I1013 16:15:58.088281  8021 caffe.cpp:210] Use CPU.
I1013 16:15:58.088945  8021 solver.cpp:48] Initializing solver from parameters: 
test_iter: 100
test_interval: 100
base_lr: 0.001
display: 100
max_iter: 45000
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
stepsize: 10000
snapshot: 10000
snapshot_prefix: "/home/suyog/Data_Preprocessing_google_50classes/caffe-cnn-model/caffenet_train"
solver_mode: CPU
net: "/home/suyog/Data_Preprocessing_google_50classes/caffe-cnn-model/cifar10_quick_train_test.prototxt"
train_state {
  level: 0
  stage: ""
}
I1013 16:15:58.089128  8021 solver.cpp:91] Creating training net from net file: /home/suyog/Data_Preprocessing_google_50classes/caffe-cnn-model/cifar10_quick_train_test.prototxt
I1013 16:15:58.090140  8021 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer cifar
I1013 16:15:58.090193  8021 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I1013 16:15:58.090436  8021 net.cpp:58] Initializing net from parameters: 
name: "CIFAR10_quick"
state {
  phase: TRAIN
  level: 0
  stage: ""
}
layer {
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mean_file: "/home/suyog/Data_Preprocessing_google_50classes/random_data_selection_5classes/mean.binaryproto"
  }
  data_param {
    source: "/home/suyog/Data_Preprocessing_google_50classes/random_data_selection_5classes/database/train_lmdb"
    batch_size: 50
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.0001
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "pool1"
  top: "pool1"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 64
    weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 5
    weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}
I1013 16:15:58.090605  8021 layer_factory.hpp:77] Creating layer cifar
I1013 16:15:58.091424  8021 net.cpp:100] Creating Layer cifar
I1013 16:15:58.091452  8021 net.cpp:408] cifar -> data
I1013 16:15:58.091507  8021 net.cpp:408] cifar -> label
I1013 16:15:58.091575  8021 data_transformer.cpp:25] Loading mean file from: /home/suyog/Data_Preprocessing_google_50classes/random_data_selection_5classes/mean.binaryproto
I1013 16:15:58.091598  8025 db_lmdb.cpp:35] Opened lmdb /home/suyog/Data_Preprocessing_google_50classes/random_data_selection_5classes/database/train_lmdb
I1013 16:15:58.091740  8021 data_layer.cpp:41] output data size: 50,3,32,32
I1013 16:15:58.094466  8021 net.cpp:150] Setting up cifar
I1013 16:15:58.094547  8021 net.cpp:157] Top shape: 50 3 32 32 (153600)
I1013 16:15:58.094573  8021 net.cpp:157] Top shape: 50 (50)
I1013 16:15:58.094588  8021 net.cpp:165] Memory required for data: 614600
I1013 16:15:58.094616  8021 layer_factory.hpp:77] Creating layer conv1
I1013 16:15:58.094671  8021 net.cpp:100] Creating Layer conv1
I1013 16:15:58.094694  8021 net.cpp:434] conv1 <- data
I1013 16:15:58.094732  8021 net.cpp:408] conv1 -> conv1
I1013 16:15:58.095170  8021 net.cpp:150] Setting up conv1
I1013 16:15:58.095239  8021 net.cpp:157] Top shape: 50 32 32 32 (1638400)
I1013 16:15:58.095285  8021 net.cpp:165] Memory required for data: 7168200
I1013 16:15:58.095355  8021 layer_factory.hpp:77] Creating layer pool1
I1013 16:15:58.095413  8021 net.cpp:100] Creating Layer pool1
I1013 16:15:58.095458  8021 net.cpp:434] pool1 <- conv1
I1013 16:15:58.095485  8021 net.cpp:408] pool1 -> pool1
I1013 16:15:58.095536  8021 net.cpp:150] Setting up pool1
I1013 16:15:58.095557  8021 net.cpp:157] Top shape: 50 32 16 16 (409600)
I1013 16:15:58.095571  8021 net.cpp:165] Memory required for data: 8806600
I1013 16:15:58.095585  8021 layer_factory.hpp:77] Creating layer relu1
I1013 16:15:58.095645  8021 net.cpp:100] Creating Layer relu1
I1013 16:15:58.095691  8021 net.cpp:434] relu1 <- pool1
I1013 16:15:58.095739  8021 net.cpp:395] relu1 -> pool1 (in-place)
I1013 16:15:58.095765  8021 net.cpp:150] Setting up relu1
I1013 16:15:58.095791  8021 net.cpp:157] Top shape: 50 32 16 16 (409600)
I1013 16:15:58.095808  8021 net.cpp:165] Memory required for data: 10445000
I1013 16:15:58.095821  8021 layer_factory.hpp:77] Creating layer conv2
I1013 16:15:58.095861  8021 net.cpp:100] Creating Layer conv2
I1013 16:15:58.095882  8021 net.cpp:434] conv2 <- pool1
I1013 16:15:58.095906  8021 net.cpp:408] conv2 -> conv2
I1013 16:15:58.098799  8021 net.cpp:150] Setting up conv2
I1013 16:15:58.098875  8021 net.cpp:157] Top shape: 50 32 16 16 (409600)
I1013 16:15:58.098894  8021 net.cpp:165] Memory required for data: 12083400
I1013 16:15:58.098924  8021 layer_factory.hpp:77] Creating layer relu2
I1013 16:15:58.098945  8021 net.cpp:100] Creating Layer relu2
I1013 16:15:58.098961  8021 net.cpp:434] relu2 <- conv2
I1013 16:15:58.098981  8021 net.cpp:395] relu2 -> conv2 (in-place)
I1013 16:15:58.099004  8021 net.cpp:150] Setting up relu2
I1013 16:15:58.099025  8021 net.cpp:157] Top shape: 50 32 16 16 (409600)
I1013 16:15:58.099040  8021 net.cpp:165] Memory required for data: 13721800
I1013 16:15:58.099056  8021 layer_factory.hpp:77] Creating layer pool2
I1013 16:15:58.099074  8021 net.cpp:100] Creating Layer pool2
I1013 16:15:58.099089  8021 net.cpp:434] pool2 <- conv2
I1013 16:15:58.099109  8021 net.cpp:408] pool2 -> pool2
I1013 16:15:58.099134  8021 net.cpp:150] Setting up pool2
I1013 16:15:58.099153  8021 net.cpp:157] Top shape: 50 32 8 8 (102400)
I1013 16:15:58.099169  8021 net.cpp:165] Memory required for data: 14131400
I1013 16:15:58.099184  8021 layer_factory.hpp:77] Creating layer conv3
I1013 16:15:58.099211  8021 net.cpp:100] Creating Layer conv3
I1013 16:15:58.099226  8021 net.cpp:434] conv3 <- pool2
I1013 16:15:58.099248  8021 net.cpp:408] conv3 -> conv3
I1013 16:15:58.104881  8021 net.cpp:150] Setting up conv3
I1013 16:15:58.104913  8021 net.cpp:157] Top shape: 50 64 8 8 (204800)
I1013 16:15:58.104930  8021 net.cpp:165] Memory required for data: 14950600
I1013 16:15:58.104960  8021 layer_factory.hpp:77] Creating layer relu3
I1013 16:15:58.104991  8021 net.cpp:100] Creating Layer relu3
I1013 16:15:58.105011  8021 net.cpp:434] relu3 <- conv3
I1013 16:15:58.105031  8021 net.cpp:395] relu3 -> conv3 (in-place)
I1013 16:15:58.105082  8021 net.cpp:150] Setting up relu3
I1013 16:15:58.105104  8021 net.cpp:157] Top shape: 50 64 8 8 (204800)
I1013 16:15:58.105119  8021 net.cpp:165] Memory required for data: 15769800
I1013 16:15:58.105134  8021 layer_factory.hpp:77] Creating layer pool3
I1013 16:15:58.105156  8021 net.cpp:100] Creating Layer pool3
I1013 16:15:58.105171  8021 net.cpp:434] pool3 <- conv3
I1013 16:15:58.105190  8021 net.cpp:408] pool3 -> pool3
I1013 16:15:58.105214  8021 net.cpp:150] Setting up pool3
I1013 16:15:58.105233  8021 net.cpp:157] Top shape: 50 64 4 4 (51200)
I1013 16:15:58.105247  8021 net.cpp:165] Memory required for data: 15974600
I1013 16:15:58.105262  8021 layer_factory.hpp:77] Creating layer ip1
I1013 16:15:58.105284  8021 net.cpp:100] Creating Layer ip1
I1013 16:15:58.105299  8021 net.cpp:434] ip1 <- pool3
I1013 16:15:58.105322  8021 net.cpp:408] ip1 -> ip1
I1013 16:15:58.112370  8021 net.cpp:150] Setting up ip1
I1013 16:15:58.112395  8021 net.cpp:157] Top shape: 50 64 (3200)
I1013 16:15:58.112408  8021 net.cpp:165] Memory required for data: 15987400
I1013 16:15:58.112431  8021 layer_factory.hpp:77] Creating layer ip2
I1013 16:15:58.112455  8021 net.cpp:100] Creating Layer ip2
I1013 16:15:58.112470  8021 net.cpp:434] ip2 <- ip1
I1013 16:15:58.112491  8021 net.cpp:408] ip2 -> ip2
I1013 16:15:58.112566  8021 net.cpp:150] Setting up ip2
I1013 16:15:58.112587  8021 net.cpp:157] Top shape: 50 5 (250)
I1013 16:15:58.112601  8021 net.cpp:165] Memory required for data: 15988400
I1013 16:15:58.112634  8021 layer_factory.hpp:77] Creating layer loss
I1013 16:15:58.112668  8021 net.cpp:100] Creating Layer loss
I1013 16:15:58.112686  8021 net.cpp:434] loss <- ip2
I1013 16:15:58.112704  8021 net.cpp:434] loss <- label
I1013 16:15:58.112728  8021 net.cpp:408] loss -> loss
I1013 16:15:58.112771  8021 layer_factory.hpp:77] Creating layer loss
I1013 16:15:58.112819  8021 net.cpp:150] Setting up loss
I1013 16:15:58.112845  8021 net.cpp:157] Top shape: (1)
I1013 16:15:58.112860  8021 net.cpp:160]     with loss weight 1
I1013 16:15:58.112905  8021 net.cpp:165] Memory required for data: 15988404
I1013 16:15:58.112921  8021 net.cpp:226] loss needs backward computation.
I1013 16:15:58.112936  8021 net.cpp:226] ip2 needs backward computation.
I1013 16:15:58.112951  8021 net.cpp:226] ip1 needs backward computation.
I1013 16:15:58.112965  8021 net.cpp:226] pool3 needs backward computation.
I1013 16:15:58.112980  8021 net.cpp:226] relu3 needs backward computation.
I1013 16:15:58.112994  8021 net.cpp:226] conv3 needs backward computation.
I1013 16:15:58.113009  8021 net.cpp:226] pool2 needs backward computation.
I1013 16:15:58.113024  8021 net.cpp:226] relu2 needs backward computation.
I1013 16:15:58.113039  8021 net.cpp:226] conv2 needs backward computation.
I1013 16:15:58.113052  8021 net.cpp:226] relu1 needs backward computation.
I1013 16:15:58.113067  8021 net.cpp:226] pool1 needs backward computation.
I1013 16:15:58.113081  8021 net.cpp:226] conv1 needs backward computation.
I1013 16:15:58.113097  8021 net.cpp:228] cifar does not need backward computation.
I1013 16:15:58.113111  8021 net.cpp:270] This network produces output loss
I1013 16:15:58.113142  8021 net.cpp:283] Network initialization done.
I1013 16:15:58.114223  8021 solver.cpp:181] Creating test net (#0) specified by net file: /home/suyog/Data_Preprocessing_google_50classes/caffe-cnn-model/cifar10_quick_train_test.prototxt
I1013 16:15:58.114306  8021 net.cpp:322] The NetState phase (1) differed from the phase (0) specified by a rule in layer cifar
I1013 16:15:58.114603  8021 net.cpp:58] Initializing net from parameters: 
name: "CIFAR10_quick"
state {
  phase: TEST
}
layer {
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mean_file: "/home/suyog/Data_Preprocessing_google_50classes/random_data_selection_5classes/mean.binaryproto"
  }
  data_param {
    source: "/home/suyog/Data_Preprocessing_google_50classes/random_data_selection_5classes/database/validation_lmdb"
    batch_size: 25
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.0001
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "pool1"
  top: "pool1"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 64
    weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 5
    weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "ip2"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}
I1013 16:15:58.114867  8021 layer_factory.hpp:77] Creating layer cifar
I1013 16:15:58.115025  8021 net.cpp:100] Creating Layer cifar
I1013 16:15:58.115067  8021 net.cpp:408] cifar -> data
I1013 16:15:58.115093  8021 net.cpp:408] cifar -> label
I1013 16:15:58.115118  8021 data_transformer.cpp:25] Loading mean file from: /home/suyog/Data_Preprocessing_google_50classes/random_data_selection_5classes/mean.binaryproto
I1013 16:15:58.115288  8027 db_lmdb.cpp:35] Opened lmdb /home/suyog/Data_Preprocessing_google_50classes/random_data_selection_5classes/database/validation_lmdb
I1013 16:15:58.115552  8021 data_layer.cpp:41] output data size: 25,3,32,32
I1013 16:15:58.116864  8021 net.cpp:150] Setting up cifar
I1013 16:15:58.116926  8021 net.cpp:157] Top shape: 25 3 32 32 (76800)
I1013 16:15:58.116946  8021 net.cpp:157] Top shape: 25 (25)
I1013 16:15:58.116996  8021 net.cpp:165] Memory required for data: 307300
I1013 16:15:58.117030  8021 layer_factory.hpp:77] Creating layer label_cifar_1_split
I1013 16:15:58.117064  8021 net.cpp:100] Creating Layer label_cifar_1_split
I1013 16:15:58.117086  8021 net.cpp:434] label_cifar_1_split <- label
I1013 16:15:58.117110  8021 net.cpp:408] label_cifar_1_split -> label_cifar_1_split_0
I1013 16:15:58.117152  8021 net.cpp:408] label_cifar_1_split -> label_cifar_1_split_1
I1013 16:15:58.117184  8021 net.cpp:150] Setting up label_cifar_1_split
I1013 16:15:58.117211  8021 net.cpp:157] Top shape: 25 (25)
I1013 16:15:58.117244  8021 net.cpp:157] Top shape: 25 (25)
I1013 16:15:58.117255  8021 net.cpp:165] Memory required for data: 307500
I1013 16:15:58.117323  8021 layer_factory.hpp:77] Creating layer conv1
I1013 16:15:58.117359  8021 net.cpp:100] Creating Layer conv1
I1013 16:15:58.117373  8021 net.cpp:434] conv1 <- data
I1013 16:15:58.117396  8021 net.cpp:408] conv1 -> conv1
I1013 16:15:58.117748  8021 net.cpp:150] Setting up conv1
I1013 16:15:58.117780  8021 net.cpp:157] Top shape: 25 32 32 32 (819200)
I1013 16:15:58.117794  8021 net.cpp:165] Memory required for data: 3584300
I1013 16:15:58.117823  8021 layer_factory.hpp:77] Creating layer pool1
I1013 16:15:58.117849  8021 net.cpp:100] Creating Layer pool1
I1013 16:15:58.117866  8021 net.cpp:434] pool1 <- conv1
I1013 16:15:58.117894  8021 net.cpp:408] pool1 -> pool1
I1013 16:15:58.117920  8021 net.cpp:150] Setting up pool1
I1013 16:15:58.117940  8021 net.cpp:157] Top shape: 25 32 16 16 (204800)
I1013 16:15:58.117960  8021 net.cpp:165] Memory required for data: 4403500
I1013 16:15:58.117980  8021 layer_factory.hpp:77] Creating layer relu1
I1013 16:15:58.118003  8021 net.cpp:100] Creating Layer relu1
I1013 16:15:58.118017  8021 net.cpp:434] relu1 <- pool1
I1013 16:15:58.118039  8021 net.cpp:395] relu1 -> pool1 (in-place)
I1013 16:15:58.118058  8021 net.cpp:150] Setting up relu1
I1013 16:15:58.118080  8021 net.cpp:157] Top shape: 25 32 16 16 (204800)
I1013 16:15:58.118094  8021 net.cpp:165] Memory required for data: 5222700
I1013 16:15:58.118108  8021 layer_factory.hpp:77] Creating layer conv2
I1013 16:15:58.118132  8021 net.cpp:100] Creating Layer conv2
I1013 16:15:58.118146  8021 net.cpp:434] conv2 <- pool1
I1013 16:15:58.118170  8021 net.cpp:408] conv2 -> conv2
I1013 16:15:58.121057  8021 net.cpp:150] Setting up conv2
I1013 16:15:58.121086  8021 net.cpp:157] Top shape: 25 32 16 16 (204800)
I1013 16:15:58.121099  8021 net.cpp:165] Memory required for data: 6041900
I1013 16:15:58.121124  8021 layer_factory.hpp:77] Creating layer relu2
I1013 16:15:58.121140  8021 net.cpp:100] Creating Layer relu2
I1013 16:15:58.121153  8021 net.cpp:434] relu2 <- conv2
I1013 16:15:58.121172  8021 net.cpp:395] relu2 -> conv2 (in-place)
I1013 16:15:58.121191  8021 net.cpp:150] Setting up relu2
I1013 16:15:58.121207  8021 net.cpp:157] Top shape: 25 32 16 16 (204800)
I1013 16:15:58.121219  8021 net.cpp:165] Memory required for data: 6861100
I1013 16:15:58.121232  8021 layer_factory.hpp:77] Creating layer pool2
I1013 16:15:58.121248  8021 net.cpp:100] Creating Layer pool2
I1013 16:15:58.121259  8021 net.cpp:434] pool2 <- conv2
I1013 16:15:58.121279  8021 net.cpp:408] pool2 -> pool2
I1013 16:15:58.121299  8021 net.cpp:150] Setting up pool2
I1013 16:15:58.121321  8021 net.cpp:157] Top shape: 25 32 8 8 (51200)
I1013 16:15:58.121335  8021 net.cpp:165] Memory required for data: 7065900
I1013 16:15:58.121350  8021 layer_factory.hpp:77] Creating layer conv3
I1013 16:15:58.121382  8021 net.cpp:100] Creating Layer conv3
I1013 16:15:58.121402  8021 net.cpp:434] conv3 <- pool2
I1013 16:15:58.121433  8021 net.cpp:408] conv3 -> conv3
I1013 16:15:58.127456  8021 net.cpp:150] Setting up conv3
I1013 16:15:58.127547  8021 net.cpp:157] Top shape: 25 64 8 8 (102400)
I1013 16:15:58.127566  8021 net.cpp:165] Memory required for data: 7475500
I1013 16:15:58.127609  8021 layer_factory.hpp:77] Creating layer relu3
I1013 16:15:58.127640  8021 net.cpp:100] Creating Layer relu3
I1013 16:15:58.127658  8021 net.cpp:434] relu3 <- conv3
I1013 16:15:58.127681  8021 net.cpp:395] relu3 -> conv3 (in-place)
I1013 16:15:58.127707  8021 net.cpp:150] Setting up relu3
I1013 16:15:58.127724  8021 net.cpp:157] Top shape: 25 64 8 8 (102400)
I1013 16:15:58.127737  8021 net.cpp:165] Memory required for data: 7885100
I1013 16:15:58.127749  8021 layer_factory.hpp:77] Creating layer pool3
I1013 16:15:58.127769  8021 net.cpp:100] Creating Layer pool3
I1013 16:15:58.127781  8021 net.cpp:434] pool3 <- conv3
I1013 16:15:58.127802  8021 net.cpp:408] pool3 -> pool3
I1013 16:15:58.127830  8021 net.cpp:150] Setting up pool3
I1013 16:15:58.127849  8021 net.cpp:157] Top shape: 25 64 4 4 (25600)
I1013 16:15:58.127863  8021 net.cpp:165] Memory required for data: 7987500
I1013 16:15:58.127936  8021 layer_factory.hpp:77] Creating layer ip1
I1013 16:15:58.127966  8021 net.cpp:100] Creating Layer ip1
I1013 16:15:58.127979  8021 net.cpp:434] ip1 <- pool3
I1013 16:15:58.127997  8021 net.cpp:408] ip1 -> ip1
I1013 16:15:58.135145  8021 net.cpp:150] Setting up ip1
I1013 16:15:58.135185  8021 net.cpp:157] Top shape: 25 64 (1600)
I1013 16:15:58.135200  8021 net.cpp:165] Memory required for data: 7993900
I1013 16:15:58.135222  8021 layer_factory.hpp:77] Creating layer ip2
I1013 16:15:58.135249  8021 net.cpp:100] Creating Layer ip2
I1013 16:15:58.135263  8021 net.cpp:434] ip2 <- ip1
I1013 16:15:58.135282  8021 net.cpp:408] ip2 -> ip2
I1013 16:15:58.135357  8021 net.cpp:150] Setting up ip2
I1013 16:15:58.135382  8021 net.cpp:157] Top shape: 25 5 (125)
I1013 16:15:58.135396  8021 net.cpp:165] Memory required for data: 7994400
I1013 16:15:58.135421  8021 layer_factory.hpp:77] Creating layer ip2_ip2_0_split
I1013 16:15:58.135440  8021 net.cpp:100] Creating Layer ip2_ip2_0_split
I1013 16:15:58.135452  8021 net.cpp:434] ip2_ip2_0_split <- ip2
I1013 16:15:58.135469  8021 net.cpp:408] ip2_ip2_0_split -> ip2_ip2_0_split_0
I1013 16:15:58.135493  8021 net.cpp:408] ip2_ip2_0_split -> ip2_ip2_0_split_1
I1013 16:15:58.135520  8021 net.cpp:150] Setting up ip2_ip2_0_split
I1013 16:15:58.135541  8021 net.cpp:157] Top shape: 25 5 (125)
I1013 16:15:58.135557  8021 net.cpp:157] Top shape: 25 5 (125)
I1013 16:15:58.135568  8021 net.cpp:165] Memory required for data: 7995400
I1013 16:15:58.135581  8021 layer_factory.hpp:77] Creating layer accuracy
I1013 16:15:58.135601  8021 net.cpp:100] Creating Layer accuracy
I1013 16:15:58.135613  8021 net.cpp:434] accuracy <- ip2_ip2_0_split_0
I1013 16:15:58.135627  8021 net.cpp:434] accuracy <- label_cifar_1_split_0
I1013 16:15:58.135645  8021 net.cpp:408] accuracy -> accuracy
I1013 16:15:58.135677  8021 net.cpp:150] Setting up accuracy
I1013 16:15:58.135697  8021 net.cpp:157] Top shape: (1)
I1013 16:15:58.135710  8021 net.cpp:165] Memory required for data: 7995404
I1013 16:15:58.135721  8021 layer_factory.hpp:77] Creating layer loss
I1013 16:15:58.135740  8021 net.cpp:100] Creating Layer loss
I1013 16:15:58.135752  8021 net.cpp:434] loss <- ip2_ip2_0_split_1
I1013 16:15:58.135766  8021 net.cpp:434] loss <- label_cifar_1_split_1
I1013 16:15:58.135788  8021 net.cpp:408] loss -> loss
I1013 16:15:58.135812  8021 layer_factory.hpp:77] Creating layer loss
I1013 16:15:58.135848  8021 net.cpp:150] Setting up loss
I1013 16:15:58.135866  8021 net.cpp:157] Top shape: (1)
I1013 16:15:58.135879  8021 net.cpp:160]     with loss weight 1
I1013 16:15:58.135910  8021 net.cpp:165] Memory required for data: 7995408
I1013 16:15:58.135923  8021 net.cpp:226] loss needs backward computation.
I1013 16:15:58.135937  8021 net.cpp:228] accuracy does not need backward computation.
I1013 16:15:58.135953  8021 net.cpp:226] ip2_ip2_0_split needs backward computation.
I1013 16:15:58.135965  8021 net.cpp:226] ip2 needs backward computation.
I1013 16:15:58.135978  8021 net.cpp:226] ip1 needs backward computation.
I1013 16:15:58.135993  8021 net.cpp:226] pool3 needs backward computation.
I1013 16:15:58.136008  8021 net.cpp:226] relu3 needs backward computation.
I1013 16:15:58.136019  8021 net.cpp:226] conv3 needs backward computation.
I1013 16:15:58.136032  8021 net.cpp:226] pool2 needs backward computation.
I1013 16:15:58.136046  8021 net.cpp:226] relu2 needs backward computation.
I1013 16:15:58.136059  8021 net.cpp:226] conv2 needs backward computation.
I1013 16:15:58.136073  8021 net.cpp:226] relu1 needs backward computation.
I1013 16:15:58.136085  8021 net.cpp:226] pool1 needs backward computation.
I1013 16:15:58.136098  8021 net.cpp:226] conv1 needs backward computation.
I1013 16:15:58.136112  8021 net.cpp:228] label_cifar_1_split does not need backward computation.
I1013 16:15:58.136126  8021 net.cpp:228] cifar does not need backward computation.
I1013 16:15:58.136138  8021 net.cpp:270] This network produces output accuracy
I1013 16:15:58.136150  8021 net.cpp:270] This network produces output loss
I1013 16:15:58.136189  8021 net.cpp:283] Network initialization done.
I1013 16:15:58.136405  8021 solver.cpp:60] Solver scaffolding done.
I1013 16:15:58.136483  8021 caffe.cpp:251] Starting Optimization
I1013 16:15:58.136502  8021 solver.cpp:279] Solving CIFAR10_quick
I1013 16:15:58.136515  8021 solver.cpp:280] Learning Rate Policy: step
I1013 16:15:58.137092  8021 solver.cpp:337] Iteration 0, Testing net (#0)
I1013 16:16:13.444034  8021 solver.cpp:404]     Test net output #0: accuracy = 0.2172
I1013 16:16:13.444135  8021 solver.cpp:404]     Test net output #1: loss = 1.60908 (* 1 = 1.60908 loss)
I1013 16:16:14.207366  8021 solver.cpp:228] Iteration 0, loss = 1.60871
I1013 16:16:14.207456  8021 solver.cpp:244]     Train net output #0: loss = 1.60871 (* 1 = 1.60871 loss)
I1013 16:16:14.207492  8021 sgd_solver.cpp:106] Iteration 0, lr = 0.001
I1013 16:17:27.529104  8021 solver.cpp:337] Iteration 100, Testing net (#0)
I1013 16:17:42.660562  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4788
I1013 16:17:42.660657  8021 solver.cpp:404]     Test net output #1: loss = 1.25406 (* 1 = 1.25406 loss)
I1013 16:17:43.393798  8021 solver.cpp:228] Iteration 100, loss = 0.537735
I1013 16:17:43.393888  8021 solver.cpp:244]     Train net output #0: loss = 0.537735 (* 1 = 0.537735 loss)
I1013 16:17:43.393909  8021 sgd_solver.cpp:106] Iteration 100, lr = 0.001
I1013 16:18:56.081717  8021 solver.cpp:337] Iteration 200, Testing net (#0)
I1013 16:19:11.682070  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1013 16:19:11.682199  8021 solver.cpp:404]     Test net output #1: loss = 1.87148 (* 1 = 1.87148 loss)
I1013 16:19:12.465229  8021 solver.cpp:228] Iteration 200, loss = 0.123204
I1013 16:19:12.465322  8021 solver.cpp:244]     Train net output #0: loss = 0.123204 (* 1 = 0.123204 loss)
I1013 16:19:12.465342  8021 sgd_solver.cpp:106] Iteration 200, lr = 0.001
I1013 16:20:26.873852  8021 solver.cpp:337] Iteration 300, Testing net (#0)
I1013 16:20:42.095093  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5212
I1013 16:20:42.095183  8021 solver.cpp:404]     Test net output #1: loss = 2.05132 (* 1 = 2.05132 loss)
I1013 16:20:42.827240  8021 solver.cpp:228] Iteration 300, loss = 0.0142856
I1013 16:20:42.827333  8021 solver.cpp:244]     Train net output #0: loss = 0.0142856 (* 1 = 0.0142856 loss)
I1013 16:20:42.827355  8021 sgd_solver.cpp:106] Iteration 300, lr = 0.001
I1013 16:21:55.290596  8021 solver.cpp:337] Iteration 400, Testing net (#0)
I1013 16:22:10.496050  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5436
I1013 16:22:10.496145  8021 solver.cpp:404]     Test net output #1: loss = 2.48814 (* 1 = 2.48814 loss)
I1013 16:22:11.228646  8021 solver.cpp:228] Iteration 400, loss = 0.0154572
I1013 16:22:11.228740  8021 solver.cpp:244]     Train net output #0: loss = 0.0154571 (* 1 = 0.0154571 loss)
I1013 16:22:11.228765  8021 sgd_solver.cpp:106] Iteration 400, lr = 0.001
I1013 16:23:24.144747  8021 solver.cpp:337] Iteration 500, Testing net (#0)
I1013 16:23:39.335551  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1013 16:23:39.335646  8021 solver.cpp:404]     Test net output #1: loss = 2.33955 (* 1 = 2.33955 loss)
I1013 16:23:40.065006  8021 solver.cpp:228] Iteration 500, loss = 0.00150991
I1013 16:23:40.065100  8021 solver.cpp:244]     Train net output #0: loss = 0.00150991 (* 1 = 0.00150991 loss)
I1013 16:23:40.065124  8021 sgd_solver.cpp:106] Iteration 500, lr = 0.001
I1013 16:24:52.739423  8021 solver.cpp:337] Iteration 600, Testing net (#0)
I1013 16:25:07.925089  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1013 16:25:07.925181  8021 solver.cpp:404]     Test net output #1: loss = 2.50213 (* 1 = 2.50213 loss)
I1013 16:25:08.659127  8021 solver.cpp:228] Iteration 600, loss = 0.000492455
I1013 16:25:08.659217  8021 solver.cpp:244]     Train net output #0: loss = 0.000492456 (* 1 = 0.000492456 loss)
I1013 16:25:08.659240  8021 sgd_solver.cpp:106] Iteration 600, lr = 0.001
I1013 16:26:21.267957  8021 solver.cpp:337] Iteration 700, Testing net (#0)
I1013 16:26:36.485932  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1013 16:26:36.486026  8021 solver.cpp:404]     Test net output #1: loss = 2.59534 (* 1 = 2.59534 loss)
I1013 16:26:37.217521  8021 solver.cpp:228] Iteration 700, loss = 0.000349752
I1013 16:26:37.217617  8021 solver.cpp:244]     Train net output #0: loss = 0.000349752 (* 1 = 0.000349752 loss)
I1013 16:26:37.217638  8021 sgd_solver.cpp:106] Iteration 700, lr = 0.001
I1013 16:27:49.708825  8021 solver.cpp:337] Iteration 800, Testing net (#0)
I1013 16:28:04.914970  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1013 16:28:04.915066  8021 solver.cpp:404]     Test net output #1: loss = 2.66545 (* 1 = 2.66545 loss)
I1013 16:28:05.644376  8021 solver.cpp:228] Iteration 800, loss = 0.000225827
I1013 16:28:05.644470  8021 solver.cpp:244]     Train net output #0: loss = 0.000225827 (* 1 = 0.000225827 loss)
I1013 16:28:05.644492  8021 sgd_solver.cpp:106] Iteration 800, lr = 0.001
I1013 16:29:18.316665  8021 solver.cpp:337] Iteration 900, Testing net (#0)
I1013 16:29:33.435895  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1013 16:29:33.435987  8021 solver.cpp:404]     Test net output #1: loss = 2.73522 (* 1 = 2.73522 loss)
I1013 16:29:34.164582  8021 solver.cpp:228] Iteration 900, loss = 0.000108249
I1013 16:29:34.164685  8021 solver.cpp:244]     Train net output #0: loss = 0.000108249 (* 1 = 0.000108249 loss)
I1013 16:29:34.164707  8021 sgd_solver.cpp:106] Iteration 900, lr = 0.001
I1013 16:30:46.724519  8021 solver.cpp:337] Iteration 1000, Testing net (#0)
I1013 16:31:01.804668  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1013 16:31:01.804755  8021 solver.cpp:404]     Test net output #1: loss = 2.7731 (* 1 = 2.7731 loss)
I1013 16:31:02.538260  8021 solver.cpp:228] Iteration 1000, loss = 8.99688e-05
I1013 16:31:02.538354  8021 solver.cpp:244]     Train net output #0: loss = 8.99692e-05 (* 1 = 8.99692e-05 loss)
I1013 16:31:02.538377  8021 sgd_solver.cpp:106] Iteration 1000, lr = 0.001
I1013 16:32:15.029021  8021 solver.cpp:337] Iteration 1100, Testing net (#0)
I1013 16:32:30.189664  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1013 16:32:30.189750  8021 solver.cpp:404]     Test net output #1: loss = 2.82163 (* 1 = 2.82163 loss)
I1013 16:32:30.920301  8021 solver.cpp:228] Iteration 1100, loss = 9.09176e-05
I1013 16:32:30.920399  8021 solver.cpp:244]     Train net output #0: loss = 9.0918e-05 (* 1 = 9.0918e-05 loss)
I1013 16:32:30.920421  8021 sgd_solver.cpp:106] Iteration 1100, lr = 0.001
I1013 16:33:43.339565  8021 solver.cpp:337] Iteration 1200, Testing net (#0)
I1013 16:33:58.459414  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1013 16:33:58.459511  8021 solver.cpp:404]     Test net output #1: loss = 2.85547 (* 1 = 2.85547 loss)
I1013 16:33:59.191020  8021 solver.cpp:228] Iteration 1200, loss = 0.00013073
I1013 16:33:59.191119  8021 solver.cpp:244]     Train net output #0: loss = 0.00013073 (* 1 = 0.00013073 loss)
I1013 16:33:59.191143  8021 sgd_solver.cpp:106] Iteration 1200, lr = 0.001
I1013 16:35:11.783473  8021 solver.cpp:337] Iteration 1300, Testing net (#0)
I1013 16:35:26.886237  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1013 16:35:26.886332  8021 solver.cpp:404]     Test net output #1: loss = 2.88083 (* 1 = 2.88083 loss)
I1013 16:35:27.615320  8021 solver.cpp:228] Iteration 1300, loss = 0.000188332
I1013 16:35:27.615409  8021 solver.cpp:244]     Train net output #0: loss = 0.000188332 (* 1 = 0.000188332 loss)
I1013 16:35:27.615432  8021 sgd_solver.cpp:106] Iteration 1300, lr = 0.001
I1013 16:36:40.748535  8021 solver.cpp:337] Iteration 1400, Testing net (#0)
I1013 16:36:55.786293  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1013 16:36:55.786386  8021 solver.cpp:404]     Test net output #1: loss = 2.91644 (* 1 = 2.91644 loss)
I1013 16:36:56.516630  8021 solver.cpp:228] Iteration 1400, loss = 0.000192845
I1013 16:36:56.516717  8021 solver.cpp:244]     Train net output #0: loss = 0.000192846 (* 1 = 0.000192846 loss)
I1013 16:36:56.516747  8021 sgd_solver.cpp:106] Iteration 1400, lr = 0.001
I1013 16:38:08.979943  8021 solver.cpp:337] Iteration 1500, Testing net (#0)
I1013 16:38:24.189126  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1013 16:38:24.189221  8021 solver.cpp:404]     Test net output #1: loss = 2.93454 (* 1 = 2.93454 loss)
I1013 16:38:24.921314  8021 solver.cpp:228] Iteration 1500, loss = 0.00011184
I1013 16:38:24.921406  8021 solver.cpp:244]     Train net output #0: loss = 0.000111841 (* 1 = 0.000111841 loss)
I1013 16:38:24.921437  8021 sgd_solver.cpp:106] Iteration 1500, lr = 0.001
I1013 16:39:40.244261  8021 solver.cpp:337] Iteration 1600, Testing net (#0)
I1013 16:39:56.400315  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1013 16:39:56.400414  8021 solver.cpp:404]     Test net output #1: loss = 2.95468 (* 1 = 2.95468 loss)
I1013 16:39:57.154307  8021 solver.cpp:228] Iteration 1600, loss = 9.04762e-05
I1013 16:39:57.154403  8021 solver.cpp:244]     Train net output #0: loss = 9.04766e-05 (* 1 = 9.04766e-05 loss)
I1013 16:39:57.154425  8021 sgd_solver.cpp:106] Iteration 1600, lr = 0.001
I1013 16:41:12.529786  8021 solver.cpp:337] Iteration 1700, Testing net (#0)
I1013 16:41:27.903934  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1013 16:41:27.904178  8021 solver.cpp:404]     Test net output #1: loss = 2.98095 (* 1 = 2.98095 loss)
I1013 16:41:28.643784  8021 solver.cpp:228] Iteration 1700, loss = 6.49307e-05
I1013 16:41:28.643874  8021 solver.cpp:244]     Train net output #0: loss = 6.49311e-05 (* 1 = 6.49311e-05 loss)
I1013 16:41:28.643898  8021 sgd_solver.cpp:106] Iteration 1700, lr = 0.001
I1013 16:42:44.582510  8021 solver.cpp:337] Iteration 1800, Testing net (#0)
I1013 16:42:59.825376  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5012
I1013 16:42:59.825470  8021 solver.cpp:404]     Test net output #1: loss = 2.99166 (* 1 = 2.99166 loss)
I1013 16:43:00.559612  8021 solver.cpp:228] Iteration 1800, loss = 5.21402e-05
I1013 16:43:00.559707  8021 solver.cpp:244]     Train net output #0: loss = 5.21406e-05 (* 1 = 5.21406e-05 loss)
I1013 16:43:00.559731  8021 sgd_solver.cpp:106] Iteration 1800, lr = 0.001
I1013 16:44:15.146240  8021 solver.cpp:337] Iteration 1900, Testing net (#0)
I1013 16:44:30.842798  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1013 16:44:30.842902  8021 solver.cpp:404]     Test net output #1: loss = 3.00706 (* 1 = 3.00706 loss)
I1013 16:44:31.678660  8021 solver.cpp:228] Iteration 1900, loss = 4.67543e-05
I1013 16:44:31.678797  8021 solver.cpp:244]     Train net output #0: loss = 4.67547e-05 (* 1 = 4.67547e-05 loss)
I1013 16:44:31.678822  8021 sgd_solver.cpp:106] Iteration 1900, lr = 0.001
I1013 16:45:49.022356  8021 solver.cpp:337] Iteration 2000, Testing net (#0)
I1013 16:46:04.132489  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1013 16:46:04.132588  8021 solver.cpp:404]     Test net output #1: loss = 3.03036 (* 1 = 3.03036 loss)
I1013 16:46:04.862521  8021 solver.cpp:228] Iteration 2000, loss = 3.83915e-05
I1013 16:46:04.862618  8021 solver.cpp:244]     Train net output #0: loss = 3.83919e-05 (* 1 = 3.83919e-05 loss)
I1013 16:46:04.862642  8021 sgd_solver.cpp:106] Iteration 2000, lr = 0.001
I1013 16:47:17.657531  8021 solver.cpp:337] Iteration 2100, Testing net (#0)
I1013 16:47:32.779830  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1013 16:47:32.779927  8021 solver.cpp:404]     Test net output #1: loss = 3.03634 (* 1 = 3.03634 loss)
I1013 16:47:33.510296  8021 solver.cpp:228] Iteration 2100, loss = 8.00801e-05
I1013 16:47:33.510397  8021 solver.cpp:244]     Train net output #0: loss = 8.00805e-05 (* 1 = 8.00805e-05 loss)
I1013 16:47:33.510421  8021 sgd_solver.cpp:106] Iteration 2100, lr = 0.001
I1013 16:48:46.097100  8021 solver.cpp:337] Iteration 2200, Testing net (#0)
I1013 16:49:01.222853  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1013 16:49:01.222942  8021 solver.cpp:404]     Test net output #1: loss = 3.0497 (* 1 = 3.0497 loss)
I1013 16:49:01.957222  8021 solver.cpp:228] Iteration 2200, loss = 0.000128547
I1013 16:49:01.957317  8021 solver.cpp:244]     Train net output #0: loss = 0.000128548 (* 1 = 0.000128548 loss)
I1013 16:49:01.957340  8021 sgd_solver.cpp:106] Iteration 2200, lr = 0.001
I1013 16:50:15.048223  8021 solver.cpp:337] Iteration 2300, Testing net (#0)
I1013 16:50:30.929006  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1013 16:50:30.929097  8021 solver.cpp:404]     Test net output #1: loss = 3.06722 (* 1 = 3.06722 loss)
I1013 16:50:31.671350  8021 solver.cpp:228] Iteration 2300, loss = 0.000119272
I1013 16:50:31.671440  8021 solver.cpp:244]     Train net output #0: loss = 0.000119273 (* 1 = 0.000119273 loss)
I1013 16:50:31.671465  8021 sgd_solver.cpp:106] Iteration 2300, lr = 0.001
I1013 16:51:46.809245  8021 solver.cpp:337] Iteration 2400, Testing net (#0)
I1013 16:52:02.900434  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1013 16:52:02.900527  8021 solver.cpp:404]     Test net output #1: loss = 3.07807 (* 1 = 3.07807 loss)
I1013 16:52:03.640610  8021 solver.cpp:228] Iteration 2400, loss = 6.90288e-05
I1013 16:52:03.640710  8021 solver.cpp:244]     Train net output #0: loss = 6.90292e-05 (* 1 = 6.90292e-05 loss)
I1013 16:52:03.640733  8021 sgd_solver.cpp:106] Iteration 2400, lr = 0.001
I1013 16:53:18.285104  8021 solver.cpp:337] Iteration 2500, Testing net (#0)
I1013 16:53:34.046141  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1013 16:53:34.046234  8021 solver.cpp:404]     Test net output #1: loss = 3.08032 (* 1 = 3.08032 loss)
I1013 16:53:34.793397  8021 solver.cpp:228] Iteration 2500, loss = 6.68219e-05
I1013 16:53:34.793519  8021 solver.cpp:244]     Train net output #0: loss = 6.68223e-05 (* 1 = 6.68223e-05 loss)
I1013 16:53:34.793545  8021 sgd_solver.cpp:106] Iteration 2500, lr = 0.001
I1013 16:54:49.891621  8021 solver.cpp:337] Iteration 2600, Testing net (#0)
I1013 16:55:05.471302  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1013 16:55:05.471395  8021 solver.cpp:404]     Test net output #1: loss = 3.10073 (* 1 = 3.10073 loss)
I1013 16:55:06.207422  8021 solver.cpp:228] Iteration 2600, loss = 3.9106e-05
I1013 16:55:06.207516  8021 solver.cpp:244]     Train net output #0: loss = 3.91064e-05 (* 1 = 3.91064e-05 loss)
I1013 16:55:06.207540  8021 sgd_solver.cpp:106] Iteration 2600, lr = 0.001
I1013 16:56:19.764559  8021 solver.cpp:337] Iteration 2700, Testing net (#0)
I1013 16:56:35.147779  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1013 16:56:35.147877  8021 solver.cpp:404]     Test net output #1: loss = 3.10986 (* 1 = 3.10986 loss)
I1013 16:56:35.885359  8021 solver.cpp:228] Iteration 2700, loss = 4.08461e-05
I1013 16:56:35.885457  8021 solver.cpp:244]     Train net output #0: loss = 4.08465e-05 (* 1 = 4.08465e-05 loss)
I1013 16:56:35.885478  8021 sgd_solver.cpp:106] Iteration 2700, lr = 0.001
I1013 16:57:50.139039  8021 solver.cpp:337] Iteration 2800, Testing net (#0)
I1013 16:58:05.588426  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1013 16:58:05.588520  8021 solver.cpp:404]     Test net output #1: loss = 3.112 (* 1 = 3.112 loss)
I1013 16:58:06.327328  8021 solver.cpp:228] Iteration 2800, loss = 3.28057e-05
I1013 16:58:06.327422  8021 solver.cpp:244]     Train net output #0: loss = 3.28061e-05 (* 1 = 3.28061e-05 loss)
I1013 16:58:06.327448  8021 sgd_solver.cpp:106] Iteration 2800, lr = 0.001
I1013 16:59:20.311936  8021 solver.cpp:337] Iteration 2900, Testing net (#0)
I1013 16:59:35.636276  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1013 16:59:35.636370  8021 solver.cpp:404]     Test net output #1: loss = 3.12993 (* 1 = 3.12993 loss)
I1013 16:59:36.384717  8021 solver.cpp:228] Iteration 2900, loss = 3.87113e-05
I1013 16:59:36.384836  8021 solver.cpp:244]     Train net output #0: loss = 3.87117e-05 (* 1 = 3.87117e-05 loss)
I1013 16:59:36.384863  8021 sgd_solver.cpp:106] Iteration 2900, lr = 0.001
I1013 17:00:50.032827  8021 solver.cpp:337] Iteration 3000, Testing net (#0)
I1013 17:01:05.302206  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1013 17:01:05.302307  8021 solver.cpp:404]     Test net output #1: loss = 3.13172 (* 1 = 3.13172 loss)
I1013 17:01:06.047449  8021 solver.cpp:228] Iteration 3000, loss = 7.33335e-05
I1013 17:01:06.047549  8021 solver.cpp:244]     Train net output #0: loss = 7.33339e-05 (* 1 = 7.33339e-05 loss)
I1013 17:01:06.047572  8021 sgd_solver.cpp:106] Iteration 3000, lr = 0.001
I1013 17:02:19.835431  8021 solver.cpp:337] Iteration 3100, Testing net (#0)
I1013 17:02:35.105007  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1013 17:02:35.105108  8021 solver.cpp:404]     Test net output #1: loss = 3.13786 (* 1 = 3.13786 loss)
I1013 17:02:35.844110  8021 solver.cpp:228] Iteration 3100, loss = 0.000112331
I1013 17:02:35.844200  8021 solver.cpp:244]     Train net output #0: loss = 0.000112331 (* 1 = 0.000112331 loss)
I1013 17:02:35.844223  8021 sgd_solver.cpp:106] Iteration 3100, lr = 0.001
I1013 17:03:49.664033  8021 solver.cpp:337] Iteration 3200, Testing net (#0)
I1013 17:04:05.019300  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1013 17:04:05.019394  8021 solver.cpp:404]     Test net output #1: loss = 3.15766 (* 1 = 3.15766 loss)
I1013 17:04:05.753327  8021 solver.cpp:228] Iteration 3200, loss = 7.93311e-05
I1013 17:04:05.753425  8021 solver.cpp:244]     Train net output #0: loss = 7.93315e-05 (* 1 = 7.93315e-05 loss)
I1013 17:04:05.753448  8021 sgd_solver.cpp:106] Iteration 3200, lr = 0.001
I1013 17:05:19.487588  8021 solver.cpp:337] Iteration 3300, Testing net (#0)
I1013 17:05:34.719971  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1013 17:05:34.720073  8021 solver.cpp:404]     Test net output #1: loss = 3.15129 (* 1 = 3.15129 loss)
I1013 17:05:35.459321  8021 solver.cpp:228] Iteration 3300, loss = 5.77511e-05
I1013 17:05:35.459422  8021 solver.cpp:244]     Train net output #0: loss = 5.77515e-05 (* 1 = 5.77515e-05 loss)
I1013 17:05:35.459445  8021 sgd_solver.cpp:106] Iteration 3300, lr = 0.001
I1013 17:06:49.617261  8021 solver.cpp:337] Iteration 3400, Testing net (#0)
I1013 17:07:05.498452  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1013 17:07:05.498548  8021 solver.cpp:404]     Test net output #1: loss = 3.16461 (* 1 = 3.16461 loss)
I1013 17:07:06.299715  8021 solver.cpp:228] Iteration 3400, loss = 4.63708e-05
I1013 17:07:06.299816  8021 solver.cpp:244]     Train net output #0: loss = 4.63712e-05 (* 1 = 4.63712e-05 loss)
I1013 17:07:06.299839  8021 sgd_solver.cpp:106] Iteration 3400, lr = 0.001
I1013 17:08:20.779106  8021 solver.cpp:337] Iteration 3500, Testing net (#0)
I1013 17:08:36.177659  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1013 17:08:36.177769  8021 solver.cpp:404]     Test net output #1: loss = 3.1701 (* 1 = 3.1701 loss)
I1013 17:08:36.914448  8021 solver.cpp:228] Iteration 3500, loss = 2.78769e-05
I1013 17:08:36.914546  8021 solver.cpp:244]     Train net output #0: loss = 2.78773e-05 (* 1 = 2.78773e-05 loss)
I1013 17:08:36.914572  8021 sgd_solver.cpp:106] Iteration 3500, lr = 0.001
I1013 17:09:52.611054  8021 solver.cpp:337] Iteration 3600, Testing net (#0)
I1013 17:10:08.076454  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1013 17:10:08.076550  8021 solver.cpp:404]     Test net output #1: loss = 3.17095 (* 1 = 3.17095 loss)
I1013 17:10:08.825844  8021 solver.cpp:228] Iteration 3600, loss = 3.14716e-05
I1013 17:10:08.825944  8021 solver.cpp:244]     Train net output #0: loss = 3.1472e-05 (* 1 = 3.1472e-05 loss)
I1013 17:10:08.825968  8021 sgd_solver.cpp:106] Iteration 3600, lr = 0.001
I1013 17:11:23.069818  8021 solver.cpp:337] Iteration 3700, Testing net (#0)
I1013 17:11:38.422104  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1013 17:11:38.422206  8021 solver.cpp:404]     Test net output #1: loss = 3.18653 (* 1 = 3.18653 loss)
I1013 17:11:39.159116  8021 solver.cpp:228] Iteration 3700, loss = 2.19126e-05
I1013 17:11:39.159214  8021 solver.cpp:244]     Train net output #0: loss = 2.1913e-05 (* 1 = 2.1913e-05 loss)
I1013 17:11:39.159237  8021 sgd_solver.cpp:106] Iteration 3700, lr = 0.001
I1013 17:12:53.237937  8021 solver.cpp:337] Iteration 3800, Testing net (#0)
I1013 17:13:08.513952  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1013 17:13:08.514047  8021 solver.cpp:404]     Test net output #1: loss = 3.18448 (* 1 = 3.18448 loss)
I1013 17:13:09.254441  8021 solver.cpp:228] Iteration 3800, loss = 3.57493e-05
I1013 17:13:09.254534  8021 solver.cpp:244]     Train net output #0: loss = 3.57497e-05 (* 1 = 3.57497e-05 loss)
I1013 17:13:09.254559  8021 sgd_solver.cpp:106] Iteration 3800, lr = 0.001
I1013 17:14:24.193627  8021 solver.cpp:337] Iteration 3900, Testing net (#0)
I1013 17:14:39.549269  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1013 17:14:39.549366  8021 solver.cpp:404]     Test net output #1: loss = 3.18857 (* 1 = 3.18857 loss)
I1013 17:14:40.300066  8021 solver.cpp:228] Iteration 3900, loss = 5.65637e-05
I1013 17:14:40.300155  8021 solver.cpp:244]     Train net output #0: loss = 5.65641e-05 (* 1 = 5.65641e-05 loss)
I1013 17:14:40.300179  8021 sgd_solver.cpp:106] Iteration 3900, lr = 0.001
I1013 17:15:53.816098  8021 solver.cpp:337] Iteration 4000, Testing net (#0)
I1013 17:16:09.144594  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1013 17:16:09.144690  8021 solver.cpp:404]     Test net output #1: loss = 3.20004 (* 1 = 3.20004 loss)
I1013 17:16:09.889077  8021 solver.cpp:228] Iteration 4000, loss = 9.16929e-05
I1013 17:16:09.889333  8021 solver.cpp:244]     Train net output #0: loss = 9.16933e-05 (* 1 = 9.16933e-05 loss)
I1013 17:16:09.889365  8021 sgd_solver.cpp:106] Iteration 4000, lr = 0.001
I1013 17:17:23.679479  8021 solver.cpp:337] Iteration 4100, Testing net (#0)
I1013 17:17:39.010426  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5012
I1013 17:17:39.010519  8021 solver.cpp:404]     Test net output #1: loss = 3.1976 (* 1 = 3.1976 loss)
I1013 17:17:39.744047  8021 solver.cpp:228] Iteration 4100, loss = 5.06104e-05
I1013 17:17:39.744141  8021 solver.cpp:244]     Train net output #0: loss = 5.06107e-05 (* 1 = 5.06107e-05 loss)
I1013 17:17:39.744165  8021 sgd_solver.cpp:106] Iteration 4100, lr = 0.001
I1013 17:18:53.745375  8021 solver.cpp:337] Iteration 4200, Testing net (#0)
I1013 17:19:09.070459  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1013 17:19:09.070557  8021 solver.cpp:404]     Test net output #1: loss = 3.20175 (* 1 = 3.20175 loss)
I1013 17:19:09.802361  8021 solver.cpp:228] Iteration 4200, loss = 5.09309e-05
I1013 17:19:09.802459  8021 solver.cpp:244]     Train net output #0: loss = 5.09313e-05 (* 1 = 5.09313e-05 loss)
I1013 17:19:09.802485  8021 sgd_solver.cpp:106] Iteration 4200, lr = 0.001
I1013 17:20:23.085326  8021 solver.cpp:337] Iteration 4300, Testing net (#0)
I1013 17:20:38.363168  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1013 17:20:38.363266  8021 solver.cpp:404]     Test net output #1: loss = 3.21505 (* 1 = 3.21505 loss)
I1013 17:20:39.099055  8021 solver.cpp:228] Iteration 4300, loss = 4.07444e-05
I1013 17:20:39.099144  8021 solver.cpp:244]     Train net output #0: loss = 4.07447e-05 (* 1 = 4.07447e-05 loss)
I1013 17:20:39.099169  8021 sgd_solver.cpp:106] Iteration 4300, lr = 0.001
I1013 17:21:52.849661  8021 solver.cpp:337] Iteration 4400, Testing net (#0)
I1013 17:22:08.581614  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1013 17:22:08.581708  8021 solver.cpp:404]     Test net output #1: loss = 3.20973 (* 1 = 3.20973 loss)
I1013 17:22:09.314044  8021 solver.cpp:228] Iteration 4400, loss = 2.92016e-05
I1013 17:22:09.314148  8021 solver.cpp:244]     Train net output #0: loss = 2.9202e-05 (* 1 = 2.9202e-05 loss)
I1013 17:22:09.314170  8021 sgd_solver.cpp:106] Iteration 4400, lr = 0.001
I1013 17:23:23.447379  8021 solver.cpp:337] Iteration 4500, Testing net (#0)
I1013 17:23:38.801079  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1013 17:23:38.801169  8021 solver.cpp:404]     Test net output #1: loss = 3.2138 (* 1 = 3.2138 loss)
I1013 17:23:39.538595  8021 solver.cpp:228] Iteration 4500, loss = 2.74251e-05
I1013 17:23:39.538688  8021 solver.cpp:244]     Train net output #0: loss = 2.74255e-05 (* 1 = 2.74255e-05 loss)
I1013 17:23:39.538712  8021 sgd_solver.cpp:106] Iteration 4500, lr = 0.001
I1013 17:24:53.348191  8021 solver.cpp:337] Iteration 4600, Testing net (#0)
I1013 17:25:08.643347  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1013 17:25:08.643450  8021 solver.cpp:404]     Test net output #1: loss = 3.22356 (* 1 = 3.22356 loss)
I1013 17:25:09.382575  8021 solver.cpp:228] Iteration 4600, loss = 2.40225e-05
I1013 17:25:09.382679  8021 solver.cpp:244]     Train net output #0: loss = 2.40229e-05 (* 1 = 2.40229e-05 loss)
I1013 17:25:09.382701  8021 sgd_solver.cpp:106] Iteration 4600, lr = 0.001
I1013 17:26:23.382639  8021 solver.cpp:337] Iteration 4700, Testing net (#0)
I1013 17:26:38.552477  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1013 17:26:38.552570  8021 solver.cpp:404]     Test net output #1: loss = 3.22524 (* 1 = 3.22524 loss)
I1013 17:26:39.310006  8021 solver.cpp:228] Iteration 4700, loss = 3.93661e-05
I1013 17:26:39.310099  8021 solver.cpp:244]     Train net output #0: loss = 3.93665e-05 (* 1 = 3.93665e-05 loss)
I1013 17:26:39.310123  8021 sgd_solver.cpp:106] Iteration 4700, lr = 0.001
I1014 10:33:58.144903  8021 solver.cpp:337] Iteration 4800, Testing net (#0)
I1014 10:34:13.794467  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 10:34:13.794571  8021 solver.cpp:404]     Test net output #1: loss = 3.21891 (* 1 = 3.21891 loss)
I1014 10:34:14.549871  8021 solver.cpp:228] Iteration 4800, loss = 6.90065e-05
I1014 10:34:14.549975  8021 solver.cpp:244]     Train net output #0: loss = 6.90069e-05 (* 1 = 6.90069e-05 loss)
I1014 10:34:14.549998  8021 sgd_solver.cpp:106] Iteration 4800, lr = 0.001
I1014 10:35:29.339957  8021 solver.cpp:337] Iteration 4900, Testing net (#0)
I1014 10:35:44.767004  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 10:35:44.767107  8021 solver.cpp:404]     Test net output #1: loss = 3.23304 (* 1 = 3.23304 loss)
I1014 10:35:45.525372  8021 solver.cpp:228] Iteration 4900, loss = 7.92674e-05
I1014 10:35:45.525478  8021 solver.cpp:244]     Train net output #0: loss = 7.92678e-05 (* 1 = 7.92678e-05 loss)
I1014 10:35:45.525506  8021 sgd_solver.cpp:106] Iteration 4900, lr = 0.001
I1014 10:36:59.965539  8021 solver.cpp:337] Iteration 5000, Testing net (#0)
I1014 10:37:15.435309  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 10:37:15.435400  8021 solver.cpp:404]     Test net output #1: loss = 3.23482 (* 1 = 3.23482 loss)
I1014 10:37:16.186879  8021 solver.cpp:228] Iteration 5000, loss = 4.09696e-05
I1014 10:37:16.186975  8021 solver.cpp:244]     Train net output #0: loss = 4.097e-05 (* 1 = 4.097e-05 loss)
I1014 10:37:16.187000  8021 sgd_solver.cpp:106] Iteration 5000, lr = 0.001
I1014 10:38:30.899849  8021 solver.cpp:337] Iteration 5100, Testing net (#0)
I1014 10:38:46.328946  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 10:38:46.329047  8021 solver.cpp:404]     Test net output #1: loss = 3.23073 (* 1 = 3.23073 loss)
I1014 10:38:47.079233  8021 solver.cpp:228] Iteration 5100, loss = 4.75567e-05
I1014 10:38:47.079339  8021 solver.cpp:244]     Train net output #0: loss = 4.75571e-05 (* 1 = 4.75571e-05 loss)
I1014 10:38:47.079361  8021 sgd_solver.cpp:106] Iteration 5100, lr = 0.001
I1014 10:40:01.433818  8021 solver.cpp:337] Iteration 5200, Testing net (#0)
I1014 10:40:16.912106  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 10:40:16.912207  8021 solver.cpp:404]     Test net output #1: loss = 3.24363 (* 1 = 3.24363 loss)
I1014 10:40:17.661583  8021 solver.cpp:228] Iteration 5200, loss = 3.79175e-05
I1014 10:40:17.661674  8021 solver.cpp:244]     Train net output #0: loss = 3.79179e-05 (* 1 = 3.79179e-05 loss)
I1014 10:40:17.661697  8021 sgd_solver.cpp:106] Iteration 5200, lr = 0.001
I1014 10:41:29.889963  8021 solver.cpp:337] Iteration 5300, Testing net (#0)
I1014 10:41:44.973743  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1014 10:41:44.973846  8021 solver.cpp:404]     Test net output #1: loss = 3.23933 (* 1 = 3.23933 loss)
I1014 10:41:45.703758  8021 solver.cpp:228] Iteration 5300, loss = 2.22489e-05
I1014 10:41:45.703863  8021 solver.cpp:244]     Train net output #0: loss = 2.22492e-05 (* 1 = 2.22492e-05 loss)
I1014 10:41:45.703889  8021 sgd_solver.cpp:106] Iteration 5300, lr = 0.001
I1014 10:42:59.389225  8021 solver.cpp:337] Iteration 5400, Testing net (#0)
I1014 10:43:14.616315  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1014 10:43:14.616415  8021 solver.cpp:404]     Test net output #1: loss = 3.23978 (* 1 = 3.23978 loss)
I1014 10:43:15.413841  8021 solver.cpp:228] Iteration 5400, loss = 2.26049e-05
I1014 10:43:15.414090  8021 solver.cpp:244]     Train net output #0: loss = 2.26053e-05 (* 1 = 2.26053e-05 loss)
I1014 10:43:15.414145  8021 sgd_solver.cpp:106] Iteration 5400, lr = 0.001
I1014 10:44:33.048487  8021 solver.cpp:337] Iteration 5500, Testing net (#0)
I1014 10:44:48.626996  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 10:44:48.627095  8021 solver.cpp:404]     Test net output #1: loss = 3.25544 (* 1 = 3.25544 loss)
I1014 10:44:49.374750  8021 solver.cpp:228] Iteration 5500, loss = 2.22141e-05
I1014 10:44:49.374864  8021 solver.cpp:244]     Train net output #0: loss = 2.22145e-05 (* 1 = 2.22145e-05 loss)
I1014 10:44:49.374886  8021 sgd_solver.cpp:106] Iteration 5500, lr = 0.001
I1014 10:46:02.918193  8021 solver.cpp:337] Iteration 5600, Testing net (#0)
I1014 10:46:18.460203  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1014 10:46:18.460304  8021 solver.cpp:404]     Test net output #1: loss = 3.24413 (* 1 = 3.24413 loss)
I1014 10:46:19.231891  8021 solver.cpp:228] Iteration 5600, loss = 3.69267e-05
I1014 10:46:19.232000  8021 solver.cpp:244]     Train net output #0: loss = 3.69271e-05 (* 1 = 3.69271e-05 loss)
I1014 10:46:19.232024  8021 sgd_solver.cpp:106] Iteration 5600, lr = 0.001
I1014 10:47:34.377089  8021 solver.cpp:337] Iteration 5700, Testing net (#0)
I1014 10:47:49.466282  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1014 10:47:49.466388  8021 solver.cpp:404]     Test net output #1: loss = 3.25271 (* 1 = 3.25271 loss)
I1014 10:47:50.196540  8021 solver.cpp:228] Iteration 5700, loss = 5.90975e-05
I1014 10:47:50.196645  8021 solver.cpp:244]     Train net output #0: loss = 5.90978e-05 (* 1 = 5.90978e-05 loss)
I1014 10:47:50.196668  8021 sgd_solver.cpp:106] Iteration 5700, lr = 0.001
I1014 10:49:04.875097  8021 solver.cpp:337] Iteration 5800, Testing net (#0)
I1014 10:49:20.326803  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1014 10:49:20.326902  8021 solver.cpp:404]     Test net output #1: loss = 3.25421 (* 1 = 3.25421 loss)
I1014 10:49:21.067917  8021 solver.cpp:228] Iteration 5800, loss = 6.07067e-05
I1014 10:49:21.068011  8021 solver.cpp:244]     Train net output #0: loss = 6.07071e-05 (* 1 = 6.07071e-05 loss)
I1014 10:49:21.068037  8021 sgd_solver.cpp:106] Iteration 5800, lr = 0.001
I1014 10:50:37.621779  8021 solver.cpp:337] Iteration 5900, Testing net (#0)
I1014 10:50:52.978786  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 10:50:52.978885  8021 solver.cpp:404]     Test net output #1: loss = 3.25119 (* 1 = 3.25119 loss)
I1014 10:50:53.706923  8021 solver.cpp:228] Iteration 5900, loss = 4.77344e-05
I1014 10:50:53.707017  8021 solver.cpp:244]     Train net output #0: loss = 4.77348e-05 (* 1 = 4.77348e-05 loss)
I1014 10:50:53.707039  8021 sgd_solver.cpp:106] Iteration 5900, lr = 0.001
I1014 10:52:10.191766  8021 solver.cpp:337] Iteration 6000, Testing net (#0)
I1014 10:52:25.686954  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1014 10:52:25.687057  8021 solver.cpp:404]     Test net output #1: loss = 3.26352 (* 1 = 3.26352 loss)
I1014 10:52:26.431551  8021 solver.cpp:228] Iteration 6000, loss = 4.10538e-05
I1014 10:52:26.431645  8021 solver.cpp:244]     Train net output #0: loss = 4.10542e-05 (* 1 = 4.10542e-05 loss)
I1014 10:52:26.431671  8021 sgd_solver.cpp:106] Iteration 6000, lr = 0.001
I1014 10:53:41.921283  8021 solver.cpp:337] Iteration 6100, Testing net (#0)
I1014 10:53:57.402308  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1014 10:53:57.402411  8021 solver.cpp:404]     Test net output #1: loss = 3.25789 (* 1 = 3.25789 loss)
I1014 10:53:58.151090  8021 solver.cpp:228] Iteration 6100, loss = 2.83955e-05
I1014 10:53:58.151193  8021 solver.cpp:244]     Train net output #0: loss = 2.83958e-05 (* 1 = 2.83958e-05 loss)
I1014 10:53:58.151217  8021 sgd_solver.cpp:106] Iteration 6100, lr = 0.001
I1014 10:55:14.034095  8021 solver.cpp:337] Iteration 6200, Testing net (#0)
I1014 10:55:29.248803  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1014 10:55:29.248899  8021 solver.cpp:404]     Test net output #1: loss = 3.25936 (* 1 = 3.25936 loss)
I1014 10:55:29.980772  8021 solver.cpp:228] Iteration 6200, loss = 2.42468e-05
I1014 10:55:29.980870  8021 solver.cpp:244]     Train net output #0: loss = 2.42472e-05 (* 1 = 2.42472e-05 loss)
I1014 10:55:29.980901  8021 sgd_solver.cpp:106] Iteration 6200, lr = 0.001
I1014 10:56:44.046180  8021 solver.cpp:337] Iteration 6300, Testing net (#0)
I1014 10:56:59.468576  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1014 10:56:59.468670  8021 solver.cpp:404]     Test net output #1: loss = 3.2678 (* 1 = 3.2678 loss)
I1014 10:57:00.232153  8021 solver.cpp:228] Iteration 6300, loss = 2.14724e-05
I1014 10:57:00.232257  8021 solver.cpp:244]     Train net output #0: loss = 2.14727e-05 (* 1 = 2.14727e-05 loss)
I1014 10:57:00.232285  8021 sgd_solver.cpp:106] Iteration 6300, lr = 0.001
I1014 10:58:15.257099  8021 solver.cpp:337] Iteration 6400, Testing net (#0)
I1014 10:58:30.846734  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5012
I1014 10:58:30.846853  8021 solver.cpp:404]     Test net output #1: loss = 3.26217 (* 1 = 3.26217 loss)
I1014 10:58:31.611122  8021 solver.cpp:228] Iteration 6400, loss = 1.99369e-05
I1014 10:58:31.611222  8021 solver.cpp:244]     Train net output #0: loss = 1.99373e-05 (* 1 = 1.99373e-05 loss)
I1014 10:58:31.611245  8021 sgd_solver.cpp:106] Iteration 6400, lr = 0.001
I1014 10:59:45.440105  8021 solver.cpp:337] Iteration 6500, Testing net (#0)
I1014 11:00:00.642688  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1014 11:00:00.642804  8021 solver.cpp:404]     Test net output #1: loss = 3.2637 (* 1 = 3.2637 loss)
I1014 11:00:01.375916  8021 solver.cpp:228] Iteration 6500, loss = 3.5298e-05
I1014 11:00:01.376021  8021 solver.cpp:244]     Train net output #0: loss = 3.52984e-05 (* 1 = 3.52984e-05 loss)
I1014 11:00:01.376045  8021 sgd_solver.cpp:106] Iteration 6500, lr = 0.001
I1014 11:01:15.866184  8021 solver.cpp:337] Iteration 6600, Testing net (#0)
I1014 11:01:31.539088  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1014 11:01:31.539178  8021 solver.cpp:404]     Test net output #1: loss = 3.27452 (* 1 = 3.27452 loss)
I1014 11:01:32.285214  8021 solver.cpp:228] Iteration 6600, loss = 5.929e-05
I1014 11:01:32.285320  8021 solver.cpp:244]     Train net output #0: loss = 5.92904e-05 (* 1 = 5.92904e-05 loss)
I1014 11:01:32.285342  8021 sgd_solver.cpp:106] Iteration 6600, lr = 0.001
I1014 11:02:47.527845  8021 solver.cpp:337] Iteration 6700, Testing net (#0)
I1014 11:03:03.261131  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1014 11:03:03.261226  8021 solver.cpp:404]     Test net output #1: loss = 3.26612 (* 1 = 3.26612 loss)
I1014 11:03:04.081400  8021 solver.cpp:228] Iteration 6700, loss = 6.00664e-05
I1014 11:03:04.081499  8021 solver.cpp:244]     Train net output #0: loss = 6.00668e-05 (* 1 = 6.00668e-05 loss)
I1014 11:03:04.081522  8021 sgd_solver.cpp:106] Iteration 6700, lr = 0.001
I1014 11:04:22.163054  8021 solver.cpp:337] Iteration 6800, Testing net (#0)
I1014 11:04:37.621510  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1014 11:04:37.621610  8021 solver.cpp:404]     Test net output #1: loss = 3.26812 (* 1 = 3.26812 loss)
I1014 11:04:38.360195  8021 solver.cpp:228] Iteration 6800, loss = 4.06584e-05
I1014 11:04:38.360302  8021 solver.cpp:244]     Train net output #0: loss = 4.06588e-05 (* 1 = 4.06588e-05 loss)
I1014 11:04:38.360326  8021 sgd_solver.cpp:106] Iteration 6800, lr = 0.001
I1014 11:05:52.700242  8021 solver.cpp:337] Iteration 6900, Testing net (#0)
I1014 11:06:08.175868  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 11:06:08.175967  8021 solver.cpp:404]     Test net output #1: loss = 3.27604 (* 1 = 3.27604 loss)
I1014 11:06:08.914899  8021 solver.cpp:228] Iteration 6900, loss = 3.53169e-05
I1014 11:06:08.914994  8021 solver.cpp:244]     Train net output #0: loss = 3.53173e-05 (* 1 = 3.53173e-05 loss)
I1014 11:06:08.915021  8021 sgd_solver.cpp:106] Iteration 6900, lr = 0.001
I1014 11:07:24.298760  8021 solver.cpp:337] Iteration 7000, Testing net (#0)
I1014 11:07:39.809092  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1014 11:07:39.809188  8021 solver.cpp:404]     Test net output #1: loss = 3.27538 (* 1 = 3.27538 loss)
I1014 11:07:40.543841  8021 solver.cpp:228] Iteration 7000, loss = 2.27541e-05
I1014 11:07:40.543934  8021 solver.cpp:244]     Train net output #0: loss = 2.27545e-05 (* 1 = 2.27545e-05 loss)
I1014 11:07:40.543959  8021 sgd_solver.cpp:106] Iteration 7000, lr = 0.001
I1014 11:08:53.915395  8021 solver.cpp:337] Iteration 7100, Testing net (#0)
I1014 11:09:09.290961  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 11:09:09.291059  8021 solver.cpp:404]     Test net output #1: loss = 3.26691 (* 1 = 3.26691 loss)
I1014 11:09:10.068606  8021 solver.cpp:228] Iteration 7100, loss = 2.3703e-05
I1014 11:09:10.068702  8021 solver.cpp:244]     Train net output #0: loss = 2.37034e-05 (* 1 = 2.37034e-05 loss)
I1014 11:09:10.068727  8021 sgd_solver.cpp:106] Iteration 7100, lr = 0.001
I1014 11:10:24.957535  8021 solver.cpp:337] Iteration 7200, Testing net (#0)
I1014 11:10:40.612125  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 11:10:40.612221  8021 solver.cpp:404]     Test net output #1: loss = 3.27983 (* 1 = 3.27983 loss)
I1014 11:10:41.382596  8021 solver.cpp:228] Iteration 7200, loss = 2.12363e-05
I1014 11:10:41.382685  8021 solver.cpp:244]     Train net output #0: loss = 2.12367e-05 (* 1 = 2.12367e-05 loss)
I1014 11:10:41.382711  8021 sgd_solver.cpp:106] Iteration 7200, lr = 0.001
I1014 11:11:56.446691  8021 solver.cpp:337] Iteration 7300, Testing net (#0)
I1014 11:12:12.403892  8021 solver.cpp:404]     Test net output #0: accuracy = 0.522
I1014 11:12:12.404002  8021 solver.cpp:404]     Test net output #1: loss = 3.27922 (* 1 = 3.27922 loss)
I1014 11:12:13.156512  8021 solver.cpp:228] Iteration 7300, loss = 2.17871e-05
I1014 11:12:13.156620  8021 solver.cpp:244]     Train net output #0: loss = 2.17874e-05 (* 1 = 2.17874e-05 loss)
I1014 11:12:13.156642  8021 sgd_solver.cpp:106] Iteration 7300, lr = 0.001
I1014 11:13:27.166520  8021 solver.cpp:337] Iteration 7400, Testing net (#0)
I1014 11:13:42.770579  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 11:13:42.770679  8021 solver.cpp:404]     Test net output #1: loss = 3.27342 (* 1 = 3.27342 loss)
I1014 11:13:43.512828  8021 solver.cpp:228] Iteration 7400, loss = 3.86504e-05
I1014 11:13:43.512936  8021 solver.cpp:244]     Train net output #0: loss = 3.86507e-05 (* 1 = 3.86507e-05 loss)
I1014 11:13:43.512960  8021 sgd_solver.cpp:106] Iteration 7400, lr = 0.001
I1014 11:15:00.251536  8021 solver.cpp:337] Iteration 7500, Testing net (#0)
I1014 11:15:15.632231  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 11:15:15.632331  8021 solver.cpp:404]     Test net output #1: loss = 3.28489 (* 1 = 3.28489 loss)
I1014 11:15:16.365381  8021 solver.cpp:228] Iteration 7500, loss = 6.12127e-05
I1014 11:15:16.365483  8021 solver.cpp:244]     Train net output #0: loss = 6.1213e-05 (* 1 = 6.1213e-05 loss)
I1014 11:15:16.365509  8021 sgd_solver.cpp:106] Iteration 7500, lr = 0.001
I1014 11:16:30.476245  8021 solver.cpp:337] Iteration 7600, Testing net (#0)
I1014 11:16:46.051208  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5224
I1014 11:16:46.051302  8021 solver.cpp:404]     Test net output #1: loss = 3.27868 (* 1 = 3.27868 loss)
I1014 11:16:46.783092  8021 solver.cpp:228] Iteration 7600, loss = 5.28203e-05
I1014 11:16:46.783190  8021 solver.cpp:244]     Train net output #0: loss = 5.28207e-05 (* 1 = 5.28207e-05 loss)
I1014 11:16:46.783215  8021 sgd_solver.cpp:106] Iteration 7600, lr = 0.001
I1014 11:18:03.092990  8021 solver.cpp:337] Iteration 7700, Testing net (#0)
I1014 11:18:18.558200  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5212
I1014 11:18:18.558302  8021 solver.cpp:404]     Test net output #1: loss = 3.27757 (* 1 = 3.27757 loss)
I1014 11:18:19.302964  8021 solver.cpp:228] Iteration 7700, loss = 4.07895e-05
I1014 11:18:19.303088  8021 solver.cpp:244]     Train net output #0: loss = 4.07899e-05 (* 1 = 4.07899e-05 loss)
I1014 11:18:19.303113  8021 sgd_solver.cpp:106] Iteration 7700, lr = 0.001
I1014 11:19:34.224685  8021 solver.cpp:337] Iteration 7800, Testing net (#0)
I1014 11:19:49.866057  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 11:19:49.866153  8021 solver.cpp:404]     Test net output #1: loss = 3.29229 (* 1 = 3.29229 loss)
I1014 11:19:50.645750  8021 solver.cpp:228] Iteration 7800, loss = 3.63841e-05
I1014 11:19:50.645848  8021 solver.cpp:244]     Train net output #0: loss = 3.63845e-05 (* 1 = 3.63845e-05 loss)
I1014 11:19:50.645874  8021 sgd_solver.cpp:106] Iteration 7800, lr = 0.001
I1014 11:21:05.283701  8021 solver.cpp:337] Iteration 7900, Testing net (#0)
I1014 11:21:20.432672  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5224
I1014 11:21:20.432762  8021 solver.cpp:404]     Test net output #1: loss = 3.27962 (* 1 = 3.27962 loss)
I1014 11:21:21.212999  8021 solver.cpp:228] Iteration 7900, loss = 2.10956e-05
I1014 11:21:21.213093  8021 solver.cpp:244]     Train net output #0: loss = 2.1096e-05 (* 1 = 2.1096e-05 loss)
I1014 11:21:21.213119  8021 sgd_solver.cpp:106] Iteration 7900, lr = 0.001
I1014 11:22:36.371737  8021 solver.cpp:337] Iteration 8000, Testing net (#0)
I1014 11:22:52.220504  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5208
I1014 11:22:52.220594  8021 solver.cpp:404]     Test net output #1: loss = 3.28686 (* 1 = 3.28686 loss)
I1014 11:22:52.990201  8021 solver.cpp:228] Iteration 8000, loss = 2.63114e-05
I1014 11:22:52.990299  8021 solver.cpp:244]     Train net output #0: loss = 2.63118e-05 (* 1 = 2.63118e-05 loss)
I1014 11:22:52.990324  8021 sgd_solver.cpp:106] Iteration 8000, lr = 0.001
I1014 11:24:06.847153  8021 solver.cpp:337] Iteration 8100, Testing net (#0)
I1014 11:24:22.735718  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5224
I1014 11:24:22.735818  8021 solver.cpp:404]     Test net output #1: loss = 3.28763 (* 1 = 3.28763 loss)
I1014 11:24:23.514050  8021 solver.cpp:228] Iteration 8100, loss = 1.65679e-05
I1014 11:24:23.514144  8021 solver.cpp:244]     Train net output #0: loss = 1.65683e-05 (* 1 = 1.65683e-05 loss)
I1014 11:24:23.514170  8021 sgd_solver.cpp:106] Iteration 8100, lr = 0.001
I1014 11:25:37.684140  8021 solver.cpp:337] Iteration 8200, Testing net (#0)
I1014 11:25:52.724453  8021 solver.cpp:404]     Test net output #0: accuracy = 0.522
I1014 11:25:52.724555  8021 solver.cpp:404]     Test net output #1: loss = 3.2833 (* 1 = 3.2833 loss)
I1014 11:25:53.450296  8021 solver.cpp:228] Iteration 8200, loss = 2.5198e-05
I1014 11:25:53.450395  8021 solver.cpp:244]     Train net output #0: loss = 2.51984e-05 (* 1 = 2.51984e-05 loss)
I1014 11:25:53.450417  8021 sgd_solver.cpp:106] Iteration 8200, lr = 0.001
I1014 11:27:07.042508  8021 solver.cpp:337] Iteration 8300, Testing net (#0)
I1014 11:27:22.364511  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5208
I1014 11:27:22.364604  8021 solver.cpp:404]     Test net output #1: loss = 3.29444 (* 1 = 3.29444 loss)
I1014 11:27:23.186591  8021 solver.cpp:228] Iteration 8300, loss = 3.66761e-05
I1014 11:27:23.186691  8021 solver.cpp:244]     Train net output #0: loss = 3.66765e-05 (* 1 = 3.66765e-05 loss)
I1014 11:27:23.186715  8021 sgd_solver.cpp:106] Iteration 8300, lr = 0.001
I1014 11:28:37.530508  8021 solver.cpp:337] Iteration 8400, Testing net (#0)
I1014 11:28:53.899919  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5224
I1014 11:28:53.900017  8021 solver.cpp:404]     Test net output #1: loss = 3.28768 (* 1 = 3.28768 loss)
I1014 11:28:54.760330  8021 solver.cpp:228] Iteration 8400, loss = 6.57801e-05
I1014 11:28:54.760428  8021 solver.cpp:244]     Train net output #0: loss = 6.57805e-05 (* 1 = 6.57805e-05 loss)
I1014 11:28:54.760450  8021 sgd_solver.cpp:106] Iteration 8400, lr = 0.001
I1014 11:30:10.941957  8021 solver.cpp:337] Iteration 8500, Testing net (#0)
I1014 11:30:26.080204  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 11:30:26.080312  8021 solver.cpp:404]     Test net output #1: loss = 3.2882 (* 1 = 3.2882 loss)
I1014 11:30:26.816259  8021 solver.cpp:228] Iteration 8500, loss = 4.31408e-05
I1014 11:30:26.816361  8021 solver.cpp:244]     Train net output #0: loss = 4.31412e-05 (* 1 = 4.31412e-05 loss)
I1014 11:30:26.816385  8021 sgd_solver.cpp:106] Iteration 8500, lr = 0.001
I1014 11:31:43.309758  8021 solver.cpp:337] Iteration 8600, Testing net (#0)
I1014 11:31:58.635006  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5208
I1014 11:31:58.635102  8021 solver.cpp:404]     Test net output #1: loss = 3.29548 (* 1 = 3.29548 loss)
I1014 11:31:59.365087  8021 solver.cpp:228] Iteration 8600, loss = 4.17931e-05
I1014 11:31:59.365191  8021 solver.cpp:244]     Train net output #0: loss = 4.17935e-05 (* 1 = 4.17935e-05 loss)
I1014 11:31:59.365216  8021 sgd_solver.cpp:106] Iteration 8600, lr = 0.001
I1014 11:33:12.631512  8021 solver.cpp:337] Iteration 8700, Testing net (#0)
I1014 11:33:27.944794  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5228
I1014 11:33:27.944900  8021 solver.cpp:404]     Test net output #1: loss = 3.28851 (* 1 = 3.28851 loss)
I1014 11:33:28.678896  8021 solver.cpp:228] Iteration 8700, loss = 3.34512e-05
I1014 11:33:28.679003  8021 solver.cpp:244]     Train net output #0: loss = 3.34516e-05 (* 1 = 3.34516e-05 loss)
I1014 11:33:28.679026  8021 sgd_solver.cpp:106] Iteration 8700, lr = 0.001
I1014 11:34:42.582101  8021 solver.cpp:337] Iteration 8800, Testing net (#0)
I1014 11:34:57.927386  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 11:34:57.927489  8021 solver.cpp:404]     Test net output #1: loss = 3.28936 (* 1 = 3.28936 loss)
I1014 11:34:58.669919  8021 solver.cpp:228] Iteration 8800, loss = 2.04256e-05
I1014 11:34:58.670018  8021 solver.cpp:244]     Train net output #0: loss = 2.0426e-05 (* 1 = 2.0426e-05 loss)
I1014 11:34:58.670045  8021 sgd_solver.cpp:106] Iteration 8800, lr = 0.001
I1014 11:36:13.499444  8021 solver.cpp:337] Iteration 8900, Testing net (#0)
I1014 11:36:28.995847  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5208
I1014 11:36:28.995946  8021 solver.cpp:404]     Test net output #1: loss = 3.29927 (* 1 = 3.29927 loss)
I1014 11:36:29.744134  8021 solver.cpp:228] Iteration 8900, loss = 2.06177e-05
I1014 11:36:29.744235  8021 solver.cpp:244]     Train net output #0: loss = 2.06181e-05 (* 1 = 2.06181e-05 loss)
I1014 11:36:29.744261  8021 sgd_solver.cpp:106] Iteration 8900, lr = 0.001
I1014 11:37:48.336462  8021 solver.cpp:337] Iteration 9000, Testing net (#0)
I1014 11:38:03.989558  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5224
I1014 11:38:03.989675  8021 solver.cpp:404]     Test net output #1: loss = 3.28959 (* 1 = 3.28959 loss)
I1014 11:38:04.768113  8021 solver.cpp:228] Iteration 9000, loss = 1.77814e-05
I1014 11:38:04.768205  8021 solver.cpp:244]     Train net output #0: loss = 1.77818e-05 (* 1 = 1.77818e-05 loss)
I1014 11:38:04.768230  8021 sgd_solver.cpp:106] Iteration 9000, lr = 0.001
I1014 11:39:20.689859  8021 solver.cpp:337] Iteration 9100, Testing net (#0)
I1014 11:39:36.298295  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 11:39:36.298387  8021 solver.cpp:404]     Test net output #1: loss = 3.29095 (* 1 = 3.29095 loss)
I1014 11:39:37.074132  8021 solver.cpp:228] Iteration 9100, loss = 2.72222e-05
I1014 11:39:37.074230  8021 solver.cpp:244]     Train net output #0: loss = 2.72226e-05 (* 1 = 2.72226e-05 loss)
I1014 11:39:37.074255  8021 sgd_solver.cpp:106] Iteration 9100, lr = 0.001
I1014 11:40:53.926991  8021 solver.cpp:337] Iteration 9200, Testing net (#0)
I1014 11:41:09.166405  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 11:41:09.166508  8021 solver.cpp:404]     Test net output #1: loss = 3.29832 (* 1 = 3.29832 loss)
I1014 11:41:09.932150  8021 solver.cpp:228] Iteration 9200, loss = 4.13577e-05
I1014 11:41:09.932271  8021 solver.cpp:244]     Train net output #0: loss = 4.13581e-05 (* 1 = 4.13581e-05 loss)
I1014 11:41:09.932297  8021 sgd_solver.cpp:106] Iteration 9200, lr = 0.001
I1014 11:42:23.191313  8021 solver.cpp:337] Iteration 9300, Testing net (#0)
I1014 11:42:38.279242  8021 solver.cpp:404]     Test net output #0: accuracy = 0.522
I1014 11:42:38.279332  8021 solver.cpp:404]     Test net output #1: loss = 3.29666 (* 1 = 3.29666 loss)
I1014 11:42:39.009699  8021 solver.cpp:228] Iteration 9300, loss = 6.34768e-05
I1014 11:42:39.009805  8021 solver.cpp:244]     Train net output #0: loss = 6.34772e-05 (* 1 = 6.34772e-05 loss)
I1014 11:42:39.009827  8021 sgd_solver.cpp:106] Iteration 9300, lr = 0.001
I1014 11:43:51.599236  8021 solver.cpp:337] Iteration 9400, Testing net (#0)
I1014 11:44:06.686429  8021 solver.cpp:404]     Test net output #0: accuracy = 0.522
I1014 11:44:06.686532  8021 solver.cpp:404]     Test net output #1: loss = 3.28734 (* 1 = 3.28734 loss)
I1014 11:44:07.415482  8021 solver.cpp:228] Iteration 9400, loss = 3.58226e-05
I1014 11:44:07.415590  8021 solver.cpp:244]     Train net output #0: loss = 3.5823e-05 (* 1 = 3.5823e-05 loss)
I1014 11:44:07.415612  8021 sgd_solver.cpp:106] Iteration 9400, lr = 0.001
I1014 11:45:19.978173  8021 solver.cpp:337] Iteration 9500, Testing net (#0)
I1014 11:45:35.040444  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 11:45:35.040545  8021 solver.cpp:404]     Test net output #1: loss = 3.30005 (* 1 = 3.30005 loss)
I1014 11:45:35.769572  8021 solver.cpp:228] Iteration 9500, loss = 4.42204e-05
I1014 11:45:35.769678  8021 solver.cpp:244]     Train net output #0: loss = 4.42208e-05 (* 1 = 4.42208e-05 loss)
I1014 11:45:35.769701  8021 sgd_solver.cpp:106] Iteration 9500, lr = 0.001
I1014 11:46:48.277837  8021 solver.cpp:337] Iteration 9600, Testing net (#0)
I1014 11:47:03.385762  8021 solver.cpp:404]     Test net output #0: accuracy = 0.522
I1014 11:47:03.385864  8021 solver.cpp:404]     Test net output #1: loss = 3.29854 (* 1 = 3.29854 loss)
I1014 11:47:04.117583  8021 solver.cpp:228] Iteration 9600, loss = 3.47065e-05
I1014 11:47:04.117692  8021 solver.cpp:244]     Train net output #0: loss = 3.47069e-05 (* 1 = 3.47069e-05 loss)
I1014 11:47:04.117715  8021 sgd_solver.cpp:106] Iteration 9600, lr = 0.001
I1014 11:48:16.896350  8021 solver.cpp:337] Iteration 9700, Testing net (#0)
I1014 11:48:31.973603  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 11:48:31.973700  8021 solver.cpp:404]     Test net output #1: loss = 3.29208 (* 1 = 3.29208 loss)
I1014 11:48:32.703682  8021 solver.cpp:228] Iteration 9700, loss = 2.16216e-05
I1014 11:48:32.703789  8021 solver.cpp:244]     Train net output #0: loss = 2.1622e-05 (* 1 = 2.1622e-05 loss)
I1014 11:48:32.703812  8021 sgd_solver.cpp:106] Iteration 9700, lr = 0.001
I1014 11:49:45.371084  8021 solver.cpp:337] Iteration 9800, Testing net (#0)
I1014 11:50:00.451408  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 11:50:00.451514  8021 solver.cpp:404]     Test net output #1: loss = 3.30313 (* 1 = 3.30313 loss)
I1014 11:50:01.180102  8021 solver.cpp:228] Iteration 9800, loss = 2.17764e-05
I1014 11:50:01.180212  8021 solver.cpp:244]     Train net output #0: loss = 2.17768e-05 (* 1 = 2.17768e-05 loss)
I1014 11:50:01.180235  8021 sgd_solver.cpp:106] Iteration 9800, lr = 0.001
I1014 11:51:13.805354  8021 solver.cpp:337] Iteration 9900, Testing net (#0)
I1014 11:51:28.936964  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5224
I1014 11:51:28.937068  8021 solver.cpp:404]     Test net output #1: loss = 3.29623 (* 1 = 3.29623 loss)
I1014 11:51:29.670686  8021 solver.cpp:228] Iteration 9900, loss = 1.80199e-05
I1014 11:51:29.670802  8021 solver.cpp:244]     Train net output #0: loss = 1.80203e-05 (* 1 = 1.80203e-05 loss)
I1014 11:51:29.670828  8021 sgd_solver.cpp:106] Iteration 9900, lr = 0.001
I1014 11:52:42.311542  8021 solver.cpp:454] Snapshotting to binary proto file /home/suyog/Data_Preprocessing_google_50classes/caffe-cnn-model/caffenet_train_iter_10000.caffemodel
I1014 11:52:42.319349  8021 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/suyog/Data_Preprocessing_google_50classes/caffe-cnn-model/caffenet_train_iter_10000.solverstate
I1014 11:52:42.321599  8021 solver.cpp:337] Iteration 10000, Testing net (#0)
I1014 11:52:57.490203  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5212
I1014 11:52:57.490303  8021 solver.cpp:404]     Test net output #1: loss = 3.29432 (* 1 = 3.29432 loss)
I1014 11:52:58.224285  8021 solver.cpp:228] Iteration 10000, loss = 3.04625e-05
I1014 11:52:58.224395  8021 solver.cpp:244]     Train net output #0: loss = 3.04629e-05 (* 1 = 3.04629e-05 loss)
I1014 11:52:58.224417  8021 sgd_solver.cpp:106] Iteration 10000, lr = 0.0001
I1014 11:54:10.708870  8021 solver.cpp:337] Iteration 10100, Testing net (#0)
I1014 11:54:25.815971  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 11:54:25.816076  8021 solver.cpp:404]     Test net output #1: loss = 3.30869 (* 1 = 3.30869 loss)
I1014 11:54:26.545801  8021 solver.cpp:228] Iteration 10100, loss = 4.85227e-05
I1014 11:54:26.545909  8021 solver.cpp:244]     Train net output #0: loss = 4.85231e-05 (* 1 = 4.85231e-05 loss)
I1014 11:54:26.545933  8021 sgd_solver.cpp:106] Iteration 10100, lr = 0.0001
I1014 11:55:39.065644  8021 solver.cpp:337] Iteration 10200, Testing net (#0)
I1014 11:55:54.171545  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5224
I1014 11:55:54.171650  8021 solver.cpp:404]     Test net output #1: loss = 3.29482 (* 1 = 3.29482 loss)
I1014 11:55:54.905766  8021 solver.cpp:228] Iteration 10200, loss = 5.94877e-05
I1014 11:55:54.905869  8021 solver.cpp:244]     Train net output #0: loss = 5.94881e-05 (* 1 = 5.94881e-05 loss)
I1014 11:55:54.905892  8021 sgd_solver.cpp:106] Iteration 10200, lr = 0.0001
I1014 11:57:07.554270  8021 solver.cpp:337] Iteration 10300, Testing net (#0)
I1014 11:57:22.639376  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5208
I1014 11:57:22.639482  8021 solver.cpp:404]     Test net output #1: loss = 3.30057 (* 1 = 3.30057 loss)
I1014 11:57:23.377526  8021 solver.cpp:228] Iteration 10300, loss = 4.3771e-05
I1014 11:57:23.377636  8021 solver.cpp:244]     Train net output #0: loss = 4.37714e-05 (* 1 = 4.37714e-05 loss)
I1014 11:57:23.377660  8021 sgd_solver.cpp:106] Iteration 10300, lr = 0.0001
I1014 11:58:35.961072  8021 solver.cpp:337] Iteration 10400, Testing net (#0)
I1014 11:58:51.244007  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5224
I1014 11:58:51.244109  8021 solver.cpp:404]     Test net output #1: loss = 3.30064 (* 1 = 3.30064 loss)
I1014 11:58:51.974807  8021 solver.cpp:228] Iteration 10400, loss = 3.99834e-05
I1014 11:58:51.974912  8021 solver.cpp:244]     Train net output #0: loss = 3.99838e-05 (* 1 = 3.99838e-05 loss)
I1014 11:58:51.974934  8021 sgd_solver.cpp:106] Iteration 10400, lr = 0.0001
I1014 12:00:04.503203  8021 solver.cpp:337] Iteration 10500, Testing net (#0)
I1014 12:00:19.599694  8021 solver.cpp:404]     Test net output #0: accuracy = 0.522
I1014 12:00:19.599792  8021 solver.cpp:404]     Test net output #1: loss = 3.29542 (* 1 = 3.29542 loss)
I1014 12:00:20.332929  8021 solver.cpp:228] Iteration 10500, loss = 2.90175e-05
I1014 12:00:20.333040  8021 solver.cpp:244]     Train net output #0: loss = 2.90179e-05 (* 1 = 2.90179e-05 loss)
I1014 12:00:20.333063  8021 sgd_solver.cpp:106] Iteration 10500, lr = 0.0001
I1014 12:01:33.795116  8021 solver.cpp:337] Iteration 10600, Testing net (#0)
I1014 12:01:48.768788  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5208
I1014 12:01:48.768895  8021 solver.cpp:404]     Test net output #1: loss = 3.30566 (* 1 = 3.30566 loss)
I1014 12:01:49.495823  8021 solver.cpp:228] Iteration 10600, loss = 2.16692e-05
I1014 12:01:49.495930  8021 solver.cpp:244]     Train net output #0: loss = 2.16696e-05 (* 1 = 2.16696e-05 loss)
I1014 12:01:49.495954  8021 sgd_solver.cpp:106] Iteration 10600, lr = 0.0001
I1014 12:03:01.677763  8021 solver.cpp:337] Iteration 10700, Testing net (#0)
I1014 12:03:16.699008  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5224
I1014 12:03:16.699111  8021 solver.cpp:404]     Test net output #1: loss = 3.29795 (* 1 = 3.29795 loss)
I1014 12:03:17.427613  8021 solver.cpp:228] Iteration 10700, loss = 1.91023e-05
I1014 12:03:17.427718  8021 solver.cpp:244]     Train net output #0: loss = 1.91027e-05 (* 1 = 1.91027e-05 loss)
I1014 12:03:17.427741  8021 sgd_solver.cpp:106] Iteration 10700, lr = 0.0001
I1014 12:04:30.346125  8021 solver.cpp:337] Iteration 10800, Testing net (#0)
I1014 12:04:45.520699  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 12:04:45.520805  8021 solver.cpp:404]     Test net output #1: loss = 3.29792 (* 1 = 3.29792 loss)
I1014 12:04:46.248697  8021 solver.cpp:228] Iteration 10800, loss = 2.02421e-05
I1014 12:04:46.248808  8021 solver.cpp:244]     Train net output #0: loss = 2.02425e-05 (* 1 = 2.02425e-05 loss)
I1014 12:04:46.248831  8021 sgd_solver.cpp:106] Iteration 10800, lr = 0.0001
I1014 12:05:58.536120  8021 solver.cpp:337] Iteration 10900, Testing net (#0)
I1014 12:06:13.543920  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5208
I1014 12:06:13.544028  8021 solver.cpp:404]     Test net output #1: loss = 3.30437 (* 1 = 3.30437 loss)
I1014 12:06:14.267906  8021 solver.cpp:228] Iteration 10900, loss = 3.19693e-05
I1014 12:06:14.268015  8021 solver.cpp:244]     Train net output #0: loss = 3.19697e-05 (* 1 = 3.19697e-05 loss)
I1014 12:06:14.268038  8021 sgd_solver.cpp:106] Iteration 10900, lr = 0.0001
I1014 12:07:26.753001  8021 solver.cpp:337] Iteration 11000, Testing net (#0)
I1014 12:07:41.762305  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5228
I1014 12:07:41.762408  8021 solver.cpp:404]     Test net output #1: loss = 3.29654 (* 1 = 3.29654 loss)
I1014 12:07:42.490521  8021 solver.cpp:228] Iteration 11000, loss = 4.77526e-05
I1014 12:07:42.490618  8021 solver.cpp:244]     Train net output #0: loss = 4.7753e-05 (* 1 = 4.7753e-05 loss)
I1014 12:07:42.490640  8021 sgd_solver.cpp:106] Iteration 11000, lr = 0.0001
I1014 12:08:57.686259  8021 solver.cpp:337] Iteration 11100, Testing net (#0)
I1014 12:09:13.511577  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 12:09:13.511673  8021 solver.cpp:404]     Test net output #1: loss = 3.297 (* 1 = 3.297 loss)
I1014 12:09:14.289060  8021 solver.cpp:228] Iteration 11100, loss = 5.61293e-05
I1014 12:09:14.289191  8021 solver.cpp:244]     Train net output #0: loss = 5.61297e-05 (* 1 = 5.61297e-05 loss)
I1014 12:09:14.289222  8021 sgd_solver.cpp:106] Iteration 11100, lr = 0.0001
I1014 12:10:28.230443  8021 solver.cpp:337] Iteration 11200, Testing net (#0)
I1014 12:10:43.494040  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5208
I1014 12:10:43.494148  8021 solver.cpp:404]     Test net output #1: loss = 3.30645 (* 1 = 3.30645 loss)
I1014 12:10:44.253006  8021 solver.cpp:228] Iteration 11200, loss = 4.06762e-05
I1014 12:10:44.253113  8021 solver.cpp:244]     Train net output #0: loss = 4.06766e-05 (* 1 = 4.06766e-05 loss)
I1014 12:10:44.253135  8021 sgd_solver.cpp:106] Iteration 11200, lr = 0.0001
I1014 12:11:58.350028  8021 solver.cpp:337] Iteration 11300, Testing net (#0)
I1014 12:12:13.892979  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5224
I1014 12:12:13.893079  8021 solver.cpp:404]     Test net output #1: loss = 3.29609 (* 1 = 3.29609 loss)
I1014 12:12:14.641480  8021 solver.cpp:228] Iteration 11300, loss = 3.37491e-05
I1014 12:12:14.641584  8021 solver.cpp:244]     Train net output #0: loss = 3.37495e-05 (* 1 = 3.37495e-05 loss)
I1014 12:12:14.641607  8021 sgd_solver.cpp:106] Iteration 11300, lr = 0.0001
I1014 12:13:28.512027  8021 solver.cpp:337] Iteration 11400, Testing net (#0)
I1014 12:13:43.853754  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 12:13:43.853857  8021 solver.cpp:404]     Test net output #1: loss = 3.2969 (* 1 = 3.2969 loss)
I1014 12:13:44.597635  8021 solver.cpp:228] Iteration 11400, loss = 2.46328e-05
I1014 12:13:44.597728  8021 solver.cpp:244]     Train net output #0: loss = 2.46332e-05 (* 1 = 2.46332e-05 loss)
I1014 12:13:44.597750  8021 sgd_solver.cpp:106] Iteration 11400, lr = 0.0001
I1014 12:15:00.819861  8021 solver.cpp:337] Iteration 11500, Testing net (#0)
I1014 12:15:15.988382  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5216
I1014 12:15:15.988486  8021 solver.cpp:404]     Test net output #1: loss = 3.30385 (* 1 = 3.30385 loss)
I1014 12:15:16.729333  8021 solver.cpp:228] Iteration 11500, loss = 2.31069e-05
I1014 12:15:16.729434  8021 solver.cpp:244]     Train net output #0: loss = 2.31073e-05 (* 1 = 2.31073e-05 loss)
I1014 12:15:16.729456  8021 sgd_solver.cpp:106] Iteration 11500, lr = 0.0001
I1014 12:16:29.576776  8021 solver.cpp:337] Iteration 11600, Testing net (#0)
I1014 12:16:44.785667  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1014 12:16:44.785768  8021 solver.cpp:404]     Test net output #1: loss = 3.30147 (* 1 = 3.30147 loss)
I1014 12:16:45.510797  8021 solver.cpp:228] Iteration 11600, loss = 2.08047e-05
I1014 12:16:45.510900  8021 solver.cpp:244]     Train net output #0: loss = 2.08051e-05 (* 1 = 2.08051e-05 loss)
I1014 12:16:45.510924  8021 sgd_solver.cpp:106] Iteration 11600, lr = 0.0001
I1014 12:17:58.777755  8021 solver.cpp:337] Iteration 11700, Testing net (#0)
I1014 12:18:13.872452  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 12:18:13.872548  8021 solver.cpp:404]     Test net output #1: loss = 3.29173 (* 1 = 3.29173 loss)
I1014 12:18:14.602939  8021 solver.cpp:228] Iteration 11700, loss = 1.76932e-05
I1014 12:18:14.603036  8021 solver.cpp:244]     Train net output #0: loss = 1.76936e-05 (* 1 = 1.76936e-05 loss)
I1014 12:18:14.603061  8021 sgd_solver.cpp:106] Iteration 11700, lr = 0.0001
I1014 12:19:27.431948  8021 solver.cpp:337] Iteration 11800, Testing net (#0)
I1014 12:19:42.568848  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 12:19:42.568943  8021 solver.cpp:404]     Test net output #1: loss = 3.30399 (* 1 = 3.30399 loss)
I1014 12:19:43.301498  8021 solver.cpp:228] Iteration 11800, loss = 3.53264e-05
I1014 12:19:43.301596  8021 solver.cpp:244]     Train net output #0: loss = 3.53268e-05 (* 1 = 3.53268e-05 loss)
I1014 12:19:43.301621  8021 sgd_solver.cpp:106] Iteration 11800, lr = 0.0001
I1014 12:20:56.480824  8021 solver.cpp:337] Iteration 11900, Testing net (#0)
I1014 12:21:12.270376  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1014 12:21:12.270479  8021 solver.cpp:404]     Test net output #1: loss = 3.30185 (* 1 = 3.30185 loss)
I1014 12:21:13.061213  8021 solver.cpp:228] Iteration 11900, loss = 5.85106e-05
I1014 12:21:13.061336  8021 solver.cpp:244]     Train net output #0: loss = 5.8511e-05 (* 1 = 5.8511e-05 loss)
I1014 12:21:13.061360  8021 sgd_solver.cpp:106] Iteration 11900, lr = 0.0001
I1014 12:22:25.803850  8021 solver.cpp:337] Iteration 12000, Testing net (#0)
I1014 12:22:40.936947  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1014 12:22:40.937050  8021 solver.cpp:404]     Test net output #1: loss = 3.2948 (* 1 = 3.2948 loss)
I1014 12:22:41.685663  8021 solver.cpp:228] Iteration 12000, loss = 5.63869e-05
I1014 12:22:41.685773  8021 solver.cpp:244]     Train net output #0: loss = 5.63873e-05 (* 1 = 5.63873e-05 loss)
I1014 12:22:41.685796  8021 sgd_solver.cpp:106] Iteration 12000, lr = 0.0001
I1014 12:23:54.427330  8021 solver.cpp:337] Iteration 12100, Testing net (#0)
I1014 12:24:09.507838  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 12:24:09.507938  8021 solver.cpp:404]     Test net output #1: loss = 3.30535 (* 1 = 3.30535 loss)
I1014 12:24:10.236112  8021 solver.cpp:228] Iteration 12100, loss = 3.76062e-05
I1014 12:24:10.236219  8021 solver.cpp:244]     Train net output #0: loss = 3.76066e-05 (* 1 = 3.76066e-05 loss)
I1014 12:24:10.236243  8021 sgd_solver.cpp:106] Iteration 12100, lr = 0.0001
I1014 12:25:24.141867  8021 solver.cpp:337] Iteration 12200, Testing net (#0)
I1014 12:25:40.283798  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1014 12:25:40.283890  8021 solver.cpp:404]     Test net output #1: loss = 3.29783 (* 1 = 3.29783 loss)
I1014 12:25:41.061501  8021 solver.cpp:228] Iteration 12200, loss = 3.7191e-05
I1014 12:25:41.061597  8021 solver.cpp:244]     Train net output #0: loss = 3.71914e-05 (* 1 = 3.71914e-05 loss)
I1014 12:25:41.061620  8021 sgd_solver.cpp:106] Iteration 12200, lr = 0.0001
I1014 12:26:54.269330  8021 solver.cpp:337] Iteration 12300, Testing net (#0)
I1014 12:27:09.460445  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1014 12:27:09.460536  8021 solver.cpp:404]     Test net output #1: loss = 3.29537 (* 1 = 3.29537 loss)
I1014 12:27:10.194545  8021 solver.cpp:228] Iteration 12300, loss = 2.28971e-05
I1014 12:27:10.194644  8021 solver.cpp:244]     Train net output #0: loss = 2.28975e-05 (* 1 = 2.28975e-05 loss)
I1014 12:27:10.194669  8021 sgd_solver.cpp:106] Iteration 12300, lr = 0.0001
I1014 12:28:23.968132  8021 solver.cpp:337] Iteration 12400, Testing net (#0)
I1014 12:28:39.352430  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 12:28:39.352537  8021 solver.cpp:404]     Test net output #1: loss = 3.30942 (* 1 = 3.30942 loss)
I1014 12:28:40.109419  8021 solver.cpp:228] Iteration 12400, loss = 2.56843e-05
I1014 12:28:40.109529  8021 solver.cpp:244]     Train net output #0: loss = 2.56847e-05 (* 1 = 2.56847e-05 loss)
I1014 12:28:40.109552  8021 sgd_solver.cpp:106] Iteration 12400, lr = 0.0001
I1014 12:29:54.323956  8021 solver.cpp:337] Iteration 12500, Testing net (#0)
I1014 12:30:09.528815  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1014 12:30:09.528942  8021 solver.cpp:404]     Test net output #1: loss = 3.29559 (* 1 = 3.29559 loss)
I1014 12:30:10.263689  8021 solver.cpp:228] Iteration 12500, loss = 2.02992e-05
I1014 12:30:10.263789  8021 solver.cpp:244]     Train net output #0: loss = 2.02996e-05 (* 1 = 2.02996e-05 loss)
I1014 12:30:10.263814  8021 sgd_solver.cpp:106] Iteration 12500, lr = 0.0001
I1014 12:31:23.250644  8021 solver.cpp:337] Iteration 12600, Testing net (#0)
I1014 12:31:38.461577  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1014 12:31:38.461686  8021 solver.cpp:404]     Test net output #1: loss = 3.30132 (* 1 = 3.30132 loss)
I1014 12:31:39.220633  8021 solver.cpp:228] Iteration 12600, loss = 2.33788e-05
I1014 12:31:39.220741  8021 solver.cpp:244]     Train net output #0: loss = 2.33792e-05 (* 1 = 2.33792e-05 loss)
I1014 12:31:39.220762  8021 sgd_solver.cpp:106] Iteration 12600, lr = 0.0001
I1014 12:32:54.917935  8021 solver.cpp:337] Iteration 12700, Testing net (#0)
I1014 12:33:09.977414  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1014 12:33:09.977512  8021 solver.cpp:404]     Test net output #1: loss = 3.30143 (* 1 = 3.30143 loss)
I1014 12:33:10.709017  8021 solver.cpp:228] Iteration 12700, loss = 4.08463e-05
I1014 12:33:10.709115  8021 solver.cpp:244]     Train net output #0: loss = 4.08467e-05 (* 1 = 4.08467e-05 loss)
I1014 12:33:10.709144  8021 sgd_solver.cpp:106] Iteration 12700, lr = 0.0001
I1014 12:34:23.996263  8021 solver.cpp:337] Iteration 12800, Testing net (#0)
I1014 12:34:39.255466  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 12:34:39.255563  8021 solver.cpp:404]     Test net output #1: loss = 3.29625 (* 1 = 3.29625 loss)
I1014 12:34:40.007470  8021 solver.cpp:228] Iteration 12800, loss = 6.43337e-05
I1014 12:34:40.007573  8021 solver.cpp:244]     Train net output #0: loss = 6.43341e-05 (* 1 = 6.43341e-05 loss)
I1014 12:34:40.007598  8021 sgd_solver.cpp:106] Iteration 12800, lr = 0.0001
I1014 12:35:53.010924  8021 solver.cpp:337] Iteration 12900, Testing net (#0)
I1014 12:36:08.256345  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1014 12:36:08.256443  8021 solver.cpp:404]     Test net output #1: loss = 3.30649 (* 1 = 3.30649 loss)
I1014 12:36:08.989835  8021 solver.cpp:228] Iteration 12900, loss = 4.77056e-05
I1014 12:36:08.989933  8021 solver.cpp:244]     Train net output #0: loss = 4.7706e-05 (* 1 = 4.7706e-05 loss)
I1014 12:36:08.989958  8021 sgd_solver.cpp:106] Iteration 12900, lr = 0.0001
I1014 12:37:22.283217  8021 solver.cpp:337] Iteration 13000, Testing net (#0)
I1014 12:37:37.454296  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1014 12:37:37.454391  8021 solver.cpp:404]     Test net output #1: loss = 3.2988 (* 1 = 3.2988 loss)
I1014 12:37:38.184651  8021 solver.cpp:228] Iteration 13000, loss = 3.96067e-05
I1014 12:37:38.184749  8021 solver.cpp:244]     Train net output #0: loss = 3.96071e-05 (* 1 = 3.96071e-05 loss)
I1014 12:37:38.184772  8021 sgd_solver.cpp:106] Iteration 13000, lr = 0.0001
I1014 12:38:51.056843  8021 solver.cpp:337] Iteration 13100, Testing net (#0)
I1014 12:39:06.111196  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1014 12:39:06.111290  8021 solver.cpp:404]     Test net output #1: loss = 3.29881 (* 1 = 3.29881 loss)
I1014 12:39:06.844959  8021 solver.cpp:228] Iteration 13100, loss = 3.28444e-05
I1014 12:39:06.845059  8021 solver.cpp:244]     Train net output #0: loss = 3.28448e-05 (* 1 = 3.28448e-05 loss)
I1014 12:39:06.845091  8021 sgd_solver.cpp:106] Iteration 13100, lr = 0.0001
I1014 12:40:19.361582  8021 solver.cpp:337] Iteration 13200, Testing net (#0)
I1014 12:40:34.412444  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1014 12:40:34.412544  8021 solver.cpp:404]     Test net output #1: loss = 3.30527 (* 1 = 3.30527 loss)
I1014 12:40:35.140323  8021 solver.cpp:228] Iteration 13200, loss = 2.05901e-05
I1014 12:40:35.140425  8021 solver.cpp:244]     Train net output #0: loss = 2.05905e-05 (* 1 = 2.05905e-05 loss)
I1014 12:40:35.140449  8021 sgd_solver.cpp:106] Iteration 13200, lr = 0.0001
I1014 12:41:47.426259  8021 solver.cpp:337] Iteration 13300, Testing net (#0)
I1014 12:42:02.499927  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5012
I1014 12:42:02.500030  8021 solver.cpp:404]     Test net output #1: loss = 3.29744 (* 1 = 3.29744 loss)
I1014 12:42:03.231492  8021 solver.cpp:228] Iteration 13300, loss = 2.42466e-05
I1014 12:42:03.231595  8021 solver.cpp:244]     Train net output #0: loss = 2.4247e-05 (* 1 = 2.4247e-05 loss)
I1014 12:42:03.231617  8021 sgd_solver.cpp:106] Iteration 13300, lr = 0.0001
I1014 12:43:16.033820  8021 solver.cpp:337] Iteration 13400, Testing net (#0)
I1014 12:43:31.089493  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1014 12:43:31.089594  8021 solver.cpp:404]     Test net output #1: loss = 3.29795 (* 1 = 3.29795 loss)
I1014 12:43:31.820406  8021 solver.cpp:228] Iteration 13400, loss = 1.50657e-05
I1014 12:43:31.820509  8021 solver.cpp:244]     Train net output #0: loss = 1.50661e-05 (* 1 = 1.50661e-05 loss)
I1014 12:43:31.820533  8021 sgd_solver.cpp:106] Iteration 13400, lr = 0.0001
I1014 12:44:44.528023  8021 solver.cpp:337] Iteration 13500, Testing net (#0)
I1014 12:44:59.704131  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1014 12:44:59.704228  8021 solver.cpp:404]     Test net output #1: loss = 3.3074 (* 1 = 3.3074 loss)
I1014 12:45:00.437914  8021 solver.cpp:228] Iteration 13500, loss = 2.56319e-05
I1014 12:45:00.438014  8021 solver.cpp:244]     Train net output #0: loss = 2.56323e-05 (* 1 = 2.56323e-05 loss)
I1014 12:45:00.438035  8021 sgd_solver.cpp:106] Iteration 13500, lr = 0.0001
I1014 12:46:13.594504  8021 solver.cpp:337] Iteration 13600, Testing net (#0)
I1014 12:46:28.704362  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1014 12:46:28.704463  8021 solver.cpp:404]     Test net output #1: loss = 3.29701 (* 1 = 3.29701 loss)
I1014 12:46:29.438974  8021 solver.cpp:228] Iteration 13600, loss = 3.82747e-05
I1014 12:46:29.439080  8021 solver.cpp:244]     Train net output #0: loss = 3.82751e-05 (* 1 = 3.82751e-05 loss)
I1014 12:46:29.439103  8021 sgd_solver.cpp:106] Iteration 13600, lr = 0.0001
I1014 12:47:42.400053  8021 solver.cpp:337] Iteration 13700, Testing net (#0)
I1014 12:47:58.384991  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1014 12:47:58.385092  8021 solver.cpp:404]     Test net output #1: loss = 3.29785 (* 1 = 3.29785 loss)
I1014 12:47:59.118960  8021 solver.cpp:228] Iteration 13700, loss = 6.50455e-05
I1014 12:47:59.119066  8021 solver.cpp:244]     Train net output #0: loss = 6.50459e-05 (* 1 = 6.50459e-05 loss)
I1014 12:47:59.119089  8021 sgd_solver.cpp:106] Iteration 13700, lr = 0.0001
I1014 12:49:12.083250  8021 solver.cpp:337] Iteration 13800, Testing net (#0)
I1014 12:49:27.136010  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 12:49:27.136114  8021 solver.cpp:404]     Test net output #1: loss = 3.30481 (* 1 = 3.30481 loss)
I1014 12:49:27.865466  8021 solver.cpp:228] Iteration 13800, loss = 3.77934e-05
I1014 12:49:27.865569  8021 solver.cpp:244]     Train net output #0: loss = 3.77938e-05 (* 1 = 3.77938e-05 loss)
I1014 12:49:27.865592  8021 sgd_solver.cpp:106] Iteration 13800, lr = 0.0001
I1014 12:50:41.774396  8021 solver.cpp:337] Iteration 13900, Testing net (#0)
I1014 12:50:57.351961  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1014 12:50:57.352062  8021 solver.cpp:404]     Test net output #1: loss = 3.30241 (* 1 = 3.30241 loss)
I1014 12:50:58.082348  8021 solver.cpp:228] Iteration 13900, loss = 4.07559e-05
I1014 12:50:58.082454  8021 solver.cpp:244]     Train net output #0: loss = 4.07562e-05 (* 1 = 4.07562e-05 loss)
I1014 12:50:58.082479  8021 sgd_solver.cpp:106] Iteration 13900, lr = 0.0001
I1014 12:52:10.485450  8021 solver.cpp:337] Iteration 14000, Testing net (#0)
I1014 12:52:25.522953  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 12:52:25.523049  8021 solver.cpp:404]     Test net output #1: loss = 3.29267 (* 1 = 3.29267 loss)
I1014 12:52:26.253197  8021 solver.cpp:228] Iteration 14000, loss = 3.22626e-05
I1014 12:52:26.253309  8021 solver.cpp:244]     Train net output #0: loss = 3.2263e-05 (* 1 = 3.2263e-05 loss)
I1014 12:52:26.253331  8021 sgd_solver.cpp:106] Iteration 14000, lr = 0.0001
I1014 12:53:38.836745  8021 solver.cpp:337] Iteration 14100, Testing net (#0)
I1014 12:53:54.058991  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 12:53:54.059093  8021 solver.cpp:404]     Test net output #1: loss = 3.30494 (* 1 = 3.30494 loss)
I1014 12:53:54.843129  8021 solver.cpp:228] Iteration 14100, loss = 2.36792e-05
I1014 12:53:54.843233  8021 solver.cpp:244]     Train net output #0: loss = 2.36796e-05 (* 1 = 2.36796e-05 loss)
I1014 12:53:54.843255  8021 sgd_solver.cpp:106] Iteration 14100, lr = 0.0001
I1014 12:55:12.591373  8021 solver.cpp:337] Iteration 14200, Testing net (#0)
I1014 12:55:27.785081  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1014 12:55:27.785178  8021 solver.cpp:404]     Test net output #1: loss = 3.30278 (* 1 = 3.30278 loss)
I1014 12:55:28.514173  8021 solver.cpp:228] Iteration 14200, loss = 2.31998e-05
I1014 12:55:28.514277  8021 solver.cpp:244]     Train net output #0: loss = 2.32002e-05 (* 1 = 2.32002e-05 loss)
I1014 12:55:28.514300  8021 sgd_solver.cpp:106] Iteration 14200, lr = 0.0001
I1014 12:56:41.792937  8021 solver.cpp:337] Iteration 14300, Testing net (#0)
I1014 12:56:56.885782  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1014 12:56:56.885881  8021 solver.cpp:404]     Test net output #1: loss = 3.29574 (* 1 = 3.29574 loss)
I1014 12:56:57.618875  8021 solver.cpp:228] Iteration 14300, loss = 1.91476e-05
I1014 12:56:57.618971  8021 solver.cpp:244]     Train net output #0: loss = 1.9148e-05 (* 1 = 1.9148e-05 loss)
I1014 12:56:57.618994  8021 sgd_solver.cpp:106] Iteration 14300, lr = 0.0001
I1014 12:58:10.578025  8021 solver.cpp:337] Iteration 14400, Testing net (#0)
I1014 12:58:25.625571  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 12:58:25.625669  8021 solver.cpp:404]     Test net output #1: loss = 3.3063 (* 1 = 3.3063 loss)
I1014 12:58:26.357753  8021 solver.cpp:228] Iteration 14400, loss = 3.09418e-05
I1014 12:58:26.357862  8021 solver.cpp:244]     Train net output #0: loss = 3.09422e-05 (* 1 = 3.09422e-05 loss)
I1014 12:58:26.357887  8021 sgd_solver.cpp:106] Iteration 14400, lr = 0.0001
I1014 12:59:38.852442  8021 solver.cpp:337] Iteration 14500, Testing net (#0)
I1014 12:59:53.881863  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1014 12:59:53.881963  8021 solver.cpp:404]     Test net output #1: loss = 3.29875 (* 1 = 3.29875 loss)
I1014 12:59:54.612982  8021 solver.cpp:228] Iteration 14500, loss = 5.29742e-05
I1014 12:59:54.613088  8021 solver.cpp:244]     Train net output #0: loss = 5.29746e-05 (* 1 = 5.29746e-05 loss)
I1014 12:59:54.613111  8021 sgd_solver.cpp:106] Iteration 14500, lr = 0.0001
I1014 13:01:08.552378  8021 solver.cpp:337] Iteration 14600, Testing net (#0)
I1014 13:01:23.944543  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1014 13:01:23.944639  8021 solver.cpp:404]     Test net output #1: loss = 3.29626 (* 1 = 3.29626 loss)
I1014 13:01:24.689352  8021 solver.cpp:228] Iteration 14600, loss = 6.22892e-05
I1014 13:01:24.689451  8021 solver.cpp:244]     Train net output #0: loss = 6.22896e-05 (* 1 = 6.22896e-05 loss)
I1014 13:01:24.689473  8021 sgd_solver.cpp:106] Iteration 14600, lr = 0.0001
I1014 13:02:38.751636  8021 solver.cpp:337] Iteration 14700, Testing net (#0)
I1014 13:02:54.716212  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 13:02:54.716308  8021 solver.cpp:404]     Test net output #1: loss = 3.31033 (* 1 = 3.31033 loss)
I1014 13:02:55.479245  8021 solver.cpp:228] Iteration 14700, loss = 3.49738e-05
I1014 13:02:55.479342  8021 solver.cpp:244]     Train net output #0: loss = 3.49741e-05 (* 1 = 3.49741e-05 loss)
I1014 13:02:55.479367  8021 sgd_solver.cpp:106] Iteration 14700, lr = 0.0001
I1014 13:59:40.279644  8021 solver.cpp:337] Iteration 14800, Testing net (#0)
I1014 13:59:55.482560  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5004
I1014 13:59:55.482650  8021 solver.cpp:404]     Test net output #1: loss = 3.29652 (* 1 = 3.29652 loss)
I1014 13:59:56.219753  8021 solver.cpp:228] Iteration 14800, loss = 4.09919e-05
I1014 13:59:56.219851  8021 solver.cpp:244]     Train net output #0: loss = 4.09923e-05 (* 1 = 4.09923e-05 loss)
I1014 13:59:56.219873  8021 sgd_solver.cpp:106] Iteration 14800, lr = 0.0001
I1014 14:01:09.946557  8021 solver.cpp:337] Iteration 14900, Testing net (#0)
I1014 14:01:25.394511  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1014 14:01:25.394604  8021 solver.cpp:404]     Test net output #1: loss = 3.30221 (* 1 = 3.30221 loss)
I1014 14:01:26.135905  8021 solver.cpp:228] Iteration 14900, loss = 3.24105e-05
I1014 14:01:26.136011  8021 solver.cpp:244]     Train net output #0: loss = 3.24109e-05 (* 1 = 3.24109e-05 loss)
I1014 14:01:26.136034  8021 sgd_solver.cpp:106] Iteration 14900, lr = 0.0001
I1014 14:02:40.456142  8021 solver.cpp:337] Iteration 15000, Testing net (#0)
I1014 14:02:56.248358  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1014 14:02:56.248458  8021 solver.cpp:404]     Test net output #1: loss = 3.30236 (* 1 = 3.30236 loss)
I1014 14:02:57.002312  8021 solver.cpp:228] Iteration 15000, loss = 2.02458e-05
I1014 14:02:57.002414  8021 solver.cpp:244]     Train net output #0: loss = 2.02462e-05 (* 1 = 2.02462e-05 loss)
I1014 14:02:57.002439  8021 sgd_solver.cpp:106] Iteration 15000, lr = 0.0001
I1014 14:04:09.869490  8021 solver.cpp:337] Iteration 15100, Testing net (#0)
I1014 14:04:25.194625  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5
I1014 14:04:25.194753  8021 solver.cpp:404]     Test net output #1: loss = 3.29717 (* 1 = 3.29717 loss)
I1014 14:04:25.929908  8021 solver.cpp:228] Iteration 15100, loss = 2.08619e-05
I1014 14:04:25.930016  8021 solver.cpp:244]     Train net output #0: loss = 2.08623e-05 (* 1 = 2.08623e-05 loss)
I1014 14:04:25.930039  8021 sgd_solver.cpp:106] Iteration 15100, lr = 0.0001
I1014 14:05:39.283710  8021 solver.cpp:337] Iteration 15200, Testing net (#0)
I1014 14:05:54.591930  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1014 14:05:54.592030  8021 solver.cpp:404]     Test net output #1: loss = 3.30742 (* 1 = 3.30742 loss)
I1014 14:05:55.328811  8021 solver.cpp:228] Iteration 15200, loss = 1.9596e-05
I1014 14:05:55.328922  8021 solver.cpp:244]     Train net output #0: loss = 1.95964e-05 (* 1 = 1.95964e-05 loss)
I1014 14:05:55.328944  8021 sgd_solver.cpp:106] Iteration 15200, lr = 0.0001
I1014 14:07:08.687402  8021 solver.cpp:337] Iteration 15300, Testing net (#0)
I1014 14:07:23.909705  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5008
I1014 14:07:23.909807  8021 solver.cpp:404]     Test net output #1: loss = 3.29971 (* 1 = 3.29971 loss)
I1014 14:07:24.646607  8021 solver.cpp:228] Iteration 15300, loss = 3.17262e-05
I1014 14:07:24.646713  8021 solver.cpp:244]     Train net output #0: loss = 3.17266e-05 (* 1 = 3.17266e-05 loss)
I1014 14:07:24.646747  8021 sgd_solver.cpp:106] Iteration 15300, lr = 0.0001
I1014 14:08:39.279300  8021 solver.cpp:337] Iteration 15400, Testing net (#0)
I1014 14:08:54.907760  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1014 14:08:54.907863  8021 solver.cpp:404]     Test net output #1: loss = 3.29973 (* 1 = 3.29973 loss)
I1014 14:08:55.654381  8021 solver.cpp:228] Iteration 15400, loss = 4.94334e-05
I1014 14:08:55.654482  8021 solver.cpp:244]     Train net output #0: loss = 4.94338e-05 (* 1 = 4.94338e-05 loss)
I1014 14:08:55.654505  8021 sgd_solver.cpp:106] Iteration 15400, lr = 0.0001
I1014 14:10:08.683627  8021 solver.cpp:337] Iteration 15500, Testing net (#0)
I1014 14:10:23.949661  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1014 14:10:23.949772  8021 solver.cpp:404]     Test net output #1: loss = 3.30618 (* 1 = 3.30618 loss)
I1014 14:10:24.680308  8021 solver.cpp:228] Iteration 15500, loss = 5.17384e-05
I1014 14:10:24.680418  8021 solver.cpp:244]     Train net output #0: loss = 5.17388e-05 (* 1 = 5.17388e-05 loss)
I1014 14:10:24.680441  8021 sgd_solver.cpp:106] Iteration 15500, lr = 0.0001
I1014 14:11:38.515213  8021 solver.cpp:337] Iteration 15600, Testing net (#0)
I1014 14:11:54.026160  8021 solver.cpp:404]     Test net output #0: accuracy = 0.5012
I1014 14:11:54.026278  8021 solver.cpp:404]     Test net output #1: loss = 3.29832 (* 1 = 3.29832 loss)
I1014 14:11:54.778324  8021 solver.cpp:228] Iteration 15600, loss = 4.28078e-05
I1014 14:11:54.778431  8021 solver.cpp:244]     Train net output #0: loss = 4.28082e-05 (* 1 = 4.28082e-05 loss)
I1014 14:11:54.778456  8021 sgd_solver.cpp:106] Iteration 15600, lr = 0.0001
I1014 14:13:08.304059  8021 solver.cpp:337] Iteration 15700, Testing net (#0)
I1014 14:13:23.535876  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4996
I1014 14:13:23.535971  8021 solver.cpp:404]     Test net output #1: loss = 3.29886 (* 1 = 3.29886 loss)
I1014 14:13:24.267484  8021 solver.cpp:228] Iteration 15700, loss = 3.82746e-05
I1014 14:13:24.267583  8021 solver.cpp:244]     Train net output #0: loss = 3.8275e-05 (* 1 = 3.8275e-05 loss)
I1014 14:13:24.267609  8021 sgd_solver.cpp:106] Iteration 15700, lr = 0.0001
I1014 14:14:37.954911  8021 solver.cpp:337] Iteration 15800, Testing net (#0)
I1014 14:14:53.862020  8021 solver.cpp:404]     Test net output #0: accuracy = 0.4992
I1014 14:14:53.862149  8021 solver.cpp:404]     Test net output #1: loss = 3.30832 (* 1 = 3.30832 loss)
I1014 14:14:54.639605  8021 solver.cpp:228] Iteration 15800, loss = 2.63161e-05
I1014 14:14:54.639737  8021 solver.cpp:244]     Train net output #0: loss = 2.63165e-05 (* 1 = 2.63165e-05 loss)
I1014 14:14:54.639760  8021 sgd_solver.cpp:106] Iteration 15800, lr = 0.0001