ALL_CNN_C.log

I0130 12:52:39.850857 32230 caffe.cpp:217] Using GPUs 0
I0130 12:52:39.851308 32230 caffe.cpp:222] GPU 0: GeForce GTX TITAN X
I0130 12:52:40.054941 32230 solver.cpp:48] Initializing solver from parameters: 
test_iter: 50
test_interval: 200
base_lr: 0.05
display: 200
max_iter: 70000
lr_policy: "multistep"
gamma: 0.1
momentum: 0.9
weight_decay: 0.001
snapshot: 2000
snapshot_prefix: "ALL_CNN_C"
solver_mode: GPU
device_id: 0
debug_info: false
net: "ALL_CNN_C_train_val.prototxt"
train_state {
  level: 0
  stage: ""
}
stepvalue: 40000
stepvalue: 50000
stepvalue: 60000
type: "SGD"
I0130 12:52:40.055204 32230 solver.cpp:91] Creating training net from net file: ALL_CNN_C_train_val.prototxt
I0130 12:52:40.055415 32230 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer data
I0130 12:52:40.055522 32230 net.cpp:58] Initializing net from parameters: 
name: "ALL-CNN-C"
state {
  phase: TRAIN
  level: 0
  stage: ""
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  data_param {
    source: "cifar-10_train_lmdb"
    batch_size: 256
    backend: LMDB
  }
}
layer {
  name: "drop1"
  type: "Dropout"
  bottom: "data"
  top: "data"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 96
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 96
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "conv2"
  top: "conv3"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 96
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "drop2"
  type: "Dropout"
  bottom: "conv3"
  top: "conv3"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "conv6"
  type: "Convolution"
  bottom: "conv5"
  top: "conv6"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "conv6"
  top: "conv6"
}
layer {
  name: "drop3"
  type: "Dropout"
  bottom: "conv6"
  top: "conv6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "conv7"
  type: "Convolution"
  bottom: "conv6"
  top: "conv7"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 192
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "conv7"
  top: "conv7"
}
layer {
  name: "conv8"
  type: "Convolution"
  bottom: "conv7"
  top: "conv8"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 192
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu8"
  type: "ReLU"
  bottom: "conv8"
  top: "conv8"
}
layer {
  name: "conv9"
  type: "Convolution"
  bottom: "conv8"
  top: "conv9"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 10
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu9"
  type: "ReLU"
  bottom: "conv9"
  top: "conv9"
}
layer {
  name: "pool"
  type: "Pooling"
  bottom: "conv9"
  top: "pool"
  pooling_param {
    pool: AVE
    global_pooling: true
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "pool"
  bottom: "label"
  top: "accuracy"
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "pool"
  bottom: "label"
  top: "loss"
}
I0130 12:52:40.055610 32230 layer_factory.hpp:77] Creating layer data
I0130 12:52:40.055814 32230 net.cpp:100] Creating Layer data
I0130 12:52:40.055820 32230 net.cpp:408] data -> data
I0130 12:52:40.055835 32230 net.cpp:408] data -> label
I0130 12:52:40.056236 32246 db_lmdb.cpp:35] Opened lmdb cifar-10_train_lmdb
I0130 12:52:40.063201 32230 data_layer.cpp:41] output data size: 256,3,32,32
I0130 12:52:40.067507 32230 net.cpp:150] Setting up data
I0130 12:52:40.067526 32230 net.cpp:157] Top shape: 256 3 32 32 (786432)
I0130 12:52:40.067529 32230 net.cpp:157] Top shape: 256 (256)
I0130 12:52:40.067531 32230 net.cpp:165] Memory required for data: 3146752
I0130 12:52:40.067545 32230 layer_factory.hpp:77] Creating layer label_data_1_split
I0130 12:52:40.067556 32230 net.cpp:100] Creating Layer label_data_1_split
I0130 12:52:40.067562 32230 net.cpp:434] label_data_1_split <- label
I0130 12:52:40.067575 32230 net.cpp:408] label_data_1_split -> label_data_1_split_0
I0130 12:52:40.067585 32230 net.cpp:408] label_data_1_split -> label_data_1_split_1
I0130 12:52:40.067651 32230 net.cpp:150] Setting up label_data_1_split
I0130 12:52:40.067703 32230 net.cpp:157] Top shape: 256 (256)
I0130 12:52:40.067706 32230 net.cpp:157] Top shape: 256 (256)
I0130 12:52:40.067708 32230 net.cpp:165] Memory required for data: 3148800
I0130 12:52:40.067710 32230 layer_factory.hpp:77] Creating layer drop1
I0130 12:52:40.067714 32230 net.cpp:100] Creating Layer drop1
I0130 12:52:40.067716 32230 net.cpp:434] drop1 <- data
I0130 12:52:40.067719 32230 net.cpp:395] drop1 -> data (in-place)
I0130 12:52:40.067746 32230 net.cpp:150] Setting up drop1
I0130 12:52:40.067750 32230 net.cpp:157] Top shape: 256 3 32 32 (786432)
I0130 12:52:40.067752 32230 net.cpp:165] Memory required for data: 6294528
I0130 12:52:40.067754 32230 layer_factory.hpp:77] Creating layer conv1
I0130 12:52:40.067765 32230 net.cpp:100] Creating Layer conv1
I0130 12:52:40.067767 32230 net.cpp:434] conv1 <- data
I0130 12:52:40.067771 32230 net.cpp:408] conv1 -> conv1
I0130 12:52:40.225661 32230 net.cpp:150] Setting up conv1
I0130 12:52:40.225680 32230 net.cpp:157] Top shape: 256 96 32 32 (25165824)
I0130 12:52:40.225682 32230 net.cpp:165] Memory required for data: 106957824
I0130 12:52:40.225706 32230 layer_factory.hpp:77] Creating layer relu1
I0130 12:52:40.225713 32230 net.cpp:100] Creating Layer relu1
I0130 12:52:40.225716 32230 net.cpp:434] relu1 <- conv1
I0130 12:52:40.225720 32230 net.cpp:395] relu1 -> conv1 (in-place)
I0130 12:52:40.226183 32230 net.cpp:150] Setting up relu1
I0130 12:52:40.226199 32230 net.cpp:157] Top shape: 256 96 32 32 (25165824)
I0130 12:52:40.226202 32230 net.cpp:165] Memory required for data: 207621120
I0130 12:52:40.226204 32230 layer_factory.hpp:77] Creating layer conv2
I0130 12:52:40.226214 32230 net.cpp:100] Creating Layer conv2
I0130 12:52:40.226217 32230 net.cpp:434] conv2 <- conv1
I0130 12:52:40.226222 32230 net.cpp:408] conv2 -> conv2
I0130 12:52:40.228440 32230 net.cpp:150] Setting up conv2
I0130 12:52:40.228448 32230 net.cpp:157] Top shape: 256 96 32 32 (25165824)
I0130 12:52:40.228451 32230 net.cpp:165] Memory required for data: 308284416
I0130 12:52:40.228456 32230 layer_factory.hpp:77] Creating layer relu2
I0130 12:52:40.228459 32230 net.cpp:100] Creating Layer relu2
I0130 12:52:40.228461 32230 net.cpp:434] relu2 <- conv2
I0130 12:52:40.228464 32230 net.cpp:395] relu2 -> conv2 (in-place)
I0130 12:52:40.228579 32230 net.cpp:150] Setting up relu2
I0130 12:52:40.228584 32230 net.cpp:157] Top shape: 256 96 32 32 (25165824)
I0130 12:52:40.228585 32230 net.cpp:165] Memory required for data: 408947712
I0130 12:52:40.228586 32230 layer_factory.hpp:77] Creating layer conv3
I0130 12:52:40.228592 32230 net.cpp:100] Creating Layer conv3
I0130 12:52:40.228595 32230 net.cpp:434] conv3 <- conv2
I0130 12:52:40.228597 32230 net.cpp:408] conv3 -> conv3
I0130 12:52:40.230530 32230 net.cpp:150] Setting up conv3
I0130 12:52:40.230538 32230 net.cpp:157] Top shape: 256 96 16 16 (6291456)
I0130 12:52:40.230540 32230 net.cpp:165] Memory required for data: 434113536
I0130 12:52:40.230545 32230 layer_factory.hpp:77] Creating layer relu3
I0130 12:52:40.230550 32230 net.cpp:100] Creating Layer relu3
I0130 12:52:40.230551 32230 net.cpp:434] relu3 <- conv3
I0130 12:52:40.230554 32230 net.cpp:395] relu3 -> conv3 (in-place)
I0130 12:52:40.231003 32230 net.cpp:150] Setting up relu3
I0130 12:52:40.231009 32230 net.cpp:157] Top shape: 256 96 16 16 (6291456)
I0130 12:52:40.231011 32230 net.cpp:165] Memory required for data: 459279360
I0130 12:52:40.231012 32230 layer_factory.hpp:77] Creating layer drop2
I0130 12:52:40.231016 32230 net.cpp:100] Creating Layer drop2
I0130 12:52:40.231019 32230 net.cpp:434] drop2 <- conv3
I0130 12:52:40.231021 32230 net.cpp:395] drop2 -> conv3 (in-place)
I0130 12:52:40.231034 32230 net.cpp:150] Setting up drop2
I0130 12:52:40.231037 32230 net.cpp:157] Top shape: 256 96 16 16 (6291456)
I0130 12:52:40.231039 32230 net.cpp:165] Memory required for data: 484445184
I0130 12:52:40.231040 32230 layer_factory.hpp:77] Creating layer conv4
I0130 12:52:40.231045 32230 net.cpp:100] Creating Layer conv4
I0130 12:52:40.231046 32230 net.cpp:434] conv4 <- conv3
I0130 12:52:40.231050 32230 net.cpp:408] conv4 -> conv4
I0130 12:52:40.233346 32230 net.cpp:150] Setting up conv4
I0130 12:52:40.233355 32230 net.cpp:157] Top shape: 256 192 16 16 (12582912)
I0130 12:52:40.233357 32230 net.cpp:165] Memory required for data: 534776832
I0130 12:52:40.233361 32230 layer_factory.hpp:77] Creating layer relu4
I0130 12:52:40.233364 32230 net.cpp:100] Creating Layer relu4
I0130 12:52:40.233366 32230 net.cpp:434] relu4 <- conv4
I0130 12:52:40.233368 32230 net.cpp:395] relu4 -> conv4 (in-place)
I0130 12:52:40.233830 32230 net.cpp:150] Setting up relu4
I0130 12:52:40.233836 32230 net.cpp:157] Top shape: 256 192 16 16 (12582912)
I0130 12:52:40.233839 32230 net.cpp:165] Memory required for data: 585108480
I0130 12:52:40.233840 32230 layer_factory.hpp:77] Creating layer conv5
I0130 12:52:40.233847 32230 net.cpp:100] Creating Layer conv5
I0130 12:52:40.233850 32230 net.cpp:434] conv5 <- conv4
I0130 12:52:40.233852 32230 net.cpp:408] conv5 -> conv5
I0130 12:52:40.237483 32230 net.cpp:150] Setting up conv5
I0130 12:52:40.237493 32230 net.cpp:157] Top shape: 256 192 16 16 (12582912)
I0130 12:52:40.237496 32230 net.cpp:165] Memory required for data: 635440128
I0130 12:52:40.237504 32230 layer_factory.hpp:77] Creating layer relu5
I0130 12:52:40.237507 32230 net.cpp:100] Creating Layer relu5
I0130 12:52:40.237510 32230 net.cpp:434] relu5 <- conv5
I0130 12:52:40.237516 32230 net.cpp:395] relu5 -> conv5 (in-place)
I0130 12:52:40.237681 32230 net.cpp:150] Setting up relu5
I0130 12:52:40.237687 32230 net.cpp:157] Top shape: 256 192 16 16 (12582912)
I0130 12:52:40.237689 32230 net.cpp:165] Memory required for data: 685771776
I0130 12:52:40.237691 32230 layer_factory.hpp:77] Creating layer conv6
I0130 12:52:40.237697 32230 net.cpp:100] Creating Layer conv6
I0130 12:52:40.237699 32230 net.cpp:434] conv6 <- conv5
I0130 12:52:40.237704 32230 net.cpp:408] conv6 -> conv6
I0130 12:52:40.241044 32230 net.cpp:150] Setting up conv6
I0130 12:52:40.241053 32230 net.cpp:157] Top shape: 256 192 8 8 (3145728)
I0130 12:52:40.241055 32230 net.cpp:165] Memory required for data: 698354688
I0130 12:52:40.241060 32230 layer_factory.hpp:77] Creating layer relu6
I0130 12:52:40.241065 32230 net.cpp:100] Creating Layer relu6
I0130 12:52:40.241066 32230 net.cpp:434] relu6 <- conv6
I0130 12:52:40.241068 32230 net.cpp:395] relu6 -> conv6 (in-place)
I0130 12:52:40.241533 32230 net.cpp:150] Setting up relu6
I0130 12:52:40.241539 32230 net.cpp:157] Top shape: 256 192 8 8 (3145728)
I0130 12:52:40.241541 32230 net.cpp:165] Memory required for data: 710937600
I0130 12:52:40.241542 32230 layer_factory.hpp:77] Creating layer drop3
I0130 12:52:40.241549 32230 net.cpp:100] Creating Layer drop3
I0130 12:52:40.241551 32230 net.cpp:434] drop3 <- conv6
I0130 12:52:40.241554 32230 net.cpp:395] drop3 -> conv6 (in-place)
I0130 12:52:40.241571 32230 net.cpp:150] Setting up drop3
I0130 12:52:40.241575 32230 net.cpp:157] Top shape: 256 192 8 8 (3145728)
I0130 12:52:40.241576 32230 net.cpp:165] Memory required for data: 723520512
I0130 12:52:40.241577 32230 layer_factory.hpp:77] Creating layer conv7
I0130 12:52:40.241585 32230 net.cpp:100] Creating Layer conv7
I0130 12:52:40.241586 32230 net.cpp:434] conv7 <- conv6
I0130 12:52:40.241590 32230 net.cpp:408] conv7 -> conv7
I0130 12:52:40.244515 32230 net.cpp:150] Setting up conv7
I0130 12:52:40.244523 32230 net.cpp:157] Top shape: 256 192 6 6 (1769472)
I0130 12:52:40.244524 32230 net.cpp:165] Memory required for data: 730598400
I0130 12:52:40.244529 32230 layer_factory.hpp:77] Creating layer relu7
I0130 12:52:40.244532 32230 net.cpp:100] Creating Layer relu7
I0130 12:52:40.244534 32230 net.cpp:434] relu7 <- conv7
I0130 12:52:40.244537 32230 net.cpp:395] relu7 -> conv7 (in-place)
I0130 12:52:40.245004 32230 net.cpp:150] Setting up relu7
I0130 12:52:40.245010 32230 net.cpp:157] Top shape: 256 192 6 6 (1769472)
I0130 12:52:40.245012 32230 net.cpp:165] Memory required for data: 737676288
I0130 12:52:40.245013 32230 layer_factory.hpp:77] Creating layer conv8
I0130 12:52:40.245020 32230 net.cpp:100] Creating Layer conv8
I0130 12:52:40.245023 32230 net.cpp:434] conv8 <- conv7
I0130 12:52:40.245026 32230 net.cpp:408] conv8 -> conv8
I0130 12:52:40.247067 32230 net.cpp:150] Setting up conv8
I0130 12:52:40.247076 32230 net.cpp:157] Top shape: 256 192 6 6 (1769472)
I0130 12:52:40.247077 32230 net.cpp:165] Memory required for data: 744754176
I0130 12:52:40.247081 32230 layer_factory.hpp:77] Creating layer relu8
I0130 12:52:40.247084 32230 net.cpp:100] Creating Layer relu8
I0130 12:52:40.247087 32230 net.cpp:434] relu8 <- conv8
I0130 12:52:40.247089 32230 net.cpp:395] relu8 -> conv8 (in-place)
I0130 12:52:40.247206 32230 net.cpp:150] Setting up relu8
I0130 12:52:40.247210 32230 net.cpp:157] Top shape: 256 192 6 6 (1769472)
I0130 12:52:40.247212 32230 net.cpp:165] Memory required for data: 751832064
I0130 12:52:40.247215 32230 layer_factory.hpp:77] Creating layer conv9
I0130 12:52:40.247220 32230 net.cpp:100] Creating Layer conv9
I0130 12:52:40.247223 32230 net.cpp:434] conv9 <- conv8
I0130 12:52:40.247228 32230 net.cpp:408] conv9 -> conv9
I0130 12:52:40.248474 32230 net.cpp:150] Setting up conv9
I0130 12:52:40.248481 32230 net.cpp:157] Top shape: 256 10 6 6 (92160)
I0130 12:52:40.248482 32230 net.cpp:165] Memory required for data: 752200704
I0130 12:52:40.248489 32230 layer_factory.hpp:77] Creating layer relu9
I0130 12:52:40.248492 32230 net.cpp:100] Creating Layer relu9
I0130 12:52:40.248494 32230 net.cpp:434] relu9 <- conv9
I0130 12:52:40.248503 32230 net.cpp:395] relu9 -> conv9 (in-place)
I0130 12:52:40.248965 32230 net.cpp:150] Setting up relu9
I0130 12:52:40.248971 32230 net.cpp:157] Top shape: 256 10 6 6 (92160)
I0130 12:52:40.248973 32230 net.cpp:165] Memory required for data: 752569344
I0130 12:52:40.248975 32230 layer_factory.hpp:77] Creating layer pool
I0130 12:52:40.248980 32230 net.cpp:100] Creating Layer pool
I0130 12:52:40.248981 32230 net.cpp:434] pool <- conv9
I0130 12:52:40.248986 32230 net.cpp:408] pool -> pool
I0130 12:52:40.249127 32230 net.cpp:150] Setting up pool
I0130 12:52:40.249133 32230 net.cpp:157] Top shape: 256 10 1 1 (2560)
I0130 12:52:40.249135 32230 net.cpp:165] Memory required for data: 752579584
I0130 12:52:40.249136 32230 layer_factory.hpp:77] Creating layer pool_pool_0_split
I0130 12:52:40.249140 32230 net.cpp:100] Creating Layer pool_pool_0_split
I0130 12:52:40.249142 32230 net.cpp:434] pool_pool_0_split <- pool
I0130 12:52:40.249145 32230 net.cpp:408] pool_pool_0_split -> pool_pool_0_split_0
I0130 12:52:40.249148 32230 net.cpp:408] pool_pool_0_split -> pool_pool_0_split_1
I0130 12:52:40.249174 32230 net.cpp:150] Setting up pool_pool_0_split
I0130 12:52:40.249178 32230 net.cpp:157] Top shape: 256 10 1 1 (2560)
I0130 12:52:40.249181 32230 net.cpp:157] Top shape: 256 10 1 1 (2560)
I0130 12:52:40.249181 32230 net.cpp:165] Memory required for data: 752600064
I0130 12:52:40.249183 32230 layer_factory.hpp:77] Creating layer accuracy
I0130 12:52:40.249188 32230 net.cpp:100] Creating Layer accuracy
I0130 12:52:40.249189 32230 net.cpp:434] accuracy <- pool_pool_0_split_0
I0130 12:52:40.249192 32230 net.cpp:434] accuracy <- label_data_1_split_0
I0130 12:52:40.249194 32230 net.cpp:408] accuracy -> accuracy
I0130 12:52:40.249202 32230 net.cpp:150] Setting up accuracy
I0130 12:52:40.249203 32230 net.cpp:157] Top shape: (1)
I0130 12:52:40.249205 32230 net.cpp:165] Memory required for data: 752600068
I0130 12:52:40.249207 32230 layer_factory.hpp:77] Creating layer loss
I0130 12:52:40.249209 32230 net.cpp:100] Creating Layer loss
I0130 12:52:40.249212 32230 net.cpp:434] loss <- pool_pool_0_split_1
I0130 12:52:40.249213 32230 net.cpp:434] loss <- label_data_1_split_1
I0130 12:52:40.249215 32230 net.cpp:408] loss -> loss
I0130 12:52:40.249222 32230 layer_factory.hpp:77] Creating layer loss
I0130 12:52:40.249748 32230 net.cpp:150] Setting up loss
I0130 12:52:40.249754 32230 net.cpp:157] Top shape: (1)
I0130 12:52:40.249755 32230 net.cpp:160]     with loss weight 1
I0130 12:52:40.249773 32230 net.cpp:165] Memory required for data: 752600072
I0130 12:52:40.249774 32230 net.cpp:226] loss needs backward computation.
I0130 12:52:40.249781 32230 net.cpp:228] accuracy does not need backward computation.
I0130 12:52:40.249784 32230 net.cpp:226] pool_pool_0_split needs backward computation.
I0130 12:52:40.249786 32230 net.cpp:226] pool needs backward computation.
I0130 12:52:40.249788 32230 net.cpp:226] relu9 needs backward computation.
I0130 12:52:40.249789 32230 net.cpp:226] conv9 needs backward computation.
I0130 12:52:40.249791 32230 net.cpp:226] relu8 needs backward computation.
I0130 12:52:40.249794 32230 net.cpp:226] conv8 needs backward computation.
I0130 12:52:40.249795 32230 net.cpp:226] relu7 needs backward computation.
I0130 12:52:40.249797 32230 net.cpp:226] conv7 needs backward computation.
I0130 12:52:40.249799 32230 net.cpp:226] drop3 needs backward computation.
I0130 12:52:40.249800 32230 net.cpp:226] relu6 needs backward computation.
I0130 12:52:40.249802 32230 net.cpp:226] conv6 needs backward computation.
I0130 12:52:40.249804 32230 net.cpp:226] relu5 needs backward computation.
I0130 12:52:40.249805 32230 net.cpp:226] conv5 needs backward computation.
I0130 12:52:40.249809 32230 net.cpp:226] relu4 needs backward computation.
I0130 12:52:40.249809 32230 net.cpp:226] conv4 needs backward computation.
I0130 12:52:40.249811 32230 net.cpp:226] drop2 needs backward computation.
I0130 12:52:40.249814 32230 net.cpp:226] relu3 needs backward computation.
I0130 12:52:40.249815 32230 net.cpp:226] conv3 needs backward computation.
I0130 12:52:40.249821 32230 net.cpp:226] relu2 needs backward computation.
I0130 12:52:40.249824 32230 net.cpp:226] conv2 needs backward computation.
I0130 12:52:40.249825 32230 net.cpp:226] relu1 needs backward computation.
I0130 12:52:40.249827 32230 net.cpp:226] conv1 needs backward computation.
I0130 12:52:40.249830 32230 net.cpp:228] drop1 does not need backward computation.
I0130 12:52:40.249831 32230 net.cpp:228] label_data_1_split does not need backward computation.
I0130 12:52:40.249833 32230 net.cpp:228] data does not need backward computation.
I0130 12:52:40.249835 32230 net.cpp:270] This network produces output accuracy
I0130 12:52:40.249837 32230 net.cpp:270] This network produces output loss
I0130 12:52:40.249848 32230 net.cpp:283] Network initialization done.
I0130 12:52:40.250032 32230 solver.cpp:181] Creating test net (#0) specified by net file: ALL_CNN_C_train_val.prototxt
I0130 12:52:40.250053 32230 net.cpp:322] The NetState phase (1) differed from the phase (0) specified by a rule in layer data
I0130 12:52:40.250143 32230 net.cpp:58] Initializing net from parameters: 
name: "ALL-CNN-C"
state {
  phase: TEST
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  data_param {
    source: "cifar-10_test_lmdb"
    batch_size: 200
    backend: LMDB
  }
}
layer {
  name: "drop1"
  type: "Dropout"
  bottom: "data"
  top: "data"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 96
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 96
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "conv2"
  top: "conv3"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 96
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "drop2"
  type: "Dropout"
  bottom: "conv3"
  top: "conv3"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "conv6"
  type: "Convolution"
  bottom: "conv5"
  top: "conv6"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "conv6"
  top: "conv6"
}
layer {
  name: "drop3"
  type: "Dropout"
  bottom: "conv6"
  top: "conv6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "conv7"
  type: "Convolution"
  bottom: "conv6"
  top: "conv7"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 192
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "conv7"
  top: "conv7"
}
layer {
  name: "conv8"
  type: "Convolution"
  bottom: "conv7"
  top: "conv8"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 192
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu8"
  type: "ReLU"
  bottom: "conv8"
  top: "conv8"
}
layer {
  name: "conv9"
  type: "Convolution"
  bottom: "conv8"
  top: "conv9"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 10
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu9"
  type: "ReLU"
  bottom: "conv9"
  top: "conv9"
}
layer {
  name: "pool"
  type: "Pooling"
  bottom: "conv9"
  top: "pool"
  pooling_param {
    pool: AVE
    global_pooling: true
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "pool"
  bottom: "label"
  top: "accuracy"
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "pool"
  bottom: "label"
  top: "loss"
}
I0130 12:52:40.250202 32230 layer_factory.hpp:77] Creating layer data
I0130 12:52:40.250322 32230 net.cpp:100] Creating Layer data
I0130 12:52:40.250329 32230 net.cpp:408] data -> data
I0130 12:52:40.250332 32230 net.cpp:408] data -> label
I0130 12:52:40.250844 32248 db_lmdb.cpp:35] Opened lmdb cifar-10_test_lmdb
I0130 12:52:40.250937 32230 data_layer.cpp:41] output data size: 200,3,32,32
I0130 12:52:40.254842 32230 net.cpp:150] Setting up data
I0130 12:52:40.254861 32230 net.cpp:157] Top shape: 200 3 32 32 (614400)
I0130 12:52:40.254864 32230 net.cpp:157] Top shape: 200 (200)
I0130 12:52:40.254865 32230 net.cpp:165] Memory required for data: 2458400
I0130 12:52:40.254869 32230 layer_factory.hpp:77] Creating layer label_data_1_split
I0130 12:52:40.254879 32230 net.cpp:100] Creating Layer label_data_1_split
I0130 12:52:40.254884 32230 net.cpp:434] label_data_1_split <- label
I0130 12:52:40.254889 32230 net.cpp:408] label_data_1_split -> label_data_1_split_0
I0130 12:52:40.254895 32230 net.cpp:408] label_data_1_split -> label_data_1_split_1
I0130 12:52:40.254956 32230 net.cpp:150] Setting up label_data_1_split
I0130 12:52:40.254961 32230 net.cpp:157] Top shape: 200 (200)
I0130 12:52:40.254962 32230 net.cpp:157] Top shape: 200 (200)
I0130 12:52:40.254964 32230 net.cpp:165] Memory required for data: 2460000
I0130 12:52:40.254966 32230 layer_factory.hpp:77] Creating layer drop1
I0130 12:52:40.254971 32230 net.cpp:100] Creating Layer drop1
I0130 12:52:40.254972 32230 net.cpp:434] drop1 <- data
I0130 12:52:40.254976 32230 net.cpp:395] drop1 -> data (in-place)
I0130 12:52:40.255007 32230 net.cpp:150] Setting up drop1
I0130 12:52:40.255010 32230 net.cpp:157] Top shape: 200 3 32 32 (614400)
I0130 12:52:40.255012 32230 net.cpp:165] Memory required for data: 4917600
I0130 12:52:40.255014 32230 layer_factory.hpp:77] Creating layer conv1
I0130 12:52:40.255023 32230 net.cpp:100] Creating Layer conv1
I0130 12:52:40.255026 32230 net.cpp:434] conv1 <- data
I0130 12:52:40.255030 32230 net.cpp:408] conv1 -> conv1
I0130 12:52:40.256852 32230 net.cpp:150] Setting up conv1
I0130 12:52:40.256865 32230 net.cpp:157] Top shape: 200 96 32 32 (19660800)
I0130 12:52:40.256867 32230 net.cpp:165] Memory required for data: 83560800
I0130 12:52:40.256875 32230 layer_factory.hpp:77] Creating layer relu1
I0130 12:52:40.256886 32230 net.cpp:100] Creating Layer relu1
I0130 12:52:40.256901 32230 net.cpp:434] relu1 <- conv1
I0130 12:52:40.256904 32230 net.cpp:395] relu1 -> conv1 (in-place)
I0130 12:52:40.257618 32230 net.cpp:150] Setting up relu1
I0130 12:52:40.257627 32230 net.cpp:157] Top shape: 200 96 32 32 (19660800)
I0130 12:52:40.257630 32230 net.cpp:165] Memory required for data: 162204000
I0130 12:52:40.257633 32230 layer_factory.hpp:77] Creating layer conv2
I0130 12:52:40.257643 32230 net.cpp:100] Creating Layer conv2
I0130 12:52:40.257647 32230 net.cpp:434] conv2 <- conv1
I0130 12:52:40.257652 32230 net.cpp:408] conv2 -> conv2
I0130 12:52:40.259987 32230 net.cpp:150] Setting up conv2
I0130 12:52:40.259999 32230 net.cpp:157] Top shape: 200 96 32 32 (19660800)
I0130 12:52:40.260000 32230 net.cpp:165] Memory required for data: 240847200
I0130 12:52:40.260009 32230 layer_factory.hpp:77] Creating layer relu2
I0130 12:52:40.260012 32230 net.cpp:100] Creating Layer relu2
I0130 12:52:40.260015 32230 net.cpp:434] relu2 <- conv2
I0130 12:52:40.260017 32230 net.cpp:395] relu2 -> conv2 (in-place)
I0130 12:52:40.260152 32230 net.cpp:150] Setting up relu2
I0130 12:52:40.260157 32230 net.cpp:157] Top shape: 200 96 32 32 (19660800)
I0130 12:52:40.260159 32230 net.cpp:165] Memory required for data: 319490400
I0130 12:52:40.260160 32230 layer_factory.hpp:77] Creating layer conv3
I0130 12:52:40.260169 32230 net.cpp:100] Creating Layer conv3
I0130 12:52:40.260170 32230 net.cpp:434] conv3 <- conv2
I0130 12:52:40.260174 32230 net.cpp:408] conv3 -> conv3
I0130 12:52:40.261921 32230 net.cpp:150] Setting up conv3
I0130 12:52:40.261929 32230 net.cpp:157] Top shape: 200 96 16 16 (4915200)
I0130 12:52:40.261931 32230 net.cpp:165] Memory required for data: 339151200
I0130 12:52:40.261936 32230 layer_factory.hpp:77] Creating layer relu3
I0130 12:52:40.261942 32230 net.cpp:100] Creating Layer relu3
I0130 12:52:40.261945 32230 net.cpp:434] relu3 <- conv3
I0130 12:52:40.261948 32230 net.cpp:395] relu3 -> conv3 (in-place)
I0130 12:52:40.262420 32230 net.cpp:150] Setting up relu3
I0130 12:52:40.262428 32230 net.cpp:157] Top shape: 200 96 16 16 (4915200)
I0130 12:52:40.262429 32230 net.cpp:165] Memory required for data: 358812000
I0130 12:52:40.262431 32230 layer_factory.hpp:77] Creating layer drop2
I0130 12:52:40.262435 32230 net.cpp:100] Creating Layer drop2
I0130 12:52:40.262437 32230 net.cpp:434] drop2 <- conv3
I0130 12:52:40.262439 32230 net.cpp:395] drop2 -> conv3 (in-place)
I0130 12:52:40.262456 32230 net.cpp:150] Setting up drop2
I0130 12:52:40.262459 32230 net.cpp:157] Top shape: 200 96 16 16 (4915200)
I0130 12:52:40.262461 32230 net.cpp:165] Memory required for data: 378472800
I0130 12:52:40.262464 32230 layer_factory.hpp:77] Creating layer conv4
I0130 12:52:40.262472 32230 net.cpp:100] Creating Layer conv4
I0130 12:52:40.262475 32230 net.cpp:434] conv4 <- conv3
I0130 12:52:40.262480 32230 net.cpp:408] conv4 -> conv4
I0130 12:52:40.264899 32230 net.cpp:150] Setting up conv4
I0130 12:52:40.264907 32230 net.cpp:157] Top shape: 200 192 16 16 (9830400)
I0130 12:52:40.264909 32230 net.cpp:165] Memory required for data: 417794400
I0130 12:52:40.264914 32230 layer_factory.hpp:77] Creating layer relu4
I0130 12:52:40.264917 32230 net.cpp:100] Creating Layer relu4
I0130 12:52:40.264919 32230 net.cpp:434] relu4 <- conv4
I0130 12:52:40.264922 32230 net.cpp:395] relu4 -> conv4 (in-place)
I0130 12:52:40.265388 32230 net.cpp:150] Setting up relu4
I0130 12:52:40.265394 32230 net.cpp:157] Top shape: 200 192 16 16 (9830400)
I0130 12:52:40.265396 32230 net.cpp:165] Memory required for data: 457116000
I0130 12:52:40.265398 32230 layer_factory.hpp:77] Creating layer conv5
I0130 12:52:40.265404 32230 net.cpp:100] Creating Layer conv5
I0130 12:52:40.265406 32230 net.cpp:434] conv5 <- conv4
I0130 12:52:40.265410 32230 net.cpp:408] conv5 -> conv5
I0130 12:52:40.269035 32230 net.cpp:150] Setting up conv5
I0130 12:52:40.269044 32230 net.cpp:157] Top shape: 200 192 16 16 (9830400)
I0130 12:52:40.269047 32230 net.cpp:165] Memory required for data: 496437600
I0130 12:52:40.269052 32230 layer_factory.hpp:77] Creating layer relu5
I0130 12:52:40.269060 32230 net.cpp:100] Creating Layer relu5
I0130 12:52:40.269062 32230 net.cpp:434] relu5 <- conv5
I0130 12:52:40.269067 32230 net.cpp:395] relu5 -> conv5 (in-place)
I0130 12:52:40.269208 32230 net.cpp:150] Setting up relu5
I0130 12:52:40.269214 32230 net.cpp:157] Top shape: 200 192 16 16 (9830400)
I0130 12:52:40.269217 32230 net.cpp:165] Memory required for data: 535759200
I0130 12:52:40.269218 32230 layer_factory.hpp:77] Creating layer conv6
I0130 12:52:40.269223 32230 net.cpp:100] Creating Layer conv6
I0130 12:52:40.269225 32230 net.cpp:434] conv6 <- conv5
I0130 12:52:40.269230 32230 net.cpp:408] conv6 -> conv6
I0130 12:52:40.272866 32230 net.cpp:150] Setting up conv6
I0130 12:52:40.272874 32230 net.cpp:157] Top shape: 200 192 8 8 (2457600)
I0130 12:52:40.272877 32230 net.cpp:165] Memory required for data: 545589600
I0130 12:52:40.272881 32230 layer_factory.hpp:77] Creating layer relu6
I0130 12:52:40.272883 32230 net.cpp:100] Creating Layer relu6
I0130 12:52:40.272886 32230 net.cpp:434] relu6 <- conv6
I0130 12:52:40.272888 32230 net.cpp:395] relu6 -> conv6 (in-place)
I0130 12:52:40.273375 32230 net.cpp:150] Setting up relu6
I0130 12:52:40.273381 32230 net.cpp:157] Top shape: 200 192 8 8 (2457600)
I0130 12:52:40.273383 32230 net.cpp:165] Memory required for data: 555420000
I0130 12:52:40.273386 32230 layer_factory.hpp:77] Creating layer drop3
I0130 12:52:40.273392 32230 net.cpp:100] Creating Layer drop3
I0130 12:52:40.273394 32230 net.cpp:434] drop3 <- conv6
I0130 12:52:40.273396 32230 net.cpp:395] drop3 -> conv6 (in-place)
I0130 12:52:40.273417 32230 net.cpp:150] Setting up drop3
I0130 12:52:40.273422 32230 net.cpp:157] Top shape: 200 192 8 8 (2457600)
I0130 12:52:40.273424 32230 net.cpp:165] Memory required for data: 565250400
I0130 12:52:40.273427 32230 layer_factory.hpp:77] Creating layer conv7
I0130 12:52:40.273437 32230 net.cpp:100] Creating Layer conv7
I0130 12:52:40.273439 32230 net.cpp:434] conv7 <- conv6
I0130 12:52:40.273443 32230 net.cpp:408] conv7 -> conv7
I0130 12:52:40.276507 32230 net.cpp:150] Setting up conv7
I0130 12:52:40.276515 32230 net.cpp:157] Top shape: 200 192 6 6 (1382400)
I0130 12:52:40.276518 32230 net.cpp:165] Memory required for data: 570780000
I0130 12:52:40.276522 32230 layer_factory.hpp:77] Creating layer relu7
I0130 12:52:40.276525 32230 net.cpp:100] Creating Layer relu7
I0130 12:52:40.276527 32230 net.cpp:434] relu7 <- conv7
I0130 12:52:40.276532 32230 net.cpp:395] relu7 -> conv7 (in-place)
I0130 12:52:40.277011 32230 net.cpp:150] Setting up relu7
I0130 12:52:40.277017 32230 net.cpp:157] Top shape: 200 192 6 6 (1382400)
I0130 12:52:40.277019 32230 net.cpp:165] Memory required for data: 576309600
I0130 12:52:40.277021 32230 layer_factory.hpp:77] Creating layer conv8
I0130 12:52:40.277029 32230 net.cpp:100] Creating Layer conv8
I0130 12:52:40.277030 32230 net.cpp:434] conv8 <- conv7
I0130 12:52:40.277034 32230 net.cpp:408] conv8 -> conv8
I0130 12:52:40.278501 32230 net.cpp:150] Setting up conv8
I0130 12:52:40.278507 32230 net.cpp:157] Top shape: 200 192 6 6 (1382400)
I0130 12:52:40.278509 32230 net.cpp:165] Memory required for data: 581839200
I0130 12:52:40.278512 32230 layer_factory.hpp:77] Creating layer relu8
I0130 12:52:40.278517 32230 net.cpp:100] Creating Layer relu8
I0130 12:52:40.278518 32230 net.cpp:434] relu8 <- conv8
I0130 12:52:40.278522 32230 net.cpp:395] relu8 -> conv8 (in-place)
I0130 12:52:40.278645 32230 net.cpp:150] Setting up relu8
I0130 12:52:40.278650 32230 net.cpp:157] Top shape: 200 192 6 6 (1382400)
I0130 12:52:40.278652 32230 net.cpp:165] Memory required for data: 587368800
I0130 12:52:40.278654 32230 layer_factory.hpp:77] Creating layer conv9
I0130 12:52:40.278659 32230 net.cpp:100] Creating Layer conv9
I0130 12:52:40.278661 32230 net.cpp:434] conv9 <- conv8
I0130 12:52:40.278666 32230 net.cpp:408] conv9 -> conv9
I0130 12:52:40.279954 32230 net.cpp:150] Setting up conv9
I0130 12:52:40.279963 32230 net.cpp:157] Top shape: 200 10 6 6 (72000)
I0130 12:52:40.279964 32230 net.cpp:165] Memory required for data: 587656800
I0130 12:52:40.279969 32230 layer_factory.hpp:77] Creating layer relu9
I0130 12:52:40.279980 32230 net.cpp:100] Creating Layer relu9
I0130 12:52:40.279983 32230 net.cpp:434] relu9 <- conv9
I0130 12:52:40.279985 32230 net.cpp:395] relu9 -> conv9 (in-place)
I0130 12:52:40.280455 32230 net.cpp:150] Setting up relu9
I0130 12:52:40.280462 32230 net.cpp:157] Top shape: 200 10 6 6 (72000)
I0130 12:52:40.280463 32230 net.cpp:165] Memory required for data: 587944800
I0130 12:52:40.280465 32230 layer_factory.hpp:77] Creating layer pool
I0130 12:52:40.280472 32230 net.cpp:100] Creating Layer pool
I0130 12:52:40.280472 32230 net.cpp:434] pool <- conv9
I0130 12:52:40.280475 32230 net.cpp:408] pool -> pool
I0130 12:52:40.280609 32230 net.cpp:150] Setting up pool
I0130 12:52:40.280613 32230 net.cpp:157] Top shape: 200 10 1 1 (2000)
I0130 12:52:40.280616 32230 net.cpp:165] Memory required for data: 587952800
I0130 12:52:40.280616 32230 layer_factory.hpp:77] Creating layer pool_pool_0_split
I0130 12:52:40.280622 32230 net.cpp:100] Creating Layer pool_pool_0_split
I0130 12:52:40.280623 32230 net.cpp:434] pool_pool_0_split <- pool
I0130 12:52:40.280627 32230 net.cpp:408] pool_pool_0_split -> pool_pool_0_split_0
I0130 12:52:40.280630 32230 net.cpp:408] pool_pool_0_split -> pool_pool_0_split_1
I0130 12:52:40.280658 32230 net.cpp:150] Setting up pool_pool_0_split
I0130 12:52:40.280663 32230 net.cpp:157] Top shape: 200 10 1 1 (2000)
I0130 12:52:40.280664 32230 net.cpp:157] Top shape: 200 10 1 1 (2000)
I0130 12:52:40.280665 32230 net.cpp:165] Memory required for data: 587968800
I0130 12:52:40.280668 32230 layer_factory.hpp:77] Creating layer accuracy
I0130 12:52:40.280670 32230 net.cpp:100] Creating Layer accuracy
I0130 12:52:40.280673 32230 net.cpp:434] accuracy <- pool_pool_0_split_0
I0130 12:52:40.280674 32230 net.cpp:434] accuracy <- label_data_1_split_0
I0130 12:52:40.280678 32230 net.cpp:408] accuracy -> accuracy
I0130 12:52:40.280684 32230 net.cpp:150] Setting up accuracy
I0130 12:52:40.280686 32230 net.cpp:157] Top shape: (1)
I0130 12:52:40.280688 32230 net.cpp:165] Memory required for data: 587968804
I0130 12:52:40.280689 32230 layer_factory.hpp:77] Creating layer loss
I0130 12:52:40.280694 32230 net.cpp:100] Creating Layer loss
I0130 12:52:40.280695 32230 net.cpp:434] loss <- pool_pool_0_split_1
I0130 12:52:40.280697 32230 net.cpp:434] loss <- label_data_1_split_1
I0130 12:52:40.280701 32230 net.cpp:408] loss -> loss
I0130 12:52:40.280705 32230 layer_factory.hpp:77] Creating layer loss
I0130 12:52:40.281255 32230 net.cpp:150] Setting up loss
I0130 12:52:40.281261 32230 net.cpp:157] Top shape: (1)
I0130 12:52:40.281263 32230 net.cpp:160]     with loss weight 1
I0130 12:52:40.281270 32230 net.cpp:165] Memory required for data: 587968808
I0130 12:52:40.281272 32230 net.cpp:226] loss needs backward computation.
I0130 12:52:40.281275 32230 net.cpp:228] accuracy does not need backward computation.
I0130 12:52:40.281277 32230 net.cpp:226] pool_pool_0_split needs backward computation.
I0130 12:52:40.281280 32230 net.cpp:226] pool needs backward computation.
I0130 12:52:40.281280 32230 net.cpp:226] relu9 needs backward computation.
I0130 12:52:40.281282 32230 net.cpp:226] conv9 needs backward computation.
I0130 12:52:40.281285 32230 net.cpp:226] relu8 needs backward computation.
I0130 12:52:40.281286 32230 net.cpp:226] conv8 needs backward computation.
I0130 12:52:40.281287 32230 net.cpp:226] relu7 needs backward computation.
I0130 12:52:40.281288 32230 net.cpp:226] conv7 needs backward computation.
I0130 12:52:40.281291 32230 net.cpp:226] drop3 needs backward computation.
I0130 12:52:40.281292 32230 net.cpp:226] relu6 needs backward computation.
I0130 12:52:40.281293 32230 net.cpp:226] conv6 needs backward computation.
I0130 12:52:40.281296 32230 net.cpp:226] relu5 needs backward computation.
I0130 12:52:40.281297 32230 net.cpp:226] conv5 needs backward computation.
I0130 12:52:40.281299 32230 net.cpp:226] relu4 needs backward computation.
I0130 12:52:40.281301 32230 net.cpp:226] conv4 needs backward computation.
I0130 12:52:40.281302 32230 net.cpp:226] drop2 needs backward computation.
I0130 12:52:40.281311 32230 net.cpp:226] relu3 needs backward computation.
I0130 12:52:40.281311 32230 net.cpp:226] conv3 needs backward computation.
I0130 12:52:40.281313 32230 net.cpp:226] relu2 needs backward computation.
I0130 12:52:40.281316 32230 net.cpp:226] conv2 needs backward computation.
I0130 12:52:40.281317 32230 net.cpp:226] relu1 needs backward computation.
I0130 12:52:40.281318 32230 net.cpp:226] conv1 needs backward computation.
I0130 12:52:40.281322 32230 net.cpp:228] drop1 does not need backward computation.
I0130 12:52:40.281324 32230 net.cpp:228] label_data_1_split does not need backward computation.
I0130 12:52:40.281328 32230 net.cpp:228] data does not need backward computation.
I0130 12:52:40.281332 32230 net.cpp:270] This network produces output accuracy
I0130 12:52:40.281335 32230 net.cpp:270] This network produces output loss
I0130 12:52:40.281347 32230 net.cpp:283] Network initialization done.
I0130 12:52:40.281395 32230 solver.cpp:60] Solver scaffolding done.
I0130 12:52:40.281889 32230 caffe.cpp:251] Starting Optimization
I0130 12:52:40.281893 32230 solver.cpp:279] Solving ALL-CNN-C
I0130 12:52:40.281895 32230 solver.cpp:280] Learning Rate Policy: multistep
I0130 12:52:40.283884 32230 solver.cpp:337] Iteration 0, Testing net (#0)
I0130 12:52:41.305521 32230 solver.cpp:404]     Test net output #0: accuracy = 0.1
I0130 12:52:41.305559 32230 solver.cpp:404]     Test net output #1: loss = 2.30314 (* 1 = 2.30314 loss)
I0130 12:52:41.344702 32230 solver.cpp:228] Iteration 0, loss = 2.3049
I0130 12:52:41.344720 32230 solver.cpp:244]     Train net output #0: accuracy = 0.105469
I0130 12:52:41.344727 32230 solver.cpp:244]     Train net output #1: loss = 2.3049 (* 1 = 2.3049 loss)
I0130 12:52:41.344738 32230 sgd_solver.cpp:106] Iteration 0, lr = 0.05
I0130 12:53:05.626216 32230 solver.cpp:337] Iteration 200, Testing net (#0)
I0130 12:53:06.745398 32230 solver.cpp:404]     Test net output #0: accuracy = 0.1418
I0130 12:53:06.745420 32230 solver.cpp:404]     Test net output #1: loss = 2.19372 (* 1 = 2.19372 loss)
I0130 12:53:06.774139 32230 solver.cpp:228] Iteration 200, loss = 2.16758
I0130 12:53:06.774158 32230 solver.cpp:244]     Train net output #0: accuracy = 0.171875
I0130 12:53:06.774163 32230 solver.cpp:244]     Train net output #1: loss = 2.16758 (* 1 = 2.16758 loss)
I0130 12:53:06.774168 32230 sgd_solver.cpp:106] Iteration 200, lr = 0.05
I0130 12:53:30.854717 32230 solver.cpp:337] Iteration 400, Testing net (#0)
I0130 12:53:31.961309 32230 solver.cpp:404]     Test net output #0: accuracy = 0.1803
I0130 12:53:31.961331 32230 solver.cpp:404]     Test net output #1: loss = 2.162 (* 1 = 2.162 loss)
I0130 12:53:31.990056 32230 solver.cpp:228] Iteration 400, loss = 2.11223
I0130 12:53:31.990072 32230 solver.cpp:244]     Train net output #0: accuracy = 0.222656
I0130 12:53:31.990077 32230 solver.cpp:244]     Train net output #1: loss = 2.11223 (* 1 = 2.11223 loss)
I0130 12:53:31.990082 32230 sgd_solver.cpp:106] Iteration 400, lr = 0.05
I0130 12:53:56.705780 32230 solver.cpp:337] Iteration 600, Testing net (#0)
I0130 12:53:57.896416 32230 solver.cpp:404]     Test net output #0: accuracy = 0.2212
I0130 12:53:57.896437 32230 solver.cpp:404]     Test net output #1: loss = 2.13483 (* 1 = 2.13483 loss)
I0130 12:53:57.927547 32230 solver.cpp:228] Iteration 600, loss = 2.13579
I0130 12:53:57.927566 32230 solver.cpp:244]     Train net output #0: accuracy = 0.183594
I0130 12:53:57.927570 32230 solver.cpp:244]     Train net output #1: loss = 2.13579 (* 1 = 2.13579 loss)
I0130 12:53:57.927574 32230 sgd_solver.cpp:106] Iteration 600, lr = 0.05
I0130 12:54:23.607870 32230 solver.cpp:337] Iteration 800, Testing net (#0)
I0130 12:54:24.748229 32230 solver.cpp:404]     Test net output #0: accuracy = 0.2015
I0130 12:54:24.748250 32230 solver.cpp:404]     Test net output #1: loss = 2.18737 (* 1 = 2.18737 loss)
I0130 12:54:24.777535 32230 solver.cpp:228] Iteration 800, loss = 2.21507
I0130 12:54:24.777551 32230 solver.cpp:244]     Train net output #0: accuracy = 0.160156
I0130 12:54:24.777557 32230 solver.cpp:244]     Train net output #1: loss = 2.21507 (* 1 = 2.21507 loss)
I0130 12:54:24.777560 32230 sgd_solver.cpp:106] Iteration 800, lr = 0.05
I0130 12:54:50.527695 32230 solver.cpp:337] Iteration 1000, Testing net (#0)
I0130 12:54:51.686780 32230 solver.cpp:404]     Test net output #0: accuracy = 0.2681
I0130 12:54:51.686805 32230 solver.cpp:404]     Test net output #1: loss = 2.03789 (* 1 = 2.03789 loss)
I0130 12:54:51.716672 32230 solver.cpp:228] Iteration 1000, loss = 2.06765
I0130 12:54:51.716692 32230 solver.cpp:244]     Train net output #0: accuracy = 0.273438
I0130 12:54:51.716701 32230 solver.cpp:244]     Train net output #1: loss = 2.06765 (* 1 = 2.06765 loss)
I0130 12:54:51.716706 32230 sgd_solver.cpp:106] Iteration 1000, lr = 0.05
I0130 12:55:17.102916 32230 solver.cpp:337] Iteration 1200, Testing net (#0)
I0130 12:55:18.257356 32230 solver.cpp:404]     Test net output #0: accuracy = 0.339
I0130 12:55:18.257380 32230 solver.cpp:404]     Test net output #1: loss = 1.77996 (* 1 = 1.77996 loss)
I0130 12:55:18.286975 32230 solver.cpp:228] Iteration 1200, loss = 1.76054
I0130 12:55:18.286990 32230 solver.cpp:244]     Train net output #0: accuracy = 0.324219
I0130 12:55:18.286995 32230 solver.cpp:244]     Train net output #1: loss = 1.76054 (* 1 = 1.76054 loss)
I0130 12:55:18.286999 32230 sgd_solver.cpp:106] Iteration 1200, lr = 0.05
I0130 12:55:43.849498 32230 solver.cpp:337] Iteration 1400, Testing net (#0)
I0130 12:55:44.990844 32230 solver.cpp:404]     Test net output #0: accuracy = 0.4102
I0130 12:55:44.990862 32230 solver.cpp:404]     Test net output #1: loss = 1.56416 (* 1 = 1.56416 loss)
I0130 12:55:45.020391 32230 solver.cpp:228] Iteration 1400, loss = 1.5537
I0130 12:55:45.020408 32230 solver.cpp:244]     Train net output #0: accuracy = 0.390625
I0130 12:55:45.020414 32230 solver.cpp:244]     Train net output #1: loss = 1.5537 (* 1 = 1.5537 loss)
I0130 12:55:45.020418 32230 sgd_solver.cpp:106] Iteration 1400, lr = 0.05
I0130 12:56:10.386245 32230 solver.cpp:337] Iteration 1600, Testing net (#0)
I0130 12:56:11.534373 32230 solver.cpp:404]     Test net output #0: accuracy = 0.5065
I0130 12:56:11.534396 32230 solver.cpp:404]     Test net output #1: loss = 1.34425 (* 1 = 1.34425 loss)
I0130 12:56:11.563735 32230 solver.cpp:228] Iteration 1600, loss = 1.42572
I0130 12:56:11.563750 32230 solver.cpp:244]     Train net output #0: accuracy = 0.507812
I0130 12:56:11.563757 32230 solver.cpp:244]     Train net output #1: loss = 1.42572 (* 1 = 1.42572 loss)
I0130 12:56:11.563762 32230 sgd_solver.cpp:106] Iteration 1600, lr = 0.05
I0130 12:56:36.718914 32230 solver.cpp:337] Iteration 1800, Testing net (#0)
I0130 12:56:37.860664 32230 solver.cpp:404]     Test net output #0: accuracy = 0.5394
I0130 12:56:37.860683 32230 solver.cpp:404]     Test net output #1: loss = 1.25078 (* 1 = 1.25078 loss)
I0130 12:56:37.890094 32230 solver.cpp:228] Iteration 1800, loss = 1.30239
I0130 12:56:37.890110 32230 solver.cpp:244]     Train net output #0: accuracy = 0.503906
I0130 12:56:37.890116 32230 solver.cpp:244]     Train net output #1: loss = 1.30239 (* 1 = 1.30239 loss)
I0130 12:56:37.890121 32230 sgd_solver.cpp:106] Iteration 1800, lr = 0.05
I0130 12:57:03.249788 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_2000.caffemodel
I0130 12:57:03.389417 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_2000.solverstate
I0130 12:57:03.415629 32230 solver.cpp:337] Iteration 2000, Testing net (#0)
I0130 12:57:04.453369 32230 solver.cpp:404]     Test net output #0: accuracy = 0.563
I0130 12:57:04.453389 32230 solver.cpp:404]     Test net output #1: loss = 1.21104 (* 1 = 1.21104 loss)
I0130 12:57:04.483546 32230 solver.cpp:228] Iteration 2000, loss = 1.15476
I0130 12:57:04.483566 32230 solver.cpp:244]     Train net output #0: accuracy = 0.582031
I0130 12:57:04.483572 32230 solver.cpp:244]     Train net output #1: loss = 1.15476 (* 1 = 1.15476 loss)
I0130 12:57:04.483577 32230 sgd_solver.cpp:106] Iteration 2000, lr = 0.05
I0130 12:57:29.496450 32230 solver.cpp:337] Iteration 2200, Testing net (#0)
I0130 12:57:30.644467 32230 solver.cpp:404]     Test net output #0: accuracy = 0.5626
I0130 12:57:30.644503 32230 solver.cpp:404]     Test net output #1: loss = 1.23057 (* 1 = 1.23057 loss)
I0130 12:57:30.674479 32230 solver.cpp:228] Iteration 2200, loss = 1.20948
I0130 12:57:30.674497 32230 solver.cpp:244]     Train net output #0: accuracy = 0.5625
I0130 12:57:30.674504 32230 solver.cpp:244]     Train net output #1: loss = 1.20948 (* 1 = 1.20948 loss)
I0130 12:57:30.674507 32230 sgd_solver.cpp:106] Iteration 2200, lr = 0.05
I0130 12:57:55.558645 32230 solver.cpp:337] Iteration 2400, Testing net (#0)
I0130 12:57:56.691289 32230 solver.cpp:404]     Test net output #0: accuracy = 0.6173
I0130 12:57:56.691311 32230 solver.cpp:404]     Test net output #1: loss = 1.07234 (* 1 = 1.07234 loss)
I0130 12:57:56.720753 32230 solver.cpp:228] Iteration 2400, loss = 1.00854
I0130 12:57:56.720770 32230 solver.cpp:244]     Train net output #0: accuracy = 0.65625
I0130 12:57:56.720777 32230 solver.cpp:244]     Train net output #1: loss = 1.00854 (* 1 = 1.00854 loss)
I0130 12:57:56.720795 32230 sgd_solver.cpp:106] Iteration 2400, lr = 0.05
I0130 12:58:21.548960 32230 solver.cpp:337] Iteration 2600, Testing net (#0)
I0130 12:58:22.681352 32230 solver.cpp:404]     Test net output #0: accuracy = 0.638
I0130 12:58:22.681375 32230 solver.cpp:404]     Test net output #1: loss = 1.02192 (* 1 = 1.02192 loss)
I0130 12:58:22.711026 32230 solver.cpp:228] Iteration 2600, loss = 1.08663
I0130 12:58:22.711042 32230 solver.cpp:244]     Train net output #0: accuracy = 0.636719
I0130 12:58:22.711069 32230 solver.cpp:244]     Train net output #1: loss = 1.08663 (* 1 = 1.08663 loss)
I0130 12:58:22.711086 32230 sgd_solver.cpp:106] Iteration 2600, lr = 0.05
I0130 12:58:47.588369 32230 solver.cpp:337] Iteration 2800, Testing net (#0)
I0130 12:58:48.721168 32230 solver.cpp:404]     Test net output #0: accuracy = 0.6712
I0130 12:58:48.721202 32230 solver.cpp:404]     Test net output #1: loss = 0.954357 (* 1 = 0.954357 loss)
I0130 12:58:48.750705 32230 solver.cpp:228] Iteration 2800, loss = 1.00912
I0130 12:58:48.750720 32230 solver.cpp:244]     Train net output #0: accuracy = 0.640625
I0130 12:58:48.750741 32230 solver.cpp:244]     Train net output #1: loss = 1.00912 (* 1 = 1.00912 loss)
I0130 12:58:48.750746 32230 sgd_solver.cpp:106] Iteration 2800, lr = 0.05
I0130 12:59:13.606735 32230 solver.cpp:337] Iteration 3000, Testing net (#0)
I0130 12:59:14.738909 32230 solver.cpp:404]     Test net output #0: accuracy = 0.6865
I0130 12:59:14.738932 32230 solver.cpp:404]     Test net output #1: loss = 0.900075 (* 1 = 0.900075 loss)
I0130 12:59:14.768313 32230 solver.cpp:228] Iteration 3000, loss = 0.936519
I0130 12:59:14.768329 32230 solver.cpp:244]     Train net output #0: accuracy = 0.65625
I0130 12:59:14.768334 32230 solver.cpp:244]     Train net output #1: loss = 0.936519 (* 1 = 0.936519 loss)
I0130 12:59:14.768338 32230 sgd_solver.cpp:106] Iteration 3000, lr = 0.05
I0130 12:59:39.444012 32230 solver.cpp:337] Iteration 3200, Testing net (#0)
I0130 12:59:40.575798 32230 solver.cpp:404]     Test net output #0: accuracy = 0.6994
I0130 12:59:40.575820 32230 solver.cpp:404]     Test net output #1: loss = 0.855564 (* 1 = 0.855564 loss)
I0130 12:59:40.605222 32230 solver.cpp:228] Iteration 3200, loss = 0.886699
I0130 12:59:40.605237 32230 solver.cpp:244]     Train net output #0: accuracy = 0.664062
I0130 12:59:40.605243 32230 solver.cpp:244]     Train net output #1: loss = 0.886699 (* 1 = 0.886699 loss)
I0130 12:59:40.605248 32230 sgd_solver.cpp:106] Iteration 3200, lr = 0.05
I0130 13:00:05.474380 32230 solver.cpp:337] Iteration 3400, Testing net (#0)
I0130 13:00:06.614481 32230 solver.cpp:404]     Test net output #0: accuracy = 0.69
I0130 13:00:06.614506 32230 solver.cpp:404]     Test net output #1: loss = 0.869783 (* 1 = 0.869783 loss)
I0130 13:00:06.643838 32230 solver.cpp:228] Iteration 3400, loss = 0.851026
I0130 13:00:06.643853 32230 solver.cpp:244]     Train net output #0: accuracy = 0.699219
I0130 13:00:06.643858 32230 solver.cpp:244]     Train net output #1: loss = 0.851026 (* 1 = 0.851026 loss)
I0130 13:00:06.643862 32230 sgd_solver.cpp:106] Iteration 3400, lr = 0.05
I0130 13:00:31.606324 32230 solver.cpp:337] Iteration 3600, Testing net (#0)
I0130 13:00:32.730911 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7186
I0130 13:00:32.730932 32230 solver.cpp:404]     Test net output #1: loss = 0.798128 (* 1 = 0.798128 loss)
I0130 13:00:32.760092 32230 solver.cpp:228] Iteration 3600, loss = 0.895453
I0130 13:00:32.760108 32230 solver.cpp:244]     Train net output #0: accuracy = 0.671875
I0130 13:00:32.760113 32230 solver.cpp:244]     Train net output #1: loss = 0.895453 (* 1 = 0.895453 loss)
I0130 13:00:32.760118 32230 sgd_solver.cpp:106] Iteration 3600, lr = 0.05
I0130 13:00:57.522670 32230 solver.cpp:337] Iteration 3800, Testing net (#0)
I0130 13:00:58.679762 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7327
I0130 13:00:58.679782 32230 solver.cpp:404]     Test net output #1: loss = 0.776284 (* 1 = 0.776284 loss)
I0130 13:00:58.709445 32230 solver.cpp:228] Iteration 3800, loss = 0.790329
I0130 13:00:58.709461 32230 solver.cpp:244]     Train net output #0: accuracy = 0.765625
I0130 13:00:58.709467 32230 solver.cpp:244]     Train net output #1: loss = 0.790329 (* 1 = 0.790329 loss)
I0130 13:00:58.709472 32230 sgd_solver.cpp:106] Iteration 3800, lr = 0.05
I0130 13:01:23.751117 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_4000.caffemodel
I0130 13:01:23.885578 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_4000.solverstate
I0130 13:01:23.912660 32230 solver.cpp:337] Iteration 4000, Testing net (#0)
I0130 13:01:24.929913 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7391
I0130 13:01:24.929934 32230 solver.cpp:404]     Test net output #1: loss = 0.747804 (* 1 = 0.747804 loss)
I0130 13:01:24.959013 32230 solver.cpp:228] Iteration 4000, loss = 0.660554
I0130 13:01:24.959029 32230 solver.cpp:244]     Train net output #0: accuracy = 0.789062
I0130 13:01:24.959035 32230 solver.cpp:244]     Train net output #1: loss = 0.660554 (* 1 = 0.660554 loss)
I0130 13:01:24.959039 32230 sgd_solver.cpp:106] Iteration 4000, lr = 0.05
I0130 13:01:49.750247 32230 solver.cpp:337] Iteration 4200, Testing net (#0)
I0130 13:01:50.873296 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7363
I0130 13:01:50.873333 32230 solver.cpp:404]     Test net output #1: loss = 0.772987 (* 1 = 0.772987 loss)
I0130 13:01:50.902406 32230 solver.cpp:228] Iteration 4200, loss = 0.78273
I0130 13:01:50.902423 32230 solver.cpp:244]     Train net output #0: accuracy = 0.707031
I0130 13:01:50.902428 32230 solver.cpp:244]     Train net output #1: loss = 0.78273 (* 1 = 0.78273 loss)
I0130 13:01:50.902432 32230 sgd_solver.cpp:106] Iteration 4200, lr = 0.05
I0130 13:02:16.136010 32230 solver.cpp:337] Iteration 4400, Testing net (#0)
I0130 13:02:17.260046 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7696
I0130 13:02:17.260082 32230 solver.cpp:404]     Test net output #1: loss = 0.665444 (* 1 = 0.665444 loss)
I0130 13:02:17.289057 32230 solver.cpp:228] Iteration 4400, loss = 0.593806
I0130 13:02:17.289073 32230 solver.cpp:244]     Train net output #0: accuracy = 0.8125
I0130 13:02:17.289079 32230 solver.cpp:244]     Train net output #1: loss = 0.593806 (* 1 = 0.593806 loss)
I0130 13:02:17.289083 32230 sgd_solver.cpp:106] Iteration 4400, lr = 0.05
I0130 13:02:42.185492 32230 solver.cpp:337] Iteration 4600, Testing net (#0)
I0130 13:02:43.310448 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7675
I0130 13:02:43.310470 32230 solver.cpp:404]     Test net output #1: loss = 0.66591 (* 1 = 0.66591 loss)
I0130 13:02:43.337960 32230 solver.cpp:228] Iteration 4600, loss = 0.603916
I0130 13:02:43.337975 32230 solver.cpp:244]     Train net output #0: accuracy = 0.785156
I0130 13:02:43.337980 32230 solver.cpp:244]     Train net output #1: loss = 0.603916 (* 1 = 0.603916 loss)
I0130 13:02:43.337985 32230 sgd_solver.cpp:106] Iteration 4600, lr = 0.05
I0130 13:03:08.104501 32230 solver.cpp:337] Iteration 4800, Testing net (#0)
I0130 13:03:09.237418 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7611
I0130 13:03:09.237438 32230 solver.cpp:404]     Test net output #1: loss = 0.70183 (* 1 = 0.70183 loss)
I0130 13:03:09.266764 32230 solver.cpp:228] Iteration 4800, loss = 0.640249
I0130 13:03:09.266780 32230 solver.cpp:244]     Train net output #0: accuracy = 0.789062
I0130 13:03:09.266800 32230 solver.cpp:244]     Train net output #1: loss = 0.640249 (* 1 = 0.640249 loss)
I0130 13:03:09.266804 32230 sgd_solver.cpp:106] Iteration 4800, lr = 0.05
I0130 13:03:34.077261 32230 solver.cpp:337] Iteration 5000, Testing net (#0)
I0130 13:03:35.200786 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7706
I0130 13:03:35.200825 32230 solver.cpp:404]     Test net output #1: loss = 0.654895 (* 1 = 0.654895 loss)
I0130 13:03:35.230093 32230 solver.cpp:228] Iteration 5000, loss = 0.62804
I0130 13:03:35.230108 32230 solver.cpp:244]     Train net output #0: accuracy = 0.75
I0130 13:03:35.230113 32230 solver.cpp:244]     Train net output #1: loss = 0.62804 (* 1 = 0.62804 loss)
I0130 13:03:35.230118 32230 sgd_solver.cpp:106] Iteration 5000, lr = 0.05
I0130 13:03:59.931351 32230 solver.cpp:337] Iteration 5200, Testing net (#0)
I0130 13:04:01.056813 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7871
I0130 13:04:01.056851 32230 solver.cpp:404]     Test net output #1: loss = 0.627731 (* 1 = 0.627731 loss)
I0130 13:04:01.086168 32230 solver.cpp:228] Iteration 5200, loss = 0.652318
I0130 13:04:01.086184 32230 solver.cpp:244]     Train net output #0: accuracy = 0.78125
I0130 13:04:01.086205 32230 solver.cpp:244]     Train net output #1: loss = 0.652318 (* 1 = 0.652318 loss)
I0130 13:04:01.086210 32230 sgd_solver.cpp:106] Iteration 5200, lr = 0.05
I0130 13:04:25.871716 32230 solver.cpp:337] Iteration 5400, Testing net (#0)
I0130 13:04:26.995501 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7888
I0130 13:04:26.995523 32230 solver.cpp:404]     Test net output #1: loss = 0.619245 (* 1 = 0.619245 loss)
I0130 13:04:27.024708 32230 solver.cpp:228] Iteration 5400, loss = 0.603992
I0130 13:04:27.024724 32230 solver.cpp:244]     Train net output #0: accuracy = 0.792969
I0130 13:04:27.024745 32230 solver.cpp:244]     Train net output #1: loss = 0.603992 (* 1 = 0.603992 loss)
I0130 13:04:27.024749 32230 sgd_solver.cpp:106] Iteration 5400, lr = 0.05
I0130 13:04:51.764089 32230 solver.cpp:337] Iteration 5600, Testing net (#0)
I0130 13:04:52.893862 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7997
I0130 13:04:52.893885 32230 solver.cpp:404]     Test net output #1: loss = 0.596755 (* 1 = 0.596755 loss)
I0130 13:04:52.922977 32230 solver.cpp:228] Iteration 5600, loss = 0.605493
I0130 13:04:52.922992 32230 solver.cpp:244]     Train net output #0: accuracy = 0.789062
I0130 13:04:52.922998 32230 solver.cpp:244]     Train net output #1: loss = 0.605493 (* 1 = 0.605493 loss)
I0130 13:04:52.923002 32230 sgd_solver.cpp:106] Iteration 5600, lr = 0.05
I0130 13:05:17.841192 32230 solver.cpp:337] Iteration 5800, Testing net (#0)
I0130 13:05:18.972111 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7787
I0130 13:05:18.972133 32230 solver.cpp:404]     Test net output #1: loss = 0.637987 (* 1 = 0.637987 loss)
I0130 13:05:19.001535 32230 solver.cpp:228] Iteration 5800, loss = 0.589742
I0130 13:05:19.001552 32230 solver.cpp:244]     Train net output #0: accuracy = 0.800781
I0130 13:05:19.001557 32230 solver.cpp:244]     Train net output #1: loss = 0.589742 (* 1 = 0.589742 loss)
I0130 13:05:19.001577 32230 sgd_solver.cpp:106] Iteration 5800, lr = 0.05
I0130 13:05:44.238260 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_6000.caffemodel
I0130 13:05:44.380086 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_6000.solverstate
I0130 13:05:44.407111 32230 solver.cpp:337] Iteration 6000, Testing net (#0)
I0130 13:05:45.501541 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7986
I0130 13:05:45.501564 32230 solver.cpp:404]     Test net output #1: loss = 0.589735 (* 1 = 0.589735 loss)
I0130 13:05:45.530534 32230 solver.cpp:228] Iteration 6000, loss = 0.496633
I0130 13:05:45.530550 32230 solver.cpp:244]     Train net output #0: accuracy = 0.847656
I0130 13:05:45.530555 32230 solver.cpp:244]     Train net output #1: loss = 0.496633 (* 1 = 0.496633 loss)
I0130 13:05:45.530558 32230 sgd_solver.cpp:106] Iteration 6000, lr = 0.05
I0130 13:06:10.534991 32230 solver.cpp:337] Iteration 6200, Testing net (#0)
I0130 13:06:11.700153 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8051
I0130 13:06:11.700192 32230 solver.cpp:404]     Test net output #1: loss = 0.569335 (* 1 = 0.569335 loss)
I0130 13:06:11.729475 32230 solver.cpp:228] Iteration 6200, loss = 0.524236
I0130 13:06:11.729491 32230 solver.cpp:244]     Train net output #0: accuracy = 0.804688
I0130 13:06:11.729497 32230 solver.cpp:244]     Train net output #1: loss = 0.524236 (* 1 = 0.524236 loss)
I0130 13:06:11.729502 32230 sgd_solver.cpp:106] Iteration 6200, lr = 0.05
I0130 13:06:36.629771 32230 solver.cpp:337] Iteration 6400, Testing net (#0)
I0130 13:06:37.759244 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8063
I0130 13:06:37.759265 32230 solver.cpp:404]     Test net output #1: loss = 0.574714 (* 1 = 0.574714 loss)
I0130 13:06:37.788733 32230 solver.cpp:228] Iteration 6400, loss = 0.601718
I0130 13:06:37.788749 32230 solver.cpp:244]     Train net output #0: accuracy = 0.792969
I0130 13:06:37.788754 32230 solver.cpp:244]     Train net output #1: loss = 0.601718 (* 1 = 0.601718 loss)
I0130 13:06:37.788758 32230 sgd_solver.cpp:106] Iteration 6400, lr = 0.05
I0130 13:07:02.824863 32230 solver.cpp:337] Iteration 6600, Testing net (#0)
I0130 13:07:03.967890 32230 solver.cpp:404]     Test net output #0: accuracy = 0.7988
I0130 13:07:03.967914 32230 solver.cpp:404]     Test net output #1: loss = 0.579575 (* 1 = 0.579575 loss)
I0130 13:07:04.000017 32230 solver.cpp:228] Iteration 6600, loss = 0.637687
I0130 13:07:04.000037 32230 solver.cpp:244]     Train net output #0: accuracy = 0.789062
I0130 13:07:04.000042 32230 solver.cpp:244]     Train net output #1: loss = 0.637687 (* 1 = 0.637687 loss)
I0130 13:07:04.000047 32230 sgd_solver.cpp:106] Iteration 6600, lr = 0.05
I0130 13:07:29.059792 32230 solver.cpp:337] Iteration 6800, Testing net (#0)
I0130 13:07:30.185055 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8183
I0130 13:07:30.185077 32230 solver.cpp:404]     Test net output #1: loss = 0.531636 (* 1 = 0.531636 loss)
I0130 13:07:30.216712 32230 solver.cpp:228] Iteration 6800, loss = 0.634499
I0130 13:07:30.216728 32230 solver.cpp:244]     Train net output #0: accuracy = 0.785156
I0130 13:07:30.216734 32230 solver.cpp:244]     Train net output #1: loss = 0.634499 (* 1 = 0.634499 loss)
I0130 13:07:30.216739 32230 sgd_solver.cpp:106] Iteration 6800, lr = 0.05
I0130 13:07:54.871057 32230 solver.cpp:337] Iteration 7000, Testing net (#0)
I0130 13:07:55.993170 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8125
I0130 13:07:55.993191 32230 solver.cpp:404]     Test net output #1: loss = 0.54303 (* 1 = 0.54303 loss)
I0130 13:07:56.022305 32230 solver.cpp:228] Iteration 7000, loss = 0.40821
I0130 13:07:56.022320 32230 solver.cpp:244]     Train net output #0: accuracy = 0.851562
I0130 13:07:56.022326 32230 solver.cpp:244]     Train net output #1: loss = 0.40821 (* 1 = 0.40821 loss)
I0130 13:07:56.022330 32230 sgd_solver.cpp:106] Iteration 7000, lr = 0.05
I0130 13:08:21.213856 32230 solver.cpp:337] Iteration 7200, Testing net (#0)
I0130 13:08:22.383935 32230 solver.cpp:404]     Test net output #0: accuracy = 0.817
I0130 13:08:22.383958 32230 solver.cpp:404]     Test net output #1: loss = 0.545454 (* 1 = 0.545454 loss)
I0130 13:08:22.413039 32230 solver.cpp:228] Iteration 7200, loss = 0.536607
I0130 13:08:22.413054 32230 solver.cpp:244]     Train net output #0: accuracy = 0.78125
I0130 13:08:22.413059 32230 solver.cpp:244]     Train net output #1: loss = 0.536607 (* 1 = 0.536607 loss)
I0130 13:08:22.413064 32230 sgd_solver.cpp:106] Iteration 7200, lr = 0.05
I0130 13:08:48.191815 32230 solver.cpp:337] Iteration 7400, Testing net (#0)
I0130 13:08:49.359859 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8203
I0130 13:08:49.359881 32230 solver.cpp:404]     Test net output #1: loss = 0.529211 (* 1 = 0.529211 loss)
I0130 13:08:49.389462 32230 solver.cpp:228] Iteration 7400, loss = 0.473193
I0130 13:08:49.389479 32230 solver.cpp:244]     Train net output #0: accuracy = 0.828125
I0130 13:08:49.389485 32230 solver.cpp:244]     Train net output #1: loss = 0.473193 (* 1 = 0.473193 loss)
I0130 13:08:49.389489 32230 sgd_solver.cpp:106] Iteration 7400, lr = 0.05
I0130 13:09:14.596359 32230 solver.cpp:337] Iteration 7600, Testing net (#0)
I0130 13:09:15.719573 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8273
I0130 13:09:15.719594 32230 solver.cpp:404]     Test net output #1: loss = 0.510654 (* 1 = 0.510654 loss)
I0130 13:09:15.748728 32230 solver.cpp:228] Iteration 7600, loss = 0.417851
I0130 13:09:15.748745 32230 solver.cpp:244]     Train net output #0: accuracy = 0.875
I0130 13:09:15.748750 32230 solver.cpp:244]     Train net output #1: loss = 0.417851 (* 1 = 0.417851 loss)
I0130 13:09:15.748754 32230 sgd_solver.cpp:106] Iteration 7600, lr = 0.05
I0130 13:09:40.449276 32230 solver.cpp:337] Iteration 7800, Testing net (#0)
I0130 13:09:41.570864 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8062
I0130 13:09:41.570901 32230 solver.cpp:404]     Test net output #1: loss = 0.584346 (* 1 = 0.584346 loss)
I0130 13:09:41.599740 32230 solver.cpp:228] Iteration 7800, loss = 0.523843
I0130 13:09:41.599756 32230 solver.cpp:244]     Train net output #0: accuracy = 0.816406
I0130 13:09:41.599761 32230 solver.cpp:244]     Train net output #1: loss = 0.523843 (* 1 = 0.523843 loss)
I0130 13:09:41.599764 32230 sgd_solver.cpp:106] Iteration 7800, lr = 0.05
I0130 13:10:06.575634 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_8000.caffemodel
I0130 13:10:06.709841 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_8000.solverstate
I0130 13:10:06.738312 32230 solver.cpp:337] Iteration 8000, Testing net (#0)
I0130 13:10:07.757122 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8225
I0130 13:10:07.757143 32230 solver.cpp:404]     Test net output #1: loss = 0.526463 (* 1 = 0.526463 loss)
I0130 13:10:07.785966 32230 solver.cpp:228] Iteration 8000, loss = 0.565781
I0130 13:10:07.785984 32230 solver.cpp:244]     Train net output #0: accuracy = 0.796875
I0130 13:10:07.785989 32230 solver.cpp:244]     Train net output #1: loss = 0.565781 (* 1 = 0.565781 loss)
I0130 13:10:07.785993 32230 sgd_solver.cpp:106] Iteration 8000, lr = 0.05
I0130 13:10:32.265275 32230 solver.cpp:337] Iteration 8200, Testing net (#0)
I0130 13:10:33.387512 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8127
I0130 13:10:33.387542 32230 solver.cpp:404]     Test net output #1: loss = 0.561818 (* 1 = 0.561818 loss)
I0130 13:10:33.416406 32230 solver.cpp:228] Iteration 8200, loss = 0.582175
I0130 13:10:33.416426 32230 solver.cpp:244]     Train net output #0: accuracy = 0.8125
I0130 13:10:33.416435 32230 solver.cpp:244]     Train net output #1: loss = 0.582175 (* 1 = 0.582175 loss)
I0130 13:10:33.416445 32230 sgd_solver.cpp:106] Iteration 8200, lr = 0.05
I0130 13:10:58.060879 32230 solver.cpp:337] Iteration 8400, Testing net (#0)
I0130 13:10:59.183003 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8318
I0130 13:10:59.183025 32230 solver.cpp:404]     Test net output #1: loss = 0.494155 (* 1 = 0.494155 loss)
I0130 13:10:59.212066 32230 solver.cpp:228] Iteration 8400, loss = 0.473918
I0130 13:10:59.212082 32230 solver.cpp:244]     Train net output #0: accuracy = 0.832031
I0130 13:10:59.212088 32230 solver.cpp:244]     Train net output #1: loss = 0.473918 (* 1 = 0.473918 loss)
I0130 13:10:59.212092 32230 sgd_solver.cpp:106] Iteration 8400, lr = 0.05
I0130 13:11:23.882745 32230 solver.cpp:337] Iteration 8600, Testing net (#0)
I0130 13:11:25.005158 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8183
I0130 13:11:25.005193 32230 solver.cpp:404]     Test net output #1: loss = 0.547728 (* 1 = 0.547728 loss)
I0130 13:11:25.034087 32230 solver.cpp:228] Iteration 8600, loss = 0.512918
I0130 13:11:25.034103 32230 solver.cpp:244]     Train net output #0: accuracy = 0.816406
I0130 13:11:25.034108 32230 solver.cpp:244]     Train net output #1: loss = 0.512918 (* 1 = 0.512918 loss)
I0130 13:11:25.034112 32230 sgd_solver.cpp:106] Iteration 8600, lr = 0.05
I0130 13:11:49.676018 32230 solver.cpp:337] Iteration 8800, Testing net (#0)
I0130 13:11:50.798115 32230 solver.cpp:404]     Test net output #0: accuracy = 0.821
I0130 13:11:50.798151 32230 solver.cpp:404]     Test net output #1: loss = 0.53191 (* 1 = 0.53191 loss)
I0130 13:11:50.827113 32230 solver.cpp:228] Iteration 8800, loss = 0.521741
I0130 13:11:50.827129 32230 solver.cpp:244]     Train net output #0: accuracy = 0.808594
I0130 13:11:50.827134 32230 solver.cpp:244]     Train net output #1: loss = 0.521741 (* 1 = 0.521741 loss)
I0130 13:11:50.827138 32230 sgd_solver.cpp:106] Iteration 8800, lr = 0.05
I0130 13:12:15.481606 32230 solver.cpp:337] Iteration 9000, Testing net (#0)
I0130 13:12:16.611414 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8336
I0130 13:12:16.611450 32230 solver.cpp:404]     Test net output #1: loss = 0.49814 (* 1 = 0.49814 loss)
I0130 13:12:16.640739 32230 solver.cpp:228] Iteration 9000, loss = 0.464741
I0130 13:12:16.640755 32230 solver.cpp:244]     Train net output #0: accuracy = 0.84375
I0130 13:12:16.640761 32230 solver.cpp:244]     Train net output #1: loss = 0.464741 (* 1 = 0.464741 loss)
I0130 13:12:16.640765 32230 sgd_solver.cpp:106] Iteration 9000, lr = 0.05
I0130 13:12:41.395215 32230 solver.cpp:337] Iteration 9200, Testing net (#0)
I0130 13:12:42.525028 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8303
I0130 13:12:42.525051 32230 solver.cpp:404]     Test net output #1: loss = 0.510985 (* 1 = 0.510985 loss)
I0130 13:12:42.554605 32230 solver.cpp:228] Iteration 9200, loss = 0.522781
I0130 13:12:42.554622 32230 solver.cpp:244]     Train net output #0: accuracy = 0.84375
I0130 13:12:42.554628 32230 solver.cpp:244]     Train net output #1: loss = 0.522781 (* 1 = 0.522781 loss)
I0130 13:12:42.554633 32230 sgd_solver.cpp:106] Iteration 9200, lr = 0.05
I0130 13:13:07.291477 32230 solver.cpp:337] Iteration 9400, Testing net (#0)
I0130 13:13:08.423246 32230 solver.cpp:404]     Test net output #0: accuracy = 0.84
I0130 13:13:08.423269 32230 solver.cpp:404]     Test net output #1: loss = 0.475115 (* 1 = 0.475115 loss)
I0130 13:13:08.452214 32230 solver.cpp:228] Iteration 9400, loss = 0.359648
I0130 13:13:08.452229 32230 solver.cpp:244]     Train net output #0: accuracy = 0.878906
I0130 13:13:08.452234 32230 solver.cpp:244]     Train net output #1: loss = 0.359648 (* 1 = 0.359648 loss)
I0130 13:13:08.452237 32230 sgd_solver.cpp:106] Iteration 9400, lr = 0.05
I0130 13:13:33.308080 32230 solver.cpp:337] Iteration 9600, Testing net (#0)
I0130 13:13:34.437013 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8352
I0130 13:13:34.437034 32230 solver.cpp:404]     Test net output #1: loss = 0.483519 (* 1 = 0.483519 loss)
I0130 13:13:34.466063 32230 solver.cpp:228] Iteration 9600, loss = 0.37684
I0130 13:13:34.466078 32230 solver.cpp:244]     Train net output #0: accuracy = 0.875
I0130 13:13:34.466084 32230 solver.cpp:244]     Train net output #1: loss = 0.37684 (* 1 = 0.37684 loss)
I0130 13:13:34.466087 32230 sgd_solver.cpp:106] Iteration 9600, lr = 0.05
I0130 13:13:59.144850 32230 solver.cpp:337] Iteration 9800, Testing net (#0)
I0130 13:14:00.263289 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8336
I0130 13:14:00.263309 32230 solver.cpp:404]     Test net output #1: loss = 0.487565 (* 1 = 0.487565 loss)
I0130 13:14:00.292171 32230 solver.cpp:228] Iteration 9800, loss = 0.449978
I0130 13:14:00.292186 32230 solver.cpp:244]     Train net output #0: accuracy = 0.855469
I0130 13:14:00.292192 32230 solver.cpp:244]     Train net output #1: loss = 0.449978 (* 1 = 0.449978 loss)
I0130 13:14:00.292196 32230 sgd_solver.cpp:106] Iteration 9800, lr = 0.05
I0130 13:14:25.009840 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_10000.caffemodel
I0130 13:14:25.144227 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_10000.solverstate
I0130 13:14:25.173146 32230 solver.cpp:337] Iteration 10000, Testing net (#0)
I0130 13:14:26.201640 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8144
I0130 13:14:26.201663 32230 solver.cpp:404]     Test net output #1: loss = 0.53561 (* 1 = 0.53561 loss)
I0130 13:14:26.230439 32230 solver.cpp:228] Iteration 10000, loss = 0.429904
I0130 13:14:26.230458 32230 solver.cpp:244]     Train net output #0: accuracy = 0.847656
I0130 13:14:26.230463 32230 solver.cpp:244]     Train net output #1: loss = 0.429904 (* 1 = 0.429904 loss)
I0130 13:14:26.230468 32230 sgd_solver.cpp:106] Iteration 10000, lr = 0.05
I0130 13:14:50.978665 32230 solver.cpp:337] Iteration 10200, Testing net (#0)
I0130 13:14:52.101222 32230 solver.cpp:404]     Test net output #0: accuracy = 0.84
I0130 13:14:52.101243 32230 solver.cpp:404]     Test net output #1: loss = 0.47481 (* 1 = 0.47481 loss)
I0130 13:14:52.129964 32230 solver.cpp:228] Iteration 10200, loss = 0.300481
I0130 13:14:52.129979 32230 solver.cpp:244]     Train net output #0: accuracy = 0.914062
I0130 13:14:52.129986 32230 solver.cpp:244]     Train net output #1: loss = 0.300481 (* 1 = 0.300481 loss)
I0130 13:14:52.129989 32230 sgd_solver.cpp:106] Iteration 10200, lr = 0.05
I0130 13:15:16.731175 32230 solver.cpp:337] Iteration 10400, Testing net (#0)
I0130 13:15:17.856686 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8363
I0130 13:15:17.856708 32230 solver.cpp:404]     Test net output #1: loss = 0.505055 (* 1 = 0.505055 loss)
I0130 13:15:17.885588 32230 solver.cpp:228] Iteration 10400, loss = 0.455537
I0130 13:15:17.885602 32230 solver.cpp:244]     Train net output #0: accuracy = 0.835938
I0130 13:15:17.885608 32230 solver.cpp:244]     Train net output #1: loss = 0.455537 (* 1 = 0.455537 loss)
I0130 13:15:17.885613 32230 sgd_solver.cpp:106] Iteration 10400, lr = 0.05
I0130 13:15:42.754945 32230 solver.cpp:337] Iteration 10600, Testing net (#0)
I0130 13:15:44.004027 32230 solver.cpp:404]     Test net output #0: accuracy = 0.825
I0130 13:15:44.004046 32230 solver.cpp:404]     Test net output #1: loss = 0.527136 (* 1 = 0.527136 loss)
I0130 13:15:44.033843 32230 solver.cpp:228] Iteration 10600, loss = 0.525698
I0130 13:15:44.033854 32230 solver.cpp:244]     Train net output #0: accuracy = 0.796875
I0130 13:15:44.033859 32230 solver.cpp:244]     Train net output #1: loss = 0.525698 (* 1 = 0.525698 loss)
I0130 13:15:44.033861 32230 sgd_solver.cpp:106] Iteration 10600, lr = 0.05
I0130 13:16:08.962882 32230 solver.cpp:337] Iteration 10800, Testing net (#0)
I0130 13:16:10.084421 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8227
I0130 13:16:10.084442 32230 solver.cpp:404]     Test net output #1: loss = 0.525323 (* 1 = 0.525323 loss)
I0130 13:16:10.113598 32230 solver.cpp:228] Iteration 10800, loss = 0.486002
I0130 13:16:10.113615 32230 solver.cpp:244]     Train net output #0: accuracy = 0.835938
I0130 13:16:10.113620 32230 solver.cpp:244]     Train net output #1: loss = 0.486002 (* 1 = 0.486002 loss)
I0130 13:16:10.113625 32230 sgd_solver.cpp:106] Iteration 10800, lr = 0.05
I0130 13:16:36.120565 32230 solver.cpp:337] Iteration 11000, Testing net (#0)
I0130 13:16:37.238528 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8364
I0130 13:16:37.238566 32230 solver.cpp:404]     Test net output #1: loss = 0.487618 (* 1 = 0.487618 loss)
I0130 13:16:37.267799 32230 solver.cpp:228] Iteration 11000, loss = 0.477415
I0130 13:16:37.267814 32230 solver.cpp:244]     Train net output #0: accuracy = 0.816406
I0130 13:16:37.267819 32230 solver.cpp:244]     Train net output #1: loss = 0.477415 (* 1 = 0.477415 loss)
I0130 13:16:37.267823 32230 sgd_solver.cpp:106] Iteration 11000, lr = 0.05
I0130 13:17:02.441642 32230 solver.cpp:337] Iteration 11200, Testing net (#0)
I0130 13:17:03.567656 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8357
I0130 13:17:03.567675 32230 solver.cpp:404]     Test net output #1: loss = 0.489886 (* 1 = 0.489886 loss)
I0130 13:17:03.596702 32230 solver.cpp:228] Iteration 11200, loss = 0.447877
I0130 13:17:03.596717 32230 solver.cpp:244]     Train net output #0: accuracy = 0.84375
I0130 13:17:03.596724 32230 solver.cpp:244]     Train net output #1: loss = 0.447877 (* 1 = 0.447877 loss)
I0130 13:17:03.596726 32230 sgd_solver.cpp:106] Iteration 11200, lr = 0.05
I0130 13:17:28.581254 32230 solver.cpp:337] Iteration 11400, Testing net (#0)
I0130 13:17:29.709381 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8278
I0130 13:17:29.709404 32230 solver.cpp:404]     Test net output #1: loss = 0.5145 (* 1 = 0.5145 loss)
I0130 13:17:29.738591 32230 solver.cpp:228] Iteration 11400, loss = 0.44836
I0130 13:17:29.738611 32230 solver.cpp:244]     Train net output #0: accuracy = 0.855469
I0130 13:17:29.738617 32230 solver.cpp:244]     Train net output #1: loss = 0.44836 (* 1 = 0.44836 loss)
I0130 13:17:29.738621 32230 sgd_solver.cpp:106] Iteration 11400, lr = 0.05
I0130 13:17:54.888087 32230 solver.cpp:337] Iteration 11600, Testing net (#0)
I0130 13:17:56.012295 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8301
I0130 13:17:56.012315 32230 solver.cpp:404]     Test net output #1: loss = 0.519654 (* 1 = 0.519654 loss)
I0130 13:17:56.041764 32230 solver.cpp:228] Iteration 11600, loss = 0.509517
I0130 13:17:56.041781 32230 solver.cpp:244]     Train net output #0: accuracy = 0.816406
I0130 13:17:56.041786 32230 solver.cpp:244]     Train net output #1: loss = 0.509517 (* 1 = 0.509517 loss)
I0130 13:17:56.041790 32230 sgd_solver.cpp:106] Iteration 11600, lr = 0.05
I0130 13:18:20.974033 32230 solver.cpp:337] Iteration 11800, Testing net (#0)
I0130 13:18:22.142601 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8124
I0130 13:18:22.142623 32230 solver.cpp:404]     Test net output #1: loss = 0.560531 (* 1 = 0.560531 loss)
I0130 13:18:22.172458 32230 solver.cpp:228] Iteration 11800, loss = 0.429285
I0130 13:18:22.172477 32230 solver.cpp:244]     Train net output #0: accuracy = 0.867188
I0130 13:18:22.172482 32230 solver.cpp:244]     Train net output #1: loss = 0.429285 (* 1 = 0.429285 loss)
I0130 13:18:22.172487 32230 sgd_solver.cpp:106] Iteration 11800, lr = 0.05
I0130 13:18:46.777333 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_12000.caffemodel
I0130 13:18:46.916980 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_12000.solverstate
I0130 13:18:46.945605 32230 solver.cpp:337] Iteration 12000, Testing net (#0)
I0130 13:18:47.991750 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8404
I0130 13:18:47.991773 32230 solver.cpp:404]     Test net output #1: loss = 0.467811 (* 1 = 0.467811 loss)
I0130 13:18:48.018975 32230 solver.cpp:228] Iteration 12000, loss = 0.360828
I0130 13:18:48.018995 32230 solver.cpp:244]     Train net output #0: accuracy = 0.867188
I0130 13:18:48.019001 32230 solver.cpp:244]     Train net output #1: loss = 0.360828 (* 1 = 0.360828 loss)
I0130 13:18:48.019006 32230 sgd_solver.cpp:106] Iteration 12000, lr = 0.05
I0130 13:19:12.843541 32230 solver.cpp:337] Iteration 12200, Testing net (#0)
I0130 13:19:13.974082 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8314
I0130 13:19:13.974104 32230 solver.cpp:404]     Test net output #1: loss = 0.496704 (* 1 = 0.496704 loss)
I0130 13:19:14.003197 32230 solver.cpp:228] Iteration 12200, loss = 0.375297
I0130 13:19:14.003216 32230 solver.cpp:244]     Train net output #0: accuracy = 0.859375
I0130 13:19:14.003221 32230 solver.cpp:244]     Train net output #1: loss = 0.375297 (* 1 = 0.375297 loss)
I0130 13:19:14.003226 32230 sgd_solver.cpp:106] Iteration 12200, lr = 0.05
I0130 13:19:39.058027 32230 solver.cpp:337] Iteration 12400, Testing net (#0)
I0130 13:19:40.177840 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8443
I0130 13:19:40.177865 32230 solver.cpp:404]     Test net output #1: loss = 0.458618 (* 1 = 0.458618 loss)
I0130 13:19:40.206631 32230 solver.cpp:228] Iteration 12400, loss = 0.433444
I0130 13:19:40.206650 32230 solver.cpp:244]     Train net output #0: accuracy = 0.832031
I0130 13:19:40.206656 32230 solver.cpp:244]     Train net output #1: loss = 0.433444 (* 1 = 0.433444 loss)
I0130 13:19:40.206661 32230 sgd_solver.cpp:106] Iteration 12400, lr = 0.05
I0130 13:20:05.217011 32230 solver.cpp:337] Iteration 12600, Testing net (#0)
I0130 13:20:06.345445 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8409
I0130 13:20:06.345468 32230 solver.cpp:404]     Test net output #1: loss = 0.474562 (* 1 = 0.474562 loss)
I0130 13:20:06.374600 32230 solver.cpp:228] Iteration 12600, loss = 0.418465
I0130 13:20:06.374619 32230 solver.cpp:244]     Train net output #0: accuracy = 0.855469
I0130 13:20:06.374624 32230 solver.cpp:244]     Train net output #1: loss = 0.418465 (* 1 = 0.418465 loss)
I0130 13:20:06.374629 32230 sgd_solver.cpp:106] Iteration 12600, lr = 0.05
I0130 13:20:31.153303 32230 solver.cpp:337] Iteration 12800, Testing net (#0)
I0130 13:20:32.298579 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8307
I0130 13:20:32.298604 32230 solver.cpp:404]     Test net output #1: loss = 0.511013 (* 1 = 0.511013 loss)
I0130 13:20:32.327601 32230 solver.cpp:228] Iteration 12800, loss = 0.488615
I0130 13:20:32.327622 32230 solver.cpp:244]     Train net output #0: accuracy = 0.796875
I0130 13:20:32.327627 32230 solver.cpp:244]     Train net output #1: loss = 0.488615 (* 1 = 0.488615 loss)
I0130 13:20:32.327632 32230 sgd_solver.cpp:106] Iteration 12800, lr = 0.05
I0130 13:20:57.134165 32230 solver.cpp:337] Iteration 13000, Testing net (#0)
I0130 13:20:58.259639 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8492
I0130 13:20:58.259660 32230 solver.cpp:404]     Test net output #1: loss = 0.446509 (* 1 = 0.446509 loss)
I0130 13:20:58.287143 32230 solver.cpp:228] Iteration 13000, loss = 0.277044
I0130 13:20:58.287160 32230 solver.cpp:244]     Train net output #0: accuracy = 0.910156
I0130 13:20:58.287166 32230 solver.cpp:244]     Train net output #1: loss = 0.277044 (* 1 = 0.277044 loss)
I0130 13:20:58.287169 32230 sgd_solver.cpp:106] Iteration 13000, lr = 0.05
I0130 13:21:23.317178 32230 solver.cpp:337] Iteration 13200, Testing net (#0)
I0130 13:21:24.435221 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8287
I0130 13:21:24.435243 32230 solver.cpp:404]     Test net output #1: loss = 0.514363 (* 1 = 0.514363 loss)
I0130 13:21:24.463959 32230 solver.cpp:228] Iteration 13200, loss = 0.394416
I0130 13:21:24.463975 32230 solver.cpp:244]     Train net output #0: accuracy = 0.847656
I0130 13:21:24.463981 32230 solver.cpp:244]     Train net output #1: loss = 0.394416 (* 1 = 0.394416 loss)
I0130 13:21:24.463984 32230 sgd_solver.cpp:106] Iteration 13200, lr = 0.05
I0130 13:21:49.082784 32230 solver.cpp:337] Iteration 13400, Testing net (#0)
I0130 13:21:50.205350 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8439
I0130 13:21:50.205374 32230 solver.cpp:404]     Test net output #1: loss = 0.478534 (* 1 = 0.478534 loss)
I0130 13:21:50.234416 32230 solver.cpp:228] Iteration 13400, loss = 0.377385
I0130 13:21:50.234432 32230 solver.cpp:244]     Train net output #0: accuracy = 0.855469
I0130 13:21:50.234438 32230 solver.cpp:244]     Train net output #1: loss = 0.377385 (* 1 = 0.377385 loss)
I0130 13:21:50.234442 32230 sgd_solver.cpp:106] Iteration 13400, lr = 0.05
I0130 13:22:14.808558 32230 solver.cpp:337] Iteration 13600, Testing net (#0)
I0130 13:22:15.930181 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8185
I0130 13:22:15.930203 32230 solver.cpp:404]     Test net output #1: loss = 0.546331 (* 1 = 0.546331 loss)
I0130 13:22:15.958904 32230 solver.cpp:228] Iteration 13600, loss = 0.451854
I0130 13:22:15.958920 32230 solver.cpp:244]     Train net output #0: accuracy = 0.855469
I0130 13:22:15.958925 32230 solver.cpp:244]     Train net output #1: loss = 0.451854 (* 1 = 0.451854 loss)
I0130 13:22:15.958930 32230 sgd_solver.cpp:106] Iteration 13600, lr = 0.05
I0130 13:22:40.592067 32230 solver.cpp:337] Iteration 13800, Testing net (#0)
I0130 13:22:41.714442 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8347
I0130 13:22:41.714475 32230 solver.cpp:404]     Test net output #1: loss = 0.485942 (* 1 = 0.485942 loss)
I0130 13:22:41.743572 32230 solver.cpp:228] Iteration 13800, loss = 0.591662
I0130 13:22:41.743589 32230 solver.cpp:244]     Train net output #0: accuracy = 0.816406
I0130 13:22:41.743593 32230 solver.cpp:244]     Train net output #1: loss = 0.591662 (* 1 = 0.591662 loss)
I0130 13:22:41.743597 32230 sgd_solver.cpp:106] Iteration 13800, lr = 0.05
I0130 13:23:06.395800 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_14000.caffemodel
I0130 13:23:06.530946 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_14000.solverstate
I0130 13:23:06.558862 32230 solver.cpp:337] Iteration 14000, Testing net (#0)
I0130 13:23:07.583974 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8539
I0130 13:23:07.583997 32230 solver.cpp:404]     Test net output #1: loss = 0.437259 (* 1 = 0.437259 loss)
I0130 13:23:07.612596 32230 solver.cpp:228] Iteration 14000, loss = 0.379222
I0130 13:23:07.612613 32230 solver.cpp:244]     Train net output #0: accuracy = 0.890625
I0130 13:23:07.612619 32230 solver.cpp:244]     Train net output #1: loss = 0.379222 (* 1 = 0.379222 loss)
I0130 13:23:07.612623 32230 sgd_solver.cpp:106] Iteration 14000, lr = 0.05
I0130 13:23:32.313449 32230 solver.cpp:337] Iteration 14200, Testing net (#0)
I0130 13:23:33.435869 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8254
I0130 13:23:33.435890 32230 solver.cpp:404]     Test net output #1: loss = 0.511413 (* 1 = 0.511413 loss)
I0130 13:23:33.464850 32230 solver.cpp:228] Iteration 14200, loss = 0.361
I0130 13:23:33.464869 32230 solver.cpp:244]     Train net output #0: accuracy = 0.855469
I0130 13:23:33.464875 32230 solver.cpp:244]     Train net output #1: loss = 0.361 (* 1 = 0.361 loss)
I0130 13:23:33.464880 32230 sgd_solver.cpp:106] Iteration 14200, lr = 0.05
I0130 13:23:58.228929 32230 solver.cpp:337] Iteration 14400, Testing net (#0)
I0130 13:23:59.358217 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8292
I0130 13:23:59.358237 32230 solver.cpp:404]     Test net output #1: loss = 0.515778 (* 1 = 0.515778 loss)
I0130 13:23:59.386958 32230 solver.cpp:228] Iteration 14400, loss = 0.521438
I0130 13:23:59.386976 32230 solver.cpp:244]     Train net output #0: accuracy = 0.824219
I0130 13:23:59.386981 32230 solver.cpp:244]     Train net output #1: loss = 0.521438 (* 1 = 0.521438 loss)
I0130 13:23:59.386983 32230 sgd_solver.cpp:106] Iteration 14400, lr = 0.05
I0130 13:24:24.149750 32230 solver.cpp:337] Iteration 14600, Testing net (#0)
I0130 13:24:25.270861 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8488
I0130 13:24:25.270896 32230 solver.cpp:404]     Test net output #1: loss = 0.454766 (* 1 = 0.454766 loss)
I0130 13:24:25.299883 32230 solver.cpp:228] Iteration 14600, loss = 0.319703
I0130 13:24:25.299901 32230 solver.cpp:244]     Train net output #0: accuracy = 0.898438
I0130 13:24:25.299906 32230 solver.cpp:244]     Train net output #1: loss = 0.319703 (* 1 = 0.319703 loss)
I0130 13:24:25.299911 32230 sgd_solver.cpp:106] Iteration 14600, lr = 0.05
I0130 13:24:50.187225 32230 solver.cpp:337] Iteration 14800, Testing net (#0)
I0130 13:24:51.353821 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8529
I0130 13:24:51.353842 32230 solver.cpp:404]     Test net output #1: loss = 0.436352 (* 1 = 0.436352 loss)
I0130 13:24:51.382766 32230 solver.cpp:228] Iteration 14800, loss = 0.289835
I0130 13:24:51.382782 32230 solver.cpp:244]     Train net output #0: accuracy = 0.890625
I0130 13:24:51.382788 32230 solver.cpp:244]     Train net output #1: loss = 0.289835 (* 1 = 0.289835 loss)
I0130 13:24:51.382792 32230 sgd_solver.cpp:106] Iteration 14800, lr = 0.05
I0130 13:25:16.206974 32230 solver.cpp:337] Iteration 15000, Testing net (#0)
I0130 13:25:17.335989 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8423
I0130 13:25:17.336010 32230 solver.cpp:404]     Test net output #1: loss = 0.474019 (* 1 = 0.474019 loss)
I0130 13:25:17.364890 32230 solver.cpp:228] Iteration 15000, loss = 0.358753
I0130 13:25:17.364907 32230 solver.cpp:244]     Train net output #0: accuracy = 0.867188
I0130 13:25:17.364912 32230 solver.cpp:244]     Train net output #1: loss = 0.358753 (* 1 = 0.358753 loss)
I0130 13:25:17.364915 32230 sgd_solver.cpp:106] Iteration 15000, lr = 0.05
I0130 13:25:42.140383 32230 solver.cpp:337] Iteration 15200, Testing net (#0)
I0130 13:25:43.264760 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8388
I0130 13:25:43.264781 32230 solver.cpp:404]     Test net output #1: loss = 0.489555 (* 1 = 0.489555 loss)
I0130 13:25:43.293936 32230 solver.cpp:228] Iteration 15200, loss = 0.268809
I0130 13:25:43.293952 32230 solver.cpp:244]     Train net output #0: accuracy = 0.925781
I0130 13:25:43.293959 32230 solver.cpp:244]     Train net output #1: loss = 0.268809 (* 1 = 0.268809 loss)
I0130 13:25:43.293964 32230 sgd_solver.cpp:106] Iteration 15200, lr = 0.05
I0130 13:26:08.115247 32230 solver.cpp:337] Iteration 15400, Testing net (#0)
I0130 13:26:09.248226 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8491
I0130 13:26:09.248250 32230 solver.cpp:404]     Test net output #1: loss = 0.441353 (* 1 = 0.441353 loss)
I0130 13:26:09.277074 32230 solver.cpp:228] Iteration 15400, loss = 0.3231
I0130 13:26:09.277089 32230 solver.cpp:244]     Train net output #0: accuracy = 0.886719
I0130 13:26:09.277094 32230 solver.cpp:244]     Train net output #1: loss = 0.3231 (* 1 = 0.3231 loss)
I0130 13:26:09.277098 32230 sgd_solver.cpp:106] Iteration 15400, lr = 0.05
I0130 13:26:34.167145 32230 solver.cpp:337] Iteration 15600, Testing net (#0)
I0130 13:26:35.297227 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8463
I0130 13:26:35.297250 32230 solver.cpp:404]     Test net output #1: loss = 0.455984 (* 1 = 0.455984 loss)
I0130 13:26:35.327409 32230 solver.cpp:228] Iteration 15600, loss = 0.460758
I0130 13:26:35.327426 32230 solver.cpp:244]     Train net output #0: accuracy = 0.851562
I0130 13:26:35.327433 32230 solver.cpp:244]     Train net output #1: loss = 0.460758 (* 1 = 0.460758 loss)
I0130 13:26:35.327438 32230 sgd_solver.cpp:106] Iteration 15600, lr = 0.05
I0130 13:27:00.373749 32230 solver.cpp:337] Iteration 15800, Testing net (#0)
I0130 13:27:01.495474 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8539
I0130 13:27:01.495498 32230 solver.cpp:404]     Test net output #1: loss = 0.421261 (* 1 = 0.421261 loss)
I0130 13:27:01.524724 32230 solver.cpp:228] Iteration 15800, loss = 0.437311
I0130 13:27:01.524744 32230 solver.cpp:244]     Train net output #0: accuracy = 0.835938
I0130 13:27:01.524751 32230 solver.cpp:244]     Train net output #1: loss = 0.437311 (* 1 = 0.437311 loss)
I0130 13:27:01.524756 32230 sgd_solver.cpp:106] Iteration 15800, lr = 0.05
I0130 13:27:26.682301 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_16000.caffemodel
I0130 13:27:26.842983 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_16000.solverstate
I0130 13:27:26.871665 32230 solver.cpp:337] Iteration 16000, Testing net (#0)
I0130 13:27:27.979998 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8458
I0130 13:27:27.980037 32230 solver.cpp:404]     Test net output #1: loss = 0.455055 (* 1 = 0.455055 loss)
I0130 13:27:28.009559 32230 solver.cpp:228] Iteration 16000, loss = 0.341983
I0130 13:27:28.009577 32230 solver.cpp:244]     Train net output #0: accuracy = 0.871094
I0130 13:27:28.009582 32230 solver.cpp:244]     Train net output #1: loss = 0.341983 (* 1 = 0.341983 loss)
I0130 13:27:28.009587 32230 sgd_solver.cpp:106] Iteration 16000, lr = 0.05
I0130 13:27:52.859485 32230 solver.cpp:337] Iteration 16200, Testing net (#0)
I0130 13:27:53.996767 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8265
I0130 13:27:53.996788 32230 solver.cpp:404]     Test net output #1: loss = 0.512061 (* 1 = 0.512061 loss)
I0130 13:27:54.026130 32230 solver.cpp:228] Iteration 16200, loss = 0.392216
I0130 13:27:54.026149 32230 solver.cpp:244]     Train net output #0: accuracy = 0.871094
I0130 13:27:54.026154 32230 solver.cpp:244]     Train net output #1: loss = 0.392216 (* 1 = 0.392216 loss)
I0130 13:27:54.026157 32230 sgd_solver.cpp:106] Iteration 16200, lr = 0.05
I0130 13:28:19.002184 32230 solver.cpp:337] Iteration 16400, Testing net (#0)
I0130 13:28:20.179210 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8312
I0130 13:28:20.179234 32230 solver.cpp:404]     Test net output #1: loss = 0.502988 (* 1 = 0.502988 loss)
I0130 13:28:20.208971 32230 solver.cpp:228] Iteration 16400, loss = 0.369379
I0130 13:28:20.208992 32230 solver.cpp:244]     Train net output #0: accuracy = 0.875
I0130 13:28:20.208997 32230 solver.cpp:244]     Train net output #1: loss = 0.369379 (* 1 = 0.369379 loss)
I0130 13:28:20.209002 32230 sgd_solver.cpp:106] Iteration 16400, lr = 0.05
I0130 13:28:45.252769 32230 solver.cpp:337] Iteration 16600, Testing net (#0)
I0130 13:28:46.430696 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8455
I0130 13:28:46.430718 32230 solver.cpp:404]     Test net output #1: loss = 0.471446 (* 1 = 0.471446 loss)
I0130 13:28:46.459656 32230 solver.cpp:228] Iteration 16600, loss = 0.385731
I0130 13:28:46.459676 32230 solver.cpp:244]     Train net output #0: accuracy = 0.875
I0130 13:28:46.459682 32230 solver.cpp:244]     Train net output #1: loss = 0.385731 (* 1 = 0.385731 loss)
I0130 13:28:46.459686 32230 sgd_solver.cpp:106] Iteration 16600, lr = 0.05
I0130 13:29:11.225428 32230 solver.cpp:337] Iteration 16800, Testing net (#0)
I0130 13:29:12.391764 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8489
I0130 13:29:12.391790 32230 solver.cpp:404]     Test net output #1: loss = 0.457551 (* 1 = 0.457551 loss)
I0130 13:29:12.420755 32230 solver.cpp:228] Iteration 16800, loss = 0.441183
I0130 13:29:12.420778 32230 solver.cpp:244]     Train net output #0: accuracy = 0.851562
I0130 13:29:12.420783 32230 solver.cpp:244]     Train net output #1: loss = 0.441183 (* 1 = 0.441183 loss)
I0130 13:29:12.420788 32230 sgd_solver.cpp:106] Iteration 16800, lr = 0.05
I0130 13:29:37.679245 32230 solver.cpp:337] Iteration 17000, Testing net (#0)
I0130 13:29:38.835080 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8634
I0130 13:29:38.835101 32230 solver.cpp:404]     Test net output #1: loss = 0.407771 (* 1 = 0.407771 loss)
I0130 13:29:38.864128 32230 solver.cpp:228] Iteration 17000, loss = 0.39543
I0130 13:29:38.864145 32230 solver.cpp:244]     Train net output #0: accuracy = 0.871094
I0130 13:29:38.864150 32230 solver.cpp:244]     Train net output #1: loss = 0.39543 (* 1 = 0.39543 loss)
I0130 13:29:38.864153 32230 sgd_solver.cpp:106] Iteration 17000, lr = 0.05
I0130 13:30:03.749680 32230 solver.cpp:337] Iteration 17200, Testing net (#0)
I0130 13:30:04.869678 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8475
I0130 13:30:04.869699 32230 solver.cpp:404]     Test net output #1: loss = 0.465701 (* 1 = 0.465701 loss)
I0130 13:30:04.898705 32230 solver.cpp:228] Iteration 17200, loss = 0.465345
I0130 13:30:04.898721 32230 solver.cpp:244]     Train net output #0: accuracy = 0.835938
I0130 13:30:04.898727 32230 solver.cpp:244]     Train net output #1: loss = 0.465345 (* 1 = 0.465345 loss)
I0130 13:30:04.898731 32230 sgd_solver.cpp:106] Iteration 17200, lr = 0.05
I0130 13:30:29.955950 32230 solver.cpp:337] Iteration 17400, Testing net (#0)
I0130 13:30:31.124297 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8411
I0130 13:30:31.124336 32230 solver.cpp:404]     Test net output #1: loss = 0.504942 (* 1 = 0.504942 loss)
I0130 13:30:31.153182 32230 solver.cpp:228] Iteration 17400, loss = 0.46593
I0130 13:30:31.153198 32230 solver.cpp:244]     Train net output #0: accuracy = 0.832031
I0130 13:30:31.153203 32230 solver.cpp:244]     Train net output #1: loss = 0.46593 (* 1 = 0.46593 loss)
I0130 13:30:31.153208 32230 sgd_solver.cpp:106] Iteration 17400, lr = 0.05
I0130 13:30:56.154510 32230 solver.cpp:337] Iteration 17600, Testing net (#0)
I0130 13:30:57.275353 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8433
I0130 13:30:57.275389 32230 solver.cpp:404]     Test net output #1: loss = 0.475435 (* 1 = 0.475435 loss)
I0130 13:30:57.304497 32230 solver.cpp:228] Iteration 17600, loss = 0.425234
I0130 13:30:57.304512 32230 solver.cpp:244]     Train net output #0: accuracy = 0.867188
I0130 13:30:57.304517 32230 solver.cpp:244]     Train net output #1: loss = 0.425234 (* 1 = 0.425234 loss)
I0130 13:30:57.304520 32230 sgd_solver.cpp:106] Iteration 17600, lr = 0.05
I0130 13:31:22.107970 32230 solver.cpp:337] Iteration 17800, Testing net (#0)
I0130 13:31:23.231766 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8428
I0130 13:31:23.231791 32230 solver.cpp:404]     Test net output #1: loss = 0.46439 (* 1 = 0.46439 loss)
I0130 13:31:23.260690 32230 solver.cpp:228] Iteration 17800, loss = 0.472989
I0130 13:31:23.260705 32230 solver.cpp:244]     Train net output #0: accuracy = 0.851562
I0130 13:31:23.260710 32230 solver.cpp:244]     Train net output #1: loss = 0.472989 (* 1 = 0.472989 loss)
I0130 13:31:23.260715 32230 sgd_solver.cpp:106] Iteration 17800, lr = 0.05
I0130 13:31:48.047049 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_18000.caffemodel
I0130 13:31:48.182651 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_18000.solverstate
I0130 13:31:48.210299 32230 solver.cpp:337] Iteration 18000, Testing net (#0)
I0130 13:31:49.252295 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8486
I0130 13:31:49.252316 32230 solver.cpp:404]     Test net output #1: loss = 0.463197 (* 1 = 0.463197 loss)
I0130 13:31:49.285440 32230 solver.cpp:228] Iteration 18000, loss = 0.320159
I0130 13:31:49.285459 32230 solver.cpp:244]     Train net output #0: accuracy = 0.878906
I0130 13:31:49.285464 32230 solver.cpp:244]     Train net output #1: loss = 0.320159 (* 1 = 0.320159 loss)
I0130 13:31:49.285468 32230 sgd_solver.cpp:106] Iteration 18000, lr = 0.05
I0130 13:32:14.068186 32230 solver.cpp:337] Iteration 18200, Testing net (#0)
I0130 13:32:15.191865 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8535
I0130 13:32:15.191892 32230 solver.cpp:404]     Test net output #1: loss = 0.451482 (* 1 = 0.451482 loss)
I0130 13:32:15.220940 32230 solver.cpp:228] Iteration 18200, loss = 0.402475
I0130 13:32:15.220957 32230 solver.cpp:244]     Train net output #0: accuracy = 0.878906
I0130 13:32:15.220963 32230 solver.cpp:244]     Train net output #1: loss = 0.402475 (* 1 = 0.402475 loss)
I0130 13:32:15.220968 32230 sgd_solver.cpp:106] Iteration 18200, lr = 0.05
I0130 13:32:40.055071 32230 solver.cpp:337] Iteration 18400, Testing net (#0)
I0130 13:32:41.178375 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8389
I0130 13:32:41.178397 32230 solver.cpp:404]     Test net output #1: loss = 0.473584 (* 1 = 0.473584 loss)
I0130 13:32:41.207190 32230 solver.cpp:228] Iteration 18400, loss = 0.393105
I0130 13:32:41.207206 32230 solver.cpp:244]     Train net output #0: accuracy = 0.867188
I0130 13:32:41.207211 32230 solver.cpp:244]     Train net output #1: loss = 0.393105 (* 1 = 0.393105 loss)
I0130 13:32:41.207216 32230 sgd_solver.cpp:106] Iteration 18400, lr = 0.05
I0130 13:33:05.836633 32230 solver.cpp:337] Iteration 18600, Testing net (#0)
I0130 13:33:06.955591 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8467
I0130 13:33:06.955612 32230 solver.cpp:404]     Test net output #1: loss = 0.447464 (* 1 = 0.447464 loss)
I0130 13:33:06.982874 32230 solver.cpp:228] Iteration 18600, loss = 0.331093
I0130 13:33:06.982892 32230 solver.cpp:244]     Train net output #0: accuracy = 0.886719
I0130 13:33:06.982898 32230 solver.cpp:244]     Train net output #1: loss = 0.331093 (* 1 = 0.331093 loss)
I0130 13:33:06.982903 32230 sgd_solver.cpp:106] Iteration 18600, lr = 0.05
I0130 13:33:31.755381 32230 solver.cpp:337] Iteration 18800, Testing net (#0)
I0130 13:33:32.879092 32230 solver.cpp:404]     Test net output #0: accuracy = 0.85
I0130 13:33:32.879115 32230 solver.cpp:404]     Test net output #1: loss = 0.455053 (* 1 = 0.455053 loss)
I0130 13:33:32.907850 32230 solver.cpp:228] Iteration 18800, loss = 0.353327
I0130 13:33:32.907865 32230 solver.cpp:244]     Train net output #0: accuracy = 0.878906
I0130 13:33:32.907869 32230 solver.cpp:244]     Train net output #1: loss = 0.353327 (* 1 = 0.353327 loss)
I0130 13:33:32.907873 32230 sgd_solver.cpp:106] Iteration 18800, lr = 0.05
I0130 13:33:57.750622 32230 solver.cpp:337] Iteration 19000, Testing net (#0)
I0130 13:33:58.868896 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8531
I0130 13:33:58.868916 32230 solver.cpp:404]     Test net output #1: loss = 0.438599 (* 1 = 0.438599 loss)
I0130 13:33:58.895784 32230 solver.cpp:228] Iteration 19000, loss = 0.375222
I0130 13:33:58.895802 32230 solver.cpp:244]     Train net output #0: accuracy = 0.871094
I0130 13:33:58.895807 32230 solver.cpp:244]     Train net output #1: loss = 0.375222 (* 1 = 0.375222 loss)
I0130 13:33:58.895812 32230 sgd_solver.cpp:106] Iteration 19000, lr = 0.05
I0130 13:34:23.603979 32230 solver.cpp:337] Iteration 19200, Testing net (#0)
I0130 13:34:24.744735 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8404
I0130 13:34:24.744758 32230 solver.cpp:404]     Test net output #1: loss = 0.468474 (* 1 = 0.468474 loss)
I0130 13:34:24.773869 32230 solver.cpp:228] Iteration 19200, loss = 0.411248
I0130 13:34:24.773885 32230 solver.cpp:244]     Train net output #0: accuracy = 0.871094
I0130 13:34:24.773890 32230 solver.cpp:244]     Train net output #1: loss = 0.411248 (* 1 = 0.411248 loss)
I0130 13:34:24.773893 32230 sgd_solver.cpp:106] Iteration 19200, lr = 0.05
I0130 13:34:49.723353 32230 solver.cpp:337] Iteration 19400, Testing net (#0)
I0130 13:34:50.862242 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8509
I0130 13:34:50.862264 32230 solver.cpp:404]     Test net output #1: loss = 0.440216 (* 1 = 0.440216 loss)
I0130 13:34:50.890938 32230 solver.cpp:228] Iteration 19400, loss = 0.396921
I0130 13:34:50.890954 32230 solver.cpp:244]     Train net output #0: accuracy = 0.875
I0130 13:34:50.890959 32230 solver.cpp:244]     Train net output #1: loss = 0.396921 (* 1 = 0.396921 loss)
I0130 13:34:50.890962 32230 sgd_solver.cpp:106] Iteration 19400, lr = 0.05
I0130 13:35:15.967257 32230 solver.cpp:337] Iteration 19600, Testing net (#0)
I0130 13:35:17.100536 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8625
I0130 13:35:17.100558 32230 solver.cpp:404]     Test net output #1: loss = 0.408169 (* 1 = 0.408169 loss)
I0130 13:35:17.129437 32230 solver.cpp:228] Iteration 19600, loss = 0.331595
I0130 13:35:17.129451 32230 solver.cpp:244]     Train net output #0: accuracy = 0.875
I0130 13:35:17.129456 32230 solver.cpp:244]     Train net output #1: loss = 0.331595 (* 1 = 0.331595 loss)
I0130 13:35:17.129461 32230 sgd_solver.cpp:106] Iteration 19600, lr = 0.05
I0130 13:35:41.898175 32230 solver.cpp:337] Iteration 19800, Testing net (#0)
I0130 13:35:43.020774 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8492
I0130 13:35:43.020797 32230 solver.cpp:404]     Test net output #1: loss = 0.442462 (* 1 = 0.442462 loss)
I0130 13:35:43.049649 32230 solver.cpp:228] Iteration 19800, loss = 0.339728
I0130 13:35:43.049666 32230 solver.cpp:244]     Train net output #0: accuracy = 0.882812
I0130 13:35:43.049671 32230 solver.cpp:244]     Train net output #1: loss = 0.339728 (* 1 = 0.339728 loss)
I0130 13:35:43.049676 32230 sgd_solver.cpp:106] Iteration 19800, lr = 0.05
I0130 13:36:07.654911 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_20000.caffemodel
I0130 13:36:07.790199 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_20000.solverstate
I0130 13:36:07.819525 32230 solver.cpp:337] Iteration 20000, Testing net (#0)
I0130 13:36:08.842711 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8454
I0130 13:36:08.842731 32230 solver.cpp:404]     Test net output #1: loss = 0.454773 (* 1 = 0.454773 loss)
I0130 13:36:08.871348 32230 solver.cpp:228] Iteration 20000, loss = 0.375575
I0130 13:36:08.871366 32230 solver.cpp:244]     Train net output #0: accuracy = 0.851562
I0130 13:36:08.871371 32230 solver.cpp:244]     Train net output #1: loss = 0.375575 (* 1 = 0.375575 loss)
I0130 13:36:08.871376 32230 sgd_solver.cpp:106] Iteration 20000, lr = 0.05
I0130 13:36:33.367517 32230 solver.cpp:337] Iteration 20200, Testing net (#0)
I0130 13:36:34.489450 32230 solver.cpp:404]     Test net output #0: accuracy = 0.855
I0130 13:36:34.489472 32230 solver.cpp:404]     Test net output #1: loss = 0.438098 (* 1 = 0.438098 loss)
I0130 13:36:34.518414 32230 solver.cpp:228] Iteration 20200, loss = 0.399306
I0130 13:36:34.518429 32230 solver.cpp:244]     Train net output #0: accuracy = 0.871094
I0130 13:36:34.518435 32230 solver.cpp:244]     Train net output #1: loss = 0.399306 (* 1 = 0.399306 loss)
I0130 13:36:34.518438 32230 sgd_solver.cpp:106] Iteration 20200, lr = 0.05
I0130 13:36:59.151649 32230 solver.cpp:337] Iteration 20400, Testing net (#0)
I0130 13:37:00.275670 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8522
I0130 13:37:00.275689 32230 solver.cpp:404]     Test net output #1: loss = 0.439398 (* 1 = 0.439398 loss)
I0130 13:37:00.304563 32230 solver.cpp:228] Iteration 20400, loss = 0.387996
I0130 13:37:00.304579 32230 solver.cpp:244]     Train net output #0: accuracy = 0.855469
I0130 13:37:00.304584 32230 solver.cpp:244]     Train net output #1: loss = 0.387996 (* 1 = 0.387996 loss)
I0130 13:37:00.304589 32230 sgd_solver.cpp:106] Iteration 20400, lr = 0.05
I0130 13:37:25.538988 32230 solver.cpp:337] Iteration 20600, Testing net (#0)
I0130 13:37:26.675750 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8459
I0130 13:37:26.675772 32230 solver.cpp:404]     Test net output #1: loss = 0.452939 (* 1 = 0.452939 loss)
I0130 13:37:26.706029 32230 solver.cpp:228] Iteration 20600, loss = 0.290684
I0130 13:37:26.706045 32230 solver.cpp:244]     Train net output #0: accuracy = 0.90625
I0130 13:37:26.706053 32230 solver.cpp:244]     Train net output #1: loss = 0.290684 (* 1 = 0.290684 loss)
I0130 13:37:26.706055 32230 sgd_solver.cpp:106] Iteration 20600, lr = 0.05
I0130 13:37:51.442361 32230 solver.cpp:337] Iteration 20800, Testing net (#0)
I0130 13:37:52.603286 32230 solver.cpp:404]     Test net output #0: accuracy = 0.847
I0130 13:37:52.603307 32230 solver.cpp:404]     Test net output #1: loss = 0.443194 (* 1 = 0.443194 loss)
I0130 13:37:52.632154 32230 solver.cpp:228] Iteration 20800, loss = 0.328557
I0130 13:37:52.632171 32230 solver.cpp:244]     Train net output #0: accuracy = 0.878906
I0130 13:37:52.632177 32230 solver.cpp:244]     Train net output #1: loss = 0.328557 (* 1 = 0.328557 loss)
I0130 13:37:52.632181 32230 sgd_solver.cpp:106] Iteration 20800, lr = 0.05
I0130 13:38:17.579519 32230 solver.cpp:337] Iteration 21000, Testing net (#0)
I0130 13:38:18.716418 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8609
I0130 13:38:18.716454 32230 solver.cpp:404]     Test net output #1: loss = 0.411659 (* 1 = 0.411659 loss)
I0130 13:38:18.745507 32230 solver.cpp:228] Iteration 21000, loss = 0.275624
I0130 13:38:18.745523 32230 solver.cpp:244]     Train net output #0: accuracy = 0.898438
I0130 13:38:18.745528 32230 solver.cpp:244]     Train net output #1: loss = 0.275624 (* 1 = 0.275624 loss)
I0130 13:38:18.745532 32230 sgd_solver.cpp:106] Iteration 21000, lr = 0.05
I0130 13:38:43.650648 32230 solver.cpp:337] Iteration 21200, Testing net (#0)
I0130 13:38:44.786890 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8385
I0130 13:38:44.786929 32230 solver.cpp:404]     Test net output #1: loss = 0.487179 (* 1 = 0.487179 loss)
I0130 13:38:44.815970 32230 solver.cpp:228] Iteration 21200, loss = 0.34942
I0130 13:38:44.815987 32230 solver.cpp:244]     Train net output #0: accuracy = 0.882812
I0130 13:38:44.815992 32230 solver.cpp:244]     Train net output #1: loss = 0.34942 (* 1 = 0.34942 loss)
I0130 13:38:44.815996 32230 sgd_solver.cpp:106] Iteration 21200, lr = 0.05
I0130 13:39:09.588382 32230 solver.cpp:337] Iteration 21400, Testing net (#0)
I0130 13:39:10.711104 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8531
I0130 13:39:10.711125 32230 solver.cpp:404]     Test net output #1: loss = 0.430417 (* 1 = 0.430417 loss)
I0130 13:39:10.740211 32230 solver.cpp:228] Iteration 21400, loss = 0.331629
I0130 13:39:10.740227 32230 solver.cpp:244]     Train net output #0: accuracy = 0.886719
I0130 13:39:10.740232 32230 solver.cpp:244]     Train net output #1: loss = 0.331629 (* 1 = 0.331629 loss)
I0130 13:39:10.740237 32230 sgd_solver.cpp:106] Iteration 21400, lr = 0.05
I0130 13:39:35.332304 32230 solver.cpp:337] Iteration 21600, Testing net (#0)
I0130 13:39:36.455879 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8516
I0130 13:39:36.455900 32230 solver.cpp:404]     Test net output #1: loss = 0.440971 (* 1 = 0.440971 loss)
I0130 13:39:36.484868 32230 solver.cpp:228] Iteration 21600, loss = 0.32194
I0130 13:39:36.484884 32230 solver.cpp:244]     Train net output #0: accuracy = 0.902344
I0130 13:39:36.484889 32230 solver.cpp:244]     Train net output #1: loss = 0.32194 (* 1 = 0.32194 loss)
I0130 13:39:36.484892 32230 sgd_solver.cpp:106] Iteration 21600, lr = 0.05
I0130 13:40:01.081626 32230 solver.cpp:337] Iteration 21800, Testing net (#0)
I0130 13:40:02.203750 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8525
I0130 13:40:02.203785 32230 solver.cpp:404]     Test net output #1: loss = 0.433452 (* 1 = 0.433452 loss)
I0130 13:40:02.232656 32230 solver.cpp:228] Iteration 21800, loss = 0.433899
I0130 13:40:02.232672 32230 solver.cpp:244]     Train net output #0: accuracy = 0.847656
I0130 13:40:02.232677 32230 solver.cpp:244]     Train net output #1: loss = 0.433899 (* 1 = 0.433899 loss)
I0130 13:40:02.232681 32230 sgd_solver.cpp:106] Iteration 21800, lr = 0.05
I0130 13:40:26.950994 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_22000.caffemodel
I0130 13:40:27.085638 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_22000.solverstate
I0130 13:40:27.114503 32230 solver.cpp:337] Iteration 22000, Testing net (#0)
I0130 13:40:28.137406 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8556
I0130 13:40:28.137426 32230 solver.cpp:404]     Test net output #1: loss = 0.459648 (* 1 = 0.459648 loss)
I0130 13:40:28.166530 32230 solver.cpp:228] Iteration 22000, loss = 0.361984
I0130 13:40:28.166546 32230 solver.cpp:244]     Train net output #0: accuracy = 0.875
I0130 13:40:28.166553 32230 solver.cpp:244]     Train net output #1: loss = 0.361984 (* 1 = 0.361984 loss)
I0130 13:40:28.166556 32230 sgd_solver.cpp:106] Iteration 22000, lr = 0.05
I0130 13:40:53.123968 32230 solver.cpp:337] Iteration 22200, Testing net (#0)
I0130 13:40:54.285749 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8634
I0130 13:40:54.285768 32230 solver.cpp:404]     Test net output #1: loss = 0.412692 (* 1 = 0.412692 loss)
I0130 13:40:54.314945 32230 solver.cpp:228] Iteration 22200, loss = 0.38206
I0130 13:40:54.314960 32230 solver.cpp:244]     Train net output #0: accuracy = 0.871094
I0130 13:40:54.314966 32230 solver.cpp:244]     Train net output #1: loss = 0.38206 (* 1 = 0.38206 loss)
I0130 13:40:54.314970 32230 sgd_solver.cpp:106] Iteration 22200, lr = 0.05
I0130 13:41:19.626623 32230 solver.cpp:337] Iteration 22400, Testing net (#0)
I0130 13:41:20.771729 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8414
I0130 13:41:20.771769 32230 solver.cpp:404]     Test net output #1: loss = 0.473846 (* 1 = 0.473846 loss)
I0130 13:41:20.800467 32230 solver.cpp:228] Iteration 22400, loss = 0.356831
I0130 13:41:20.800482 32230 solver.cpp:244]     Train net output #0: accuracy = 0.878906
I0130 13:41:20.800487 32230 solver.cpp:244]     Train net output #1: loss = 0.356831 (* 1 = 0.356831 loss)
I0130 13:41:20.800490 32230 sgd_solver.cpp:106] Iteration 22400, lr = 0.05
I0130 13:41:45.541452 32230 solver.cpp:337] Iteration 22600, Testing net (#0)
I0130 13:41:46.678869 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8485
I0130 13:41:46.678890 32230 solver.cpp:404]     Test net output #1: loss = 0.438094 (* 1 = 0.438094 loss)
I0130 13:41:46.709239 32230 solver.cpp:228] Iteration 22600, loss = 0.319493
I0130 13:41:46.709256 32230 solver.cpp:244]     Train net output #0: accuracy = 0.890625
I0130 13:41:46.709261 32230 solver.cpp:244]     Train net output #1: loss = 0.319493 (* 1 = 0.319493 loss)
I0130 13:41:46.709266 32230 sgd_solver.cpp:106] Iteration 22600, lr = 0.05
I0130 13:42:11.971359 32230 solver.cpp:337] Iteration 22800, Testing net (#0)
I0130 13:42:13.095167 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8537
I0130 13:42:13.095204 32230 solver.cpp:404]     Test net output #1: loss = 0.426192 (* 1 = 0.426192 loss)
I0130 13:42:13.124469 32230 solver.cpp:228] Iteration 22800, loss = 0.370173
I0130 13:42:13.124485 32230 solver.cpp:244]     Train net output #0: accuracy = 0.878906
I0130 13:42:13.124491 32230 solver.cpp:244]     Train net output #1: loss = 0.370173 (* 1 = 0.370173 loss)
I0130 13:42:13.124495 32230 sgd_solver.cpp:106] Iteration 22800, lr = 0.05
I0130 13:42:38.121585 32230 solver.cpp:337] Iteration 23000, Testing net (#0)
I0130 13:42:39.267081 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8474
I0130 13:42:39.267104 32230 solver.cpp:404]     Test net output #1: loss = 0.441305 (* 1 = 0.441305 loss)
I0130 13:42:39.296253 32230 solver.cpp:228] Iteration 23000, loss = 0.327891
I0130 13:42:39.296268 32230 solver.cpp:244]     Train net output #0: accuracy = 0.878906
I0130 13:42:39.296273 32230 solver.cpp:244]     Train net output #1: loss = 0.327891 (* 1 = 0.327891 loss)
I0130 13:42:39.296277 32230 sgd_solver.cpp:106] Iteration 23000, lr = 0.05
I0130 13:43:04.066354 32230 solver.cpp:337] Iteration 23200, Testing net (#0)
I0130 13:43:05.192248 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8505
I0130 13:43:05.192270 32230 solver.cpp:404]     Test net output #1: loss = 0.440336 (* 1 = 0.440336 loss)
I0130 13:43:05.221002 32230 solver.cpp:228] Iteration 23200, loss = 0.324843
I0130 13:43:05.221015 32230 solver.cpp:244]     Train net output #0: accuracy = 0.910156
I0130 13:43:05.221020 32230 solver.cpp:244]     Train net output #1: loss = 0.324843 (* 1 = 0.324843 loss)
I0130 13:43:05.221025 32230 sgd_solver.cpp:106] Iteration 23200, lr = 0.05
I0130 13:43:29.927054 32230 solver.cpp:337] Iteration 23400, Testing net (#0)
I0130 13:43:31.051627 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8481
I0130 13:43:31.051651 32230 solver.cpp:404]     Test net output #1: loss = 0.450393 (* 1 = 0.450393 loss)
I0130 13:43:31.080754 32230 solver.cpp:228] Iteration 23400, loss = 0.396468
I0130 13:43:31.080770 32230 solver.cpp:244]     Train net output #0: accuracy = 0.851562
I0130 13:43:31.080775 32230 solver.cpp:244]     Train net output #1: loss = 0.396468 (* 1 = 0.396468 loss)
I0130 13:43:31.080778 32230 sgd_solver.cpp:106] Iteration 23400, lr = 0.05
I0130 13:43:56.149557 32230 solver.cpp:337] Iteration 23600, Testing net (#0)
I0130 13:43:57.274351 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8552
I0130 13:43:57.274372 32230 solver.cpp:404]     Test net output #1: loss = 0.429071 (* 1 = 0.429071 loss)
I0130 13:43:57.303318 32230 solver.cpp:228] Iteration 23600, loss = 0.268993
I0130 13:43:57.303331 32230 solver.cpp:244]     Train net output #0: accuracy = 0.929688
I0130 13:43:57.303338 32230 solver.cpp:244]     Train net output #1: loss = 0.268993 (* 1 = 0.268993 loss)
I0130 13:43:57.303341 32230 sgd_solver.cpp:106] Iteration 23600, lr = 0.05
I0130 13:44:22.871776 32230 solver.cpp:337] Iteration 23800, Testing net (#0)
I0130 13:44:24.065460 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8585
I0130 13:44:24.065484 32230 solver.cpp:404]     Test net output #1: loss = 0.417609 (* 1 = 0.417609 loss)
I0130 13:44:24.094261 32230 solver.cpp:228] Iteration 23800, loss = 0.425105
I0130 13:44:24.094277 32230 solver.cpp:244]     Train net output #0: accuracy = 0.84375
I0130 13:44:24.094282 32230 solver.cpp:244]     Train net output #1: loss = 0.425105 (* 1 = 0.425105 loss)
I0130 13:44:24.094286 32230 sgd_solver.cpp:106] Iteration 23800, lr = 0.05
I0130 13:44:49.477069 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_24000.caffemodel
I0130 13:44:49.610906 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_24000.solverstate
I0130 13:44:49.639127 32230 solver.cpp:337] Iteration 24000, Testing net (#0)
I0130 13:44:50.659376 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8504
I0130 13:44:50.659399 32230 solver.cpp:404]     Test net output #1: loss = 0.443283 (* 1 = 0.443283 loss)
I0130 13:44:50.688210 32230 solver.cpp:228] Iteration 24000, loss = 0.364351
I0130 13:44:50.688227 32230 solver.cpp:244]     Train net output #0: accuracy = 0.878906
I0130 13:44:50.688233 32230 solver.cpp:244]     Train net output #1: loss = 0.364351 (* 1 = 0.364351 loss)
I0130 13:44:50.688236 32230 sgd_solver.cpp:106] Iteration 24000, lr = 0.05
I0130 13:45:15.245019 32230 solver.cpp:337] Iteration 24200, Testing net (#0)
I0130 13:45:16.367075 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8543
I0130 13:45:16.367095 32230 solver.cpp:404]     Test net output #1: loss = 0.424281 (* 1 = 0.424281 loss)
I0130 13:45:16.396088 32230 solver.cpp:228] Iteration 24200, loss = 0.303591
I0130 13:45:16.396106 32230 solver.cpp:244]     Train net output #0: accuracy = 0.894531
I0130 13:45:16.396112 32230 solver.cpp:244]     Train net output #1: loss = 0.303591 (* 1 = 0.303591 loss)
I0130 13:45:16.396116 32230 sgd_solver.cpp:106] Iteration 24200, lr = 0.05
I0130 13:45:41.055304 32230 solver.cpp:337] Iteration 24400, Testing net (#0)
I0130 13:45:42.178232 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8518
I0130 13:45:42.178254 32230 solver.cpp:404]     Test net output #1: loss = 0.43054 (* 1 = 0.43054 loss)
I0130 13:45:42.207031 32230 solver.cpp:228] Iteration 24400, loss = 0.377988
I0130 13:45:42.207048 32230 solver.cpp:244]     Train net output #0: accuracy = 0.867188
I0130 13:45:42.207054 32230 solver.cpp:244]     Train net output #1: loss = 0.377988 (* 1 = 0.377988 loss)
I0130 13:45:42.207058 32230 sgd_solver.cpp:106] Iteration 24400, lr = 0.05
I0130 13:46:06.812425 32230 solver.cpp:337] Iteration 24600, Testing net (#0)
I0130 13:46:07.940861 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8642
I0130 13:46:07.940886 32230 solver.cpp:404]     Test net output #1: loss = 0.416467 (* 1 = 0.416467 loss)
I0130 13:46:07.969904 32230 solver.cpp:228] Iteration 24600, loss = 0.321171
I0130 13:46:07.969923 32230 solver.cpp:244]     Train net output #0: accuracy = 0.890625
I0130 13:46:07.969930 32230 solver.cpp:244]     Train net output #1: loss = 0.321171 (* 1 = 0.321171 loss)
I0130 13:46:07.969934 32230 sgd_solver.cpp:106] Iteration 24600, lr = 0.05
I0130 13:46:32.583076 32230 solver.cpp:337] Iteration 24800, Testing net (#0)
I0130 13:46:33.705176 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8609
I0130 13:46:33.705199 32230 solver.cpp:404]     Test net output #1: loss = 0.416054 (* 1 = 0.416054 loss)
I0130 13:46:33.733975 32230 solver.cpp:228] Iteration 24800, loss = 0.265756
I0130 13:46:33.733990 32230 solver.cpp:244]     Train net output #0: accuracy = 0.902344
I0130 13:46:33.733996 32230 solver.cpp:244]     Train net output #1: loss = 0.265756 (* 1 = 0.265756 loss)
I0130 13:46:33.734000 32230 sgd_solver.cpp:106] Iteration 24800, lr = 0.05
I0130 13:46:58.388134 32230 solver.cpp:337] Iteration 25000, Testing net (#0)
I0130 13:46:59.511541 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8612
I0130 13:46:59.511565 32230 solver.cpp:404]     Test net output #1: loss = 0.412393 (* 1 = 0.412393 loss)
I0130 13:46:59.540632 32230 solver.cpp:228] Iteration 25000, loss = 0.216334
I0130 13:46:59.540649 32230 solver.cpp:244]     Train net output #0: accuracy = 0.90625
I0130 13:46:59.540657 32230 solver.cpp:244]     Train net output #1: loss = 0.216334 (* 1 = 0.216334 loss)
I0130 13:46:59.540662 32230 sgd_solver.cpp:106] Iteration 25000, lr = 0.05
I0130 13:47:24.115157 32230 solver.cpp:337] Iteration 25200, Testing net (#0)
I0130 13:47:25.237164 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8567
I0130 13:47:25.237186 32230 solver.cpp:404]     Test net output #1: loss = 0.429382 (* 1 = 0.429382 loss)
I0130 13:47:25.266293 32230 solver.cpp:228] Iteration 25200, loss = 0.258499
I0130 13:47:25.266309 32230 solver.cpp:244]     Train net output #0: accuracy = 0.910156
I0130 13:47:25.266314 32230 solver.cpp:244]     Train net output #1: loss = 0.258499 (* 1 = 0.258499 loss)
I0130 13:47:25.266319 32230 sgd_solver.cpp:106] Iteration 25200, lr = 0.05
I0130 13:47:49.881973 32230 solver.cpp:337] Iteration 25400, Testing net (#0)
I0130 13:47:51.004461 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8522
I0130 13:47:51.004482 32230 solver.cpp:404]     Test net output #1: loss = 0.453915 (* 1 = 0.453915 loss)
I0130 13:47:51.033504 32230 solver.cpp:228] Iteration 25400, loss = 0.258333
I0130 13:47:51.033520 32230 solver.cpp:244]     Train net output #0: accuracy = 0.90625
I0130 13:47:51.033525 32230 solver.cpp:244]     Train net output #1: loss = 0.258333 (* 1 = 0.258333 loss)
I0130 13:47:51.033529 32230 sgd_solver.cpp:106] Iteration 25400, lr = 0.05
I0130 13:48:15.653738 32230 solver.cpp:337] Iteration 25600, Testing net (#0)
I0130 13:48:16.775629 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8683
I0130 13:48:16.775652 32230 solver.cpp:404]     Test net output #1: loss = 0.408247 (* 1 = 0.408247 loss)
I0130 13:48:16.804540 32230 solver.cpp:228] Iteration 25600, loss = 0.292412
I0130 13:48:16.804555 32230 solver.cpp:244]     Train net output #0: accuracy = 0.902344
I0130 13:48:16.804560 32230 solver.cpp:244]     Train net output #1: loss = 0.292412 (* 1 = 0.292412 loss)
I0130 13:48:16.804564 32230 sgd_solver.cpp:106] Iteration 25600, lr = 0.05
I0130 13:48:41.396706 32230 solver.cpp:337] Iteration 25800, Testing net (#0)
I0130 13:48:42.518833 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8569
I0130 13:48:42.518869 32230 solver.cpp:404]     Test net output #1: loss = 0.445911 (* 1 = 0.445911 loss)
I0130 13:48:42.547804 32230 solver.cpp:228] Iteration 25800, loss = 0.407816
I0130 13:48:42.547819 32230 solver.cpp:244]     Train net output #0: accuracy = 0.867188
I0130 13:48:42.547824 32230 solver.cpp:244]     Train net output #1: loss = 0.407816 (* 1 = 0.407816 loss)
I0130 13:48:42.547828 32230 sgd_solver.cpp:106] Iteration 25800, lr = 0.05
I0130 13:49:07.115669 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_26000.caffemodel
I0130 13:49:07.249816 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_26000.solverstate
I0130 13:49:07.277993 32230 solver.cpp:337] Iteration 26000, Testing net (#0)
I0130 13:49:08.308270 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8578
I0130 13:49:08.308312 32230 solver.cpp:404]     Test net output #1: loss = 0.435368 (* 1 = 0.435368 loss)
I0130 13:49:08.337618 32230 solver.cpp:228] Iteration 26000, loss = 0.424765
I0130 13:49:08.337646 32230 solver.cpp:244]     Train net output #0: accuracy = 0.839844
I0130 13:49:08.337653 32230 solver.cpp:244]     Train net output #1: loss = 0.424765 (* 1 = 0.424765 loss)
I0130 13:49:08.337659 32230 sgd_solver.cpp:106] Iteration 26000, lr = 0.05
I0130 13:49:32.929883 32230 solver.cpp:337] Iteration 26200, Testing net (#0)
I0130 13:49:34.052244 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8615
I0130 13:49:34.052268 32230 solver.cpp:404]     Test net output #1: loss = 0.427815 (* 1 = 0.427815 loss)
I0130 13:49:34.081295 32230 solver.cpp:228] Iteration 26200, loss = 0.278845
I0130 13:49:34.081311 32230 solver.cpp:244]     Train net output #0: accuracy = 0.914062
I0130 13:49:34.081316 32230 solver.cpp:244]     Train net output #1: loss = 0.278845 (* 1 = 0.278845 loss)
I0130 13:49:34.081321 32230 sgd_solver.cpp:106] Iteration 26200, lr = 0.05
I0130 13:49:58.677760 32230 solver.cpp:337] Iteration 26400, Testing net (#0)
I0130 13:49:59.800334 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8605
I0130 13:49:59.800355 32230 solver.cpp:404]     Test net output #1: loss = 0.424513 (* 1 = 0.424513 loss)
I0130 13:49:59.829102 32230 solver.cpp:228] Iteration 26400, loss = 0.301584
I0130 13:49:59.829118 32230 solver.cpp:244]     Train net output #0: accuracy = 0.882812
I0130 13:49:59.829123 32230 solver.cpp:244]     Train net output #1: loss = 0.301584 (* 1 = 0.301584 loss)
I0130 13:49:59.829128 32230 sgd_solver.cpp:106] Iteration 26400, lr = 0.05
I0130 13:50:24.935217 32230 solver.cpp:337] Iteration 26600, Testing net (#0)
I0130 13:50:26.170403 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8459
I0130 13:50:26.170423 32230 solver.cpp:404]     Test net output #1: loss = 0.467816 (* 1 = 0.467816 loss)
I0130 13:50:26.199141 32230 solver.cpp:228] Iteration 26600, loss = 0.404128
I0130 13:50:26.199156 32230 solver.cpp:244]     Train net output #0: accuracy = 0.835938
I0130 13:50:26.199162 32230 solver.cpp:244]     Train net output #1: loss = 0.404128 (* 1 = 0.404128 loss)
I0130 13:50:26.199167 32230 sgd_solver.cpp:106] Iteration 26600, lr = 0.05
I0130 13:50:51.010247 32230 solver.cpp:337] Iteration 26800, Testing net (#0)
I0130 13:50:52.211733 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8583
I0130 13:50:52.211757 32230 solver.cpp:404]     Test net output #1: loss = 0.42266 (* 1 = 0.42266 loss)
I0130 13:50:52.241312 32230 solver.cpp:228] Iteration 26800, loss = 0.324339
I0130 13:50:52.241328 32230 solver.cpp:244]     Train net output #0: accuracy = 0.878906
I0130 13:50:52.241333 32230 solver.cpp:244]     Train net output #1: loss = 0.324339 (* 1 = 0.324339 loss)
I0130 13:50:52.241338 32230 sgd_solver.cpp:106] Iteration 26800, lr = 0.05
I0130 13:51:17.214421 32230 solver.cpp:337] Iteration 27000, Testing net (#0)
I0130 13:51:18.348552 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8601
I0130 13:51:18.348589 32230 solver.cpp:404]     Test net output #1: loss = 0.42463 (* 1 = 0.42463 loss)
I0130 13:51:18.378170 32230 solver.cpp:228] Iteration 27000, loss = 0.346946
I0130 13:51:18.378186 32230 solver.cpp:244]     Train net output #0: accuracy = 0.882812
I0130 13:51:18.378191 32230 solver.cpp:244]     Train net output #1: loss = 0.346946 (* 1 = 0.346946 loss)
I0130 13:51:18.378196 32230 sgd_solver.cpp:106] Iteration 27000, lr = 0.05
I0130 13:51:43.277251 32230 solver.cpp:337] Iteration 27200, Testing net (#0)
I0130 13:51:44.400619 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8559
I0130 13:51:44.400640 32230 solver.cpp:404]     Test net output #1: loss = 0.448849 (* 1 = 0.448849 loss)
I0130 13:51:44.429553 32230 solver.cpp:228] Iteration 27200, loss = 0.378514
I0130 13:51:44.429570 32230 solver.cpp:244]     Train net output #0: accuracy = 0.875
I0130 13:51:44.429575 32230 solver.cpp:244]     Train net output #1: loss = 0.378514 (* 1 = 0.378514 loss)
I0130 13:51:44.429579 32230 sgd_solver.cpp:106] Iteration 27200, lr = 0.05
I0130 13:52:09.449414 32230 solver.cpp:337] Iteration 27400, Testing net (#0)
I0130 13:52:10.595290 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8622
I0130 13:52:10.595310 32230 solver.cpp:404]     Test net output #1: loss = 0.405422 (* 1 = 0.405422 loss)
I0130 13:52:10.624270 32230 solver.cpp:228] Iteration 27400, loss = 0.327845
I0130 13:52:10.624289 32230 solver.cpp:244]     Train net output #0: accuracy = 0.890625
I0130 13:52:10.624294 32230 solver.cpp:244]     Train net output #1: loss = 0.327845 (* 1 = 0.327845 loss)
I0130 13:52:10.624315 32230 sgd_solver.cpp:106] Iteration 27400, lr = 0.05
I0130 13:52:35.529287 32230 solver.cpp:337] Iteration 27600, Testing net (#0)
I0130 13:52:36.672412 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8607
I0130 13:52:36.672435 32230 solver.cpp:404]     Test net output #1: loss = 0.406192 (* 1 = 0.406192 loss)
I0130 13:52:36.701721 32230 solver.cpp:228] Iteration 27600, loss = 0.29573
I0130 13:52:36.701750 32230 solver.cpp:244]     Train net output #0: accuracy = 0.894531
I0130 13:52:36.701759 32230 solver.cpp:244]     Train net output #1: loss = 0.29573 (* 1 = 0.29573 loss)
I0130 13:52:36.701766 32230 sgd_solver.cpp:106] Iteration 27600, lr = 0.05
I0130 13:53:01.739140 32230 solver.cpp:337] Iteration 27800, Testing net (#0)
I0130 13:53:02.857026 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8588
I0130 13:53:02.857049 32230 solver.cpp:404]     Test net output #1: loss = 0.416885 (* 1 = 0.416885 loss)
I0130 13:53:02.885496 32230 solver.cpp:228] Iteration 27800, loss = 0.31593
I0130 13:53:02.885512 32230 solver.cpp:244]     Train net output #0: accuracy = 0.890625
I0130 13:53:02.885517 32230 solver.cpp:244]     Train net output #1: loss = 0.31593 (* 1 = 0.31593 loss)
I0130 13:53:02.885521 32230 sgd_solver.cpp:106] Iteration 27800, lr = 0.05
I0130 13:53:27.491333 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_28000.caffemodel
I0130 13:53:27.625879 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_28000.solverstate
I0130 13:53:27.654052 32230 solver.cpp:337] Iteration 28000, Testing net (#0)
I0130 13:53:28.676548 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8614
I0130 13:53:28.676570 32230 solver.cpp:404]     Test net output #1: loss = 0.419012 (* 1 = 0.419012 loss)
I0130 13:53:28.705477 32230 solver.cpp:228] Iteration 28000, loss = 0.287207
I0130 13:53:28.705495 32230 solver.cpp:244]     Train net output #0: accuracy = 0.902344
I0130 13:53:28.705502 32230 solver.cpp:244]     Train net output #1: loss = 0.287207 (* 1 = 0.287207 loss)
I0130 13:53:28.705505 32230 sgd_solver.cpp:106] Iteration 28000, lr = 0.05
I0130 13:53:53.352229 32230 solver.cpp:337] Iteration 28200, Testing net (#0)
I0130 13:53:54.476379 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8491
I0130 13:53:54.476402 32230 solver.cpp:404]     Test net output #1: loss = 0.45654 (* 1 = 0.45654 loss)
I0130 13:53:54.503778 32230 solver.cpp:228] Iteration 28200, loss = 0.330023
I0130 13:53:54.503796 32230 solver.cpp:244]     Train net output #0: accuracy = 0.882812
I0130 13:53:54.503803 32230 solver.cpp:244]     Train net output #1: loss = 0.330023 (* 1 = 0.330023 loss)
I0130 13:53:54.503806 32230 sgd_solver.cpp:106] Iteration 28200, lr = 0.05
I0130 13:54:19.222440 32230 solver.cpp:337] Iteration 28400, Testing net (#0)
I0130 13:54:20.350908 32230 solver.cpp:404]     Test net output #0: accuracy = 0.865
I0130 13:54:20.350929 32230 solver.cpp:404]     Test net output #1: loss = 0.41409 (* 1 = 0.41409 loss)
I0130 13:54:20.379614 32230 solver.cpp:228] Iteration 28400, loss = 0.348225
I0130 13:54:20.379631 32230 solver.cpp:244]     Train net output #0: accuracy = 0.875
I0130 13:54:20.379637 32230 solver.cpp:244]     Train net output #1: loss = 0.348225 (* 1 = 0.348225 loss)
I0130 13:54:20.379642 32230 sgd_solver.cpp:106] Iteration 28400, lr = 0.05
I0130 13:54:44.975989 32230 solver.cpp:337] Iteration 28600, Testing net (#0)
I0130 13:54:46.099534 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8506
I0130 13:54:46.099555 32230 solver.cpp:404]     Test net output #1: loss = 0.445657 (* 1 = 0.445657 loss)
I0130 13:54:46.128582 32230 solver.cpp:228] Iteration 28600, loss = 0.361211
I0130 13:54:46.128598 32230 solver.cpp:244]     Train net output #0: accuracy = 0.871094
I0130 13:54:46.128604 32230 solver.cpp:244]     Train net output #1: loss = 0.361211 (* 1 = 0.361211 loss)
I0130 13:54:46.128608 32230 sgd_solver.cpp:106] Iteration 28600, lr = 0.05
I0130 13:55:11.075515 32230 solver.cpp:337] Iteration 28800, Testing net (#0)
I0130 13:55:12.231744 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8579
I0130 13:55:12.231767 32230 solver.cpp:404]     Test net output #1: loss = 0.436922 (* 1 = 0.436922 loss)
I0130 13:55:12.260782 32230 solver.cpp:228] Iteration 28800, loss = 0.449242
I0130 13:55:12.260802 32230 solver.cpp:244]     Train net output #0: accuracy = 0.867188
I0130 13:55:12.260807 32230 solver.cpp:244]     Train net output #1: loss = 0.449242 (* 1 = 0.449242 loss)
I0130 13:55:12.260810 32230 sgd_solver.cpp:106] Iteration 28800, lr = 0.05
I0130 13:55:37.101936 32230 solver.cpp:337] Iteration 29000, Testing net (#0)
I0130 13:55:38.223839 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8581
I0130 13:55:38.223862 32230 solver.cpp:404]     Test net output #1: loss = 0.436058 (* 1 = 0.436058 loss)
I0130 13:55:38.252825 32230 solver.cpp:228] Iteration 29000, loss = 0.306783
I0130 13:55:38.252843 32230 solver.cpp:244]     Train net output #0: accuracy = 0.890625
I0130 13:55:38.252848 32230 solver.cpp:244]     Train net output #1: loss = 0.306783 (* 1 = 0.306783 loss)
I0130 13:55:38.252853 32230 sgd_solver.cpp:106] Iteration 29000, lr = 0.05
I0130 13:56:02.923101 32230 solver.cpp:337] Iteration 29200, Testing net (#0)
I0130 13:56:04.046046 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8637
I0130 13:56:04.046066 32230 solver.cpp:404]     Test net output #1: loss = 0.41554 (* 1 = 0.41554 loss)
I0130 13:56:04.075065 32230 solver.cpp:228] Iteration 29200, loss = 0.301913
I0130 13:56:04.075083 32230 solver.cpp:244]     Train net output #0: accuracy = 0.871094
I0130 13:56:04.075088 32230 solver.cpp:244]     Train net output #1: loss = 0.301913 (* 1 = 0.301913 loss)
I0130 13:56:04.075093 32230 sgd_solver.cpp:106] Iteration 29200, lr = 0.05
I0130 13:56:28.718323 32230 solver.cpp:337] Iteration 29400, Testing net (#0)
I0130 13:56:29.840016 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8644
I0130 13:56:29.840036 32230 solver.cpp:404]     Test net output #1: loss = 0.426302 (* 1 = 0.426302 loss)
I0130 13:56:29.868937 32230 solver.cpp:228] Iteration 29400, loss = 0.242373
I0130 13:56:29.868954 32230 solver.cpp:244]     Train net output #0: accuracy = 0.894531
I0130 13:56:29.868960 32230 solver.cpp:244]     Train net output #1: loss = 0.242373 (* 1 = 0.242373 loss)
I0130 13:56:29.868964 32230 sgd_solver.cpp:106] Iteration 29400, lr = 0.05
I0130 13:56:54.853746 32230 solver.cpp:337] Iteration 29600, Testing net (#0)
I0130 13:56:56.014363 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8529
I0130 13:56:56.014384 32230 solver.cpp:404]     Test net output #1: loss = 0.450457 (* 1 = 0.450457 loss)
I0130 13:56:56.043977 32230 solver.cpp:228] Iteration 29600, loss = 0.324548
I0130 13:56:56.043995 32230 solver.cpp:244]     Train net output #0: accuracy = 0.890625
I0130 13:56:56.044001 32230 solver.cpp:244]     Train net output #1: loss = 0.324548 (* 1 = 0.324548 loss)
I0130 13:56:56.044006 32230 sgd_solver.cpp:106] Iteration 29600, lr = 0.05
I0130 13:57:20.754657 32230 solver.cpp:337] Iteration 29800, Testing net (#0)
I0130 13:57:21.903332 32230 solver.cpp:404]     Test net output #0: accuracy = 0.858
I0130 13:57:21.903355 32230 solver.cpp:404]     Test net output #1: loss = 0.430256 (* 1 = 0.430256 loss)
I0130 13:57:21.932337 32230 solver.cpp:228] Iteration 29800, loss = 0.286515
I0130 13:57:21.932353 32230 solver.cpp:244]     Train net output #0: accuracy = 0.914062
I0130 13:57:21.932359 32230 solver.cpp:244]     Train net output #1: loss = 0.286515 (* 1 = 0.286515 loss)
I0130 13:57:21.932363 32230 sgd_solver.cpp:106] Iteration 29800, lr = 0.05
I0130 13:57:46.781643 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_30000.caffemodel
I0130 13:57:46.918809 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_30000.solverstate
I0130 13:57:46.946337 32230 solver.cpp:337] Iteration 30000, Testing net (#0)
I0130 13:57:47.998759 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8572
I0130 13:57:47.998783 32230 solver.cpp:404]     Test net output #1: loss = 0.430424 (* 1 = 0.430424 loss)
I0130 13:57:48.027851 32230 solver.cpp:228] Iteration 30000, loss = 0.282028
I0130 13:57:48.027869 32230 solver.cpp:244]     Train net output #0: accuracy = 0.914062
I0130 13:57:48.027875 32230 solver.cpp:244]     Train net output #1: loss = 0.282028 (* 1 = 0.282028 loss)
I0130 13:57:48.027879 32230 sgd_solver.cpp:106] Iteration 30000, lr = 0.05
I0130 13:58:12.708542 32230 solver.cpp:337] Iteration 30200, Testing net (#0)
I0130 13:58:13.840173 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8717
I0130 13:58:13.840193 32230 solver.cpp:404]     Test net output #1: loss = 0.398656 (* 1 = 0.398656 loss)
I0130 13:58:13.869166 32230 solver.cpp:228] Iteration 30200, loss = 0.21318
I0130 13:58:13.869182 32230 solver.cpp:244]     Train net output #0: accuracy = 0.910156
I0130 13:58:13.869189 32230 solver.cpp:244]     Train net output #1: loss = 0.21318 (* 1 = 0.21318 loss)
I0130 13:58:13.869192 32230 sgd_solver.cpp:106] Iteration 30200, lr = 0.05
I0130 13:58:38.904997 32230 solver.cpp:337] Iteration 30400, Testing net (#0)
I0130 13:58:40.028406 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8562
I0130 13:58:40.028429 32230 solver.cpp:404]     Test net output #1: loss = 0.449634 (* 1 = 0.449634 loss)
I0130 13:58:40.057302 32230 solver.cpp:228] Iteration 30400, loss = 0.349878
I0130 13:58:40.057320 32230 solver.cpp:244]     Train net output #0: accuracy = 0.867188
I0130 13:58:40.057327 32230 solver.cpp:244]     Train net output #1: loss = 0.349878 (* 1 = 0.349878 loss)
I0130 13:58:40.057330 32230 sgd_solver.cpp:106] Iteration 30400, lr = 0.05
I0130 13:59:04.922163 32230 solver.cpp:337] Iteration 30600, Testing net (#0)
I0130 13:59:06.042826 32230 solver.cpp:404]     Test net output #0: accuracy = 0.853
I0130 13:59:06.042848 32230 solver.cpp:404]     Test net output #1: loss = 0.438001 (* 1 = 0.438001 loss)
I0130 13:59:06.071840 32230 solver.cpp:228] Iteration 30600, loss = 0.360702
I0130 13:59:06.071856 32230 solver.cpp:244]     Train net output #0: accuracy = 0.890625
I0130 13:59:06.071861 32230 solver.cpp:244]     Train net output #1: loss = 0.360702 (* 1 = 0.360702 loss)
I0130 13:59:06.071866 32230 sgd_solver.cpp:106] Iteration 30600, lr = 0.05
I0130 13:59:30.722476 32230 solver.cpp:337] Iteration 30800, Testing net (#0)
I0130 13:59:31.845526 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8459
I0130 13:59:31.845551 32230 solver.cpp:404]     Test net output #1: loss = 0.444579 (* 1 = 0.444579 loss)
I0130 13:59:31.874476 32230 solver.cpp:228] Iteration 30800, loss = 0.359833
I0130 13:59:31.874490 32230 solver.cpp:244]     Train net output #0: accuracy = 0.863281
I0130 13:59:31.874495 32230 solver.cpp:244]     Train net output #1: loss = 0.359833 (* 1 = 0.359833 loss)
I0130 13:59:31.874500 32230 sgd_solver.cpp:106] Iteration 30800, lr = 0.05
I0130 13:59:56.756916 32230 solver.cpp:337] Iteration 31000, Testing net (#0)
I0130 13:59:57.878087 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8612
I0130 13:59:57.878108 32230 solver.cpp:404]     Test net output #1: loss = 0.410789 (* 1 = 0.410789 loss)
I0130 13:59:57.907202 32230 solver.cpp:228] Iteration 31000, loss = 0.269809
I0130 13:59:57.907215 32230 solver.cpp:244]     Train net output #0: accuracy = 0.917969
I0130 13:59:57.907220 32230 solver.cpp:244]     Train net output #1: loss = 0.269809 (* 1 = 0.269809 loss)
I0130 13:59:57.907225 32230 sgd_solver.cpp:106] Iteration 31000, lr = 0.05
I0130 14:00:22.894702 32230 solver.cpp:337] Iteration 31200, Testing net (#0)
I0130 14:00:24.026258 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8538
I0130 14:00:24.026280 32230 solver.cpp:404]     Test net output #1: loss = 0.432374 (* 1 = 0.432374 loss)
I0130 14:00:24.055330 32230 solver.cpp:228] Iteration 31200, loss = 0.282313
I0130 14:00:24.055344 32230 solver.cpp:244]     Train net output #0: accuracy = 0.898438
I0130 14:00:24.055351 32230 solver.cpp:244]     Train net output #1: loss = 0.282313 (* 1 = 0.282313 loss)
I0130 14:00:24.055356 32230 sgd_solver.cpp:106] Iteration 31200, lr = 0.05
I0130 14:00:48.801607 32230 solver.cpp:337] Iteration 31400, Testing net (#0)
I0130 14:00:49.922927 32230 solver.cpp:404]     Test net output #0: accuracy = 0.847
I0130 14:00:49.922950 32230 solver.cpp:404]     Test net output #1: loss = 0.469247 (* 1 = 0.469247 loss)
I0130 14:00:49.952080 32230 solver.cpp:228] Iteration 31400, loss = 0.273163
I0130 14:00:49.952100 32230 solver.cpp:244]     Train net output #0: accuracy = 0.910156
I0130 14:00:49.952105 32230 solver.cpp:244]     Train net output #1: loss = 0.273163 (* 1 = 0.273163 loss)
I0130 14:00:49.952109 32230 sgd_solver.cpp:106] Iteration 31400, lr = 0.05
I0130 14:01:15.049177 32230 solver.cpp:337] Iteration 31600, Testing net (#0)
I0130 14:01:16.232136 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8503
I0130 14:01:16.232157 32230 solver.cpp:404]     Test net output #1: loss = 0.461052 (* 1 = 0.461052 loss)
I0130 14:01:16.263525 32230 solver.cpp:228] Iteration 31600, loss = 0.382816
I0130 14:01:16.263545 32230 solver.cpp:244]     Train net output #0: accuracy = 0.851562
I0130 14:01:16.263552 32230 solver.cpp:244]     Train net output #1: loss = 0.382816 (* 1 = 0.382816 loss)
I0130 14:01:16.263556 32230 sgd_solver.cpp:106] Iteration 31600, lr = 0.05
I0130 14:01:41.647768 32230 solver.cpp:337] Iteration 31800, Testing net (#0)
I0130 14:01:42.812206 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8598
I0130 14:01:42.812228 32230 solver.cpp:404]     Test net output #1: loss = 0.439507 (* 1 = 0.439507 loss)
I0130 14:01:42.841225 32230 solver.cpp:228] Iteration 31800, loss = 0.331397
I0130 14:01:42.841243 32230 solver.cpp:244]     Train net output #0: accuracy = 0.882812
I0130 14:01:42.841248 32230 solver.cpp:244]     Train net output #1: loss = 0.331397 (* 1 = 0.331397 loss)
I0130 14:01:42.841253 32230 sgd_solver.cpp:106] Iteration 31800, lr = 0.05
I0130 14:02:07.658956 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_32000.caffemodel
I0130 14:02:07.792912 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_32000.solverstate
I0130 14:02:07.822757 32230 solver.cpp:337] Iteration 32000, Testing net (#0)
I0130 14:02:08.897802 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8486
I0130 14:02:08.897825 32230 solver.cpp:404]     Test net output #1: loss = 0.469624 (* 1 = 0.469624 loss)
I0130 14:02:08.926949 32230 solver.cpp:228] Iteration 32000, loss = 0.360318
I0130 14:02:08.926967 32230 solver.cpp:244]     Train net output #0: accuracy = 0.851562
I0130 14:02:08.926975 32230 solver.cpp:244]     Train net output #1: loss = 0.360318 (* 1 = 0.360318 loss)
I0130 14:02:08.926978 32230 sgd_solver.cpp:106] Iteration 32000, lr = 0.05
I0130 14:02:34.048828 32230 solver.cpp:337] Iteration 32200, Testing net (#0)
I0130 14:02:35.200104 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8584
I0130 14:02:35.200134 32230 solver.cpp:404]     Test net output #1: loss = 0.425978 (* 1 = 0.425978 loss)
I0130 14:02:35.232106 32230 solver.cpp:228] Iteration 32200, loss = 0.37319
I0130 14:02:35.232134 32230 solver.cpp:244]     Train net output #0: accuracy = 0.886719
I0130 14:02:35.232143 32230 solver.cpp:244]     Train net output #1: loss = 0.37319 (* 1 = 0.37319 loss)
I0130 14:02:35.232151 32230 sgd_solver.cpp:106] Iteration 32200, lr = 0.05
I0130 14:03:00.026623 32230 solver.cpp:337] Iteration 32400, Testing net (#0)
I0130 14:03:01.161497 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8604
I0130 14:03:01.161516 32230 solver.cpp:404]     Test net output #1: loss = 0.414433 (* 1 = 0.414433 loss)
I0130 14:03:01.190608 32230 solver.cpp:228] Iteration 32400, loss = 0.302865
I0130 14:03:01.190624 32230 solver.cpp:244]     Train net output #0: accuracy = 0.90625
I0130 14:03:01.190629 32230 solver.cpp:244]     Train net output #1: loss = 0.302865 (* 1 = 0.302865 loss)
I0130 14:03:01.190634 32230 sgd_solver.cpp:106] Iteration 32400, lr = 0.05
I0130 14:03:26.122515 32230 solver.cpp:337] Iteration 32600, Testing net (#0)
I0130 14:03:27.256505 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8517
I0130 14:03:27.256528 32230 solver.cpp:404]     Test net output #1: loss = 0.441023 (* 1 = 0.441023 loss)
I0130 14:03:27.285547 32230 solver.cpp:228] Iteration 32600, loss = 0.341786
I0130 14:03:27.285563 32230 solver.cpp:244]     Train net output #0: accuracy = 0.902344
I0130 14:03:27.285569 32230 solver.cpp:244]     Train net output #1: loss = 0.341786 (* 1 = 0.341786 loss)
I0130 14:03:27.285573 32230 sgd_solver.cpp:106] Iteration 32600, lr = 0.05
I0130 14:03:52.155544 32230 solver.cpp:337] Iteration 32800, Testing net (#0)
I0130 14:03:53.277328 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8504
I0130 14:03:53.277361 32230 solver.cpp:404]     Test net output #1: loss = 0.451803 (* 1 = 0.451803 loss)
I0130 14:03:53.306332 32230 solver.cpp:228] Iteration 32800, loss = 0.37865
I0130 14:03:53.306349 32230 solver.cpp:244]     Train net output #0: accuracy = 0.847656
I0130 14:03:53.306354 32230 solver.cpp:244]     Train net output #1: loss = 0.37865 (* 1 = 0.37865 loss)
I0130 14:03:53.306357 32230 sgd_solver.cpp:106] Iteration 32800, lr = 0.05
I0130 14:04:18.047293 32230 solver.cpp:337] Iteration 33000, Testing net (#0)
I0130 14:04:19.174708 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8496
I0130 14:04:19.174744 32230 solver.cpp:404]     Test net output #1: loss = 0.443441 (* 1 = 0.443441 loss)
I0130 14:04:19.202193 32230 solver.cpp:228] Iteration 33000, loss = 0.330281
I0130 14:04:19.202208 32230 solver.cpp:244]     Train net output #0: accuracy = 0.90625
I0130 14:04:19.202214 32230 solver.cpp:244]     Train net output #1: loss = 0.330281 (* 1 = 0.330281 loss)
I0130 14:04:19.202217 32230 sgd_solver.cpp:106] Iteration 33000, lr = 0.05
I0130 14:04:43.874565 32230 solver.cpp:337] Iteration 33200, Testing net (#0)
I0130 14:04:44.997033 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8549
I0130 14:04:44.997052 32230 solver.cpp:404]     Test net output #1: loss = 0.444717 (* 1 = 0.444717 loss)
I0130 14:04:45.025950 32230 solver.cpp:228] Iteration 33200, loss = 0.390402
I0130 14:04:45.025964 32230 solver.cpp:244]     Train net output #0: accuracy = 0.867188
I0130 14:04:45.025969 32230 solver.cpp:244]     Train net output #1: loss = 0.390402 (* 1 = 0.390402 loss)
I0130 14:04:45.025974 32230 sgd_solver.cpp:106] Iteration 33200, lr = 0.05
I0130 14:05:09.680807 32230 solver.cpp:337] Iteration 33400, Testing net (#0)
I0130 14:05:10.802129 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8619
I0130 14:05:10.802167 32230 solver.cpp:404]     Test net output #1: loss = 0.424306 (* 1 = 0.424306 loss)
I0130 14:05:10.831024 32230 solver.cpp:228] Iteration 33400, loss = 0.308374
I0130 14:05:10.831040 32230 solver.cpp:244]     Train net output #0: accuracy = 0.894531
I0130 14:05:10.831048 32230 solver.cpp:244]     Train net output #1: loss = 0.308374 (* 1 = 0.308374 loss)
I0130 14:05:10.831051 32230 sgd_solver.cpp:106] Iteration 33400, lr = 0.05
I0130 14:05:35.467515 32230 solver.cpp:337] Iteration 33600, Testing net (#0)
I0130 14:05:36.590037 32230 solver.cpp:404]     Test net output #0: accuracy = 0.86
I0130 14:05:36.590073 32230 solver.cpp:404]     Test net output #1: loss = 0.421585 (* 1 = 0.421585 loss)
I0130 14:05:36.618827 32230 solver.cpp:228] Iteration 33600, loss = 0.342051
I0130 14:05:36.618855 32230 solver.cpp:244]     Train net output #0: accuracy = 0.886719
I0130 14:05:36.618860 32230 solver.cpp:244]     Train net output #1: loss = 0.342051 (* 1 = 0.342051 loss)
I0130 14:05:36.618880 32230 sgd_solver.cpp:106] Iteration 33600, lr = 0.05
I0130 14:06:01.474512 32230 solver.cpp:337] Iteration 33800, Testing net (#0)
I0130 14:06:02.605511 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8508
I0130 14:06:02.605535 32230 solver.cpp:404]     Test net output #1: loss = 0.44792 (* 1 = 0.44792 loss)
I0130 14:06:02.634646 32230 solver.cpp:228] Iteration 33800, loss = 0.263193
I0130 14:06:02.634662 32230 solver.cpp:244]     Train net output #0: accuracy = 0.917969
I0130 14:06:02.634667 32230 solver.cpp:244]     Train net output #1: loss = 0.263193 (* 1 = 0.263193 loss)
I0130 14:06:02.634671 32230 sgd_solver.cpp:106] Iteration 33800, lr = 0.05
I0130 14:06:27.500378 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_34000.caffemodel
I0130 14:06:27.641850 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_34000.solverstate
I0130 14:06:27.670661 32230 solver.cpp:337] Iteration 34000, Testing net (#0)
I0130 14:06:28.730461 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8661
I0130 14:06:28.730484 32230 solver.cpp:404]     Test net output #1: loss = 0.414298 (* 1 = 0.414298 loss)
I0130 14:06:28.759624 32230 solver.cpp:228] Iteration 34000, loss = 0.313067
I0130 14:06:28.759641 32230 solver.cpp:244]     Train net output #0: accuracy = 0.890625
I0130 14:06:28.759646 32230 solver.cpp:244]     Train net output #1: loss = 0.313067 (* 1 = 0.313067 loss)
I0130 14:06:28.759651 32230 sgd_solver.cpp:106] Iteration 34000, lr = 0.05
I0130 14:06:53.578994 32230 solver.cpp:337] Iteration 34200, Testing net (#0)
I0130 14:06:54.709167 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8645
I0130 14:06:54.709202 32230 solver.cpp:404]     Test net output #1: loss = 0.400176 (* 1 = 0.400176 loss)
I0130 14:06:54.738348 32230 solver.cpp:228] Iteration 34200, loss = 0.278384
I0130 14:06:54.738360 32230 solver.cpp:244]     Train net output #0: accuracy = 0.894531
I0130 14:06:54.738365 32230 solver.cpp:244]     Train net output #1: loss = 0.278384 (* 1 = 0.278384 loss)
I0130 14:06:54.738369 32230 sgd_solver.cpp:106] Iteration 34200, lr = 0.05
I0130 14:07:19.740901 32230 solver.cpp:337] Iteration 34400, Testing net (#0)
I0130 14:07:20.913411 32230 solver.cpp:404]     Test net output #0: accuracy = 0.873
I0130 14:07:20.913434 32230 solver.cpp:404]     Test net output #1: loss = 0.375619 (* 1 = 0.375619 loss)
I0130 14:07:20.942469 32230 solver.cpp:228] Iteration 34400, loss = 0.222084
I0130 14:07:20.942486 32230 solver.cpp:244]     Train net output #0: accuracy = 0.933594
I0130 14:07:20.942490 32230 solver.cpp:244]     Train net output #1: loss = 0.222084 (* 1 = 0.222084 loss)
I0130 14:07:20.942493 32230 sgd_solver.cpp:106] Iteration 34400, lr = 0.05
I0130 14:07:46.095453 32230 solver.cpp:337] Iteration 34600, Testing net (#0)
I0130 14:07:47.244457 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8589
I0130 14:07:47.244477 32230 solver.cpp:404]     Test net output #1: loss = 0.457924 (* 1 = 0.457924 loss)
I0130 14:07:47.273532 32230 solver.cpp:228] Iteration 34600, loss = 0.333805
I0130 14:07:47.273545 32230 solver.cpp:244]     Train net output #0: accuracy = 0.894531
I0130 14:07:47.273550 32230 solver.cpp:244]     Train net output #1: loss = 0.333805 (* 1 = 0.333805 loss)
I0130 14:07:47.273555 32230 sgd_solver.cpp:106] Iteration 34600, lr = 0.05
I0130 14:08:12.375361 32230 solver.cpp:337] Iteration 34800, Testing net (#0)
I0130 14:08:13.517933 32230 solver.cpp:404]     Test net output #0: accuracy = 0.87
I0130 14:08:13.517969 32230 solver.cpp:404]     Test net output #1: loss = 0.391046 (* 1 = 0.391046 loss)
I0130 14:08:13.546743 32230 solver.cpp:228] Iteration 34800, loss = 0.243364
I0130 14:08:13.546756 32230 solver.cpp:244]     Train net output #0: accuracy = 0.929688
I0130 14:08:13.546761 32230 solver.cpp:244]     Train net output #1: loss = 0.243364 (* 1 = 0.243364 loss)
I0130 14:08:13.546782 32230 sgd_solver.cpp:106] Iteration 34800, lr = 0.05
I0130 14:08:39.019593 32230 solver.cpp:337] Iteration 35000, Testing net (#0)
I0130 14:08:40.156481 32230 solver.cpp:404]     Test net output #0: accuracy = 0.855
I0130 14:08:40.156517 32230 solver.cpp:404]     Test net output #1: loss = 0.434182 (* 1 = 0.434182 loss)
I0130 14:08:40.183766 32230 solver.cpp:228] Iteration 35000, loss = 0.360464
I0130 14:08:40.183781 32230 solver.cpp:244]     Train net output #0: accuracy = 0.894531
I0130 14:08:40.183786 32230 solver.cpp:244]     Train net output #1: loss = 0.360464 (* 1 = 0.360464 loss)
I0130 14:08:40.183790 32230 sgd_solver.cpp:106] Iteration 35000, lr = 0.05
I0130 14:09:04.905627 32230 solver.cpp:337] Iteration 35200, Testing net (#0)
I0130 14:09:06.028939 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8541
I0130 14:09:06.028962 32230 solver.cpp:404]     Test net output #1: loss = 0.439018 (* 1 = 0.439018 loss)
I0130 14:09:06.057919 32230 solver.cpp:228] Iteration 35200, loss = 0.301771
I0130 14:09:06.057934 32230 solver.cpp:244]     Train net output #0: accuracy = 0.902344
I0130 14:09:06.057940 32230 solver.cpp:244]     Train net output #1: loss = 0.301771 (* 1 = 0.301771 loss)
I0130 14:09:06.057945 32230 sgd_solver.cpp:106] Iteration 35200, lr = 0.05
I0130 14:09:30.684190 32230 solver.cpp:337] Iteration 35400, Testing net (#0)
I0130 14:09:31.806432 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8562
I0130 14:09:31.806455 32230 solver.cpp:404]     Test net output #1: loss = 0.437631 (* 1 = 0.437631 loss)
I0130 14:09:31.835561 32230 solver.cpp:228] Iteration 35400, loss = 0.267534
I0130 14:09:31.835579 32230 solver.cpp:244]     Train net output #0: accuracy = 0.902344
I0130 14:09:31.835585 32230 solver.cpp:244]     Train net output #1: loss = 0.267534 (* 1 = 0.267534 loss)
I0130 14:09:31.835590 32230 sgd_solver.cpp:106] Iteration 35400, lr = 0.05
I0130 14:09:56.466861 32230 solver.cpp:337] Iteration 35600, Testing net (#0)
I0130 14:09:57.589299 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8612
I0130 14:09:57.589323 32230 solver.cpp:404]     Test net output #1: loss = 0.421087 (* 1 = 0.421087 loss)
I0130 14:09:57.618088 32230 solver.cpp:228] Iteration 35600, loss = 0.340982
I0130 14:09:57.618104 32230 solver.cpp:244]     Train net output #0: accuracy = 0.890625
I0130 14:09:57.618109 32230 solver.cpp:244]     Train net output #1: loss = 0.340982 (* 1 = 0.340982 loss)
I0130 14:09:57.618113 32230 sgd_solver.cpp:106] Iteration 35600, lr = 0.05
I0130 14:10:22.231983 32230 solver.cpp:337] Iteration 35800, Testing net (#0)
I0130 14:10:23.354508 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8571
I0130 14:10:23.354532 32230 solver.cpp:404]     Test net output #1: loss = 0.424944 (* 1 = 0.424944 loss)
I0130 14:10:23.381983 32230 solver.cpp:228] Iteration 35800, loss = 0.287186
I0130 14:10:23.381999 32230 solver.cpp:244]     Train net output #0: accuracy = 0.917969
I0130 14:10:23.382004 32230 solver.cpp:244]     Train net output #1: loss = 0.287186 (* 1 = 0.287186 loss)
I0130 14:10:23.382009 32230 sgd_solver.cpp:106] Iteration 35800, lr = 0.05
I0130 14:10:48.004384 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_36000.caffemodel
I0130 14:10:48.139246 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_36000.solverstate
I0130 14:10:48.168680 32230 solver.cpp:337] Iteration 36000, Testing net (#0)
I0130 14:10:49.190922 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8555
I0130 14:10:49.190945 32230 solver.cpp:404]     Test net output #1: loss = 0.434703 (* 1 = 0.434703 loss)
I0130 14:10:49.219781 32230 solver.cpp:228] Iteration 36000, loss = 0.325804
I0130 14:10:49.219796 32230 solver.cpp:244]     Train net output #0: accuracy = 0.890625
I0130 14:10:49.219802 32230 solver.cpp:244]     Train net output #1: loss = 0.325804 (* 1 = 0.325804 loss)
I0130 14:10:49.219806 32230 sgd_solver.cpp:106] Iteration 36000, lr = 0.05
I0130 14:11:13.817229 32230 solver.cpp:337] Iteration 36200, Testing net (#0)
I0130 14:11:14.940011 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8675
I0130 14:11:14.940032 32230 solver.cpp:404]     Test net output #1: loss = 0.392245 (* 1 = 0.392245 loss)
I0130 14:11:14.968921 32230 solver.cpp:228] Iteration 36200, loss = 0.316469
I0130 14:11:14.968937 32230 solver.cpp:244]     Train net output #0: accuracy = 0.910156
I0130 14:11:14.968942 32230 solver.cpp:244]     Train net output #1: loss = 0.316469 (* 1 = 0.316469 loss)
I0130 14:11:14.968946 32230 sgd_solver.cpp:106] Iteration 36200, lr = 0.05
I0130 14:11:39.708246 32230 solver.cpp:337] Iteration 36400, Testing net (#0)
I0130 14:11:40.832185 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8599
I0130 14:11:40.832206 32230 solver.cpp:404]     Test net output #1: loss = 0.440304 (* 1 = 0.440304 loss)
I0130 14:11:40.861438 32230 solver.cpp:228] Iteration 36400, loss = 0.320008
I0130 14:11:40.861455 32230 solver.cpp:244]     Train net output #0: accuracy = 0.894531
I0130 14:11:40.861460 32230 solver.cpp:244]     Train net output #1: loss = 0.320008 (* 1 = 0.320008 loss)
I0130 14:11:40.861464 32230 sgd_solver.cpp:106] Iteration 36400, lr = 0.05
I0130 14:12:05.489681 32230 solver.cpp:337] Iteration 36600, Testing net (#0)
I0130 14:12:06.614181 32230 solver.cpp:404]     Test net output #0: accuracy = 0.862
I0130 14:12:06.614200 32230 solver.cpp:404]     Test net output #1: loss = 0.410578 (* 1 = 0.410578 loss)
I0130 14:12:06.643374 32230 solver.cpp:228] Iteration 36600, loss = 0.284469
I0130 14:12:06.643390 32230 solver.cpp:244]     Train net output #0: accuracy = 0.898438
I0130 14:12:06.643396 32230 solver.cpp:244]     Train net output #1: loss = 0.284469 (* 1 = 0.284469 loss)
I0130 14:12:06.643400 32230 sgd_solver.cpp:106] Iteration 36600, lr = 0.05
I0130 14:12:31.277361 32230 solver.cpp:337] Iteration 36800, Testing net (#0)
I0130 14:12:32.401366 32230 solver.cpp:404]     Test net output #0: accuracy = 0.841
I0130 14:12:32.401387 32230 solver.cpp:404]     Test net output #1: loss = 0.501256 (* 1 = 0.501256 loss)
I0130 14:12:32.430548 32230 solver.cpp:228] Iteration 36800, loss = 0.408279
I0130 14:12:32.430564 32230 solver.cpp:244]     Train net output #0: accuracy = 0.855469
I0130 14:12:32.430570 32230 solver.cpp:244]     Train net output #1: loss = 0.408279 (* 1 = 0.408279 loss)
I0130 14:12:32.430574 32230 sgd_solver.cpp:106] Iteration 36800, lr = 0.05
I0130 14:12:57.141669 32230 solver.cpp:337] Iteration 37000, Testing net (#0)
I0130 14:12:58.264632 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8599
I0130 14:12:58.264655 32230 solver.cpp:404]     Test net output #1: loss = 0.419102 (* 1 = 0.419102 loss)
I0130 14:12:58.293784 32230 solver.cpp:228] Iteration 37000, loss = 0.290038
I0130 14:12:58.293799 32230 solver.cpp:244]     Train net output #0: accuracy = 0.898438
I0130 14:12:58.293804 32230 solver.cpp:244]     Train net output #1: loss = 0.290038 (* 1 = 0.290038 loss)
I0130 14:12:58.293808 32230 sgd_solver.cpp:106] Iteration 37000, lr = 0.05
I0130 14:13:22.909808 32230 solver.cpp:337] Iteration 37200, Testing net (#0)
I0130 14:13:24.033658 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8529
I0130 14:13:24.033679 32230 solver.cpp:404]     Test net output #1: loss = 0.438827 (* 1 = 0.438827 loss)
I0130 14:13:24.063041 32230 solver.cpp:228] Iteration 37200, loss = 0.306217
I0130 14:13:24.063060 32230 solver.cpp:244]     Train net output #0: accuracy = 0.882812
I0130 14:13:24.063064 32230 solver.cpp:244]     Train net output #1: loss = 0.306217 (* 1 = 0.306217 loss)
I0130 14:13:24.063068 32230 sgd_solver.cpp:106] Iteration 37200, lr = 0.05
I0130 14:13:48.684553 32230 solver.cpp:337] Iteration 37400, Testing net (#0)
I0130 14:13:49.807859 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8619
I0130 14:13:49.807881 32230 solver.cpp:404]     Test net output #1: loss = 0.412909 (* 1 = 0.412909 loss)
I0130 14:13:49.837206 32230 solver.cpp:228] Iteration 37400, loss = 0.273489
I0130 14:13:49.837222 32230 solver.cpp:244]     Train net output #0: accuracy = 0.898438
I0130 14:13:49.837227 32230 solver.cpp:244]     Train net output #1: loss = 0.273489 (* 1 = 0.273489 loss)
I0130 14:13:49.837231 32230 sgd_solver.cpp:106] Iteration 37400, lr = 0.05
I0130 14:14:14.452589 32230 solver.cpp:337] Iteration 37600, Testing net (#0)
I0130 14:14:15.575989 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8637
I0130 14:14:15.576014 32230 solver.cpp:404]     Test net output #1: loss = 0.406111 (* 1 = 0.406111 loss)
I0130 14:14:15.605135 32230 solver.cpp:228] Iteration 37600, loss = 0.302984
I0130 14:14:15.605154 32230 solver.cpp:244]     Train net output #0: accuracy = 0.898438
I0130 14:14:15.605161 32230 solver.cpp:244]     Train net output #1: loss = 0.302984 (* 1 = 0.302984 loss)
I0130 14:14:15.605166 32230 sgd_solver.cpp:106] Iteration 37600, lr = 0.05
I0130 14:14:40.220078 32230 solver.cpp:337] Iteration 37800, Testing net (#0)
I0130 14:14:41.343729 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8628
I0130 14:14:41.343750 32230 solver.cpp:404]     Test net output #1: loss = 0.424213 (* 1 = 0.424213 loss)
I0130 14:14:41.373034 32230 solver.cpp:228] Iteration 37800, loss = 0.302711
I0130 14:14:41.373051 32230 solver.cpp:244]     Train net output #0: accuracy = 0.886719
I0130 14:14:41.373056 32230 solver.cpp:244]     Train net output #1: loss = 0.302711 (* 1 = 0.302711 loss)
I0130 14:14:41.373060 32230 sgd_solver.cpp:106] Iteration 37800, lr = 0.05
I0130 14:15:05.970386 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_38000.caffemodel
I0130 14:15:06.106365 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_38000.solverstate
I0130 14:15:06.134724 32230 solver.cpp:337] Iteration 38000, Testing net (#0)
I0130 14:15:07.154858 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8619
I0130 14:15:07.154882 32230 solver.cpp:404]     Test net output #1: loss = 0.407177 (* 1 = 0.407177 loss)
I0130 14:15:07.184092 32230 solver.cpp:228] Iteration 38000, loss = 0.221685
I0130 14:15:07.184110 32230 solver.cpp:244]     Train net output #0: accuracy = 0.921875
I0130 14:15:07.184116 32230 solver.cpp:244]     Train net output #1: loss = 0.221685 (* 1 = 0.221685 loss)
I0130 14:15:07.184121 32230 sgd_solver.cpp:106] Iteration 38000, lr = 0.05
I0130 14:15:31.734140 32230 solver.cpp:337] Iteration 38200, Testing net (#0)
I0130 14:15:32.861183 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8662
I0130 14:15:32.861204 32230 solver.cpp:404]     Test net output #1: loss = 0.408509 (* 1 = 0.408509 loss)
I0130 14:15:32.890527 32230 solver.cpp:228] Iteration 38200, loss = 0.226423
I0130 14:15:32.890545 32230 solver.cpp:244]     Train net output #0: accuracy = 0.910156
I0130 14:15:32.890552 32230 solver.cpp:244]     Train net output #1: loss = 0.226423 (* 1 = 0.226423 loss)
I0130 14:15:32.890554 32230 sgd_solver.cpp:106] Iteration 38200, lr = 0.05
I0130 14:15:57.506127 32230 solver.cpp:337] Iteration 38400, Testing net (#0)
I0130 14:15:58.629734 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8764
I0130 14:15:58.629771 32230 solver.cpp:404]     Test net output #1: loss = 0.385033 (* 1 = 0.385033 loss)
I0130 14:15:58.658854 32230 solver.cpp:228] Iteration 38400, loss = 0.186885
I0130 14:15:58.658871 32230 solver.cpp:244]     Train net output #0: accuracy = 0.9375
I0130 14:15:58.658879 32230 solver.cpp:244]     Train net output #1: loss = 0.186885 (* 1 = 0.186885 loss)
I0130 14:15:58.658882 32230 sgd_solver.cpp:106] Iteration 38400, lr = 0.05
I0130 14:16:23.234007 32230 solver.cpp:337] Iteration 38600, Testing net (#0)
I0130 14:16:24.356501 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8598
I0130 14:16:24.356528 32230 solver.cpp:404]     Test net output #1: loss = 0.429932 (* 1 = 0.429932 loss)
I0130 14:16:24.385634 32230 solver.cpp:228] Iteration 38600, loss = 0.342465
I0130 14:16:24.385653 32230 solver.cpp:244]     Train net output #0: accuracy = 0.886719
I0130 14:16:24.385658 32230 solver.cpp:244]     Train net output #1: loss = 0.342465 (* 1 = 0.342465 loss)
I0130 14:16:24.385663 32230 sgd_solver.cpp:106] Iteration 38600, lr = 0.05
I0130 14:16:48.990392 32230 solver.cpp:337] Iteration 38800, Testing net (#0)
I0130 14:16:50.114128 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8566
I0130 14:16:50.114150 32230 solver.cpp:404]     Test net output #1: loss = 0.463668 (* 1 = 0.463668 loss)
I0130 14:16:50.143291 32230 solver.cpp:228] Iteration 38800, loss = 0.470055
I0130 14:16:50.143311 32230 solver.cpp:244]     Train net output #0: accuracy = 0.832031
I0130 14:16:50.143316 32230 solver.cpp:244]     Train net output #1: loss = 0.470055 (* 1 = 0.470055 loss)
I0130 14:16:50.143321 32230 sgd_solver.cpp:106] Iteration 38800, lr = 0.05
I0130 14:17:14.823508 32230 solver.cpp:337] Iteration 39000, Testing net (#0)
I0130 14:17:15.947836 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8626
I0130 14:17:15.947856 32230 solver.cpp:404]     Test net output #1: loss = 0.414222 (* 1 = 0.414222 loss)
I0130 14:17:15.977146 32230 solver.cpp:228] Iteration 39000, loss = 0.320478
I0130 14:17:15.977164 32230 solver.cpp:244]     Train net output #0: accuracy = 0.90625
I0130 14:17:15.977169 32230 solver.cpp:244]     Train net output #1: loss = 0.320478 (* 1 = 0.320478 loss)
I0130 14:17:15.977174 32230 sgd_solver.cpp:106] Iteration 39000, lr = 0.05
I0130 14:17:40.573691 32230 solver.cpp:337] Iteration 39200, Testing net (#0)
I0130 14:17:41.697089 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8618
I0130 14:17:41.697113 32230 solver.cpp:404]     Test net output #1: loss = 0.424388 (* 1 = 0.424388 loss)
I0130 14:17:41.726460 32230 solver.cpp:228] Iteration 39200, loss = 0.262001
I0130 14:17:41.726476 32230 solver.cpp:244]     Train net output #0: accuracy = 0.914062
I0130 14:17:41.726482 32230 solver.cpp:244]     Train net output #1: loss = 0.262001 (* 1 = 0.262001 loss)
I0130 14:17:41.726486 32230 sgd_solver.cpp:106] Iteration 39200, lr = 0.05
I0130 14:18:06.320901 32230 solver.cpp:337] Iteration 39400, Testing net (#0)
I0130 14:18:07.444941 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8461
I0130 14:18:07.444965 32230 solver.cpp:404]     Test net output #1: loss = 0.479003 (* 1 = 0.479003 loss)
I0130 14:18:07.474143 32230 solver.cpp:228] Iteration 39400, loss = 0.373884
I0130 14:18:07.474159 32230 solver.cpp:244]     Train net output #0: accuracy = 0.867188
I0130 14:18:07.474164 32230 solver.cpp:244]     Train net output #1: loss = 0.373884 (* 1 = 0.373884 loss)
I0130 14:18:07.474167 32230 sgd_solver.cpp:106] Iteration 39400, lr = 0.05
I0130 14:18:32.138890 32230 solver.cpp:337] Iteration 39600, Testing net (#0)
I0130 14:18:33.262799 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8653
I0130 14:18:33.262820 32230 solver.cpp:404]     Test net output #1: loss = 0.407976 (* 1 = 0.407976 loss)
I0130 14:18:33.291936 32230 solver.cpp:228] Iteration 39600, loss = 0.263676
I0130 14:18:33.291955 32230 solver.cpp:244]     Train net output #0: accuracy = 0.917969
I0130 14:18:33.291961 32230 solver.cpp:244]     Train net output #1: loss = 0.263676 (* 1 = 0.263676 loss)
I0130 14:18:33.291965 32230 sgd_solver.cpp:106] Iteration 39600, lr = 0.05
I0130 14:18:57.930356 32230 solver.cpp:337] Iteration 39800, Testing net (#0)
I0130 14:18:59.058547 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8603
I0130 14:18:59.058569 32230 solver.cpp:404]     Test net output #1: loss = 0.417128 (* 1 = 0.417128 loss)
I0130 14:18:59.087623 32230 solver.cpp:228] Iteration 39800, loss = 0.230979
I0130 14:18:59.087646 32230 solver.cpp:244]     Train net output #0: accuracy = 0.917969
I0130 14:18:59.087652 32230 solver.cpp:244]     Train net output #1: loss = 0.230979 (* 1 = 0.230979 loss)
I0130 14:18:59.087657 32230 sgd_solver.cpp:106] Iteration 39800, lr = 0.05
I0130 14:19:23.729634 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_40000.caffemodel
I0130 14:19:23.866839 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_40000.solverstate
I0130 14:19:23.894609 32230 solver.cpp:337] Iteration 40000, Testing net (#0)
I0130 14:19:24.922312 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8565
I0130 14:19:24.922335 32230 solver.cpp:404]     Test net output #1: loss = 0.423359 (* 1 = 0.423359 loss)
I0130 14:19:24.951450 32230 solver.cpp:228] Iteration 40000, loss = 0.259958
I0130 14:19:24.951468 32230 solver.cpp:244]     Train net output #0: accuracy = 0.898438
I0130 14:19:24.951474 32230 solver.cpp:244]     Train net output #1: loss = 0.259958 (* 1 = 0.259958 loss)
I0130 14:19:24.951478 32230 sgd_solver.cpp:46] MultiStep Status: Iteration 40000, step = 1
I0130 14:19:24.951480 32230 sgd_solver.cpp:106] Iteration 40000, lr = 0.005
I0130 14:19:49.607427 32230 solver.cpp:337] Iteration 40200, Testing net (#0)
I0130 14:19:50.731631 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8898
I0130 14:19:50.731653 32230 solver.cpp:404]     Test net output #1: loss = 0.335187 (* 1 = 0.335187 loss)
I0130 14:19:50.760776 32230 solver.cpp:228] Iteration 40200, loss = 0.161363
I0130 14:19:50.760793 32230 solver.cpp:244]     Train net output #0: accuracy = 0.941406
I0130 14:19:50.760799 32230 solver.cpp:244]     Train net output #1: loss = 0.161363 (* 1 = 0.161363 loss)
I0130 14:19:50.760803 32230 sgd_solver.cpp:106] Iteration 40200, lr = 0.005
I0130 14:20:15.402364 32230 solver.cpp:337] Iteration 40400, Testing net (#0)
I0130 14:20:16.525990 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8931
I0130 14:20:16.526031 32230 solver.cpp:404]     Test net output #1: loss = 0.3413 (* 1 = 0.3413 loss)
I0130 14:20:16.555088 32230 solver.cpp:228] Iteration 40400, loss = 0.188705
I0130 14:20:16.555106 32230 solver.cpp:244]     Train net output #0: accuracy = 0.917969
I0130 14:20:16.555111 32230 solver.cpp:244]     Train net output #1: loss = 0.188705 (* 1 = 0.188705 loss)
I0130 14:20:16.555116 32230 sgd_solver.cpp:106] Iteration 40400, lr = 0.005
I0130 14:20:41.190230 32230 solver.cpp:337] Iteration 40600, Testing net (#0)
I0130 14:20:42.314880 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8955
I0130 14:20:42.314903 32230 solver.cpp:404]     Test net output #1: loss = 0.32953 (* 1 = 0.32953 loss)
I0130 14:20:42.344295 32230 solver.cpp:228] Iteration 40600, loss = 0.126902
I0130 14:20:42.344314 32230 solver.cpp:244]     Train net output #0: accuracy = 0.953125
I0130 14:20:42.344321 32230 solver.cpp:244]     Train net output #1: loss = 0.126902 (* 1 = 0.126902 loss)
I0130 14:20:42.344326 32230 sgd_solver.cpp:106] Iteration 40600, lr = 0.005
I0130 14:21:06.975906 32230 solver.cpp:337] Iteration 40800, Testing net (#0)
I0130 14:21:08.100432 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8929
I0130 14:21:08.100458 32230 solver.cpp:404]     Test net output #1: loss = 0.333007 (* 1 = 0.333007 loss)
I0130 14:21:08.129796 32230 solver.cpp:228] Iteration 40800, loss = 0.126387
I0130 14:21:08.129815 32230 solver.cpp:244]     Train net output #0: accuracy = 0.957031
I0130 14:21:08.129822 32230 solver.cpp:244]     Train net output #1: loss = 0.126387 (* 1 = 0.126387 loss)
I0130 14:21:08.129827 32230 sgd_solver.cpp:106] Iteration 40800, lr = 0.005
I0130 14:21:32.770359 32230 solver.cpp:337] Iteration 41000, Testing net (#0)
I0130 14:21:33.895485 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8952
I0130 14:21:33.895509 32230 solver.cpp:404]     Test net output #1: loss = 0.329661 (* 1 = 0.329661 loss)
I0130 14:21:33.924551 32230 solver.cpp:228] Iteration 41000, loss = 0.0990786
I0130 14:21:33.924569 32230 solver.cpp:244]     Train net output #0: accuracy = 0.964844
I0130 14:21:33.924576 32230 solver.cpp:244]     Train net output #1: loss = 0.0990786 (* 1 = 0.0990786 loss)
I0130 14:21:33.924582 32230 sgd_solver.cpp:106] Iteration 41000, lr = 0.005
I0130 14:21:58.548146 32230 solver.cpp:337] Iteration 41200, Testing net (#0)
I0130 14:21:59.672860 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8965
I0130 14:21:59.672884 32230 solver.cpp:404]     Test net output #1: loss = 0.322842 (* 1 = 0.322842 loss)
I0130 14:21:59.701889 32230 solver.cpp:228] Iteration 41200, loss = 0.140497
I0130 14:21:59.701907 32230 solver.cpp:244]     Train net output #0: accuracy = 0.941406
I0130 14:21:59.701915 32230 solver.cpp:244]     Train net output #1: loss = 0.140497 (* 1 = 0.140497 loss)
I0130 14:21:59.701920 32230 sgd_solver.cpp:106] Iteration 41200, lr = 0.005
I0130 14:22:24.333685 32230 solver.cpp:337] Iteration 41400, Testing net (#0)
I0130 14:22:25.457387 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8974
I0130 14:22:25.457409 32230 solver.cpp:404]     Test net output #1: loss = 0.328401 (* 1 = 0.328401 loss)
I0130 14:22:25.486518 32230 solver.cpp:228] Iteration 41400, loss = 0.135375
I0130 14:22:25.486536 32230 solver.cpp:244]     Train net output #0: accuracy = 0.957031
I0130 14:22:25.486543 32230 solver.cpp:244]     Train net output #1: loss = 0.135375 (* 1 = 0.135375 loss)
I0130 14:22:25.486547 32230 sgd_solver.cpp:106] Iteration 41400, lr = 0.005
I0130 14:22:50.140076 32230 solver.cpp:337] Iteration 41600, Testing net (#0)
I0130 14:22:51.264909 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8953
I0130 14:22:51.264931 32230 solver.cpp:404]     Test net output #1: loss = 0.335762 (* 1 = 0.335762 loss)
I0130 14:22:51.294106 32230 solver.cpp:228] Iteration 41600, loss = 0.120959
I0130 14:22:51.294124 32230 solver.cpp:244]     Train net output #0: accuracy = 0.960938
I0130 14:22:51.294132 32230 solver.cpp:244]     Train net output #1: loss = 0.120959 (* 1 = 0.120959 loss)
I0130 14:22:51.294137 32230 sgd_solver.cpp:106] Iteration 41600, lr = 0.005
I0130 14:23:15.942363 32230 solver.cpp:337] Iteration 41800, Testing net (#0)
I0130 14:23:17.066906 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8956
I0130 14:23:17.066928 32230 solver.cpp:404]     Test net output #1: loss = 0.331631 (* 1 = 0.331631 loss)
I0130 14:23:17.096127 32230 solver.cpp:228] Iteration 41800, loss = 0.109619
I0130 14:23:17.096143 32230 solver.cpp:244]     Train net output #0: accuracy = 0.964844
I0130 14:23:17.096148 32230 solver.cpp:244]     Train net output #1: loss = 0.109619 (* 1 = 0.109619 loss)
I0130 14:23:17.096153 32230 sgd_solver.cpp:106] Iteration 41800, lr = 0.005
I0130 14:23:41.715816 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_42000.caffemodel
I0130 14:23:41.851464 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_42000.solverstate
I0130 14:23:41.879140 32230 solver.cpp:337] Iteration 42000, Testing net (#0)
I0130 14:23:42.903193 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8975
I0130 14:23:42.903214 32230 solver.cpp:404]     Test net output #1: loss = 0.334589 (* 1 = 0.334589 loss)
I0130 14:23:42.932339 32230 solver.cpp:228] Iteration 42000, loss = 0.116837
I0130 14:23:42.932354 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 14:23:42.932360 32230 solver.cpp:244]     Train net output #1: loss = 0.116837 (* 1 = 0.116837 loss)
I0130 14:23:42.932379 32230 sgd_solver.cpp:106] Iteration 42000, lr = 0.005
I0130 14:24:07.583206 32230 solver.cpp:337] Iteration 42200, Testing net (#0)
I0130 14:24:08.716709 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8973
I0130 14:24:08.716734 32230 solver.cpp:404]     Test net output #1: loss = 0.333629 (* 1 = 0.333629 loss)
I0130 14:24:08.745983 32230 solver.cpp:228] Iteration 42200, loss = 0.165419
I0130 14:24:08.746001 32230 solver.cpp:244]     Train net output #0: accuracy = 0.945312
I0130 14:24:08.746006 32230 solver.cpp:244]     Train net output #1: loss = 0.165419 (* 1 = 0.165419 loss)
I0130 14:24:08.746011 32230 sgd_solver.cpp:106] Iteration 42200, lr = 0.005
I0130 14:24:33.507069 32230 solver.cpp:337] Iteration 42400, Testing net (#0)
I0130 14:24:34.630138 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8978
I0130 14:24:34.630159 32230 solver.cpp:404]     Test net output #1: loss = 0.333479 (* 1 = 0.333479 loss)
I0130 14:24:34.659596 32230 solver.cpp:228] Iteration 42400, loss = 0.158409
I0130 14:24:34.659610 32230 solver.cpp:244]     Train net output #0: accuracy = 0.941406
I0130 14:24:34.659631 32230 solver.cpp:244]     Train net output #1: loss = 0.158409 (* 1 = 0.158409 loss)
I0130 14:24:34.659634 32230 sgd_solver.cpp:106] Iteration 42400, lr = 0.005
I0130 14:24:59.435685 32230 solver.cpp:337] Iteration 42600, Testing net (#0)
I0130 14:25:00.567000 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8974
I0130 14:25:00.567021 32230 solver.cpp:404]     Test net output #1: loss = 0.338413 (* 1 = 0.338413 loss)
I0130 14:25:00.596210 32230 solver.cpp:228] Iteration 42600, loss = 0.12904
I0130 14:25:00.596225 32230 solver.cpp:244]     Train net output #0: accuracy = 0.957031
I0130 14:25:00.596230 32230 solver.cpp:244]     Train net output #1: loss = 0.12904 (* 1 = 0.12904 loss)
I0130 14:25:00.596249 32230 sgd_solver.cpp:106] Iteration 42600, lr = 0.005
I0130 14:25:26.131301 32230 solver.cpp:337] Iteration 42800, Testing net (#0)
I0130 14:25:27.375164 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8991
I0130 14:25:27.375187 32230 solver.cpp:404]     Test net output #1: loss = 0.327529 (* 1 = 0.327529 loss)
I0130 14:25:27.405066 32230 solver.cpp:228] Iteration 42800, loss = 0.0971255
I0130 14:25:27.405076 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 14:25:27.405083 32230 solver.cpp:244]     Train net output #1: loss = 0.0971255 (* 1 = 0.0971255 loss)
I0130 14:25:27.405087 32230 sgd_solver.cpp:106] Iteration 42800, lr = 0.005
I0130 14:25:52.351184 32230 solver.cpp:337] Iteration 43000, Testing net (#0)
I0130 14:25:53.483747 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8977
I0130 14:25:53.483770 32230 solver.cpp:404]     Test net output #1: loss = 0.334341 (* 1 = 0.334341 loss)
I0130 14:25:53.513087 32230 solver.cpp:228] Iteration 43000, loss = 0.153823
I0130 14:25:53.513103 32230 solver.cpp:244]     Train net output #0: accuracy = 0.945312
I0130 14:25:53.513108 32230 solver.cpp:244]     Train net output #1: loss = 0.153823 (* 1 = 0.153823 loss)
I0130 14:25:53.513113 32230 sgd_solver.cpp:106] Iteration 43000, lr = 0.005
I0130 14:26:18.851078 32230 solver.cpp:337] Iteration 43200, Testing net (#0)
I0130 14:26:19.978467 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8973
I0130 14:26:19.978492 32230 solver.cpp:404]     Test net output #1: loss = 0.335925 (* 1 = 0.335925 loss)
I0130 14:26:20.007812 32230 solver.cpp:228] Iteration 43200, loss = 0.115703
I0130 14:26:20.007829 32230 solver.cpp:244]     Train net output #0: accuracy = 0.960938
I0130 14:26:20.007836 32230 solver.cpp:244]     Train net output #1: loss = 0.115703 (* 1 = 0.115703 loss)
I0130 14:26:20.007840 32230 sgd_solver.cpp:106] Iteration 43200, lr = 0.005
I0130 14:26:45.357926 32230 solver.cpp:337] Iteration 43400, Testing net (#0)
I0130 14:26:46.488307 32230 solver.cpp:404]     Test net output #0: accuracy = 0.898
I0130 14:26:46.488329 32230 solver.cpp:404]     Test net output #1: loss = 0.342106 (* 1 = 0.342106 loss)
I0130 14:26:46.517796 32230 solver.cpp:228] Iteration 43400, loss = 0.130223
I0130 14:26:46.517810 32230 solver.cpp:244]     Train net output #0: accuracy = 0.960938
I0130 14:26:46.517815 32230 solver.cpp:244]     Train net output #1: loss = 0.130223 (* 1 = 0.130223 loss)
I0130 14:26:46.517818 32230 sgd_solver.cpp:106] Iteration 43400, lr = 0.005
I0130 14:27:11.947752 32230 solver.cpp:337] Iteration 43600, Testing net (#0)
I0130 14:27:13.189749 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8941
I0130 14:27:13.189785 32230 solver.cpp:404]     Test net output #1: loss = 0.34915 (* 1 = 0.34915 loss)
I0130 14:27:13.221316 32230 solver.cpp:228] Iteration 43600, loss = 0.13835
I0130 14:27:13.221325 32230 solver.cpp:244]     Train net output #0: accuracy = 0.941406
I0130 14:27:13.221331 32230 solver.cpp:244]     Train net output #1: loss = 0.13835 (* 1 = 0.13835 loss)
I0130 14:27:13.221335 32230 sgd_solver.cpp:106] Iteration 43600, lr = 0.005
I0130 14:27:38.429836 32230 solver.cpp:337] Iteration 43800, Testing net (#0)
I0130 14:27:39.678194 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8957
I0130 14:27:39.678231 32230 solver.cpp:404]     Test net output #1: loss = 0.343581 (* 1 = 0.343581 loss)
I0130 14:27:39.708374 32230 solver.cpp:228] Iteration 43800, loss = 0.135845
I0130 14:27:39.708384 32230 solver.cpp:244]     Train net output #0: accuracy = 0.957031
I0130 14:27:39.708389 32230 solver.cpp:244]     Train net output #1: loss = 0.135845 (* 1 = 0.135845 loss)
I0130 14:27:39.708410 32230 sgd_solver.cpp:106] Iteration 43800, lr = 0.005
I0130 14:28:05.309058 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_44000.caffemodel
I0130 14:28:05.443224 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_44000.solverstate
I0130 14:28:05.471796 32230 solver.cpp:337] Iteration 44000, Testing net (#0)
I0130 14:28:06.577639 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8957
I0130 14:28:06.577677 32230 solver.cpp:404]     Test net output #1: loss = 0.336206 (* 1 = 0.336206 loss)
I0130 14:28:06.607146 32230 solver.cpp:228] Iteration 44000, loss = 0.127798
I0130 14:28:06.607156 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 14:28:06.607177 32230 solver.cpp:244]     Train net output #1: loss = 0.127798 (* 1 = 0.127798 loss)
I0130 14:28:06.607180 32230 sgd_solver.cpp:106] Iteration 44000, lr = 0.005
I0130 14:28:32.119674 32230 solver.cpp:337] Iteration 44200, Testing net (#0)
I0130 14:28:33.242432 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8954
I0130 14:28:33.242470 32230 solver.cpp:404]     Test net output #1: loss = 0.345236 (* 1 = 0.345236 loss)
I0130 14:28:33.271482 32230 solver.cpp:228] Iteration 44200, loss = 0.169806
I0130 14:28:33.271497 32230 solver.cpp:244]     Train net output #0: accuracy = 0.949219
I0130 14:28:33.271502 32230 solver.cpp:244]     Train net output #1: loss = 0.169806 (* 1 = 0.169806 loss)
I0130 14:28:33.271505 32230 sgd_solver.cpp:106] Iteration 44200, lr = 0.005
I0130 14:28:58.297391 32230 solver.cpp:337] Iteration 44400, Testing net (#0)
I0130 14:28:59.434213 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8951
I0130 14:28:59.434233 32230 solver.cpp:404]     Test net output #1: loss = 0.336091 (* 1 = 0.336091 loss)
I0130 14:28:59.463296 32230 solver.cpp:228] Iteration 44400, loss = 0.154482
I0130 14:28:59.463341 32230 solver.cpp:244]     Train net output #0: accuracy = 0.9375
I0130 14:28:59.463347 32230 solver.cpp:244]     Train net output #1: loss = 0.154482 (* 1 = 0.154482 loss)
I0130 14:28:59.463351 32230 sgd_solver.cpp:106] Iteration 44400, lr = 0.005
I0130 14:29:24.606415 32230 solver.cpp:337] Iteration 44600, Testing net (#0)
I0130 14:29:25.752298 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8923
I0130 14:29:25.752321 32230 solver.cpp:404]     Test net output #1: loss = 0.343235 (* 1 = 0.343235 loss)
I0130 14:29:25.781589 32230 solver.cpp:228] Iteration 44600, loss = 0.0978689
I0130 14:29:25.781605 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 14:29:25.781610 32230 solver.cpp:244]     Train net output #1: loss = 0.0978688 (* 1 = 0.0978688 loss)
I0130 14:29:25.781628 32230 sgd_solver.cpp:106] Iteration 44600, lr = 0.005
I0130 14:29:50.952219 32230 solver.cpp:337] Iteration 44800, Testing net (#0)
I0130 14:29:52.148967 32230 solver.cpp:404]     Test net output #0: accuracy = 0.896
I0130 14:29:52.148989 32230 solver.cpp:404]     Test net output #1: loss = 0.333176 (* 1 = 0.333176 loss)
I0130 14:29:52.179972 32230 solver.cpp:228] Iteration 44800, loss = 0.148311
I0130 14:29:52.179992 32230 solver.cpp:244]     Train net output #0: accuracy = 0.949219
I0130 14:29:52.179998 32230 solver.cpp:244]     Train net output #1: loss = 0.148311 (* 1 = 0.148311 loss)
I0130 14:29:52.180002 32230 sgd_solver.cpp:106] Iteration 44800, lr = 0.005
I0130 14:30:17.286267 32230 solver.cpp:337] Iteration 45000, Testing net (#0)
I0130 14:30:18.444664 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8943
I0130 14:30:18.444684 32230 solver.cpp:404]     Test net output #1: loss = 0.34171 (* 1 = 0.34171 loss)
I0130 14:30:18.474622 32230 solver.cpp:228] Iteration 45000, loss = 0.107083
I0130 14:30:18.474634 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 14:30:18.474639 32230 solver.cpp:244]     Train net output #1: loss = 0.107083 (* 1 = 0.107083 loss)
I0130 14:30:18.474643 32230 sgd_solver.cpp:106] Iteration 45000, lr = 0.005
I0130 14:30:43.435155 32230 solver.cpp:337] Iteration 45200, Testing net (#0)
I0130 14:30:44.565345 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8859
I0130 14:30:44.565366 32230 solver.cpp:404]     Test net output #1: loss = 0.354087 (* 1 = 0.354087 loss)
I0130 14:30:44.594264 32230 solver.cpp:228] Iteration 45200, loss = 0.11686
I0130 14:30:44.594279 32230 solver.cpp:244]     Train net output #0: accuracy = 0.957031
I0130 14:30:44.594283 32230 solver.cpp:244]     Train net output #1: loss = 0.11686 (* 1 = 0.11686 loss)
I0130 14:30:44.594287 32230 sgd_solver.cpp:106] Iteration 45200, lr = 0.005
I0130 14:31:09.795413 32230 solver.cpp:337] Iteration 45400, Testing net (#0)
I0130 14:31:10.928225 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8991
I0130 14:31:10.928247 32230 solver.cpp:404]     Test net output #1: loss = 0.333134 (* 1 = 0.333134 loss)
I0130 14:31:10.957314 32230 solver.cpp:228] Iteration 45400, loss = 0.114118
I0130 14:31:10.957327 32230 solver.cpp:244]     Train net output #0: accuracy = 0.953125
I0130 14:31:10.957332 32230 solver.cpp:244]     Train net output #1: loss = 0.114118 (* 1 = 0.114118 loss)
I0130 14:31:10.957336 32230 sgd_solver.cpp:106] Iteration 45400, lr = 0.005
I0130 14:31:36.746078 32230 solver.cpp:337] Iteration 45600, Testing net (#0)
I0130 14:31:37.916134 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8984
I0130 14:31:37.916157 32230 solver.cpp:404]     Test net output #1: loss = 0.338851 (* 1 = 0.338851 loss)
I0130 14:31:37.946779 32230 solver.cpp:228] Iteration 45600, loss = 0.0940692
I0130 14:31:37.946799 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 14:31:37.946805 32230 solver.cpp:244]     Train net output #1: loss = 0.0940692 (* 1 = 0.0940692 loss)
I0130 14:31:37.946808 32230 sgd_solver.cpp:106] Iteration 45600, lr = 0.005
I0130 14:32:02.971015 32230 solver.cpp:337] Iteration 45800, Testing net (#0)
I0130 14:32:04.100870 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8981
I0130 14:32:04.100893 32230 solver.cpp:404]     Test net output #1: loss = 0.33311 (* 1 = 0.33311 loss)
I0130 14:32:04.130089 32230 solver.cpp:228] Iteration 45800, loss = 0.102188
I0130 14:32:04.130106 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 14:32:04.130112 32230 solver.cpp:244]     Train net output #1: loss = 0.102188 (* 1 = 0.102188 loss)
I0130 14:32:04.130116 32230 sgd_solver.cpp:106] Iteration 45800, lr = 0.005
I0130 14:32:29.180121 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_46000.caffemodel
I0130 14:32:29.317416 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_46000.solverstate
I0130 14:32:29.345299 32230 solver.cpp:337] Iteration 46000, Testing net (#0)
I0130 14:32:30.378885 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8938
I0130 14:32:30.378906 32230 solver.cpp:404]     Test net output #1: loss = 0.331785 (* 1 = 0.331785 loss)
I0130 14:32:30.408246 32230 solver.cpp:228] Iteration 46000, loss = 0.0808324
I0130 14:32:30.408265 32230 solver.cpp:244]     Train net output #0: accuracy = 0.964844
I0130 14:32:30.408272 32230 solver.cpp:244]     Train net output #1: loss = 0.0808324 (* 1 = 0.0808324 loss)
I0130 14:32:30.408290 32230 sgd_solver.cpp:106] Iteration 46000, lr = 0.005
I0130 14:32:55.569707 32230 solver.cpp:337] Iteration 46200, Testing net (#0)
I0130 14:32:56.705688 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8991
I0130 14:32:56.705708 32230 solver.cpp:404]     Test net output #1: loss = 0.328101 (* 1 = 0.328101 loss)
I0130 14:32:56.734939 32230 solver.cpp:228] Iteration 46200, loss = 0.0709879
I0130 14:32:56.734956 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 14:32:56.734961 32230 solver.cpp:244]     Train net output #1: loss = 0.0709879 (* 1 = 0.0709879 loss)
I0130 14:32:56.734964 32230 sgd_solver.cpp:106] Iteration 46200, lr = 0.005
I0130 14:33:21.614348 32230 solver.cpp:337] Iteration 46400, Testing net (#0)
I0130 14:33:22.737715 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8926
I0130 14:33:22.737737 32230 solver.cpp:404]     Test net output #1: loss = 0.358106 (* 1 = 0.358106 loss)
I0130 14:33:22.767002 32230 solver.cpp:228] Iteration 46400, loss = 0.124669
I0130 14:33:22.767019 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 14:33:22.767024 32230 solver.cpp:244]     Train net output #1: loss = 0.124669 (* 1 = 0.124669 loss)
I0130 14:33:22.767027 32230 sgd_solver.cpp:106] Iteration 46400, lr = 0.005
I0130 14:33:47.486618 32230 solver.cpp:337] Iteration 46600, Testing net (#0)
I0130 14:33:48.619719 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8953
I0130 14:33:48.619740 32230 solver.cpp:404]     Test net output #1: loss = 0.348059 (* 1 = 0.348059 loss)
I0130 14:33:48.649283 32230 solver.cpp:228] Iteration 46600, loss = 0.0598322
I0130 14:33:48.649298 32230 solver.cpp:244]     Train net output #0: accuracy = 0.996094
I0130 14:33:48.649318 32230 solver.cpp:244]     Train net output #1: loss = 0.0598322 (* 1 = 0.0598322 loss)
I0130 14:33:48.649322 32230 sgd_solver.cpp:106] Iteration 46600, lr = 0.005
I0130 14:34:13.480197 32230 solver.cpp:337] Iteration 46800, Testing net (#0)
I0130 14:34:14.602385 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8956
I0130 14:34:14.602406 32230 solver.cpp:404]     Test net output #1: loss = 0.346183 (* 1 = 0.346183 loss)
I0130 14:34:14.631422 32230 solver.cpp:228] Iteration 46800, loss = 0.114981
I0130 14:34:14.631438 32230 solver.cpp:244]     Train net output #0: accuracy = 0.957031
I0130 14:34:14.631443 32230 solver.cpp:244]     Train net output #1: loss = 0.114981 (* 1 = 0.114981 loss)
I0130 14:34:14.631463 32230 sgd_solver.cpp:106] Iteration 46800, lr = 0.005
I0130 14:34:39.237845 32230 solver.cpp:337] Iteration 47000, Testing net (#0)
I0130 14:34:40.362232 32230 solver.cpp:404]     Test net output #0: accuracy = 0.896
I0130 14:34:40.362256 32230 solver.cpp:404]     Test net output #1: loss = 0.345474 (* 1 = 0.345474 loss)
I0130 14:34:40.391309 32230 solver.cpp:228] Iteration 47000, loss = 0.155703
I0130 14:34:40.391324 32230 solver.cpp:244]     Train net output #0: accuracy = 0.9375
I0130 14:34:40.391330 32230 solver.cpp:244]     Train net output #1: loss = 0.155703 (* 1 = 0.155703 loss)
I0130 14:34:40.391333 32230 sgd_solver.cpp:106] Iteration 47000, lr = 0.005
I0130 14:35:05.226943 32230 solver.cpp:337] Iteration 47200, Testing net (#0)
I0130 14:35:06.359349 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8948
I0130 14:35:06.359372 32230 solver.cpp:404]     Test net output #1: loss = 0.346107 (* 1 = 0.346107 loss)
I0130 14:35:06.388905 32230 solver.cpp:228] Iteration 47200, loss = 0.106153
I0130 14:35:06.388921 32230 solver.cpp:244]     Train net output #0: accuracy = 0.957031
I0130 14:35:06.388926 32230 solver.cpp:244]     Train net output #1: loss = 0.106153 (* 1 = 0.106153 loss)
I0130 14:35:06.388931 32230 sgd_solver.cpp:106] Iteration 47200, lr = 0.005
I0130 14:35:31.069188 32230 solver.cpp:337] Iteration 47400, Testing net (#0)
I0130 14:35:32.193786 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8936
I0130 14:35:32.193805 32230 solver.cpp:404]     Test net output #1: loss = 0.353943 (* 1 = 0.353943 loss)
I0130 14:35:32.222952 32230 solver.cpp:228] Iteration 47400, loss = 0.0796511
I0130 14:35:32.222970 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 14:35:32.222975 32230 solver.cpp:244]     Train net output #1: loss = 0.0796511 (* 1 = 0.0796511 loss)
I0130 14:35:32.222978 32230 sgd_solver.cpp:106] Iteration 47400, lr = 0.005
I0130 14:35:56.977015 32230 solver.cpp:337] Iteration 47600, Testing net (#0)
I0130 14:35:58.109295 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8946
I0130 14:35:58.109318 32230 solver.cpp:404]     Test net output #1: loss = 0.348589 (* 1 = 0.348589 loss)
I0130 14:35:58.138795 32230 solver.cpp:228] Iteration 47600, loss = 0.0805118
I0130 14:35:58.138813 32230 solver.cpp:244]     Train net output #0: accuracy = 0.960938
I0130 14:35:58.138818 32230 solver.cpp:244]     Train net output #1: loss = 0.0805118 (* 1 = 0.0805118 loss)
I0130 14:35:58.138837 32230 sgd_solver.cpp:106] Iteration 47600, lr = 0.005
I0130 14:36:22.847915 32230 solver.cpp:337] Iteration 47800, Testing net (#0)
I0130 14:36:23.971436 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8938
I0130 14:36:23.971458 32230 solver.cpp:404]     Test net output #1: loss = 0.351559 (* 1 = 0.351559 loss)
I0130 14:36:24.000612 32230 solver.cpp:228] Iteration 47800, loss = 0.0968338
I0130 14:36:24.000628 32230 solver.cpp:244]     Train net output #0: accuracy = 0.964844
I0130 14:36:24.000634 32230 solver.cpp:244]     Train net output #1: loss = 0.0968337 (* 1 = 0.0968337 loss)
I0130 14:36:24.000638 32230 sgd_solver.cpp:106] Iteration 47800, lr = 0.005
I0130 14:36:48.805233 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_48000.caffemodel
I0130 14:36:48.942054 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_48000.solverstate
I0130 14:36:48.969720 32230 solver.cpp:337] Iteration 48000, Testing net (#0)
I0130 14:36:49.997246 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8955
I0130 14:36:49.997283 32230 solver.cpp:404]     Test net output #1: loss = 0.340106 (* 1 = 0.340106 loss)
I0130 14:36:50.026648 32230 solver.cpp:228] Iteration 48000, loss = 0.0814094
I0130 14:36:50.026664 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 14:36:50.026684 32230 solver.cpp:244]     Train net output #1: loss = 0.0814094 (* 1 = 0.0814094 loss)
I0130 14:36:50.026690 32230 sgd_solver.cpp:106] Iteration 48000, lr = 0.005
I0130 14:37:14.717190 32230 solver.cpp:337] Iteration 48200, Testing net (#0)
I0130 14:37:15.849519 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8944
I0130 14:37:15.849547 32230 solver.cpp:404]     Test net output #1: loss = 0.342916 (* 1 = 0.342916 loss)
I0130 14:37:15.878960 32230 solver.cpp:228] Iteration 48200, loss = 0.135063
I0130 14:37:15.878976 32230 solver.cpp:244]     Train net output #0: accuracy = 0.953125
I0130 14:37:15.878981 32230 solver.cpp:244]     Train net output #1: loss = 0.135063 (* 1 = 0.135063 loss)
I0130 14:37:15.878985 32230 sgd_solver.cpp:106] Iteration 48200, lr = 0.005
I0130 14:37:40.713207 32230 solver.cpp:337] Iteration 48400, Testing net (#0)
I0130 14:37:41.837411 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8941
I0130 14:37:41.837432 32230 solver.cpp:404]     Test net output #1: loss = 0.340655 (* 1 = 0.340655 loss)
I0130 14:37:41.866716 32230 solver.cpp:228] Iteration 48400, loss = 0.105784
I0130 14:37:41.866732 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 14:37:41.866737 32230 solver.cpp:244]     Train net output #1: loss = 0.105784 (* 1 = 0.105784 loss)
I0130 14:37:41.866741 32230 sgd_solver.cpp:106] Iteration 48400, lr = 0.005
I0130 14:38:06.510601 32230 solver.cpp:337] Iteration 48600, Testing net (#0)
I0130 14:38:07.643486 32230 solver.cpp:404]     Test net output #0: accuracy = 0.892
I0130 14:38:07.643507 32230 solver.cpp:404]     Test net output #1: loss = 0.354414 (* 1 = 0.354414 loss)
I0130 14:38:07.672822 32230 solver.cpp:228] Iteration 48600, loss = 0.10484
I0130 14:38:07.672837 32230 solver.cpp:244]     Train net output #0: accuracy = 0.960938
I0130 14:38:07.672842 32230 solver.cpp:244]     Train net output #1: loss = 0.10484 (* 1 = 0.10484 loss)
I0130 14:38:07.672845 32230 sgd_solver.cpp:106] Iteration 48600, lr = 0.005
I0130 14:38:32.541390 32230 solver.cpp:337] Iteration 48800, Testing net (#0)
I0130 14:38:33.674144 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8953
I0130 14:38:33.674167 32230 solver.cpp:404]     Test net output #1: loss = 0.348633 (* 1 = 0.348633 loss)
I0130 14:38:33.703601 32230 solver.cpp:228] Iteration 48800, loss = 0.112849
I0130 14:38:33.703616 32230 solver.cpp:244]     Train net output #0: accuracy = 0.960938
I0130 14:38:33.703622 32230 solver.cpp:244]     Train net output #1: loss = 0.112849 (* 1 = 0.112849 loss)
I0130 14:38:33.703626 32230 sgd_solver.cpp:106] Iteration 48800, lr = 0.005
I0130 14:38:58.417780 32230 solver.cpp:337] Iteration 49000, Testing net (#0)
I0130 14:38:59.551267 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8891
I0130 14:38:59.551291 32230 solver.cpp:404]     Test net output #1: loss = 0.364274 (* 1 = 0.364274 loss)
I0130 14:38:59.580564 32230 solver.cpp:228] Iteration 49000, loss = 0.0967405
I0130 14:38:59.580580 32230 solver.cpp:244]     Train net output #0: accuracy = 0.964844
I0130 14:38:59.580586 32230 solver.cpp:244]     Train net output #1: loss = 0.0967404 (* 1 = 0.0967404 loss)
I0130 14:38:59.580605 32230 sgd_solver.cpp:106] Iteration 49000, lr = 0.005
I0130 14:39:24.446797 32230 solver.cpp:337] Iteration 49200, Testing net (#0)
I0130 14:39:25.571420 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8937
I0130 14:39:25.571444 32230 solver.cpp:404]     Test net output #1: loss = 0.35177 (* 1 = 0.35177 loss)
I0130 14:39:25.600600 32230 solver.cpp:228] Iteration 49200, loss = 0.103727
I0130 14:39:25.600616 32230 solver.cpp:244]     Train net output #0: accuracy = 0.960938
I0130 14:39:25.600621 32230 solver.cpp:244]     Train net output #1: loss = 0.103727 (* 1 = 0.103727 loss)
I0130 14:39:25.600625 32230 sgd_solver.cpp:106] Iteration 49200, lr = 0.005
I0130 14:39:50.212467 32230 solver.cpp:337] Iteration 49400, Testing net (#0)
I0130 14:39:51.341109 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8955
I0130 14:39:51.341131 32230 solver.cpp:404]     Test net output #1: loss = 0.343949 (* 1 = 0.343949 loss)
I0130 14:39:51.370355 32230 solver.cpp:228] Iteration 49400, loss = 0.106098
I0130 14:39:51.370384 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 14:39:51.370404 32230 solver.cpp:244]     Train net output #1: loss = 0.106098 (* 1 = 0.106098 loss)
I0130 14:39:51.370409 32230 sgd_solver.cpp:106] Iteration 49400, lr = 0.005
I0130 14:40:16.221490 32230 solver.cpp:337] Iteration 49600, Testing net (#0)
I0130 14:40:17.354032 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8898
I0130 14:40:17.354055 32230 solver.cpp:404]     Test net output #1: loss = 0.36457 (* 1 = 0.36457 loss)
I0130 14:40:17.383520 32230 solver.cpp:228] Iteration 49600, loss = 0.120991
I0130 14:40:17.383535 32230 solver.cpp:244]     Train net output #0: accuracy = 0.960938
I0130 14:40:17.383541 32230 solver.cpp:244]     Train net output #1: loss = 0.120991 (* 1 = 0.120991 loss)
I0130 14:40:17.383545 32230 sgd_solver.cpp:106] Iteration 49600, lr = 0.005
I0130 14:40:42.069257 32230 solver.cpp:337] Iteration 49800, Testing net (#0)
I0130 14:40:43.193102 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8936
I0130 14:40:43.193125 32230 solver.cpp:404]     Test net output #1: loss = 0.352717 (* 1 = 0.352717 loss)
I0130 14:40:43.222349 32230 solver.cpp:228] Iteration 49800, loss = 0.0714137
I0130 14:40:43.222364 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 14:40:43.222369 32230 solver.cpp:244]     Train net output #1: loss = 0.0714137 (* 1 = 0.0714137 loss)
I0130 14:40:43.222373 32230 sgd_solver.cpp:106] Iteration 49800, lr = 0.005
I0130 14:41:08.016672 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_50000.caffemodel
I0130 14:41:08.153621 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_50000.solverstate
I0130 14:41:08.181072 32230 solver.cpp:337] Iteration 50000, Testing net (#0)
I0130 14:41:09.210450 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8963
I0130 14:41:09.210474 32230 solver.cpp:404]     Test net output #1: loss = 0.343417 (* 1 = 0.343417 loss)
I0130 14:41:09.239768 32230 solver.cpp:228] Iteration 50000, loss = 0.0515495
I0130 14:41:09.239784 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 14:41:09.239804 32230 solver.cpp:244]     Train net output #1: loss = 0.0515495 (* 1 = 0.0515495 loss)
I0130 14:41:09.239807 32230 sgd_solver.cpp:46] MultiStep Status: Iteration 50000, step = 2
I0130 14:41:09.239810 32230 sgd_solver.cpp:106] Iteration 50000, lr = 0.0005
I0130 14:41:33.916538 32230 solver.cpp:337] Iteration 50200, Testing net (#0)
I0130 14:41:35.049154 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9022
I0130 14:41:35.049175 32230 solver.cpp:404]     Test net output #1: loss = 0.331378 (* 1 = 0.331378 loss)
I0130 14:41:35.078655 32230 solver.cpp:228] Iteration 50200, loss = 0.0697659
I0130 14:41:35.078670 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 14:41:35.078675 32230 solver.cpp:244]     Train net output #1: loss = 0.0697659 (* 1 = 0.0697659 loss)
I0130 14:41:35.078678 32230 sgd_solver.cpp:106] Iteration 50200, lr = 0.0005
I0130 14:41:59.869941 32230 solver.cpp:337] Iteration 50400, Testing net (#0)
I0130 14:42:00.995322 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9021
I0130 14:42:00.995345 32230 solver.cpp:404]     Test net output #1: loss = 0.33321 (* 1 = 0.33321 loss)
I0130 14:42:01.024649 32230 solver.cpp:228] Iteration 50400, loss = 0.04139
I0130 14:42:01.024665 32230 solver.cpp:244]     Train net output #0: accuracy = 0.992188
I0130 14:42:01.024670 32230 solver.cpp:244]     Train net output #1: loss = 0.04139 (* 1 = 0.04139 loss)
I0130 14:42:01.024675 32230 sgd_solver.cpp:106] Iteration 50400, lr = 0.0005
I0130 14:42:25.746601 32230 solver.cpp:337] Iteration 50600, Testing net (#0)
I0130 14:42:26.870558 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9023
I0130 14:42:26.870580 32230 solver.cpp:404]     Test net output #1: loss = 0.334055 (* 1 = 0.334055 loss)
I0130 14:42:26.899899 32230 solver.cpp:228] Iteration 50600, loss = 0.0799627
I0130 14:42:26.899912 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 14:42:26.899919 32230 solver.cpp:244]     Train net output #1: loss = 0.0799627 (* 1 = 0.0799627 loss)
I0130 14:42:26.899922 32230 sgd_solver.cpp:106] Iteration 50600, lr = 0.0005
I0130 14:42:52.157234 32230 solver.cpp:337] Iteration 50800, Testing net (#0)
I0130 14:42:53.343709 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9026
I0130 14:42:53.343747 32230 solver.cpp:404]     Test net output #1: loss = 0.334634 (* 1 = 0.334634 loss)
I0130 14:42:53.374017 32230 solver.cpp:228] Iteration 50800, loss = 0.0924383
I0130 14:42:53.374034 32230 solver.cpp:244]     Train net output #0: accuracy = 0.960938
I0130 14:42:53.374040 32230 solver.cpp:244]     Train net output #1: loss = 0.0924383 (* 1 = 0.0924383 loss)
I0130 14:42:53.374044 32230 sgd_solver.cpp:106] Iteration 50800, lr = 0.0005
I0130 14:43:18.813931 32230 solver.cpp:337] Iteration 51000, Testing net (#0)
I0130 14:43:19.984843 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9
I0130 14:43:19.984863 32230 solver.cpp:404]     Test net output #1: loss = 0.339607 (* 1 = 0.339607 loss)
I0130 14:43:20.013685 32230 solver.cpp:228] Iteration 51000, loss = 0.0524254
I0130 14:43:20.013701 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 14:43:20.013707 32230 solver.cpp:244]     Train net output #1: loss = 0.0524254 (* 1 = 0.0524254 loss)
I0130 14:43:20.013711 32230 sgd_solver.cpp:106] Iteration 51000, lr = 0.0005
I0130 14:43:45.786702 32230 solver.cpp:337] Iteration 51200, Testing net (#0)
I0130 14:43:46.911198 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9019
I0130 14:43:46.911217 32230 solver.cpp:404]     Test net output #1: loss = 0.340108 (* 1 = 0.340108 loss)
I0130 14:43:46.940385 32230 solver.cpp:228] Iteration 51200, loss = 0.0548838
I0130 14:43:46.940404 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 14:43:46.940412 32230 solver.cpp:244]     Train net output #1: loss = 0.0548838 (* 1 = 0.0548838 loss)
I0130 14:43:46.940417 32230 sgd_solver.cpp:106] Iteration 51200, lr = 0.0005
I0130 14:44:12.663239 32230 solver.cpp:337] Iteration 51400, Testing net (#0)
I0130 14:44:13.789940 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8999
I0130 14:44:13.789961 32230 solver.cpp:404]     Test net output #1: loss = 0.338148 (* 1 = 0.338148 loss)
I0130 14:44:13.819118 32230 solver.cpp:228] Iteration 51400, loss = 0.0613732
I0130 14:44:13.819133 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 14:44:13.819139 32230 solver.cpp:244]     Train net output #1: loss = 0.0613732 (* 1 = 0.0613732 loss)
I0130 14:44:13.819142 32230 sgd_solver.cpp:106] Iteration 51400, lr = 0.0005
I0130 14:44:39.171736 32230 solver.cpp:337] Iteration 51600, Testing net (#0)
I0130 14:44:40.331991 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9009
I0130 14:44:40.332013 32230 solver.cpp:404]     Test net output #1: loss = 0.340072 (* 1 = 0.340072 loss)
I0130 14:44:40.361202 32230 solver.cpp:228] Iteration 51600, loss = 0.0618855
I0130 14:44:40.361218 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 14:44:40.361224 32230 solver.cpp:244]     Train net output #1: loss = 0.0618856 (* 1 = 0.0618856 loss)
I0130 14:44:40.361228 32230 sgd_solver.cpp:106] Iteration 51600, lr = 0.0005
I0130 14:45:05.761045 32230 solver.cpp:337] Iteration 51800, Testing net (#0)
I0130 14:45:06.917172 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9002
I0130 14:45:06.917194 32230 solver.cpp:404]     Test net output #1: loss = 0.341569 (* 1 = 0.341569 loss)
I0130 14:45:06.945935 32230 solver.cpp:228] Iteration 51800, loss = 0.110874
I0130 14:45:06.945950 32230 solver.cpp:244]     Train net output #0: accuracy = 0.964844
I0130 14:45:06.945955 32230 solver.cpp:244]     Train net output #1: loss = 0.110874 (* 1 = 0.110874 loss)
I0130 14:45:06.945960 32230 sgd_solver.cpp:106] Iteration 51800, lr = 0.0005
I0130 14:45:32.015305 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_52000.caffemodel
I0130 14:45:32.152299 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_52000.solverstate
I0130 14:45:32.181203 32230 solver.cpp:337] Iteration 52000, Testing net (#0)
I0130 14:45:33.211125 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9025
I0130 14:45:33.211143 32230 solver.cpp:404]     Test net output #1: loss = 0.33919 (* 1 = 0.33919 loss)
I0130 14:45:33.240386 32230 solver.cpp:228] Iteration 52000, loss = 0.0657455
I0130 14:45:33.240402 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 14:45:33.240408 32230 solver.cpp:244]     Train net output #1: loss = 0.0657456 (* 1 = 0.0657456 loss)
I0130 14:45:33.240412 32230 sgd_solver.cpp:106] Iteration 52000, lr = 0.0005
I0130 14:45:58.136265 32230 solver.cpp:337] Iteration 52200, Testing net (#0)
I0130 14:45:59.276170 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9002
I0130 14:45:59.276193 32230 solver.cpp:404]     Test net output #1: loss = 0.344688 (* 1 = 0.344688 loss)
I0130 14:45:59.305340 32230 solver.cpp:228] Iteration 52200, loss = 0.0774598
I0130 14:45:59.305356 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 14:45:59.305362 32230 solver.cpp:244]     Train net output #1: loss = 0.0774598 (* 1 = 0.0774598 loss)
I0130 14:45:59.305367 32230 sgd_solver.cpp:106] Iteration 52200, lr = 0.0005
I0130 14:46:24.287039 32230 solver.cpp:337] Iteration 52400, Testing net (#0)
I0130 14:46:25.457200 32230 solver.cpp:404]     Test net output #0: accuracy = 0.901
I0130 14:46:25.457219 32230 solver.cpp:404]     Test net output #1: loss = 0.342734 (* 1 = 0.342734 loss)
I0130 14:46:25.486781 32230 solver.cpp:228] Iteration 52400, loss = 0.079993
I0130 14:46:25.486796 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 14:46:25.486802 32230 solver.cpp:244]     Train net output #1: loss = 0.079993 (* 1 = 0.079993 loss)
I0130 14:46:25.486806 32230 sgd_solver.cpp:106] Iteration 52400, lr = 0.0005
I0130 14:46:50.606055 32230 solver.cpp:337] Iteration 52600, Testing net (#0)
I0130 14:46:51.763713 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8999
I0130 14:46:51.763751 32230 solver.cpp:404]     Test net output #1: loss = 0.344507 (* 1 = 0.344507 loss)
I0130 14:46:51.792858 32230 solver.cpp:228] Iteration 52600, loss = 0.0417336
I0130 14:46:51.792872 32230 solver.cpp:244]     Train net output #0: accuracy = 0.988281
I0130 14:46:51.792878 32230 solver.cpp:244]     Train net output #1: loss = 0.0417337 (* 1 = 0.0417337 loss)
I0130 14:46:51.792882 32230 sgd_solver.cpp:106] Iteration 52600, lr = 0.0005
I0130 14:47:16.698573 32230 solver.cpp:337] Iteration 52800, Testing net (#0)
I0130 14:47:17.845134 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9004
I0130 14:47:17.845154 32230 solver.cpp:404]     Test net output #1: loss = 0.341893 (* 1 = 0.341893 loss)
I0130 14:47:17.874075 32230 solver.cpp:228] Iteration 52800, loss = 0.0675498
I0130 14:47:17.874090 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 14:47:17.874095 32230 solver.cpp:244]     Train net output #1: loss = 0.0675498 (* 1 = 0.0675498 loss)
I0130 14:47:17.874100 32230 sgd_solver.cpp:106] Iteration 52800, lr = 0.0005
I0130 14:47:43.352423 32230 solver.cpp:337] Iteration 53000, Testing net (#0)
I0130 14:47:44.476905 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9009
I0130 14:47:44.476925 32230 solver.cpp:404]     Test net output #1: loss = 0.344739 (* 1 = 0.344739 loss)
I0130 14:47:44.505930 32230 solver.cpp:228] Iteration 53000, loss = 0.0838843
I0130 14:47:44.505949 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 14:47:44.505954 32230 solver.cpp:244]     Train net output #1: loss = 0.0838843 (* 1 = 0.0838843 loss)
I0130 14:47:44.505957 32230 sgd_solver.cpp:106] Iteration 53000, lr = 0.0005
I0130 14:48:09.406792 32230 solver.cpp:337] Iteration 53200, Testing net (#0)
I0130 14:48:10.539902 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9015
I0130 14:48:10.539922 32230 solver.cpp:404]     Test net output #1: loss = 0.343689 (* 1 = 0.343689 loss)
I0130 14:48:10.569155 32230 solver.cpp:228] Iteration 53200, loss = 0.0706859
I0130 14:48:10.569171 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 14:48:10.569177 32230 solver.cpp:244]     Train net output #1: loss = 0.0706859 (* 1 = 0.0706859 loss)
I0130 14:48:10.569180 32230 sgd_solver.cpp:106] Iteration 53200, lr = 0.0005
I0130 14:48:35.338723 32230 solver.cpp:337] Iteration 53400, Testing net (#0)
I0130 14:48:36.493018 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9024
I0130 14:48:36.493038 32230 solver.cpp:404]     Test net output #1: loss = 0.340979 (* 1 = 0.340979 loss)
I0130 14:48:36.522117 32230 solver.cpp:228] Iteration 53400, loss = 0.0920252
I0130 14:48:36.522132 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 14:48:36.522138 32230 solver.cpp:244]     Train net output #1: loss = 0.0920252 (* 1 = 0.0920252 loss)
I0130 14:48:36.522142 32230 sgd_solver.cpp:106] Iteration 53400, lr = 0.0005
I0130 14:49:01.554996 32230 solver.cpp:337] Iteration 53600, Testing net (#0)
I0130 14:49:02.680718 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9018
I0130 14:49:02.680740 32230 solver.cpp:404]     Test net output #1: loss = 0.343194 (* 1 = 0.343194 loss)
I0130 14:49:02.709801 32230 solver.cpp:228] Iteration 53600, loss = 0.0840994
I0130 14:49:02.709820 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 14:49:02.709825 32230 solver.cpp:244]     Train net output #1: loss = 0.0840994 (* 1 = 0.0840994 loss)
I0130 14:49:02.709830 32230 sgd_solver.cpp:106] Iteration 53600, lr = 0.0005
I0130 14:49:27.631083 32230 solver.cpp:337] Iteration 53800, Testing net (#0)
I0130 14:49:28.806987 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9006
I0130 14:49:28.807005 32230 solver.cpp:404]     Test net output #1: loss = 0.345707 (* 1 = 0.345707 loss)
I0130 14:49:28.836298 32230 solver.cpp:228] Iteration 53800, loss = 0.106143
I0130 14:49:28.836313 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 14:49:28.836319 32230 solver.cpp:244]     Train net output #1: loss = 0.106143 (* 1 = 0.106143 loss)
I0130 14:49:28.836323 32230 sgd_solver.cpp:106] Iteration 53800, lr = 0.0005
I0130 14:49:54.467847 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_54000.caffemodel
I0130 14:49:54.612573 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_54000.solverstate
I0130 14:49:54.642552 32230 solver.cpp:337] Iteration 54000, Testing net (#0)
I0130 14:49:55.671303 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9021
I0130 14:49:55.671326 32230 solver.cpp:404]     Test net output #1: loss = 0.339786 (* 1 = 0.339786 loss)
I0130 14:49:55.700505 32230 solver.cpp:228] Iteration 54000, loss = 0.0544848
I0130 14:49:55.700523 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 14:49:55.700528 32230 solver.cpp:244]     Train net output #1: loss = 0.0544848 (* 1 = 0.0544848 loss)
I0130 14:49:55.700533 32230 sgd_solver.cpp:106] Iteration 54000, lr = 0.0005
I0130 14:50:20.889997 32230 solver.cpp:337] Iteration 54200, Testing net (#0)
I0130 14:50:22.037619 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9003
I0130 14:50:22.037654 32230 solver.cpp:404]     Test net output #1: loss = 0.340332 (* 1 = 0.340332 loss)
I0130 14:50:22.066797 32230 solver.cpp:228] Iteration 54200, loss = 0.0876137
I0130 14:50:22.066814 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 14:50:22.066820 32230 solver.cpp:244]     Train net output #1: loss = 0.0876137 (* 1 = 0.0876137 loss)
I0130 14:50:22.066824 32230 sgd_solver.cpp:106] Iteration 54200, lr = 0.0005
I0130 14:50:47.107698 32230 solver.cpp:337] Iteration 54400, Testing net (#0)
I0130 14:50:48.238155 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9017
I0130 14:50:48.238175 32230 solver.cpp:404]     Test net output #1: loss = 0.336373 (* 1 = 0.336373 loss)
I0130 14:50:48.267458 32230 solver.cpp:228] Iteration 54400, loss = 0.0503111
I0130 14:50:48.267475 32230 solver.cpp:244]     Train net output #0: accuracy = 0.996094
I0130 14:50:48.267482 32230 solver.cpp:244]     Train net output #1: loss = 0.0503112 (* 1 = 0.0503112 loss)
I0130 14:50:48.267487 32230 sgd_solver.cpp:106] Iteration 54400, lr = 0.0005
I0130 14:51:13.238374 32230 solver.cpp:337] Iteration 54600, Testing net (#0)
I0130 14:51:14.375906 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9022
I0130 14:51:14.375926 32230 solver.cpp:404]     Test net output #1: loss = 0.338899 (* 1 = 0.338899 loss)
I0130 14:51:14.405555 32230 solver.cpp:228] Iteration 54600, loss = 0.067714
I0130 14:51:14.405571 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 14:51:14.405576 32230 solver.cpp:244]     Train net output #1: loss = 0.0677141 (* 1 = 0.0677141 loss)
I0130 14:51:14.405581 32230 sgd_solver.cpp:106] Iteration 54600, lr = 0.0005
I0130 14:51:39.692915 32230 solver.cpp:337] Iteration 54800, Testing net (#0)
I0130 14:51:40.816190 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9007
I0130 14:51:40.816211 32230 solver.cpp:404]     Test net output #1: loss = 0.344136 (* 1 = 0.344136 loss)
I0130 14:51:40.843642 32230 solver.cpp:228] Iteration 54800, loss = 0.0650635
I0130 14:51:40.843657 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 14:51:40.843662 32230 solver.cpp:244]     Train net output #1: loss = 0.0650636 (* 1 = 0.0650636 loss)
I0130 14:51:40.843667 32230 sgd_solver.cpp:106] Iteration 54800, lr = 0.0005
I0130 14:52:05.792075 32230 solver.cpp:337] Iteration 55000, Testing net (#0)
I0130 14:52:06.924649 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9008
I0130 14:52:06.924685 32230 solver.cpp:404]     Test net output #1: loss = 0.342559 (* 1 = 0.342559 loss)
I0130 14:52:06.952155 32230 solver.cpp:228] Iteration 55000, loss = 0.0477835
I0130 14:52:06.952170 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 14:52:06.952174 32230 solver.cpp:244]     Train net output #1: loss = 0.0477835 (* 1 = 0.0477835 loss)
I0130 14:52:06.952178 32230 sgd_solver.cpp:106] Iteration 55000, lr = 0.0005
I0130 14:52:31.674340 32230 solver.cpp:337] Iteration 55200, Testing net (#0)
I0130 14:52:32.798792 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8991
I0130 14:52:32.798830 32230 solver.cpp:404]     Test net output #1: loss = 0.346741 (* 1 = 0.346741 loss)
I0130 14:52:32.828076 32230 solver.cpp:228] Iteration 55200, loss = 0.0707032
I0130 14:52:32.828091 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 14:52:32.828097 32230 solver.cpp:244]     Train net output #1: loss = 0.0707032 (* 1 = 0.0707032 loss)
I0130 14:52:32.828101 32230 sgd_solver.cpp:106] Iteration 55200, lr = 0.0005
I0130 14:52:57.640410 32230 solver.cpp:337] Iteration 55400, Testing net (#0)
I0130 14:52:58.770555 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9012
I0130 14:52:58.770592 32230 solver.cpp:404]     Test net output #1: loss = 0.341771 (* 1 = 0.341771 loss)
I0130 14:52:58.798259 32230 solver.cpp:228] Iteration 55400, loss = 0.0609666
I0130 14:52:58.798274 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 14:52:58.798280 32230 solver.cpp:244]     Train net output #1: loss = 0.0609667 (* 1 = 0.0609667 loss)
I0130 14:52:58.798283 32230 sgd_solver.cpp:106] Iteration 55400, lr = 0.0005
I0130 14:53:23.600347 32230 solver.cpp:337] Iteration 55600, Testing net (#0)
I0130 14:53:24.740888 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9011
I0130 14:53:24.740907 32230 solver.cpp:404]     Test net output #1: loss = 0.341494 (* 1 = 0.341494 loss)
I0130 14:53:24.768278 32230 solver.cpp:228] Iteration 55600, loss = 0.065249
I0130 14:53:24.768295 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 14:53:24.768299 32230 solver.cpp:244]     Train net output #1: loss = 0.065249 (* 1 = 0.065249 loss)
I0130 14:53:24.768302 32230 sgd_solver.cpp:106] Iteration 55600, lr = 0.0005
I0130 14:53:49.444082 32230 solver.cpp:337] Iteration 55800, Testing net (#0)
I0130 14:53:50.577841 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9025
I0130 14:53:50.577877 32230 solver.cpp:404]     Test net output #1: loss = 0.34 (* 1 = 0.34 loss)
I0130 14:53:50.607075 32230 solver.cpp:228] Iteration 55800, loss = 0.0736427
I0130 14:53:50.607092 32230 solver.cpp:244]     Train net output #0: accuracy = 0.964844
I0130 14:53:50.607097 32230 solver.cpp:244]     Train net output #1: loss = 0.0736428 (* 1 = 0.0736428 loss)
I0130 14:53:50.607102 32230 sgd_solver.cpp:106] Iteration 55800, lr = 0.0005
I0130 14:54:15.494199 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_56000.caffemodel
I0130 14:54:15.631942 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_56000.solverstate
I0130 14:54:15.660048 32230 solver.cpp:337] Iteration 56000, Testing net (#0)
I0130 14:54:16.687983 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9009
I0130 14:54:16.688004 32230 solver.cpp:404]     Test net output #1: loss = 0.342637 (* 1 = 0.342637 loss)
I0130 14:54:16.717141 32230 solver.cpp:228] Iteration 56000, loss = 0.104627
I0130 14:54:16.717157 32230 solver.cpp:244]     Train net output #0: accuracy = 0.964844
I0130 14:54:16.717162 32230 solver.cpp:244]     Train net output #1: loss = 0.104627 (* 1 = 0.104627 loss)
I0130 14:54:16.717181 32230 sgd_solver.cpp:106] Iteration 56000, lr = 0.0005
I0130 14:54:41.333791 32230 solver.cpp:337] Iteration 56200, Testing net (#0)
I0130 14:54:42.457449 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9004
I0130 14:54:42.457486 32230 solver.cpp:404]     Test net output #1: loss = 0.344737 (* 1 = 0.344737 loss)
I0130 14:54:42.486716 32230 solver.cpp:228] Iteration 56200, loss = 0.0722482
I0130 14:54:42.486747 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 14:54:42.486753 32230 solver.cpp:244]     Train net output #1: loss = 0.0722482 (* 1 = 0.0722482 loss)
I0130 14:54:42.486757 32230 sgd_solver.cpp:106] Iteration 56200, lr = 0.0005
I0130 14:55:07.335556 32230 solver.cpp:337] Iteration 56400, Testing net (#0)
I0130 14:55:08.468621 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9021
I0130 14:55:08.468642 32230 solver.cpp:404]     Test net output #1: loss = 0.344212 (* 1 = 0.344212 loss)
I0130 14:55:08.497938 32230 solver.cpp:228] Iteration 56400, loss = 0.0864795
I0130 14:55:08.497954 32230 solver.cpp:244]     Train net output #0: accuracy = 0.964844
I0130 14:55:08.497959 32230 solver.cpp:244]     Train net output #1: loss = 0.0864795 (* 1 = 0.0864795 loss)
I0130 14:55:08.497964 32230 sgd_solver.cpp:106] Iteration 56400, lr = 0.0005
I0130 14:55:33.316088 32230 solver.cpp:337] Iteration 56600, Testing net (#0)
I0130 14:55:34.440590 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9006
I0130 14:55:34.440614 32230 solver.cpp:404]     Test net output #1: loss = 0.347844 (* 1 = 0.347844 loss)
I0130 14:55:34.470031 32230 solver.cpp:228] Iteration 56600, loss = 0.0730935
I0130 14:55:34.470048 32230 solver.cpp:244]     Train net output #0: accuracy = 0.964844
I0130 14:55:34.470054 32230 solver.cpp:244]     Train net output #1: loss = 0.0730935 (* 1 = 0.0730935 loss)
I0130 14:55:34.470058 32230 sgd_solver.cpp:106] Iteration 56600, lr = 0.0005
I0130 14:55:59.103579 32230 solver.cpp:337] Iteration 56800, Testing net (#0)
I0130 14:56:00.227602 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8996
I0130 14:56:00.227622 32230 solver.cpp:404]     Test net output #1: loss = 0.347674 (* 1 = 0.347674 loss)
I0130 14:56:00.256749 32230 solver.cpp:228] Iteration 56800, loss = 0.0660686
I0130 14:56:00.256767 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 14:56:00.256772 32230 solver.cpp:244]     Train net output #1: loss = 0.0660686 (* 1 = 0.0660686 loss)
I0130 14:56:00.256778 32230 sgd_solver.cpp:106] Iteration 56800, lr = 0.0005
I0130 14:56:25.088587 32230 solver.cpp:337] Iteration 57000, Testing net (#0)
I0130 14:56:26.222211 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9001
I0130 14:56:26.222240 32230 solver.cpp:404]     Test net output #1: loss = 0.34672 (* 1 = 0.34672 loss)
I0130 14:56:26.251579 32230 solver.cpp:228] Iteration 57000, loss = 0.0682623
I0130 14:56:26.251593 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 14:56:26.251598 32230 solver.cpp:244]     Train net output #1: loss = 0.0682623 (* 1 = 0.0682623 loss)
I0130 14:56:26.251602 32230 sgd_solver.cpp:106] Iteration 57000, lr = 0.0005
I0130 14:56:50.965407 32230 solver.cpp:337] Iteration 57200, Testing net (#0)
I0130 14:56:52.089756 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9005
I0130 14:56:52.089792 32230 solver.cpp:404]     Test net output #1: loss = 0.342724 (* 1 = 0.342724 loss)
I0130 14:56:52.119073 32230 solver.cpp:228] Iteration 57200, loss = 0.0814631
I0130 14:56:52.119088 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 14:56:52.119094 32230 solver.cpp:244]     Train net output #1: loss = 0.0814631 (* 1 = 0.0814631 loss)
I0130 14:56:52.119098 32230 sgd_solver.cpp:106] Iteration 57200, lr = 0.0005
I0130 14:57:16.923171 32230 solver.cpp:337] Iteration 57400, Testing net (#0)
I0130 14:57:18.049419 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9016
I0130 14:57:18.049460 32230 solver.cpp:404]     Test net output #1: loss = 0.346506 (* 1 = 0.346506 loss)
I0130 14:57:18.078589 32230 solver.cpp:228] Iteration 57400, loss = 0.0676048
I0130 14:57:18.078609 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 14:57:18.078614 32230 solver.cpp:244]     Train net output #1: loss = 0.0676048 (* 1 = 0.0676048 loss)
I0130 14:57:18.078620 32230 sgd_solver.cpp:106] Iteration 57400, lr = 0.0005
I0130 14:57:42.868080 32230 solver.cpp:337] Iteration 57600, Testing net (#0)
I0130 14:57:44.003612 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8996
I0130 14:57:44.003633 32230 solver.cpp:404]     Test net output #1: loss = 0.348428 (* 1 = 0.348428 loss)
I0130 14:57:44.032624 32230 solver.cpp:228] Iteration 57600, loss = 0.0627705
I0130 14:57:44.032655 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 14:57:44.032661 32230 solver.cpp:244]     Train net output #1: loss = 0.0627706 (* 1 = 0.0627706 loss)
I0130 14:57:44.032665 32230 sgd_solver.cpp:106] Iteration 57600, lr = 0.0005
I0130 14:58:08.895489 32230 solver.cpp:337] Iteration 57800, Testing net (#0)
I0130 14:58:10.029225 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8977
I0130 14:58:10.029247 32230 solver.cpp:404]     Test net output #1: loss = 0.34907 (* 1 = 0.34907 loss)
I0130 14:58:10.058538 32230 solver.cpp:228] Iteration 57800, loss = 0.076275
I0130 14:58:10.058555 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 14:58:10.058562 32230 solver.cpp:244]     Train net output #1: loss = 0.076275 (* 1 = 0.076275 loss)
I0130 14:58:10.058564 32230 sgd_solver.cpp:106] Iteration 57800, lr = 0.0005
I0130 14:58:34.837719 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_58000.caffemodel
I0130 14:58:34.972846 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_58000.solverstate
I0130 14:58:35.001040 32230 solver.cpp:337] Iteration 58000, Testing net (#0)
I0130 14:58:36.028455 32230 solver.cpp:404]     Test net output #0: accuracy = 0.901
I0130 14:58:36.028479 32230 solver.cpp:404]     Test net output #1: loss = 0.344847 (* 1 = 0.344847 loss)
I0130 14:58:36.057718 32230 solver.cpp:228] Iteration 58000, loss = 0.0492413
I0130 14:58:36.057734 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 14:58:36.057739 32230 solver.cpp:244]     Train net output #1: loss = 0.0492414 (* 1 = 0.0492414 loss)
I0130 14:58:36.057744 32230 sgd_solver.cpp:106] Iteration 58000, lr = 0.0005
I0130 14:59:00.860702 32230 solver.cpp:337] Iteration 58200, Testing net (#0)
I0130 14:59:01.994413 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9007
I0130 14:59:01.994436 32230 solver.cpp:404]     Test net output #1: loss = 0.3447 (* 1 = 0.3447 loss)
I0130 14:59:02.023716 32230 solver.cpp:228] Iteration 58200, loss = 0.0652522
I0130 14:59:02.023732 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 14:59:02.023737 32230 solver.cpp:244]     Train net output #1: loss = 0.0652522 (* 1 = 0.0652522 loss)
I0130 14:59:02.023741 32230 sgd_solver.cpp:106] Iteration 58200, lr = 0.0005
I0130 14:59:26.956418 32230 solver.cpp:337] Iteration 58400, Testing net (#0)
I0130 14:59:28.085371 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9002
I0130 14:59:28.085423 32230 solver.cpp:404]     Test net output #1: loss = 0.344999 (* 1 = 0.344999 loss)
I0130 14:59:28.112773 32230 solver.cpp:228] Iteration 58400, loss = 0.0830112
I0130 14:59:28.112787 32230 solver.cpp:244]     Train net output #0: accuracy = 0.957031
I0130 14:59:28.112808 32230 solver.cpp:244]     Train net output #1: loss = 0.0830112 (* 1 = 0.0830112 loss)
I0130 14:59:28.112812 32230 sgd_solver.cpp:106] Iteration 58400, lr = 0.0005
I0130 14:59:52.951040 32230 solver.cpp:337] Iteration 58600, Testing net (#0)
I0130 14:59:54.084481 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9021
I0130 14:59:54.084518 32230 solver.cpp:404]     Test net output #1: loss = 0.340141 (* 1 = 0.340141 loss)
I0130 14:59:54.114047 32230 solver.cpp:228] Iteration 58600, loss = 0.074916
I0130 14:59:54.114061 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 14:59:54.114079 32230 solver.cpp:244]     Train net output #1: loss = 0.0749161 (* 1 = 0.0749161 loss)
I0130 14:59:54.114084 32230 sgd_solver.cpp:106] Iteration 58600, lr = 0.0005
I0130 15:00:19.012352 32230 solver.cpp:337] Iteration 58800, Testing net (#0)
I0130 15:00:20.185645 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9021
I0130 15:00:20.185665 32230 solver.cpp:404]     Test net output #1: loss = 0.344073 (* 1 = 0.344073 loss)
I0130 15:00:20.216588 32230 solver.cpp:228] Iteration 58800, loss = 0.0609141
I0130 15:00:20.216605 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:00:20.216611 32230 solver.cpp:244]     Train net output #1: loss = 0.0609141 (* 1 = 0.0609141 loss)
I0130 15:00:20.216616 32230 sgd_solver.cpp:106] Iteration 58800, lr = 0.0005
I0130 15:00:44.913197 32230 solver.cpp:337] Iteration 59000, Testing net (#0)
I0130 15:00:46.045881 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9012
I0130 15:00:46.045903 32230 solver.cpp:404]     Test net output #1: loss = 0.343302 (* 1 = 0.343302 loss)
I0130 15:00:46.075086 32230 solver.cpp:228] Iteration 59000, loss = 0.0956062
I0130 15:00:46.075101 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 15:00:46.075106 32230 solver.cpp:244]     Train net output #1: loss = 0.0956063 (* 1 = 0.0956063 loss)
I0130 15:00:46.075110 32230 sgd_solver.cpp:106] Iteration 59000, lr = 0.0005
I0130 15:01:10.978469 32230 solver.cpp:337] Iteration 59200, Testing net (#0)
I0130 15:01:12.113492 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9028
I0130 15:01:12.113515 32230 solver.cpp:404]     Test net output #1: loss = 0.34249 (* 1 = 0.34249 loss)
I0130 15:01:12.142765 32230 solver.cpp:228] Iteration 59200, loss = 0.0758819
I0130 15:01:12.142779 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:01:12.142786 32230 solver.cpp:244]     Train net output #1: loss = 0.0758819 (* 1 = 0.0758819 loss)
I0130 15:01:12.142789 32230 sgd_solver.cpp:106] Iteration 59200, lr = 0.0005
I0130 15:01:36.990217 32230 solver.cpp:337] Iteration 59400, Testing net (#0)
I0130 15:01:38.115607 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9005
I0130 15:01:38.115648 32230 solver.cpp:404]     Test net output #1: loss = 0.34604 (* 1 = 0.34604 loss)
I0130 15:01:38.145042 32230 solver.cpp:228] Iteration 59400, loss = 0.0930256
I0130 15:01:38.145056 32230 solver.cpp:244]     Train net output #0: accuracy = 0.960938
I0130 15:01:38.145077 32230 solver.cpp:244]     Train net output #1: loss = 0.0930256 (* 1 = 0.0930256 loss)
I0130 15:01:38.145081 32230 sgd_solver.cpp:106] Iteration 59400, lr = 0.0005
I0130 15:02:02.983752 32230 solver.cpp:337] Iteration 59600, Testing net (#0)
I0130 15:02:04.117722 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9013
I0130 15:02:04.117748 32230 solver.cpp:404]     Test net output #1: loss = 0.343736 (* 1 = 0.343736 loss)
I0130 15:02:04.147259 32230 solver.cpp:228] Iteration 59600, loss = 0.0438373
I0130 15:02:04.147274 32230 solver.cpp:244]     Train net output #0: accuracy = 1
I0130 15:02:04.147280 32230 solver.cpp:244]     Train net output #1: loss = 0.0438374 (* 1 = 0.0438374 loss)
I0130 15:02:04.147284 32230 sgd_solver.cpp:106] Iteration 59600, lr = 0.0005
I0130 15:02:28.995599 32230 solver.cpp:337] Iteration 59800, Testing net (#0)
I0130 15:02:30.128062 32230 solver.cpp:404]     Test net output #0: accuracy = 0.8996
I0130 15:02:30.128103 32230 solver.cpp:404]     Test net output #1: loss = 0.345825 (* 1 = 0.345825 loss)
I0130 15:02:30.157388 32230 solver.cpp:228] Iteration 59800, loss = 0.112503
I0130 15:02:30.157403 32230 solver.cpp:244]     Train net output #0: accuracy = 0.964844
I0130 15:02:30.157408 32230 solver.cpp:244]     Train net output #1: loss = 0.112503 (* 1 = 0.112503 loss)
I0130 15:02:30.157413 32230 sgd_solver.cpp:106] Iteration 59800, lr = 0.0005
I0130 15:02:54.997150 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_60000.caffemodel
I0130 15:02:55.138514 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_60000.solverstate
I0130 15:02:55.166601 32230 solver.cpp:337] Iteration 60000, Testing net (#0)
I0130 15:02:56.249209 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9022
I0130 15:02:56.249244 32230 solver.cpp:404]     Test net output #1: loss = 0.346296 (* 1 = 0.346296 loss)
I0130 15:02:56.278610 32230 solver.cpp:228] Iteration 60000, loss = 0.0762527
I0130 15:02:56.278626 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 15:02:56.278632 32230 solver.cpp:244]     Train net output #1: loss = 0.0762527 (* 1 = 0.0762527 loss)
I0130 15:02:56.278636 32230 sgd_solver.cpp:46] MultiStep Status: Iteration 60000, step = 3
I0130 15:02:56.278640 32230 sgd_solver.cpp:106] Iteration 60000, lr = 5e-05
I0130 15:03:21.476538 32230 solver.cpp:337] Iteration 60200, Testing net (#0)
I0130 15:03:22.611812 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9021
I0130 15:03:22.611832 32230 solver.cpp:404]     Test net output #1: loss = 0.344852 (* 1 = 0.344852 loss)
I0130 15:03:22.640923 32230 solver.cpp:228] Iteration 60200, loss = 0.0774904
I0130 15:03:22.640940 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 15:03:22.640945 32230 solver.cpp:244]     Train net output #1: loss = 0.0774904 (* 1 = 0.0774904 loss)
I0130 15:03:22.640950 32230 sgd_solver.cpp:106] Iteration 60200, lr = 5e-05
I0130 15:03:47.574422 32230 solver.cpp:337] Iteration 60400, Testing net (#0)
I0130 15:03:48.706885 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9018
I0130 15:03:48.706907 32230 solver.cpp:404]     Test net output #1: loss = 0.345124 (* 1 = 0.345124 loss)
I0130 15:03:48.736232 32230 solver.cpp:228] Iteration 60400, loss = 0.0636248
I0130 15:03:48.736246 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:03:48.736251 32230 solver.cpp:244]     Train net output #1: loss = 0.0636248 (* 1 = 0.0636248 loss)
I0130 15:03:48.736255 32230 sgd_solver.cpp:106] Iteration 60400, lr = 5e-05
I0130 15:04:13.554245 32230 solver.cpp:337] Iteration 60600, Testing net (#0)
I0130 15:04:14.678124 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9006
I0130 15:04:14.678144 32230 solver.cpp:404]     Test net output #1: loss = 0.345634 (* 1 = 0.345634 loss)
I0130 15:04:14.707303 32230 solver.cpp:228] Iteration 60600, loss = 0.0684254
I0130 15:04:14.707319 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 15:04:14.707324 32230 solver.cpp:244]     Train net output #1: loss = 0.0684255 (* 1 = 0.0684255 loss)
I0130 15:04:14.707329 32230 sgd_solver.cpp:106] Iteration 60600, lr = 5e-05
I0130 15:04:39.459523 32230 solver.cpp:337] Iteration 60800, Testing net (#0)
I0130 15:04:40.592291 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9014
I0130 15:04:40.592314 32230 solver.cpp:404]     Test net output #1: loss = 0.345609 (* 1 = 0.345609 loss)
I0130 15:04:40.621762 32230 solver.cpp:228] Iteration 60800, loss = 0.0899478
I0130 15:04:40.621776 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 15:04:40.621783 32230 solver.cpp:244]     Train net output #1: loss = 0.0899479 (* 1 = 0.0899479 loss)
I0130 15:04:40.621788 32230 sgd_solver.cpp:106] Iteration 60800, lr = 5e-05
I0130 15:05:05.788496 32230 solver.cpp:337] Iteration 61000, Testing net (#0)
I0130 15:05:06.912778 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9014
I0130 15:05:06.912803 32230 solver.cpp:404]     Test net output #1: loss = 0.34478 (* 1 = 0.34478 loss)
I0130 15:05:06.942324 32230 solver.cpp:228] Iteration 61000, loss = 0.0607978
I0130 15:05:06.942342 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 15:05:06.942363 32230 solver.cpp:244]     Train net output #1: loss = 0.0607978 (* 1 = 0.0607978 loss)
I0130 15:05:06.942368 32230 sgd_solver.cpp:106] Iteration 61000, lr = 5e-05
I0130 15:05:31.817026 32230 solver.cpp:337] Iteration 61200, Testing net (#0)
I0130 15:05:32.950863 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9012
I0130 15:05:32.950902 32230 solver.cpp:404]     Test net output #1: loss = 0.344331 (* 1 = 0.344331 loss)
I0130 15:05:32.980219 32230 solver.cpp:228] Iteration 61200, loss = 0.0965747
I0130 15:05:32.980235 32230 solver.cpp:244]     Train net output #0: accuracy = 0.953125
I0130 15:05:32.980242 32230 solver.cpp:244]     Train net output #1: loss = 0.0965747 (* 1 = 0.0965747 loss)
I0130 15:05:32.980245 32230 sgd_solver.cpp:106] Iteration 61200, lr = 5e-05
I0130 15:05:58.048905 32230 solver.cpp:337] Iteration 61400, Testing net (#0)
I0130 15:05:59.172201 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9006
I0130 15:05:59.172220 32230 solver.cpp:404]     Test net output #1: loss = 0.344473 (* 1 = 0.344473 loss)
I0130 15:05:59.201247 32230 solver.cpp:228] Iteration 61400, loss = 0.0684053
I0130 15:05:59.201262 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:05:59.201269 32230 solver.cpp:244]     Train net output #1: loss = 0.0684053 (* 1 = 0.0684053 loss)
I0130 15:05:59.201273 32230 sgd_solver.cpp:106] Iteration 61400, lr = 5e-05
I0130 15:06:24.061758 32230 solver.cpp:337] Iteration 61600, Testing net (#0)
I0130 15:06:25.193940 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9008
I0130 15:06:25.193979 32230 solver.cpp:404]     Test net output #1: loss = 0.344402 (* 1 = 0.344402 loss)
I0130 15:06:25.223302 32230 solver.cpp:228] Iteration 61600, loss = 0.0830928
I0130 15:06:25.223317 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:06:25.223322 32230 solver.cpp:244]     Train net output #1: loss = 0.0830929 (* 1 = 0.0830929 loss)
I0130 15:06:25.223327 32230 sgd_solver.cpp:106] Iteration 61600, lr = 5e-05
I0130 15:06:50.747949 32230 solver.cpp:337] Iteration 61800, Testing net (#0)
I0130 15:06:51.937820 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9006
I0130 15:06:51.937840 32230 solver.cpp:404]     Test net output #1: loss = 0.344877 (* 1 = 0.344877 loss)
I0130 15:06:51.967671 32230 solver.cpp:228] Iteration 61800, loss = 0.0690773
I0130 15:06:51.967684 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:06:51.967689 32230 solver.cpp:244]     Train net output #1: loss = 0.0690773 (* 1 = 0.0690773 loss)
I0130 15:06:51.967694 32230 sgd_solver.cpp:106] Iteration 61800, lr = 5e-05
I0130 15:07:17.449313 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_62000.caffemodel
I0130 15:07:17.585105 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_62000.solverstate
I0130 15:07:17.613876 32230 solver.cpp:337] Iteration 62000, Testing net (#0)
I0130 15:07:18.684412 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9011
I0130 15:07:18.684432 32230 solver.cpp:404]     Test net output #1: loss = 0.344539 (* 1 = 0.344539 loss)
I0130 15:07:18.714042 32230 solver.cpp:228] Iteration 62000, loss = 0.0609382
I0130 15:07:18.714053 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 15:07:18.714059 32230 solver.cpp:244]     Train net output #1: loss = 0.0609383 (* 1 = 0.0609383 loss)
I0130 15:07:18.714064 32230 sgd_solver.cpp:106] Iteration 62000, lr = 5e-05
I0130 15:07:44.135499 32230 solver.cpp:337] Iteration 62200, Testing net (#0)
I0130 15:07:45.274499 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9008
I0130 15:07:45.274538 32230 solver.cpp:404]     Test net output #1: loss = 0.344598 (* 1 = 0.344598 loss)
I0130 15:07:45.304294 32230 solver.cpp:228] Iteration 62200, loss = 0.0768101
I0130 15:07:45.304311 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 15:07:45.304317 32230 solver.cpp:244]     Train net output #1: loss = 0.0768102 (* 1 = 0.0768102 loss)
I0130 15:07:45.304322 32230 sgd_solver.cpp:106] Iteration 62200, lr = 5e-05
I0130 15:08:11.049751 32230 solver.cpp:337] Iteration 62400, Testing net (#0)
I0130 15:08:12.199700 32230 solver.cpp:404]     Test net output #0: accuracy = 0.901
I0130 15:08:12.199720 32230 solver.cpp:404]     Test net output #1: loss = 0.345086 (* 1 = 0.345086 loss)
I0130 15:08:12.229123 32230 solver.cpp:228] Iteration 62400, loss = 0.068104
I0130 15:08:12.229140 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:08:12.229146 32230 solver.cpp:244]     Train net output #1: loss = 0.0681041 (* 1 = 0.0681041 loss)
I0130 15:08:12.229151 32230 sgd_solver.cpp:106] Iteration 62400, lr = 5e-05
I0130 15:08:37.562322 32230 solver.cpp:337] Iteration 62600, Testing net (#0)
I0130 15:08:38.695297 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9005
I0130 15:08:38.695317 32230 solver.cpp:404]     Test net output #1: loss = 0.345073 (* 1 = 0.345073 loss)
I0130 15:08:38.724581 32230 solver.cpp:228] Iteration 62600, loss = 0.0611603
I0130 15:08:38.724599 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:08:38.724606 32230 solver.cpp:244]     Train net output #1: loss = 0.0611603 (* 1 = 0.0611603 loss)
I0130 15:08:38.724611 32230 sgd_solver.cpp:106] Iteration 62600, lr = 5e-05
I0130 15:09:03.660987 32230 solver.cpp:337] Iteration 62800, Testing net (#0)
I0130 15:09:04.800493 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9011
I0130 15:09:04.800530 32230 solver.cpp:404]     Test net output #1: loss = 0.344098 (* 1 = 0.344098 loss)
I0130 15:09:04.829902 32230 solver.cpp:228] Iteration 62800, loss = 0.0433622
I0130 15:09:04.829918 32230 solver.cpp:244]     Train net output #0: accuracy = 0.988281
I0130 15:09:04.829923 32230 solver.cpp:244]     Train net output #1: loss = 0.0433623 (* 1 = 0.0433623 loss)
I0130 15:09:04.829928 32230 sgd_solver.cpp:106] Iteration 62800, lr = 5e-05
I0130 15:09:30.185245 32230 solver.cpp:337] Iteration 63000, Testing net (#0)
I0130 15:09:31.332856 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9019
I0130 15:09:31.332876 32230 solver.cpp:404]     Test net output #1: loss = 0.344023 (* 1 = 0.344023 loss)
I0130 15:09:31.362699 32230 solver.cpp:228] Iteration 63000, loss = 0.03682
I0130 15:09:31.362712 32230 solver.cpp:244]     Train net output #0: accuracy = 0.992188
I0130 15:09:31.362717 32230 solver.cpp:244]     Train net output #1: loss = 0.0368201 (* 1 = 0.0368201 loss)
I0130 15:09:31.362721 32230 sgd_solver.cpp:106] Iteration 63000, lr = 5e-05
I0130 15:09:56.635819 32230 solver.cpp:337] Iteration 63200, Testing net (#0)
I0130 15:09:57.766464 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9011
I0130 15:09:57.766484 32230 solver.cpp:404]     Test net output #1: loss = 0.344887 (* 1 = 0.344887 loss)
I0130 15:09:57.795819 32230 solver.cpp:228] Iteration 63200, loss = 0.0455001
I0130 15:09:57.795833 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 15:09:57.795840 32230 solver.cpp:244]     Train net output #1: loss = 0.0455002 (* 1 = 0.0455002 loss)
I0130 15:09:57.795846 32230 sgd_solver.cpp:106] Iteration 63200, lr = 5e-05
I0130 15:10:22.931169 32230 solver.cpp:337] Iteration 63400, Testing net (#0)
I0130 15:10:24.054530 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9009
I0130 15:10:24.054550 32230 solver.cpp:404]     Test net output #1: loss = 0.344935 (* 1 = 0.344935 loss)
I0130 15:10:24.083639 32230 solver.cpp:228] Iteration 63400, loss = 0.0617623
I0130 15:10:24.083657 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 15:10:24.083662 32230 solver.cpp:244]     Train net output #1: loss = 0.0617624 (* 1 = 0.0617624 loss)
I0130 15:10:24.083667 32230 sgd_solver.cpp:106] Iteration 63400, lr = 5e-05
I0130 15:10:49.298315 32230 solver.cpp:337] Iteration 63600, Testing net (#0)
I0130 15:10:50.541200 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9006
I0130 15:10:50.541219 32230 solver.cpp:404]     Test net output #1: loss = 0.345017 (* 1 = 0.345017 loss)
I0130 15:10:50.570984 32230 solver.cpp:228] Iteration 63600, loss = 0.0629212
I0130 15:10:50.570996 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:10:50.571002 32230 solver.cpp:244]     Train net output #1: loss = 0.0629212 (* 1 = 0.0629212 loss)
I0130 15:10:50.571005 32230 sgd_solver.cpp:106] Iteration 63600, lr = 5e-05
I0130 15:11:15.805281 32230 solver.cpp:337] Iteration 63800, Testing net (#0)
I0130 15:11:16.928381 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9009
I0130 15:11:16.928403 32230 solver.cpp:404]     Test net output #1: loss = 0.345147 (* 1 = 0.345147 loss)
I0130 15:11:16.957610 32230 solver.cpp:228] Iteration 63800, loss = 0.085279
I0130 15:11:16.957626 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 15:11:16.957633 32230 solver.cpp:244]     Train net output #1: loss = 0.0852791 (* 1 = 0.0852791 loss)
I0130 15:11:16.957638 32230 sgd_solver.cpp:106] Iteration 63800, lr = 5e-05
I0130 15:11:42.565363 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_64000.caffemodel
I0130 15:11:42.699604 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_64000.solverstate
I0130 15:11:42.728099 32230 solver.cpp:337] Iteration 64000, Testing net (#0)
I0130 15:11:43.791167 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9008
I0130 15:11:43.791190 32230 solver.cpp:404]     Test net output #1: loss = 0.344516 (* 1 = 0.344516 loss)
I0130 15:11:43.820531 32230 solver.cpp:228] Iteration 64000, loss = 0.0579608
I0130 15:11:43.820547 32230 solver.cpp:244]     Train net output #0: accuracy = 0.988281
I0130 15:11:43.820554 32230 solver.cpp:244]     Train net output #1: loss = 0.0579608 (* 1 = 0.0579608 loss)
I0130 15:11:43.820557 32230 sgd_solver.cpp:106] Iteration 64000, lr = 5e-05
I0130 15:12:08.946745 32230 solver.cpp:337] Iteration 64200, Testing net (#0)
I0130 15:12:10.088281 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9011
I0130 15:12:10.088317 32230 solver.cpp:404]     Test net output #1: loss = 0.34473 (* 1 = 0.34473 loss)
I0130 15:12:10.117655 32230 solver.cpp:228] Iteration 64200, loss = 0.0638383
I0130 15:12:10.117669 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 15:12:10.117674 32230 solver.cpp:244]     Train net output #1: loss = 0.0638384 (* 1 = 0.0638384 loss)
I0130 15:12:10.117702 32230 sgd_solver.cpp:106] Iteration 64200, lr = 5e-05
I0130 15:12:35.429044 32230 solver.cpp:337] Iteration 64400, Testing net (#0)
I0130 15:12:36.551173 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9011
I0130 15:12:36.551198 32230 solver.cpp:404]     Test net output #1: loss = 0.344113 (* 1 = 0.344113 loss)
I0130 15:12:36.580510 32230 solver.cpp:228] Iteration 64400, loss = 0.112863
I0130 15:12:36.580526 32230 solver.cpp:244]     Train net output #0: accuracy = 0.957031
I0130 15:12:36.580548 32230 solver.cpp:244]     Train net output #1: loss = 0.112863 (* 1 = 0.112863 loss)
I0130 15:12:36.580552 32230 sgd_solver.cpp:106] Iteration 64400, lr = 5e-05
I0130 15:13:01.310511 32230 solver.cpp:337] Iteration 64600, Testing net (#0)
I0130 15:13:02.444025 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9007
I0130 15:13:02.444046 32230 solver.cpp:404]     Test net output #1: loss = 0.344754 (* 1 = 0.344754 loss)
I0130 15:13:02.473356 32230 solver.cpp:228] Iteration 64600, loss = 0.0721339
I0130 15:13:02.473386 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 15:13:02.473390 32230 solver.cpp:244]     Train net output #1: loss = 0.0721339 (* 1 = 0.0721339 loss)
I0130 15:13:02.473395 32230 sgd_solver.cpp:106] Iteration 64600, lr = 5e-05
I0130 15:13:27.288987 32230 solver.cpp:337] Iteration 64800, Testing net (#0)
I0130 15:13:28.414295 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9012
I0130 15:13:28.414330 32230 solver.cpp:404]     Test net output #1: loss = 0.344392 (* 1 = 0.344392 loss)
I0130 15:13:28.443388 32230 solver.cpp:228] Iteration 64800, loss = 0.0331234
I0130 15:13:28.443403 32230 solver.cpp:244]     Train net output #0: accuracy = 0.996094
I0130 15:13:28.443408 32230 solver.cpp:244]     Train net output #1: loss = 0.0331235 (* 1 = 0.0331235 loss)
I0130 15:13:28.443413 32230 sgd_solver.cpp:106] Iteration 64800, lr = 5e-05
I0130 15:13:53.170608 32230 solver.cpp:337] Iteration 65000, Testing net (#0)
I0130 15:13:54.295970 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9006
I0130 15:13:54.296008 32230 solver.cpp:404]     Test net output #1: loss = 0.344852 (* 1 = 0.344852 loss)
I0130 15:13:54.325279 32230 solver.cpp:228] Iteration 65000, loss = 0.0506591
I0130 15:13:54.325295 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 15:13:54.325300 32230 solver.cpp:244]     Train net output #1: loss = 0.0506592 (* 1 = 0.0506592 loss)
I0130 15:13:54.325305 32230 sgd_solver.cpp:106] Iteration 65000, lr = 5e-05
I0130 15:14:19.161453 32230 solver.cpp:337] Iteration 65200, Testing net (#0)
I0130 15:14:20.296156 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9014
I0130 15:14:20.296182 32230 solver.cpp:404]     Test net output #1: loss = 0.344587 (* 1 = 0.344587 loss)
I0130 15:14:20.325588 32230 solver.cpp:228] Iteration 65200, loss = 0.0503691
I0130 15:14:20.325604 32230 solver.cpp:244]     Train net output #0: accuracy = 0.988281
I0130 15:14:20.325613 32230 solver.cpp:244]     Train net output #1: loss = 0.0503692 (* 1 = 0.0503692 loss)
I0130 15:14:20.325633 32230 sgd_solver.cpp:106] Iteration 65200, lr = 5e-05
I0130 15:14:45.147119 32230 solver.cpp:337] Iteration 65400, Testing net (#0)
I0130 15:14:46.271121 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9005
I0130 15:14:46.271144 32230 solver.cpp:404]     Test net output #1: loss = 0.34537 (* 1 = 0.34537 loss)
I0130 15:14:46.300400 32230 solver.cpp:228] Iteration 65400, loss = 0.0636789
I0130 15:14:46.300415 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 15:14:46.300422 32230 solver.cpp:244]     Train net output #1: loss = 0.063679 (* 1 = 0.063679 loss)
I0130 15:14:46.300442 32230 sgd_solver.cpp:106] Iteration 65400, lr = 5e-05
I0130 15:15:11.079661 32230 solver.cpp:337] Iteration 65600, Testing net (#0)
I0130 15:15:12.214323 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9012
I0130 15:15:12.214344 32230 solver.cpp:404]     Test net output #1: loss = 0.344684 (* 1 = 0.344684 loss)
I0130 15:15:12.243769 32230 solver.cpp:228] Iteration 65600, loss = 0.0717593
I0130 15:15:12.243787 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 15:15:12.243791 32230 solver.cpp:244]     Train net output #1: loss = 0.0717593 (* 1 = 0.0717593 loss)
I0130 15:15:12.243811 32230 sgd_solver.cpp:106] Iteration 65600, lr = 5e-05
I0130 15:15:37.093662 32230 solver.cpp:337] Iteration 65800, Testing net (#0)
I0130 15:15:38.219949 32230 solver.cpp:404]     Test net output #0: accuracy = 0.901
I0130 15:15:38.219970 32230 solver.cpp:404]     Test net output #1: loss = 0.344697 (* 1 = 0.344697 loss)
I0130 15:15:38.249258 32230 solver.cpp:228] Iteration 65800, loss = 0.0708783
I0130 15:15:38.249286 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 15:15:38.249291 32230 solver.cpp:244]     Train net output #1: loss = 0.0708784 (* 1 = 0.0708784 loss)
I0130 15:15:38.249311 32230 sgd_solver.cpp:106] Iteration 65800, lr = 5e-05
I0130 15:16:02.970083 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_66000.caffemodel
I0130 15:16:03.108496 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_66000.solverstate
I0130 15:16:03.137588 32230 solver.cpp:337] Iteration 66000, Testing net (#0)
I0130 15:16:04.165840 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9009
I0130 15:16:04.165884 32230 solver.cpp:404]     Test net output #1: loss = 0.345264 (* 1 = 0.345264 loss)
I0130 15:16:04.195396 32230 solver.cpp:228] Iteration 66000, loss = 0.0543164
I0130 15:16:04.195416 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:16:04.195425 32230 solver.cpp:244]     Train net output #1: loss = 0.0543165 (* 1 = 0.0543165 loss)
I0130 15:16:04.195432 32230 sgd_solver.cpp:106] Iteration 66000, lr = 5e-05
I0130 15:16:28.937031 32230 solver.cpp:337] Iteration 66200, Testing net (#0)
I0130 15:16:30.070296 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9014
I0130 15:16:30.070332 32230 solver.cpp:404]     Test net output #1: loss = 0.34571 (* 1 = 0.34571 loss)
I0130 15:16:30.099896 32230 solver.cpp:228] Iteration 66200, loss = 0.0737116
I0130 15:16:30.099918 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 15:16:30.099927 32230 solver.cpp:244]     Train net output #1: loss = 0.0737116 (* 1 = 0.0737116 loss)
I0130 15:16:30.099932 32230 sgd_solver.cpp:106] Iteration 66200, lr = 5e-05
I0130 15:16:54.945937 32230 solver.cpp:337] Iteration 66400, Testing net (#0)
I0130 15:16:56.070698 32230 solver.cpp:404]     Test net output #0: accuracy = 0.901
I0130 15:16:56.070735 32230 solver.cpp:404]     Test net output #1: loss = 0.344977 (* 1 = 0.344977 loss)
I0130 15:16:56.099932 32230 solver.cpp:228] Iteration 66400, loss = 0.0313737
I0130 15:16:56.099947 32230 solver.cpp:244]     Train net output #0: accuracy = 0.996094
I0130 15:16:56.099968 32230 solver.cpp:244]     Train net output #1: loss = 0.0313738 (* 1 = 0.0313738 loss)
I0130 15:16:56.099972 32230 sgd_solver.cpp:106] Iteration 66400, lr = 5e-05
I0130 15:17:21.264173 32230 solver.cpp:337] Iteration 66600, Testing net (#0)
I0130 15:17:22.412411 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9011
I0130 15:17:22.412434 32230 solver.cpp:404]     Test net output #1: loss = 0.344732 (* 1 = 0.344732 loss)
I0130 15:17:22.441282 32230 solver.cpp:228] Iteration 66600, loss = 0.0593035
I0130 15:17:22.441298 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 15:17:22.441303 32230 solver.cpp:244]     Train net output #1: loss = 0.0593036 (* 1 = 0.0593036 loss)
I0130 15:17:22.441308 32230 sgd_solver.cpp:106] Iteration 66600, lr = 5e-05
I0130 15:17:47.753741 32230 solver.cpp:337] Iteration 66800, Testing net (#0)
I0130 15:17:48.927661 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9013
I0130 15:17:48.927685 32230 solver.cpp:404]     Test net output #1: loss = 0.344734 (* 1 = 0.344734 loss)
I0130 15:17:48.956954 32230 solver.cpp:228] Iteration 66800, loss = 0.085436
I0130 15:17:48.956977 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 15:17:48.956987 32230 solver.cpp:244]     Train net output #1: loss = 0.085436 (* 1 = 0.085436 loss)
I0130 15:17:48.956993 32230 sgd_solver.cpp:106] Iteration 66800, lr = 5e-05
I0130 15:18:14.233397 32230 solver.cpp:337] Iteration 67000, Testing net (#0)
I0130 15:18:15.395999 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9014
I0130 15:18:15.396021 32230 solver.cpp:404]     Test net output #1: loss = 0.345029 (* 1 = 0.345029 loss)
I0130 15:18:15.425842 32230 solver.cpp:228] Iteration 67000, loss = 0.074852
I0130 15:18:15.425860 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 15:18:15.425865 32230 solver.cpp:244]     Train net output #1: loss = 0.0748521 (* 1 = 0.0748521 loss)
I0130 15:18:15.425870 32230 sgd_solver.cpp:106] Iteration 67000, lr = 5e-05
I0130 15:18:40.183647 32230 solver.cpp:337] Iteration 67200, Testing net (#0)
I0130 15:18:41.316041 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9015
I0130 15:18:41.316078 32230 solver.cpp:404]     Test net output #1: loss = 0.344929 (* 1 = 0.344929 loss)
I0130 15:18:41.345454 32230 solver.cpp:228] Iteration 67200, loss = 0.0670519
I0130 15:18:41.345470 32230 solver.cpp:244]     Train net output #0: accuracy = 0.988281
I0130 15:18:41.345476 32230 solver.cpp:244]     Train net output #1: loss = 0.067052 (* 1 = 0.067052 loss)
I0130 15:18:41.345480 32230 sgd_solver.cpp:106] Iteration 67200, lr = 5e-05
I0130 15:19:06.206326 32230 solver.cpp:337] Iteration 67400, Testing net (#0)
I0130 15:19:07.352764 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9012
I0130 15:19:07.352787 32230 solver.cpp:404]     Test net output #1: loss = 0.345769 (* 1 = 0.345769 loss)
I0130 15:19:07.382107 32230 solver.cpp:228] Iteration 67400, loss = 0.0648328
I0130 15:19:07.382122 32230 solver.cpp:244]     Train net output #0: accuracy = 0.984375
I0130 15:19:07.382128 32230 solver.cpp:244]     Train net output #1: loss = 0.0648329 (* 1 = 0.0648329 loss)
I0130 15:19:07.382133 32230 sgd_solver.cpp:106] Iteration 67400, lr = 5e-05
I0130 15:19:32.244278 32230 solver.cpp:337] Iteration 67600, Testing net (#0)
I0130 15:19:33.374508 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9021
I0130 15:19:33.374529 32230 solver.cpp:404]     Test net output #1: loss = 0.344972 (* 1 = 0.344972 loss)
I0130 15:19:33.404028 32230 solver.cpp:228] Iteration 67600, loss = 0.0845662
I0130 15:19:33.404047 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 15:19:33.404052 32230 solver.cpp:244]     Train net output #1: loss = 0.0845663 (* 1 = 0.0845663 loss)
I0130 15:19:33.404057 32230 sgd_solver.cpp:106] Iteration 67600, lr = 5e-05
I0130 15:19:58.489996 32230 solver.cpp:337] Iteration 67800, Testing net (#0)
I0130 15:19:59.660451 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9019
I0130 15:19:59.660473 32230 solver.cpp:404]     Test net output #1: loss = 0.345965 (* 1 = 0.345965 loss)
I0130 15:19:59.690395 32230 solver.cpp:228] Iteration 67800, loss = 0.0910973
I0130 15:19:59.690412 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 15:19:59.690418 32230 solver.cpp:244]     Train net output #1: loss = 0.0910973 (* 1 = 0.0910973 loss)
I0130 15:19:59.690424 32230 sgd_solver.cpp:106] Iteration 67800, lr = 5e-05
I0130 15:20:25.001302 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_68000.caffemodel
I0130 15:20:25.137470 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_68000.solverstate
I0130 15:20:25.167161 32230 solver.cpp:337] Iteration 68000, Testing net (#0)
I0130 15:20:26.192502 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9017
I0130 15:20:26.192526 32230 solver.cpp:404]     Test net output #1: loss = 0.345198 (* 1 = 0.345198 loss)
I0130 15:20:26.221513 32230 solver.cpp:228] Iteration 68000, loss = 0.0593584
I0130 15:20:26.221529 32230 solver.cpp:244]     Train net output #0: accuracy = 0.972656
I0130 15:20:26.221535 32230 solver.cpp:244]     Train net output #1: loss = 0.0593584 (* 1 = 0.0593584 loss)
I0130 15:20:26.221539 32230 sgd_solver.cpp:106] Iteration 68000, lr = 5e-05
I0130 15:20:51.161126 32230 solver.cpp:337] Iteration 68200, Testing net (#0)
I0130 15:20:52.289273 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9014
I0130 15:20:52.289295 32230 solver.cpp:404]     Test net output #1: loss = 0.345557 (* 1 = 0.345557 loss)
I0130 15:20:52.318455 32230 solver.cpp:228] Iteration 68200, loss = 0.0753458
I0130 15:20:52.318470 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 15:20:52.318475 32230 solver.cpp:244]     Train net output #1: loss = 0.0753459 (* 1 = 0.0753459 loss)
I0130 15:20:52.318481 32230 sgd_solver.cpp:106] Iteration 68200, lr = 5e-05
I0130 15:21:17.447366 32230 solver.cpp:337] Iteration 68400, Testing net (#0)
I0130 15:21:18.570845 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9021
I0130 15:21:18.570869 32230 solver.cpp:404]     Test net output #1: loss = 0.345711 (* 1 = 0.345711 loss)
I0130 15:21:18.599992 32230 solver.cpp:228] Iteration 68400, loss = 0.043393
I0130 15:21:18.600005 32230 solver.cpp:244]     Train net output #0: accuracy = 0.992188
I0130 15:21:18.600011 32230 solver.cpp:244]     Train net output #1: loss = 0.0433931 (* 1 = 0.0433931 loss)
I0130 15:21:18.600015 32230 sgd_solver.cpp:106] Iteration 68400, lr = 5e-05
I0130 15:21:43.420111 32230 solver.cpp:337] Iteration 68600, Testing net (#0)
I0130 15:21:44.543650 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9016
I0130 15:21:44.543687 32230 solver.cpp:404]     Test net output #1: loss = 0.345828 (* 1 = 0.345828 loss)
I0130 15:21:44.572906 32230 solver.cpp:228] Iteration 68600, loss = 0.0661783
I0130 15:21:44.572921 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:21:44.572926 32230 solver.cpp:244]     Train net output #1: loss = 0.0661784 (* 1 = 0.0661784 loss)
I0130 15:21:44.572931 32230 sgd_solver.cpp:106] Iteration 68600, lr = 5e-05
I0130 15:22:09.393929 32230 solver.cpp:337] Iteration 68800, Testing net (#0)
I0130 15:22:10.525178 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9012
I0130 15:22:10.525213 32230 solver.cpp:404]     Test net output #1: loss = 0.345538 (* 1 = 0.345538 loss)
I0130 15:22:10.554193 32230 solver.cpp:228] Iteration 68800, loss = 0.0670853
I0130 15:22:10.554208 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 15:22:10.554214 32230 solver.cpp:244]     Train net output #1: loss = 0.0670853 (* 1 = 0.0670853 loss)
I0130 15:22:10.554219 32230 sgd_solver.cpp:106] Iteration 68800, lr = 5e-05
I0130 15:22:35.259748 32230 solver.cpp:337] Iteration 69000, Testing net (#0)
I0130 15:22:36.383198 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9012
I0130 15:22:36.383234 32230 solver.cpp:404]     Test net output #1: loss = 0.345405 (* 1 = 0.345405 loss)
I0130 15:22:36.411931 32230 solver.cpp:228] Iteration 69000, loss = 0.0637356
I0130 15:22:36.411947 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:22:36.411952 32230 solver.cpp:244]     Train net output #1: loss = 0.0637356 (* 1 = 0.0637356 loss)
I0130 15:22:36.411958 32230 sgd_solver.cpp:106] Iteration 69000, lr = 5e-05
I0130 15:23:01.191423 32230 solver.cpp:337] Iteration 69200, Testing net (#0)
I0130 15:23:02.323045 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9018
I0130 15:23:02.323084 32230 solver.cpp:404]     Test net output #1: loss = 0.345515 (* 1 = 0.345515 loss)
I0130 15:23:02.352061 32230 solver.cpp:228] Iteration 69200, loss = 0.0785886
I0130 15:23:02.352092 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:23:02.352097 32230 solver.cpp:244]     Train net output #1: loss = 0.0785886 (* 1 = 0.0785886 loss)
I0130 15:23:02.352100 32230 sgd_solver.cpp:106] Iteration 69200, lr = 5e-05
I0130 15:23:27.164649 32230 solver.cpp:337] Iteration 69400, Testing net (#0)
I0130 15:23:28.288661 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9014
I0130 15:23:28.288700 32230 solver.cpp:404]     Test net output #1: loss = 0.346058 (* 1 = 0.346058 loss)
I0130 15:23:28.317811 32230 solver.cpp:228] Iteration 69400, loss = 0.0805638
I0130 15:23:28.317826 32230 solver.cpp:244]     Train net output #0: accuracy = 0.980469
I0130 15:23:28.317831 32230 solver.cpp:244]     Train net output #1: loss = 0.0805639 (* 1 = 0.0805639 loss)
I0130 15:23:28.317834 32230 sgd_solver.cpp:106] Iteration 69400, lr = 5e-05
I0130 15:23:52.970428 32230 solver.cpp:337] Iteration 69600, Testing net (#0)
I0130 15:23:54.093256 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9012
I0130 15:23:54.093297 32230 solver.cpp:404]     Test net output #1: loss = 0.346308 (* 1 = 0.346308 loss)
I0130 15:23:54.122428 32230 solver.cpp:228] Iteration 69600, loss = 0.067192
I0130 15:23:54.122442 32230 solver.cpp:244]     Train net output #0: accuracy = 0.96875
I0130 15:23:54.122448 32230 solver.cpp:244]     Train net output #1: loss = 0.0671921 (* 1 = 0.0671921 loss)
I0130 15:23:54.122468 32230 sgd_solver.cpp:106] Iteration 69600, lr = 5e-05
I0130 15:24:18.948745 32230 solver.cpp:337] Iteration 69800, Testing net (#0)
I0130 15:24:20.096652 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9008
I0130 15:24:20.096673 32230 solver.cpp:404]     Test net output #1: loss = 0.346292 (* 1 = 0.346292 loss)
I0130 15:24:20.125910 32230 solver.cpp:228] Iteration 69800, loss = 0.0680205
I0130 15:24:20.125926 32230 solver.cpp:244]     Train net output #0: accuracy = 0.976562
I0130 15:24:20.125932 32230 solver.cpp:244]     Train net output #1: loss = 0.0680206 (* 1 = 0.0680206 loss)
I0130 15:24:20.125937 32230 sgd_solver.cpp:106] Iteration 69800, lr = 5e-05
I0130 15:24:44.862947 32230 solver.cpp:454] Snapshotting to binary proto file ALL_CNN_C_iter_70000.caffemodel
I0130 15:24:45.001533 32230 sgd_solver.cpp:273] Snapshotting solver state to binary proto file ALL_CNN_C_iter_70000.solverstate
I0130 15:24:45.057334 32230 solver.cpp:317] Iteration 70000, loss = 0.0613564
I0130 15:24:45.057353 32230 solver.cpp:337] Iteration 70000, Testing net (#0)
I0130 15:24:46.082324 32230 solver.cpp:404]     Test net output #0: accuracy = 0.9013
I0130 15:24:46.082345 32230 solver.cpp:404]     Test net output #1: loss = 0.345126 (* 1 = 0.345126 loss)
I0130 15:24:46.082348 32230 solver.cpp:322] Optimization Done.
I0130 15:24:46.082350 32230 caffe.cpp:254] Optimization Done.