I0312 15:14:58.786797  3400 caffe.cpp:218] Using GPUs 0
I0312 15:14:58.802232  3400 caffe.cpp:223] GPU 0: GeForce GTX 1070
I0312 15:14:59.004849  3400 solver.cpp:44] Initializing solver from parameters: 
test_iter: 56
test_interval: 28
base_lr: 0.001
display: 20
max_iter: 5000
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize: 100000
snapshot: 4999
snapshot_prefix: "models/bvlc_alexnet/caffe_alexnet_sinatrain"
solver_mode: GPU
device_id: 0
net: "examples/alexnetfinetune/train_valsina.prototxt"
train_state {
  level: 0
  stage: ""
}
type: "SGD"
I0312 15:14:59.004992  3400 solver.cpp:87] Creating training net from net file: examples/alexnetfinetune/train_valsina.prototxt
I0312 15:14:59.005278  3400 net.cpp:296] The NetState phase (0) differed from the phase (1) specified by a rule in layer data
I0312 15:14:59.005290  3400 net.cpp:296] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0312 15:14:59.005439  3400 net.cpp:53] Initializing net from parameters: 
name: "AlexNet"
state {
  phase: TRAIN
  level: 0
  stage: ""
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 227
    mean_file: "examples/Mydataset_train_lmdb/mean_imagetest.binaryproto"
  }
  data_param {
    source: "examples/Mydataset_train_lmdb"
    batch_size: 256
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "conv1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "norm1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "conv2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "norm2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "xavier"
      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: "xavier"
      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: "xavier"
      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: 4
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc8"
  bottom: "label"
  top: "loss"
}
I0312 15:14:59.005563  3400 layer_factory.hpp:77] Creating layer data
I0312 15:14:59.005635  3400 db_lmdb.cpp:35] Opened lmdb examples/Mydataset_train_lmdb
I0312 15:14:59.005655  3400 net.cpp:86] Creating Layer data
I0312 15:14:59.005661  3400 net.cpp:382] data -> data
I0312 15:14:59.005674  3400 net.cpp:382] data -> label
I0312 15:14:59.005686  3400 data_transformer.cpp:25] Loading mean file from: examples/Mydataset_train_lmdb/mean_imagetest.binaryproto
I0312 15:14:59.008136  3400 data_layer.cpp:45] output data size: 256,3,227,227
I0312 15:14:59.186409  3400 net.cpp:124] Setting up data
I0312 15:14:59.186440  3400 net.cpp:131] Top shape: 256 3 227 227 (39574272)
I0312 15:14:59.186458  3400 net.cpp:131] Top shape: 256 (256)
I0312 15:14:59.186460  3400 net.cpp:139] Memory required for data: 158298112
I0312 15:14:59.186468  3400 layer_factory.hpp:77] Creating layer conv1
I0312 15:14:59.186497  3400 net.cpp:86] Creating Layer conv1
I0312 15:14:59.186501  3400 net.cpp:408] conv1 <- data
I0312 15:14:59.186511  3400 net.cpp:382] conv1 -> conv1
I0312 15:14:59.398895  3400 net.cpp:124] Setting up conv1
I0312 15:14:59.398912  3400 net.cpp:131] Top shape: 256 96 55 55 (74342400)
I0312 15:14:59.398914  3400 net.cpp:139] Memory required for data: 455667712
I0312 15:14:59.398947  3400 layer_factory.hpp:77] Creating layer relu1
I0312 15:14:59.398955  3400 net.cpp:86] Creating Layer relu1
I0312 15:14:59.398972  3400 net.cpp:408] relu1 <- conv1
I0312 15:14:59.398977  3400 net.cpp:369] relu1 -> conv1 (in-place)
I0312 15:14:59.399150  3400 net.cpp:124] Setting up relu1
I0312 15:14:59.399157  3400 net.cpp:131] Top shape: 256 96 55 55 (74342400)
I0312 15:14:59.399159  3400 net.cpp:139] Memory required for data: 753037312
I0312 15:14:59.399160  3400 layer_factory.hpp:77] Creating layer norm1
I0312 15:14:59.399168  3400 net.cpp:86] Creating Layer norm1
I0312 15:14:59.399183  3400 net.cpp:408] norm1 <- conv1
I0312 15:14:59.399186  3400 net.cpp:382] norm1 -> norm1
I0312 15:14:59.399544  3400 net.cpp:124] Setting up norm1
I0312 15:14:59.399552  3400 net.cpp:131] Top shape: 256 96 55 55 (74342400)
I0312 15:14:59.399554  3400 net.cpp:139] Memory required for data: 1050406912
I0312 15:14:59.399556  3400 layer_factory.hpp:77] Creating layer pool1
I0312 15:14:59.399575  3400 net.cpp:86] Creating Layer pool1
I0312 15:14:59.399605  3400 net.cpp:408] pool1 <- norm1
I0312 15:14:59.399607  3400 net.cpp:382] pool1 -> pool1
I0312 15:14:59.399665  3400 net.cpp:124] Setting up pool1
I0312 15:14:59.399669  3400 net.cpp:131] Top shape: 256 96 27 27 (17915904)
I0312 15:14:59.399672  3400 net.cpp:139] Memory required for data: 1122070528
I0312 15:14:59.399673  3400 layer_factory.hpp:77] Creating layer conv2
I0312 15:14:59.399680  3400 net.cpp:86] Creating Layer conv2
I0312 15:14:59.399696  3400 net.cpp:408] conv2 <- pool1
I0312 15:14:59.399699  3400 net.cpp:382] conv2 -> conv2
I0312 15:14:59.403538  3400 net.cpp:124] Setting up conv2
I0312 15:14:59.403551  3400 net.cpp:131] Top shape: 256 256 27 27 (47775744)
I0312 15:14:59.403554  3400 net.cpp:139] Memory required for data: 1313173504
I0312 15:14:59.403578  3400 layer_factory.hpp:77] Creating layer relu2
I0312 15:14:59.403583  3400 net.cpp:86] Creating Layer relu2
I0312 15:14:59.403585  3400 net.cpp:408] relu2 <- conv2
I0312 15:14:59.403589  3400 net.cpp:369] relu2 -> conv2 (in-place)
I0312 15:14:59.403933  3400 net.cpp:124] Setting up relu2
I0312 15:14:59.403940  3400 net.cpp:131] Top shape: 256 256 27 27 (47775744)
I0312 15:14:59.403942  3400 net.cpp:139] Memory required for data: 1504276480
I0312 15:14:59.403945  3400 layer_factory.hpp:77] Creating layer norm2
I0312 15:14:59.403949  3400 net.cpp:86] Creating Layer norm2
I0312 15:14:59.403965  3400 net.cpp:408] norm2 <- conv2
I0312 15:14:59.403969  3400 net.cpp:382] norm2 -> norm2
I0312 15:14:59.404144  3400 net.cpp:124] Setting up norm2
I0312 15:14:59.404148  3400 net.cpp:131] Top shape: 256 256 27 27 (47775744)
I0312 15:14:59.404150  3400 net.cpp:139] Memory required for data: 1695379456
I0312 15:14:59.404152  3400 layer_factory.hpp:77] Creating layer pool2
I0312 15:14:59.404157  3400 net.cpp:86] Creating Layer pool2
I0312 15:14:59.404160  3400 net.cpp:408] pool2 <- norm2
I0312 15:14:59.404176  3400 net.cpp:382] pool2 -> pool2
I0312 15:14:59.404214  3400 net.cpp:124] Setting up pool2
I0312 15:14:59.404219  3400 net.cpp:131] Top shape: 256 256 13 13 (11075584)
I0312 15:14:59.404222  3400 net.cpp:139] Memory required for data: 1739681792
I0312 15:14:59.404223  3400 layer_factory.hpp:77] Creating layer conv3
I0312 15:14:59.404229  3400 net.cpp:86] Creating Layer conv3
I0312 15:14:59.404232  3400 net.cpp:408] conv3 <- pool2
I0312 15:14:59.404235  3400 net.cpp:382] conv3 -> conv3
I0312 15:14:59.413219  3400 net.cpp:124] Setting up conv3
I0312 15:14:59.413250  3400 net.cpp:131] Top shape: 256 384 13 13 (16613376)
I0312 15:14:59.413254  3400 net.cpp:139] Memory required for data: 1806135296
I0312 15:14:59.413264  3400 layer_factory.hpp:77] Creating layer relu3
I0312 15:14:59.413285  3400 net.cpp:86] Creating Layer relu3
I0312 15:14:59.413286  3400 net.cpp:408] relu3 <- conv3
I0312 15:14:59.413291  3400 net.cpp:369] relu3 -> conv3 (in-place)
I0312 15:14:59.413511  3400 net.cpp:124] Setting up relu3
I0312 15:14:59.413516  3400 net.cpp:131] Top shape: 256 384 13 13 (16613376)
I0312 15:14:59.413532  3400 net.cpp:139] Memory required for data: 1872588800
I0312 15:14:59.413533  3400 layer_factory.hpp:77] Creating layer conv4
I0312 15:14:59.413542  3400 net.cpp:86] Creating Layer conv4
I0312 15:14:59.413543  3400 net.cpp:408] conv4 <- conv3
I0312 15:14:59.413563  3400 net.cpp:382] conv4 -> conv4
I0312 15:14:59.420562  3400 net.cpp:124] Setting up conv4
I0312 15:14:59.420594  3400 net.cpp:131] Top shape: 256 384 13 13 (16613376)
I0312 15:14:59.420596  3400 net.cpp:139] Memory required for data: 1939042304
I0312 15:14:59.420603  3400 layer_factory.hpp:77] Creating layer relu4
I0312 15:14:59.420610  3400 net.cpp:86] Creating Layer relu4
I0312 15:14:59.420614  3400 net.cpp:408] relu4 <- conv4
I0312 15:14:59.420631  3400 net.cpp:369] relu4 -> conv4 (in-place)
I0312 15:14:59.420794  3400 net.cpp:124] Setting up relu4
I0312 15:14:59.420799  3400 net.cpp:131] Top shape: 256 384 13 13 (16613376)
I0312 15:14:59.420815  3400 net.cpp:139] Memory required for data: 2005495808
I0312 15:14:59.420817  3400 layer_factory.hpp:77] Creating layer conv5
I0312 15:14:59.420851  3400 net.cpp:86] Creating Layer conv5
I0312 15:14:59.420855  3400 net.cpp:408] conv5 <- conv4
I0312 15:14:59.420861  3400 net.cpp:382] conv5 -> conv5
I0312 15:14:59.425384  3400 net.cpp:124] Setting up conv5
I0312 15:14:59.425401  3400 net.cpp:131] Top shape: 256 256 13 13 (11075584)
I0312 15:14:59.425403  3400 net.cpp:139] Memory required for data: 2049798144
I0312 15:14:59.425429  3400 layer_factory.hpp:77] Creating layer relu5
I0312 15:14:59.425436  3400 net.cpp:86] Creating Layer relu5
I0312 15:14:59.425438  3400 net.cpp:408] relu5 <- conv5
I0312 15:14:59.425443  3400 net.cpp:369] relu5 -> conv5 (in-place)
I0312 15:14:59.425593  3400 net.cpp:124] Setting up relu5
I0312 15:14:59.425601  3400 net.cpp:131] Top shape: 256 256 13 13 (11075584)
I0312 15:14:59.425602  3400 net.cpp:139] Memory required for data: 2094100480
I0312 15:14:59.425604  3400 layer_factory.hpp:77] Creating layer pool5
I0312 15:14:59.425623  3400 net.cpp:86] Creating Layer pool5
I0312 15:14:59.425626  3400 net.cpp:408] pool5 <- conv5
I0312 15:14:59.425628  3400 net.cpp:382] pool5 -> pool5
I0312 15:14:59.425691  3400 net.cpp:124] Setting up pool5
I0312 15:14:59.425709  3400 net.cpp:131] Top shape: 256 256 6 6 (2359296)
I0312 15:14:59.425711  3400 net.cpp:139] Memory required for data: 2103537664
I0312 15:14:59.425714  3400 layer_factory.hpp:77] Creating layer fc6
I0312 15:14:59.425735  3400 net.cpp:86] Creating Layer fc6
I0312 15:14:59.425737  3400 net.cpp:408] fc6 <- pool5
I0312 15:14:59.425741  3400 net.cpp:382] fc6 -> fc6
I0312 15:14:59.609557  3400 net.cpp:124] Setting up fc6
I0312 15:14:59.609572  3400 net.cpp:131] Top shape: 256 4096 (1048576)
I0312 15:14:59.609575  3400 net.cpp:139] Memory required for data: 2107731968
I0312 15:14:59.609596  3400 layer_factory.hpp:77] Creating layer relu6
I0312 15:14:59.609601  3400 net.cpp:86] Creating Layer relu6
I0312 15:14:59.609604  3400 net.cpp:408] relu6 <- fc6
I0312 15:14:59.609608  3400 net.cpp:369] relu6 -> fc6 (in-place)
I0312 15:14:59.609860  3400 net.cpp:124] Setting up relu6
I0312 15:14:59.609865  3400 net.cpp:131] Top shape: 256 4096 (1048576)
I0312 15:14:59.609868  3400 net.cpp:139] Memory required for data: 2111926272
I0312 15:14:59.609869  3400 layer_factory.hpp:77] Creating layer drop6
I0312 15:14:59.609874  3400 net.cpp:86] Creating Layer drop6
I0312 15:14:59.609876  3400 net.cpp:408] drop6 <- fc6
I0312 15:14:59.609894  3400 net.cpp:369] drop6 -> fc6 (in-place)
I0312 15:14:59.609930  3400 net.cpp:124] Setting up drop6
I0312 15:14:59.609948  3400 net.cpp:131] Top shape: 256 4096 (1048576)
I0312 15:14:59.609951  3400 net.cpp:139] Memory required for data: 2116120576
I0312 15:14:59.609952  3400 layer_factory.hpp:77] Creating layer fc7
I0312 15:14:59.609969  3400 net.cpp:86] Creating Layer fc7
I0312 15:14:59.609972  3400 net.cpp:408] fc7 <- fc6
I0312 15:14:59.609975  3400 net.cpp:382] fc7 -> fc7
I0312 15:14:59.688920  3400 net.cpp:124] Setting up fc7
I0312 15:14:59.688936  3400 net.cpp:131] Top shape: 256 4096 (1048576)
I0312 15:14:59.688940  3400 net.cpp:139] Memory required for data: 2120314880
I0312 15:14:59.688959  3400 layer_factory.hpp:77] Creating layer relu7
I0312 15:14:59.688966  3400 net.cpp:86] Creating Layer relu7
I0312 15:14:59.688968  3400 net.cpp:408] relu7 <- fc7
I0312 15:14:59.688973  3400 net.cpp:369] relu7 -> fc7 (in-place)
I0312 15:14:59.689429  3400 net.cpp:124] Setting up relu7
I0312 15:14:59.689437  3400 net.cpp:131] Top shape: 256 4096 (1048576)
I0312 15:14:59.689440  3400 net.cpp:139] Memory required for data: 2124509184
I0312 15:14:59.689441  3400 layer_factory.hpp:77] Creating layer drop7
I0312 15:14:59.689462  3400 net.cpp:86] Creating Layer drop7
I0312 15:14:59.689465  3400 net.cpp:408] drop7 <- fc7
I0312 15:14:59.689467  3400 net.cpp:369] drop7 -> fc7 (in-place)
I0312 15:14:59.689502  3400 net.cpp:124] Setting up drop7
I0312 15:14:59.689519  3400 net.cpp:131] Top shape: 256 4096 (1048576)
I0312 15:14:59.689522  3400 net.cpp:139] Memory required for data: 2128703488
I0312 15:14:59.689522  3400 layer_factory.hpp:77] Creating layer fc8
I0312 15:14:59.689553  3400 net.cpp:86] Creating Layer fc8
I0312 15:14:59.689554  3400 net.cpp:408] fc8 <- fc7
I0312 15:14:59.689558  3400 net.cpp:382] fc8 -> fc8
I0312 15:14:59.690343  3400 net.cpp:124] Setting up fc8
I0312 15:14:59.690353  3400 net.cpp:131] Top shape: 256 4 (1024)
I0312 15:14:59.690356  3400 net.cpp:139] Memory required for data: 2128707584
I0312 15:14:59.690359  3400 layer_factory.hpp:77] Creating layer loss
I0312 15:14:59.690363  3400 net.cpp:86] Creating Layer loss
I0312 15:14:59.690379  3400 net.cpp:408] loss <- fc8
I0312 15:14:59.690382  3400 net.cpp:408] loss <- label
I0312 15:14:59.690387  3400 net.cpp:382] loss -> loss
I0312 15:14:59.690409  3400 layer_factory.hpp:77] Creating layer loss
I0312 15:14:59.690651  3400 net.cpp:124] Setting up loss
I0312 15:14:59.690657  3400 net.cpp:131] Top shape: (1)
I0312 15:14:59.690659  3400 net.cpp:134]     with loss weight 1
I0312 15:14:59.690685  3400 net.cpp:139] Memory required for data: 2128707588
I0312 15:14:59.690686  3400 net.cpp:200] loss needs backward computation.
I0312 15:14:59.690691  3400 net.cpp:200] fc8 needs backward computation.
I0312 15:14:59.690692  3400 net.cpp:200] drop7 needs backward computation.
I0312 15:14:59.690695  3400 net.cpp:200] relu7 needs backward computation.
I0312 15:14:59.690696  3400 net.cpp:200] fc7 needs backward computation.
I0312 15:14:59.690698  3400 net.cpp:200] drop6 needs backward computation.
I0312 15:14:59.690699  3400 net.cpp:200] relu6 needs backward computation.
I0312 15:14:59.690716  3400 net.cpp:200] fc6 needs backward computation.
I0312 15:14:59.690718  3400 net.cpp:200] pool5 needs backward computation.
I0312 15:14:59.690722  3400 net.cpp:200] relu5 needs backward computation.
I0312 15:14:59.690738  3400 net.cpp:200] conv5 needs backward computation.
I0312 15:14:59.690740  3400 net.cpp:200] relu4 needs backward computation.
I0312 15:14:59.690742  3400 net.cpp:200] conv4 needs backward computation.
I0312 15:14:59.690745  3400 net.cpp:200] relu3 needs backward computation.
I0312 15:14:59.690747  3400 net.cpp:200] conv3 needs backward computation.
I0312 15:14:59.690749  3400 net.cpp:200] pool2 needs backward computation.
I0312 15:14:59.690752  3400 net.cpp:200] norm2 needs backward computation.
I0312 15:14:59.690754  3400 net.cpp:200] relu2 needs backward computation.
I0312 15:14:59.690757  3400 net.cpp:200] conv2 needs backward computation.
I0312 15:14:59.690758  3400 net.cpp:200] pool1 needs backward computation.
I0312 15:14:59.690760  3400 net.cpp:200] norm1 needs backward computation.
I0312 15:14:59.690763  3400 net.cpp:200] relu1 needs backward computation.
I0312 15:14:59.690765  3400 net.cpp:200] conv1 needs backward computation.
I0312 15:14:59.690768  3400 net.cpp:202] data does not need backward computation.
I0312 15:14:59.690770  3400 net.cpp:244] This network produces output loss
I0312 15:14:59.690780  3400 net.cpp:257] Network initialization done.
I0312 15:14:59.691009  3400 solver.cpp:173] Creating test net (#0) specified by net file: examples/alexnetfinetune/train_valsina.prototxt
I0312 15:14:59.691061  3400 net.cpp:296] The NetState phase (1) differed from the phase (0) specified by a rule in layer data
I0312 15:14:59.691197  3400 net.cpp:53] Initializing net from parameters: 
name: "AlexNet"
state {
  phase: TEST
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mirror: false
    crop_size: 227
    mean_file: "examples/Mydataset_test_lmdb/mean_imagetest.binaryproto"
  }
  data_param {
    source: "examples/Mydataset_test_lmdb"
    batch_size: 50
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "conv1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "norm1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "conv2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "norm2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "xavier"
      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: "xavier"
      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: "xavier"
      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: 4
    weight_filler {
      type: "xavier"
      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"
}
I0312 15:14:59.691308  3400 layer_factory.hpp:77] Creating layer data
I0312 15:14:59.691346  3400 db_lmdb.cpp:35] Opened lmdb examples/Mydataset_test_lmdb
I0312 15:14:59.691357  3400 net.cpp:86] Creating Layer data
I0312 15:14:59.691364  3400 net.cpp:382] data -> data
I0312 15:14:59.691368  3400 net.cpp:382] data -> label
I0312 15:14:59.691373  3400 data_transformer.cpp:25] Loading mean file from: examples/Mydataset_test_lmdb/mean_imagetest.binaryproto
I0312 15:14:59.692934  3400 data_layer.cpp:45] output data size: 50,3,227,227
I0312 15:14:59.731097  3400 net.cpp:124] Setting up data
I0312 15:14:59.731127  3400 net.cpp:131] Top shape: 50 3 227 227 (7729350)
I0312 15:14:59.731145  3400 net.cpp:131] Top shape: 50 (50)
I0312 15:14:59.731147  3400 net.cpp:139] Memory required for data: 30917600
I0312 15:14:59.731151  3400 layer_factory.hpp:77] Creating layer label_data_1_split
I0312 15:14:59.731160  3400 net.cpp:86] Creating Layer label_data_1_split
I0312 15:14:59.731163  3400 net.cpp:408] label_data_1_split <- label
I0312 15:14:59.731181  3400 net.cpp:382] label_data_1_split -> label_data_1_split_0
I0312 15:14:59.731189  3400 net.cpp:382] label_data_1_split -> label_data_1_split_1
I0312 15:14:59.731282  3400 net.cpp:124] Setting up label_data_1_split
I0312 15:14:59.731287  3400 net.cpp:131] Top shape: 50 (50)
I0312 15:14:59.731303  3400 net.cpp:131] Top shape: 50 (50)
I0312 15:14:59.731305  3400 net.cpp:139] Memory required for data: 30918000
I0312 15:14:59.731308  3400 layer_factory.hpp:77] Creating layer conv1
I0312 15:14:59.731317  3400 net.cpp:86] Creating Layer conv1
I0312 15:14:59.731318  3400 net.cpp:408] conv1 <- data
I0312 15:14:59.731323  3400 net.cpp:382] conv1 -> conv1
I0312 15:14:59.734802  3400 net.cpp:124] Setting up conv1
I0312 15:14:59.734823  3400 net.cpp:131] Top shape: 50 96 55 55 (14520000)
I0312 15:14:59.734827  3400 net.cpp:139] Memory required for data: 88998000
I0312 15:14:59.734833  3400 layer_factory.hpp:77] Creating layer relu1
I0312 15:14:59.734853  3400 net.cpp:86] Creating Layer relu1
I0312 15:14:59.734854  3400 net.cpp:408] relu1 <- conv1
I0312 15:14:59.734858  3400 net.cpp:369] relu1 -> conv1 (in-place)
I0312 15:14:59.735003  3400 net.cpp:124] Setting up relu1
I0312 15:14:59.735009  3400 net.cpp:131] Top shape: 50 96 55 55 (14520000)
I0312 15:14:59.735025  3400 net.cpp:139] Memory required for data: 147078000
I0312 15:14:59.735028  3400 layer_factory.hpp:77] Creating layer norm1
I0312 15:14:59.735033  3400 net.cpp:86] Creating Layer norm1
I0312 15:14:59.735035  3400 net.cpp:408] norm1 <- conv1
I0312 15:14:59.735051  3400 net.cpp:382] norm1 -> norm1
I0312 15:14:59.735203  3400 net.cpp:124] Setting up norm1
I0312 15:14:59.735208  3400 net.cpp:131] Top shape: 50 96 55 55 (14520000)
I0312 15:14:59.735225  3400 net.cpp:139] Memory required for data: 205158000
I0312 15:14:59.735226  3400 layer_factory.hpp:77] Creating layer pool1
I0312 15:14:59.735230  3400 net.cpp:86] Creating Layer pool1
I0312 15:14:59.735232  3400 net.cpp:408] pool1 <- norm1
I0312 15:14:59.735235  3400 net.cpp:382] pool1 -> pool1
I0312 15:14:59.735262  3400 net.cpp:124] Setting up pool1
I0312 15:14:59.735266  3400 net.cpp:131] Top shape: 50 96 27 27 (3499200)
I0312 15:14:59.735268  3400 net.cpp:139] Memory required for data: 219154800
I0312 15:14:59.735270  3400 layer_factory.hpp:77] Creating layer conv2
I0312 15:14:59.735275  3400 net.cpp:86] Creating Layer conv2
I0312 15:14:59.735277  3400 net.cpp:408] conv2 <- pool1
I0312 15:14:59.735280  3400 net.cpp:382] conv2 -> conv2
I0312 15:14:59.738873  3400 net.cpp:124] Setting up conv2
I0312 15:14:59.738884  3400 net.cpp:131] Top shape: 50 256 27 27 (9331200)
I0312 15:14:59.738900  3400 net.cpp:139] Memory required for data: 256479600
I0312 15:14:59.738909  3400 layer_factory.hpp:77] Creating layer relu2
I0312 15:14:59.738914  3400 net.cpp:86] Creating Layer relu2
I0312 15:14:59.738930  3400 net.cpp:408] relu2 <- conv2
I0312 15:14:59.738934  3400 net.cpp:369] relu2 -> conv2 (in-place)
I0312 15:14:59.739276  3400 net.cpp:124] Setting up relu2
I0312 15:14:59.739284  3400 net.cpp:131] Top shape: 50 256 27 27 (9331200)
I0312 15:14:59.739300  3400 net.cpp:139] Memory required for data: 293804400
I0312 15:14:59.739302  3400 layer_factory.hpp:77] Creating layer norm2
I0312 15:14:59.739310  3400 net.cpp:86] Creating Layer norm2
I0312 15:14:59.739325  3400 net.cpp:408] norm2 <- conv2
I0312 15:14:59.739328  3400 net.cpp:382] norm2 -> norm2
I0312 15:14:59.739482  3400 net.cpp:124] Setting up norm2
I0312 15:14:59.739488  3400 net.cpp:131] Top shape: 50 256 27 27 (9331200)
I0312 15:14:59.739504  3400 net.cpp:139] Memory required for data: 331129200
I0312 15:14:59.739506  3400 layer_factory.hpp:77] Creating layer pool2
I0312 15:14:59.739511  3400 net.cpp:86] Creating Layer pool2
I0312 15:14:59.739512  3400 net.cpp:408] pool2 <- norm2
I0312 15:14:59.739516  3400 net.cpp:382] pool2 -> pool2
I0312 15:14:59.739542  3400 net.cpp:124] Setting up pool2
I0312 15:14:59.739559  3400 net.cpp:131] Top shape: 50 256 13 13 (2163200)
I0312 15:14:59.739560  3400 net.cpp:139] Memory required for data: 339782000
I0312 15:14:59.739562  3400 layer_factory.hpp:77] Creating layer conv3
I0312 15:14:59.739583  3400 net.cpp:86] Creating Layer conv3
I0312 15:14:59.739584  3400 net.cpp:408] conv3 <- pool2
I0312 15:14:59.739588  3400 net.cpp:382] conv3 -> conv3
I0312 15:14:59.748337  3400 net.cpp:124] Setting up conv3
I0312 15:14:59.748355  3400 net.cpp:131] Top shape: 50 384 13 13 (3244800)
I0312 15:14:59.748358  3400 net.cpp:139] Memory required for data: 352761200
I0312 15:14:59.748370  3400 layer_factory.hpp:77] Creating layer relu3
I0312 15:14:59.748378  3400 net.cpp:86] Creating Layer relu3
I0312 15:14:59.748401  3400 net.cpp:408] relu3 <- conv3
I0312 15:14:59.748407  3400 net.cpp:369] relu3 -> conv3 (in-place)
I0312 15:14:59.748574  3400 net.cpp:124] Setting up relu3
I0312 15:14:59.748580  3400 net.cpp:131] Top shape: 50 384 13 13 (3244800)
I0312 15:14:59.748584  3400 net.cpp:139] Memory required for data: 365740400
I0312 15:14:59.748585  3400 layer_factory.hpp:77] Creating layer conv4
I0312 15:14:59.748592  3400 net.cpp:86] Creating Layer conv4
I0312 15:14:59.748595  3400 net.cpp:408] conv4 <- conv3
I0312 15:14:59.748600  3400 net.cpp:382] conv4 -> conv4
I0312 15:14:59.757342  3400 net.cpp:124] Setting up conv4
I0312 15:14:59.757372  3400 net.cpp:131] Top shape: 50 384 13 13 (3244800)
I0312 15:14:59.757376  3400 net.cpp:139] Memory required for data: 378719600
I0312 15:14:59.757382  3400 layer_factory.hpp:77] Creating layer relu4
I0312 15:14:59.757403  3400 net.cpp:86] Creating Layer relu4
I0312 15:14:59.757406  3400 net.cpp:408] relu4 <- conv4
I0312 15:14:59.757412  3400 net.cpp:369] relu4 -> conv4 (in-place)
I0312 15:14:59.757581  3400 net.cpp:124] Setting up relu4
I0312 15:14:59.757587  3400 net.cpp:131] Top shape: 50 384 13 13 (3244800)
I0312 15:14:59.757603  3400 net.cpp:139] Memory required for data: 391698800
I0312 15:14:59.757606  3400 layer_factory.hpp:77] Creating layer conv5
I0312 15:14:59.757612  3400 net.cpp:86] Creating Layer conv5
I0312 15:14:59.757628  3400 net.cpp:408] conv5 <- conv4
I0312 15:14:59.757632  3400 net.cpp:382] conv5 -> conv5
I0312 15:14:59.761788  3400 net.cpp:124] Setting up conv5
I0312 15:14:59.761816  3400 net.cpp:131] Top shape: 50 256 13 13 (2163200)
I0312 15:14:59.761818  3400 net.cpp:139] Memory required for data: 400351600
I0312 15:14:59.761828  3400 layer_factory.hpp:77] Creating layer relu5
I0312 15:14:59.761849  3400 net.cpp:86] Creating Layer relu5
I0312 15:14:59.761852  3400 net.cpp:408] relu5 <- conv5
I0312 15:14:59.761855  3400 net.cpp:369] relu5 -> conv5 (in-place)
I0312 15:14:59.762032  3400 net.cpp:124] Setting up relu5
I0312 15:14:59.762037  3400 net.cpp:131] Top shape: 50 256 13 13 (2163200)
I0312 15:14:59.762054  3400 net.cpp:139] Memory required for data: 409004400
I0312 15:14:59.762056  3400 layer_factory.hpp:77] Creating layer pool5
I0312 15:14:59.762063  3400 net.cpp:86] Creating Layer pool5
I0312 15:14:59.762079  3400 net.cpp:408] pool5 <- conv5
I0312 15:14:59.762100  3400 net.cpp:382] pool5 -> pool5
I0312 15:14:59.762141  3400 net.cpp:124] Setting up pool5
I0312 15:14:59.762146  3400 net.cpp:131] Top shape: 50 256 6 6 (460800)
I0312 15:14:59.762148  3400 net.cpp:139] Memory required for data: 410847600
I0312 15:14:59.762151  3400 layer_factory.hpp:77] Creating layer fc6
I0312 15:14:59.762156  3400 net.cpp:86] Creating Layer fc6
I0312 15:14:59.762157  3400 net.cpp:408] fc6 <- pool5
I0312 15:14:59.762161  3400 net.cpp:382] fc6 -> fc6
I0312 15:14:59.942030  3400 net.cpp:124] Setting up fc6
I0312 15:14:59.942045  3400 net.cpp:131] Top shape: 50 4096 (204800)
I0312 15:14:59.942047  3400 net.cpp:139] Memory required for data: 411666800
I0312 15:14:59.942054  3400 layer_factory.hpp:77] Creating layer relu6
I0312 15:14:59.942072  3400 net.cpp:86] Creating Layer relu6
I0312 15:14:59.942075  3400 net.cpp:408] relu6 <- fc6
I0312 15:14:59.942080  3400 net.cpp:369] relu6 -> fc6 (in-place)
I0312 15:14:59.942311  3400 net.cpp:124] Setting up relu6
I0312 15:14:59.942315  3400 net.cpp:131] Top shape: 50 4096 (204800)
I0312 15:14:59.942317  3400 net.cpp:139] Memory required for data: 412486000
I0312 15:14:59.942319  3400 layer_factory.hpp:77] Creating layer drop6
I0312 15:14:59.942323  3400 net.cpp:86] Creating Layer drop6
I0312 15:14:59.942325  3400 net.cpp:408] drop6 <- fc6
I0312 15:14:59.942342  3400 net.cpp:369] drop6 -> fc6 (in-place)
I0312 15:14:59.942391  3400 net.cpp:124] Setting up drop6
I0312 15:14:59.942395  3400 net.cpp:131] Top shape: 50 4096 (204800)
I0312 15:14:59.942397  3400 net.cpp:139] Memory required for data: 413305200
I0312 15:14:59.942399  3400 layer_factory.hpp:77] Creating layer fc7
I0312 15:14:59.942404  3400 net.cpp:86] Creating Layer fc7
I0312 15:14:59.942405  3400 net.cpp:408] fc7 <- fc6
I0312 15:14:59.942409  3400 net.cpp:382] fc7 -> fc7
I0312 15:15:00.021935  3400 net.cpp:124] Setting up fc7
I0312 15:15:00.021950  3400 net.cpp:131] Top shape: 50 4096 (204800)
I0312 15:15:00.021952  3400 net.cpp:139] Memory required for data: 414124400
I0312 15:15:00.021958  3400 layer_factory.hpp:77] Creating layer relu7
I0312 15:15:00.021980  3400 net.cpp:86] Creating Layer relu7
I0312 15:15:00.021981  3400 net.cpp:408] relu7 <- fc7
I0312 15:15:00.021986  3400 net.cpp:369] relu7 -> fc7 (in-place)
I0312 15:15:00.022466  3400 net.cpp:124] Setting up relu7
I0312 15:15:00.022475  3400 net.cpp:131] Top shape: 50 4096 (204800)
I0312 15:15:00.022476  3400 net.cpp:139] Memory required for data: 414943600
I0312 15:15:00.022478  3400 layer_factory.hpp:77] Creating layer drop7
I0312 15:15:00.022482  3400 net.cpp:86] Creating Layer drop7
I0312 15:15:00.022498  3400 net.cpp:408] drop7 <- fc7
I0312 15:15:00.022501  3400 net.cpp:369] drop7 -> fc7 (in-place)
I0312 15:15:00.022554  3400 net.cpp:124] Setting up drop7
I0312 15:15:00.022570  3400 net.cpp:131] Top shape: 50 4096 (204800)
I0312 15:15:00.022572  3400 net.cpp:139] Memory required for data: 415762800
I0312 15:15:00.022574  3400 layer_factory.hpp:77] Creating layer fc8
I0312 15:15:00.022578  3400 net.cpp:86] Creating Layer fc8
I0312 15:15:00.022580  3400 net.cpp:408] fc8 <- fc7
I0312 15:15:00.022583  3400 net.cpp:382] fc8 -> fc8
I0312 15:15:00.022816  3400 net.cpp:124] Setting up fc8
I0312 15:15:00.022821  3400 net.cpp:131] Top shape: 50 4 (200)
I0312 15:15:00.022822  3400 net.cpp:139] Memory required for data: 415763600
I0312 15:15:00.022826  3400 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0312 15:15:00.022830  3400 net.cpp:86] Creating Layer fc8_fc8_0_split
I0312 15:15:00.022831  3400 net.cpp:408] fc8_fc8_0_split <- fc8
I0312 15:15:00.022835  3400 net.cpp:382] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0312 15:15:00.022851  3400 net.cpp:382] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0312 15:15:00.022925  3400 net.cpp:124] Setting up fc8_fc8_0_split
I0312 15:15:00.022929  3400 net.cpp:131] Top shape: 50 4 (200)
I0312 15:15:00.022943  3400 net.cpp:131] Top shape: 50 4 (200)
I0312 15:15:00.022945  3400 net.cpp:139] Memory required for data: 415765200
I0312 15:15:00.022948  3400 layer_factory.hpp:77] Creating layer accuracy
I0312 15:15:00.022989  3400 net.cpp:86] Creating Layer accuracy
I0312 15:15:00.022991  3400 net.cpp:408] accuracy <- fc8_fc8_0_split_0
I0312 15:15:00.022994  3400 net.cpp:408] accuracy <- label_data_1_split_0
I0312 15:15:00.022999  3400 net.cpp:382] accuracy -> accuracy
I0312 15:15:00.023017  3400 net.cpp:124] Setting up accuracy
I0312 15:15:00.023020  3400 net.cpp:131] Top shape: (1)
I0312 15:15:00.023036  3400 net.cpp:139] Memory required for data: 415765204
I0312 15:15:00.023038  3400 layer_factory.hpp:77] Creating layer loss
I0312 15:15:00.023041  3400 net.cpp:86] Creating Layer loss
I0312 15:15:00.023043  3400 net.cpp:408] loss <- fc8_fc8_0_split_1
I0312 15:15:00.023046  3400 net.cpp:408] loss <- label_data_1_split_1
I0312 15:15:00.023049  3400 net.cpp:382] loss -> loss
I0312 15:15:00.023053  3400 layer_factory.hpp:77] Creating layer loss
I0312 15:15:00.023260  3400 net.cpp:124] Setting up loss
I0312 15:15:00.023267  3400 net.cpp:131] Top shape: (1)
I0312 15:15:00.023268  3400 net.cpp:134]     with loss weight 1
I0312 15:15:00.023274  3400 net.cpp:139] Memory required for data: 415765208
I0312 15:15:00.023277  3400 net.cpp:200] loss needs backward computation.
I0312 15:15:00.023293  3400 net.cpp:202] accuracy does not need backward computation.
I0312 15:15:00.023296  3400 net.cpp:200] fc8_fc8_0_split needs backward computation.
I0312 15:15:00.023298  3400 net.cpp:200] fc8 needs backward computation.
I0312 15:15:00.023299  3400 net.cpp:200] drop7 needs backward computation.
I0312 15:15:00.023301  3400 net.cpp:200] relu7 needs backward computation.
I0312 15:15:00.023317  3400 net.cpp:200] fc7 needs backward computation.
I0312 15:15:00.023319  3400 net.cpp:200] drop6 needs backward computation.
I0312 15:15:00.023321  3400 net.cpp:200] relu6 needs backward computation.
I0312 15:15:00.023324  3400 net.cpp:200] fc6 needs backward computation.
I0312 15:15:00.023326  3400 net.cpp:200] pool5 needs backward computation.
I0312 15:15:00.023342  3400 net.cpp:200] relu5 needs backward computation.
I0312 15:15:00.023344  3400 net.cpp:200] conv5 needs backward computation.
I0312 15:15:00.023346  3400 net.cpp:200] relu4 needs backward computation.
I0312 15:15:00.023349  3400 net.cpp:200] conv4 needs backward computation.
I0312 15:15:00.023350  3400 net.cpp:200] relu3 needs backward computation.
I0312 15:15:00.023351  3400 net.cpp:200] conv3 needs backward computation.
I0312 15:15:00.023353  3400 net.cpp:200] pool2 needs backward computation.
I0312 15:15:00.023356  3400 net.cpp:200] norm2 needs backward computation.
I0312 15:15:00.023358  3400 net.cpp:200] relu2 needs backward computation.
I0312 15:15:00.023360  3400 net.cpp:200] conv2 needs backward computation.
I0312 15:15:00.023362  3400 net.cpp:200] pool1 needs backward computation.
I0312 15:15:00.023365  3400 net.cpp:200] norm1 needs backward computation.
I0312 15:15:00.023366  3400 net.cpp:200] relu1 needs backward computation.
I0312 15:15:00.023368  3400 net.cpp:200] conv1 needs backward computation.
I0312 15:15:00.023370  3400 net.cpp:202] label_data_1_split does not need backward computation.
I0312 15:15:00.023373  3400 net.cpp:202] data does not need backward computation.
I0312 15:15:00.023375  3400 net.cpp:244] This network produces output accuracy
I0312 15:15:00.023377  3400 net.cpp:244] This network produces output loss
I0312 15:15:00.023392  3400 net.cpp:257] Network initialization done.
I0312 15:15:00.023463  3400 solver.cpp:56] Solver scaffolding done.
I0312 15:15:00.023947  3400 caffe.cpp:248] Starting Optimization
I0312 15:15:00.023952  3400 solver.cpp:273] Solving AlexNet
I0312 15:15:00.023952  3400 solver.cpp:274] Learning Rate Policy: step
I0312 15:15:00.025804  3400 solver.cpp:331] Iteration 0, Testing net (#0)
I0312 15:15:00.196871  3400 blocking_queue.cpp:49] Waiting for data
I0312 15:15:01.865114  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:15:01.888545  3400 solver.cpp:398]     Test net output #0: accuracy = 0.180714
I0312 15:15:01.888568  3400 solver.cpp:398]     Test net output #1: loss = 1.59707 (* 1 = 1.59707 loss)
I0312 15:15:02.152133  3400 solver.cpp:219] Iteration 0 (0 iter/s, 2.12822s/20 iters), loss = 1.79293
I0312 15:15:02.154552  3400 solver.cpp:238]     Train net output #0: loss = 1.79293 (* 1 = 1.79293 loss)
I0312 15:15:02.154572  3400 sgd_solver.cpp:105] Iteration 0, lr = 0.001
I0312 15:15:07.469552  3400 solver.cpp:219] Iteration 20 (3.76284 iter/s, 5.31514s/20 iters), loss = 1.15939
I0312 15:15:07.481657  3400 solver.cpp:238]     Train net output #0: loss = 1.15939 (* 1 = 1.15939 loss)
I0312 15:15:07.481684  3400 sgd_solver.cpp:105] Iteration 20, lr = 0.001
I0312 15:15:08.407680  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:15:09.181119  3400 solver.cpp:331] Iteration 28, Testing net (#0)
I0312 15:15:11.139320  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:15:11.163296  3400 solver.cpp:398]     Test net output #0: accuracy = 0.545357
I0312 15:15:11.163332  3400 solver.cpp:398]     Test net output #1: loss = 1.17676 (* 1 = 1.17676 loss)
I0312 15:15:14.600911  3400 solver.cpp:219] Iteration 40 (2.80925 iter/s, 7.11935s/20 iters), loss = 1.20282
I0312 15:15:14.613029  3400 solver.cpp:238]     Train net output #0: loss = 1.20282 (* 1 = 1.20282 loss)
I0312 15:15:14.613056  3400 sgd_solver.cpp:105] Iteration 40, lr = 0.001
I0312 15:15:16.898764  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:15:18.446493  3400 solver.cpp:331] Iteration 56, Testing net (#0)
I0312 15:15:20.401070  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:15:20.426092  3400 solver.cpp:398]     Test net output #0: accuracy = 0.545714
I0312 15:15:20.426120  3400 solver.cpp:398]     Test net output #1: loss = 1.15208 (* 1 = 1.15208 loss)
I0312 15:15:21.738221  3400 solver.cpp:219] Iteration 60 (2.80694 iter/s, 7.12519s/20 iters), loss = 1.21732
I0312 15:15:21.750447  3400 solver.cpp:238]     Train net output #0: loss = 1.21732 (* 1 = 1.21732 loss)
I0312 15:15:21.750473  3400 sgd_solver.cpp:105] Iteration 60, lr = 0.001
I0312 15:15:25.605252  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:15:27.077924  3400 solver.cpp:219] Iteration 80 (3.75416 iter/s, 5.32742s/20 iters), loss = 1.18855
I0312 15:15:27.089962  3400 solver.cpp:238]     Train net output #0: loss = 1.18855 (* 1 = 1.18855 loss)
I0312 15:15:27.089987  3400 sgd_solver.cpp:105] Iteration 80, lr = 0.001
I0312 15:15:27.723026  3400 solver.cpp:331] Iteration 84, Testing net (#0)
I0312 15:15:29.689395  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:15:29.715106  3400 solver.cpp:398]     Test net output #0: accuracy = 0.545357
I0312 15:15:29.715128  3400 solver.cpp:398]     Test net output #1: loss = 1.13127 (* 1 = 1.13127 loss)
I0312 15:15:34.118387  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:15:34.229269  3400 solver.cpp:219] Iteration 100 (2.80146 iter/s, 7.13915s/20 iters), loss = 1.13812
I0312 15:15:34.241354  3400 solver.cpp:238]     Train net output #0: loss = 1.13812 (* 1 = 1.13812 loss)
I0312 15:15:34.241380  3400 sgd_solver.cpp:105] Iteration 100, lr = 0.001
I0312 15:15:37.011456  3400 solver.cpp:331] Iteration 112, Testing net (#0)
I0312 15:15:38.965276  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:15:38.991606  3400 solver.cpp:398]     Test net output #0: accuracy = 0.546071
I0312 15:15:38.991643  3400 solver.cpp:398]     Test net output #1: loss = 1.0943 (* 1 = 1.0943 loss)
I0312 15:15:41.372874  3400 solver.cpp:219] Iteration 120 (2.80454 iter/s, 7.1313s/20 iters), loss = 1.08922
I0312 15:15:41.385037  3400 solver.cpp:238]     Train net output #0: loss = 1.08922 (* 1 = 1.08922 loss)
I0312 15:15:41.385051  3400 sgd_solver.cpp:105] Iteration 120, lr = 0.001
I0312 15:15:42.835633  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:15:46.298776  3400 solver.cpp:331] Iteration 140, Testing net (#0)
I0312 15:15:48.270100  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:15:48.298099  3400 solver.cpp:398]     Test net output #0: accuracy = 0.553214
I0312 15:15:48.298120  3400 solver.cpp:398]     Test net output #1: loss = 1.0419 (* 1 = 1.0419 loss)
I0312 15:15:48.556108  3400 solver.cpp:219] Iteration 140 (2.7891 iter/s, 7.17077s/20 iters), loss = 1.13393
I0312 15:15:48.558588  3400 solver.cpp:238]     Train net output #0: loss = 1.13393 (* 1 = 1.13393 loss)
I0312 15:15:48.558614  3400 sgd_solver.cpp:105] Iteration 140, lr = 0.001
I0312 15:15:51.358734  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:15:53.889725  3400 solver.cpp:219] Iteration 160 (3.75173 iter/s, 5.33087s/20 iters), loss = 1.12009
I0312 15:15:53.901734  3400 solver.cpp:238]     Train net output #0: loss = 1.12009 (* 1 = 1.12009 loss)
I0312 15:15:53.901760  3400 sgd_solver.cpp:105] Iteration 160, lr = 0.001
I0312 15:15:55.606535  3400 solver.cpp:331] Iteration 168, Testing net (#0)
I0312 15:15:57.567255  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:15:57.596175  3400 solver.cpp:398]     Test net output #0: accuracy = 0.569286
I0312 15:15:57.596201  3400 solver.cpp:398]     Test net output #1: loss = 0.994541 (* 1 = 0.994541 loss)
I0312 15:16:00.104831  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:16:01.059020  3400 solver.cpp:219] Iteration 180 (2.79451 iter/s, 7.15688s/20 iters), loss = 1.077
I0312 15:16:01.071106  3400 solver.cpp:238]     Train net output #0: loss = 1.077 (* 1 = 1.077 loss)
I0312 15:16:01.071132  3400 sgd_solver.cpp:105] Iteration 180, lr = 0.001
I0312 15:16:04.928994  3400 solver.cpp:331] Iteration 196, Testing net (#0)
I0312 15:16:06.895695  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:16:06.925457  3400 solver.cpp:398]     Test net output #0: accuracy = 0.576071
I0312 15:16:06.925496  3400 solver.cpp:398]     Test net output #1: loss = 0.971296 (* 1 = 0.971296 loss)
I0312 15:16:08.246050  3400 solver.cpp:219] Iteration 200 (2.78767 iter/s, 7.17446s/20 iters), loss = 0.94749
I0312 15:16:08.258175  3400 solver.cpp:238]     Train net output #0: loss = 0.94749 (* 1 = 0.94749 loss)
I0312 15:16:08.258188  3400 sgd_solver.cpp:105] Iteration 200, lr = 0.001
I0312 15:16:08.667268  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:16:13.626134  3400 solver.cpp:219] Iteration 220 (3.72607 iter/s, 5.36759s/20 iters), loss = 0.933928
I0312 15:16:13.638236  3400 solver.cpp:238]     Train net output #0: loss = 0.933928 (* 1 = 0.933928 loss)
I0312 15:16:13.638248  3400 sgd_solver.cpp:105] Iteration 220, lr = 0.001
I0312 15:16:14.275890  3400 solver.cpp:331] Iteration 224, Testing net (#0)
I0312 15:16:16.253525  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:16:16.284018  3400 solver.cpp:398]     Test net output #0: accuracy = 0.579643
I0312 15:16:16.284056  3400 solver.cpp:398]     Test net output #1: loss = 0.951704 (* 1 = 0.951704 loss)
I0312 15:16:17.237542  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:16:20.832008  3400 solver.cpp:219] Iteration 240 (2.78039 iter/s, 7.19324s/20 iters), loss = 0.987299
I0312 15:16:20.844086  3400 solver.cpp:238]     Train net output #0: loss = 0.987299 (* 1 = 0.987299 loss)
I0312 15:16:20.844097  3400 sgd_solver.cpp:105] Iteration 240, lr = 0.001
I0312 15:16:23.635071  3400 solver.cpp:331] Iteration 252, Testing net (#0)
I0312 15:16:23.995193  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:16:25.599102  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:16:25.630218  3400 solver.cpp:398]     Test net output #0: accuracy = 0.5975
I0312 15:16:25.630256  3400 solver.cpp:398]     Test net output #1: loss = 0.937659 (* 1 = 0.937659 loss)
I0312 15:16:28.027734  3400 solver.cpp:219] Iteration 260 (2.78432 iter/s, 7.18308s/20 iters), loss = 0.990188
I0312 15:16:28.039851  3400 solver.cpp:238]     Train net output #0: loss = 0.990188 (* 1 = 0.990188 loss)
I0312 15:16:28.039878  3400 sgd_solver.cpp:105] Iteration 260, lr = 0.001
I0312 15:16:32.976518  3400 solver.cpp:331] Iteration 280, Testing net (#0)
I0312 15:16:32.978247  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:16:34.949976  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:16:34.981762  3400 solver.cpp:398]     Test net output #0: accuracy = 0.5875
I0312 15:16:34.981802  3400 solver.cpp:398]     Test net output #1: loss = 0.942969 (* 1 = 0.942969 loss)
I0312 15:16:35.241040  3400 solver.cpp:219] Iteration 280 (2.77755 iter/s, 7.20059s/20 iters), loss = 1.05443
I0312 15:16:35.243535  3400 solver.cpp:238]     Train net output #0: loss = 1.05443 (* 1 = 1.05443 loss)
I0312 15:16:35.243561  3400 sgd_solver.cpp:105] Iteration 280, lr = 0.001
I0312 15:16:40.607734  3400 solver.cpp:219] Iteration 300 (3.72875 iter/s, 5.36373s/20 iters), loss = 0.853462
I0312 15:16:40.619915  3400 solver.cpp:238]     Train net output #0: loss = 0.853462 (* 1 = 0.853462 loss)
I0312 15:16:40.619928  3400 sgd_solver.cpp:105] Iteration 300, lr = 0.001
I0312 15:16:41.549270  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:16:42.333878  3400 solver.cpp:331] Iteration 308, Testing net (#0)
I0312 15:16:44.299496  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:16:44.332072  3400 solver.cpp:398]     Test net output #0: accuracy = 0.605714
I0312 15:16:44.332109  3400 solver.cpp:398]     Test net output #1: loss = 0.900171 (* 1 = 0.900171 loss)
I0312 15:16:47.805300  3400 solver.cpp:219] Iteration 320 (2.78368 iter/s, 7.18474s/20 iters), loss = 0.975026
I0312 15:16:47.817509  3400 solver.cpp:238]     Train net output #0: loss = 0.975026 (* 1 = 0.975026 loss)
I0312 15:16:47.817523  3400 sgd_solver.cpp:105] Iteration 320, lr = 0.001
I0312 15:16:50.130303  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:16:51.691027  3400 solver.cpp:331] Iteration 336, Testing net (#0)
I0312 15:16:53.658074  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:16:53.691260  3400 solver.cpp:398]     Test net output #0: accuracy = 0.618214
I0312 15:16:53.691298  3400 solver.cpp:398]     Test net output #1: loss = 0.902877 (* 1 = 0.902877 loss)
I0312 15:16:55.010742  3400 solver.cpp:219] Iteration 340 (2.78065 iter/s, 7.19257s/20 iters), loss = 0.900574
I0312 15:16:55.022836  3400 solver.cpp:238]     Train net output #0: loss = 0.900574 (* 1 = 0.900574 loss)
I0312 15:16:55.022864  3400 sgd_solver.cpp:105] Iteration 340, lr = 0.001
I0312 15:16:58.919059  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:00.406522  3400 solver.cpp:219] Iteration 360 (3.71528 iter/s, 5.38317s/20 iters), loss = 0.93244
I0312 15:17:00.418678  3400 solver.cpp:238]     Train net output #0: loss = 0.93244 (* 1 = 0.93244 loss)
I0312 15:17:00.418704  3400 sgd_solver.cpp:105] Iteration 360, lr = 0.001
I0312 15:17:01.056758  3400 solver.cpp:331] Iteration 364, Testing net (#0)
I0312 15:17:03.015851  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:03.050194  3400 solver.cpp:398]     Test net output #0: accuracy = 0.623929
I0312 15:17:03.050235  3400 solver.cpp:398]     Test net output #1: loss = 0.867694 (* 1 = 0.867694 loss)
I0312 15:17:06.637147  3400 blocking_queue.cpp:49] Waiting for data
I0312 15:17:07.504999  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:07.612885  3400 solver.cpp:219] Iteration 380 (2.78029 iter/s, 7.1935s/20 iters), loss = 0.872377
I0312 15:17:07.624950  3400 solver.cpp:238]     Train net output #0: loss = 0.872377 (* 1 = 0.872377 loss)
I0312 15:17:07.624976  3400 sgd_solver.cpp:105] Iteration 380, lr = 0.001
I0312 15:17:10.419472  3400 solver.cpp:331] Iteration 392, Testing net (#0)
I0312 15:17:12.390650  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:12.425534  3400 solver.cpp:398]     Test net output #0: accuracy = 0.615714
I0312 15:17:12.425559  3400 solver.cpp:398]     Test net output #1: loss = 0.915761 (* 1 = 0.915761 loss)
I0312 15:17:14.830678  3400 solver.cpp:219] Iteration 400 (2.77585 iter/s, 7.205s/20 iters), loss = 0.836408
I0312 15:17:14.842885  3400 solver.cpp:238]     Train net output #0: loss = 0.836408 (* 1 = 0.836408 loss)
I0312 15:17:14.842911  3400 sgd_solver.cpp:105] Iteration 400, lr = 0.001
I0312 15:17:16.303714  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:19.794162  3400 solver.cpp:331] Iteration 420, Testing net (#0)
I0312 15:17:21.750341  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:21.785835  3400 solver.cpp:398]     Test net output #0: accuracy = 0.645714
I0312 15:17:21.785862  3400 solver.cpp:398]     Test net output #1: loss = 0.831009 (* 1 = 0.831009 loss)
I0312 15:17:22.045446  3400 solver.cpp:219] Iteration 420 (2.77678 iter/s, 7.20259s/20 iters), loss = 0.956732
I0312 15:17:22.047906  3400 solver.cpp:238]     Train net output #0: loss = 0.956732 (* 1 = 0.956732 loss)
I0312 15:17:22.047932  3400 sgd_solver.cpp:105] Iteration 420, lr = 0.001
I0312 15:17:24.887140  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:27.430994  3400 solver.cpp:219] Iteration 440 (3.71484 iter/s, 5.38382s/20 iters), loss = 0.953717
I0312 15:17:27.443086  3400 solver.cpp:238]     Train net output #0: loss = 0.953717 (* 1 = 0.953717 loss)
I0312 15:17:27.443112  3400 sgd_solver.cpp:105] Iteration 440, lr = 0.001
I0312 15:17:29.158365  3400 solver.cpp:331] Iteration 448, Testing net (#0)
I0312 15:17:31.106226  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:31.162901  3400 solver.cpp:398]     Test net output #0: accuracy = 0.661071
I0312 15:17:31.162943  3400 solver.cpp:398]     Test net output #1: loss = 0.824015 (* 1 = 0.824015 loss)
I0312 15:17:33.689541  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:34.648578  3400 solver.cpp:219] Iteration 460 (2.77531 iter/s, 7.2064s/20 iters), loss = 0.888829
I0312 15:17:34.660645  3400 solver.cpp:238]     Train net output #0: loss = 0.888829 (* 1 = 0.888829 loss)
I0312 15:17:34.660656  3400 sgd_solver.cpp:105] Iteration 460, lr = 0.001
I0312 15:17:38.539413  3400 solver.cpp:331] Iteration 476, Testing net (#0)
I0312 15:17:40.475811  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:40.535271  3400 solver.cpp:398]     Test net output #0: accuracy = 0.6625
I0312 15:17:40.535308  3400 solver.cpp:398]     Test net output #1: loss = 0.80534 (* 1 = 0.80534 loss)
I0312 15:17:41.858047  3400 solver.cpp:219] Iteration 480 (2.77846 iter/s, 7.19822s/20 iters), loss = 0.850351
I0312 15:17:41.870193  3400 solver.cpp:238]     Train net output #0: loss = 0.850351 (* 1 = 0.850351 loss)
I0312 15:17:41.870219  3400 sgd_solver.cpp:105] Iteration 480, lr = 0.001
I0312 15:17:42.282505  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:47.264785  3400 solver.cpp:219] Iteration 500 (3.70703 iter/s, 5.39515s/20 iters), loss = 0.766672
I0312 15:17:47.276868  3400 solver.cpp:238]     Train net output #0: loss = 0.766672 (* 1 = 0.766672 loss)
I0312 15:17:47.276895  3400 sgd_solver.cpp:105] Iteration 500, lr = 0.001
I0312 15:17:47.915307  3400 solver.cpp:331] Iteration 504, Testing net (#0)
I0312 15:17:49.864032  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:49.922363  3400 solver.cpp:398]     Test net output #0: accuracy = 0.663571
I0312 15:17:49.922412  3400 solver.cpp:398]     Test net output #1: loss = 0.785628 (* 1 = 0.785628 loss)
I0312 15:17:51.095754  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:54.490452  3400 solver.cpp:219] Iteration 520 (2.77228 iter/s, 7.21427s/20 iters), loss = 0.812744
I0312 15:17:54.502517  3400 solver.cpp:238]     Train net output #0: loss = 0.812744 (* 1 = 0.812744 loss)
I0312 15:17:54.502542  3400 sgd_solver.cpp:105] Iteration 520, lr = 0.001
I0312 15:17:57.298475  3400 solver.cpp:331] Iteration 532, Testing net (#0)
I0312 15:17:57.663458  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:59.226677  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:17:59.286490  3400 solver.cpp:398]     Test net output #0: accuracy = 0.687143
I0312 15:17:59.286527  3400 solver.cpp:398]     Test net output #1: loss = 0.76098 (* 1 = 0.76098 loss)
I0312 15:18:01.688042  3400 solver.cpp:219] Iteration 540 (2.78314 iter/s, 7.18613s/20 iters), loss = 0.804691
I0312 15:18:01.700240  3400 solver.cpp:238]     Train net output #0: loss = 0.804691 (* 1 = 0.804691 loss)
I0312 15:18:01.700268  3400 sgd_solver.cpp:105] Iteration 540, lr = 0.001
I0312 15:18:06.657513  3400 solver.cpp:331] Iteration 560, Testing net (#0)
I0312 15:18:06.663669  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:18:08.591706  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:18:08.651191  3400 solver.cpp:398]     Test net output #0: accuracy = 0.674643
I0312 15:18:08.651216  3400 solver.cpp:398]     Test net output #1: loss = 0.746633 (* 1 = 0.746633 loss)
I0312 15:18:08.914775  3400 solver.cpp:219] Iteration 560 (2.77197 iter/s, 7.21508s/20 iters), loss = 0.824877
I0312 15:18:08.917230  3400 solver.cpp:238]     Train net output #0: loss = 0.824877 (* 1 = 0.824877 loss)
I0312 15:18:08.917256  3400 sgd_solver.cpp:105] Iteration 560, lr = 0.001
I0312 15:18:14.304972  3400 solver.cpp:219] Iteration 580 (3.71189 iter/s, 5.3881s/20 iters), loss = 0.647147
I0312 15:18:14.317092  3400 solver.cpp:238]     Train net output #0: loss = 0.647147 (* 1 = 0.647147 loss)
I0312 15:18:14.317106  3400 sgd_solver.cpp:105] Iteration 580, lr = 0.001
I0312 15:18:15.262302  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:18:16.038519  3400 solver.cpp:331] Iteration 588, Testing net (#0)
I0312 15:18:17.986201  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:18:18.046329  3400 solver.cpp:398]     Test net output #0: accuracy = 0.705
I0312 15:18:18.046355  3400 solver.cpp:398]     Test net output #1: loss = 0.714921 (* 1 = 0.714921 loss)
I0312 15:18:21.529448  3400 solver.cpp:219] Iteration 600 (2.77285 iter/s, 7.21279s/20 iters), loss = 0.788523
I0312 15:18:21.541590  3400 solver.cpp:238]     Train net output #0: loss = 0.788523 (* 1 = 0.788523 loss)
I0312 15:18:21.541604  3400 sgd_solver.cpp:105] Iteration 600, lr = 0.001
I0312 15:18:24.077775  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:18:25.428196  3400 solver.cpp:331] Iteration 616, Testing net (#0)
I0312 15:18:27.360915  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:18:27.422842  3400 solver.cpp:398]     Test net output #0: accuracy = 0.668571
I0312 15:18:27.422868  3400 solver.cpp:398]     Test net output #1: loss = 0.782879 (* 1 = 0.782879 loss)
I0312 15:18:28.751802  3400 solver.cpp:219] Iteration 620 (2.7737 iter/s, 7.21058s/20 iters), loss = 0.679942
I0312 15:18:28.763985  3400 solver.cpp:238]     Train net output #0: loss = 0.679942 (* 1 = 0.679942 loss)
I0312 15:18:28.763998  3400 sgd_solver.cpp:105] Iteration 620, lr = 0.001
I0312 15:18:32.677551  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:18:34.164574  3400 solver.cpp:219] Iteration 640 (3.70313 iter/s, 5.40083s/20 iters), loss = 0.774282
I0312 15:18:34.176654  3400 solver.cpp:238]     Train net output #0: loss = 0.774282 (* 1 = 0.774282 loss)
I0312 15:18:34.176666  3400 sgd_solver.cpp:105] Iteration 640, lr = 0.001
I0312 15:18:34.817320  3400 solver.cpp:331] Iteration 644, Testing net (#0)
I0312 15:18:36.734540  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:18:36.797863  3400 solver.cpp:398]     Test net output #0: accuracy = 0.705715
I0312 15:18:36.797888  3400 solver.cpp:398]     Test net output #1: loss = 0.721174 (* 1 = 0.721174 loss)
I0312 15:18:41.261659  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:18:41.364552  3400 solver.cpp:219] Iteration 660 (2.78235 iter/s, 7.18816s/20 iters), loss = 0.650005
I0312 15:18:41.376653  3400 solver.cpp:238]     Train net output #0: loss = 0.650005 (* 1 = 0.650005 loss)
I0312 15:18:41.376667  3400 sgd_solver.cpp:105] Iteration 660, lr = 0.001
I0312 15:18:44.176199  3400 solver.cpp:331] Iteration 672, Testing net (#0)
I0312 15:18:46.116256  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:18:46.180114  3400 solver.cpp:398]     Test net output #0: accuracy = 0.744643
I0312 15:18:46.180153  3400 solver.cpp:398]     Test net output #1: loss = 0.65881 (* 1 = 0.65881 loss)
I0312 15:18:48.590128  3400 solver.cpp:219] Iteration 680 (2.7725 iter/s, 7.21371s/20 iters), loss = 0.676694
I0312 15:18:48.602308  3400 solver.cpp:238]     Train net output #0: loss = 0.676694 (* 1 = 0.676694 loss)
I0312 15:18:48.602334  3400 sgd_solver.cpp:105] Iteration 680, lr = 0.001
I0312 15:18:50.076055  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:18:53.563628  3400 solver.cpp:331] Iteration 700, Testing net (#0)
I0312 15:18:55.500000  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:18:55.564798  3400 solver.cpp:398]     Test net output #0: accuracy = 0.726429
I0312 15:18:55.564836  3400 solver.cpp:398]     Test net output #1: loss = 0.674512 (* 1 = 0.674512 loss)
I0312 15:18:55.826586  3400 solver.cpp:219] Iteration 700 (2.76838 iter/s, 7.22445s/20 iters), loss = 0.651219
I0312 15:18:55.829041  3400 solver.cpp:238]     Train net output #0: loss = 0.651219 (* 1 = 0.651219 loss)
I0312 15:18:55.829067  3400 sgd_solver.cpp:105] Iteration 700, lr = 0.001
I0312 15:18:58.672683  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:01.227725  3400 solver.cpp:219] Iteration 720 (3.70454 iter/s, 5.39878s/20 iters), loss = 0.647588
I0312 15:19:01.239888  3400 solver.cpp:238]     Train net output #0: loss = 0.647588 (* 1 = 0.647588 loss)
I0312 15:19:01.239915  3400 sgd_solver.cpp:105] Iteration 720, lr = 0.001
I0312 15:19:02.964723  3400 solver.cpp:331] Iteration 728, Testing net (#0)
I0312 15:19:04.926232  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:04.989537  3400 solver.cpp:398]     Test net output #0: accuracy = 0.753929
I0312 15:19:04.989574  3400 solver.cpp:398]     Test net output #1: loss = 0.619521 (* 1 = 0.619521 loss)
I0312 15:19:07.528949  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:08.485841  3400 solver.cpp:219] Iteration 740 (2.76012 iter/s, 7.24605s/20 iters), loss = 0.675941
I0312 15:19:08.497941  3400 solver.cpp:238]     Train net output #0: loss = 0.675941 (* 1 = 0.675941 loss)
I0312 15:19:08.497967  3400 sgd_solver.cpp:105] Iteration 740, lr = 0.001
I0312 15:19:12.381670  3400 solver.cpp:331] Iteration 756, Testing net (#0)
I0312 15:19:14.249562  3400 blocking_queue.cpp:49] Waiting for data
I0312 15:19:14.319602  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:14.385287  3400 solver.cpp:398]     Test net output #0: accuracy = 0.761072
I0312 15:19:14.385324  3400 solver.cpp:398]     Test net output #1: loss = 0.59158 (* 1 = 0.59158 loss)
I0312 15:19:15.715216  3400 solver.cpp:219] Iteration 760 (2.77111 iter/s, 7.21733s/20 iters), loss = 0.626121
I0312 15:19:15.727432  3400 solver.cpp:238]     Train net output #0: loss = 0.626121 (* 1 = 0.626121 loss)
I0312 15:19:15.727458  3400 sgd_solver.cpp:105] Iteration 760, lr = 0.001
I0312 15:19:16.149528  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:21.129549  3400 solver.cpp:219] Iteration 780 (3.70224 iter/s, 5.40214s/20 iters), loss = 0.540354
I0312 15:19:21.141628  3400 solver.cpp:238]     Train net output #0: loss = 0.540354 (* 1 = 0.540354 loss)
I0312 15:19:21.141654  3400 sgd_solver.cpp:105] Iteration 780, lr = 0.001
I0312 15:19:21.782236  3400 solver.cpp:331] Iteration 784, Testing net (#0)
I0312 15:19:23.720639  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:23.784689  3400 solver.cpp:398]     Test net output #0: accuracy = 0.769643
I0312 15:19:23.784726  3400 solver.cpp:398]     Test net output #1: loss = 0.570081 (* 1 = 0.570081 loss)
I0312 15:19:24.963053  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:28.356974  3400 solver.cpp:219] Iteration 800 (2.77187 iter/s, 7.21534s/20 iters), loss = 0.51104
I0312 15:19:28.369065  3400 solver.cpp:238]     Train net output #0: loss = 0.51104 (* 1 = 0.51104 loss)
I0312 15:19:28.369092  3400 sgd_solver.cpp:105] Iteration 800, lr = 0.001
I0312 15:19:31.176674  3400 solver.cpp:331] Iteration 812, Testing net (#0)
I0312 15:19:31.564457  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:33.127796  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:33.192548  3400 solver.cpp:398]     Test net output #0: accuracy = 0.771429
I0312 15:19:33.192596  3400 solver.cpp:398]     Test net output #1: loss = 0.571397 (* 1 = 0.571397 loss)
I0312 15:19:35.609904  3400 solver.cpp:219] Iteration 820 (2.76213 iter/s, 7.24079s/20 iters), loss = 0.533786
I0312 15:19:35.622110  3400 solver.cpp:238]     Train net output #0: loss = 0.533786 (* 1 = 0.533786 loss)
I0312 15:19:35.622138  3400 sgd_solver.cpp:105] Iteration 820, lr = 0.001
I0312 15:19:40.597286  3400 solver.cpp:331] Iteration 840, Testing net (#0)
I0312 15:19:40.608041  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:42.550683  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:42.616173  3400 solver.cpp:398]     Test net output #0: accuracy = 0.803929
I0312 15:19:42.616212  3400 solver.cpp:398]     Test net output #1: loss = 0.520615 (* 1 = 0.520615 loss)
I0312 15:19:42.878137  3400 solver.cpp:219] Iteration 840 (2.75636 iter/s, 7.25595s/20 iters), loss = 0.560993
I0312 15:19:42.880589  3400 solver.cpp:238]     Train net output #0: loss = 0.560993 (* 1 = 0.560993 loss)
I0312 15:19:42.880614  3400 sgd_solver.cpp:105] Iteration 840, lr = 0.001
I0312 15:19:48.282490  3400 solver.cpp:219] Iteration 860 (3.70246 iter/s, 5.40182s/20 iters), loss = 0.479458
I0312 15:19:48.294679  3400 solver.cpp:238]     Train net output #0: loss = 0.479458 (* 1 = 0.479458 loss)
I0312 15:19:48.294693  3400 sgd_solver.cpp:105] Iteration 860, lr = 0.001
I0312 15:19:49.247689  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:50.023023  3400 solver.cpp:331] Iteration 868, Testing net (#0)
I0312 15:19:51.958149  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:52.024778  3400 solver.cpp:398]     Test net output #0: accuracy = 0.774286
I0312 15:19:52.024813  3400 solver.cpp:398]     Test net output #1: loss = 0.53657 (* 1 = 0.53657 loss)
I0312 15:19:55.531849  3400 solver.cpp:219] Iteration 880 (2.76356 iter/s, 7.23704s/20 iters), loss = 0.514099
I0312 15:19:55.544028  3400 solver.cpp:238]     Train net output #0: loss = 0.514099 (* 1 = 0.514099 loss)
I0312 15:19:55.544040  3400 sgd_solver.cpp:105] Iteration 880, lr = 0.001
I0312 15:19:58.087602  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:19:59.442054  3400 solver.cpp:331] Iteration 896, Testing net (#0)
I0312 15:20:01.371603  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:20:01.438855  3400 solver.cpp:398]     Test net output #0: accuracy = 0.789643
I0312 15:20:01.438877  3400 solver.cpp:398]     Test net output #1: loss = 0.534242 (* 1 = 0.534242 loss)
I0312 15:20:02.769948  3400 solver.cpp:219] Iteration 900 (2.76787 iter/s, 7.22576s/20 iters), loss = 0.401579
I0312 15:20:02.782141  3400 solver.cpp:238]     Train net output #0: loss = 0.401579 (* 1 = 0.401579 loss)
I0312 15:20:02.782167  3400 sgd_solver.cpp:105] Iteration 900, lr = 0.001
I0312 15:20:06.710672  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:20:08.196038  3400 solver.cpp:219] Iteration 920 (3.69429 iter/s, 5.41376s/20 iters), loss = 0.418515
I0312 15:20:08.208111  3400 solver.cpp:238]     Train net output #0: loss = 0.418515 (* 1 = 0.418515 loss)
I0312 15:20:08.208137  3400 sgd_solver.cpp:105] Iteration 920, lr = 0.001
I0312 15:20:08.853616  3400 solver.cpp:331] Iteration 924, Testing net (#0)
I0312 15:20:10.750771  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:20:10.840065  3400 solver.cpp:398]     Test net output #0: accuracy = 0.803214
I0312 15:20:10.840090  3400 solver.cpp:398]     Test net output #1: loss = 0.477671 (* 1 = 0.477671 loss)
I0312 15:20:15.432298  3400 solver.cpp:219] Iteration 940 (2.76856 iter/s, 7.22398s/20 iters), loss = 0.388416
I0312 15:20:15.444432  3400 solver.cpp:238]     Train net output #0: loss = 0.388416 (* 1 = 0.388416 loss)
I0312 15:20:15.444445  3400 sgd_solver.cpp:105] Iteration 940, lr = 0.001
I0312 15:20:15.460371  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:20:18.259165  3400 solver.cpp:331] Iteration 952, Testing net (#0)
I0312 15:20:20.171815  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:20:20.260933  3400 solver.cpp:398]     Test net output #0: accuracy = 0.850714
I0312 15:20:20.260957  3400 solver.cpp:398]     Test net output #1: loss = 0.431527 (* 1 = 0.431527 loss)
I0312 15:20:22.679644  3400 solver.cpp:219] Iteration 960 (2.76435 iter/s, 7.23498s/20 iters), loss = 0.511667
I0312 15:20:22.691884  3400 solver.cpp:238]     Train net output #0: loss = 0.511667 (* 1 = 0.511667 loss)
I0312 15:20:22.691895  3400 sgd_solver.cpp:105] Iteration 960, lr = 0.001
I0312 15:20:24.174374  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:20:27.672485  3400 solver.cpp:331] Iteration 980, Testing net (#0)
I0312 15:20:29.607878  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:20:29.697804  3400 solver.cpp:398]     Test net output #0: accuracy = 0.837857
I0312 15:20:29.697826  3400 solver.cpp:398]     Test net output #1: loss = 0.446211 (* 1 = 0.446211 loss)
I0312 15:20:29.961614  3400 solver.cpp:219] Iteration 980 (2.75123 iter/s, 7.26947s/20 iters), loss = 0.405516
I0312 15:20:29.964067  3400 solver.cpp:238]     Train net output #0: loss = 0.405516 (* 1 = 0.405516 loss)
I0312 15:20:29.964093  3400 sgd_solver.cpp:105] Iteration 980, lr = 0.001
I0312 15:20:32.825044  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:20:35.375099  3400 solver.cpp:219] Iteration 1000 (3.6963 iter/s, 5.41082s/20 iters), loss = 0.360694
I0312 15:20:35.387202  3400 solver.cpp:238]     Train net output #0: loss = 0.360694 (* 1 = 0.360694 loss)
I0312 15:20:35.387228  3400 sgd_solver.cpp:105] Iteration 1000, lr = 0.001
I0312 15:20:37.113565  3400 solver.cpp:331] Iteration 1008, Testing net (#0)
I0312 15:20:39.028877  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:20:39.120355  3400 solver.cpp:398]     Test net output #0: accuracy = 0.8525
I0312 15:20:39.120378  3400 solver.cpp:398]     Test net output #1: loss = 0.390573 (* 1 = 0.390573 loss)
I0312 15:20:41.674085  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:20:42.630245  3400 solver.cpp:219] Iteration 1020 (2.76138 iter/s, 7.24275s/20 iters), loss = 0.371531
I0312 15:20:42.642331  3400 solver.cpp:238]     Train net output #0: loss = 0.371531 (* 1 = 0.371531 loss)
I0312 15:20:42.642356  3400 sgd_solver.cpp:105] Iteration 1020, lr = 0.001
I0312 15:20:46.539854  3400 solver.cpp:331] Iteration 1036, Testing net (#0)
I0312 15:20:48.450417  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:20:48.541676  3400 solver.cpp:398]     Test net output #0: accuracy = 0.834643
I0312 15:20:48.541698  3400 solver.cpp:398]     Test net output #1: loss = 0.413479 (* 1 = 0.413479 loss)
I0312 15:20:49.877884  3400 solver.cpp:219] Iteration 1040 (2.76425 iter/s, 7.23524s/20 iters), loss = 0.321864
I0312 15:20:49.890053  3400 solver.cpp:238]     Train net output #0: loss = 0.321864 (* 1 = 0.321864 loss)
I0312 15:20:49.890080  3400 sgd_solver.cpp:105] Iteration 1040, lr = 0.001
I0312 15:20:50.314105  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:20:55.310025  3400 solver.cpp:219] Iteration 1060 (3.69022 iter/s, 5.41973s/20 iters), loss = 0.333701
I0312 15:20:55.322134  3400 solver.cpp:238]     Train net output #0: loss = 0.333701 (* 1 = 0.333701 loss)
I0312 15:20:55.322160  3400 sgd_solver.cpp:105] Iteration 1060, lr = 0.001
I0312 15:20:55.963469  3400 solver.cpp:331] Iteration 1064, Testing net (#0)
I0312 15:20:57.854028  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:20:57.945955  3400 solver.cpp:398]     Test net output #0: accuracy = 0.870357
I0312 15:20:57.945991  3400 solver.cpp:398]     Test net output #1: loss = 0.362958 (* 1 = 0.362958 loss)
I0312 15:20:59.134594  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:21:02.540894  3400 solver.cpp:219] Iteration 1080 (2.77069 iter/s, 7.21842s/20 iters), loss = 0.247844
I0312 15:21:02.552929  3400 solver.cpp:238]     Train net output #0: loss = 0.247844 (* 1 = 0.247844 loss)
I0312 15:21:02.552954  3400 sgd_solver.cpp:105] Iteration 1080, lr = 0.001
I0312 15:21:05.366915  3400 solver.cpp:331] Iteration 1092, Testing net (#0)
I0312 15:21:05.742332  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:21:07.267343  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:21:07.360766  3400 solver.cpp:398]     Test net output #0: accuracy = 0.865714
I0312 15:21:07.360790  3400 solver.cpp:398]     Test net output #1: loss = 0.366755 (* 1 = 0.366755 loss)
I0312 15:21:09.781147  3400 solver.cpp:219] Iteration 1100 (2.76707 iter/s, 7.22786s/20 iters), loss = 0.253973
I0312 15:21:09.793283  3400 solver.cpp:238]     Train net output #0: loss = 0.253973 (* 1 = 0.253973 loss)
I0312 15:21:09.793310  3400 sgd_solver.cpp:105] Iteration 1100, lr = 0.001
I0312 15:21:14.780355  3400 solver.cpp:331] Iteration 1120, Testing net (#0)
I0312 15:21:14.795826  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:21:16.703181  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:21:16.801316  3400 solver.cpp:398]     Test net output #0: accuracy = 0.859286
I0312 15:21:16.801353  3400 solver.cpp:398]     Test net output #1: loss = 0.374792 (* 1 = 0.374792 loss)
I0312 15:21:17.066205  3400 solver.cpp:219] Iteration 1120 (2.75007 iter/s, 7.27254s/20 iters), loss = 0.292386
I0312 15:21:17.068648  3400 solver.cpp:238]     Train net output #0: loss = 0.292386 (* 1 = 0.292386 loss)
I0312 15:21:17.068673  3400 sgd_solver.cpp:105] Iteration 1120, lr = 0.001
I0312 15:21:22.483883  3400 solver.cpp:219] Iteration 1140 (3.69348 iter/s, 5.41494s/20 iters), loss = 0.211931
I0312 15:21:22.496067  3400 solver.cpp:238]     Train net output #0: loss = 0.211931 (* 1 = 0.211931 loss)
I0312 15:21:22.496094  3400 sgd_solver.cpp:105] Iteration 1140, lr = 0.001
I0312 15:21:23.456593  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:21:24.232170  3400 solver.cpp:331] Iteration 1148, Testing net (#0)
I0312 15:21:25.605412  3400 blocking_queue.cpp:49] Waiting for data
I0312 15:21:26.153647  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:21:26.248821  3400 solver.cpp:398]     Test net output #0: accuracy = 0.899643
I0312 15:21:26.248862  3400 solver.cpp:398]     Test net output #1: loss = 0.310715 (* 1 = 0.310715 loss)
I0312 15:21:29.756299  3400 solver.cpp:219] Iteration 1160 (2.75489 iter/s, 7.25983s/20 iters), loss = 0.255113
I0312 15:21:29.768452  3400 solver.cpp:238]     Train net output #0: loss = 0.255113 (* 1 = 0.255113 loss)
I0312 15:21:29.768478  3400 sgd_solver.cpp:105] Iteration 1160, lr = 0.001
I0312 15:21:32.324887  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:21:33.668165  3400 solver.cpp:331] Iteration 1176, Testing net (#0)
I0312 15:21:35.576524  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:21:35.674839  3400 solver.cpp:398]     Test net output #0: accuracy = 0.853214
I0312 15:21:35.674876  3400 solver.cpp:398]     Test net output #1: loss = 0.406227 (* 1 = 0.406227 loss)
I0312 15:21:37.006021  3400 solver.cpp:219] Iteration 1180 (2.76352 iter/s, 7.23715s/20 iters), loss = 0.188535
I0312 15:21:37.018227  3400 solver.cpp:238]     Train net output #0: loss = 0.188535 (* 1 = 0.188535 loss)
I0312 15:21:37.018254  3400 sgd_solver.cpp:105] Iteration 1180, lr = 0.001
I0312 15:21:40.960352  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:21:42.445075  3400 solver.cpp:219] Iteration 1200 (3.6856 iter/s, 5.42653s/20 iters), loss = 0.213094
I0312 15:21:42.457192  3400 solver.cpp:238]     Train net output #0: loss = 0.213094 (* 1 = 0.213094 loss)
I0312 15:21:42.457219  3400 sgd_solver.cpp:105] Iteration 1200, lr = 0.001
I0312 15:21:43.096578  3400 solver.cpp:331] Iteration 1204, Testing net (#0)
I0312 15:21:45.010499  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:21:45.107622  3400 solver.cpp:398]     Test net output #0: accuracy = 0.876786
I0312 15:21:45.107659  3400 solver.cpp:398]     Test net output #1: loss = 0.336491 (* 1 = 0.336491 loss)
I0312 15:21:49.706220  3400 solver.cpp:219] Iteration 1220 (2.75916 iter/s, 7.24859s/20 iters), loss = 0.194658
I0312 15:21:49.718585  3400 solver.cpp:238]     Train net output #0: loss = 0.194658 (* 1 = 0.194658 loss)
I0312 15:21:49.718612  3400 sgd_solver.cpp:105] Iteration 1220, lr = 0.001
I0312 15:21:49.734843  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:21:52.537127  3400 solver.cpp:331] Iteration 1232, Testing net (#0)
I0312 15:21:54.427505  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:21:54.524472  3400 solver.cpp:398]     Test net output #0: accuracy = 0.877857
I0312 15:21:54.524494  3400 solver.cpp:398]     Test net output #1: loss = 0.358809 (* 1 = 0.358809 loss)
I0312 15:21:56.944597  3400 solver.cpp:219] Iteration 1240 (2.76795 iter/s, 7.22556s/20 iters), loss = 0.181977
I0312 15:21:56.956811  3400 solver.cpp:238]     Train net output #0: loss = 0.181977 (* 1 = 0.181977 loss)
I0312 15:21:56.956838  3400 sgd_solver.cpp:105] Iteration 1240, lr = 0.001
I0312 15:21:58.448406  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:01.946749  3400 solver.cpp:331] Iteration 1260, Testing net (#0)
I0312 15:22:03.851243  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:03.948640  3400 solver.cpp:398]     Test net output #0: accuracy = 0.906071
I0312 15:22:03.948668  3400 solver.cpp:398]     Test net output #1: loss = 0.290488 (* 1 = 0.290488 loss)
I0312 15:22:04.216552  3400 solver.cpp:219] Iteration 1260 (2.75509 iter/s, 7.25928s/20 iters), loss = 0.186585
I0312 15:22:04.219038  3400 solver.cpp:238]     Train net output #0: loss = 0.186585 (* 1 = 0.186585 loss)
I0312 15:22:04.219063  3400 sgd_solver.cpp:105] Iteration 1260, lr = 0.001
I0312 15:22:07.296706  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:09.635771  3400 solver.cpp:219] Iteration 1280 (3.6925 iter/s, 5.41638s/20 iters), loss = 0.130299
I0312 15:22:09.647881  3400 solver.cpp:238]     Train net output #0: loss = 0.130299 (* 1 = 0.130299 loss)
I0312 15:22:09.647907  3400 sgd_solver.cpp:105] Iteration 1280, lr = 0.001
I0312 15:22:11.379429  3400 solver.cpp:331] Iteration 1288, Testing net (#0)
I0312 15:22:13.277011  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:13.379159  3400 solver.cpp:398]     Test net output #0: accuracy = 0.908928
I0312 15:22:13.379182  3400 solver.cpp:398]     Test net output #1: loss = 0.274184 (* 1 = 0.274184 loss)
I0312 15:22:15.938383  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:16.887956  3400 solver.cpp:219] Iteration 1300 (2.76259 iter/s, 7.2396s/20 iters), loss = 0.183653
I0312 15:22:16.900012  3400 solver.cpp:238]     Train net output #0: loss = 0.183653 (* 1 = 0.183653 loss)
I0312 15:22:16.900038  3400 sgd_solver.cpp:105] Iteration 1300, lr = 0.001
I0312 15:22:20.801398  3400 solver.cpp:331] Iteration 1316, Testing net (#0)
I0312 15:22:22.702507  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:22.801537  3400 solver.cpp:398]     Test net output #0: accuracy = 0.848572
I0312 15:22:22.801573  3400 solver.cpp:398]     Test net output #1: loss = 0.439101 (* 1 = 0.439101 loss)
I0312 15:22:24.137284  3400 solver.cpp:219] Iteration 1320 (2.76366 iter/s, 7.23678s/20 iters), loss = 0.209299
I0312 15:22:24.149406  3400 solver.cpp:238]     Train net output #0: loss = 0.209299 (* 1 = 0.209299 loss)
I0312 15:22:24.149432  3400 sgd_solver.cpp:105] Iteration 1320, lr = 0.001
I0312 15:22:24.579705  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:29.573554  3400 solver.cpp:219] Iteration 1340 (3.68747 iter/s, 5.42378s/20 iters), loss = 0.156826
I0312 15:22:29.585666  3400 solver.cpp:238]     Train net output #0: loss = 0.156826 (* 1 = 0.156826 loss)
I0312 15:22:29.585678  3400 sgd_solver.cpp:105] Iteration 1340, lr = 0.001
I0312 15:22:30.229128  3400 solver.cpp:331] Iteration 1344, Testing net (#0)
I0312 15:22:32.126505  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:32.226131  3400 solver.cpp:398]     Test net output #0: accuracy = 0.888929
I0312 15:22:32.226167  3400 solver.cpp:398]     Test net output #1: loss = 0.340275 (* 1 = 0.340275 loss)
I0312 15:22:33.418944  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:36.817004  3400 solver.cpp:219] Iteration 1360 (2.76593 iter/s, 7.23084s/20 iters), loss = 0.0888245
I0312 15:22:36.829107  3400 solver.cpp:238]     Train net output #0: loss = 0.0888245 (* 1 = 0.0888245 loss)
I0312 15:22:36.829133  3400 sgd_solver.cpp:105] Iteration 1360, lr = 0.001
I0312 15:22:39.645694  3400 solver.cpp:331] Iteration 1372, Testing net (#0)
I0312 15:22:40.027101  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:41.530071  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:41.651494  3400 solver.cpp:398]     Test net output #0: accuracy = 0.906071
I0312 15:22:41.651532  3400 solver.cpp:398]     Test net output #1: loss = 0.307399 (* 1 = 0.307399 loss)
I0312 15:22:44.074694  3400 solver.cpp:219] Iteration 1380 (2.7605 iter/s, 7.24508s/20 iters), loss = 0.197561
I0312 15:22:44.086817  3400 solver.cpp:238]     Train net output #0: loss = 0.197561 (* 1 = 0.197561 loss)
I0312 15:22:44.086843  3400 sgd_solver.cpp:105] Iteration 1380, lr = 0.001
I0312 15:22:49.074193  3400 solver.cpp:331] Iteration 1400, Testing net (#0)
I0312 15:22:49.094249  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:50.938649  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:51.060781  3400 solver.cpp:398]     Test net output #0: accuracy = 0.927142
I0312 15:22:51.060820  3400 solver.cpp:398]     Test net output #1: loss = 0.236449 (* 1 = 0.236449 loss)
I0312 15:22:51.324509  3400 solver.cpp:219] Iteration 1400 (2.76351 iter/s, 7.23718s/20 iters), loss = 0.110642
I0312 15:22:51.326978  3400 solver.cpp:238]     Train net output #0: loss = 0.110642 (* 1 = 0.110642 loss)
I0312 15:22:51.327003  3400 sgd_solver.cpp:105] Iteration 1400, lr = 0.001
I0312 15:22:56.742542  3400 solver.cpp:219] Iteration 1420 (3.69332 iter/s, 5.41518s/20 iters), loss = 0.0974992
I0312 15:22:56.754711  3400 solver.cpp:238]     Train net output #0: loss = 0.0974992 (* 1 = 0.0974992 loss)
I0312 15:22:56.754739  3400 sgd_solver.cpp:105] Iteration 1420, lr = 0.001
I0312 15:22:57.721212  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:22:58.488878  3400 solver.cpp:331] Iteration 1428, Testing net (#0)
I0312 15:23:00.368564  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:23:00.492012  3400 solver.cpp:398]     Test net output #0: accuracy = 0.934285
I0312 15:23:00.492048  3400 solver.cpp:398]     Test net output #1: loss = 0.218519 (* 1 = 0.218519 loss)
I0312 15:23:04.001441  3400 solver.cpp:219] Iteration 1440 (2.76007 iter/s, 7.2462s/20 iters), loss = 0.0782407
I0312 15:23:04.013653  3400 solver.cpp:238]     Train net output #0: loss = 0.0782407 (* 1 = 0.0782407 loss)
I0312 15:23:04.013679  3400 sgd_solver.cpp:105] Iteration 1440, lr = 0.001
I0312 15:23:06.572952  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:23:07.918545  3400 solver.cpp:331] Iteration 1456, Testing net (#0)
I0312 15:23:09.800770  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:23:09.924667  3400 solver.cpp:398]     Test net output #0: accuracy = 0.921428
I0312 15:23:09.924705  3400 solver.cpp:398]     Test net output #1: loss = 0.245003 (* 1 = 0.245003 loss)
I0312 15:23:11.262861  3400 solver.cpp:219] Iteration 1460 (2.75912 iter/s, 7.24868s/20 iters), loss = 0.0540647
I0312 15:23:11.274978  3400 solver.cpp:238]     Train net output #0: loss = 0.0540647 (* 1 = 0.0540647 loss)
I0312 15:23:11.275005  3400 sgd_solver.cpp:105] Iteration 1460, lr = 0.001
I0312 15:23:15.221263  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:23:16.701591  3400 solver.cpp:219] Iteration 1480 (3.68581 iter/s, 5.42621s/20 iters), loss = 0.0991732
I0312 15:23:16.713642  3400 solver.cpp:238]     Train net output #0: loss = 0.0991732 (* 1 = 0.0991732 loss)
I0312 15:23:16.713668  3400 sgd_solver.cpp:105] Iteration 1480, lr = 0.001
I0312 15:23:17.355805  3400 solver.cpp:331] Iteration 1484, Testing net (#0)
I0312 15:23:19.250823  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:23:19.375488  3400 solver.cpp:398]     Test net output #0: accuracy = 0.906428
I0312 15:23:19.375527  3400 solver.cpp:398]     Test net output #1: loss = 0.290387 (* 1 = 0.290387 loss)
I0312 15:23:23.975798  3400 solver.cpp:219] Iteration 1500 (2.75421 iter/s, 7.26161s/20 iters), loss = 0.13034
I0312 15:23:23.987970  3400 solver.cpp:238]     Train net output #0: loss = 0.13034 (* 1 = 0.13034 loss)
I0312 15:23:23.987998  3400 sgd_solver.cpp:105] Iteration 1500, lr = 0.001
I0312 15:23:24.014277  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:23:26.810633  3400 solver.cpp:331] Iteration 1512, Testing net (#0)
I0312 15:23:28.708775  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:23:28.834867  3400 solver.cpp:398]     Test net output #0: accuracy = 0.870714
I0312 15:23:28.834903  3400 solver.cpp:398]     Test net output #1: loss = 0.387578 (* 1 = 0.387578 loss)
I0312 15:23:31.260344  3400 solver.cpp:219] Iteration 1520 (2.75034 iter/s, 7.27182s/20 iters), loss = 0.0944722
I0312 15:23:31.272511  3400 solver.cpp:238]     Train net output #0: loss = 0.0944722 (* 1 = 0.0944722 loss)
I0312 15:23:31.272537  3400 sgd_solver.cpp:105] Iteration 1520, lr = 0.001
I0312 15:23:32.768396  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:23:36.259568  3400 solver.cpp:331] Iteration 1540, Testing net (#0)
I0312 15:23:37.462826  3400 blocking_queue.cpp:49] Waiting for data
I0312 15:23:38.148380  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:23:38.274374  3400 solver.cpp:398]     Test net output #0: accuracy = 0.933928
I0312 15:23:38.274395  3400 solver.cpp:398]     Test net output #1: loss = 0.230134 (* 1 = 0.230134 loss)
I0312 15:23:38.536557  3400 solver.cpp:219] Iteration 1540 (2.7535 iter/s, 7.26349s/20 iters), loss = 0.0971224
I0312 15:23:38.539016  3400 solver.cpp:238]     Train net output #0: loss = 0.0971224 (* 1 = 0.0971224 loss)
I0312 15:23:38.539042  3400 sgd_solver.cpp:105] Iteration 1540, lr = 0.001
I0312 15:23:41.627427  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:23:43.955335  3400 solver.cpp:219] Iteration 1560 (3.69283 iter/s, 5.41589s/20 iters), loss = 0.0968255
I0312 15:23:43.967442  3400 solver.cpp:238]     Train net output #0: loss = 0.0968255 (* 1 = 0.0968255 loss)
I0312 15:23:43.967468  3400 sgd_solver.cpp:105] Iteration 1560, lr = 0.001
I0312 15:23:45.698551  3400 solver.cpp:331] Iteration 1568, Testing net (#0)
I0312 15:23:47.568752  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:23:47.699048  3400 solver.cpp:398]     Test net output #0: accuracy = 0.926786
I0312 15:23:47.699085  3400 solver.cpp:398]     Test net output #1: loss = 0.266172 (* 1 = 0.266172 loss)
I0312 15:23:50.263005  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:23:51.213078  3400 solver.cpp:219] Iteration 1580 (2.7605 iter/s, 7.24507s/20 iters), loss = 0.148818
I0312 15:23:51.225193  3400 solver.cpp:238]     Train net output #0: loss = 0.148818 (* 1 = 0.148818 loss)
I0312 15:23:51.225219  3400 sgd_solver.cpp:105] Iteration 1580, lr = 0.001
I0312 15:23:55.124668  3400 solver.cpp:331] Iteration 1596, Testing net (#0)
I0312 15:23:56.997790  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:23:57.125257  3400 solver.cpp:398]     Test net output #0: accuracy = 0.931071
I0312 15:23:57.125279  3400 solver.cpp:398]     Test net output #1: loss = 0.243579 (* 1 = 0.243579 loss)
I0312 15:23:58.459100  3400 solver.cpp:219] Iteration 1600 (2.76497 iter/s, 7.23334s/20 iters), loss = 0.0652831
I0312 15:23:58.471344  3400 solver.cpp:238]     Train net output #0: loss = 0.0652831 (* 1 = 0.0652831 loss)
I0312 15:23:58.471356  3400 sgd_solver.cpp:105] Iteration 1600, lr = 0.001
I0312 15:23:59.114401  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:24:04.013156  3400 solver.cpp:219] Iteration 1620 (3.60921 iter/s, 5.54137s/20 iters), loss = 0.0935076
I0312 15:24:04.025998  3400 solver.cpp:238]     Train net output #0: loss = 0.0935076 (* 1 = 0.0935076 loss)
I0312 15:24:04.026031  3400 sgd_solver.cpp:105] Iteration 1620, lr = 0.001
I0312 15:24:04.707993  3400 solver.cpp:331] Iteration 1624, Testing net (#0)
I0312 15:24:06.577738  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:24:06.710077  3400 solver.cpp:398]     Test net output #0: accuracy = 0.935714
I0312 15:24:06.710114  3400 solver.cpp:398]     Test net output #1: loss = 0.241938 (* 1 = 0.241938 loss)
I0312 15:24:07.955536  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:24:11.454594  3400 solver.cpp:219] Iteration 1640 (2.69251 iter/s, 7.42801s/20 iters), loss = 0.051428
I0312 15:24:11.467205  3400 solver.cpp:238]     Train net output #0: loss = 0.051428 (* 1 = 0.051428 loss)
I0312 15:24:11.467224  3400 sgd_solver.cpp:105] Iteration 1640, lr = 0.001
I0312 15:24:14.320205  3400 solver.cpp:331] Iteration 1652, Testing net (#0)
I0312 15:24:14.724130  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:24:16.194334  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:24:16.326746  3400 solver.cpp:398]     Test net output #0: accuracy = 0.942142
I0312 15:24:16.326769  3400 solver.cpp:398]     Test net output #1: loss = 0.223745 (* 1 = 0.223745 loss)
I0312 15:24:18.896901  3400 solver.cpp:219] Iteration 1660 (2.69211 iter/s, 7.42911s/20 iters), loss = 0.0416095
I0312 15:24:18.909481  3400 solver.cpp:238]     Train net output #0: loss = 0.0416095 (* 1 = 0.0416095 loss)
I0312 15:24:18.909512  3400 sgd_solver.cpp:105] Iteration 1660, lr = 0.001
I0312 15:24:24.042721  3400 solver.cpp:331] Iteration 1680, Testing net (#0)
I0312 15:24:24.061594  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:24:25.921290  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:24:26.051421  3400 solver.cpp:398]     Test net output #0: accuracy = 0.945714
I0312 15:24:26.051460  3400 solver.cpp:398]     Test net output #1: loss = 0.21022 (* 1 = 0.21022 loss)
I0312 15:24:26.313695  3400 solver.cpp:219] Iteration 1680 (2.70138 iter/s, 7.40363s/20 iters), loss = 0.0470645
I0312 15:24:26.316110  3400 solver.cpp:238]     Train net output #0: loss = 0.0470645 (* 1 = 0.0470645 loss)
I0312 15:24:26.316123  3400 sgd_solver.cpp:105] Iteration 1680, lr = 0.001
I0312 15:24:31.898216  3400 solver.cpp:219] Iteration 1700 (3.58317 iter/s, 5.58164s/20 iters), loss = 0.0492646
I0312 15:24:31.910471  3400 solver.cpp:238]     Train net output #0: loss = 0.0492646 (* 1 = 0.0492646 loss)
I0312 15:24:31.910604  3400 sgd_solver.cpp:105] Iteration 1700, lr = 0.001
I0312 15:24:32.905202  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:24:33.710945  3400 solver.cpp:331] Iteration 1708, Testing net (#0)
I0312 15:24:35.588150  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:24:35.719116  3400 solver.cpp:398]     Test net output #0: accuracy = 0.947143
I0312 15:24:35.719139  3400 solver.cpp:398]     Test net output #1: loss = 0.223752 (* 1 = 0.223752 loss)
I0312 15:24:39.229243  3400 solver.cpp:219] Iteration 1720 (2.73291 iter/s, 7.3182s/20 iters), loss = 0.0587909
I0312 15:24:39.241425  3400 solver.cpp:238]     Train net output #0: loss = 0.0587909 (* 1 = 0.0587909 loss)
I0312 15:24:39.241437  3400 sgd_solver.cpp:105] Iteration 1720, lr = 0.001
I0312 15:24:41.805457  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:24:43.144937  3400 solver.cpp:331] Iteration 1736, Testing net (#0)
I0312 15:24:45.041338  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:24:45.173462  3400 solver.cpp:398]     Test net output #0: accuracy = 0.948214
I0312 15:24:45.173501  3400 solver.cpp:398]     Test net output #1: loss = 0.210534 (* 1 = 0.210534 loss)
I0312 15:24:46.514678  3400 solver.cpp:219] Iteration 1740 (2.75003 iter/s, 7.27266s/20 iters), loss = 0.0363963
I0312 15:24:46.526796  3400 solver.cpp:238]     Train net output #0: loss = 0.0363963 (* 1 = 0.0363963 loss)
I0312 15:24:46.526823  3400 sgd_solver.cpp:105] Iteration 1740, lr = 0.001
I0312 15:24:50.481462  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:24:52.024276  3400 solver.cpp:219] Iteration 1760 (3.63833 iter/s, 5.49702s/20 iters), loss = 0.0759064
I0312 15:24:52.036573  3400 solver.cpp:238]     Train net output #0: loss = 0.0759064 (* 1 = 0.0759064 loss)
I0312 15:24:52.036598  3400 sgd_solver.cpp:105] Iteration 1760, lr = 0.001
I0312 15:24:52.677892  3400 solver.cpp:331] Iteration 1764, Testing net (#0)
I0312 15:24:54.558871  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:24:54.692844  3400 solver.cpp:398]     Test net output #0: accuracy = 0.943928
I0312 15:24:54.692884  3400 solver.cpp:398]     Test net output #1: loss = 0.217515 (* 1 = 0.217515 loss)
I0312 15:24:59.339237  3400 solver.cpp:219] Iteration 1780 (2.73893 iter/s, 7.30212s/20 iters), loss = 0.0333237
I0312 15:24:59.352099  3400 solver.cpp:238]     Train net output #0: loss = 0.0333237 (* 1 = 0.0333237 loss)
I0312 15:24:59.352130  3400 sgd_solver.cpp:105] Iteration 1780, lr = 0.001
I0312 15:24:59.370473  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:25:02.257624  3400 solver.cpp:331] Iteration 1792, Testing net (#0)
I0312 15:25:04.165477  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:25:04.298642  3400 solver.cpp:398]     Test net output #0: accuracy = 0.940357
I0312 15:25:04.298684  3400 solver.cpp:398]     Test net output #1: loss = 0.236867 (* 1 = 0.236867 loss)
I0312 15:25:06.756495  3400 solver.cpp:219] Iteration 1800 (2.70132 iter/s, 7.40379s/20 iters), loss = 0.0709304
I0312 15:25:06.768586  3400 solver.cpp:238]     Train net output #0: loss = 0.0709304 (* 1 = 0.0709304 loss)
I0312 15:25:06.768617  3400 sgd_solver.cpp:105] Iteration 1800, lr = 0.001
I0312 15:25:08.273730  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:25:11.793817  3400 solver.cpp:331] Iteration 1820, Testing net (#0)
I0312 15:25:13.656452  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:25:13.791921  3400 solver.cpp:398]     Test net output #0: accuracy = 0.945
I0312 15:25:13.791944  3400 solver.cpp:398]     Test net output #1: loss = 0.217189 (* 1 = 0.217189 loss)
I0312 15:25:14.056071  3400 solver.cpp:219] Iteration 1820 (2.74466 iter/s, 7.28688s/20 iters), loss = 0.0877381
I0312 15:25:14.058534  3400 solver.cpp:238]     Train net output #0: loss = 0.0877381 (* 1 = 0.0877381 loss)
I0312 15:25:14.058560  3400 sgd_solver.cpp:105] Iteration 1820, lr = 0.001
I0312 15:25:17.148061  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:25:19.475175  3400 solver.cpp:219] Iteration 1840 (3.69264 iter/s, 5.41619s/20 iters), loss = 0.0563678
I0312 15:25:19.487390  3400 solver.cpp:238]     Train net output #0: loss = 0.0563678 (* 1 = 0.0563678 loss)
I0312 15:25:19.487438  3400 sgd_solver.cpp:105] Iteration 1840, lr = 0.001
I0312 15:25:21.228039  3400 solver.cpp:331] Iteration 1848, Testing net (#0)
I0312 15:25:23.099680  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:25:23.255745  3400 solver.cpp:398]     Test net output #0: accuracy = 0.943214
I0312 15:25:23.255784  3400 solver.cpp:398]     Test net output #1: loss = 0.229499 (* 1 = 0.229499 loss)
I0312 15:25:25.825199  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:25:26.769888  3400 solver.cpp:219] Iteration 1860 (2.74653 iter/s, 7.2819s/20 iters), loss = 0.0632004
I0312 15:25:26.782080  3400 solver.cpp:238]     Train net output #0: loss = 0.0632004 (* 1 = 0.0632004 loss)
I0312 15:25:26.782095  3400 sgd_solver.cpp:105] Iteration 1860, lr = 0.001
I0312 15:25:30.710422  3400 solver.cpp:331] Iteration 1876, Testing net (#0)
I0312 15:25:32.561218  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:25:32.713716  3400 solver.cpp:398]     Test net output #0: accuracy = 0.949285
I0312 15:25:32.713754  3400 solver.cpp:398]     Test net output #1: loss = 0.206619 (* 1 = 0.206619 loss)
I0312 15:25:34.049382  3400 solver.cpp:219] Iteration 1880 (2.75228 iter/s, 7.2667s/20 iters), loss = 0.0472578
I0312 15:25:34.061617  3400 solver.cpp:238]     Train net output #0: loss = 0.0472578 (* 1 = 0.0472578 loss)
I0312 15:25:34.061643  3400 sgd_solver.cpp:105] Iteration 1880, lr = 0.001
I0312 15:25:34.703518  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:25:39.492034  3400 solver.cpp:219] Iteration 1900 (3.68327 iter/s, 5.42996s/20 iters), loss = 0.0901867
I0312 15:25:39.504140  3400 solver.cpp:238]     Train net output #0: loss = 0.0901867 (* 1 = 0.0901867 loss)
I0312 15:25:39.504153  3400 sgd_solver.cpp:105] Iteration 1900, lr = 0.001
I0312 15:25:40.148438  3400 solver.cpp:331] Iteration 1904, Testing net (#0)
I0312 15:25:41.973055  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:25:42.131355  3400 solver.cpp:398]     Test net output #0: accuracy = 0.941428
I0312 15:25:42.131391  3400 solver.cpp:398]     Test net output #1: loss = 0.243495 (* 1 = 0.243495 loss)
I0312 15:25:43.339576  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:25:46.729701  3400 solver.cpp:219] Iteration 1920 (2.76818 iter/s, 7.22495s/20 iters), loss = 0.0168991
I0312 15:25:46.741763  3400 solver.cpp:238]     Train net output #0: loss = 0.0168991 (* 1 = 0.0168991 loss)
I0312 15:25:46.741789  3400 sgd_solver.cpp:105] Iteration 1920, lr = 0.001
I0312 15:25:49.562788  3400 solver.cpp:331] Iteration 1932, Testing net (#0)
I0312 15:25:50.356412  3400 blocking_queue.cpp:49] Waiting for data
I0312 15:25:51.415866  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:25:51.577615  3400 solver.cpp:398]     Test net output #0: accuracy = 0.9475
I0312 15:25:51.577651  3400 solver.cpp:398]     Test net output #1: loss = 0.22666 (* 1 = 0.22666 loss)
I0312 15:25:51.677505  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:25:54.010398  3400 solver.cpp:219] Iteration 1940 (2.75162 iter/s, 7.26846s/20 iters), loss = 0.0458259
I0312 15:25:54.022474  3400 solver.cpp:238]     Train net output #0: loss = 0.0458259 (* 1 = 0.0458259 loss)
I0312 15:25:54.022500  3400 sgd_solver.cpp:105] Iteration 1940, lr = 0.001
I0312 15:25:59.010727  3400 solver.cpp:331] Iteration 1960, Testing net (#0)
I0312 15:25:59.040019  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:26:00.851655  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:26:01.011333  3400 solver.cpp:398]     Test net output #0: accuracy = 0.9525
I0312 15:26:01.011358  3400 solver.cpp:398]     Test net output #1: loss = 0.21101 (* 1 = 0.21101 loss)
I0312 15:26:01.274606  3400 solver.cpp:219] Iteration 1960 (2.75748 iter/s, 7.25299s/20 iters), loss = 0.0183012
I0312 15:26:01.277048  3400 solver.cpp:238]     Train net output #0: loss = 0.0183012 (* 1 = 0.0183012 loss)
I0312 15:26:01.277072  3400 sgd_solver.cpp:105] Iteration 1960, lr = 0.001
I0312 15:26:06.691892  3400 solver.cpp:219] Iteration 1980 (3.69313 iter/s, 5.41546s/20 iters), loss = 0.0222976
I0312 15:26:06.704053  3400 solver.cpp:238]     Train net output #0: loss = 0.0222976 (* 1 = 0.0222976 loss)
I0312 15:26:06.704079  3400 sgd_solver.cpp:105] Iteration 1980, lr = 0.001
I0312 15:26:07.678004  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:26:08.437675  3400 solver.cpp:331] Iteration 1988, Testing net (#0)
I0312 15:26:10.284507  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:26:10.444213  3400 solver.cpp:398]     Test net output #0: accuracy = 0.9525
I0312 15:26:10.444252  3400 solver.cpp:398]     Test net output #1: loss = 0.202441 (* 1 = 0.202441 loss)
I0312 15:26:13.946060  3400 solver.cpp:219] Iteration 2000 (2.76136 iter/s, 7.2428s/20 iters), loss = 0.0436784
I0312 15:26:13.958148  3400 solver.cpp:238]     Train net output #0: loss = 0.0436784 (* 1 = 0.0436784 loss)
I0312 15:26:13.958174  3400 sgd_solver.cpp:105] Iteration 2000, lr = 0.001
I0312 15:26:16.527683  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:26:17.866642  3400 solver.cpp:331] Iteration 2016, Testing net (#0)
I0312 15:26:19.723129  3410 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:26:19.882864  3400 solver.cpp:398]     Test net output #0: accuracy = 0.953214
I0312 15:26:19.882899  3400 solver.cpp:398]     Test net output #1: loss = 0.21853 (* 1 = 0.21853 loss)
I0312 15:26:21.214287  3400 solver.cpp:219] Iteration 2020 (2.756 iter/s, 7.2569s/20 iters), loss = 0.0215966
I0312 15:26:21.226387  3400 solver.cpp:238]     Train net output #0: loss = 0.0215966 (* 1 = 0.0215966 loss)
I0312 15:26:21.226413  3400 sgd_solver.cpp:105] Iteration 2020, lr = 0.001
I0312 15:26:25.183472  3409 data_layer.cpp:73] Restarting data prefetching from start.
I0312 15:26:26.659919  3400 solver.cpp:219] Iteration 2040 (3.68047 iter/s, 5.43408s/20 iters), loss = 0.0152768
I0312 15:26:26.672003  3400 solver.cpp:238]     Train net output #0: loss = 0.0152768 (* 1 = 0.0152768 loss)
I0312 15:26:26.672027  3400 sgd_solver.cpp:105] Iteration 2040, lr = 0.001