1.4993 (* 1 = 1.4993 loss)
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I0428 13:56:06.148135 11373 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
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I0428 13:56:11.317235 11373 solver.cpp:218] Iteration 4680 (2.32158 iter/s, 5.1689s/12 iters), loss = 1.26624
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I0428 13:56:11.317375 11373 solver.cpp:237] Train net output #0: loss = 1.26624 (* 1 = 1.26624 loss)
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I0428 13:56:11.317385 11373 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
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I0428 13:56:15.887183 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
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I0428 13:56:17.972051 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
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I0428 13:56:19.012905 11373 solver.cpp:330] Iteration 4692, Testing net (#0)
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I0428 13:56:19.012925 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:56:21.756680 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:56:23.813019 11373 solver.cpp:397] Test net output #0: accuracy = 0.40625
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I0428 13:56:23.813048 11373 solver.cpp:397] Test net output #1: loss = 2.59024 (* 1 = 2.59024 loss)
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I0428 13:56:23.978559 11373 solver.cpp:218] Iteration 4692 (0.947816 iter/s, 12.6607s/12 iters), loss = 1.3625
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I0428 13:56:23.980157 11373 solver.cpp:237] Train net output #0: loss = 1.3625 (* 1 = 1.3625 loss)
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I0428 13:56:23.980167 11373 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
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I0428 13:56:28.164886 11373 solver.cpp:218] Iteration 4704 (2.86769 iter/s, 4.18455s/12 iters), loss = 1.39492
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I0428 13:56:28.164935 11373 solver.cpp:237] Train net output #0: loss = 1.39492 (* 1 = 1.39492 loss)
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I0428 13:56:28.164947 11373 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
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I0428 13:56:33.046638 11373 solver.cpp:218] Iteration 4716 (2.45826 iter/s, 4.8815s/12 iters), loss = 1.37625
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I0428 13:56:33.046690 11373 solver.cpp:237] Train net output #0: loss = 1.37625 (* 1 = 1.37625 loss)
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I0428 13:56:33.046703 11373 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
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I0428 13:56:37.969571 11373 solver.cpp:218] Iteration 4728 (2.4377 iter/s, 4.92268s/12 iters), loss = 1.261
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I0428 13:56:37.969609 11373 solver.cpp:237] Train net output #0: loss = 1.261 (* 1 = 1.261 loss)
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I0428 13:56:37.969619 11373 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
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I0428 13:56:42.872211 11373 solver.cpp:218] Iteration 4740 (2.44888 iter/s, 4.9002s/12 iters), loss = 1.25669
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I0428 13:56:42.872354 11373 solver.cpp:237] Train net output #0: loss = 1.25669 (* 1 = 1.25669 loss)
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I0428 13:56:42.872368 11373 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
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I0428 13:56:47.668913 11373 solver.cpp:218] Iteration 4752 (2.50296 iter/s, 4.79432s/12 iters), loss = 1.38498
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I0428 13:56:47.668956 11373 solver.cpp:237] Train net output #0: loss = 1.38498 (* 1 = 1.38498 loss)
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I0428 13:56:47.668963 11373 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
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I0428 13:56:48.191326 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:56:52.531514 11373 solver.cpp:218] Iteration 4764 (2.46794 iter/s, 4.86235s/12 iters), loss = 1.33
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I0428 13:56:52.531545 11373 solver.cpp:237] Train net output #0: loss = 1.33 (* 1 = 1.33 loss)
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I0428 13:56:52.531553 11373 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
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I0428 13:56:57.507417 11373 solver.cpp:218] Iteration 4776 (2.41174 iter/s, 4.97567s/12 iters), loss = 1.28333
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I0428 13:56:57.507458 11373 solver.cpp:237] Train net output #0: loss = 1.28333 (* 1 = 1.28333 loss)
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I0428 13:56:57.507467 11373 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
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I0428 13:57:02.480420 11373 solver.cpp:218] Iteration 4788 (2.41315 iter/s, 4.97276s/12 iters), loss = 1.26755
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I0428 13:57:02.480459 11373 solver.cpp:237] Train net output #0: loss = 1.26755 (* 1 = 1.26755 loss)
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I0428 13:57:02.480468 11373 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
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I0428 13:57:04.499388 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
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I0428 13:57:11.278741 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
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I0428 13:57:14.902151 11373 solver.cpp:330] Iteration 4794, Testing net (#0)
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I0428 13:57:14.902312 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:57:17.665369 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:57:19.829567 11373 solver.cpp:397] Test net output #0: accuracy = 0.416054
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I0428 13:57:19.829596 11373 solver.cpp:397] Test net output #1: loss = 2.54459 (* 1 = 2.54459 loss)
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I0428 13:57:21.626999 11373 solver.cpp:218] Iteration 4800 (0.626769 iter/s, 19.1458s/12 iters), loss = 1.33337
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I0428 13:57:21.627056 11373 solver.cpp:237] Train net output #0: loss = 1.33337 (* 1 = 1.33337 loss)
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I0428 13:57:21.627068 11373 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
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I0428 13:57:26.810968 11373 solver.cpp:218] Iteration 4812 (2.31495 iter/s, 5.1837s/12 iters), loss = 1.14206
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I0428 13:57:26.811013 11373 solver.cpp:237] Train net output #0: loss = 1.14206 (* 1 = 1.14206 loss)
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I0428 13:57:26.811024 11373 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
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I0428 13:57:31.727871 11373 solver.cpp:218] Iteration 4824 (2.44177 iter/s, 4.91447s/12 iters), loss = 1.05061
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I0428 13:57:31.727913 11373 solver.cpp:237] Train net output #0: loss = 1.05061 (* 1 = 1.05061 loss)
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I0428 13:57:31.727924 11373 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
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I0428 13:57:36.601707 11373 solver.cpp:218] Iteration 4836 (2.46225 iter/s, 4.8736s/12 iters), loss = 1.15477
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I0428 13:57:36.601747 11373 solver.cpp:237] Train net output #0: loss = 1.15477 (* 1 = 1.15477 loss)
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I0428 13:57:36.601755 11373 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
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I0428 13:57:38.657753 11373 blocking_queue.cpp:49] Waiting for data
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I0428 13:57:41.686619 11373 solver.cpp:218] Iteration 4848 (2.36004 iter/s, 5.08466s/12 iters), loss = 1.31448
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I0428 13:57:41.686676 11373 solver.cpp:237] Train net output #0: loss = 1.31448 (* 1 = 1.31448 loss)
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I0428 13:57:41.686686 11373 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
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I0428 13:57:44.302953 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:57:46.696327 11373 solver.cpp:218] Iteration 4860 (2.3963 iter/s, 5.00771s/12 iters), loss = 1.03316
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I0428 13:57:46.696763 11373 solver.cpp:237] Train net output #0: loss = 1.03316 (* 1 = 1.03316 loss)
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I0428 13:57:46.696774 11373 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
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I0428 13:57:51.682864 11373 solver.cpp:218] Iteration 4872 (2.40679 iter/s, 4.9859s/12 iters), loss = 1.23223
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I0428 13:57:51.682905 11373 solver.cpp:237] Train net output #0: loss = 1.23223 (* 1 = 1.23223 loss)
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I0428 13:57:51.682914 11373 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
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I0428 13:57:56.777319 11373 solver.cpp:218] Iteration 4884 (2.35663 iter/s, 5.09202s/12 iters), loss = 1.21104
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I0428 13:57:56.777367 11373 solver.cpp:237] Train net output #0: loss = 1.21104 (* 1 = 1.21104 loss)
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I0428 13:57:56.777377 11373 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
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I0428 13:58:01.134256 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
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I0428 13:58:05.332901 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
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I0428 13:58:09.177551 11373 solver.cpp:330] Iteration 4896, Testing net (#0)
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I0428 13:58:09.177569 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:58:11.925137 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:58:14.102456 11373 solver.cpp:397] Test net output #0: accuracy = 0.436887
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I0428 13:58:14.102483 11373 solver.cpp:397] Test net output #1: loss = 2.45759 (* 1 = 2.45759 loss)
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I0428 13:58:14.201455 11373 solver.cpp:218] Iteration 4896 (0.688728 iter/s, 17.4234s/12 iters), loss = 0.884871
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I0428 13:58:14.201514 11373 solver.cpp:237] Train net output #0: loss = 0.884871 (* 1 = 0.884871 loss)
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I0428 13:58:14.201525 11373 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
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I0428 13:58:18.160951 11373 solver.cpp:218] Iteration 4908 (3.03086 iter/s, 3.95928s/12 iters), loss = 1.09046
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I0428 13:58:18.161072 11373 solver.cpp:237] Train net output #0: loss = 1.09046 (* 1 = 1.09046 loss)
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I0428 13:58:18.161082 11373 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
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I0428 13:58:23.051602 11373 solver.cpp:218] Iteration 4920 (2.45382 iter/s, 4.89033s/12 iters), loss = 1.04117
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I0428 13:58:23.051656 11373 solver.cpp:237] Train net output #0: loss = 1.04117 (* 1 = 1.04117 loss)
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I0428 13:58:23.051668 11373 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
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I0428 13:58:27.801095 11373 solver.cpp:218] Iteration 4932 (2.52789 iter/s, 4.74705s/12 iters), loss = 1.27781
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I0428 13:58:27.801136 11373 solver.cpp:237] Train net output #0: loss = 1.27781 (* 1 = 1.27781 loss)
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I0428 13:58:27.801144 11373 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
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I0428 13:58:32.777407 11373 solver.cpp:218] Iteration 4944 (2.41261 iter/s, 4.97386s/12 iters), loss = 0.915464
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I0428 13:58:32.777460 11373 solver.cpp:237] Train net output #0: loss = 0.915464 (* 1 = 0.915464 loss)
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I0428 13:58:32.777473 11373 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
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I0428 13:58:37.482605 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:58:37.767432 11373 solver.cpp:218] Iteration 4956 (2.40492 iter/s, 4.98977s/12 iters), loss = 1.1752
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I0428 13:58:37.767493 11373 solver.cpp:237] Train net output #0: loss = 1.1752 (* 1 = 1.1752 loss)
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I0428 13:58:37.767506 11373 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
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I0428 13:58:42.516836 11373 solver.cpp:218] Iteration 4968 (2.52792 iter/s, 4.74699s/12 iters), loss = 1.30386
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I0428 13:58:42.516897 11373 solver.cpp:237] Train net output #0: loss = 1.30386 (* 1 = 1.30386 loss)
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I0428 13:58:42.516911 11373 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
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I0428 13:58:47.438479 11373 solver.cpp:218] Iteration 4980 (2.43834 iter/s, 4.92138s/12 iters), loss = 1.16474
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I0428 13:58:47.438529 11373 solver.cpp:237] Train net output #0: loss = 1.16474 (* 1 = 1.16474 loss)
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I0428 13:58:47.438541 11373 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
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I0428 13:58:52.398137 11373 solver.cpp:218] Iteration 4992 (2.4207 iter/s, 4.95723s/12 iters), loss = 0.967057
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I0428 13:58:52.398267 11373 solver.cpp:237] Train net output #0: loss = 0.967057 (* 1 = 0.967057 loss)
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I0428 13:58:52.398277 11373 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
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I0428 13:58:54.286229 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
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I0428 13:58:57.394425 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
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I0428 13:59:01.575307 11373 solver.cpp:330] Iteration 4998, Testing net (#0)
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I0428 13:59:01.575328 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:59:04.293900 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:59:06.441876 11373 solver.cpp:397] Test net output #0: accuracy = 0.434436
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I0428 13:59:06.441906 11373 solver.cpp:397] Test net output #1: loss = 2.46356 (* 1 = 2.46356 loss)
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I0428 13:59:08.144290 11373 solver.cpp:218] Iteration 5004 (0.76223 iter/s, 15.7433s/12 iters), loss = 1.01988
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I0428 13:59:08.144330 11373 solver.cpp:237] Train net output #0: loss = 1.01988 (* 1 = 1.01988 loss)
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I0428 13:59:08.144338 11373 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
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I0428 13:59:12.939720 11373 solver.cpp:218] Iteration 5016 (2.5025 iter/s, 4.7952s/12 iters), loss = 1.14645
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I0428 13:59:12.939761 11373 solver.cpp:237] Train net output #0: loss = 1.14645 (* 1 = 1.14645 loss)
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I0428 13:59:12.939770 11373 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
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I0428 13:59:17.893453 11373 solver.cpp:218] Iteration 5028 (2.42253 iter/s, 4.95349s/12 iters), loss = 1.13344
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I0428 13:59:17.893493 11373 solver.cpp:237] Train net output #0: loss = 1.13344 (* 1 = 1.13344 loss)
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I0428 13:59:17.893502 11373 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
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I0428 13:59:22.790963 11373 solver.cpp:218] Iteration 5040 (2.45144 iter/s, 4.89508s/12 iters), loss = 1.02942
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I0428 13:59:22.791095 11373 solver.cpp:237] Train net output #0: loss = 1.02942 (* 1 = 1.02942 loss)
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I0428 13:59:22.791106 11373 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
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I0428 13:59:27.967404 11373 solver.cpp:218] Iteration 5052 (2.31835 iter/s, 5.1761s/12 iters), loss = 1.01803
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I0428 13:59:27.967448 11373 solver.cpp:237] Train net output #0: loss = 1.01803 (* 1 = 1.01803 loss)
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I0428 13:59:27.967458 11373 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
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I0428 13:59:29.964377 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:59:33.091384 11373 solver.cpp:218] Iteration 5064 (2.34205 iter/s, 5.12371s/12 iters), loss = 1.0478
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I0428 13:59:33.091429 11373 solver.cpp:237] Train net output #0: loss = 1.0478 (* 1 = 1.0478 loss)
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I0428 13:59:33.091439 11373 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
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I0428 13:59:37.758551 11373 solver.cpp:218] Iteration 5076 (2.5725 iter/s, 4.66473s/12 iters), loss = 1.26807
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I0428 13:59:37.758613 11373 solver.cpp:237] Train net output #0: loss = 1.26807 (* 1 = 1.26807 loss)
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I0428 13:59:37.758626 11373 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
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I0428 13:59:42.670825 11373 solver.cpp:218] Iteration 5088 (2.44299 iter/s, 4.91201s/12 iters), loss = 1.09465
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I0428 13:59:42.670873 11373 solver.cpp:237] Train net output #0: loss = 1.09465 (* 1 = 1.09465 loss)
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I0428 13:59:42.670887 11373 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
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I0428 13:59:46.982756 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
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I0428 13:59:48.268638 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
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I0428 13:59:49.264384 11373 solver.cpp:330] Iteration 5100, Testing net (#0)
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I0428 13:59:49.264402 11373 net.cpp:676] Ignoring source layer train-data
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I0428 13:59:51.820118 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 13:59:54.011473 11373 solver.cpp:397] Test net output #0: accuracy = 0.421569
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I0428 13:59:54.011585 11373 solver.cpp:397] Test net output #1: loss = 2.51802 (* 1 = 2.51802 loss)
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I0428 13:59:54.181766 11373 solver.cpp:218] Iteration 5100 (1.04273 iter/s, 11.5083s/12 iters), loss = 1.02876
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I0428 13:59:54.183434 11373 solver.cpp:237] Train net output #0: loss = 1.02876 (* 1 = 1.02876 loss)
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I0428 13:59:54.183447 11373 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
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I0428 13:59:58.088853 11373 solver.cpp:218] Iteration 5112 (3.07278 iter/s, 3.90526s/12 iters), loss = 1.1349
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I0428 13:59:58.088893 11373 solver.cpp:237] Train net output #0: loss = 1.1349 (* 1 = 1.1349 loss)
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I0428 13:59:58.088902 11373 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
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I0428 14:00:03.353224 11373 solver.cpp:218] Iteration 5124 (2.27959 iter/s, 5.26411s/12 iters), loss = 1.00481
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I0428 14:00:03.353266 11373 solver.cpp:237] Train net output #0: loss = 1.00481 (* 1 = 1.00481 loss)
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I0428 14:00:03.353274 11373 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
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I0428 14:00:08.124011 11373 solver.cpp:218] Iteration 5136 (2.51659 iter/s, 4.76835s/12 iters), loss = 1.12002
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I0428 14:00:08.124053 11373 solver.cpp:237] Train net output #0: loss = 1.12002 (* 1 = 1.12002 loss)
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I0428 14:00:08.124063 11373 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
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I0428 14:00:13.131240 11373 solver.cpp:218] Iteration 5148 (2.39771 iter/s, 5.00478s/12 iters), loss = 1.03183
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I0428 14:00:13.131290 11373 solver.cpp:237] Train net output #0: loss = 1.03183 (* 1 = 1.03183 loss)
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I0428 14:00:13.131301 11373 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
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I0428 14:00:16.967653 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:00:17.901680 11373 solver.cpp:218] Iteration 5160 (2.51562 iter/s, 4.7702s/12 iters), loss = 1.21724
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I0428 14:00:17.901729 11373 solver.cpp:237] Train net output #0: loss = 1.21724 (* 1 = 1.21724 loss)
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I0428 14:00:17.901741 11373 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
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I0428 14:00:22.495494 11373 solver.cpp:218] Iteration 5172 (2.61359 iter/s, 4.59139s/12 iters), loss = 1.07968
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I0428 14:00:22.495545 11373 solver.cpp:237] Train net output #0: loss = 1.07968 (* 1 = 1.07968 loss)
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I0428 14:00:22.495558 11373 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
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I0428 14:00:27.199194 11373 solver.cpp:218] Iteration 5184 (2.55131 iter/s, 4.70346s/12 iters), loss = 0.996259
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I0428 14:00:27.199337 11373 solver.cpp:237] Train net output #0: loss = 0.996259 (* 1 = 0.996259 loss)
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I0428 14:00:27.199349 11373 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
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I0428 14:00:32.084522 11373 solver.cpp:218] Iteration 5196 (2.45706 iter/s, 4.88389s/12 iters), loss = 0.90751
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I0428 14:00:32.084563 11373 solver.cpp:237] Train net output #0: loss = 0.90751 (* 1 = 0.90751 loss)
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I0428 14:00:32.084573 11373 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
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I0428 14:00:34.081765 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
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I0428 14:00:35.443214 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
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I0428 14:00:36.450871 11373 solver.cpp:330] Iteration 5202, Testing net (#0)
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I0428 14:00:36.450891 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:00:38.941140 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:00:41.228026 11373 solver.cpp:397] Test net output #0: accuracy = 0.425245
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I0428 14:00:41.228063 11373 solver.cpp:397] Test net output #1: loss = 2.48675 (* 1 = 2.48675 loss)
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I0428 14:00:43.203706 11373 solver.cpp:218] Iteration 5208 (1.07926 iter/s, 11.1187s/12 iters), loss = 1.11661
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I0428 14:00:43.203759 11373 solver.cpp:237] Train net output #0: loss = 1.11661 (* 1 = 1.11661 loss)
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I0428 14:00:43.203771 11373 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
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I0428 14:00:48.165729 11373 solver.cpp:218] Iteration 5220 (2.41849 iter/s, 4.96177s/12 iters), loss = 0.936797
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I0428 14:00:48.165793 11373 solver.cpp:237] Train net output #0: loss = 0.936797 (* 1 = 0.936797 loss)
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I0428 14:00:48.165807 11373 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
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I0428 14:00:53.088122 11373 solver.cpp:218] Iteration 5232 (2.43905 iter/s, 4.91995s/12 iters), loss = 0.698968
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I0428 14:00:53.088166 11373 solver.cpp:237] Train net output #0: loss = 0.698968 (* 1 = 0.698968 loss)
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I0428 14:00:53.088176 11373 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
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I0428 14:00:58.075227 11373 solver.cpp:218] Iteration 5244 (2.40738 iter/s, 4.98466s/12 iters), loss = 1.00875
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I0428 14:00:58.075338 11373 solver.cpp:237] Train net output #0: loss = 1.00875 (* 1 = 1.00875 loss)
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I0428 14:00:58.075349 11373 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
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I0428 14:01:02.975129 11373 solver.cpp:218] Iteration 5256 (2.45025 iter/s, 4.89745s/12 iters), loss = 1.16311
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I0428 14:01:02.975175 11373 solver.cpp:237] Train net output #0: loss = 1.16311 (* 1 = 1.16311 loss)
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I0428 14:01:02.975188 11373 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
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I0428 14:01:04.189013 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:01:08.069278 11373 solver.cpp:218] Iteration 5268 (2.35576 iter/s, 5.0939s/12 iters), loss = 1.13707
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I0428 14:01:08.069315 11373 solver.cpp:237] Train net output #0: loss = 1.13707 (* 1 = 1.13707 loss)
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I0428 14:01:08.069325 11373 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
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I0428 14:01:12.897233 11373 solver.cpp:218] Iteration 5280 (2.48565 iter/s, 4.82772s/12 iters), loss = 1.47005
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I0428 14:01:12.897287 11373 solver.cpp:237] Train net output #0: loss = 1.47005 (* 1 = 1.47005 loss)
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I0428 14:01:12.897298 11373 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
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I0428 14:01:17.634243 11373 solver.cpp:218] Iteration 5292 (2.53338 iter/s, 4.73676s/12 iters), loss = 0.888539
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I0428 14:01:17.634294 11373 solver.cpp:237] Train net output #0: loss = 0.888539 (* 1 = 0.888539 loss)
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I0428 14:01:17.634306 11373 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
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I0428 14:01:22.209576 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
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I0428 14:01:27.045143 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
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I0428 14:01:31.184926 11373 solver.cpp:330] Iteration 5304, Testing net (#0)
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I0428 14:01:31.185016 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:01:33.855720 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:01:36.230108 11373 solver.cpp:397] Test net output #0: accuracy = 0.439338
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I0428 14:01:36.230136 11373 solver.cpp:397] Test net output #1: loss = 2.54372 (* 1 = 2.54372 loss)
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I0428 14:01:36.303066 11373 solver.cpp:218] Iteration 5304 (0.642809 iter/s, 18.6681s/12 iters), loss = 0.936509
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I0428 14:01:36.303110 11373 solver.cpp:237] Train net output #0: loss = 0.936509 (* 1 = 0.936509 loss)
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I0428 14:01:36.303118 11373 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
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I0428 14:01:40.421372 11373 solver.cpp:218] Iteration 5316 (2.91397 iter/s, 4.11809s/12 iters), loss = 0.953282
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I0428 14:01:40.421411 11373 solver.cpp:237] Train net output #0: loss = 0.953282 (* 1 = 0.953282 loss)
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I0428 14:01:40.421420 11373 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
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I0428 14:01:45.284133 11373 solver.cpp:218] Iteration 5328 (2.46897 iter/s, 4.86032s/12 iters), loss = 0.881156
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I0428 14:01:45.284189 11373 solver.cpp:237] Train net output #0: loss = 0.881156 (* 1 = 0.881156 loss)
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I0428 14:01:45.284200 11373 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
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I0428 14:01:50.118727 11373 solver.cpp:218] Iteration 5340 (2.48224 iter/s, 4.83434s/12 iters), loss = 0.86573
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I0428 14:01:50.118780 11373 solver.cpp:237] Train net output #0: loss = 0.86573 (* 1 = 0.86573 loss)
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I0428 14:01:50.118791 11373 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
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I0428 14:01:54.966698 11373 solver.cpp:218] Iteration 5352 (2.47539 iter/s, 4.84772s/12 iters), loss = 1.1479
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I0428 14:01:54.966760 11373 solver.cpp:237] Train net output #0: loss = 1.1479 (* 1 = 1.1479 loss)
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I0428 14:01:54.966773 11373 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
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I0428 14:01:58.204252 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:01:59.804284 11373 solver.cpp:218] Iteration 5364 (2.48071 iter/s, 4.83733s/12 iters), loss = 1.0397
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I0428 14:01:59.804327 11373 solver.cpp:237] Train net output #0: loss = 1.0397 (* 1 = 1.0397 loss)
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I0428 14:01:59.804337 11373 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
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I0428 14:02:04.603050 11373 solver.cpp:218] Iteration 5376 (2.50191 iter/s, 4.79633s/12 iters), loss = 0.948923
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I0428 14:02:04.603152 11373 solver.cpp:237] Train net output #0: loss = 0.948923 (* 1 = 0.948923 loss)
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I0428 14:02:04.603160 11373 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
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I0428 14:02:09.649160 11373 solver.cpp:218] Iteration 5388 (2.37821 iter/s, 5.0458s/12 iters), loss = 0.9863
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I0428 14:02:09.649216 11373 solver.cpp:237] Train net output #0: loss = 0.9863 (* 1 = 0.9863 loss)
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I0428 14:02:09.649227 11373 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
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I0428 14:02:14.468986 11373 solver.cpp:218] Iteration 5400 (2.48984 iter/s, 4.81958s/12 iters), loss = 1.08317
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I0428 14:02:14.469035 11373 solver.cpp:237] Train net output #0: loss = 1.08317 (* 1 = 1.08317 loss)
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I0428 14:02:14.469048 11373 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
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I0428 14:02:16.355271 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
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I0428 14:02:19.200317 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
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I0428 14:02:21.730068 11373 solver.cpp:330] Iteration 5406, Testing net (#0)
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I0428 14:02:21.730089 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:02:24.161237 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:02:26.685804 11373 solver.cpp:397] Test net output #0: accuracy = 0.439951
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I0428 14:02:26.685842 11373 solver.cpp:397] Test net output #1: loss = 2.63902 (* 1 = 2.63902 loss)
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I0428 14:02:28.451376 11373 solver.cpp:218] Iteration 5412 (0.858258 iter/s, 13.9818s/12 iters), loss = 0.96391
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I0428 14:02:28.451431 11373 solver.cpp:237] Train net output #0: loss = 0.96391 (* 1 = 0.96391 loss)
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I0428 14:02:28.451442 11373 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
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I0428 14:02:33.283269 11373 solver.cpp:218] Iteration 5424 (2.48363 iter/s, 4.83165s/12 iters), loss = 0.854345
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I0428 14:02:33.283310 11373 solver.cpp:237] Train net output #0: loss = 0.854345 (* 1 = 0.854345 loss)
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I0428 14:02:33.283318 11373 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
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I0428 14:02:38.042918 11373 solver.cpp:218] Iteration 5436 (2.52249 iter/s, 4.75721s/12 iters), loss = 0.863695
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I0428 14:02:38.043067 11373 solver.cpp:237] Train net output #0: loss = 0.863695 (* 1 = 0.863695 loss)
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I0428 14:02:38.043078 11373 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
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I0428 14:02:42.832537 11373 solver.cpp:218] Iteration 5448 (2.5056 iter/s, 4.78927s/12 iters), loss = 0.95789
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I0428 14:02:42.832592 11373 solver.cpp:237] Train net output #0: loss = 0.95789 (* 1 = 0.95789 loss)
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I0428 14:02:42.832603 11373 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
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I0428 14:02:47.635949 11373 solver.cpp:218] Iteration 5460 (2.49836 iter/s, 4.80315s/12 iters), loss = 1.18145
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I0428 14:02:47.635987 11373 solver.cpp:237] Train net output #0: loss = 1.18145 (* 1 = 1.18145 loss)
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I0428 14:02:47.635996 11373 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
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I0428 14:02:48.179296 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:02:52.660210 11373 solver.cpp:218] Iteration 5472 (2.38852 iter/s, 5.02402s/12 iters), loss = 1.05543
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I0428 14:02:52.660249 11373 solver.cpp:237] Train net output #0: loss = 1.05543 (* 1 = 1.05543 loss)
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I0428 14:02:52.660260 11373 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
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I0428 14:02:57.362768 11373 solver.cpp:218] Iteration 5484 (2.55193 iter/s, 4.70233s/12 iters), loss = 0.948888
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I0428 14:02:57.362809 11373 solver.cpp:237] Train net output #0: loss = 0.948888 (* 1 = 0.948888 loss)
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I0428 14:02:57.362818 11373 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
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I0428 14:03:02.324132 11373 solver.cpp:218] Iteration 5496 (2.41881 iter/s, 4.96113s/12 iters), loss = 0.976805
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I0428 14:03:02.324172 11373 solver.cpp:237] Train net output #0: loss = 0.976805 (* 1 = 0.976805 loss)
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I0428 14:03:02.324179 11373 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
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I0428 14:03:06.833787 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
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I0428 14:03:08.089403 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
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I0428 14:03:09.751133 11373 solver.cpp:330] Iteration 5508, Testing net (#0)
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I0428 14:03:09.751157 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:03:12.183580 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:03:14.767127 11373 solver.cpp:397] Test net output #0: accuracy = 0.448529
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I0428 14:03:14.767154 11373 solver.cpp:397] Test net output #1: loss = 2.51847 (* 1 = 2.51847 loss)
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I0428 14:03:14.885535 11373 solver.cpp:218] Iteration 5508 (0.955347 iter/s, 12.5609s/12 iters), loss = 0.900953
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I0428 14:03:14.885581 11373 solver.cpp:237] Train net output #0: loss = 0.900953 (* 1 = 0.900953 loss)
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I0428 14:03:14.885591 11373 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
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I0428 14:03:19.080677 11373 solver.cpp:218] Iteration 5520 (2.86059 iter/s, 4.19493s/12 iters), loss = 1.06263
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I0428 14:03:19.080716 11373 solver.cpp:237] Train net output #0: loss = 1.06263 (* 1 = 1.06263 loss)
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I0428 14:03:19.080724 11373 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
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I0428 14:03:21.456336 11373 blocking_queue.cpp:49] Waiting for data
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I0428 14:03:23.978451 11373 solver.cpp:218] Iteration 5532 (2.45021 iter/s, 4.89753s/12 iters), loss = 0.808152
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I0428 14:03:23.978497 11373 solver.cpp:237] Train net output #0: loss = 0.808152 (* 1 = 0.808152 loss)
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I0428 14:03:23.978507 11373 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
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I0428 14:03:28.840306 11373 solver.cpp:218] Iteration 5544 (2.46942 iter/s, 4.85944s/12 iters), loss = 0.884146
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I0428 14:03:28.840346 11373 solver.cpp:237] Train net output #0: loss = 0.884146 (* 1 = 0.884146 loss)
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I0428 14:03:28.840355 11373 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
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I0428 14:03:33.741812 11373 solver.cpp:218] Iteration 5556 (2.44835 iter/s, 4.90127s/12 iters), loss = 0.717529
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I0428 14:03:33.741854 11373 solver.cpp:237] Train net output #0: loss = 0.717529 (* 1 = 0.717529 loss)
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I0428 14:03:33.741864 11373 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
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I0428 14:03:36.625077 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:03:39.071223 11373 solver.cpp:218] Iteration 5568 (2.25177 iter/s, 5.32915s/12 iters), loss = 1.00307
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I0428 14:03:39.071388 11373 solver.cpp:237] Train net output #0: loss = 1.00307 (* 1 = 1.00307 loss)
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I0428 14:03:39.071399 11373 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
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I0428 14:03:44.010645 11373 solver.cpp:218] Iteration 5580 (2.43063 iter/s, 4.93699s/12 iters), loss = 1.00578
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I0428 14:03:44.010687 11373 solver.cpp:237] Train net output #0: loss = 1.00578 (* 1 = 1.00578 loss)
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I0428 14:03:44.010696 11373 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
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I0428 14:03:49.034490 11373 solver.cpp:218] Iteration 5592 (2.38873 iter/s, 5.0236s/12 iters), loss = 1.05123
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I0428 14:03:49.034529 11373 solver.cpp:237] Train net output #0: loss = 1.05123 (* 1 = 1.05123 loss)
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I0428 14:03:49.034538 11373 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
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I0428 14:03:54.292312 11373 solver.cpp:218] Iteration 5604 (2.28242 iter/s, 5.25757s/12 iters), loss = 1.13446
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I0428 14:03:54.292366 11373 solver.cpp:237] Train net output #0: loss = 1.13446 (* 1 = 1.13446 loss)
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I0428 14:03:54.292378 11373 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
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I0428 14:03:56.298617 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
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I0428 14:04:00.696091 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
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I0428 14:04:02.985117 11373 solver.cpp:330] Iteration 5610, Testing net (#0)
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I0428 14:04:02.985138 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:04:05.322708 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:04:07.791254 11373 solver.cpp:397] Test net output #0: accuracy = 0.452206
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I0428 14:04:07.791293 11373 solver.cpp:397] Test net output #1: loss = 2.47788 (* 1 = 2.47788 loss)
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I0428 14:04:09.642274 11373 solver.cpp:218] Iteration 5616 (0.781793 iter/s, 15.3493s/12 iters), loss = 0.81401
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I0428 14:04:09.642421 11373 solver.cpp:237] Train net output #0: loss = 0.81401 (* 1 = 0.81401 loss)
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I0428 14:04:09.642436 11373 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
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I0428 14:04:14.532332 11373 solver.cpp:218] Iteration 5628 (2.45517 iter/s, 4.88765s/12 iters), loss = 0.656837
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I0428 14:04:14.532375 11373 solver.cpp:237] Train net output #0: loss = 0.656837 (* 1 = 0.656837 loss)
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I0428 14:04:14.532387 11373 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
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I0428 14:04:19.485705 11373 solver.cpp:218] Iteration 5640 (2.42378 iter/s, 4.95095s/12 iters), loss = 0.762522
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I0428 14:04:19.485745 11373 solver.cpp:237] Train net output #0: loss = 0.762522 (* 1 = 0.762522 loss)
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I0428 14:04:19.485754 11373 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
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I0428 14:04:24.632752 11373 solver.cpp:218] Iteration 5652 (2.33155 iter/s, 5.1468s/12 iters), loss = 0.651878
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I0428 14:04:24.632800 11373 solver.cpp:237] Train net output #0: loss = 0.651878 (* 1 = 0.651878 loss)
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I0428 14:04:24.632812 11373 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
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I0428 14:04:29.424417 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:04:29.648018 11373 solver.cpp:218] Iteration 5664 (2.39281 iter/s, 5.01502s/12 iters), loss = 0.958076
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I0428 14:04:29.648062 11373 solver.cpp:237] Train net output #0: loss = 0.958076 (* 1 = 0.958076 loss)
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I0428 14:04:29.648074 11373 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
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I0428 14:04:34.846787 11373 solver.cpp:218] Iteration 5676 (2.30835 iter/s, 5.19852s/12 iters), loss = 0.780264
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I0428 14:04:34.846825 11373 solver.cpp:237] Train net output #0: loss = 0.780264 (* 1 = 0.780264 loss)
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I0428 14:04:34.846833 11373 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
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I0428 14:04:39.747922 11373 solver.cpp:218] Iteration 5688 (2.44961 iter/s, 4.89875s/12 iters), loss = 1.00148
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I0428 14:04:39.750831 11373 solver.cpp:237] Train net output #0: loss = 1.00148 (* 1 = 1.00148 loss)
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I0428 14:04:39.750844 11373 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
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I0428 14:04:44.825971 11373 solver.cpp:218] Iteration 5700 (2.36456 iter/s, 5.07494s/12 iters), loss = 0.780705
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I0428 14:04:44.826009 11373 solver.cpp:237] Train net output #0: loss = 0.780705 (* 1 = 0.780705 loss)
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I0428 14:04:44.826020 11373 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
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I0428 14:04:49.273315 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
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I0428 14:04:52.848691 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
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I0428 14:04:59.053606 11373 solver.cpp:330] Iteration 5712, Testing net (#0)
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I0428 14:04:59.053632 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:05:01.415252 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:05:04.119766 11373 solver.cpp:397] Test net output #0: accuracy = 0.463235
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I0428 14:05:04.119808 11373 solver.cpp:397] Test net output #1: loss = 2.49666 (* 1 = 2.49666 loss)
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I0428 14:05:04.215561 11373 solver.cpp:218] Iteration 5712 (0.618984 iter/s, 19.3866s/12 iters), loss = 0.916622
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I0428 14:05:04.215615 11373 solver.cpp:237] Train net output #0: loss = 0.916622 (* 1 = 0.916622 loss)
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I0428 14:05:04.215627 11373 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
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I0428 14:05:08.360540 11373 solver.cpp:218] Iteration 5724 (2.89523 iter/s, 4.14475s/12 iters), loss = 0.784113
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I0428 14:05:08.360584 11373 solver.cpp:237] Train net output #0: loss = 0.784113 (* 1 = 0.784113 loss)
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I0428 14:05:08.360591 11373 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
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I0428 14:05:13.321676 11373 solver.cpp:218] Iteration 5736 (2.41892 iter/s, 4.96089s/12 iters), loss = 0.615441
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I0428 14:05:13.321797 11373 solver.cpp:237] Train net output #0: loss = 0.615441 (* 1 = 0.615441 loss)
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I0428 14:05:13.321810 11373 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
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I0428 14:05:18.368063 11373 solver.cpp:218] Iteration 5748 (2.37809 iter/s, 5.04607s/12 iters), loss = 0.766017
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I0428 14:05:18.368104 11373 solver.cpp:237] Train net output #0: loss = 0.766017 (* 1 = 0.766017 loss)
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I0428 14:05:18.368113 11373 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
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I0428 14:05:23.210182 11373 solver.cpp:218] Iteration 5760 (2.47838 iter/s, 4.84188s/12 iters), loss = 0.618697
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I0428 14:05:23.210232 11373 solver.cpp:237] Train net output #0: loss = 0.618697 (* 1 = 0.618697 loss)
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I0428 14:05:23.210245 11373 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
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I0428 14:05:25.226691 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:05:28.424378 11373 solver.cpp:218] Iteration 5772 (2.30152 iter/s, 5.21394s/12 iters), loss = 0.762399
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I0428 14:05:28.424420 11373 solver.cpp:237] Train net output #0: loss = 0.762399 (* 1 = 0.762399 loss)
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I0428 14:05:28.424429 11373 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
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I0428 14:05:33.345851 11373 solver.cpp:218] Iteration 5784 (2.43841 iter/s, 4.92123s/12 iters), loss = 0.777706
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I0428 14:05:33.346012 11373 solver.cpp:237] Train net output #0: loss = 0.777706 (* 1 = 0.777706 loss)
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I0428 14:05:33.346025 11373 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
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I0428 14:05:38.398799 11373 solver.cpp:218] Iteration 5796 (2.37502 iter/s, 5.05259s/12 iters), loss = 0.669988
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I0428 14:05:38.398839 11373 solver.cpp:237] Train net output #0: loss = 0.669988 (* 1 = 0.669988 loss)
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I0428 14:05:38.398850 11373 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
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I0428 14:05:43.215025 11373 solver.cpp:218] Iteration 5808 (2.4917 iter/s, 4.81599s/12 iters), loss = 0.592537
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I0428 14:05:43.215068 11373 solver.cpp:237] Train net output #0: loss = 0.592537 (* 1 = 0.592537 loss)
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I0428 14:05:43.215076 11373 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
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I0428 14:05:45.271813 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
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I0428 14:05:52.252542 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
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I0428 14:05:59.308691 11373 solver.cpp:330] Iteration 5814, Testing net (#0)
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I0428 14:05:59.308710 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:06:01.566542 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:06:04.058089 11373 solver.cpp:397] Test net output #0: accuracy = 0.438726
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I0428 14:06:04.058130 11373 solver.cpp:397] Test net output #1: loss = 2.52593 (* 1 = 2.52593 loss)
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I0428 14:06:05.754298 11373 solver.cpp:218] Iteration 5820 (0.532425 iter/s, 22.5384s/12 iters), loss = 1.05298
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I0428 14:06:05.754341 11373 solver.cpp:237] Train net output #0: loss = 1.05298 (* 1 = 1.05298 loss)
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I0428 14:06:05.754350 11373 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
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I0428 14:06:10.630640 11373 solver.cpp:218] Iteration 5832 (2.46098 iter/s, 4.8761s/12 iters), loss = 0.607462
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I0428 14:06:10.630683 11373 solver.cpp:237] Train net output #0: loss = 0.607462 (* 1 = 0.607462 loss)
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I0428 14:06:10.630692 11373 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
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I0428 14:06:15.620380 11373 solver.cpp:218] Iteration 5844 (2.40505 iter/s, 4.9895s/12 iters), loss = 0.721153
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I0428 14:06:15.620540 11373 solver.cpp:237] Train net output #0: loss = 0.721153 (* 1 = 0.721153 loss)
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I0428 14:06:15.620549 11373 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
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I0428 14:06:20.668076 11373 solver.cpp:218] Iteration 5856 (2.37847 iter/s, 5.04526s/12 iters), loss = 0.752828
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I0428 14:06:20.668115 11373 solver.cpp:237] Train net output #0: loss = 0.752828 (* 1 = 0.752828 loss)
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I0428 14:06:20.668123 11373 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
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I0428 14:06:24.931370 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:06:25.791652 11373 solver.cpp:218] Iteration 5868 (2.34324 iter/s, 5.12112s/12 iters), loss = 0.777322
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I0428 14:06:25.791705 11373 solver.cpp:237] Train net output #0: loss = 0.777322 (* 1 = 0.777322 loss)
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I0428 14:06:25.791718 11373 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
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I0428 14:06:30.743674 11373 solver.cpp:218] Iteration 5880 (2.42338 iter/s, 4.95177s/12 iters), loss = 0.781401
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I0428 14:06:30.743713 11373 solver.cpp:237] Train net output #0: loss = 0.781401 (* 1 = 0.781401 loss)
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I0428 14:06:30.743722 11373 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
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I0428 14:06:35.691506 11373 solver.cpp:218] Iteration 5892 (2.42542 iter/s, 4.9476s/12 iters), loss = 0.799485
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I0428 14:06:35.691545 11373 solver.cpp:237] Train net output #0: loss = 0.799485 (* 1 = 0.799485 loss)
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I0428 14:06:35.691555 11373 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
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I0428 14:06:40.458542 11373 solver.cpp:218] Iteration 5904 (2.51857 iter/s, 4.76461s/12 iters), loss = 0.676162
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I0428 14:06:40.458602 11373 solver.cpp:237] Train net output #0: loss = 0.676162 (* 1 = 0.676162 loss)
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I0428 14:06:40.458616 11373 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
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I0428 14:06:45.156359 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
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I0428 14:06:50.525257 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
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I0428 14:06:57.787910 11373 solver.cpp:330] Iteration 5916, Testing net (#0)
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I0428 14:06:57.787936 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:07:00.263672 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:07:02.811827 11373 solver.cpp:397] Test net output #0: accuracy = 0.471201
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I0428 14:07:02.811861 11373 solver.cpp:397] Test net output #1: loss = 2.47838 (* 1 = 2.47838 loss)
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I0428 14:07:02.980232 11373 solver.cpp:218] Iteration 5916 (0.532841 iter/s, 22.5208s/12 iters), loss = 0.679392
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I0428 14:07:02.981914 11373 solver.cpp:237] Train net output #0: loss = 0.679392 (* 1 = 0.679392 loss)
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I0428 14:07:02.981930 11373 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
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I0428 14:07:06.958664 11373 solver.cpp:218] Iteration 5928 (3.01766 iter/s, 3.97659s/12 iters), loss = 0.776745
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I0428 14:07:06.958707 11373 solver.cpp:237] Train net output #0: loss = 0.776745 (* 1 = 0.776745 loss)
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I0428 14:07:06.958716 11373 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
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I0428 14:07:11.952136 11373 solver.cpp:218] Iteration 5940 (2.40326 iter/s, 4.99323s/12 iters), loss = 0.5953
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I0428 14:07:11.952180 11373 solver.cpp:237] Train net output #0: loss = 0.5953 (* 1 = 0.5953 loss)
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I0428 14:07:11.952189 11373 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
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I0428 14:07:16.751921 11373 solver.cpp:218] Iteration 5952 (2.50137 iter/s, 4.79736s/12 iters), loss = 0.695021
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I0428 14:07:16.751960 11373 solver.cpp:237] Train net output #0: loss = 0.695021 (* 1 = 0.695021 loss)
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I0428 14:07:16.751969 11373 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
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I0428 14:07:21.831475 11373 solver.cpp:218] Iteration 5964 (2.36253 iter/s, 5.07931s/12 iters), loss = 0.630277
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I0428 14:07:21.831599 11373 solver.cpp:237] Train net output #0: loss = 0.630277 (* 1 = 0.630277 loss)
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I0428 14:07:21.831610 11373 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
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I0428 14:07:23.021065 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:07:26.800590 11373 solver.cpp:218] Iteration 5976 (2.4161 iter/s, 4.96668s/12 iters), loss = 0.71897
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I0428 14:07:26.800632 11373 solver.cpp:237] Train net output #0: loss = 0.71897 (* 1 = 0.71897 loss)
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I0428 14:07:26.800642 11373 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
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I0428 14:07:31.864775 11373 solver.cpp:218] Iteration 5988 (2.37073 iter/s, 5.06174s/12 iters), loss = 0.865469
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I0428 14:07:31.864837 11373 solver.cpp:237] Train net output #0: loss = 0.865469 (* 1 = 0.865469 loss)
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I0428 14:07:31.864850 11373 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
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I0428 14:07:36.773973 11373 solver.cpp:218] Iteration 6000 (2.44452 iter/s, 4.90894s/12 iters), loss = 0.567841
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I0428 14:07:36.774021 11373 solver.cpp:237] Train net output #0: loss = 0.567841 (* 1 = 0.567841 loss)
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I0428 14:07:36.774032 11373 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
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I0428 14:07:41.752702 11373 solver.cpp:218] Iteration 6012 (2.41037 iter/s, 4.97848s/12 iters), loss = 0.65463
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I0428 14:07:41.752753 11373 solver.cpp:237] Train net output #0: loss = 0.65463 (* 1 = 0.65463 loss)
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I0428 14:07:41.752764 11373 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
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I0428 14:07:43.805665 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
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I0428 14:07:49.210038 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
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I0428 14:07:53.242249 11373 solver.cpp:330] Iteration 6018, Testing net (#0)
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I0428 14:07:53.242556 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:07:55.512053 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:07:58.114396 11373 solver.cpp:397] Test net output #0: accuracy = 0.462623
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I0428 14:07:58.114434 11373 solver.cpp:397] Test net output #1: loss = 2.54395 (* 1 = 2.54395 loss)
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I0428 14:07:59.997092 11373 solver.cpp:218] Iteration 6024 (0.657763 iter/s, 18.2437s/12 iters), loss = 0.588365
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I0428 14:07:59.997138 11373 solver.cpp:237] Train net output #0: loss = 0.588365 (* 1 = 0.588365 loss)
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I0428 14:07:59.997150 11373 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
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I0428 14:08:04.857214 11373 solver.cpp:218] Iteration 6036 (2.47031 iter/s, 4.8577s/12 iters), loss = 0.778435
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I0428 14:08:04.857260 11373 solver.cpp:237] Train net output #0: loss = 0.778435 (* 1 = 0.778435 loss)
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I0428 14:08:04.857270 11373 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
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I0428 14:08:09.935755 11373 solver.cpp:218] Iteration 6048 (2.363 iter/s, 5.07829s/12 iters), loss = 0.726883
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I0428 14:08:09.935811 11373 solver.cpp:237] Train net output #0: loss = 0.726883 (* 1 = 0.726883 loss)
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I0428 14:08:09.935827 11373 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
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I0428 14:08:14.772802 11373 solver.cpp:218] Iteration 6060 (2.4813 iter/s, 4.83618s/12 iters), loss = 0.577405
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I0428 14:08:14.772871 11373 solver.cpp:237] Train net output #0: loss = 0.577405 (* 1 = 0.577405 loss)
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I0428 14:08:14.772884 11373 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
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I0428 14:08:18.117182 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:08:19.690384 11373 solver.cpp:218] Iteration 6072 (2.44143 iter/s, 4.91515s/12 iters), loss = 0.610366
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I0428 14:08:19.690428 11373 solver.cpp:237] Train net output #0: loss = 0.610366 (* 1 = 0.610366 loss)
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I0428 14:08:19.690436 11373 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
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I0428 14:08:24.968520 11373 solver.cpp:218] Iteration 6084 (2.27365 iter/s, 5.27786s/12 iters), loss = 0.591814
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I0428 14:08:24.971352 11373 solver.cpp:237] Train net output #0: loss = 0.591814 (* 1 = 0.591814 loss)
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I0428 14:08:24.971366 11373 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
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I0428 14:08:29.943298 11373 solver.cpp:218] Iteration 6096 (2.41363 iter/s, 4.97176s/12 iters), loss = 0.734386
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I0428 14:08:29.943339 11373 solver.cpp:237] Train net output #0: loss = 0.734386 (* 1 = 0.734386 loss)
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I0428 14:08:29.943351 11373 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
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I0428 14:08:34.912765 11373 solver.cpp:218] Iteration 6108 (2.41519 iter/s, 4.96855s/12 iters), loss = 0.686714
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I0428 14:08:34.912824 11373 solver.cpp:237] Train net output #0: loss = 0.686714 (* 1 = 0.686714 loss)
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I0428 14:08:34.912837 11373 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
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I0428 14:08:39.541146 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
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I0428 14:08:40.843168 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
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I0428 14:08:41.852820 11373 solver.cpp:330] Iteration 6120, Testing net (#0)
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I0428 14:08:41.852849 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:08:43.959637 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:08:46.618034 11373 solver.cpp:397] Test net output #0: accuracy = 0.466299
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I0428 14:08:46.618074 11373 solver.cpp:397] Test net output #1: loss = 2.51021 (* 1 = 2.51021 loss)
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I0428 14:08:46.769731 11373 solver.cpp:218] Iteration 6120 (1.01229 iter/s, 11.8543s/12 iters), loss = 0.552409
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I0428 14:08:46.771589 11373 solver.cpp:237] Train net output #0: loss = 0.552409 (* 1 = 0.552409 loss)
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I0428 14:08:46.771605 11373 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
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I0428 14:08:50.719991 11373 solver.cpp:218] Iteration 6132 (3.03933 iter/s, 3.94824s/12 iters), loss = 0.763351
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I0428 14:08:50.720036 11373 solver.cpp:237] Train net output #0: loss = 0.763351 (* 1 = 0.763351 loss)
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I0428 14:08:50.720047 11373 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
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I0428 14:08:55.532934 11373 solver.cpp:218] Iteration 6144 (2.4934 iter/s, 4.81271s/12 iters), loss = 0.699842
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I0428 14:08:55.540429 11373 solver.cpp:237] Train net output #0: loss = 0.699842 (* 1 = 0.699842 loss)
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I0428 14:08:55.540446 11373 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
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I0428 14:09:00.632906 11373 solver.cpp:218] Iteration 6156 (2.35651 iter/s, 5.09228s/12 iters), loss = 0.893837
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I0428 14:09:00.632966 11373 solver.cpp:237] Train net output #0: loss = 0.893837 (* 1 = 0.893837 loss)
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I0428 14:09:00.632978 11373 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
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I0428 14:09:05.340961 11373 solver.cpp:218] Iteration 6168 (2.54896 iter/s, 4.70781s/12 iters), loss = 0.755684
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I0428 14:09:05.341007 11373 solver.cpp:237] Train net output #0: loss = 0.755684 (* 1 = 0.755684 loss)
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I0428 14:09:05.341017 11373 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
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I0428 14:09:05.917482 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:09:10.413345 11373 solver.cpp:218] Iteration 6180 (2.36587 iter/s, 5.07213s/12 iters), loss = 0.788675
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I0428 14:09:10.413388 11373 solver.cpp:237] Train net output #0: loss = 0.788675 (* 1 = 0.788675 loss)
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I0428 14:09:10.413398 11373 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
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I0428 14:09:15.386582 11373 solver.cpp:218] Iteration 6192 (2.41409 iter/s, 4.97081s/12 iters), loss = 0.770184
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I0428 14:09:15.386624 11373 solver.cpp:237] Train net output #0: loss = 0.770184 (* 1 = 0.770184 loss)
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I0428 14:09:15.386633 11373 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
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I0428 14:09:20.189950 11373 solver.cpp:218] Iteration 6204 (2.49837 iter/s, 4.80313s/12 iters), loss = 0.655237
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I0428 14:09:20.189990 11373 solver.cpp:237] Train net output #0: loss = 0.655237 (* 1 = 0.655237 loss)
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I0428 14:09:20.189999 11373 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
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I0428 14:09:25.100865 11373 solver.cpp:218] Iteration 6216 (2.44476 iter/s, 4.90845s/12 iters), loss = 0.536059
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I0428 14:09:25.100903 11373 solver.cpp:237] Train net output #0: loss = 0.536059 (* 1 = 0.536059 loss)
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I0428 14:09:25.100909 11373 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
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I0428 14:09:27.047439 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
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I0428 14:09:28.424710 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
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I0428 14:09:29.430842 11373 solver.cpp:330] Iteration 6222, Testing net (#0)
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I0428 14:09:29.430866 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:09:31.494864 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:09:32.851982 11373 blocking_queue.cpp:49] Waiting for data
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I0428 14:09:34.212345 11373 solver.cpp:397] Test net output #0: accuracy = 0.471814
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I0428 14:09:34.212386 11373 solver.cpp:397] Test net output #1: loss = 2.57321 (* 1 = 2.57321 loss)
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I0428 14:09:35.916862 11373 solver.cpp:218] Iteration 6228 (1.10951 iter/s, 10.8155s/12 iters), loss = 0.533921
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I0428 14:09:35.916915 11373 solver.cpp:237] Train net output #0: loss = 0.533921 (* 1 = 0.533921 loss)
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I0428 14:09:35.916926 11373 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
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I0428 14:09:40.748807 11373 solver.cpp:218] Iteration 6240 (2.4836 iter/s, 4.8317s/12 iters), loss = 0.641548
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I0428 14:09:40.748847 11373 solver.cpp:237] Train net output #0: loss = 0.641548 (* 1 = 0.641548 loss)
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I0428 14:09:40.748857 11373 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
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I0428 14:09:45.679014 11373 solver.cpp:218] Iteration 6252 (2.43519 iter/s, 4.92775s/12 iters), loss = 0.578973
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I0428 14:09:45.679054 11373 solver.cpp:237] Train net output #0: loss = 0.578973 (* 1 = 0.578973 loss)
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I0428 14:09:45.679061 11373 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
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I0428 14:09:50.632599 11373 solver.cpp:218] Iteration 6264 (2.42368 iter/s, 4.95116s/12 iters), loss = 0.677435
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I0428 14:09:50.632638 11373 solver.cpp:237] Train net output #0: loss = 0.677435 (* 1 = 0.677435 loss)
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I0428 14:09:50.632647 11373 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
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I0428 14:09:53.134007 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:09:55.361948 11373 solver.cpp:218] Iteration 6276 (2.53865 iter/s, 4.72691s/12 iters), loss = 0.78155
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I0428 14:09:55.361994 11373 solver.cpp:237] Train net output #0: loss = 0.78155 (* 1 = 0.78155 loss)
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I0428 14:09:55.362004 11373 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
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I0428 14:10:00.498564 11373 solver.cpp:218] Iteration 6288 (2.33729 iter/s, 5.13414s/12 iters), loss = 0.739041
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I0428 14:10:00.498692 11373 solver.cpp:237] Train net output #0: loss = 0.739041 (* 1 = 0.739041 loss)
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I0428 14:10:00.498704 11373 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
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I0428 14:10:05.555423 11373 solver.cpp:218] Iteration 6300 (2.37317 iter/s, 5.05653s/12 iters), loss = 0.658695
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I0428 14:10:05.555481 11373 solver.cpp:237] Train net output #0: loss = 0.658695 (* 1 = 0.658695 loss)
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I0428 14:10:05.555493 11373 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
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I0428 14:10:10.479701 11373 solver.cpp:218] Iteration 6312 (2.43811 iter/s, 4.92185s/12 iters), loss = 0.56413
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I0428 14:10:10.479743 11373 solver.cpp:237] Train net output #0: loss = 0.56413 (* 1 = 0.56413 loss)
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I0428 14:10:10.479753 11373 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
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I0428 14:10:14.889364 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
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I0428 14:10:16.510797 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
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I0428 14:10:18.734472 11373 solver.cpp:330] Iteration 6324, Testing net (#0)
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I0428 14:10:18.734498 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:10:20.830596 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:10:23.586984 11373 solver.cpp:397] Test net output #0: accuracy = 0.458946
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I0428 14:10:23.587021 11373 solver.cpp:397] Test net output #1: loss = 2.54833 (* 1 = 2.54833 loss)
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I0428 14:10:23.659250 11373 solver.cpp:218] Iteration 6324 (0.910539 iter/s, 13.179s/12 iters), loss = 0.47899
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I0428 14:10:23.659307 11373 solver.cpp:237] Train net output #0: loss = 0.47899 (* 1 = 0.47899 loss)
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I0428 14:10:23.659320 11373 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
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I0428 14:10:27.884743 11373 solver.cpp:218] Iteration 6336 (2.84006 iter/s, 4.22526s/12 iters), loss = 0.566314
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I0428 14:10:27.884799 11373 solver.cpp:237] Train net output #0: loss = 0.566314 (* 1 = 0.566314 loss)
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I0428 14:10:27.884812 11373 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
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I0428 14:10:32.764075 11373 solver.cpp:218] Iteration 6348 (2.45948 iter/s, 4.87908s/12 iters), loss = 0.586595
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I0428 14:10:32.766222 11373 solver.cpp:237] Train net output #0: loss = 0.586595 (* 1 = 0.586595 loss)
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I0428 14:10:32.766237 11373 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
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I0428 14:10:37.810914 11373 solver.cpp:218] Iteration 6360 (2.37888 iter/s, 5.0444s/12 iters), loss = 0.416388
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I0428 14:10:37.810966 11373 solver.cpp:237] Train net output #0: loss = 0.416388 (* 1 = 0.416388 loss)
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I0428 14:10:37.810978 11373 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
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I0428 14:10:42.692677 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:10:42.838091 11373 solver.cpp:218] Iteration 6372 (2.38819 iter/s, 5.02473s/12 iters), loss = 0.589158
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I0428 14:10:42.838155 11373 solver.cpp:237] Train net output #0: loss = 0.589158 (* 1 = 0.589158 loss)
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I0428 14:10:42.838167 11373 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
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I0428 14:10:47.658457 11373 solver.cpp:218] Iteration 6384 (2.48957 iter/s, 4.82011s/12 iters), loss = 0.655279
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I0428 14:10:47.658504 11373 solver.cpp:237] Train net output #0: loss = 0.655279 (* 1 = 0.655279 loss)
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I0428 14:10:47.658517 11373 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
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I0428 14:10:52.813391 11373 solver.cpp:218] Iteration 6396 (2.32897 iter/s, 5.1525s/12 iters), loss = 0.668541
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I0428 14:10:52.813429 11373 solver.cpp:237] Train net output #0: loss = 0.668541 (* 1 = 0.668541 loss)
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I0428 14:10:52.813438 11373 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
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I0428 14:10:57.900373 11373 solver.cpp:218] Iteration 6408 (2.35907 iter/s, 5.08674s/12 iters), loss = 0.463684
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I0428 14:10:57.900413 11373 solver.cpp:237] Train net output #0: loss = 0.463684 (* 1 = 0.463684 loss)
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I0428 14:10:57.900421 11373 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
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I0428 14:11:02.863257 11373 solver.cpp:218] Iteration 6420 (2.41807 iter/s, 4.96265s/12 iters), loss = 0.730055
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I0428 14:11:02.864284 11373 solver.cpp:237] Train net output #0: loss = 0.730055 (* 1 = 0.730055 loss)
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I0428 14:11:02.864295 11373 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
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I0428 14:11:04.940060 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
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I0428 14:11:06.227458 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
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I0428 14:11:07.301059 11373 solver.cpp:330] Iteration 6426, Testing net (#0)
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I0428 14:11:07.301082 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:11:09.360553 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:11:12.129092 11373 solver.cpp:397] Test net output #0: accuracy = 0.454657
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I0428 14:11:12.129119 11373 solver.cpp:397] Test net output #1: loss = 2.70564 (* 1 = 2.70564 loss)
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I0428 14:11:13.791455 11373 solver.cpp:218] Iteration 6432 (1.09822 iter/s, 10.9268s/12 iters), loss = 0.495397
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I0428 14:11:13.791499 11373 solver.cpp:237] Train net output #0: loss = 0.495397 (* 1 = 0.495397 loss)
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I0428 14:11:13.791509 11373 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
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I0428 14:11:18.936720 11373 solver.cpp:218] Iteration 6444 (2.33236 iter/s, 5.145s/12 iters), loss = 0.533813
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I0428 14:11:18.936759 11373 solver.cpp:237] Train net output #0: loss = 0.533813 (* 1 = 0.533813 loss)
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I0428 14:11:18.936767 11373 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
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I0428 14:11:23.889901 11373 solver.cpp:218] Iteration 6456 (2.4228 iter/s, 4.95294s/12 iters), loss = 0.698981
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I0428 14:11:23.889945 11373 solver.cpp:237] Train net output #0: loss = 0.698981 (* 1 = 0.698981 loss)
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I0428 14:11:23.889955 11373 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
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I0428 14:11:28.898151 11373 solver.cpp:218] Iteration 6468 (2.39616 iter/s, 5.008s/12 iters), loss = 0.447926
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I0428 14:11:28.898198 11373 solver.cpp:237] Train net output #0: loss = 0.447926 (* 1 = 0.447926 loss)
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I0428 14:11:28.898207 11373 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
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I0428 14:11:30.765506 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:11:33.780393 11373 solver.cpp:218] Iteration 6480 (2.45801 iter/s, 4.882s/12 iters), loss = 0.721384
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I0428 14:11:33.780567 11373 solver.cpp:237] Train net output #0: loss = 0.721384 (* 1 = 0.721384 loss)
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I0428 14:11:33.780582 11373 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
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I0428 14:11:38.893064 11373 solver.cpp:218] Iteration 6492 (2.34728 iter/s, 5.1123s/12 iters), loss = 0.779752
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I0428 14:11:38.893106 11373 solver.cpp:237] Train net output #0: loss = 0.779752 (* 1 = 0.779752 loss)
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I0428 14:11:38.893115 11373 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
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I0428 14:11:43.810549 11373 solver.cpp:218] Iteration 6504 (2.44039 iter/s, 4.91724s/12 iters), loss = 0.518139
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I0428 14:11:43.810592 11373 solver.cpp:237] Train net output #0: loss = 0.518139 (* 1 = 0.518139 loss)
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I0428 14:11:43.810603 11373 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
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I0428 14:11:48.557261 11373 solver.cpp:218] Iteration 6516 (2.52936 iter/s, 4.74428s/12 iters), loss = 0.708146
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I0428 14:11:48.557301 11373 solver.cpp:237] Train net output #0: loss = 0.708146 (* 1 = 0.708146 loss)
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I0428 14:11:48.557312 11373 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
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I0428 14:11:52.976126 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
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I0428 14:11:55.831290 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
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I0428 14:11:59.437361 11373 solver.cpp:330] Iteration 6528, Testing net (#0)
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I0428 14:11:59.437381 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:12:01.461628 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:12:04.326485 11373 solver.cpp:397] Test net output #0: accuracy = 0.477941
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I0428 14:12:04.326571 11373 solver.cpp:397] Test net output #1: loss = 2.59783 (* 1 = 2.59783 loss)
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I0428 14:12:04.505856 11373 solver.cpp:218] Iteration 6528 (0.752553 iter/s, 15.9457s/12 iters), loss = 0.547836
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I0428 14:12:04.507481 11373 solver.cpp:237] Train net output #0: loss = 0.547836 (* 1 = 0.547836 loss)
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I0428 14:12:04.507494 11373 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
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I0428 14:12:08.540102 11373 solver.cpp:218] Iteration 6540 (2.97585 iter/s, 4.03246s/12 iters), loss = 0.500078
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I0428 14:12:08.540143 11373 solver.cpp:237] Train net output #0: loss = 0.500078 (* 1 = 0.500078 loss)
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I0428 14:12:08.540151 11373 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
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I0428 14:12:13.549883 11373 solver.cpp:218] Iteration 6552 (2.39543 iter/s, 5.00954s/12 iters), loss = 0.597907
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I0428 14:12:13.549928 11373 solver.cpp:237] Train net output #0: loss = 0.597907 (* 1 = 0.597907 loss)
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I0428 14:12:13.549937 11373 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
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I0428 14:12:18.747968 11373 solver.cpp:218] Iteration 6564 (2.30865 iter/s, 5.19784s/12 iters), loss = 0.69981
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I0428 14:12:18.748008 11373 solver.cpp:237] Train net output #0: loss = 0.69981 (* 1 = 0.69981 loss)
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I0428 14:12:18.748018 11373 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
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I0428 14:12:22.927593 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:12:23.733247 11373 solver.cpp:218] Iteration 6576 (2.4072 iter/s, 4.98504s/12 iters), loss = 0.678449
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I0428 14:12:23.733286 11373 solver.cpp:237] Train net output #0: loss = 0.678449 (* 1 = 0.678449 loss)
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I0428 14:12:23.733296 11373 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
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I0428 14:12:28.565496 11373 solver.cpp:218] Iteration 6588 (2.48346 iter/s, 4.83197s/12 iters), loss = 0.514225
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I0428 14:12:28.565552 11373 solver.cpp:237] Train net output #0: loss = 0.514225 (* 1 = 0.514225 loss)
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I0428 14:12:28.565565 11373 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
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I0428 14:12:33.370132 11373 solver.cpp:218] Iteration 6600 (2.49773 iter/s, 4.80437s/12 iters), loss = 0.473599
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I0428 14:12:33.370185 11373 solver.cpp:237] Train net output #0: loss = 0.473599 (* 1 = 0.473599 loss)
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I0428 14:12:33.370198 11373 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
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I0428 14:12:38.510881 11373 solver.cpp:218] Iteration 6612 (2.33442 iter/s, 5.14047s/12 iters), loss = 0.534486
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I0428 14:12:38.511023 11373 solver.cpp:237] Train net output #0: loss = 0.534486 (* 1 = 0.534486 loss)
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I0428 14:12:38.511037 11373 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
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I0428 14:12:43.421013 11373 solver.cpp:218] Iteration 6624 (2.44512 iter/s, 4.90774s/12 iters), loss = 0.717533
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I0428 14:12:43.421067 11373 solver.cpp:237] Train net output #0: loss = 0.717533 (* 1 = 0.717533 loss)
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I0428 14:12:43.421078 11373 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
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I0428 14:12:45.483618 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
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I0428 14:12:47.340657 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
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I0428 14:12:51.510609 11373 solver.cpp:330] Iteration 6630, Testing net (#0)
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I0428 14:12:51.510629 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:12:53.396723 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:12:56.239362 11373 solver.cpp:397] Test net output #0: accuracy = 0.450368
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I0428 14:12:56.239389 11373 solver.cpp:397] Test net output #1: loss = 2.71357 (* 1 = 2.71357 loss)
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I0428 14:12:58.069768 11373 solver.cpp:218] Iteration 6636 (0.819216 iter/s, 14.6481s/12 iters), loss = 0.489265
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I0428 14:12:58.069816 11373 solver.cpp:237] Train net output #0: loss = 0.489265 (* 1 = 0.489265 loss)
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I0428 14:12:58.069825 11373 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
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I0428 14:13:02.986862 11373 solver.cpp:218] Iteration 6648 (2.44167 iter/s, 4.91466s/12 iters), loss = 0.571837
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I0428 14:13:02.986919 11373 solver.cpp:237] Train net output #0: loss = 0.571837 (* 1 = 0.571837 loss)
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I0428 14:13:02.986929 11373 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
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I0428 14:13:08.024555 11373 solver.cpp:218] Iteration 6660 (2.38217 iter/s, 5.03742s/12 iters), loss = 0.697063
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I0428 14:13:08.024597 11373 solver.cpp:237] Train net output #0: loss = 0.697063 (* 1 = 0.697063 loss)
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I0428 14:13:08.024606 11373 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
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I0428 14:13:12.873095 11373 solver.cpp:218] Iteration 6672 (2.47622 iter/s, 4.84611s/12 iters), loss = 0.677767
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I0428 14:13:12.873226 11373 solver.cpp:237] Train net output #0: loss = 0.677767 (* 1 = 0.677767 loss)
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I0428 14:13:12.873239 11373 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
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I0428 14:13:14.068600 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:13:17.751332 11373 solver.cpp:218] Iteration 6684 (2.46113 iter/s, 4.8758s/12 iters), loss = 0.454435
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I0428 14:13:17.751386 11373 solver.cpp:237] Train net output #0: loss = 0.454435 (* 1 = 0.454435 loss)
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I0428 14:13:17.751399 11373 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
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I0428 14:13:22.728353 11373 solver.cpp:218] Iteration 6696 (2.41226 iter/s, 4.97459s/12 iters), loss = 0.69468
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I0428 14:13:22.728391 11373 solver.cpp:237] Train net output #0: loss = 0.69468 (* 1 = 0.69468 loss)
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I0428 14:13:22.728400 11373 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
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I0428 14:13:27.625278 11373 solver.cpp:218] Iteration 6708 (2.45063 iter/s, 4.8967s/12 iters), loss = 0.404529
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I0428 14:13:27.625316 11373 solver.cpp:237] Train net output #0: loss = 0.404529 (* 1 = 0.404529 loss)
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I0428 14:13:27.625326 11373 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
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I0428 14:13:32.590057 11373 solver.cpp:218] Iteration 6720 (2.41714 iter/s, 4.96454s/12 iters), loss = 0.534145
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I0428 14:13:32.590093 11373 solver.cpp:237] Train net output #0: loss = 0.534145 (* 1 = 0.534145 loss)
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I0428 14:13:32.590102 11373 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
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I0428 14:13:36.870836 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
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I0428 14:13:38.195103 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
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I0428 14:13:39.229001 11373 solver.cpp:330] Iteration 6732, Testing net (#0)
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I0428 14:13:39.229022 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:13:41.063097 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:13:44.044374 11373 solver.cpp:397] Test net output #0: accuracy = 0.464461
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I0428 14:13:44.044564 11373 solver.cpp:397] Test net output #1: loss = 2.65044 (* 1 = 2.65044 loss)
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I0428 14:13:44.205945 11373 solver.cpp:218] Iteration 6732 (1.03311 iter/s, 11.6154s/12 iters), loss = 0.394689
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I0428 14:13:44.207598 11373 solver.cpp:237] Train net output #0: loss = 0.394689 (* 1 = 0.394689 loss)
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I0428 14:13:44.207613 11373 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
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I0428 14:13:48.258728 11373 solver.cpp:218] Iteration 6744 (2.96225 iter/s, 4.05097s/12 iters), loss = 0.715062
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I0428 14:13:48.258775 11373 solver.cpp:237] Train net output #0: loss = 0.715062 (* 1 = 0.715062 loss)
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I0428 14:13:48.258786 11373 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
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I0428 14:13:53.058418 11373 solver.cpp:218] Iteration 6756 (2.50029 iter/s, 4.79945s/12 iters), loss = 0.621595
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I0428 14:13:53.058459 11373 solver.cpp:237] Train net output #0: loss = 0.621595 (* 1 = 0.621595 loss)
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I0428 14:13:53.058467 11373 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
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I0428 14:13:57.932713 11373 solver.cpp:218] Iteration 6768 (2.46313 iter/s, 4.87184s/12 iters), loss = 0.521483
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I0428 14:13:57.932758 11373 solver.cpp:237] Train net output #0: loss = 0.521483 (* 1 = 0.521483 loss)
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I0428 14:13:57.932772 11373 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
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I0428 14:14:01.256254 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:14:02.697566 11373 solver.cpp:218] Iteration 6780 (2.51857 iter/s, 4.76461s/12 iters), loss = 0.484281
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I0428 14:14:02.697611 11373 solver.cpp:237] Train net output #0: loss = 0.484281 (* 1 = 0.484281 loss)
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I0428 14:14:02.697624 11373 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
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I0428 14:14:07.468874 11373 solver.cpp:218] Iteration 6792 (2.51517 iter/s, 4.77104s/12 iters), loss = 0.546999
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I0428 14:14:07.468931 11373 solver.cpp:237] Train net output #0: loss = 0.546999 (* 1 = 0.546999 loss)
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I0428 14:14:07.468943 11373 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
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I0428 14:14:12.376021 11373 solver.cpp:218] Iteration 6804 (2.44664 iter/s, 4.90469s/12 iters), loss = 0.530404
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I0428 14:14:12.376077 11373 solver.cpp:237] Train net output #0: loss = 0.530404 (* 1 = 0.530404 loss)
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I0428 14:14:12.376089 11373 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
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I0428 14:14:17.140033 11373 solver.cpp:218] Iteration 6816 (2.52017 iter/s, 4.76159s/12 iters), loss = 0.437341
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I0428 14:14:17.140152 11373 solver.cpp:237] Train net output #0: loss = 0.437341 (* 1 = 0.437341 loss)
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I0428 14:14:17.140166 11373 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
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I0428 14:14:22.131211 11373 solver.cpp:218] Iteration 6828 (2.4044 iter/s, 4.99086s/12 iters), loss = 0.428356
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I0428 14:14:22.131269 11373 solver.cpp:237] Train net output #0: loss = 0.428356 (* 1 = 0.428356 loss)
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I0428 14:14:22.131284 11373 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
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I0428 14:14:24.127382 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
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I0428 14:14:27.254175 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
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I0428 14:14:29.038368 11373 solver.cpp:330] Iteration 6834, Testing net (#0)
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I0428 14:14:29.038386 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:14:30.944651 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:14:33.918960 11373 solver.cpp:397] Test net output #0: accuracy = 0.471201
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I0428 14:14:33.918988 11373 solver.cpp:397] Test net output #1: loss = 2.66148 (* 1 = 2.66148 loss)
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I0428 14:14:35.784286 11373 solver.cpp:218] Iteration 6840 (0.879099 iter/s, 13.6503s/12 iters), loss = 0.50145
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I0428 14:14:35.784337 11373 solver.cpp:237] Train net output #0: loss = 0.50145 (* 1 = 0.50145 loss)
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I0428 14:14:35.784348 11373 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
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I0428 14:14:40.449043 11373 solver.cpp:218] Iteration 6852 (2.57261 iter/s, 4.66452s/12 iters), loss = 0.632872
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I0428 14:14:40.449095 11373 solver.cpp:237] Train net output #0: loss = 0.632872 (* 1 = 0.632872 loss)
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I0428 14:14:40.449107 11373 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
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I0428 14:14:45.330648 11373 solver.cpp:218] Iteration 6864 (2.45833 iter/s, 4.88136s/12 iters), loss = 0.440849
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I0428 14:14:45.330698 11373 solver.cpp:237] Train net output #0: loss = 0.440849 (* 1 = 0.440849 loss)
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I0428 14:14:45.330709 11373 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
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I0428 14:14:50.390220 11373 solver.cpp:218] Iteration 6876 (2.37186 iter/s, 5.05932s/12 iters), loss = 0.537742
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I0428 14:14:50.390470 11373 solver.cpp:237] Train net output #0: loss = 0.537742 (* 1 = 0.537742 loss)
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I0428 14:14:50.390480 11373 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
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I0428 14:14:50.866133 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:14:55.203097 11373 solver.cpp:218] Iteration 6888 (2.49459 iter/s, 4.81042s/12 iters), loss = 0.425858
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I0428 14:14:55.203143 11373 solver.cpp:237] Train net output #0: loss = 0.425858 (* 1 = 0.425858 loss)
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I0428 14:14:55.203155 11373 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
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I0428 14:15:00.152129 11373 solver.cpp:218] Iteration 6900 (2.42484 iter/s, 4.94878s/12 iters), loss = 0.536799
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I0428 14:15:00.152184 11373 solver.cpp:237] Train net output #0: loss = 0.536799 (* 1 = 0.536799 loss)
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I0428 14:15:00.152197 11373 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
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I0428 14:15:05.340680 11373 solver.cpp:218] Iteration 6912 (2.3129 iter/s, 5.18829s/12 iters), loss = 0.280023
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I0428 14:15:05.340731 11373 solver.cpp:237] Train net output #0: loss = 0.280023 (* 1 = 0.280023 loss)
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I0428 14:15:05.340744 11373 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
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I0428 14:15:10.180927 11373 solver.cpp:218] Iteration 6924 (2.47934 iter/s, 4.84001s/12 iters), loss = 0.576856
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I0428 14:15:10.180963 11373 solver.cpp:237] Train net output #0: loss = 0.576856 (* 1 = 0.576856 loss)
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I0428 14:15:10.180971 11373 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
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I0428 14:15:14.552096 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
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I0428 14:15:17.756614 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
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I0428 14:15:21.255925 11373 solver.cpp:330] Iteration 6936, Testing net (#0)
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I0428 14:15:21.256011 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:15:21.863929 11373 blocking_queue.cpp:49] Waiting for data
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I0428 14:15:22.959739 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:15:25.931731 11373 solver.cpp:397] Test net output #0: accuracy = 0.46201
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I0428 14:15:25.931766 11373 solver.cpp:397] Test net output #1: loss = 2.51607 (* 1 = 2.51607 loss)
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I0428 14:15:26.095854 11373 solver.cpp:218] Iteration 6936 (0.754039 iter/s, 15.9143s/12 iters), loss = 0.495466
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I0428 14:15:26.097554 11373 solver.cpp:237] Train net output #0: loss = 0.495466 (* 1 = 0.495466 loss)
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I0428 14:15:26.097566 11373 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
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I0428 14:15:30.161157 11373 solver.cpp:218] Iteration 6948 (2.95316 iter/s, 4.06344s/12 iters), loss = 0.503095
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I0428 14:15:30.161204 11373 solver.cpp:237] Train net output #0: loss = 0.503095 (* 1 = 0.503095 loss)
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I0428 14:15:30.161216 11373 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
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I0428 14:15:35.109109 11373 solver.cpp:218] Iteration 6960 (2.42537 iter/s, 4.94771s/12 iters), loss = 0.380888
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I0428 14:15:35.109158 11373 solver.cpp:237] Train net output #0: loss = 0.380888 (* 1 = 0.380888 loss)
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I0428 14:15:35.109172 11373 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
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I0428 14:15:39.909977 11373 solver.cpp:218] Iteration 6972 (2.49967 iter/s, 4.80063s/12 iters), loss = 0.446673
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I0428 14:15:39.910019 11373 solver.cpp:237] Train net output #0: loss = 0.446673 (* 1 = 0.446673 loss)
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I0428 14:15:39.910029 11373 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
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I0428 14:15:42.711480 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:15:44.926328 11373 solver.cpp:218] Iteration 6984 (2.39229 iter/s, 5.01611s/12 iters), loss = 0.709009
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I0428 14:15:44.926367 11373 solver.cpp:237] Train net output #0: loss = 0.709009 (* 1 = 0.709009 loss)
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I0428 14:15:44.926378 11373 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
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I0428 14:15:49.832095 11373 solver.cpp:218] Iteration 6996 (2.44622 iter/s, 4.90553s/12 iters), loss = 0.395547
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I0428 14:15:49.832139 11373 solver.cpp:237] Train net output #0: loss = 0.395547 (* 1 = 0.395547 loss)
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I0428 14:15:49.832150 11373 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
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I0428 14:15:55.166961 11373 solver.cpp:218] Iteration 7008 (2.2504 iter/s, 5.33239s/12 iters), loss = 0.480094
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I0428 14:15:55.167136 11373 solver.cpp:237] Train net output #0: loss = 0.480094 (* 1 = 0.480094 loss)
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I0428 14:15:55.167150 11373 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
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I0428 14:15:59.780726 11373 solver.cpp:218] Iteration 7020 (2.60229 iter/s, 4.61132s/12 iters), loss = 0.389632
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I0428 14:15:59.780772 11373 solver.cpp:237] Train net output #0: loss = 0.389632 (* 1 = 0.389632 loss)
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I0428 14:15:59.780781 11373 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
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I0428 14:16:04.459353 11373 solver.cpp:218] Iteration 7032 (2.56618 iter/s, 4.67621s/12 iters), loss = 0.464675
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I0428 14:16:04.459398 11373 solver.cpp:237] Train net output #0: loss = 0.464675 (* 1 = 0.464675 loss)
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I0428 14:16:04.459406 11373 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
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I0428 14:16:06.421536 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
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I0428 14:16:09.175046 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
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I0428 14:16:10.449920 11373 solver.cpp:330] Iteration 7038, Testing net (#0)
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I0428 14:16:10.449945 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:16:12.153214 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:16:14.978739 11373 solver.cpp:397] Test net output #0: accuracy = 0.458946
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I0428 14:16:14.978785 11373 solver.cpp:397] Test net output #1: loss = 2.66867 (* 1 = 2.66867 loss)
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I0428 14:16:16.726387 11373 solver.cpp:218] Iteration 7044 (0.978272 iter/s, 12.2665s/12 iters), loss = 0.385187
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I0428 14:16:16.726428 11373 solver.cpp:237] Train net output #0: loss = 0.385187 (* 1 = 0.385187 loss)
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I0428 14:16:16.726438 11373 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
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I0428 14:16:21.433182 11373 solver.cpp:218] Iteration 7056 (2.54963 iter/s, 4.70657s/12 iters), loss = 0.661194
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I0428 14:16:21.433226 11373 solver.cpp:237] Train net output #0: loss = 0.661194 (* 1 = 0.661194 loss)
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I0428 14:16:21.433235 11373 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
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I0428 14:16:26.146308 11373 solver.cpp:218] Iteration 7068 (2.54621 iter/s, 4.7129s/12 iters), loss = 0.440121
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I0428 14:16:26.146440 11373 solver.cpp:237] Train net output #0: loss = 0.440121 (* 1 = 0.440121 loss)
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I0428 14:16:26.146450 11373 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
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I0428 14:16:30.806963 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:16:30.902598 11373 solver.cpp:218] Iteration 7080 (2.52314 iter/s, 4.75597s/12 iters), loss = 0.630924
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I0428 14:16:30.902638 11373 solver.cpp:237] Train net output #0: loss = 0.630924 (* 1 = 0.630924 loss)
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I0428 14:16:30.902647 11373 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
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I0428 14:16:35.684262 11373 solver.cpp:218] Iteration 7092 (2.50971 iter/s, 4.78143s/12 iters), loss = 0.492031
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I0428 14:16:35.684295 11373 solver.cpp:237] Train net output #0: loss = 0.492031 (* 1 = 0.492031 loss)
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I0428 14:16:35.684304 11373 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
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I0428 14:16:40.408017 11373 solver.cpp:218] Iteration 7104 (2.54047 iter/s, 4.72353s/12 iters), loss = 0.511536
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I0428 14:16:40.408066 11373 solver.cpp:237] Train net output #0: loss = 0.511536 (* 1 = 0.511536 loss)
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I0428 14:16:40.408074 11373 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
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I0428 14:16:45.189582 11373 solver.cpp:218] Iteration 7116 (2.50977 iter/s, 4.78132s/12 iters), loss = 0.561806
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I0428 14:16:45.189623 11373 solver.cpp:237] Train net output #0: loss = 0.561806 (* 1 = 0.561806 loss)
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I0428 14:16:45.189633 11373 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
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I0428 14:16:49.927709 11373 solver.cpp:218] Iteration 7128 (2.53277 iter/s, 4.73789s/12 iters), loss = 0.521786
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I0428 14:16:49.927750 11373 solver.cpp:237] Train net output #0: loss = 0.521786 (* 1 = 0.521786 loss)
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I0428 14:16:49.927759 11373 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
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I0428 14:16:54.299080 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
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I0428 14:16:55.595578 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
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I0428 14:16:56.621826 11373 solver.cpp:330] Iteration 7140, Testing net (#0)
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I0428 14:16:56.623893 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:16:58.243724 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:17:01.063572 11373 solver.cpp:397] Test net output #0: accuracy = 0.453431
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I0428 14:17:01.063602 11373 solver.cpp:397] Test net output #1: loss = 2.76973 (* 1 = 2.76973 loss)
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I0428 14:17:01.142349 11373 solver.cpp:218] Iteration 7140 (1.07008 iter/s, 11.2142s/12 iters), loss = 0.367865
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I0428 14:17:01.142391 11373 solver.cpp:237] Train net output #0: loss = 0.367865 (* 1 = 0.367865 loss)
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I0428 14:17:01.142400 11373 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
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I0428 14:17:05.246539 11373 solver.cpp:218] Iteration 7152 (2.92399 iter/s, 4.10398s/12 iters), loss = 0.438564
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I0428 14:17:05.246575 11373 solver.cpp:237] Train net output #0: loss = 0.438564 (* 1 = 0.438564 loss)
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I0428 14:17:05.246584 11373 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
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I0428 14:17:09.983402 11373 solver.cpp:218] Iteration 7164 (2.53345 iter/s, 4.73662s/12 iters), loss = 0.414319
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I0428 14:17:09.983446 11373 solver.cpp:237] Train net output #0: loss = 0.414319 (* 1 = 0.414319 loss)
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I0428 14:17:09.983456 11373 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
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I0428 14:17:14.829447 11373 solver.cpp:218] Iteration 7176 (2.47637 iter/s, 4.8458s/12 iters), loss = 0.506873
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I0428 14:17:14.829488 11373 solver.cpp:237] Train net output #0: loss = 0.506873 (* 1 = 0.506873 loss)
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I0428 14:17:14.829499 11373 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
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I0428 14:17:16.868451 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:17:19.666172 11373 solver.cpp:218] Iteration 7188 (2.48115 iter/s, 4.83647s/12 iters), loss = 0.628574
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I0428 14:17:19.666232 11373 solver.cpp:237] Train net output #0: loss = 0.628574 (* 1 = 0.628574 loss)
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I0428 14:17:19.666242 11373 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
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I0428 14:17:24.432253 11373 solver.cpp:218] Iteration 7200 (2.51793 iter/s, 4.76583s/12 iters), loss = 0.546556
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I0428 14:17:24.432297 11373 solver.cpp:237] Train net output #0: loss = 0.546556 (* 1 = 0.546556 loss)
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I0428 14:17:24.432307 11373 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
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I0428 14:17:29.522123 11373 solver.cpp:218] Iteration 7212 (2.35775 iter/s, 5.08961s/12 iters), loss = 0.420121
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I0428 14:17:29.522253 11373 solver.cpp:237] Train net output #0: loss = 0.420121 (* 1 = 0.420121 loss)
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I0428 14:17:29.522264 11373 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
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I0428 14:17:34.455109 11373 solver.cpp:218] Iteration 7224 (2.43277 iter/s, 4.93265s/12 iters), loss = 0.595028
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I0428 14:17:34.455152 11373 solver.cpp:237] Train net output #0: loss = 0.595028 (* 1 = 0.595028 loss)
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I0428 14:17:34.455161 11373 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
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I0428 14:17:39.251045 11373 solver.cpp:218] Iteration 7236 (2.50225 iter/s, 4.79569s/12 iters), loss = 0.524584
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I0428 14:17:39.251085 11373 solver.cpp:237] Train net output #0: loss = 0.524584 (* 1 = 0.524584 loss)
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I0428 14:17:39.251093 11373 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
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I0428 14:17:41.388360 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
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I0428 14:17:42.607640 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
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I0428 14:17:43.593526 11373 solver.cpp:330] Iteration 7242, Testing net (#0)
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I0428 14:17:43.593546 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:17:45.082096 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:17:47.941452 11373 solver.cpp:397] Test net output #0: accuracy = 0.477941
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I0428 14:17:47.941495 11373 solver.cpp:397] Test net output #1: loss = 2.65926 (* 1 = 2.65926 loss)
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I0428 14:17:49.708573 11373 solver.cpp:218] Iteration 7248 (1.14755 iter/s, 10.4571s/12 iters), loss = 0.530105
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I0428 14:17:49.708623 11373 solver.cpp:237] Train net output #0: loss = 0.530105 (* 1 = 0.530105 loss)
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I0428 14:17:49.708633 11373 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
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I0428 14:17:54.585125 11373 solver.cpp:218] Iteration 7260 (2.46088 iter/s, 4.8763s/12 iters), loss = 0.452333
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I0428 14:17:54.585165 11373 solver.cpp:237] Train net output #0: loss = 0.452333 (* 1 = 0.452333 loss)
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I0428 14:17:54.585175 11373 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
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I0428 14:17:59.439857 11373 solver.cpp:218] Iteration 7272 (2.47194 iter/s, 4.85449s/12 iters), loss = 0.37986
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I0428 14:17:59.439910 11373 solver.cpp:237] Train net output #0: loss = 0.37986 (* 1 = 0.37986 loss)
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I0428 14:17:59.439924 11373 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
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I0428 14:18:03.516408 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:18:04.237349 11373 solver.cpp:218] Iteration 7284 (2.50144 iter/s, 4.79723s/12 iters), loss = 0.422824
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I0428 14:18:04.237402 11373 solver.cpp:237] Train net output #0: loss = 0.422824 (* 1 = 0.422824 loss)
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I0428 14:18:04.237413 11373 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
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I0428 14:18:09.093274 11373 solver.cpp:218] Iteration 7296 (2.47134 iter/s, 4.85567s/12 iters), loss = 0.306414
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I0428 14:18:09.093319 11373 solver.cpp:237] Train net output #0: loss = 0.306414 (* 1 = 0.306414 loss)
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I0428 14:18:09.093328 11373 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
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I0428 14:18:13.829597 11373 solver.cpp:218] Iteration 7308 (2.53374 iter/s, 4.73608s/12 iters), loss = 0.348409
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I0428 14:18:13.829646 11373 solver.cpp:237] Train net output #0: loss = 0.348409 (* 1 = 0.348409 loss)
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I0428 14:18:13.829659 11373 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
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I0428 14:18:19.057197 11373 solver.cpp:218] Iteration 7320 (2.29563 iter/s, 5.22733s/12 iters), loss = 0.435009
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I0428 14:18:19.057241 11373 solver.cpp:237] Train net output #0: loss = 0.435009 (* 1 = 0.435009 loss)
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I0428 14:18:19.057250 11373 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
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I0428 14:18:23.872094 11373 solver.cpp:218] Iteration 7332 (2.49239 iter/s, 4.81465s/12 iters), loss = 0.423525
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I0428 14:18:23.872140 11373 solver.cpp:237] Train net output #0: loss = 0.423525 (* 1 = 0.423525 loss)
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I0428 14:18:23.872153 11373 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
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I0428 14:18:28.272347 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
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I0428 14:18:32.884548 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
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I0428 14:18:38.192059 11373 solver.cpp:330] Iteration 7344, Testing net (#0)
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I0428 14:18:38.192152 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:18:39.745618 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:18:42.791270 11373 solver.cpp:397] Test net output #0: accuracy = 0.474877
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I0428 14:18:42.791299 11373 solver.cpp:397] Test net output #1: loss = 2.73785 (* 1 = 2.73785 loss)
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I0428 14:18:42.868556 11373 solver.cpp:218] Iteration 7344 (0.631723 iter/s, 18.9957s/12 iters), loss = 0.429476
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I0428 14:18:42.868619 11373 solver.cpp:237] Train net output #0: loss = 0.429476 (* 1 = 0.429476 loss)
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I0428 14:18:42.868631 11373 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
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I0428 14:18:46.895355 11373 solver.cpp:218] Iteration 7356 (2.98022 iter/s, 4.02656s/12 iters), loss = 0.311959
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I0428 14:18:46.895418 11373 solver.cpp:237] Train net output #0: loss = 0.311959 (* 1 = 0.311959 loss)
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I0428 14:18:46.895432 11373 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
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I0428 14:18:51.677747 11373 solver.cpp:218] Iteration 7368 (2.50934 iter/s, 4.78214s/12 iters), loss = 0.369921
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I0428 14:18:51.677784 11373 solver.cpp:237] Train net output #0: loss = 0.369921 (* 1 = 0.369921 loss)
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I0428 14:18:51.677791 11373 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
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I0428 14:18:56.388037 11373 solver.cpp:218] Iteration 7380 (2.54774 iter/s, 4.71005s/12 iters), loss = 0.265587
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I0428 14:18:56.388077 11373 solver.cpp:237] Train net output #0: loss = 0.265587 (* 1 = 0.265587 loss)
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I0428 14:18:56.388085 11373 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
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I0428 14:18:57.694128 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:19:01.127894 11373 solver.cpp:218] Iteration 7392 (2.53185 iter/s, 4.73962s/12 iters), loss = 0.316736
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I0428 14:19:01.127934 11373 solver.cpp:237] Train net output #0: loss = 0.316736 (* 1 = 0.316736 loss)
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I0428 14:19:01.127943 11373 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
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I0428 14:19:06.039368 11373 solver.cpp:218] Iteration 7404 (2.44338 iter/s, 4.91122s/12 iters), loss = 0.44712
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I0428 14:19:06.039420 11373 solver.cpp:237] Train net output #0: loss = 0.44712 (* 1 = 0.44712 loss)
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I0428 14:19:06.039429 11373 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
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I0428 14:19:10.867573 11373 solver.cpp:218] Iteration 7416 (2.48553 iter/s, 4.82795s/12 iters), loss = 0.370926
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I0428 14:19:10.867671 11373 solver.cpp:237] Train net output #0: loss = 0.370926 (* 1 = 0.370926 loss)
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I0428 14:19:10.867681 11373 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
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I0428 14:19:15.670830 11373 solver.cpp:218] Iteration 7428 (2.49846 iter/s, 4.80296s/12 iters), loss = 0.238135
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I0428 14:19:15.670871 11373 solver.cpp:237] Train net output #0: loss = 0.238135 (* 1 = 0.238135 loss)
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I0428 14:19:15.670881 11373 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
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I0428 14:19:20.429679 11373 solver.cpp:218] Iteration 7440 (2.52175 iter/s, 4.7586s/12 iters), loss = 0.349326
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I0428 14:19:20.429719 11373 solver.cpp:237] Train net output #0: loss = 0.349326 (* 1 = 0.349326 loss)
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I0428 14:19:20.429728 11373 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
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I0428 14:19:22.535388 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
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I0428 14:19:25.873059 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
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I0428 14:19:29.056828 11373 solver.cpp:330] Iteration 7446, Testing net (#0)
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I0428 14:19:29.056849 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:19:30.546867 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:19:33.525113 11373 solver.cpp:397] Test net output #0: accuracy = 0.46875
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I0428 14:19:33.525148 11373 solver.cpp:397] Test net output #1: loss = 2.86431 (* 1 = 2.86431 loss)
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I0428 14:19:35.260125 11373 solver.cpp:218] Iteration 7452 (0.809181 iter/s, 14.8298s/12 iters), loss = 0.358078
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I0428 14:19:35.260170 11373 solver.cpp:237] Train net output #0: loss = 0.358078 (* 1 = 0.358078 loss)
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I0428 14:19:35.260180 11373 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
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I0428 14:19:40.048902 11373 solver.cpp:218] Iteration 7464 (2.50599 iter/s, 4.78853s/12 iters), loss = 0.347822
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I0428 14:19:40.048957 11373 solver.cpp:237] Train net output #0: loss = 0.347822 (* 1 = 0.347822 loss)
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I0428 14:19:40.048969 11373 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
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I0428 14:19:44.774838 11373 solver.cpp:218] Iteration 7476 (2.53932 iter/s, 4.72568s/12 iters), loss = 0.363371
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I0428 14:19:44.774979 11373 solver.cpp:237] Train net output #0: loss = 0.363371 (* 1 = 0.363371 loss)
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I0428 14:19:44.774991 11373 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
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I0428 14:19:48.164445 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:19:49.623165 11373 solver.cpp:218] Iteration 7488 (2.47526 iter/s, 4.84798s/12 iters), loss = 0.508416
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I0428 14:19:49.623209 11373 solver.cpp:237] Train net output #0: loss = 0.508416 (* 1 = 0.508416 loss)
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I0428 14:19:49.623219 11373 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
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I0428 14:19:54.658915 11373 solver.cpp:218] Iteration 7500 (2.38308 iter/s, 5.0355s/12 iters), loss = 0.29228
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I0428 14:19:54.658958 11373 solver.cpp:237] Train net output #0: loss = 0.29228 (* 1 = 0.29228 loss)
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I0428 14:19:54.658967 11373 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
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I0428 14:19:59.465302 11373 solver.cpp:218] Iteration 7512 (2.4968 iter/s, 4.80615s/12 iters), loss = 0.294177
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I0428 14:19:59.465342 11373 solver.cpp:237] Train net output #0: loss = 0.294177 (* 1 = 0.294177 loss)
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I0428 14:19:59.465350 11373 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
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I0428 14:20:04.365321 11373 solver.cpp:218] Iteration 7524 (2.44909 iter/s, 4.89977s/12 iters), loss = 0.383741
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I0428 14:20:04.365370 11373 solver.cpp:237] Train net output #0: loss = 0.383741 (* 1 = 0.383741 loss)
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I0428 14:20:04.365382 11373 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
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I0428 14:20:09.240375 11373 solver.cpp:218] Iteration 7536 (2.46164 iter/s, 4.8748s/12 iters), loss = 0.28932
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I0428 14:20:09.240417 11373 solver.cpp:237] Train net output #0: loss = 0.28932 (* 1 = 0.28932 loss)
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I0428 14:20:09.240427 11373 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
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I0428 14:20:13.715370 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
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I0428 14:20:15.001524 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
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I0428 14:20:17.618568 11373 solver.cpp:330] Iteration 7548, Testing net (#0)
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I0428 14:20:17.618587 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:20:19.093626 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:20:22.303546 11373 solver.cpp:397] Test net output #0: accuracy = 0.481005
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I0428 14:20:22.303584 11373 solver.cpp:397] Test net output #1: loss = 2.72474 (* 1 = 2.72474 loss)
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I0428 14:20:22.406741 11373 solver.cpp:218] Iteration 7548 (0.911452 iter/s, 13.1658s/12 iters), loss = 0.345882
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I0428 14:20:22.408263 11373 solver.cpp:237] Train net output #0: loss = 0.345882 (* 1 = 0.345882 loss)
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I0428 14:20:22.408277 11373 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
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I0428 14:20:26.657485 11373 solver.cpp:218] Iteration 7560 (2.82416 iter/s, 4.24905s/12 iters), loss = 0.309141
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I0428 14:20:26.657531 11373 solver.cpp:237] Train net output #0: loss = 0.309141 (* 1 = 0.309141 loss)
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I0428 14:20:26.657541 11373 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
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I0428 14:20:31.507480 11373 solver.cpp:218] Iteration 7572 (2.47436 iter/s, 4.84975s/12 iters), loss = 0.35641
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I0428 14:20:31.507517 11373 solver.cpp:237] Train net output #0: loss = 0.35641 (* 1 = 0.35641 loss)
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I0428 14:20:31.507526 11373 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
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I0428 14:20:36.470438 11373 solver.cpp:218] Iteration 7584 (2.41803 iter/s, 4.96271s/12 iters), loss = 0.42768
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I0428 14:20:36.470494 11373 solver.cpp:237] Train net output #0: loss = 0.42768 (* 1 = 0.42768 loss)
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I0428 14:20:36.470505 11373 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
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I0428 14:20:37.144255 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:20:41.622433 11373 solver.cpp:218] Iteration 7596 (2.32933 iter/s, 5.1517s/12 iters), loss = 0.31126
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I0428 14:20:41.622478 11373 solver.cpp:237] Train net output #0: loss = 0.31126 (* 1 = 0.31126 loss)
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I0428 14:20:41.622488 11373 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
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I0428 14:20:46.615916 11373 solver.cpp:218] Iteration 7608 (2.40325 iter/s, 4.99323s/12 iters), loss = 0.323292
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I0428 14:20:46.616179 11373 solver.cpp:237] Train net output #0: loss = 0.323292 (* 1 = 0.323292 loss)
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I0428 14:20:46.616192 11373 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
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I0428 14:20:51.485823 11373 solver.cpp:218] Iteration 7620 (2.46435 iter/s, 4.86945s/12 iters), loss = 0.31752
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I0428 14:20:51.485867 11373 solver.cpp:237] Train net output #0: loss = 0.31752 (* 1 = 0.31752 loss)
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I0428 14:20:51.485875 11373 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
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I0428 14:20:53.737977 11373 blocking_queue.cpp:49] Waiting for data
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I0428 14:20:56.385605 11373 solver.cpp:218] Iteration 7632 (2.44922 iter/s, 4.89952s/12 iters), loss = 0.202161
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I0428 14:20:56.385659 11373 solver.cpp:237] Train net output #0: loss = 0.202161 (* 1 = 0.202161 loss)
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I0428 14:20:56.385673 11373 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
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I0428 14:21:01.302968 11373 solver.cpp:218] Iteration 7644 (2.44046 iter/s, 4.9171s/12 iters), loss = 0.326206
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I0428 14:21:01.303009 11373 solver.cpp:237] Train net output #0: loss = 0.326206 (* 1 = 0.326206 loss)
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I0428 14:21:01.303017 11373 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
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I0428 14:21:03.356143 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
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I0428 14:21:05.848644 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
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I0428 14:21:07.440420 11373 solver.cpp:330] Iteration 7650, Testing net (#0)
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I0428 14:21:07.440443 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:21:08.950994 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:21:12.137346 11373 solver.cpp:397] Test net output #0: accuracy = 0.496936
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I0428 14:21:12.137375 11373 solver.cpp:397] Test net output #1: loss = 2.65604 (* 1 = 2.65604 loss)
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I0428 14:21:13.840970 11373 solver.cpp:218] Iteration 7656 (0.957134 iter/s, 12.5374s/12 iters), loss = 0.283503
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I0428 14:21:13.841022 11373 solver.cpp:237] Train net output #0: loss = 0.283503 (* 1 = 0.283503 loss)
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I0428 14:21:13.841032 11373 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
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I0428 14:21:18.584379 11373 solver.cpp:218] Iteration 7668 (2.52996 iter/s, 4.74316s/12 iters), loss = 0.413542
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I0428 14:21:18.584523 11373 solver.cpp:237] Train net output #0: loss = 0.413542 (* 1 = 0.413542 loss)
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I0428 14:21:18.584534 11373 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
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I0428 14:21:23.368681 11373 solver.cpp:218] Iteration 7680 (2.50838 iter/s, 4.78396s/12 iters), loss = 0.347872
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I0428 14:21:23.368721 11373 solver.cpp:237] Train net output #0: loss = 0.347872 (* 1 = 0.347872 loss)
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I0428 14:21:23.368729 11373 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
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I0428 14:21:26.149829 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:21:28.322280 11373 solver.cpp:218] Iteration 7692 (2.4226 iter/s, 4.95335s/12 iters), loss = 0.156766
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I0428 14:21:28.322335 11373 solver.cpp:237] Train net output #0: loss = 0.156766 (* 1 = 0.156766 loss)
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I0428 14:21:28.322347 11373 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
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I0428 14:21:33.327656 11373 solver.cpp:218] Iteration 7704 (2.39756 iter/s, 5.00509s/12 iters), loss = 0.397211
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I0428 14:21:33.327694 11373 solver.cpp:237] Train net output #0: loss = 0.397211 (* 1 = 0.397211 loss)
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I0428 14:21:33.327703 11373 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
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I0428 14:21:38.278098 11373 solver.cpp:218] Iteration 7716 (2.42415 iter/s, 4.9502s/12 iters), loss = 0.427203
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I0428 14:21:38.278137 11373 solver.cpp:237] Train net output #0: loss = 0.427203 (* 1 = 0.427203 loss)
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I0428 14:21:38.278146 11373 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
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I0428 14:21:43.213749 11373 solver.cpp:218] Iteration 7728 (2.43141 iter/s, 4.9354s/12 iters), loss = 0.323059
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I0428 14:21:43.213795 11373 solver.cpp:237] Train net output #0: loss = 0.323059 (* 1 = 0.323059 loss)
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I0428 14:21:43.213804 11373 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
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I0428 14:21:48.198874 11373 solver.cpp:218] Iteration 7740 (2.4073 iter/s, 4.98485s/12 iters), loss = 0.359791
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I0428 14:21:48.198911 11373 solver.cpp:237] Train net output #0: loss = 0.359791 (* 1 = 0.359791 loss)
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I0428 14:21:48.198920 11373 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
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I0428 14:21:52.529472 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
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I0428 14:21:54.073830 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
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I0428 14:21:55.081799 11373 solver.cpp:330] Iteration 7752, Testing net (#0)
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I0428 14:21:55.081820 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:21:56.427994 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:21:59.591199 11373 solver.cpp:397] Test net output #0: accuracy = 0.477328
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I0428 14:21:59.591235 11373 solver.cpp:397] Test net output #1: loss = 2.82277 (* 1 = 2.82277 loss)
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I0428 14:21:59.722932 11373 solver.cpp:218] Iteration 7752 (1.04134 iter/s, 11.5236s/12 iters), loss = 0.241566
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I0428 14:21:59.724545 11373 solver.cpp:237] Train net output #0: loss = 0.241566 (* 1 = 0.241566 loss)
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I0428 14:21:59.724557 11373 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
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I0428 14:22:03.864230 11373 solver.cpp:218] Iteration 7764 (2.89889 iter/s, 4.13951s/12 iters), loss = 0.203829
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I0428 14:22:03.864272 11373 solver.cpp:237] Train net output #0: loss = 0.203829 (* 1 = 0.203829 loss)
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I0428 14:22:03.864281 11373 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
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I0428 14:22:08.842182 11373 solver.cpp:218] Iteration 7776 (2.41075 iter/s, 4.9777s/12 iters), loss = 0.274837
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I0428 14:22:08.842239 11373 solver.cpp:237] Train net output #0: loss = 0.274837 (* 1 = 0.274837 loss)
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I0428 14:22:08.842252 11373 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
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I0428 14:22:13.680143 11373 solver.cpp:218] Iteration 7788 (2.48052 iter/s, 4.8377s/12 iters), loss = 0.462795
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I0428 14:22:13.680198 11373 solver.cpp:237] Train net output #0: loss = 0.462795 (* 1 = 0.462795 loss)
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I0428 14:22:13.680210 11373 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
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I0428 14:22:13.686882 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:22:18.531929 11373 solver.cpp:218] Iteration 7800 (2.47346 iter/s, 4.8515s/12 iters), loss = 0.326967
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I0428 14:22:18.531982 11373 solver.cpp:237] Train net output #0: loss = 0.326967 (* 1 = 0.326967 loss)
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I0428 14:22:18.531996 11373 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
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I0428 14:22:23.731966 11373 solver.cpp:218] Iteration 7812 (2.30779 iter/s, 5.19977s/12 iters), loss = 0.337836
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I0428 14:22:23.732085 11373 solver.cpp:237] Train net output #0: loss = 0.337836 (* 1 = 0.337836 loss)
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I0428 14:22:23.732095 11373 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
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I0428 14:22:28.763465 11373 solver.cpp:218] Iteration 7824 (2.38513 iter/s, 5.03117s/12 iters), loss = 0.384218
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I0428 14:22:28.763504 11373 solver.cpp:237] Train net output #0: loss = 0.384218 (* 1 = 0.384218 loss)
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I0428 14:22:28.763511 11373 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
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I0428 14:22:33.683163 11373 solver.cpp:218] Iteration 7836 (2.4393 iter/s, 4.91945s/12 iters), loss = 0.309541
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I0428 14:22:33.683218 11373 solver.cpp:237] Train net output #0: loss = 0.309541 (* 1 = 0.309541 loss)
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I0428 14:22:33.683228 11373 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
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I0428 14:22:38.712898 11373 solver.cpp:218] Iteration 7848 (2.38594 iter/s, 5.02947s/12 iters), loss = 0.325199
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I0428 14:22:38.712954 11373 solver.cpp:237] Train net output #0: loss = 0.325199 (* 1 = 0.325199 loss)
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I0428 14:22:38.712965 11373 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
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I0428 14:22:40.786120 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
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I0428 14:22:45.684384 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
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I0428 14:22:48.396000 11373 solver.cpp:330] Iteration 7854, Testing net (#0)
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I0428 14:22:48.396025 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:22:49.718879 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:22:52.983462 11373 solver.cpp:397] Test net output #0: accuracy = 0.490196
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I0428 14:22:52.983505 11373 solver.cpp:397] Test net output #1: loss = 2.78002 (* 1 = 2.78002 loss)
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I0428 14:22:54.869973 11373 solver.cpp:218] Iteration 7860 (0.74274 iter/s, 16.1564s/12 iters), loss = 0.468699
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I0428 14:22:54.872706 11373 solver.cpp:237] Train net output #0: loss = 0.468699 (* 1 = 0.468699 loss)
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I0428 14:22:54.872716 11373 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
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I0428 14:22:59.822587 11373 solver.cpp:218] Iteration 7872 (2.4244 iter/s, 4.94967s/12 iters), loss = 0.279037
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I0428 14:22:59.822629 11373 solver.cpp:237] Train net output #0: loss = 0.279037 (* 1 = 0.279037 loss)
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I0428 14:22:59.822639 11373 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
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I0428 14:23:04.797989 11373 solver.cpp:218] Iteration 7884 (2.412 iter/s, 4.97512s/12 iters), loss = 0.264194
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I0428 14:23:04.798038 11373 solver.cpp:237] Train net output #0: loss = 0.264194 (* 1 = 0.264194 loss)
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I0428 14:23:04.798048 11373 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
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I0428 14:23:06.925575 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:23:09.897024 11373 solver.cpp:218] Iteration 7896 (2.35352 iter/s, 5.09875s/12 iters), loss = 0.265907
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I0428 14:23:09.897068 11373 solver.cpp:237] Train net output #0: loss = 0.265907 (* 1 = 0.265907 loss)
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I0428 14:23:09.897078 11373 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
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I0428 14:23:14.809710 11373 solver.cpp:218] Iteration 7908 (2.44279 iter/s, 4.91241s/12 iters), loss = 0.303496
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I0428 14:23:14.809759 11373 solver.cpp:237] Train net output #0: loss = 0.303496 (* 1 = 0.303496 loss)
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I0428 14:23:14.809770 11373 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
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I0428 14:23:19.733846 11373 solver.cpp:218] Iteration 7920 (2.4371 iter/s, 4.92388s/12 iters), loss = 0.219333
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I0428 14:23:19.733891 11373 solver.cpp:237] Train net output #0: loss = 0.219333 (* 1 = 0.219333 loss)
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I0428 14:23:19.733901 11373 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
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I0428 14:23:24.754541 11373 solver.cpp:218] Iteration 7932 (2.39131 iter/s, 5.01817s/12 iters), loss = 0.398712
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I0428 14:23:24.754585 11373 solver.cpp:237] Train net output #0: loss = 0.398712 (* 1 = 0.398712 loss)
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I0428 14:23:24.754595 11373 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
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I0428 14:23:29.880857 11373 solver.cpp:218] Iteration 7944 (2.34106 iter/s, 5.12588s/12 iters), loss = 0.338619
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I0428 14:23:29.880964 11373 solver.cpp:237] Train net output #0: loss = 0.338619 (* 1 = 0.338619 loss)
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I0428 14:23:29.880975 11373 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
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I0428 14:23:34.429558 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
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I0428 14:23:40.291965 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
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I0428 14:23:47.559895 11373 solver.cpp:330] Iteration 7956, Testing net (#0)
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I0428 14:23:47.559916 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:23:48.812512 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:23:51.960420 11373 solver.cpp:397] Test net output #0: accuracy = 0.483456
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I0428 14:23:51.960448 11373 solver.cpp:397] Test net output #1: loss = 2.77497 (* 1 = 2.77497 loss)
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I0428 14:23:52.053273 11373 solver.cpp:218] Iteration 7956 (0.541237 iter/s, 22.1714s/12 iters), loss = 0.260068
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I0428 14:23:52.053328 11373 solver.cpp:237] Train net output #0: loss = 0.260068 (* 1 = 0.260068 loss)
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I0428 14:23:52.053339 11373 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
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I0428 14:23:56.164755 11373 solver.cpp:218] Iteration 7968 (2.91883 iter/s, 4.11123s/12 iters), loss = 0.265792
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I0428 14:23:56.164799 11373 solver.cpp:237] Train net output #0: loss = 0.265792 (* 1 = 0.265792 loss)
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I0428 14:23:56.164808 11373 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
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I0428 14:24:01.023648 11373 solver.cpp:218] Iteration 7980 (2.46984 iter/s, 4.85861s/12 iters), loss = 0.251368
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I0428 14:24:01.023818 11373 solver.cpp:237] Train net output #0: loss = 0.251368 (* 1 = 0.251368 loss)
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I0428 14:24:01.023831 11373 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
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I0428 14:24:05.055812 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:24:05.784584 11373 solver.cpp:218] Iteration 7992 (2.52071 iter/s, 4.76056s/12 iters), loss = 0.189123
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I0428 14:24:05.784642 11373 solver.cpp:237] Train net output #0: loss = 0.189123 (* 1 = 0.189123 loss)
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I0428 14:24:05.784655 11373 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
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I0428 14:24:10.556548 11373 solver.cpp:218] Iteration 8004 (2.51484 iter/s, 4.77167s/12 iters), loss = 0.185855
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I0428 14:24:10.556587 11373 solver.cpp:237] Train net output #0: loss = 0.185855 (* 1 = 0.185855 loss)
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I0428 14:24:10.556597 11373 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
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I0428 14:24:15.528578 11373 solver.cpp:218] Iteration 8016 (2.41362 iter/s, 4.97179s/12 iters), loss = 0.294835
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I0428 14:24:15.528620 11373 solver.cpp:237] Train net output #0: loss = 0.294835 (* 1 = 0.294835 loss)
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I0428 14:24:15.528630 11373 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
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I0428 14:24:20.390707 11373 solver.cpp:218] Iteration 8028 (2.46818 iter/s, 4.86188s/12 iters), loss = 0.314312
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I0428 14:24:20.390753 11373 solver.cpp:237] Train net output #0: loss = 0.314312 (* 1 = 0.314312 loss)
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I0428 14:24:20.390760 11373 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
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I0428 14:24:25.417737 11373 solver.cpp:218] Iteration 8040 (2.38722 iter/s, 5.02677s/12 iters), loss = 0.250221
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I0428 14:24:25.417781 11373 solver.cpp:237] Train net output #0: loss = 0.250221 (* 1 = 0.250221 loss)
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I0428 14:24:25.417791 11373 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
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I0428 14:24:30.498010 11373 solver.cpp:218] Iteration 8052 (2.3622 iter/s, 5.08001s/12 iters), loss = 0.179802
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I0428 14:24:30.498068 11373 solver.cpp:237] Train net output #0: loss = 0.179802 (* 1 = 0.179802 loss)
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I0428 14:24:30.498080 11373 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
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I0428 14:24:32.546455 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
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I0428 14:24:35.486862 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
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I0428 14:24:37.372843 11373 solver.cpp:330] Iteration 8058, Testing net (#0)
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I0428 14:24:37.372864 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:24:38.631400 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:24:41.938556 11373 solver.cpp:397] Test net output #0: accuracy = 0.504902
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I0428 14:24:41.938583 11373 solver.cpp:397] Test net output #1: loss = 2.62588 (* 1 = 2.62588 loss)
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I0428 14:24:43.642688 11373 solver.cpp:218] Iteration 8064 (0.912958 iter/s, 13.1441s/12 iters), loss = 0.186267
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I0428 14:24:43.642729 11373 solver.cpp:237] Train net output #0: loss = 0.186267 (* 1 = 0.186267 loss)
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I0428 14:24:43.642737 11373 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
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I0428 14:24:48.459301 11373 solver.cpp:218] Iteration 8076 (2.4915 iter/s, 4.81637s/12 iters), loss = 0.30152
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I0428 14:24:48.459350 11373 solver.cpp:237] Train net output #0: loss = 0.30152 (* 1 = 0.30152 loss)
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I0428 14:24:48.459360 11373 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
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I0428 14:24:53.660557 11373 solver.cpp:218] Iteration 8088 (2.30725 iter/s, 5.20099s/12 iters), loss = 0.20362
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I0428 14:24:53.660598 11373 solver.cpp:237] Train net output #0: loss = 0.20362 (* 1 = 0.20362 loss)
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I0428 14:24:53.660606 11373 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
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I0428 14:24:55.034461 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:24:58.698215 11373 solver.cpp:218] Iteration 8100 (2.38218 iter/s, 5.0374s/12 iters), loss = 0.296657
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I0428 14:24:58.698272 11373 solver.cpp:237] Train net output #0: loss = 0.296657 (* 1 = 0.296657 loss)
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I0428 14:24:58.698283 11373 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
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I0428 14:25:03.756366 11373 solver.cpp:218] Iteration 8112 (2.37255 iter/s, 5.05786s/12 iters), loss = 0.223365
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I0428 14:25:03.756697 11373 solver.cpp:237] Train net output #0: loss = 0.223365 (* 1 = 0.223365 loss)
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I0428 14:25:03.756713 11373 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
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I0428 14:25:08.587165 11373 solver.cpp:218] Iteration 8124 (2.48433 iter/s, 4.83027s/12 iters), loss = 0.257081
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I0428 14:25:08.587215 11373 solver.cpp:237] Train net output #0: loss = 0.257081 (* 1 = 0.257081 loss)
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I0428 14:25:08.587225 11373 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
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I0428 14:25:13.494386 11373 solver.cpp:218] Iteration 8136 (2.4455 iter/s, 4.90696s/12 iters), loss = 0.367294
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I0428 14:25:13.494429 11373 solver.cpp:237] Train net output #0: loss = 0.367294 (* 1 = 0.367294 loss)
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I0428 14:25:13.494439 11373 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
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I0428 14:25:18.429958 11373 solver.cpp:218] Iteration 8148 (2.43146 iter/s, 4.9353s/12 iters), loss = 0.186839
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I0428 14:25:18.430002 11373 solver.cpp:237] Train net output #0: loss = 0.186839 (* 1 = 0.186839 loss)
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I0428 14:25:18.430011 11373 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
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I0428 14:25:22.849251 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
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I0428 14:25:25.678247 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
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I0428 14:25:26.667505 11373 solver.cpp:330] Iteration 8160, Testing net (#0)
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I0428 14:25:26.667528 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:25:27.837419 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:25:31.226897 11373 solver.cpp:397] Test net output #0: accuracy = 0.492647
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I0428 14:25:31.226935 11373 solver.cpp:397] Test net output #1: loss = 2.77621 (* 1 = 2.77621 loss)
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I0428 14:25:31.297653 11373 solver.cpp:218] Iteration 8160 (0.932611 iter/s, 12.8671s/12 iters), loss = 0.325581
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I0428 14:25:31.297698 11373 solver.cpp:237] Train net output #0: loss = 0.325581 (* 1 = 0.325581 loss)
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I0428 14:25:31.297706 11373 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
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I0428 14:25:35.699698 11373 solver.cpp:218] Iteration 8172 (2.72615 iter/s, 4.40181s/12 iters), loss = 0.274775
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I0428 14:25:35.699824 11373 solver.cpp:237] Train net output #0: loss = 0.274775 (* 1 = 0.274775 loss)
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I0428 14:25:35.699837 11373 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
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I0428 14:25:40.652032 11373 solver.cpp:218] Iteration 8184 (2.42326 iter/s, 4.952s/12 iters), loss = 0.161796
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I0428 14:25:40.652088 11373 solver.cpp:237] Train net output #0: loss = 0.161796 (* 1 = 0.161796 loss)
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I0428 14:25:40.652099 11373 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
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I0428 14:25:44.238873 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:25:45.762576 11373 solver.cpp:218] Iteration 8196 (2.34821 iter/s, 5.11027s/12 iters), loss = 0.260678
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I0428 14:25:45.762634 11373 solver.cpp:237] Train net output #0: loss = 0.260678 (* 1 = 0.260678 loss)
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I0428 14:25:45.762650 11373 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
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I0428 14:25:50.908162 11373 solver.cpp:218] Iteration 8208 (2.33222 iter/s, 5.14532s/12 iters), loss = 0.288437
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I0428 14:25:50.908205 11373 solver.cpp:237] Train net output #0: loss = 0.288437 (* 1 = 0.288437 loss)
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I0428 14:25:50.908214 11373 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
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I0428 14:25:55.904131 11373 solver.cpp:218] Iteration 8220 (2.40206 iter/s, 4.99571s/12 iters), loss = 0.206213
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I0428 14:25:55.904184 11373 solver.cpp:237] Train net output #0: loss = 0.206213 (* 1 = 0.206213 loss)
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I0428 14:25:55.904196 11373 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
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I0428 14:26:01.038267 11373 solver.cpp:218] Iteration 8232 (2.33743 iter/s, 5.13385s/12 iters), loss = 0.258369
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I0428 14:26:01.038323 11373 solver.cpp:237] Train net output #0: loss = 0.258369 (* 1 = 0.258369 loss)
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I0428 14:26:01.038339 11373 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
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I0428 14:26:05.934763 11373 solver.cpp:218] Iteration 8244 (2.45198 iter/s, 4.894s/12 iters), loss = 0.260128
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I0428 14:26:05.941385 11373 solver.cpp:237] Train net output #0: loss = 0.260128 (* 1 = 0.260128 loss)
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I0428 14:26:05.941397 11373 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
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I0428 14:26:10.868237 11373 solver.cpp:218] Iteration 8256 (2.43573 iter/s, 4.92665s/12 iters), loss = 0.231726
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I0428 14:26:10.868280 11373 solver.cpp:237] Train net output #0: loss = 0.231726 (* 1 = 0.231726 loss)
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I0428 14:26:10.868289 11373 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
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I0428 14:26:13.160971 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
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I0428 14:26:18.145836 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
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I0428 14:26:21.306383 11373 solver.cpp:330] Iteration 8262, Testing net (#0)
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I0428 14:26:21.306409 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:26:22.479926 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:26:25.707857 11373 solver.cpp:397] Test net output #0: accuracy = 0.490196
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I0428 14:26:25.707886 11373 solver.cpp:397] Test net output #1: loss = 2.75807 (* 1 = 2.75807 loss)
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I0428 14:26:27.358881 11373 solver.cpp:218] Iteration 8268 (0.727762 iter/s, 16.4889s/12 iters), loss = 0.127962
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I0428 14:26:27.358927 11373 solver.cpp:237] Train net output #0: loss = 0.127962 (* 1 = 0.127962 loss)
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I0428 14:26:27.358937 11373 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
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I0428 14:26:32.159940 11373 solver.cpp:218] Iteration 8280 (2.49958 iter/s, 4.80081s/12 iters), loss = 0.159002
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I0428 14:26:32.159983 11373 solver.cpp:237] Train net output #0: loss = 0.159002 (* 1 = 0.159002 loss)
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I0428 14:26:32.159992 11373 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
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I0428 14:26:36.912184 11373 solver.cpp:218] Iteration 8292 (2.52525 iter/s, 4.752s/12 iters), loss = 0.128471
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I0428 14:26:36.913861 11373 solver.cpp:237] Train net output #0: loss = 0.128471 (* 1 = 0.128471 loss)
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I0428 14:26:36.913875 11373 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
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I0428 14:26:37.572118 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:26:41.647285 11373 solver.cpp:218] Iteration 8304 (2.53526 iter/s, 4.73323s/12 iters), loss = 0.209025
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I0428 14:26:41.647330 11373 solver.cpp:237] Train net output #0: loss = 0.209025 (* 1 = 0.209025 loss)
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I0428 14:26:41.647338 11373 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
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I0428 14:26:44.401280 11373 blocking_queue.cpp:49] Waiting for data
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I0428 14:26:46.424726 11373 solver.cpp:218] Iteration 8316 (2.51193 iter/s, 4.7772s/12 iters), loss = 0.383778
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I0428 14:26:46.424762 11373 solver.cpp:237] Train net output #0: loss = 0.383778 (* 1 = 0.383778 loss)
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I0428 14:26:46.424770 11373 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
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I0428 14:26:51.067625 11373 solver.cpp:218] Iteration 8328 (2.58472 iter/s, 4.64267s/12 iters), loss = 0.225793
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I0428 14:26:51.067667 11373 solver.cpp:237] Train net output #0: loss = 0.225793 (* 1 = 0.225793 loss)
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I0428 14:26:51.067675 11373 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
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I0428 14:26:55.888319 11373 solver.cpp:218] Iteration 8340 (2.48939 iter/s, 4.82045s/12 iters), loss = 0.214836
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I0428 14:26:55.888357 11373 solver.cpp:237] Train net output #0: loss = 0.214836 (* 1 = 0.214836 loss)
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I0428 14:26:55.888366 11373 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
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I0428 14:27:01.705674 11373 solver.cpp:218] Iteration 8352 (2.06289 iter/s, 5.81707s/12 iters), loss = 0.311157
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I0428 14:27:01.711796 11373 solver.cpp:237] Train net output #0: loss = 0.311157 (* 1 = 0.311157 loss)
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I0428 14:27:01.711824 11373 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
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I0428 14:27:07.766819 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
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I0428 14:27:10.916450 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
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I0428 14:27:12.135824 11373 solver.cpp:330] Iteration 8364, Testing net (#0)
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I0428 14:27:12.135848 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:27:13.185740 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:27:16.411548 11373 solver.cpp:397] Test net output #0: accuracy = 0.48652
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I0428 14:27:16.411583 11373 solver.cpp:397] Test net output #1: loss = 3.02197 (* 1 = 3.02197 loss)
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I0428 14:27:16.480469 11373 solver.cpp:218] Iteration 8364 (0.812562 iter/s, 14.7681s/12 iters), loss = 0.182149
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I0428 14:27:16.480553 11373 solver.cpp:237] Train net output #0: loss = 0.182149 (* 1 = 0.182149 loss)
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I0428 14:27:16.480564 11373 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
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I0428 14:27:20.471125 11373 solver.cpp:218] Iteration 8376 (3.00722 iter/s, 3.9904s/12 iters), loss = 0.290933
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I0428 14:27:20.471168 11373 solver.cpp:237] Train net output #0: loss = 0.290933 (* 1 = 0.290933 loss)
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I0428 14:27:20.471177 11373 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
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I0428 14:27:25.259415 11373 solver.cpp:218] Iteration 8388 (2.50624 iter/s, 4.78805s/12 iters), loss = 0.265516
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I0428 14:27:25.259454 11373 solver.cpp:237] Train net output #0: loss = 0.265516 (* 1 = 0.265516 loss)
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I0428 14:27:25.259461 11373 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
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I0428 14:27:27.935734 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:27:30.000022 11373 solver.cpp:218] Iteration 8400 (2.53145 iter/s, 4.74036s/12 iters), loss = 0.245154
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I0428 14:27:30.000063 11373 solver.cpp:237] Train net output #0: loss = 0.245154 (* 1 = 0.245154 loss)
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I0428 14:27:30.000072 11373 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
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I0428 14:27:34.791090 11373 solver.cpp:218] Iteration 8412 (2.50479 iter/s, 4.79081s/12 iters), loss = 0.351318
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I0428 14:27:34.791148 11373 solver.cpp:237] Train net output #0: loss = 0.351318 (* 1 = 0.351318 loss)
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I0428 14:27:34.791162 11373 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
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I0428 14:27:39.564963 11373 solver.cpp:218] Iteration 8424 (2.51382 iter/s, 4.77362s/12 iters), loss = 0.192994
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I0428 14:27:39.565057 11373 solver.cpp:237] Train net output #0: loss = 0.192994 (* 1 = 0.192994 loss)
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I0428 14:27:39.565068 11373 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
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I0428 14:27:44.269398 11373 solver.cpp:218] Iteration 8436 (2.55094 iter/s, 4.70415s/12 iters), loss = 0.120663
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I0428 14:27:44.269439 11373 solver.cpp:237] Train net output #0: loss = 0.120663 (* 1 = 0.120663 loss)
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I0428 14:27:44.269449 11373 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
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I0428 14:27:49.020663 11373 solver.cpp:218] Iteration 8448 (2.52577 iter/s, 4.75102s/12 iters), loss = 0.211474
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I0428 14:27:49.020711 11373 solver.cpp:237] Train net output #0: loss = 0.211474 (* 1 = 0.211474 loss)
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I0428 14:27:49.020722 11373 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
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I0428 14:27:53.926000 11373 solver.cpp:218] Iteration 8460 (2.44644 iter/s, 4.90508s/12 iters), loss = 0.189889
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I0428 14:27:53.926048 11373 solver.cpp:237] Train net output #0: loss = 0.189889 (* 1 = 0.189889 loss)
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I0428 14:27:53.926061 11373 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
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I0428 14:27:55.913882 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
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I0428 14:27:57.685930 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
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I0428 14:27:58.680099 11373 solver.cpp:330] Iteration 8466, Testing net (#0)
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I0428 14:27:58.680119 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:27:59.819207 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:28:03.137614 11373 solver.cpp:397] Test net output #0: accuracy = 0.517157
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I0428 14:28:03.137643 11373 solver.cpp:397] Test net output #1: loss = 2.89527 (* 1 = 2.89527 loss)
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I0428 14:28:04.836433 11373 solver.cpp:218] Iteration 8472 (1.09992 iter/s, 10.9099s/12 iters), loss = 0.239505
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I0428 14:28:04.836480 11373 solver.cpp:237] Train net output #0: loss = 0.239505 (* 1 = 0.239505 loss)
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I0428 14:28:04.836521 11373 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
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I0428 14:28:09.543637 11373 solver.cpp:218] Iteration 8484 (2.54942 iter/s, 4.70696s/12 iters), loss = 0.175313
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I0428 14:28:09.543680 11373 solver.cpp:237] Train net output #0: loss = 0.175313 (* 1 = 0.175313 loss)
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I0428 14:28:09.543690 11373 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
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I0428 14:28:14.247669 11373 solver.cpp:218] Iteration 8496 (2.55114 iter/s, 4.70379s/12 iters), loss = 0.248364
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I0428 14:28:14.247797 11373 solver.cpp:237] Train net output #0: loss = 0.248364 (* 1 = 0.248364 loss)
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I0428 14:28:14.247807 11373 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
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I0428 14:28:14.285343 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:28:19.028612 11373 solver.cpp:218] Iteration 8508 (2.51014 iter/s, 4.78061s/12 iters), loss = 0.15937
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I0428 14:28:19.028662 11373 solver.cpp:237] Train net output #0: loss = 0.15937 (* 1 = 0.15937 loss)
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I0428 14:28:19.028672 11373 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
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I0428 14:28:23.939913 11373 solver.cpp:218] Iteration 8520 (2.44347 iter/s, 4.91104s/12 iters), loss = 0.113018
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I0428 14:28:23.939961 11373 solver.cpp:237] Train net output #0: loss = 0.113018 (* 1 = 0.113018 loss)
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I0428 14:28:23.939972 11373 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
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I0428 14:28:28.691283 11373 solver.cpp:218] Iteration 8532 (2.52572 iter/s, 4.75112s/12 iters), loss = 0.149661
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I0428 14:28:28.691323 11373 solver.cpp:237] Train net output #0: loss = 0.149661 (* 1 = 0.149661 loss)
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I0428 14:28:28.691331 11373 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
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I0428 14:28:33.520887 11373 solver.cpp:218] Iteration 8544 (2.4848 iter/s, 4.82936s/12 iters), loss = 0.242173
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I0428 14:28:33.520927 11373 solver.cpp:237] Train net output #0: loss = 0.242173 (* 1 = 0.242173 loss)
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I0428 14:28:33.520936 11373 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
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I0428 14:28:38.254053 11373 solver.cpp:218] Iteration 8556 (2.53543 iter/s, 4.73292s/12 iters), loss = 0.150019
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I0428 14:28:38.254106 11373 solver.cpp:237] Train net output #0: loss = 0.150019 (* 1 = 0.150019 loss)
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I0428 14:28:38.254114 11373 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
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I0428 14:28:42.551762 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
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I0428 14:28:44.472985 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
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I0428 14:28:45.527499 11373 solver.cpp:330] Iteration 8568, Testing net (#0)
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I0428 14:28:45.527529 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:28:46.486188 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:28:49.850704 11373 solver.cpp:397] Test net output #0: accuracy = 0.497549
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I0428 14:28:49.850733 11373 solver.cpp:397] Test net output #1: loss = 2.95107 (* 1 = 2.95107 loss)
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I0428 14:28:49.929558 11373 solver.cpp:218] Iteration 8568 (1.02784 iter/s, 11.675s/12 iters), loss = 0.235609
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I0428 14:28:49.929600 11373 solver.cpp:237] Train net output #0: loss = 0.235609 (* 1 = 0.235609 loss)
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I0428 14:28:49.929608 11373 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
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I0428 14:28:53.874246 11373 solver.cpp:218] Iteration 8580 (3.04222 iter/s, 3.94448s/12 iters), loss = 0.247221
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I0428 14:28:53.874282 11373 solver.cpp:237] Train net output #0: loss = 0.247221 (* 1 = 0.247221 loss)
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I0428 14:28:53.874292 11373 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
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I0428 14:28:58.734660 11373 solver.cpp:218] Iteration 8592 (2.46905 iter/s, 4.86017s/12 iters), loss = 0.165686
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I0428 14:28:58.734704 11373 solver.cpp:237] Train net output #0: loss = 0.165686 (* 1 = 0.165686 loss)
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I0428 14:28:58.734714 11373 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
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I0428 14:29:00.795919 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:29:03.543012 11373 solver.cpp:218] Iteration 8604 (2.49579 iter/s, 4.8081s/12 iters), loss = 0.276718
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I0428 14:29:03.543052 11373 solver.cpp:237] Train net output #0: loss = 0.276718 (* 1 = 0.276718 loss)
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I0428 14:29:03.543061 11373 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
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I0428 14:29:08.313905 11373 solver.cpp:218] Iteration 8616 (2.51538 iter/s, 4.77064s/12 iters), loss = 0.23524
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I0428 14:29:08.313957 11373 solver.cpp:237] Train net output #0: loss = 0.23524 (* 1 = 0.23524 loss)
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I0428 14:29:08.313969 11373 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
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I0428 14:29:13.018324 11373 solver.cpp:218] Iteration 8628 (2.55093 iter/s, 4.70417s/12 iters), loss = 0.330433
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I0428 14:29:13.018360 11373 solver.cpp:237] Train net output #0: loss = 0.330433 (* 1 = 0.330433 loss)
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I0428 14:29:13.018370 11373 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
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I0428 14:29:17.763588 11373 solver.cpp:218] Iteration 8640 (2.52896 iter/s, 4.74503s/12 iters), loss = 0.196533
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I0428 14:29:17.763710 11373 solver.cpp:237] Train net output #0: loss = 0.196533 (* 1 = 0.196533 loss)
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I0428 14:29:17.763718 11373 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
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I0428 14:29:22.517604 11373 solver.cpp:218] Iteration 8652 (2.52435 iter/s, 4.7537s/12 iters), loss = 0.193288
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I0428 14:29:22.517649 11373 solver.cpp:237] Train net output #0: loss = 0.193288 (* 1 = 0.193288 loss)
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I0428 14:29:22.517660 11373 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
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I0428 14:29:27.257886 11373 solver.cpp:218] Iteration 8664 (2.53163 iter/s, 4.74004s/12 iters), loss = 0.180599
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I0428 14:29:27.257928 11373 solver.cpp:237] Train net output #0: loss = 0.180599 (* 1 = 0.180599 loss)
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I0428 14:29:27.257938 11373 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
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I0428 14:29:29.189008 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
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I0428 14:29:32.870663 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
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I0428 14:29:33.881937 11373 solver.cpp:330] Iteration 8670, Testing net (#0)
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I0428 14:29:33.881975 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:29:34.856950 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:29:38.234876 11373 solver.cpp:397] Test net output #0: accuracy = 0.509804
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I0428 14:29:38.234930 11373 solver.cpp:397] Test net output #1: loss = 2.94978 (* 1 = 2.94978 loss)
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I0428 14:29:40.067548 11373 solver.cpp:218] Iteration 8676 (0.936834 iter/s, 12.8091s/12 iters), loss = 0.177871
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I0428 14:29:40.067589 11373 solver.cpp:237] Train net output #0: loss = 0.177871 (* 1 = 0.177871 loss)
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I0428 14:29:40.067598 11373 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
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I0428 14:29:44.795971 11373 solver.cpp:218] Iteration 8688 (2.53798 iter/s, 4.72818s/12 iters), loss = 0.188118
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I0428 14:29:44.796011 11373 solver.cpp:237] Train net output #0: loss = 0.188118 (* 1 = 0.188118 loss)
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I0428 14:29:44.796020 11373 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
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I0428 14:29:48.890174 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:29:49.543982 11373 solver.cpp:218] Iteration 8700 (2.5275 iter/s, 4.74777s/12 iters), loss = 0.243769
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I0428 14:29:49.544018 11373 solver.cpp:237] Train net output #0: loss = 0.243769 (* 1 = 0.243769 loss)
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I0428 14:29:49.544028 11373 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
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I0428 14:29:54.204931 11373 solver.cpp:218] Iteration 8712 (2.57471 iter/s, 4.66071s/12 iters), loss = 0.232546
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I0428 14:29:54.204967 11373 solver.cpp:237] Train net output #0: loss = 0.232546 (* 1 = 0.232546 loss)
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I0428 14:29:54.204977 11373 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
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I0428 14:29:58.924738 11373 solver.cpp:218] Iteration 8724 (2.54261 iter/s, 4.71957s/12 iters), loss = 0.110712
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I0428 14:29:58.924782 11373 solver.cpp:237] Train net output #0: loss = 0.110712 (* 1 = 0.110712 loss)
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I0428 14:29:58.924791 11373 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
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I0428 14:30:03.699337 11373 solver.cpp:218] Iteration 8736 (2.51343 iter/s, 4.77435s/12 iters), loss = 0.146763
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I0428 14:30:03.699379 11373 solver.cpp:237] Train net output #0: loss = 0.146763 (* 1 = 0.146763 loss)
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I0428 14:30:03.699389 11373 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
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I0428 14:30:08.535773 11373 solver.cpp:218] Iteration 8748 (2.4813 iter/s, 4.83618s/12 iters), loss = 0.21234
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I0428 14:30:08.535815 11373 solver.cpp:237] Train net output #0: loss = 0.21234 (* 1 = 0.21234 loss)
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I0428 14:30:08.535825 11373 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
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I0428 14:30:13.178048 11373 solver.cpp:218] Iteration 8760 (2.58508 iter/s, 4.64203s/12 iters), loss = 0.0808347
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I0428 14:30:13.178103 11373 solver.cpp:237] Train net output #0: loss = 0.0808347 (* 1 = 0.0808347 loss)
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I0428 14:30:13.178114 11373 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
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I0428 14:30:17.473201 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
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I0428 14:30:26.838533 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
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I0428 14:30:33.935236 11373 solver.cpp:330] Iteration 8772, Testing net (#0)
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I0428 14:30:33.935256 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:30:34.822121 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:30:38.223836 11373 solver.cpp:397] Test net output #0: accuracy = 0.497549
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I0428 14:30:38.223872 11373 solver.cpp:397] Test net output #1: loss = 3.08301 (* 1 = 3.08301 loss)
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I0428 14:30:38.302676 11373 solver.cpp:218] Iteration 8772 (0.477639 iter/s, 25.1236s/12 iters), loss = 0.11697
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I0428 14:30:38.302731 11373 solver.cpp:237] Train net output #0: loss = 0.11697 (* 1 = 0.11697 loss)
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I0428 14:30:38.302743 11373 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
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I0428 14:30:42.192718 11373 solver.cpp:218] Iteration 8784 (3.08497 iter/s, 3.88982s/12 iters), loss = 0.105491
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I0428 14:30:42.192755 11373 solver.cpp:237] Train net output #0: loss = 0.105491 (* 1 = 0.105491 loss)
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I0428 14:30:42.192764 11373 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
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I0428 14:30:46.963047 11373 solver.cpp:218] Iteration 8796 (2.51568 iter/s, 4.77008s/12 iters), loss = 0.268669
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I0428 14:30:46.963095 11373 solver.cpp:237] Train net output #0: loss = 0.268669 (* 1 = 0.268669 loss)
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I0428 14:30:46.963105 11373 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
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I0428 14:30:48.308979 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:30:51.681282 11373 solver.cpp:218] Iteration 8808 (2.54346 iter/s, 4.71798s/12 iters), loss = 0.241521
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I0428 14:30:51.681325 11373 solver.cpp:237] Train net output #0: loss = 0.241521 (* 1 = 0.241521 loss)
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I0428 14:30:51.681336 11373 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
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I0428 14:30:56.498677 11373 solver.cpp:218] Iteration 8820 (2.4911 iter/s, 4.81714s/12 iters), loss = 0.415466
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I0428 14:30:56.498719 11373 solver.cpp:237] Train net output #0: loss = 0.415466 (* 1 = 0.415466 loss)
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I0428 14:30:56.498729 11373 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
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I0428 14:31:01.531705 11373 solver.cpp:218] Iteration 8832 (2.38437 iter/s, 5.03277s/12 iters), loss = 0.153
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I0428 14:31:01.531810 11373 solver.cpp:237] Train net output #0: loss = 0.153 (* 1 = 0.153 loss)
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I0428 14:31:01.531821 11373 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
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I0428 14:31:06.463538 11373 solver.cpp:218] Iteration 8844 (2.43333 iter/s, 4.93152s/12 iters), loss = 0.231623
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I0428 14:31:06.463579 11373 solver.cpp:237] Train net output #0: loss = 0.231623 (* 1 = 0.231623 loss)
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I0428 14:31:06.463588 11373 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
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I0428 14:31:11.165416 11373 solver.cpp:218] Iteration 8856 (2.5523 iter/s, 4.70164s/12 iters), loss = 0.0985414
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I0428 14:31:11.165453 11373 solver.cpp:237] Train net output #0: loss = 0.0985414 (* 1 = 0.0985414 loss)
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I0428 14:31:11.165463 11373 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
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I0428 14:31:15.932832 11373 solver.cpp:218] Iteration 8868 (2.51722 iter/s, 4.76717s/12 iters), loss = 0.240201
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I0428 14:31:15.932876 11373 solver.cpp:237] Train net output #0: loss = 0.240201 (* 1 = 0.240201 loss)
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I0428 14:31:15.932885 11373 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
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I0428 14:31:17.834327 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
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I0428 14:31:22.766978 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
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I0428 14:31:29.478399 11373 solver.cpp:330] Iteration 8874, Testing net (#0)
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I0428 14:31:29.478415 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:31:30.421264 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:31:34.075557 11373 solver.cpp:397] Test net output #0: accuracy = 0.515931
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I0428 14:31:34.077045 11373 solver.cpp:397] Test net output #1: loss = 2.91668 (* 1 = 2.91668 loss)
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I0428 14:31:35.934437 11373 solver.cpp:218] Iteration 8880 (0.599978 iter/s, 20.0007s/12 iters), loss = 0.154502
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I0428 14:31:35.934487 11373 solver.cpp:237] Train net output #0: loss = 0.154502 (* 1 = 0.154502 loss)
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I0428 14:31:35.934496 11373 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
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I0428 14:31:40.748281 11373 solver.cpp:218] Iteration 8892 (2.49294 iter/s, 4.81359s/12 iters), loss = 0.122808
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I0428 14:31:40.748319 11373 solver.cpp:237] Train net output #0: loss = 0.122808 (* 1 = 0.122808 loss)
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I0428 14:31:40.748328 11373 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
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I0428 14:31:44.248104 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:31:45.598323 11373 solver.cpp:218] Iteration 8904 (2.47433 iter/s, 4.8498s/12 iters), loss = 0.209389
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I0428 14:31:45.598382 11373 solver.cpp:237] Train net output #0: loss = 0.209389 (* 1 = 0.209389 loss)
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I0428 14:31:45.598395 11373 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
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I0428 14:31:50.360421 11373 solver.cpp:218] Iteration 8916 (2.52003 iter/s, 4.76184s/12 iters), loss = 0.127727
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I0428 14:31:50.360460 11373 solver.cpp:237] Train net output #0: loss = 0.127727 (* 1 = 0.127727 loss)
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I0428 14:31:50.360469 11373 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
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I0428 14:31:55.740180 11373 solver.cpp:218] Iteration 8928 (2.23069 iter/s, 5.37949s/12 iters), loss = 0.187728
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I0428 14:31:55.740222 11373 solver.cpp:237] Train net output #0: loss = 0.187728 (* 1 = 0.187728 loss)
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I0428 14:31:55.740232 11373 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
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I0428 14:32:00.448539 11373 solver.cpp:218] Iteration 8940 (2.5489 iter/s, 4.70792s/12 iters), loss = 0.140718
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I0428 14:32:00.448585 11373 solver.cpp:237] Train net output #0: loss = 0.140718 (* 1 = 0.140718 loss)
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I0428 14:32:00.448596 11373 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
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I0428 14:32:05.513605 11373 solver.cpp:218] Iteration 8952 (2.36929 iter/s, 5.0648s/12 iters), loss = 0.11516
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I0428 14:32:05.513706 11373 solver.cpp:237] Train net output #0: loss = 0.11516 (* 1 = 0.11516 loss)
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I0428 14:32:05.513716 11373 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
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I0428 14:32:10.536193 11373 solver.cpp:218] Iteration 8964 (2.38936 iter/s, 5.02227s/12 iters), loss = 0.129362
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I0428 14:32:10.536247 11373 solver.cpp:237] Train net output #0: loss = 0.129362 (* 1 = 0.129362 loss)
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I0428 14:32:10.536258 11373 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
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I0428 14:32:14.894939 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
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I0428 14:32:18.543645 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
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I0428 14:32:23.543138 11373 solver.cpp:330] Iteration 8976, Testing net (#0)
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I0428 14:32:23.543159 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:32:24.413898 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:32:28.117753 11373 solver.cpp:397] Test net output #0: accuracy = 0.504902
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I0428 14:32:28.117785 11373 solver.cpp:397] Test net output #1: loss = 3.03776 (* 1 = 3.03776 loss)
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I0428 14:32:28.232177 11373 solver.cpp:218] Iteration 8976 (0.678149 iter/s, 17.6952s/12 iters), loss = 0.128659
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I0428 14:32:28.232218 11373 solver.cpp:237] Train net output #0: loss = 0.128659 (* 1 = 0.128659 loss)
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I0428 14:32:28.232228 11373 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
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I0428 14:32:32.467583 11373 solver.cpp:218] Iteration 8988 (2.83341 iter/s, 4.23518s/12 iters), loss = 0.261062
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I0428 14:32:32.467640 11373 solver.cpp:237] Train net output #0: loss = 0.261062 (* 1 = 0.261062 loss)
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I0428 14:32:32.467653 11373 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
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I0428 14:32:35.565827 11373 blocking_queue.cpp:49] Waiting for data
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I0428 14:32:37.156869 11373 solver.cpp:218] Iteration 9000 (2.55916 iter/s, 4.68903s/12 iters), loss = 0.119515
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I0428 14:32:37.156910 11373 solver.cpp:237] Train net output #0: loss = 0.119515 (* 1 = 0.119515 loss)
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I0428 14:32:37.156919 11373 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
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I0428 14:32:37.847280 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:32:41.887075 11373 solver.cpp:218] Iteration 9012 (2.53702 iter/s, 4.72996s/12 iters), loss = 0.18082
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I0428 14:32:41.887132 11373 solver.cpp:237] Train net output #0: loss = 0.18082 (* 1 = 0.18082 loss)
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I0428 14:32:41.887145 11373 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
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I0428 14:32:46.679551 11373 solver.cpp:218] Iteration 9024 (2.50406 iter/s, 4.79222s/12 iters), loss = 0.179679
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I0428 14:32:46.679598 11373 solver.cpp:237] Train net output #0: loss = 0.179679 (* 1 = 0.179679 loss)
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I0428 14:32:46.679607 11373 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
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I0428 14:32:51.452919 11373 solver.cpp:218] Iteration 9036 (2.51408 iter/s, 4.77312s/12 iters), loss = 0.150016
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I0428 14:32:51.452963 11373 solver.cpp:237] Train net output #0: loss = 0.150016 (* 1 = 0.150016 loss)
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I0428 14:32:51.452972 11373 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
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I0428 14:32:56.507866 11373 solver.cpp:218] Iteration 9048 (2.37404 iter/s, 5.05468s/12 iters), loss = 0.116887
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I0428 14:32:56.507923 11373 solver.cpp:237] Train net output #0: loss = 0.116887 (* 1 = 0.116887 loss)
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I0428 14:32:56.507934 11373 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
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I0428 14:33:01.766497 11373 solver.cpp:218] Iteration 9060 (2.28208 iter/s, 5.25836s/12 iters), loss = 0.13706
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I0428 14:33:01.766539 11373 solver.cpp:237] Train net output #0: loss = 0.13706 (* 1 = 0.13706 loss)
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I0428 14:33:01.766548 11373 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
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I0428 14:33:06.907500 11373 solver.cpp:218] Iteration 9072 (2.33429 iter/s, 5.14074s/12 iters), loss = 0.188583
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I0428 14:33:06.907655 11373 solver.cpp:237] Train net output #0: loss = 0.188583 (* 1 = 0.188583 loss)
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I0428 14:33:06.907672 11373 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
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I0428 14:33:09.050493 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
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I0428 14:33:12.280138 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
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I0428 14:33:14.763866 11373 solver.cpp:330] Iteration 9078, Testing net (#0)
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I0428 14:33:14.763886 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:33:15.661834 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:33:19.483870 11373 solver.cpp:397] Test net output #0: accuracy = 0.514093
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I0428 14:33:19.483907 11373 solver.cpp:397] Test net output #1: loss = 3.07974 (* 1 = 3.07974 loss)
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I0428 14:33:21.354579 11373 solver.cpp:218] Iteration 9084 (0.830781 iter/s, 14.4442s/12 iters), loss = 0.0952953
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I0428 14:33:21.354624 11373 solver.cpp:237] Train net output #0: loss = 0.0952953 (* 1 = 0.0952953 loss)
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I0428 14:33:21.354635 11373 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
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I0428 14:33:26.532258 11373 solver.cpp:218] Iteration 9096 (2.31776 iter/s, 5.17741s/12 iters), loss = 0.163032
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I0428 14:33:26.532310 11373 solver.cpp:237] Train net output #0: loss = 0.163032 (* 1 = 0.163032 loss)
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I0428 14:33:26.532321 11373 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
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I0428 14:33:29.519670 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:33:31.615046 11373 solver.cpp:218] Iteration 9108 (2.36103 iter/s, 5.08252s/12 iters), loss = 0.0984962
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I0428 14:33:31.615089 11373 solver.cpp:237] Train net output #0: loss = 0.0984962 (* 1 = 0.0984962 loss)
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I0428 14:33:31.615099 11373 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
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I0428 14:33:36.803110 11373 solver.cpp:218] Iteration 9120 (2.31312 iter/s, 5.18781s/12 iters), loss = 0.164598
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I0428 14:33:36.803158 11373 solver.cpp:237] Train net output #0: loss = 0.164598 (* 1 = 0.164598 loss)
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I0428 14:33:36.803167 11373 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
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I0428 14:33:41.860797 11373 solver.cpp:218] Iteration 9132 (2.37275 iter/s, 5.05743s/12 iters), loss = 0.110621
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I0428 14:33:41.860915 11373 solver.cpp:237] Train net output #0: loss = 0.110621 (* 1 = 0.110621 loss)
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I0428 14:33:41.860925 11373 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
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I0428 14:33:47.360982 11373 solver.cpp:218] Iteration 9144 (2.18188 iter/s, 5.49983s/12 iters), loss = 0.174177
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I0428 14:33:47.361040 11373 solver.cpp:237] Train net output #0: loss = 0.174177 (* 1 = 0.174177 loss)
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I0428 14:33:47.361052 11373 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
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I0428 14:33:52.290880 11373 solver.cpp:218] Iteration 9156 (2.43426 iter/s, 4.92964s/12 iters), loss = 0.177929
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I0428 14:33:52.290920 11373 solver.cpp:237] Train net output #0: loss = 0.17793 (* 1 = 0.17793 loss)
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I0428 14:33:52.290930 11373 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
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I0428 14:33:57.108458 11373 solver.cpp:218] Iteration 9168 (2.491 iter/s, 4.81734s/12 iters), loss = 0.120492
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I0428 14:33:57.108526 11373 solver.cpp:237] Train net output #0: loss = 0.120492 (* 1 = 0.120492 loss)
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I0428 14:33:57.108537 11373 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
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I0428 14:34:02.073434 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
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I0428 14:34:04.166110 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
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I0428 14:34:06.531159 11373 solver.cpp:330] Iteration 9180, Testing net (#0)
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I0428 14:34:06.531183 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:34:07.561228 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:34:12.735888 11373 solver.cpp:397] Test net output #0: accuracy = 0.517157
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I0428 14:34:12.736003 11373 solver.cpp:397] Test net output #1: loss = 3.10703 (* 1 = 3.10703 loss)
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I0428 14:34:12.828157 11373 solver.cpp:218] Iteration 9180 (0.763407 iter/s, 15.719s/12 iters), loss = 0.106257
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I0428 14:34:12.828212 11373 solver.cpp:237] Train net output #0: loss = 0.106257 (* 1 = 0.106257 loss)
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I0428 14:34:12.828224 11373 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
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I0428 14:34:17.035336 11373 solver.cpp:218] Iteration 9192 (2.85243 iter/s, 4.20695s/12 iters), loss = 0.147843
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I0428 14:34:17.035380 11373 solver.cpp:237] Train net output #0: loss = 0.147843 (* 1 = 0.147843 loss)
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I0428 14:34:17.035389 11373 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
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I0428 14:34:21.952934 11373 solver.cpp:218] Iteration 9204 (2.44034 iter/s, 4.91734s/12 iters), loss = 0.157678
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I0428 14:34:21.952982 11373 solver.cpp:237] Train net output #0: loss = 0.157678 (* 1 = 0.157678 loss)
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I0428 14:34:21.952992 11373 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
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I0428 14:34:22.021912 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:34:26.736521 11373 solver.cpp:218] Iteration 9216 (2.50872 iter/s, 4.78332s/12 iters), loss = 0.147849
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I0428 14:34:26.736564 11373 solver.cpp:237] Train net output #0: loss = 0.147849 (* 1 = 0.147849 loss)
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I0428 14:34:26.736573 11373 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
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I0428 14:34:31.531947 11373 solver.cpp:218] Iteration 9228 (2.50251 iter/s, 4.79519s/12 iters), loss = 0.194044
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I0428 14:34:31.531989 11373 solver.cpp:237] Train net output #0: loss = 0.194044 (* 1 = 0.194044 loss)
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I0428 14:34:31.531999 11373 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
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I0428 14:34:36.346639 11373 solver.cpp:218] Iteration 9240 (2.4925 iter/s, 4.81445s/12 iters), loss = 0.158591
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I0428 14:34:36.346678 11373 solver.cpp:237] Train net output #0: loss = 0.158591 (* 1 = 0.158591 loss)
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I0428 14:34:36.346688 11373 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
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I0428 14:34:41.168905 11373 solver.cpp:218] Iteration 9252 (2.48858 iter/s, 4.82202s/12 iters), loss = 0.144265
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I0428 14:34:41.168944 11373 solver.cpp:237] Train net output #0: loss = 0.144265 (* 1 = 0.144265 loss)
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I0428 14:34:41.168953 11373 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
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I0428 14:34:45.874186 11373 solver.cpp:218] Iteration 9264 (2.55046 iter/s, 4.70504s/12 iters), loss = 0.104635
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I0428 14:34:45.874307 11373 solver.cpp:237] Train net output #0: loss = 0.104635 (* 1 = 0.104635 loss)
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I0428 14:34:45.874316 11373 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
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I0428 14:34:50.620735 11373 solver.cpp:218] Iteration 9276 (2.52832 iter/s, 4.74623s/12 iters), loss = 0.143554
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I0428 14:34:50.620772 11373 solver.cpp:237] Train net output #0: loss = 0.143554 (* 1 = 0.143554 loss)
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I0428 14:34:50.620781 11373 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
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I0428 14:34:52.559257 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
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I0428 14:34:53.822989 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
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I0428 14:34:54.818280 11373 solver.cpp:330] Iteration 9282, Testing net (#0)
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I0428 14:34:54.818301 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:34:55.501389 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:34:59.105855 11373 solver.cpp:397] Test net output #0: accuracy = 0.518382
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I0428 14:34:59.105885 11373 solver.cpp:397] Test net output #1: loss = 2.99155 (* 1 = 2.99155 loss)
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I0428 14:35:00.759982 11373 solver.cpp:218] Iteration 9288 (1.18357 iter/s, 10.1388s/12 iters), loss = 0.161138
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I0428 14:35:00.760027 11373 solver.cpp:237] Train net output #0: loss = 0.161138 (* 1 = 0.161138 loss)
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I0428 14:35:00.760035 11373 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
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I0428 14:35:05.534036 11373 solver.cpp:218] Iteration 9300 (2.51372 iter/s, 4.7738s/12 iters), loss = 0.18829
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I0428 14:35:05.534083 11373 solver.cpp:237] Train net output #0: loss = 0.18829 (* 1 = 0.18829 loss)
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I0428 14:35:05.534092 11373 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
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I0428 14:35:07.631371 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:35:10.291587 11373 solver.cpp:218] Iteration 9312 (2.52244 iter/s, 4.7573s/12 iters), loss = 0.12722
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I0428 14:35:10.291630 11373 solver.cpp:237] Train net output #0: loss = 0.12722 (* 1 = 0.12722 loss)
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I0428 14:35:10.291640 11373 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
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I0428 14:35:15.081317 11373 solver.cpp:218] Iteration 9324 (2.50549 iter/s, 4.78948s/12 iters), loss = 0.114079
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I0428 14:35:15.081362 11373 solver.cpp:237] Train net output #0: loss = 0.114079 (* 1 = 0.114079 loss)
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I0428 14:35:15.081370 11373 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
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I0428 14:35:19.791262 11373 solver.cpp:218] Iteration 9336 (2.54793 iter/s, 4.70971s/12 iters), loss = 0.106845
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I0428 14:35:19.791455 11373 solver.cpp:237] Train net output #0: loss = 0.106845 (* 1 = 0.106845 loss)
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I0428 14:35:19.791465 11373 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
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I0428 14:35:24.359733 11373 solver.cpp:218] Iteration 9348 (2.62693 iter/s, 4.56808s/12 iters), loss = 0.147424
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I0428 14:35:24.359787 11373 solver.cpp:237] Train net output #0: loss = 0.147424 (* 1 = 0.147424 loss)
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I0428 14:35:24.359800 11373 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
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I0428 14:35:29.109273 11373 solver.cpp:218] Iteration 9360 (2.5267 iter/s, 4.74928s/12 iters), loss = 0.212597
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I0428 14:35:29.109323 11373 solver.cpp:237] Train net output #0: loss = 0.212597 (* 1 = 0.212597 loss)
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I0428 14:35:29.109335 11373 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
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I0428 14:35:33.810108 11373 solver.cpp:218] Iteration 9372 (2.55287 iter/s, 4.70059s/12 iters), loss = 0.0778684
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I0428 14:35:33.810150 11373 solver.cpp:237] Train net output #0: loss = 0.0778684 (* 1 = 0.0778684 loss)
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I0428 14:35:33.810160 11373 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
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I0428 14:35:38.033990 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
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I0428 14:35:42.495057 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
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I0428 14:35:44.805518 11373 solver.cpp:330] Iteration 9384, Testing net (#0)
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I0428 14:35:44.805544 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:35:45.536674 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:35:49.304612 11373 solver.cpp:397] Test net output #0: accuracy = 0.504289
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I0428 14:35:49.304653 11373 solver.cpp:397] Test net output #1: loss = 3.07717 (* 1 = 3.07717 loss)
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I0428 14:35:49.383610 11373 solver.cpp:218] Iteration 9384 (0.770573 iter/s, 15.5728s/12 iters), loss = 0.153343
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I0428 14:35:49.383656 11373 solver.cpp:237] Train net output #0: loss = 0.153343 (* 1 = 0.153343 loss)
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I0428 14:35:49.383667 11373 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
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I0428 14:35:53.442126 11373 solver.cpp:218] Iteration 9396 (2.95691 iter/s, 4.05829s/12 iters), loss = 0.213143
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I0428 14:35:53.442253 11373 solver.cpp:237] Train net output #0: loss = 0.213143 (* 1 = 0.213143 loss)
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I0428 14:35:53.442263 11373 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
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I0428 14:35:57.560441 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:35:58.175614 11373 solver.cpp:218] Iteration 9408 (2.5353 iter/s, 4.73316s/12 iters), loss = 0.115762
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I0428 14:35:58.175650 11373 solver.cpp:237] Train net output #0: loss = 0.115762 (* 1 = 0.115762 loss)
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I0428 14:35:58.175659 11373 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
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I0428 14:36:02.923677 11373 solver.cpp:218] Iteration 9420 (2.52748 iter/s, 4.74782s/12 iters), loss = 0.135495
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I0428 14:36:02.923729 11373 solver.cpp:237] Train net output #0: loss = 0.135495 (* 1 = 0.135495 loss)
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I0428 14:36:02.923740 11373 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
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I0428 14:36:07.652576 11373 solver.cpp:218] Iteration 9432 (2.53772 iter/s, 4.72865s/12 iters), loss = 0.14592
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I0428 14:36:07.652626 11373 solver.cpp:237] Train net output #0: loss = 0.14592 (* 1 = 0.14592 loss)
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I0428 14:36:07.652638 11373 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
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I0428 14:36:12.299108 11373 solver.cpp:218] Iteration 9444 (2.58271 iter/s, 4.64628s/12 iters), loss = 0.182608
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I0428 14:36:12.299150 11373 solver.cpp:237] Train net output #0: loss = 0.182608 (* 1 = 0.182608 loss)
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I0428 14:36:12.299158 11373 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
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I0428 14:36:17.022007 11373 solver.cpp:218] Iteration 9456 (2.54095 iter/s, 4.72265s/12 iters), loss = 0.139541
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I0428 14:36:17.022063 11373 solver.cpp:237] Train net output #0: loss = 0.139541 (* 1 = 0.139541 loss)
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I0428 14:36:17.022075 11373 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
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I0428 14:36:21.614392 11373 solver.cpp:218] Iteration 9468 (2.61316 iter/s, 4.59214s/12 iters), loss = 0.0524702
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I0428 14:36:21.614437 11373 solver.cpp:237] Train net output #0: loss = 0.0524702 (* 1 = 0.0524702 loss)
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I0428 14:36:21.614447 11373 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
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I0428 14:36:26.350533 11373 solver.cpp:218] Iteration 9480 (2.53384 iter/s, 4.7359s/12 iters), loss = 0.0945884
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I0428 14:36:26.350636 11373 solver.cpp:237] Train net output #0: loss = 0.0945884 (* 1 = 0.0945884 loss)
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I0428 14:36:26.350646 11373 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
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I0428 14:36:28.279121 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
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I0428 14:36:30.149745 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
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I0428 14:36:31.868955 11373 solver.cpp:330] Iteration 9486, Testing net (#0)
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I0428 14:36:31.868974 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:36:32.517933 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:36:36.212795 11373 solver.cpp:397] Test net output #0: accuracy = 0.52451
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I0428 14:36:36.212834 11373 solver.cpp:397] Test net output #1: loss = 2.96537 (* 1 = 2.96537 loss)
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I0428 14:36:37.913707 11373 solver.cpp:218] Iteration 9492 (1.03783 iter/s, 11.5626s/12 iters), loss = 0.099577
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I0428 14:36:37.913750 11373 solver.cpp:237] Train net output #0: loss = 0.099577 (* 1 = 0.099577 loss)
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I0428 14:36:37.913759 11373 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
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I0428 14:36:42.663194 11373 solver.cpp:218] Iteration 9504 (2.52672 iter/s, 4.74924s/12 iters), loss = 0.153754
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I0428 14:36:42.663234 11373 solver.cpp:237] Train net output #0: loss = 0.153754 (* 1 = 0.153754 loss)
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I0428 14:36:42.663242 11373 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
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I0428 14:36:44.053261 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:36:47.354039 11373 solver.cpp:218] Iteration 9516 (2.55831 iter/s, 4.6906s/12 iters), loss = 0.105351
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I0428 14:36:47.354097 11373 solver.cpp:237] Train net output #0: loss = 0.105351 (* 1 = 0.105351 loss)
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I0428 14:36:47.354112 11373 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
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I0428 14:36:52.058105 11373 solver.cpp:218] Iteration 9528 (2.55112 iter/s, 4.70381s/12 iters), loss = 0.103718
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I0428 14:36:52.058140 11373 solver.cpp:237] Train net output #0: loss = 0.103719 (* 1 = 0.103719 loss)
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I0428 14:36:52.058147 11373 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
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I0428 14:36:56.902988 11373 solver.cpp:218] Iteration 9540 (2.47696 iter/s, 4.84464s/12 iters), loss = 0.112034
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I0428 14:36:56.903122 11373 solver.cpp:237] Train net output #0: loss = 0.112034 (* 1 = 0.112034 loss)
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I0428 14:36:56.903132 11373 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
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I0428 14:37:01.577041 11373 solver.cpp:218] Iteration 9552 (2.56755 iter/s, 4.67372s/12 iters), loss = 0.0707022
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I0428 14:37:01.577080 11373 solver.cpp:237] Train net output #0: loss = 0.0707022 (* 1 = 0.0707022 loss)
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I0428 14:37:01.577090 11373 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
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I0428 14:37:06.347996 11373 solver.cpp:218] Iteration 9564 (2.51535 iter/s, 4.77071s/12 iters), loss = 0.158789
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I0428 14:37:06.348049 11373 solver.cpp:237] Train net output #0: loss = 0.158789 (* 1 = 0.158789 loss)
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I0428 14:37:06.348062 11373 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
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I0428 14:37:11.156733 11373 solver.cpp:218] Iteration 9576 (2.49559 iter/s, 4.80848s/12 iters), loss = 0.259829
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I0428 14:37:11.156782 11373 solver.cpp:237] Train net output #0: loss = 0.259829 (* 1 = 0.259829 loss)
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I0428 14:37:11.156798 11373 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
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I0428 14:37:15.408893 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
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I0428 14:37:16.658272 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
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I0428 14:37:17.671485 11373 solver.cpp:330] Iteration 9588, Testing net (#0)
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I0428 14:37:17.671509 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:37:18.240865 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:37:21.940475 11373 solver.cpp:397] Test net output #0: accuracy = 0.525735
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I0428 14:37:21.940531 11373 solver.cpp:397] Test net output #1: loss = 2.99919 (* 1 = 2.99919 loss)
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I0428 14:37:22.019268 11373 solver.cpp:218] Iteration 9588 (1.10476 iter/s, 10.862s/12 iters), loss = 0.108116
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I0428 14:37:22.019309 11373 solver.cpp:237] Train net output #0: loss = 0.108116 (* 1 = 0.108116 loss)
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I0428 14:37:22.019317 11373 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
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I0428 14:37:25.929605 11373 solver.cpp:218] Iteration 9600 (3.06896 iter/s, 3.91012s/12 iters), loss = 0.112979
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I0428 14:37:25.929679 11373 solver.cpp:237] Train net output #0: loss = 0.112979 (* 1 = 0.112979 loss)
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I0428 14:37:25.929699 11373 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
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I0428 14:37:29.406390 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:37:30.728193 11373 solver.cpp:218] Iteration 9612 (2.50088 iter/s, 4.79831s/12 iters), loss = 0.100779
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I0428 14:37:30.728232 11373 solver.cpp:237] Train net output #0: loss = 0.100779 (* 1 = 0.100779 loss)
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I0428 14:37:30.728241 11373 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
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I0428 14:37:35.481858 11373 solver.cpp:218] Iteration 9624 (2.5245 iter/s, 4.75342s/12 iters), loss = 0.109882
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I0428 14:37:35.481906 11373 solver.cpp:237] Train net output #0: loss = 0.109882 (* 1 = 0.109882 loss)
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I0428 14:37:35.481917 11373 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
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I0428 14:37:40.240617 11373 solver.cpp:218] Iteration 9636 (2.5218 iter/s, 4.7585s/12 iters), loss = 0.148403
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I0428 14:37:40.240672 11373 solver.cpp:237] Train net output #0: loss = 0.148403 (* 1 = 0.148403 loss)
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I0428 14:37:40.240686 11373 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
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I0428 14:37:44.924881 11373 solver.cpp:218] Iteration 9648 (2.56191 iter/s, 4.68401s/12 iters), loss = 0.117098
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I0428 14:37:44.924939 11373 solver.cpp:237] Train net output #0: loss = 0.117098 (* 1 = 0.117098 loss)
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I0428 14:37:44.924952 11373 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
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I0428 14:37:49.727712 11373 solver.cpp:218] Iteration 9660 (2.49866 iter/s, 4.80257s/12 iters), loss = 0.133136
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I0428 14:37:49.727769 11373 solver.cpp:237] Train net output #0: loss = 0.133136 (* 1 = 0.133136 loss)
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I0428 14:37:49.727782 11373 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
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I0428 14:37:54.455407 11373 solver.cpp:218] Iteration 9672 (2.53837 iter/s, 4.72744s/12 iters), loss = 0.0872357
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I0428 14:37:54.455461 11373 solver.cpp:237] Train net output #0: loss = 0.0872358 (* 1 = 0.0872358 loss)
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I0428 14:37:54.455472 11373 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
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I0428 14:37:59.184044 11373 solver.cpp:218] Iteration 9684 (2.53786 iter/s, 4.72839s/12 iters), loss = 0.0961587
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I0428 14:37:59.184096 11373 solver.cpp:237] Train net output #0: loss = 0.0961588 (* 1 = 0.0961588 loss)
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I0428 14:37:59.184108 11373 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
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I0428 14:38:01.106309 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
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I0428 14:38:03.847828 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
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I0428 14:38:07.288889 11373 solver.cpp:330] Iteration 9690, Testing net (#0)
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I0428 14:38:07.288908 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:38:07.882563 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:38:10.656766 11373 blocking_queue.cpp:49] Waiting for data
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I0428 14:38:11.638664 11373 solver.cpp:397] Test net output #0: accuracy = 0.520221
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I0428 14:38:11.638693 11373 solver.cpp:397] Test net output #1: loss = 3.0016 (* 1 = 3.0016 loss)
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I0428 14:38:13.202289 11373 solver.cpp:218] Iteration 9696 (0.856065 iter/s, 14.0176s/12 iters), loss = 0.113004
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I0428 14:38:13.202338 11373 solver.cpp:237] Train net output #0: loss = 0.113004 (* 1 = 0.113004 loss)
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I0428 14:38:13.202347 11373 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
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I0428 14:38:18.050437 11373 solver.cpp:218] Iteration 9708 (2.4753 iter/s, 4.84789s/12 iters), loss = 0.0761831
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I0428 14:38:18.050477 11373 solver.cpp:237] Train net output #0: loss = 0.0761831 (* 1 = 0.0761831 loss)
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I0428 14:38:18.050487 11373 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
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I0428 14:38:18.770339 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:38:22.842463 11373 solver.cpp:218] Iteration 9720 (2.50429 iter/s, 4.79177s/12 iters), loss = 0.131583
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I0428 14:38:22.842509 11373 solver.cpp:237] Train net output #0: loss = 0.131583 (* 1 = 0.131583 loss)
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I0428 14:38:22.842519 11373 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
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I0428 14:38:27.509419 11373 solver.cpp:218] Iteration 9732 (2.57141 iter/s, 4.66671s/12 iters), loss = 0.06557
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I0428 14:38:27.509471 11373 solver.cpp:237] Train net output #0: loss = 0.06557 (* 1 = 0.06557 loss)
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I0428 14:38:27.509483 11373 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
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I0428 14:38:32.412456 11373 solver.cpp:218] Iteration 9744 (2.44759 iter/s, 4.90278s/12 iters), loss = 0.106168
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I0428 14:38:32.420804 11373 solver.cpp:237] Train net output #0: loss = 0.106168 (* 1 = 0.106168 loss)
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I0428 14:38:32.420815 11373 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
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I0428 14:38:37.199196 11373 solver.cpp:218] Iteration 9756 (2.51141 iter/s, 4.77818s/12 iters), loss = 0.129483
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I0428 14:38:37.199241 11373 solver.cpp:237] Train net output #0: loss = 0.129483 (* 1 = 0.129483 loss)
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I0428 14:38:37.199250 11373 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
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I0428 14:38:42.005690 11373 solver.cpp:218] Iteration 9768 (2.49675 iter/s, 4.80625s/12 iters), loss = 0.11321
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I0428 14:38:42.005730 11373 solver.cpp:237] Train net output #0: loss = 0.11321 (* 1 = 0.11321 loss)
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I0428 14:38:42.005741 11373 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
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I0428 14:38:46.734380 11373 solver.cpp:218] Iteration 9780 (2.53783 iter/s, 4.72845s/12 iters), loss = 0.0659945
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I0428 14:38:46.734419 11373 solver.cpp:237] Train net output #0: loss = 0.0659946 (* 1 = 0.0659946 loss)
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I0428 14:38:46.734428 11373 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
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I0428 14:38:51.036994 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
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I0428 14:38:52.411332 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
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I0428 14:38:54.091714 11373 solver.cpp:330] Iteration 9792, Testing net (#0)
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I0428 14:38:54.091737 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:38:54.645690 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:38:58.472237 11373 solver.cpp:397] Test net output #0: accuracy = 0.519608
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I0428 14:38:58.472286 11373 solver.cpp:397] Test net output #1: loss = 2.99634 (* 1 = 2.99634 loss)
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I0428 14:38:58.551322 11373 solver.cpp:218] Iteration 9792 (1.01554 iter/s, 11.8164s/12 iters), loss = 0.0645566
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I0428 14:38:58.551367 11373 solver.cpp:237] Train net output #0: loss = 0.0645567 (* 1 = 0.0645567 loss)
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I0428 14:38:58.551376 11373 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
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I0428 14:39:02.519315 11373 solver.cpp:218] Iteration 9804 (3.02436 iter/s, 3.96778s/12 iters), loss = 0.0474683
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I0428 14:39:02.519402 11373 solver.cpp:237] Train net output #0: loss = 0.0474684 (* 1 = 0.0474684 loss)
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I0428 14:39:02.519412 11373 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
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I0428 14:39:05.276365 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:39:07.198244 11373 solver.cpp:218] Iteration 9816 (2.56485 iter/s, 4.67864s/12 iters), loss = 0.0776824
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I0428 14:39:07.198298 11373 solver.cpp:237] Train net output #0: loss = 0.0776825 (* 1 = 0.0776825 loss)
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I0428 14:39:07.198312 11373 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
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I0428 14:39:11.897960 11373 solver.cpp:218] Iteration 9828 (2.55348 iter/s, 4.69947s/12 iters), loss = 0.266753
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I0428 14:39:11.898000 11373 solver.cpp:237] Train net output #0: loss = 0.266753 (* 1 = 0.266753 loss)
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I0428 14:39:11.898010 11373 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
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I0428 14:39:16.631956 11373 solver.cpp:218] Iteration 9840 (2.53499 iter/s, 4.73376s/12 iters), loss = 0.147513
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I0428 14:39:16.631996 11373 solver.cpp:237] Train net output #0: loss = 0.147513 (* 1 = 0.147513 loss)
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I0428 14:39:16.632005 11373 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
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I0428 14:39:21.385993 11373 solver.cpp:218] Iteration 9852 (2.5243 iter/s, 4.75378s/12 iters), loss = 0.0833249
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I0428 14:39:21.386041 11373 solver.cpp:237] Train net output #0: loss = 0.083325 (* 1 = 0.083325 loss)
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I0428 14:39:21.386052 11373 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
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I0428 14:39:26.279047 11373 solver.cpp:218] Iteration 9864 (2.45259 iter/s, 4.89279s/12 iters), loss = 0.248342
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I0428 14:39:26.279101 11373 solver.cpp:237] Train net output #0: loss = 0.248342 (* 1 = 0.248342 loss)
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I0428 14:39:26.279114 11373 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
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I0428 14:39:31.003540 11373 solver.cpp:218] Iteration 9876 (2.5401 iter/s, 4.72423s/12 iters), loss = 0.0622629
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I0428 14:39:31.003587 11373 solver.cpp:237] Train net output #0: loss = 0.062263 (* 1 = 0.062263 loss)
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I0428 14:39:31.003597 11373 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
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I0428 14:39:35.663043 11373 solver.cpp:218] Iteration 9888 (2.57552 iter/s, 4.65925s/12 iters), loss = 0.073244
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I0428 14:39:35.663174 11373 solver.cpp:237] Train net output #0: loss = 0.0732441 (* 1 = 0.0732441 loss)
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I0428 14:39:35.663185 11373 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
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I0428 14:39:37.626152 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
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I0428 14:39:38.875115 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
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I0428 14:39:39.873876 11373 solver.cpp:330] Iteration 9894, Testing net (#0)
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I0428 14:39:39.873899 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:39:40.354804 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:39:44.189446 11373 solver.cpp:397] Test net output #0: accuracy = 0.530637
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I0428 14:39:44.189482 11373 solver.cpp:397] Test net output #1: loss = 3.10718 (* 1 = 3.10718 loss)
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I0428 14:39:45.917207 11373 solver.cpp:218] Iteration 9900 (1.17032 iter/s, 10.2536s/12 iters), loss = 0.151535
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I0428 14:39:45.917249 11373 solver.cpp:237] Train net output #0: loss = 0.151536 (* 1 = 0.151536 loss)
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I0428 14:39:45.917258 11373 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
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I0428 14:39:50.657896 11373 solver.cpp:218] Iteration 9912 (2.53141 iter/s, 4.74044s/12 iters), loss = 0.122663
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I0428 14:39:50.657940 11373 solver.cpp:237] Train net output #0: loss = 0.122663 (* 1 = 0.122663 loss)
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I0428 14:39:50.657949 11373 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
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I0428 14:39:50.758473 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:39:55.346974 11373 solver.cpp:218] Iteration 9924 (2.55927 iter/s, 4.68883s/12 iters), loss = 0.0787128
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I0428 14:39:55.347028 11373 solver.cpp:237] Train net output #0: loss = 0.0787129 (* 1 = 0.0787129 loss)
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I0428 14:39:55.347038 11373 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
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I0428 14:40:00.088922 11373 solver.cpp:218] Iteration 9936 (2.53075 iter/s, 4.74169s/12 iters), loss = 0.0718023
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I0428 14:40:00.088968 11373 solver.cpp:237] Train net output #0: loss = 0.0718024 (* 1 = 0.0718024 loss)
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I0428 14:40:00.088977 11373 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
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I0428 14:40:05.001353 11373 solver.cpp:218] Iteration 9948 (2.44291 iter/s, 4.91217s/12 iters), loss = 0.136435
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I0428 14:40:05.001400 11373 solver.cpp:237] Train net output #0: loss = 0.136435 (* 1 = 0.136435 loss)
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I0428 14:40:05.001411 11373 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
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I0428 14:40:09.709656 11373 solver.cpp:218] Iteration 9960 (2.54882 iter/s, 4.70806s/12 iters), loss = 0.0983343
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I0428 14:40:09.709764 11373 solver.cpp:237] Train net output #0: loss = 0.0983344 (* 1 = 0.0983344 loss)
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I0428 14:40:09.709774 11373 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
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I0428 14:40:14.456846 11373 solver.cpp:218] Iteration 9972 (2.52798 iter/s, 4.74688s/12 iters), loss = 0.0942689
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I0428 14:40:14.456895 11373 solver.cpp:237] Train net output #0: loss = 0.094269 (* 1 = 0.094269 loss)
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I0428 14:40:14.456910 11373 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
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I0428 14:40:19.145862 11373 solver.cpp:218] Iteration 9984 (2.55931 iter/s, 4.68877s/12 iters), loss = 0.0947112
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I0428 14:40:19.145905 11373 solver.cpp:237] Train net output #0: loss = 0.0947113 (* 1 = 0.0947113 loss)
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I0428 14:40:19.145915 11373 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
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I0428 14:40:23.449384 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
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I0428 14:40:25.042904 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
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I0428 14:40:28.024000 11373 solver.cpp:330] Iteration 9996, Testing net (#0)
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I0428 14:40:28.024024 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:40:28.456864 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:40:32.349170 11373 solver.cpp:397] Test net output #0: accuracy = 0.536765
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I0428 14:40:32.349200 11373 solver.cpp:397] Test net output #1: loss = 3.00492 (* 1 = 3.00492 loss)
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I0428 14:40:32.427955 11373 solver.cpp:218] Iteration 9996 (0.903513 iter/s, 13.2815s/12 iters), loss = 0.113864
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I0428 14:40:32.428020 11373 solver.cpp:237] Train net output #0: loss = 0.113864 (* 1 = 0.113864 loss)
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I0428 14:40:32.428031 11373 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
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I0428 14:40:36.467669 11373 solver.cpp:218] Iteration 10008 (2.97068 iter/s, 4.03948s/12 iters), loss = 0.154748
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I0428 14:40:36.467715 11373 solver.cpp:237] Train net output #0: loss = 0.154748 (* 1 = 0.154748 loss)
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I0428 14:40:36.467723 11373 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
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I0428 14:40:38.694676 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:40:41.282934 11373 solver.cpp:218] Iteration 10020 (2.4922 iter/s, 4.81501s/12 iters), loss = 0.129272
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I0428 14:40:41.283069 11373 solver.cpp:237] Train net output #0: loss = 0.129272 (* 1 = 0.129272 loss)
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I0428 14:40:41.283079 11373 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
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I0428 14:40:46.014272 11373 solver.cpp:218] Iteration 10032 (2.53646 iter/s, 4.731s/12 iters), loss = 0.108309
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I0428 14:40:46.014309 11373 solver.cpp:237] Train net output #0: loss = 0.108309 (* 1 = 0.108309 loss)
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I0428 14:40:46.014318 11373 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
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I0428 14:40:50.763060 11373 solver.cpp:218] Iteration 10044 (2.52709 iter/s, 4.74855s/12 iters), loss = 0.0871755
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I0428 14:40:50.763103 11373 solver.cpp:237] Train net output #0: loss = 0.0871755 (* 1 = 0.0871755 loss)
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I0428 14:40:50.763111 11373 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
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I0428 14:40:55.460868 11373 solver.cpp:218] Iteration 10056 (2.55451 iter/s, 4.69757s/12 iters), loss = 0.142678
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I0428 14:40:55.460909 11373 solver.cpp:237] Train net output #0: loss = 0.142678 (* 1 = 0.142678 loss)
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I0428 14:40:55.460918 11373 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
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I0428 14:41:00.240309 11373 solver.cpp:218] Iteration 10068 (2.51088 iter/s, 4.77919s/12 iters), loss = 0.0483597
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I0428 14:41:00.240352 11373 solver.cpp:237] Train net output #0: loss = 0.0483598 (* 1 = 0.0483598 loss)
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I0428 14:41:00.240361 11373 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
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I0428 14:41:04.924082 11373 solver.cpp:218] Iteration 10080 (2.56217 iter/s, 4.68352s/12 iters), loss = 0.102671
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I0428 14:41:04.924136 11373 solver.cpp:237] Train net output #0: loss = 0.102671 (* 1 = 0.102671 loss)
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I0428 14:41:04.924150 11373 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
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I0428 14:41:09.599383 11373 solver.cpp:218] Iteration 10092 (2.56682 iter/s, 4.67505s/12 iters), loss = 0.142168
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I0428 14:41:09.599428 11373 solver.cpp:237] Train net output #0: loss = 0.142168 (* 1 = 0.142168 loss)
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I0428 14:41:09.599440 11373 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
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I0428 14:41:11.525534 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
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I0428 14:41:16.211207 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
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I0428 14:41:19.707720 11373 solver.cpp:330] Iteration 10098, Testing net (#0)
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I0428 14:41:19.707741 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:41:20.093901 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:41:24.109380 11373 solver.cpp:397] Test net output #0: accuracy = 0.528799
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I0428 14:41:24.109413 11373 solver.cpp:397] Test net output #1: loss = 3.1017 (* 1 = 3.1017 loss)
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I0428 14:41:25.944227 11373 solver.cpp:218] Iteration 10104 (0.734208 iter/s, 16.3441s/12 iters), loss = 0.0645552
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I0428 14:41:25.944272 11373 solver.cpp:237] Train net output #0: loss = 0.0645552 (* 1 = 0.0645552 loss)
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I0428 14:41:25.944279 11373 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
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I0428 14:41:30.153694 11401 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:41:30.756573 11373 solver.cpp:218] Iteration 10116 (2.49371 iter/s, 4.8121s/12 iters), loss = 0.0595668
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I0428 14:41:30.756608 11373 solver.cpp:237] Train net output #0: loss = 0.0595669 (* 1 = 0.0595669 loss)
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I0428 14:41:30.756618 11373 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
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I0428 14:41:35.516286 11373 solver.cpp:218] Iteration 10128 (2.52129 iter/s, 4.75947s/12 iters), loss = 0.0559656
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I0428 14:41:35.516326 11373 solver.cpp:237] Train net output #0: loss = 0.0559657 (* 1 = 0.0559657 loss)
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I0428 14:41:35.516335 11373 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
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I0428 14:41:40.384531 11373 solver.cpp:218] Iteration 10140 (2.46509 iter/s, 4.86797s/12 iters), loss = 0.0585194
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I0428 14:41:40.384574 11373 solver.cpp:237] Train net output #0: loss = 0.0585195 (* 1 = 0.0585195 loss)
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I0428 14:41:40.384583 11373 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
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I0428 14:41:45.078173 11373 solver.cpp:218] Iteration 10152 (2.55678 iter/s, 4.6934s/12 iters), loss = 0.172883
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I0428 14:41:45.078315 11373 solver.cpp:237] Train net output #0: loss = 0.172883 (* 1 = 0.172883 loss)
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I0428 14:41:45.078325 11373 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
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I0428 14:41:49.858743 11373 solver.cpp:218] Iteration 10164 (2.51034 iter/s, 4.78022s/12 iters), loss = 0.112167
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I0428 14:41:49.858786 11373 solver.cpp:237] Train net output #0: loss = 0.112167 (* 1 = 0.112167 loss)
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I0428 14:41:49.858796 11373 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
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I0428 14:41:54.510249 11373 solver.cpp:218] Iteration 10176 (2.57994 iter/s, 4.65127s/12 iters), loss = 0.0470152
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I0428 14:41:54.510279 11373 solver.cpp:237] Train net output #0: loss = 0.0470153 (* 1 = 0.0470153 loss)
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I0428 14:41:54.510288 11373 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
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I0428 14:41:59.310741 11373 solver.cpp:218] Iteration 10188 (2.49987 iter/s, 4.80025s/12 iters), loss = 0.10272
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I0428 14:41:59.310783 11373 solver.cpp:237] Train net output #0: loss = 0.10272 (* 1 = 0.10272 loss)
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I0428 14:41:59.310792 11373 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
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I0428 14:42:03.619390 11373 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
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I0428 14:42:10.724897 11373 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
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I0428 14:42:17.160943 11373 solver.cpp:310] Iteration 10200, loss = 0.0605376
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I0428 14:42:17.161027 11373 solver.cpp:330] Iteration 10200, Testing net (#0)
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I0428 14:42:17.161033 11373 net.cpp:676] Ignoring source layer train-data
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I0428 14:42:17.532023 11427 data_layer.cpp:73] Restarting data prefetching from start.
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I0428 14:42:21.510584 11373 solver.cpp:397] Test net output #0: accuracy = 0.527574
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I0428 14:42:21.510615 11373 solver.cpp:397] Test net output #1: loss = 3.0429 (* 1 = 3.0429 loss)
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I0428 14:42:21.510622 11373 solver.cpp:315] Optimization Done.
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I0428 14:42:21.510625 11373 caffe.cpp:259] Optimization Done.
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