cifar vgg16

slight modification of vgg16 (shrinked ip layers and dropout added) trained on cifar10 with and without batch normalization
also uses prelu: https://github.com/BVLC/caffe/pull/1940
and msr weight filler: https://github.com/BVLC/caffe/pull/1883

1.png

cifar_baseline.log

I0219 15:21:20.651052 11127 caffe.cpp:99] Use GPU with device ID 0
I0219 15:21:20.773990 11127 caffe.cpp:107] Starting Optimization
I0219 15:21:20.774137 11127 solver.cpp:32] Initializing solver from parameters: 
test_iter: 100
test_interval: 1000
base_lr: 0.001
display: 200
max_iter: 60000
lr_policy: "fixed"
momentum: 0.9
weight_decay: 0.004
snapshot: 10000
snapshot_prefix: "examples/cifar10/cifar10_full"
solver_mode: GPU
net: "examples/cifar10/cifar10_full_train_test.prototxt"
I0219 15:21:20.774168 11127 solver.cpp:70] Creating training net from net file: examples/cifar10/cifar10_full_train_test.prototxt
I0219 15:21:20.774467 11127 net.cpp:256] The NetState phase (0) differed from the phase (1) specified by a rule in layer cifar
I0219 15:21:20.774478 11127 net.cpp:256] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0219 15:21:20.774566 11127 net.cpp:45] Initializing net from parameters: 
name: "CIFAR10_full"
state {
  phase: TRAIN
}
layer {
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mean_file: "examples/cifar10/mean.binaryproto"
  }
  data_param {
    source: "examples/cifar10/cifar10_train_lmdb"
    batch_size: 100
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.0001
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "pool1"
  top: "pool1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "ip1"
  param {
    lr_mult: 1
    decay_mult: 250
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip1"
  bottom: "label"
  top: "loss"
}
I0219 15:21:20.774632 11127 layer_factory.hpp:74] Creating layer cifar
I0219 15:21:20.774654 11127 net.cpp:79] Creating Layer cifar
I0219 15:21:20.774660 11127 net.cpp:337] cifar -> data
I0219 15:21:20.774688 11127 net.cpp:337] cifar -> label
I0219 15:21:20.774696 11127 net.cpp:108] Setting up cifar
I0219 15:21:20.774763 11127 db.cpp:34] Opened lmdb examples/cifar10/cifar10_train_lmdb
I0219 15:21:20.774799 11127 data_layer.cpp:65] output data size: 100,3,32,32
I0219 15:21:20.774806 11127 data_transformer.cpp:59] Loading mean file from: examples/cifar10/mean.binaryproto
I0219 15:21:20.775171 11127 net.cpp:115] Top shape: 100 3 32 32 (307200)
I0219 15:21:20.775176 11127 net.cpp:115] Top shape: 100 1 1 1 (100)
I0219 15:21:20.775179 11127 layer_factory.hpp:74] Creating layer conv1
I0219 15:21:20.775199 11127 net.cpp:79] Creating Layer conv1
I0219 15:21:20.775203 11127 net.cpp:375] conv1 <- data
I0219 15:21:20.775220 11127 net.cpp:337] conv1 -> conv1
I0219 15:21:20.775226 11127 net.cpp:108] Setting up conv1
I0219 15:21:20.775655 11127 net.cpp:115] Top shape: 100 32 32 32 (3276800)
I0219 15:21:20.775682 11127 layer_factory.hpp:74] Creating layer pool1
I0219 15:21:20.775697 11127 net.cpp:79] Creating Layer pool1
I0219 15:21:20.775699 11127 net.cpp:375] pool1 <- conv1
I0219 15:21:20.775701 11127 net.cpp:337] pool1 -> pool1
I0219 15:21:20.775707 11127 net.cpp:108] Setting up pool1
I0219 15:21:20.775713 11127 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 15:21:20.775717 11127 layer_factory.hpp:74] Creating layer relu1
I0219 15:21:20.775722 11127 net.cpp:79] Creating Layer relu1
I0219 15:21:20.775722 11127 net.cpp:375] relu1 <- pool1
I0219 15:21:20.775725 11127 net.cpp:326] relu1 -> pool1 (in-place)
I0219 15:21:20.775727 11127 net.cpp:108] Setting up relu1
I0219 15:21:20.775730 11127 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 15:21:20.775732 11127 layer_factory.hpp:74] Creating layer norm1
I0219 15:21:20.775740 11127 net.cpp:79] Creating Layer norm1
I0219 15:21:20.775743 11127 net.cpp:375] norm1 <- pool1
I0219 15:21:20.775745 11127 net.cpp:337] norm1 -> norm1
I0219 15:21:20.775748 11127 net.cpp:108] Setting up norm1
I0219 15:21:20.775789 11127 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 15:21:20.775791 11127 layer_factory.hpp:74] Creating layer conv2
I0219 15:21:20.775804 11127 net.cpp:79] Creating Layer conv2
I0219 15:21:20.775805 11127 net.cpp:375] conv2 <- norm1
I0219 15:21:20.775810 11127 net.cpp:337] conv2 -> conv2
I0219 15:21:20.775812 11127 net.cpp:108] Setting up conv2
I0219 15:21:20.776412 11127 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 15:21:20.776419 11127 layer_factory.hpp:74] Creating layer relu2
I0219 15:21:20.776432 11127 net.cpp:79] Creating Layer relu2
I0219 15:21:20.776433 11127 net.cpp:375] relu2 <- conv2
I0219 15:21:20.776437 11127 net.cpp:326] relu2 -> conv2 (in-place)
I0219 15:21:20.776439 11127 net.cpp:108] Setting up relu2
I0219 15:21:20.776440 11127 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 15:21:20.776443 11127 layer_factory.hpp:74] Creating layer pool2
I0219 15:21:20.776455 11127 net.cpp:79] Creating Layer pool2
I0219 15:21:20.776458 11127 net.cpp:375] pool2 <- conv2
I0219 15:21:20.776459 11127 net.cpp:337] pool2 -> pool2
I0219 15:21:20.776463 11127 net.cpp:108] Setting up pool2
I0219 15:21:20.776464 11127 net.cpp:115] Top shape: 100 32 8 8 (204800)
I0219 15:21:20.776466 11127 layer_factory.hpp:74] Creating layer norm2
I0219 15:21:20.776471 11127 net.cpp:79] Creating Layer norm2
I0219 15:21:20.776473 11127 net.cpp:375] norm2 <- pool2
I0219 15:21:20.776475 11127 net.cpp:337] norm2 -> norm2
I0219 15:21:20.776478 11127 net.cpp:108] Setting up norm2
I0219 15:21:20.776485 11127 net.cpp:115] Top shape: 100 32 8 8 (204800)
I0219 15:21:20.776487 11127 layer_factory.hpp:74] Creating layer conv3
I0219 15:21:20.776491 11127 net.cpp:79] Creating Layer conv3
I0219 15:21:20.776494 11127 net.cpp:375] conv3 <- norm2
I0219 15:21:20.776496 11127 net.cpp:337] conv3 -> conv3
I0219 15:21:20.776499 11127 net.cpp:108] Setting up conv3
I0219 15:21:20.777616 11127 net.cpp:115] Top shape: 100 64 8 8 (409600)
I0219 15:21:20.777622 11127 layer_factory.hpp:74] Creating layer relu3
I0219 15:21:20.777636 11127 net.cpp:79] Creating Layer relu3
I0219 15:21:20.777638 11127 net.cpp:375] relu3 <- conv3
I0219 15:21:20.777640 11127 net.cpp:326] relu3 -> conv3 (in-place)
I0219 15:21:20.777642 11127 net.cpp:108] Setting up relu3
I0219 15:21:20.777644 11127 net.cpp:115] Top shape: 100 64 8 8 (409600)
I0219 15:21:20.777647 11127 layer_factory.hpp:74] Creating layer pool3
I0219 15:21:20.777658 11127 net.cpp:79] Creating Layer pool3
I0219 15:21:20.777670 11127 net.cpp:375] pool3 <- conv3
I0219 15:21:20.777674 11127 net.cpp:337] pool3 -> pool3
I0219 15:21:20.777678 11127 net.cpp:108] Setting up pool3
I0219 15:21:20.777679 11127 net.cpp:115] Top shape: 100 64 4 4 (102400)
I0219 15:21:20.777681 11127 layer_factory.hpp:74] Creating layer ip1
I0219 15:21:20.777686 11127 net.cpp:79] Creating Layer ip1
I0219 15:21:20.777688 11127 net.cpp:375] ip1 <- pool3
I0219 15:21:20.777691 11127 net.cpp:337] ip1 -> ip1
I0219 15:21:20.777695 11127 net.cpp:108] Setting up ip1
I0219 15:21:20.777920 11127 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0219 15:21:20.777951 11127 layer_factory.hpp:74] Creating layer loss
I0219 15:21:20.777963 11127 net.cpp:79] Creating Layer loss
I0219 15:21:20.777971 11127 net.cpp:375] loss <- ip1
I0219 15:21:20.777979 11127 net.cpp:375] loss <- label
I0219 15:21:20.777989 11127 net.cpp:337] loss -> loss
I0219 15:21:20.777999 11127 net.cpp:108] Setting up loss
I0219 15:21:20.778010 11127 layer_factory.hpp:74] Creating layer loss
I0219 15:21:20.778031 11127 net.cpp:115] Top shape: 1 1 1 1 (1)
I0219 15:21:20.778040 11127 net.cpp:121]     with loss weight 1
I0219 15:21:20.778059 11127 net.cpp:166] loss needs backward computation.
I0219 15:21:20.778067 11127 net.cpp:166] ip1 needs backward computation.
I0219 15:21:20.778075 11127 net.cpp:166] pool3 needs backward computation.
I0219 15:21:20.778084 11127 net.cpp:166] relu3 needs backward computation.
I0219 15:21:20.778090 11127 net.cpp:166] conv3 needs backward computation.
I0219 15:21:20.778098 11127 net.cpp:166] norm2 needs backward computation.
I0219 15:21:20.778106 11127 net.cpp:166] pool2 needs backward computation.
I0219 15:21:20.778110 11127 net.cpp:166] relu2 needs backward computation.
I0219 15:21:20.778110 11127 net.cpp:166] conv2 needs backward computation.
I0219 15:21:20.778112 11127 net.cpp:166] norm1 needs backward computation.
I0219 15:21:20.778115 11127 net.cpp:166] relu1 needs backward computation.
I0219 15:21:20.778116 11127 net.cpp:166] pool1 needs backward computation.
I0219 15:21:20.778118 11127 net.cpp:166] conv1 needs backward computation.
I0219 15:21:20.778120 11127 net.cpp:168] cifar does not need backward computation.
I0219 15:21:20.778121 11127 net.cpp:204] This network produces output loss
I0219 15:21:20.778138 11127 net.cpp:449] Collecting Learning Rate and Weight Decay.
I0219 15:21:20.778142 11127 net.cpp:216] Network initialization done.
I0219 15:21:20.778156 11127 net.cpp:217] Memory required for data: 36049204
I0219 15:21:20.778470 11127 solver.cpp:154] Creating test net (#0) specified by net file: examples/cifar10/cifar10_full_train_test.prototxt
I0219 15:21:20.778507 11127 net.cpp:256] The NetState phase (1) differed from the phase (0) specified by a rule in layer cifar
I0219 15:21:20.778611 11127 net.cpp:45] Initializing net from parameters: 
name: "CIFAR10_full"
state {
  phase: TEST
}
layer {
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mean_file: "examples/cifar10/mean.binaryproto"
  }
  data_param {
    source: "examples/cifar10/cifar10_test_lmdb"
    batch_size: 100
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.0001
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "pool1"
  top: "pool1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "ip1"
  param {
    lr_mult: 1
    decay_mult: 250
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "ip1"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip1"
  bottom: "label"
  top: "loss"
}
I0219 15:21:20.778692 11127 layer_factory.hpp:74] Creating layer cifar
I0219 15:21:20.778697 11127 net.cpp:79] Creating Layer cifar
I0219 15:21:20.778700 11127 net.cpp:337] cifar -> data
I0219 15:21:20.778705 11127 net.cpp:337] cifar -> label
I0219 15:21:20.778709 11127 net.cpp:108] Setting up cifar
I0219 15:21:20.778741 11127 db.cpp:34] Opened lmdb examples/cifar10/cifar10_test_lmdb
I0219 15:21:20.778753 11127 data_layer.cpp:65] output data size: 100,3,32,32
I0219 15:21:20.778758 11127 data_transformer.cpp:59] Loading mean file from: examples/cifar10/mean.binaryproto
I0219 15:21:20.779192 11127 net.cpp:115] Top shape: 100 3 32 32 (307200)
I0219 15:21:20.779207 11127 net.cpp:115] Top shape: 100 1 1 1 (100)
I0219 15:21:20.779209 11127 layer_factory.hpp:74] Creating layer label_cifar_1_split
I0219 15:21:20.779224 11127 net.cpp:79] Creating Layer label_cifar_1_split
I0219 15:21:20.779227 11127 net.cpp:375] label_cifar_1_split <- label
I0219 15:21:20.779228 11127 net.cpp:337] label_cifar_1_split -> label_cifar_1_split_0
I0219 15:21:20.779232 11127 net.cpp:337] label_cifar_1_split -> label_cifar_1_split_1
I0219 15:21:20.779235 11127 net.cpp:108] Setting up label_cifar_1_split
I0219 15:21:20.779238 11127 net.cpp:115] Top shape: 100 1 1 1 (100)
I0219 15:21:20.779240 11127 net.cpp:115] Top shape: 100 1 1 1 (100)
I0219 15:21:20.779242 11127 layer_factory.hpp:74] Creating layer conv1
I0219 15:21:20.779245 11127 net.cpp:79] Creating Layer conv1
I0219 15:21:20.779247 11127 net.cpp:375] conv1 <- data
I0219 15:21:20.779253 11127 net.cpp:337] conv1 -> conv1
I0219 15:21:20.779264 11127 net.cpp:108] Setting up conv1
I0219 15:21:20.779337 11127 net.cpp:115] Top shape: 100 32 32 32 (3276800)
I0219 15:21:20.779345 11127 layer_factory.hpp:74] Creating layer pool1
I0219 15:21:20.779347 11127 net.cpp:79] Creating Layer pool1
I0219 15:21:20.779350 11127 net.cpp:375] pool1 <- conv1
I0219 15:21:20.779352 11127 net.cpp:337] pool1 -> pool1
I0219 15:21:20.779356 11127 net.cpp:108] Setting up pool1
I0219 15:21:20.779358 11127 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 15:21:20.779361 11127 layer_factory.hpp:74] Creating layer relu1
I0219 15:21:20.779363 11127 net.cpp:79] Creating Layer relu1
I0219 15:21:20.779366 11127 net.cpp:375] relu1 <- pool1
I0219 15:21:20.779367 11127 net.cpp:326] relu1 -> pool1 (in-place)
I0219 15:21:20.779369 11127 net.cpp:108] Setting up relu1
I0219 15:21:20.779371 11127 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 15:21:20.779381 11127 layer_factory.hpp:74] Creating layer norm1
I0219 15:21:20.779384 11127 net.cpp:79] Creating Layer norm1
I0219 15:21:20.779386 11127 net.cpp:375] norm1 <- pool1
I0219 15:21:20.779389 11127 net.cpp:337] norm1 -> norm1
I0219 15:21:20.779392 11127 net.cpp:108] Setting up norm1
I0219 15:21:20.779420 11127 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 15:21:20.779422 11127 layer_factory.hpp:74] Creating layer conv2
I0219 15:21:20.779425 11127 net.cpp:79] Creating Layer conv2
I0219 15:21:20.779428 11127 net.cpp:375] conv2 <- norm1
I0219 15:21:20.779429 11127 net.cpp:337] conv2 -> conv2
I0219 15:21:20.779433 11127 net.cpp:108] Setting up conv2
I0219 15:21:20.780061 11127 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 15:21:20.780067 11127 layer_factory.hpp:74] Creating layer relu2
I0219 15:21:20.780079 11127 net.cpp:79] Creating Layer relu2
I0219 15:21:20.780081 11127 net.cpp:375] relu2 <- conv2
I0219 15:21:20.780084 11127 net.cpp:326] relu2 -> conv2 (in-place)
I0219 15:21:20.780087 11127 net.cpp:108] Setting up relu2
I0219 15:21:20.780088 11127 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 15:21:20.780100 11127 layer_factory.hpp:74] Creating layer pool2
I0219 15:21:20.780103 11127 net.cpp:79] Creating Layer pool2
I0219 15:21:20.780105 11127 net.cpp:375] pool2 <- conv2
I0219 15:21:20.780107 11127 net.cpp:337] pool2 -> pool2
I0219 15:21:20.780110 11127 net.cpp:108] Setting up pool2
I0219 15:21:20.780113 11127 net.cpp:115] Top shape: 100 32 8 8 (204800)
I0219 15:21:20.780114 11127 layer_factory.hpp:74] Creating layer norm2
I0219 15:21:20.780118 11127 net.cpp:79] Creating Layer norm2
I0219 15:21:20.780119 11127 net.cpp:375] norm2 <- pool2
I0219 15:21:20.780123 11127 net.cpp:337] norm2 -> norm2
I0219 15:21:20.780125 11127 net.cpp:108] Setting up norm2
I0219 15:21:20.780133 11127 net.cpp:115] Top shape: 100 32 8 8 (204800)
I0219 15:21:20.780135 11127 layer_factory.hpp:74] Creating layer conv3
I0219 15:21:20.780138 11127 net.cpp:79] Creating Layer conv3
I0219 15:21:20.780139 11127 net.cpp:375] conv3 <- norm2
I0219 15:21:20.780143 11127 net.cpp:337] conv3 -> conv3
I0219 15:21:20.780146 11127 net.cpp:108] Setting up conv3
I0219 15:21:20.781261 11127 net.cpp:115] Top shape: 100 64 8 8 (409600)
I0219 15:21:20.781268 11127 layer_factory.hpp:74] Creating layer relu3
I0219 15:21:20.781280 11127 net.cpp:79] Creating Layer relu3
I0219 15:21:20.781281 11127 net.cpp:375] relu3 <- conv3
I0219 15:21:20.781285 11127 net.cpp:326] relu3 -> conv3 (in-place)
I0219 15:21:20.781286 11127 net.cpp:108] Setting up relu3
I0219 15:21:20.781298 11127 net.cpp:115] Top shape: 100 64 8 8 (409600)
I0219 15:21:20.781301 11127 layer_factory.hpp:74] Creating layer pool3
I0219 15:21:20.781303 11127 net.cpp:79] Creating Layer pool3
I0219 15:21:20.781306 11127 net.cpp:375] pool3 <- conv3
I0219 15:21:20.781307 11127 net.cpp:337] pool3 -> pool3
I0219 15:21:20.781311 11127 net.cpp:108] Setting up pool3
I0219 15:21:20.781312 11127 net.cpp:115] Top shape: 100 64 4 4 (102400)
I0219 15:21:20.781313 11127 layer_factory.hpp:74] Creating layer ip1
I0219 15:21:20.781317 11127 net.cpp:79] Creating Layer ip1
I0219 15:21:20.781319 11127 net.cpp:375] ip1 <- pool3
I0219 15:21:20.781322 11127 net.cpp:337] ip1 -> ip1
I0219 15:21:20.781325 11127 net.cpp:108] Setting up ip1
I0219 15:21:20.781579 11127 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0219 15:21:20.781582 11127 layer_factory.hpp:74] Creating layer ip1_ip1_0_split
I0219 15:21:20.781595 11127 net.cpp:79] Creating Layer ip1_ip1_0_split
I0219 15:21:20.781597 11127 net.cpp:375] ip1_ip1_0_split <- ip1
I0219 15:21:20.781599 11127 net.cpp:337] ip1_ip1_0_split -> ip1_ip1_0_split_0
I0219 15:21:20.781602 11127 net.cpp:337] ip1_ip1_0_split -> ip1_ip1_0_split_1
I0219 15:21:20.781615 11127 net.cpp:108] Setting up ip1_ip1_0_split
I0219 15:21:20.781617 11127 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0219 15:21:20.781618 11127 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0219 15:21:20.781620 11127 layer_factory.hpp:74] Creating layer accuracy
I0219 15:21:20.781625 11127 net.cpp:79] Creating Layer accuracy
I0219 15:21:20.781635 11127 net.cpp:375] accuracy <- ip1_ip1_0_split_0
I0219 15:21:20.781636 11127 net.cpp:375] accuracy <- label_cifar_1_split_0
I0219 15:21:20.781640 11127 net.cpp:337] accuracy -> accuracy
I0219 15:21:20.781642 11127 net.cpp:108] Setting up accuracy
I0219 15:21:20.781646 11127 net.cpp:115] Top shape: 1 1 1 1 (1)
I0219 15:21:20.781648 11127 layer_factory.hpp:74] Creating layer loss
I0219 15:21:20.781651 11127 net.cpp:79] Creating Layer loss
I0219 15:21:20.781653 11127 net.cpp:375] loss <- ip1_ip1_0_split_1
I0219 15:21:20.781656 11127 net.cpp:375] loss <- label_cifar_1_split_1
I0219 15:21:20.781658 11127 net.cpp:337] loss -> loss
I0219 15:21:20.781661 11127 net.cpp:108] Setting up loss
I0219 15:21:20.781664 11127 layer_factory.hpp:74] Creating layer loss
I0219 15:21:20.781672 11127 net.cpp:115] Top shape: 1 1 1 1 (1)
I0219 15:21:20.781676 11127 net.cpp:121]     with loss weight 1
I0219 15:21:20.781679 11127 net.cpp:166] loss needs backward computation.
I0219 15:21:20.781682 11127 net.cpp:168] accuracy does not need backward computation.
I0219 15:21:20.781682 11127 net.cpp:166] ip1_ip1_0_split needs backward computation.
I0219 15:21:20.781684 11127 net.cpp:166] ip1 needs backward computation.
I0219 15:21:20.781687 11127 net.cpp:166] pool3 needs backward computation.
I0219 15:21:20.781687 11127 net.cpp:166] relu3 needs backward computation.
I0219 15:21:20.781689 11127 net.cpp:166] conv3 needs backward computation.
I0219 15:21:20.781692 11127 net.cpp:166] norm2 needs backward computation.
I0219 15:21:20.781693 11127 net.cpp:166] pool2 needs backward computation.
I0219 15:21:20.781694 11127 net.cpp:166] relu2 needs backward computation.
I0219 15:21:20.781697 11127 net.cpp:166] conv2 needs backward computation.
I0219 15:21:20.781697 11127 net.cpp:166] norm1 needs backward computation.
I0219 15:21:20.781699 11127 net.cpp:166] relu1 needs backward computation.
I0219 15:21:20.781702 11127 net.cpp:166] pool1 needs backward computation.
I0219 15:21:20.781702 11127 net.cpp:166] conv1 needs backward computation.
I0219 15:21:20.781704 11127 net.cpp:168] label_cifar_1_split does not need backward computation.
I0219 15:21:20.781707 11127 net.cpp:168] cifar does not need backward computation.
I0219 15:21:20.781708 11127 net.cpp:204] This network produces output accuracy
I0219 15:21:20.781710 11127 net.cpp:204] This network produces output loss
I0219 15:21:20.781718 11127 net.cpp:449] Collecting Learning Rate and Weight Decay.
I0219 15:21:20.781720 11127 net.cpp:216] Network initialization done.
I0219 15:21:20.781723 11127 net.cpp:217] Memory required for data: 36058008
I0219 15:21:20.781765 11127 solver.cpp:42] Solver scaffolding done.
I0219 15:21:20.781790 11127 solver.cpp:222] Solving CIFAR10_full
I0219 15:21:20.781800 11127 solver.cpp:223] Learning Rate Policy: fixed
I0219 15:21:20.781805 11127 solver.cpp:266] Iteration 0, Testing net (#0)
I0219 15:21:23.052314 11127 solver.cpp:315]     Test net output #0: accuracy = 0.0954
I0219 15:21:23.052340 11127 solver.cpp:315]     Test net output #1: loss = 2.30256 (* 1 = 2.30256 loss)
I0219 15:21:23.079687 11127 solver.cpp:189] Iteration 0, loss = 2.30255
I0219 15:21:23.079705 11127 solver.cpp:204]     Train net output #0: loss = 2.30255 (* 1 = 2.30255 loss)
I0219 15:21:23.079710 11127 solver.cpp:470] Iteration 0, lr = 0.001
I0219 15:21:34.244202 11127 solver.cpp:189] Iteration 200, loss = 1.8655
I0219 15:21:34.244235 11127 solver.cpp:204]     Train net output #0: loss = 1.8655 (* 1 = 1.8655 loss)
I0219 15:21:34.244238 11127 solver.cpp:470] Iteration 200, lr = 0.001
I0219 15:21:45.419401 11127 solver.cpp:189] Iteration 400, loss = 1.47456
I0219 15:21:45.419432 11127 solver.cpp:204]     Train net output #0: loss = 1.47456 (* 1 = 1.47456 loss)
I0219 15:21:45.419437 11127 solver.cpp:470] Iteration 400, lr = 0.001
I0219 15:21:56.504674 11127 solver.cpp:189] Iteration 600, loss = 1.46074
I0219 15:21:56.504760 11127 solver.cpp:204]     Train net output #0: loss = 1.46074 (* 1 = 1.46074 loss)
I0219 15:21:56.504765 11127 solver.cpp:470] Iteration 600, lr = 0.001
I0219 15:22:07.585526 11127 solver.cpp:189] Iteration 800, loss = 1.3431
I0219 15:22:07.585568 11127 solver.cpp:204]     Train net output #0: loss = 1.3431 (* 1 = 1.3431 loss)
I0219 15:22:07.585573 11127 solver.cpp:470] Iteration 800, lr = 0.001
I0219 15:22:18.610458 11127 solver.cpp:266] Iteration 1000, Testing net (#0)
I0219 15:22:20.855303 11127 solver.cpp:315]     Test net output #0: accuracy = 0.5314
I0219 15:22:20.855324 11127 solver.cpp:315]     Test net output #1: loss = 1.30748 (* 1 = 1.30748 loss)
I0219 15:22:20.882680 11127 solver.cpp:189] Iteration 1000, loss = 1.33667
I0219 15:22:20.882714 11127 solver.cpp:204]     Train net output #0: loss = 1.33667 (* 1 = 1.33667 loss)
I0219 15:22:20.882719 11127 solver.cpp:470] Iteration 1000, lr = 0.001
I0219 15:22:31.932368 11127 solver.cpp:189] Iteration 1200, loss = 1.30877
I0219 15:22:31.932448 11127 solver.cpp:204]     Train net output #0: loss = 1.30877 (* 1 = 1.30877 loss)
I0219 15:22:31.932462 11127 solver.cpp:470] Iteration 1200, lr = 0.001
I0219 15:22:42.999593 11127 solver.cpp:189] Iteration 1400, loss = 1.21518
I0219 15:22:42.999618 11127 solver.cpp:204]     Train net output #0: loss = 1.21518 (* 1 = 1.21518 loss)
I0219 15:22:42.999621 11127 solver.cpp:470] Iteration 1400, lr = 0.001
I0219 15:22:54.152343 11127 solver.cpp:189] Iteration 1600, loss = 1.12812
I0219 15:22:54.152365 11127 solver.cpp:204]     Train net output #0: loss = 1.12812 (* 1 = 1.12812 loss)
I0219 15:22:54.152369 11127 solver.cpp:470] Iteration 1600, lr = 0.001
I0219 15:23:05.220461 11127 solver.cpp:189] Iteration 1800, loss = 1.08235
I0219 15:23:05.220525 11127 solver.cpp:204]     Train net output #0: loss = 1.08235 (* 1 = 1.08235 loss)
I0219 15:23:05.220530 11127 solver.cpp:470] Iteration 1800, lr = 0.001
I0219 15:23:16.371557 11127 solver.cpp:266] Iteration 2000, Testing net (#0)
I0219 15:23:18.640578 11127 solver.cpp:315]     Test net output #0: accuracy = 0.6051
I0219 15:23:18.640650 11127 solver.cpp:315]     Test net output #1: loss = 1.1281 (* 1 = 1.1281 loss)
I0219 15:23:18.668297 11127 solver.cpp:189] Iteration 2000, loss = 1.1403
I0219 15:23:18.668333 11127 solver.cpp:204]     Train net output #0: loss = 1.1403 (* 1 = 1.1403 loss)
I0219 15:23:18.668339 11127 solver.cpp:470] Iteration 2000, lr = 0.001
I0219 15:23:29.852929 11127 solver.cpp:189] Iteration 2200, loss = 1.17146
I0219 15:23:29.852962 11127 solver.cpp:204]     Train net output #0: loss = 1.17146 (* 1 = 1.17146 loss)
I0219 15:23:29.852967 11127 solver.cpp:470] Iteration 2200, lr = 0.001
I0219 15:23:41.034948 11127 solver.cpp:189] Iteration 2400, loss = 1.09494
I0219 15:23:41.035037 11127 solver.cpp:204]     Train net output #0: loss = 1.09494 (* 1 = 1.09494 loss)
I0219 15:23:41.035050 11127 solver.cpp:470] Iteration 2400, lr = 0.001
I0219 15:23:52.254334 11127 solver.cpp:189] Iteration 2600, loss = 1.00144
I0219 15:23:52.254372 11127 solver.cpp:204]     Train net output #0: loss = 1.00144 (* 1 = 1.00144 loss)
I0219 15:23:52.254377 11127 solver.cpp:470] Iteration 2600, lr = 0.001
I0219 15:24:03.438585 11127 solver.cpp:189] Iteration 2800, loss = 0.852462
I0219 15:24:03.438623 11127 solver.cpp:204]     Train net output #0: loss = 0.852462 (* 1 = 0.852462 loss)
I0219 15:24:03.438628 11127 solver.cpp:470] Iteration 2800, lr = 0.001
I0219 15:24:14.474529 11127 solver.cpp:266] Iteration 3000, Testing net (#0)
I0219 15:24:16.722628 11127 solver.cpp:315]     Test net output #0: accuracy = 0.6381
I0219 15:24:16.722651 11127 solver.cpp:315]     Test net output #1: loss = 1.01927 (* 1 = 1.01927 loss)
I0219 15:24:16.748991 11127 solver.cpp:189] Iteration 3000, loss = 0.973597
I0219 15:24:16.749006 11127 solver.cpp:204]     Train net output #0: loss = 0.973597 (* 1 = 0.973597 loss)
I0219 15:24:16.749009 11127 solver.cpp:470] Iteration 3000, lr = 0.001
I0219 15:24:27.811522 11127 solver.cpp:189] Iteration 3200, loss = 1.09173
I0219 15:24:27.811554 11127 solver.cpp:204]     Train net output #0: loss = 1.09173 (* 1 = 1.09173 loss)
I0219 15:24:27.811559 11127 solver.cpp:470] Iteration 3200, lr = 0.001
I0219 15:24:38.870769 11127 solver.cpp:189] Iteration 3400, loss = 0.998396
I0219 15:24:38.870791 11127 solver.cpp:204]     Train net output #0: loss = 0.998396 (* 1 = 0.998396 loss)
I0219 15:24:38.870795 11127 solver.cpp:470] Iteration 3400, lr = 0.001
I0219 15:24:49.933133 11127 solver.cpp:189] Iteration 3600, loss = 0.897475
I0219 15:24:49.933225 11127 solver.cpp:204]     Train net output #0: loss = 0.897475 (* 1 = 0.897475 loss)
I0219 15:24:49.933239 11127 solver.cpp:470] Iteration 3600, lr = 0.001
I0219 15:25:00.985859 11127 solver.cpp:189] Iteration 3800, loss = 0.759083
I0219 15:25:00.985884 11127 solver.cpp:204]     Train net output #0: loss = 0.759083 (* 1 = 0.759083 loss)
I0219 15:25:00.985888 11127 solver.cpp:470] Iteration 3800, lr = 0.001
I0219 15:25:11.994781 11127 solver.cpp:266] Iteration 4000, Testing net (#0)
I0219 15:25:14.237787 11127 solver.cpp:315]     Test net output #0: accuracy = 0.6558
I0219 15:25:14.237810 11127 solver.cpp:315]     Test net output #1: loss = 0.956424 (* 1 = 0.956424 loss)
I0219 15:25:14.264401 11127 solver.cpp:189] Iteration 4000, loss = 0.879901
I0219 15:25:14.264421 11127 solver.cpp:204]     Train net output #0: loss = 0.879901 (* 1 = 0.879901 loss)
I0219 15:25:14.264425 11127 solver.cpp:470] Iteration 4000, lr = 0.001
I0219 15:25:25.379326 11127 solver.cpp:189] Iteration 4200, loss = 0.97316
I0219 15:25:25.379374 11127 solver.cpp:204]     Train net output #0: loss = 0.97316 (* 1 = 0.97316 loss)
I0219 15:25:25.379380 11127 solver.cpp:470] Iteration 4200, lr = 0.001
I0219 15:25:36.418879 11127 solver.cpp:189] Iteration 4400, loss = 0.92297
I0219 15:25:36.418903 11127 solver.cpp:204]     Train net output #0: loss = 0.92297 (* 1 = 0.92297 loss)
I0219 15:25:36.418907 11127 solver.cpp:470] Iteration 4400, lr = 0.001
I0219 15:25:47.486646 11127 solver.cpp:189] Iteration 4600, loss = 0.807052
I0219 15:25:47.486671 11127 solver.cpp:204]     Train net output #0: loss = 0.807052 (* 1 = 0.807052 loss)
I0219 15:25:47.486675 11127 solver.cpp:470] Iteration 4600, lr = 0.001
I0219 15:25:58.556072 11127 solver.cpp:189] Iteration 4800, loss = 0.720424
I0219 15:25:58.556133 11127 solver.cpp:204]     Train net output #0: loss = 0.720424 (* 1 = 0.720424 loss)
I0219 15:25:58.556138 11127 solver.cpp:470] Iteration 4800, lr = 0.001
I0219 15:26:09.544183 11127 solver.cpp:266] Iteration 5000, Testing net (#0)
I0219 15:26:11.798961 11127 solver.cpp:315]     Test net output #0: accuracy = 0.6805
I0219 15:26:11.798986 11127 solver.cpp:315]     Test net output #1: loss = 0.894506 (* 1 = 0.894506 loss)
I0219 15:26:11.826916 11127 solver.cpp:189] Iteration 5000, loss = 0.828288
I0219 15:26:11.826951 11127 solver.cpp:204]     Train net output #0: loss = 0.828288 (* 1 = 0.828288 loss)
I0219 15:26:11.826956 11127 solver.cpp:470] Iteration 5000, lr = 0.001
I0219 15:26:23.211653 11127 solver.cpp:189] Iteration 5200, loss = 0.890664
I0219 15:26:23.211690 11127 solver.cpp:204]     Train net output #0: loss = 0.890664 (* 1 = 0.890664 loss)
I0219 15:26:23.211694 11127 solver.cpp:470] Iteration 5200, lr = 0.001
I0219 15:26:34.352205 11127 solver.cpp:189] Iteration 5400, loss = 0.844799
I0219 15:26:34.352288 11127 solver.cpp:204]     Train net output #0: loss = 0.844799 (* 1 = 0.844799 loss)
I0219 15:26:34.352291 11127 solver.cpp:470] Iteration 5400, lr = 0.001
I0219 15:26:45.390902 11127 solver.cpp:189] Iteration 5600, loss = 0.744831
I0219 15:26:45.390925 11127 solver.cpp:204]     Train net output #0: loss = 0.744831 (* 1 = 0.744831 loss)
I0219 15:26:45.390929 11127 solver.cpp:470] Iteration 5600, lr = 0.001
I0219 15:26:56.417596 11127 solver.cpp:189] Iteration 5800, loss = 0.685013
I0219 15:26:56.417623 11127 solver.cpp:204]     Train net output #0: loss = 0.685013 (* 1 = 0.685013 loss)
I0219 15:26:56.417626 11127 solver.cpp:470] Iteration 5800, lr = 0.001
I0219 15:27:07.388901 11127 solver.cpp:266] Iteration 6000, Testing net (#0)
I0219 15:27:09.628552 11127 solver.cpp:315]     Test net output #0: accuracy = 0.6992
I0219 15:27:09.628574 11127 solver.cpp:315]     Test net output #1: loss = 0.856518 (* 1 = 0.856518 loss)
I0219 15:27:09.654878 11127 solver.cpp:189] Iteration 6000, loss = 0.771091
I0219 15:27:09.654892 11127 solver.cpp:204]     Train net output #0: loss = 0.771091 (* 1 = 0.771091 loss)
I0219 15:27:09.654896 11127 solver.cpp:470] Iteration 6000, lr = 0.001
I0219 15:27:20.681146 11127 solver.cpp:189] Iteration 6200, loss = 0.862633
I0219 15:27:20.681170 11127 solver.cpp:204]     Train net output #0: loss = 0.862633 (* 1 = 0.862633 loss)
I0219 15:27:20.681175 11127 solver.cpp:470] Iteration 6200, lr = 0.001
I0219 15:27:31.707185 11127 solver.cpp:189] Iteration 6400, loss = 0.769153
I0219 15:27:31.707206 11127 solver.cpp:204]     Train net output #0: loss = 0.769153 (* 1 = 0.769153 loss)
I0219 15:27:31.707211 11127 solver.cpp:470] Iteration 6400, lr = 0.001
I0219 15:27:42.862913 11127 solver.cpp:189] Iteration 6600, loss = 0.711267
I0219 15:27:42.862980 11127 solver.cpp:204]     Train net output #0: loss = 0.711267 (* 1 = 0.711267 loss)
I0219 15:27:42.862985 11127 solver.cpp:470] Iteration 6600, lr = 0.001
I0219 15:27:54.051085 11127 solver.cpp:189] Iteration 6800, loss = 0.685888
I0219 15:27:54.051107 11127 solver.cpp:204]     Train net output #0: loss = 0.685888 (* 1 = 0.685888 loss)
I0219 15:27:54.051111 11127 solver.cpp:470] Iteration 6800, lr = 0.001
I0219 15:28:05.044096 11127 solver.cpp:266] Iteration 7000, Testing net (#0)
I0219 15:28:07.314334 11127 solver.cpp:315]     Test net output #0: accuracy = 0.712
I0219 15:28:07.314355 11127 solver.cpp:315]     Test net output #1: loss = 0.818867 (* 1 = 0.818867 loss)
I0219 15:28:07.340651 11127 solver.cpp:189] Iteration 7000, loss = 0.718072
I0219 15:28:07.340664 11127 solver.cpp:204]     Train net output #0: loss = 0.718072 (* 1 = 0.718072 loss)
I0219 15:28:07.340669 11127 solver.cpp:470] Iteration 7000, lr = 0.001
I0219 15:28:18.450372 11127 solver.cpp:189] Iteration 7200, loss = 0.831294
I0219 15:28:18.450435 11127 solver.cpp:204]     Train net output #0: loss = 0.831294 (* 1 = 0.831294 loss)
I0219 15:28:18.450441 11127 solver.cpp:470] Iteration 7200, lr = 0.001
I0219 15:28:29.575587 11127 solver.cpp:189] Iteration 7400, loss = 0.720045
I0219 15:28:29.575624 11127 solver.cpp:204]     Train net output #0: loss = 0.720045 (* 1 = 0.720045 loss)
I0219 15:28:29.575629 11127 solver.cpp:470] Iteration 7400, lr = 0.001
I0219 15:28:40.774260 11127 solver.cpp:189] Iteration 7600, loss = 0.688607
I0219 15:28:40.774291 11127 solver.cpp:204]     Train net output #0: loss = 0.688607 (* 1 = 0.688607 loss)
I0219 15:28:40.774296 11127 solver.cpp:470] Iteration 7600, lr = 0.001
I0219 15:28:51.916656 11127 solver.cpp:189] Iteration 7800, loss = 0.679433
I0219 15:28:51.916746 11127 solver.cpp:204]     Train net output #0: loss = 0.679433 (* 1 = 0.679433 loss)
I0219 15:28:51.916760 11127 solver.cpp:470] Iteration 7800, lr = 0.001
I0219 15:29:03.062476 11127 solver.cpp:266] Iteration 8000, Testing net (#0)
I0219 15:29:05.344347 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7194
I0219 15:29:05.344418 11127 solver.cpp:315]     Test net output #1: loss = 0.802156 (* 1 = 0.802156 loss)
I0219 15:29:05.370909 11127 solver.cpp:189] Iteration 8000, loss = 0.69294
I0219 15:29:05.370949 11127 solver.cpp:204]     Train net output #0: loss = 0.69294 (* 1 = 0.69294 loss)
I0219 15:29:05.370954 11127 solver.cpp:470] Iteration 8000, lr = 0.001
I0219 15:29:16.515523 11127 solver.cpp:189] Iteration 8200, loss = 0.804048
I0219 15:29:16.515559 11127 solver.cpp:204]     Train net output #0: loss = 0.804048 (* 1 = 0.804048 loss)
I0219 15:29:16.515564 11127 solver.cpp:470] Iteration 8200, lr = 0.001
I0219 15:29:27.667392 11127 solver.cpp:189] Iteration 8400, loss = 0.701799
I0219 15:29:27.667449 11127 solver.cpp:204]     Train net output #0: loss = 0.701799 (* 1 = 0.701799 loss)
I0219 15:29:27.667454 11127 solver.cpp:470] Iteration 8400, lr = 0.001
I0219 15:29:38.742177 11127 solver.cpp:189] Iteration 8600, loss = 0.678232
I0219 15:29:38.742216 11127 solver.cpp:204]     Train net output #0: loss = 0.678232 (* 1 = 0.678232 loss)
I0219 15:29:38.742221 11127 solver.cpp:470] Iteration 8600, lr = 0.001
I0219 15:29:49.833911 11127 solver.cpp:189] Iteration 8800, loss = 0.662438
I0219 15:29:49.833935 11127 solver.cpp:204]     Train net output #0: loss = 0.662438 (* 1 = 0.662438 loss)
I0219 15:29:49.833938 11127 solver.cpp:470] Iteration 8800, lr = 0.001
I0219 15:30:00.845211 11127 solver.cpp:266] Iteration 9000, Testing net (#0)
I0219 15:30:03.110085 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7267
I0219 15:30:03.110110 11127 solver.cpp:315]     Test net output #1: loss = 0.779834 (* 1 = 0.779834 loss)
I0219 15:30:03.136423 11127 solver.cpp:189] Iteration 9000, loss = 0.658473
I0219 15:30:03.136438 11127 solver.cpp:204]     Train net output #0: loss = 0.658473 (* 1 = 0.658473 loss)
I0219 15:30:03.136442 11127 solver.cpp:470] Iteration 9000, lr = 0.001
I0219 15:30:14.317734 11127 solver.cpp:189] Iteration 9200, loss = 0.778153
I0219 15:30:14.317775 11127 solver.cpp:204]     Train net output #0: loss = 0.778153 (* 1 = 0.778153 loss)
I0219 15:30:14.317780 11127 solver.cpp:470] Iteration 9200, lr = 0.001
I0219 15:30:25.500437 11127 solver.cpp:189] Iteration 9400, loss = 0.690631
I0219 15:30:25.500458 11127 solver.cpp:204]     Train net output #0: loss = 0.690631 (* 1 = 0.690631 loss)
I0219 15:30:25.500463 11127 solver.cpp:470] Iteration 9400, lr = 0.001
I0219 15:30:36.698978 11127 solver.cpp:189] Iteration 9600, loss = 0.66751
I0219 15:30:36.699043 11127 solver.cpp:204]     Train net output #0: loss = 0.66751 (* 1 = 0.66751 loss)
I0219 15:30:36.699048 11127 solver.cpp:470] Iteration 9600, lr = 0.001
I0219 15:30:47.891686 11127 solver.cpp:189] Iteration 9800, loss = 0.649774
I0219 15:30:47.891731 11127 solver.cpp:204]     Train net output #0: loss = 0.649774 (* 1 = 0.649774 loss)
I0219 15:30:47.891736 11127 solver.cpp:470] Iteration 9800, lr = 0.001
I0219 15:30:59.052383 11127 solver.cpp:334] Snapshotting to examples/cifar10/cifar10_full_iter_10000.caffemodel
I0219 15:30:59.053534 11127 solver.cpp:342] Snapshotting solver state to examples/cifar10/cifar10_full_iter_10000.solverstate
I0219 15:30:59.054044 11127 solver.cpp:266] Iteration 10000, Testing net (#0)
I0219 15:31:01.300050 11127 solver.cpp:315]     Test net output #0: accuracy = 0.735
I0219 15:31:01.300086 11127 solver.cpp:315]     Test net output #1: loss = 0.763701 (* 1 = 0.763701 loss)
I0219 15:31:01.326577 11127 solver.cpp:189] Iteration 10000, loss = 0.636333
I0219 15:31:01.326619 11127 solver.cpp:204]     Train net output #0: loss = 0.636333 (* 1 = 0.636333 loss)
I0219 15:31:01.326627 11127 solver.cpp:470] Iteration 10000, lr = 0.001
I0219 15:31:12.482506 11127 solver.cpp:189] Iteration 10200, loss = 0.762713
I0219 15:31:12.482619 11127 solver.cpp:204]     Train net output #0: loss = 0.762713 (* 1 = 0.762713 loss)
I0219 15:31:12.482632 11127 solver.cpp:470] Iteration 10200, lr = 0.001
I0219 15:31:23.653070 11127 solver.cpp:189] Iteration 10400, loss = 0.683774
I0219 15:31:23.653091 11127 solver.cpp:204]     Train net output #0: loss = 0.683774 (* 1 = 0.683774 loss)
I0219 15:31:23.653095 11127 solver.cpp:470] Iteration 10400, lr = 0.001
I0219 15:31:34.806217 11127 solver.cpp:189] Iteration 10600, loss = 0.661309
I0219 15:31:34.806238 11127 solver.cpp:204]     Train net output #0: loss = 0.661309 (* 1 = 0.661309 loss)
I0219 15:31:34.806243 11127 solver.cpp:470] Iteration 10600, lr = 0.001
I0219 15:31:46.032023 11127 solver.cpp:189] Iteration 10800, loss = 0.647419
I0219 15:31:46.032119 11127 solver.cpp:204]     Train net output #0: loss = 0.647419 (* 1 = 0.647419 loss)
I0219 15:31:46.032126 11127 solver.cpp:470] Iteration 10800, lr = 0.001
I0219 15:31:57.199931 11127 solver.cpp:266] Iteration 11000, Testing net (#0)
I0219 15:31:59.449734 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7442
I0219 15:31:59.449758 11127 solver.cpp:315]     Test net output #1: loss = 0.746141 (* 1 = 0.746141 loss)
I0219 15:31:59.476068 11127 solver.cpp:189] Iteration 11000, loss = 0.620968
I0219 15:31:59.476094 11127 solver.cpp:204]     Train net output #0: loss = 0.620968 (* 1 = 0.620968 loss)
I0219 15:31:59.476099 11127 solver.cpp:470] Iteration 11000, lr = 0.001
I0219 15:32:10.656828 11127 solver.cpp:189] Iteration 11200, loss = 0.745214
I0219 15:32:10.656867 11127 solver.cpp:204]     Train net output #0: loss = 0.745214 (* 1 = 0.745214 loss)
I0219 15:32:10.656872 11127 solver.cpp:470] Iteration 11200, lr = 0.001
I0219 15:32:21.878655 11127 solver.cpp:189] Iteration 11400, loss = 0.665031
I0219 15:32:21.878722 11127 solver.cpp:204]     Train net output #0: loss = 0.665031 (* 1 = 0.665031 loss)
I0219 15:32:21.878726 11127 solver.cpp:470] Iteration 11400, lr = 0.001
I0219 15:32:33.093027 11127 solver.cpp:189] Iteration 11600, loss = 0.650968
I0219 15:32:33.093050 11127 solver.cpp:204]     Train net output #0: loss = 0.650968 (* 1 = 0.650968 loss)
I0219 15:32:33.093055 11127 solver.cpp:470] Iteration 11600, lr = 0.001
I0219 15:32:44.322011 11127 solver.cpp:189] Iteration 11800, loss = 0.633544
I0219 15:32:44.322048 11127 solver.cpp:204]     Train net output #0: loss = 0.633544 (* 1 = 0.633544 loss)
I0219 15:32:44.322053 11127 solver.cpp:470] Iteration 11800, lr = 0.001
I0219 15:32:55.492895 11127 solver.cpp:266] Iteration 12000, Testing net (#0)
I0219 15:32:57.781343 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7478
I0219 15:32:57.781386 11127 solver.cpp:315]     Test net output #1: loss = 0.737381 (* 1 = 0.737381 loss)
I0219 15:32:57.807878 11127 solver.cpp:189] Iteration 12000, loss = 0.616563
I0219 15:32:57.807919 11127 solver.cpp:204]     Train net output #0: loss = 0.616563 (* 1 = 0.616563 loss)
I0219 15:32:57.807924 11127 solver.cpp:470] Iteration 12000, lr = 0.001
I0219 15:33:08.990998 11127 solver.cpp:189] Iteration 12200, loss = 0.72055
I0219 15:33:08.991039 11127 solver.cpp:204]     Train net output #0: loss = 0.72055 (* 1 = 0.72055 loss)
I0219 15:33:08.991044 11127 solver.cpp:470] Iteration 12200, lr = 0.001
I0219 15:33:20.234211 11127 solver.cpp:189] Iteration 12400, loss = 0.652243
I0219 15:33:20.234252 11127 solver.cpp:204]     Train net output #0: loss = 0.652243 (* 1 = 0.652243 loss)
I0219 15:33:20.234258 11127 solver.cpp:470] Iteration 12400, lr = 0.001
I0219 15:33:31.388180 11127 solver.cpp:189] Iteration 12600, loss = 0.645388
I0219 15:33:31.388257 11127 solver.cpp:204]     Train net output #0: loss = 0.645388 (* 1 = 0.645388 loss)
I0219 15:33:31.388263 11127 solver.cpp:470] Iteration 12600, lr = 0.001
I0219 15:33:42.529557 11127 solver.cpp:189] Iteration 12800, loss = 0.614156
I0219 15:33:42.529582 11127 solver.cpp:204]     Train net output #0: loss = 0.614156 (* 1 = 0.614156 loss)
I0219 15:33:42.529587 11127 solver.cpp:470] Iteration 12800, lr = 0.001
I0219 15:33:53.701789 11127 solver.cpp:266] Iteration 13000, Testing net (#0)
I0219 15:33:55.989331 11127 solver.cpp:315]     Test net output #0: accuracy = 0.752
I0219 15:33:55.989372 11127 solver.cpp:315]     Test net output #1: loss = 0.725771 (* 1 = 0.725771 loss)
I0219 15:33:56.017390 11127 solver.cpp:189] Iteration 13000, loss = 0.602856
I0219 15:33:56.017426 11127 solver.cpp:204]     Train net output #0: loss = 0.602856 (* 1 = 0.602856 loss)
I0219 15:33:56.017431 11127 solver.cpp:470] Iteration 13000, lr = 0.001
I0219 15:34:07.443255 11127 solver.cpp:189] Iteration 13200, loss = 0.697035
I0219 15:34:07.443496 11127 solver.cpp:204]     Train net output #0: loss = 0.697035 (* 1 = 0.697035 loss)
I0219 15:34:07.443554 11127 solver.cpp:470] Iteration 13200, lr = 0.001
I0219 15:34:18.667937 11127 solver.cpp:189] Iteration 13400, loss = 0.64616
I0219 15:34:18.667979 11127 solver.cpp:204]     Train net output #0: loss = 0.64616 (* 1 = 0.64616 loss)
I0219 15:34:18.667984 11127 solver.cpp:470] Iteration 13400, lr = 0.001
I0219 15:34:29.932700 11127 solver.cpp:189] Iteration 13600, loss = 0.635438
I0219 15:34:29.932726 11127 solver.cpp:204]     Train net output #0: loss = 0.635438 (* 1 = 0.635438 loss)
I0219 15:34:29.932731 11127 solver.cpp:470] Iteration 13600, lr = 0.001
I0219 15:34:41.268578 11127 solver.cpp:189] Iteration 13800, loss = 0.600463
I0219 15:34:41.268725 11127 solver.cpp:204]     Train net output #0: loss = 0.600463 (* 1 = 0.600463 loss)
I0219 15:34:41.268741 11127 solver.cpp:470] Iteration 13800, lr = 0.001
I0219 15:34:52.449544 11127 solver.cpp:266] Iteration 14000, Testing net (#0)
I0219 15:34:54.735854 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7542
I0219 15:34:54.735895 11127 solver.cpp:315]     Test net output #1: loss = 0.719129 (* 1 = 0.719129 loss)
I0219 15:34:54.762465 11127 solver.cpp:189] Iteration 14000, loss = 0.598773
I0219 15:34:54.762506 11127 solver.cpp:204]     Train net output #0: loss = 0.598773 (* 1 = 0.598773 loss)
I0219 15:34:54.762513 11127 solver.cpp:470] Iteration 14000, lr = 0.001
I0219 15:35:06.021595 11127 solver.cpp:189] Iteration 14200, loss = 0.669437
I0219 15:35:06.021628 11127 solver.cpp:204]     Train net output #0: loss = 0.669437 (* 1 = 0.669437 loss)
I0219 15:35:06.021632 11127 solver.cpp:470] Iteration 14200, lr = 0.001
I0219 15:35:17.250581 11127 solver.cpp:189] Iteration 14400, loss = 0.630056
I0219 15:35:17.250669 11127 solver.cpp:204]     Train net output #0: loss = 0.630056 (* 1 = 0.630056 loss)
I0219 15:35:17.250684 11127 solver.cpp:470] Iteration 14400, lr = 0.001
I0219 15:35:28.464985 11127 solver.cpp:189] Iteration 14600, loss = 0.62859
I0219 15:35:28.465008 11127 solver.cpp:204]     Train net output #0: loss = 0.62859 (* 1 = 0.62859 loss)
I0219 15:35:28.465011 11127 solver.cpp:470] Iteration 14600, lr = 0.001
I0219 15:35:39.644752 11127 solver.cpp:189] Iteration 14800, loss = 0.582546
I0219 15:35:39.644791 11127 solver.cpp:204]     Train net output #0: loss = 0.582546 (* 1 = 0.582546 loss)
I0219 15:35:39.644796 11127 solver.cpp:470] Iteration 14800, lr = 0.001
I0219 15:35:50.845198 11127 solver.cpp:266] Iteration 15000, Testing net (#0)
I0219 15:35:53.122616 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7576
I0219 15:35:53.122660 11127 solver.cpp:315]     Test net output #1: loss = 0.709873 (* 1 = 0.709873 loss)
I0219 15:35:53.149092 11127 solver.cpp:189] Iteration 15000, loss = 0.591653
I0219 15:35:53.149135 11127 solver.cpp:204]     Train net output #0: loss = 0.591653 (* 1 = 0.591653 loss)
I0219 15:35:53.149142 11127 solver.cpp:470] Iteration 15000, lr = 0.001
I0219 15:36:04.368368 11127 solver.cpp:189] Iteration 15200, loss = 0.6453
I0219 15:36:04.368432 11127 solver.cpp:204]     Train net output #0: loss = 0.6453 (* 1 = 0.6453 loss)
I0219 15:36:04.368445 11127 solver.cpp:470] Iteration 15200, lr = 0.001
I0219 15:36:15.547025 11127 solver.cpp:189] Iteration 15400, loss = 0.6161
I0219 15:36:15.547091 11127 solver.cpp:204]     Train net output #0: loss = 0.6161 (* 1 = 0.6161 loss)
I0219 15:36:15.547106 11127 solver.cpp:470] Iteration 15400, lr = 0.001
I0219 15:36:26.781271 11127 solver.cpp:189] Iteration 15600, loss = 0.615861
I0219 15:36:26.781358 11127 solver.cpp:204]     Train net output #0: loss = 0.615861 (* 1 = 0.615861 loss)
I0219 15:36:26.781371 11127 solver.cpp:470] Iteration 15600, lr = 0.001
I0219 15:36:38.005492 11127 solver.cpp:189] Iteration 15800, loss = 0.562802
I0219 15:36:38.005527 11127 solver.cpp:204]     Train net output #0: loss = 0.562802 (* 1 = 0.562802 loss)
I0219 15:36:38.005532 11127 solver.cpp:470] Iteration 15800, lr = 0.001
I0219 15:36:49.048285 11127 solver.cpp:266] Iteration 16000, Testing net (#0)
I0219 15:36:51.352851 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7639
I0219 15:36:51.352890 11127 solver.cpp:315]     Test net output #1: loss = 0.697871 (* 1 = 0.697871 loss)
I0219 15:36:51.379818 11127 solver.cpp:189] Iteration 16000, loss = 0.588968
I0219 15:36:51.379855 11127 solver.cpp:204]     Train net output #0: loss = 0.588968 (* 1 = 0.588968 loss)
I0219 15:36:51.379860 11127 solver.cpp:470] Iteration 16000, lr = 0.001
I0219 15:37:02.583000 11127 solver.cpp:189] Iteration 16200, loss = 0.63018
I0219 15:37:02.583065 11127 solver.cpp:204]     Train net output #0: loss = 0.63018 (* 1 = 0.63018 loss)
I0219 15:37:02.583070 11127 solver.cpp:470] Iteration 16200, lr = 0.001
I0219 15:37:13.784173 11127 solver.cpp:189] Iteration 16400, loss = 0.606515
I0219 15:37:13.784211 11127 solver.cpp:204]     Train net output #0: loss = 0.606515 (* 1 = 0.606515 loss)
I0219 15:37:13.784217 11127 solver.cpp:470] Iteration 16400, lr = 0.001
I0219 15:37:25.003054 11127 solver.cpp:189] Iteration 16600, loss = 0.601385
I0219 15:37:25.003096 11127 solver.cpp:204]     Train net output #0: loss = 0.601385 (* 1 = 0.601385 loss)
I0219 15:37:25.003103 11127 solver.cpp:470] Iteration 16600, lr = 0.001
I0219 15:37:36.195519 11127 solver.cpp:189] Iteration 16800, loss = 0.54881
I0219 15:37:36.195618 11127 solver.cpp:204]     Train net output #0: loss = 0.54881 (* 1 = 0.54881 loss)
I0219 15:37:36.195624 11127 solver.cpp:470] Iteration 16800, lr = 0.001
I0219 15:37:47.355922 11127 solver.cpp:266] Iteration 17000, Testing net (#0)
I0219 15:37:49.612293 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7684
I0219 15:37:49.612332 11127 solver.cpp:315]     Test net output #1: loss = 0.687888 (* 1 = 0.687888 loss)
I0219 15:37:49.638993 11127 solver.cpp:189] Iteration 17000, loss = 0.577857
I0219 15:37:49.639016 11127 solver.cpp:204]     Train net output #0: loss = 0.577857 (* 1 = 0.577857 loss)
I0219 15:37:49.639020 11127 solver.cpp:470] Iteration 17000, lr = 0.001
I0219 15:38:00.784313 11127 solver.cpp:189] Iteration 17200, loss = 0.61684
I0219 15:38:00.784338 11127 solver.cpp:204]     Train net output #0: loss = 0.61684 (* 1 = 0.61684 loss)
I0219 15:38:00.784343 11127 solver.cpp:470] Iteration 17200, lr = 0.001
I0219 15:38:11.977802 11127 solver.cpp:189] Iteration 17400, loss = 0.602018
I0219 15:38:11.977880 11127 solver.cpp:204]     Train net output #0: loss = 0.602018 (* 1 = 0.602018 loss)
I0219 15:38:11.977885 11127 solver.cpp:470] Iteration 17400, lr = 0.001
I0219 15:38:23.212402 11127 solver.cpp:189] Iteration 17600, loss = 0.590793
I0219 15:38:23.212445 11127 solver.cpp:204]     Train net output #0: loss = 0.590793 (* 1 = 0.590793 loss)
I0219 15:38:23.212451 11127 solver.cpp:470] Iteration 17600, lr = 0.001
I0219 15:38:34.429811 11127 solver.cpp:189] Iteration 17800, loss = 0.536703
I0219 15:38:34.429853 11127 solver.cpp:204]     Train net output #0: loss = 0.536703 (* 1 = 0.536703 loss)
I0219 15:38:34.429859 11127 solver.cpp:470] Iteration 17800, lr = 0.001
I0219 15:38:45.501060 11127 solver.cpp:266] Iteration 18000, Testing net (#0)
I0219 15:38:47.757928 11127 solver.cpp:315]     Test net output #0: accuracy = 0.771
I0219 15:38:47.757959 11127 solver.cpp:315]     Test net output #1: loss = 0.678328 (* 1 = 0.678328 loss)
I0219 15:38:47.785745 11127 solver.cpp:189] Iteration 18000, loss = 0.567651
I0219 15:38:47.785768 11127 solver.cpp:204]     Train net output #0: loss = 0.567651 (* 1 = 0.567651 loss)
I0219 15:38:47.785773 11127 solver.cpp:470] Iteration 18000, lr = 0.001
I0219 15:38:59.105131 11127 solver.cpp:189] Iteration 18200, loss = 0.600507
I0219 15:38:59.105180 11127 solver.cpp:204]     Train net output #0: loss = 0.600507 (* 1 = 0.600507 loss)
I0219 15:38:59.105186 11127 solver.cpp:470] Iteration 18200, lr = 0.001
I0219 15:39:10.349841 11127 solver.cpp:189] Iteration 18400, loss = 0.597401
I0219 15:39:10.349867 11127 solver.cpp:204]     Train net output #0: loss = 0.597401 (* 1 = 0.597401 loss)
I0219 15:39:10.349872 11127 solver.cpp:470] Iteration 18400, lr = 0.001
I0219 15:39:21.667392 11127 solver.cpp:189] Iteration 18600, loss = 0.585883
I0219 15:39:21.667454 11127 solver.cpp:204]     Train net output #0: loss = 0.585883 (* 1 = 0.585883 loss)
I0219 15:39:21.667459 11127 solver.cpp:470] Iteration 18600, lr = 0.001
I0219 15:39:32.971668 11127 solver.cpp:189] Iteration 18800, loss = 0.52911
I0219 15:39:32.971709 11127 solver.cpp:204]     Train net output #0: loss = 0.52911 (* 1 = 0.52911 loss)
I0219 15:39:32.971714 11127 solver.cpp:470] Iteration 18800, lr = 0.001
I0219 15:39:44.223801 11127 solver.cpp:266] Iteration 19000, Testing net (#0)
I0219 15:39:46.496927 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7722
I0219 15:39:46.496953 11127 solver.cpp:315]     Test net output #1: loss = 0.674246 (* 1 = 0.674246 loss)
I0219 15:39:46.525440 11127 solver.cpp:189] Iteration 19000, loss = 0.560295
I0219 15:39:46.525475 11127 solver.cpp:204]     Train net output #0: loss = 0.560295 (* 1 = 0.560295 loss)
I0219 15:39:46.525481 11127 solver.cpp:470] Iteration 19000, lr = 0.001
I0219 15:39:57.638224 11127 solver.cpp:189] Iteration 19200, loss = 0.592709
I0219 15:39:57.638315 11127 solver.cpp:204]     Train net output #0: loss = 0.592709 (* 1 = 0.592709 loss)
I0219 15:39:57.638330 11127 solver.cpp:470] Iteration 19200, lr = 0.001
I0219 15:40:08.814836 11127 solver.cpp:189] Iteration 19400, loss = 0.592927
I0219 15:40:08.814863 11127 solver.cpp:204]     Train net output #0: loss = 0.592927 (* 1 = 0.592927 loss)
I0219 15:40:08.814868 11127 solver.cpp:470] Iteration 19400, lr = 0.001
I0219 15:40:20.018683 11127 solver.cpp:189] Iteration 19600, loss = 0.578148
I0219 15:40:20.018721 11127 solver.cpp:204]     Train net output #0: loss = 0.578148 (* 1 = 0.578148 loss)
I0219 15:40:20.018726 11127 solver.cpp:470] Iteration 19600, lr = 0.001
I0219 15:40:31.214099 11127 solver.cpp:189] Iteration 19800, loss = 0.5216
I0219 15:40:31.214187 11127 solver.cpp:204]     Train net output #0: loss = 0.5216 (* 1 = 0.5216 loss)
I0219 15:40:31.214195 11127 solver.cpp:470] Iteration 19800, lr = 0.001
I0219 15:40:42.340410 11127 solver.cpp:334] Snapshotting to examples/cifar10/cifar10_full_iter_20000.caffemodel
I0219 15:40:42.341264 11127 solver.cpp:342] Snapshotting solver state to examples/cifar10/cifar10_full_iter_20000.solverstate
I0219 15:40:42.341724 11127 solver.cpp:266] Iteration 20000, Testing net (#0)
I0219 15:40:44.603926 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7732
I0219 15:40:44.603958 11127 solver.cpp:315]     Test net output #1: loss = 0.670051 (* 1 = 0.670051 loss)
I0219 15:40:44.631364 11127 solver.cpp:189] Iteration 20000, loss = 0.551531
I0219 15:40:44.631387 11127 solver.cpp:204]     Train net output #0: loss = 0.551531 (* 1 = 0.551531 loss)
I0219 15:40:44.631392 11127 solver.cpp:470] Iteration 20000, lr = 0.001
I0219 15:40:55.878216 11127 solver.cpp:189] Iteration 20200, loss = 0.584299
I0219 15:40:55.878249 11127 solver.cpp:204]     Train net output #0: loss = 0.584299 (* 1 = 0.584299 loss)
I0219 15:40:55.878253 11127 solver.cpp:470] Iteration 20200, lr = 0.001
I0219 15:41:07.149019 11127 solver.cpp:189] Iteration 20400, loss = 0.594247
I0219 15:41:07.149080 11127 solver.cpp:204]     Train net output #0: loss = 0.594247 (* 1 = 0.594247 loss)
I0219 15:41:07.149085 11127 solver.cpp:470] Iteration 20400, lr = 0.001
I0219 15:41:18.366217 11127 solver.cpp:189] Iteration 20600, loss = 0.569845
I0219 15:41:18.366258 11127 solver.cpp:204]     Train net output #0: loss = 0.569845 (* 1 = 0.569845 loss)
I0219 15:41:18.366264 11127 solver.cpp:470] Iteration 20600, lr = 0.001
I0219 15:41:29.563206 11127 solver.cpp:189] Iteration 20800, loss = 0.509678
I0219 15:41:29.563246 11127 solver.cpp:204]     Train net output #0: loss = 0.509678 (* 1 = 0.509678 loss)
I0219 15:41:29.563252 11127 solver.cpp:470] Iteration 20800, lr = 0.001
I0219 15:41:40.687753 11127 solver.cpp:266] Iteration 21000, Testing net (#0)
I0219 15:41:42.963218 11127 solver.cpp:315]     Test net output #0: accuracy = 0.774
I0219 15:41:42.963258 11127 solver.cpp:315]     Test net output #1: loss = 0.664476 (* 1 = 0.664476 loss)
I0219 15:41:42.990835 11127 solver.cpp:189] Iteration 21000, loss = 0.541642
I0219 15:41:42.990874 11127 solver.cpp:204]     Train net output #0: loss = 0.541642 (* 1 = 0.541642 loss)
I0219 15:41:42.990880 11127 solver.cpp:470] Iteration 21000, lr = 0.001
I0219 15:41:54.231886 11127 solver.cpp:189] Iteration 21200, loss = 0.58135
I0219 15:41:54.231912 11127 solver.cpp:204]     Train net output #0: loss = 0.58135 (* 1 = 0.58135 loss)
I0219 15:41:54.231917 11127 solver.cpp:470] Iteration 21200, lr = 0.001
I0219 15:42:05.500414 11127 solver.cpp:189] Iteration 21400, loss = 0.589187
I0219 15:42:05.500440 11127 solver.cpp:204]     Train net output #0: loss = 0.589187 (* 1 = 0.589187 loss)
I0219 15:42:05.500444 11127 solver.cpp:470] Iteration 21400, lr = 0.001
I0219 15:42:16.727833 11127 solver.cpp:189] Iteration 21600, loss = 0.564212
I0219 15:42:16.727944 11127 solver.cpp:204]     Train net output #0: loss = 0.564212 (* 1 = 0.564212 loss)
I0219 15:42:16.727959 11127 solver.cpp:470] Iteration 21600, lr = 0.001
I0219 15:42:27.942811 11127 solver.cpp:189] Iteration 21800, loss = 0.499333
I0219 15:42:27.942849 11127 solver.cpp:204]     Train net output #0: loss = 0.499333 (* 1 = 0.499333 loss)
I0219 15:42:27.942854 11127 solver.cpp:470] Iteration 21800, lr = 0.001
I0219 15:42:39.121296 11127 solver.cpp:266] Iteration 22000, Testing net (#0)
I0219 15:42:41.407315 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7759
I0219 15:42:41.407364 11127 solver.cpp:315]     Test net output #1: loss = 0.659748 (* 1 = 0.659748 loss)
I0219 15:42:41.434327 11127 solver.cpp:189] Iteration 22000, loss = 0.531585
I0219 15:42:41.434363 11127 solver.cpp:204]     Train net output #0: loss = 0.531585 (* 1 = 0.531585 loss)
I0219 15:42:41.434370 11127 solver.cpp:470] Iteration 22000, lr = 0.001
I0219 15:42:52.627387 11127 solver.cpp:189] Iteration 22200, loss = 0.575952
I0219 15:42:52.627521 11127 solver.cpp:204]     Train net output #0: loss = 0.575952 (* 1 = 0.575952 loss)
I0219 15:42:52.627531 11127 solver.cpp:470] Iteration 22200, lr = 0.001
I0219 15:43:03.870213 11127 solver.cpp:189] Iteration 22400, loss = 0.586636
I0219 15:43:03.870297 11127 solver.cpp:204]     Train net output #0: loss = 0.586636 (* 1 = 0.586636 loss)
I0219 15:43:03.870319 11127 solver.cpp:470] Iteration 22400, lr = 0.001
I0219 15:43:15.105221 11127 solver.cpp:189] Iteration 22600, loss = 0.553887
I0219 15:43:15.105260 11127 solver.cpp:204]     Train net output #0: loss = 0.553887 (* 1 = 0.553887 loss)
I0219 15:43:15.105265 11127 solver.cpp:470] Iteration 22600, lr = 0.001
I0219 15:43:26.387696 11127 solver.cpp:189] Iteration 22800, loss = 0.49046
I0219 15:43:26.387796 11127 solver.cpp:204]     Train net output #0: loss = 0.49046 (* 1 = 0.49046 loss)
I0219 15:43:26.387811 11127 solver.cpp:470] Iteration 22800, lr = 0.001
I0219 15:43:37.530050 11127 solver.cpp:266] Iteration 23000, Testing net (#0)
I0219 15:43:39.814882 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7769
I0219 15:43:39.815048 11127 solver.cpp:315]     Test net output #1: loss = 0.656337 (* 1 = 0.656337 loss)
I0219 15:43:39.842157 11127 solver.cpp:189] Iteration 23000, loss = 0.521678
I0219 15:43:39.842201 11127 solver.cpp:204]     Train net output #0: loss = 0.521678 (* 1 = 0.521678 loss)
I0219 15:43:39.842207 11127 solver.cpp:470] Iteration 23000, lr = 0.001
I0219 15:43:51.084558 11127 solver.cpp:189] Iteration 23200, loss = 0.569695
I0219 15:43:51.084599 11127 solver.cpp:204]     Train net output #0: loss = 0.569695 (* 1 = 0.569695 loss)
I0219 15:43:51.084604 11127 solver.cpp:470] Iteration 23200, lr = 0.001
I0219 15:44:02.438339 11127 solver.cpp:189] Iteration 23400, loss = 0.575117
I0219 15:44:02.438421 11127 solver.cpp:204]     Train net output #0: loss = 0.575117 (* 1 = 0.575117 loss)
I0219 15:44:02.438426 11127 solver.cpp:470] Iteration 23400, lr = 0.001
I0219 15:44:13.723721 11127 solver.cpp:189] Iteration 23600, loss = 0.546004
I0219 15:44:13.723762 11127 solver.cpp:204]     Train net output #0: loss = 0.546004 (* 1 = 0.546004 loss)
I0219 15:44:13.723767 11127 solver.cpp:470] Iteration 23600, lr = 0.001
I0219 15:44:24.949285 11127 solver.cpp:189] Iteration 23800, loss = 0.476462
I0219 15:44:24.949324 11127 solver.cpp:204]     Train net output #0: loss = 0.476462 (* 1 = 0.476462 loss)
I0219 15:44:24.949329 11127 solver.cpp:470] Iteration 23800, lr = 0.001
I0219 15:44:36.111915 11127 solver.cpp:266] Iteration 24000, Testing net (#0)
I0219 15:44:38.396082 11127 solver.cpp:315]     Test net output #0: accuracy = 0.778
I0219 15:44:38.396106 11127 solver.cpp:315]     Test net output #1: loss = 0.652743 (* 1 = 0.652743 loss)
I0219 15:44:38.422426 11127 solver.cpp:189] Iteration 24000, loss = 0.511681
I0219 15:44:38.422438 11127 solver.cpp:204]     Train net output #0: loss = 0.511681 (* 1 = 0.511681 loss)
I0219 15:44:38.422442 11127 solver.cpp:470] Iteration 24000, lr = 0.001
I0219 15:44:49.617226 11127 solver.cpp:189] Iteration 24200, loss = 0.565338
I0219 15:44:49.617264 11127 solver.cpp:204]     Train net output #0: loss = 0.565338 (* 1 = 0.565338 loss)
I0219 15:44:49.617269 11127 solver.cpp:470] Iteration 24200, lr = 0.001
I0219 15:45:00.860102 11127 solver.cpp:189] Iteration 24400, loss = 0.563713
I0219 15:45:00.860131 11127 solver.cpp:204]     Train net output #0: loss = 0.563713 (* 1 = 0.563713 loss)
I0219 15:45:00.860134 11127 solver.cpp:470] Iteration 24400, lr = 0.001
I0219 15:45:12.039381 11127 solver.cpp:189] Iteration 24600, loss = 0.535932
I0219 15:45:12.039474 11127 solver.cpp:204]     Train net output #0: loss = 0.535932 (* 1 = 0.535932 loss)
I0219 15:45:12.039479 11127 solver.cpp:470] Iteration 24600, lr = 0.001
I0219 15:45:23.230064 11127 solver.cpp:189] Iteration 24800, loss = 0.465347
I0219 15:45:23.230105 11127 solver.cpp:204]     Train net output #0: loss = 0.465347 (* 1 = 0.465347 loss)
I0219 15:45:23.230111 11127 solver.cpp:470] Iteration 24800, lr = 0.001
I0219 15:45:34.361477 11127 solver.cpp:266] Iteration 25000, Testing net (#0)
I0219 15:45:36.653710 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7789
I0219 15:45:36.653733 11127 solver.cpp:315]     Test net output #1: loss = 0.649671 (* 1 = 0.649671 loss)
I0219 15:45:36.680176 11127 solver.cpp:189] Iteration 25000, loss = 0.505349
I0219 15:45:36.680212 11127 solver.cpp:204]     Train net output #0: loss = 0.505349 (* 1 = 0.505349 loss)
I0219 15:45:36.680217 11127 solver.cpp:470] Iteration 25000, lr = 0.001
I0219 15:45:47.840952 11127 solver.cpp:189] Iteration 25200, loss = 0.562621
I0219 15:45:47.841013 11127 solver.cpp:204]     Train net output #0: loss = 0.562621 (* 1 = 0.562621 loss)
I0219 15:45:47.841019 11127 solver.cpp:470] Iteration 25200, lr = 0.001
I0219 15:45:59.106541 11127 solver.cpp:189] Iteration 25400, loss = 0.552126
I0219 15:45:59.106595 11127 solver.cpp:204]     Train net output #0: loss = 0.552126 (* 1 = 0.552126 loss)
I0219 15:45:59.106600 11127 solver.cpp:470] Iteration 25400, lr = 0.001
I0219 15:46:10.396497 11127 solver.cpp:189] Iteration 25600, loss = 0.526702
I0219 15:46:10.396528 11127 solver.cpp:204]     Train net output #0: loss = 0.526702 (* 1 = 0.526702 loss)
I0219 15:46:10.396533 11127 solver.cpp:470] Iteration 25600, lr = 0.001
I0219 15:46:21.625363 11127 solver.cpp:189] Iteration 25800, loss = 0.452978
I0219 15:46:21.625511 11127 solver.cpp:204]     Train net output #0: loss = 0.452978 (* 1 = 0.452978 loss)
I0219 15:46:21.625519 11127 solver.cpp:470] Iteration 25800, lr = 0.001
I0219 15:46:32.836311 11127 solver.cpp:266] Iteration 26000, Testing net (#0)
I0219 15:46:35.207447 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7793
I0219 15:46:35.207484 11127 solver.cpp:315]     Test net output #1: loss = 0.647796 (* 1 = 0.647796 loss)
I0219 15:46:35.234347 11127 solver.cpp:189] Iteration 26000, loss = 0.49685
I0219 15:46:35.234382 11127 solver.cpp:204]     Train net output #0: loss = 0.49685 (* 1 = 0.49685 loss)
I0219 15:46:35.234386 11127 solver.cpp:470] Iteration 26000, lr = 0.001
I0219 15:46:46.575419 11127 solver.cpp:189] Iteration 26200, loss = 0.558917
I0219 15:46:46.575459 11127 solver.cpp:204]     Train net output #0: loss = 0.558917 (* 1 = 0.558917 loss)
I0219 15:46:46.575464 11127 solver.cpp:470] Iteration 26200, lr = 0.001
I0219 15:46:57.776296 11127 solver.cpp:189] Iteration 26400, loss = 0.538381
I0219 15:46:57.776370 11127 solver.cpp:204]     Train net output #0: loss = 0.538381 (* 1 = 0.538381 loss)
I0219 15:46:57.776376 11127 solver.cpp:470] Iteration 26400, lr = 0.001
I0219 15:47:08.963250 11127 solver.cpp:189] Iteration 26600, loss = 0.520088
I0219 15:47:08.963273 11127 solver.cpp:204]     Train net output #0: loss = 0.520088 (* 1 = 0.520088 loss)
I0219 15:47:08.963276 11127 solver.cpp:470] Iteration 26600, lr = 0.001
I0219 15:47:20.131348 11127 solver.cpp:189] Iteration 26800, loss = 0.446777
I0219 15:47:20.131388 11127 solver.cpp:204]     Train net output #0: loss = 0.446777 (* 1 = 0.446777 loss)
I0219 15:47:20.131393 11127 solver.cpp:470] Iteration 26800, lr = 0.001
I0219 15:47:31.322442 11127 solver.cpp:266] Iteration 27000, Testing net (#0)
I0219 15:47:33.597966 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7787
I0219 15:47:33.598011 11127 solver.cpp:315]     Test net output #1: loss = 0.645586 (* 1 = 0.645586 loss)
I0219 15:47:33.624542 11127 solver.cpp:189] Iteration 27000, loss = 0.488994
I0219 15:47:33.624583 11127 solver.cpp:204]     Train net output #0: loss = 0.488994 (* 1 = 0.488994 loss)
I0219 15:47:33.624589 11127 solver.cpp:470] Iteration 27000, lr = 0.001
I0219 15:47:44.809128 11127 solver.cpp:189] Iteration 27200, loss = 0.555541
I0219 15:47:44.809150 11127 solver.cpp:204]     Train net output #0: loss = 0.555541 (* 1 = 0.555541 loss)
I0219 15:47:44.809155 11127 solver.cpp:470] Iteration 27200, lr = 0.001
I0219 15:47:56.020725 11127 solver.cpp:189] Iteration 27400, loss = 0.530515
I0219 15:47:56.020763 11127 solver.cpp:204]     Train net output #0: loss = 0.530515 (* 1 = 0.530515 loss)
I0219 15:47:56.020768 11127 solver.cpp:470] Iteration 27400, lr = 0.001
I0219 15:48:07.250962 11127 solver.cpp:189] Iteration 27600, loss = 0.515159
I0219 15:48:07.251054 11127 solver.cpp:204]     Train net output #0: loss = 0.515159 (* 1 = 0.515159 loss)
I0219 15:48:07.251060 11127 solver.cpp:470] Iteration 27600, lr = 0.001
I0219 15:48:18.504144 11127 solver.cpp:189] Iteration 27800, loss = 0.43665
I0219 15:48:18.504189 11127 solver.cpp:204]     Train net output #0: loss = 0.43665 (* 1 = 0.43665 loss)
I0219 15:48:18.504194 11127 solver.cpp:470] Iteration 27800, lr = 0.001
I0219 15:48:29.653031 11127 solver.cpp:266] Iteration 28000, Testing net (#0)
I0219 15:48:31.929527 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7803
I0219 15:48:31.929549 11127 solver.cpp:315]     Test net output #1: loss = 0.642865 (* 1 = 0.642865 loss)
I0219 15:48:31.955868 11127 solver.cpp:189] Iteration 28000, loss = 0.48016
I0219 15:48:31.955888 11127 solver.cpp:204]     Train net output #0: loss = 0.48016 (* 1 = 0.48016 loss)
I0219 15:48:31.955893 11127 solver.cpp:470] Iteration 28000, lr = 0.001
I0219 15:48:43.149618 11127 solver.cpp:189] Iteration 28200, loss = 0.55208
I0219 15:48:43.149705 11127 solver.cpp:204]     Train net output #0: loss = 0.55208 (* 1 = 0.55208 loss)
I0219 15:48:43.149711 11127 solver.cpp:470] Iteration 28200, lr = 0.001
I0219 15:48:54.373829 11127 solver.cpp:189] Iteration 28400, loss = 0.530041
I0219 15:48:54.373852 11127 solver.cpp:204]     Train net output #0: loss = 0.530041 (* 1 = 0.530041 loss)
I0219 15:48:54.373857 11127 solver.cpp:470] Iteration 28400, lr = 0.001
I0219 15:49:05.611449 11127 solver.cpp:189] Iteration 28600, loss = 0.514087
I0219 15:49:05.611488 11127 solver.cpp:204]     Train net output #0: loss = 0.514087 (* 1 = 0.514087 loss)
I0219 15:49:05.611493 11127 solver.cpp:470] Iteration 28600, lr = 0.001
I0219 15:49:16.870831 11127 solver.cpp:189] Iteration 28800, loss = 0.427797
I0219 15:49:16.870903 11127 solver.cpp:204]     Train net output #0: loss = 0.427797 (* 1 = 0.427797 loss)
I0219 15:49:16.870909 11127 solver.cpp:470] Iteration 28800, lr = 0.001
I0219 15:49:28.031635 11127 solver.cpp:266] Iteration 29000, Testing net (#0)
I0219 15:49:30.312110 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7799
I0219 15:49:30.312152 11127 solver.cpp:315]     Test net output #1: loss = 0.643045 (* 1 = 0.643045 loss)
I0219 15:49:30.339123 11127 solver.cpp:189] Iteration 29000, loss = 0.46969
I0219 15:49:30.339151 11127 solver.cpp:204]     Train net output #0: loss = 0.46969 (* 1 = 0.46969 loss)
I0219 15:49:30.339156 11127 solver.cpp:470] Iteration 29000, lr = 0.001
I0219 15:49:41.526854 11127 solver.cpp:189] Iteration 29200, loss = 0.547019
I0219 15:49:41.526893 11127 solver.cpp:204]     Train net output #0: loss = 0.547019 (* 1 = 0.547019 loss)
I0219 15:49:41.526900 11127 solver.cpp:470] Iteration 29200, lr = 0.001
I0219 15:49:52.752910 11127 solver.cpp:189] Iteration 29400, loss = 0.517427
I0219 15:49:52.753026 11127 solver.cpp:204]     Train net output #0: loss = 0.517427 (* 1 = 0.517427 loss)
I0219 15:49:52.753041 11127 solver.cpp:470] Iteration 29400, lr = 0.001
I0219 15:50:04.093736 11127 solver.cpp:189] Iteration 29600, loss = 0.505393
I0219 15:50:04.093775 11127 solver.cpp:204]     Train net output #0: loss = 0.505393 (* 1 = 0.505393 loss)
I0219 15:50:04.093780 11127 solver.cpp:470] Iteration 29600, lr = 0.001
I0219 15:50:15.379309 11127 solver.cpp:189] Iteration 29800, loss = 0.419618
I0219 15:50:15.379334 11127 solver.cpp:204]     Train net output #0: loss = 0.419618 (* 1 = 0.419618 loss)
I0219 15:50:15.379339 11127 solver.cpp:470] Iteration 29800, lr = 0.001
I0219 15:50:26.559525 11127 solver.cpp:334] Snapshotting to examples/cifar10/cifar10_full_iter_30000.caffemodel
I0219 15:50:26.560381 11127 solver.cpp:342] Snapshotting solver state to examples/cifar10/cifar10_full_iter_30000.solverstate
I0219 15:50:26.560799 11127 solver.cpp:266] Iteration 30000, Testing net (#0)
I0219 15:50:28.813758 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7816
I0219 15:50:28.813781 11127 solver.cpp:315]     Test net output #1: loss = 0.641281 (* 1 = 0.641281 loss)
I0219 15:50:28.841943 11127 solver.cpp:189] Iteration 30000, loss = 0.460314
I0219 15:50:28.841976 11127 solver.cpp:204]     Train net output #0: loss = 0.460314 (* 1 = 0.460314 loss)
I0219 15:50:28.841982 11127 solver.cpp:470] Iteration 30000, lr = 0.001
I0219 15:50:40.045948 11127 solver.cpp:189] Iteration 30200, loss = 0.544894
I0219 15:50:40.045972 11127 solver.cpp:204]     Train net output #0: loss = 0.544894 (* 1 = 0.544894 loss)
I0219 15:50:40.045976 11127 solver.cpp:470] Iteration 30200, lr = 0.001
I0219 15:50:51.320401 11127 solver.cpp:189] Iteration 30400, loss = 0.516428
I0219 15:50:51.320425 11127 solver.cpp:204]     Train net output #0: loss = 0.516428 (* 1 = 0.516428 loss)
I0219 15:50:51.320428 11127 solver.cpp:470] Iteration 30400, lr = 0.001
I0219 15:51:02.546123 11127 solver.cpp:189] Iteration 30600, loss = 0.506171
I0219 15:51:02.546178 11127 solver.cpp:204]     Train net output #0: loss = 0.506171 (* 1 = 0.506171 loss)
I0219 15:51:02.546182 11127 solver.cpp:470] Iteration 30600, lr = 0.001
I0219 15:51:13.747470 11127 solver.cpp:189] Iteration 30800, loss = 0.412439
I0219 15:51:13.747495 11127 solver.cpp:204]     Train net output #0: loss = 0.412439 (* 1 = 0.412439 loss)
I0219 15:51:13.747500 11127 solver.cpp:470] Iteration 30800, lr = 0.001
I0219 15:51:24.898813 11127 solver.cpp:266] Iteration 31000, Testing net (#0)
I0219 15:51:27.166060 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7811
I0219 15:51:27.166082 11127 solver.cpp:315]     Test net output #1: loss = 0.640296 (* 1 = 0.640296 loss)
I0219 15:51:27.192358 11127 solver.cpp:189] Iteration 31000, loss = 0.451952
I0219 15:51:27.192373 11127 solver.cpp:204]     Train net output #0: loss = 0.451952 (* 1 = 0.451952 loss)
I0219 15:51:27.192376 11127 solver.cpp:470] Iteration 31000, lr = 0.001
I0219 15:51:38.393074 11127 solver.cpp:189] Iteration 31200, loss = 0.541429
I0219 15:51:38.393129 11127 solver.cpp:204]     Train net output #0: loss = 0.541429 (* 1 = 0.541429 loss)
I0219 15:51:38.393134 11127 solver.cpp:470] Iteration 31200, lr = 0.001
I0219 15:51:49.632067 11127 solver.cpp:189] Iteration 31400, loss = 0.513404
I0219 15:51:49.632109 11127 solver.cpp:204]     Train net output #0: loss = 0.513404 (* 1 = 0.513404 loss)
I0219 15:51:49.632117 11127 solver.cpp:470] Iteration 31400, lr = 0.001
I0219 15:52:00.853492 11127 solver.cpp:189] Iteration 31600, loss = 0.502264
I0219 15:52:00.853516 11127 solver.cpp:204]     Train net output #0: loss = 0.502264 (* 1 = 0.502264 loss)
I0219 15:52:00.853521 11127 solver.cpp:470] Iteration 31600, lr = 0.001
I0219 15:52:12.049360 11127 solver.cpp:189] Iteration 31800, loss = 0.40481
I0219 15:52:12.049418 11127 solver.cpp:204]     Train net output #0: loss = 0.40481 (* 1 = 0.40481 loss)
I0219 15:52:12.049423 11127 solver.cpp:470] Iteration 31800, lr = 0.001
I0219 15:52:23.188340 11127 solver.cpp:266] Iteration 32000, Testing net (#0)
I0219 15:52:25.462051 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7814
I0219 15:52:25.462083 11127 solver.cpp:315]     Test net output #1: loss = 0.639066 (* 1 = 0.639066 loss)
I0219 15:52:25.489189 11127 solver.cpp:189] Iteration 32000, loss = 0.442637
I0219 15:52:25.489230 11127 solver.cpp:204]     Train net output #0: loss = 0.442637 (* 1 = 0.442637 loss)
I0219 15:52:25.489236 11127 solver.cpp:470] Iteration 32000, lr = 0.001
I0219 15:52:37.038839 11127 solver.cpp:189] Iteration 32200, loss = 0.537263
I0219 15:52:37.038882 11127 solver.cpp:204]     Train net output #0: loss = 0.537263 (* 1 = 0.537263 loss)
I0219 15:52:37.038888 11127 solver.cpp:470] Iteration 32200, lr = 0.001
I0219 15:52:48.372360 11127 solver.cpp:189] Iteration 32400, loss = 0.504399
I0219 15:52:48.372498 11127 solver.cpp:204]     Train net output #0: loss = 0.504399 (* 1 = 0.504399 loss)
I0219 15:52:48.372519 11127 solver.cpp:470] Iteration 32400, lr = 0.001
I0219 15:52:59.608458 11127 solver.cpp:189] Iteration 32600, loss = 0.498036
I0219 15:52:59.608484 11127 solver.cpp:204]     Train net output #0: loss = 0.498036 (* 1 = 0.498036 loss)
I0219 15:52:59.608489 11127 solver.cpp:470] Iteration 32600, lr = 0.001
I0219 15:53:10.820977 11127 solver.cpp:189] Iteration 32800, loss = 0.398017
I0219 15:53:10.821012 11127 solver.cpp:204]     Train net output #0: loss = 0.398017 (* 1 = 0.398017 loss)
I0219 15:53:10.821017 11127 solver.cpp:470] Iteration 32800, lr = 0.001
I0219 15:53:22.094980 11127 solver.cpp:266] Iteration 33000, Testing net (#0)
I0219 15:53:24.355921 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7821
I0219 15:53:24.355943 11127 solver.cpp:315]     Test net output #1: loss = 0.637487 (* 1 = 0.637487 loss)
I0219 15:53:24.382333 11127 solver.cpp:189] Iteration 33000, loss = 0.436281
I0219 15:53:24.382356 11127 solver.cpp:204]     Train net output #0: loss = 0.436281 (* 1 = 0.436281 loss)
I0219 15:53:24.382361 11127 solver.cpp:470] Iteration 33000, lr = 0.001
I0219 15:53:35.587749 11127 solver.cpp:189] Iteration 33200, loss = 0.536495
I0219 15:53:35.587781 11127 solver.cpp:204]     Train net output #0: loss = 0.536495 (* 1 = 0.536495 loss)
I0219 15:53:35.587786 11127 solver.cpp:470] Iteration 33200, lr = 0.001
I0219 15:53:46.733340 11127 solver.cpp:189] Iteration 33400, loss = 0.501259
I0219 15:53:46.733381 11127 solver.cpp:204]     Train net output #0: loss = 0.501259 (* 1 = 0.501259 loss)
I0219 15:53:46.733386 11127 solver.cpp:470] Iteration 33400, lr = 0.001
I0219 15:53:57.868461 11127 solver.cpp:189] Iteration 33600, loss = 0.493988
I0219 15:53:57.868532 11127 solver.cpp:204]     Train net output #0: loss = 0.493988 (* 1 = 0.493988 loss)
I0219 15:53:57.868538 11127 solver.cpp:470] Iteration 33600, lr = 0.001
I0219 15:54:09.124073 11127 solver.cpp:189] Iteration 33800, loss = 0.398505
I0219 15:54:09.124099 11127 solver.cpp:204]     Train net output #0: loss = 0.398505 (* 1 = 0.398505 loss)
I0219 15:54:09.124102 11127 solver.cpp:470] Iteration 33800, lr = 0.001
I0219 15:54:20.285045 11127 solver.cpp:266] Iteration 34000, Testing net (#0)
I0219 15:54:22.579450 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7846
I0219 15:54:22.579498 11127 solver.cpp:315]     Test net output #1: loss = 0.634624 (* 1 = 0.634624 loss)
I0219 15:54:22.607630 11127 solver.cpp:189] Iteration 34000, loss = 0.428524
I0219 15:54:22.607666 11127 solver.cpp:204]     Train net output #0: loss = 0.428524 (* 1 = 0.428524 loss)
I0219 15:54:22.607671 11127 solver.cpp:470] Iteration 34000, lr = 0.001
I0219 15:54:33.842754 11127 solver.cpp:189] Iteration 34200, loss = 0.532811
I0219 15:54:33.842816 11127 solver.cpp:204]     Train net output #0: loss = 0.532811 (* 1 = 0.532811 loss)
I0219 15:54:33.842823 11127 solver.cpp:470] Iteration 34200, lr = 0.001
I0219 15:54:45.106022 11127 solver.cpp:189] Iteration 34400, loss = 0.499216
I0219 15:54:45.106061 11127 solver.cpp:204]     Train net output #0: loss = 0.499216 (* 1 = 0.499216 loss)
I0219 15:54:45.106066 11127 solver.cpp:470] Iteration 34400, lr = 0.001
I0219 15:54:56.324795 11127 solver.cpp:189] Iteration 34600, loss = 0.490183
I0219 15:54:56.324820 11127 solver.cpp:204]     Train net output #0: loss = 0.490183 (* 1 = 0.490183 loss)
I0219 15:54:56.324826 11127 solver.cpp:470] Iteration 34600, lr = 0.001
I0219 15:55:07.530697 11127 solver.cpp:189] Iteration 34800, loss = 0.39515
I0219 15:55:07.530830 11127 solver.cpp:204]     Train net output #0: loss = 0.39515 (* 1 = 0.39515 loss)
I0219 15:55:07.530845 11127 solver.cpp:470] Iteration 34800, lr = 0.001
I0219 15:55:18.564442 11127 solver.cpp:266] Iteration 35000, Testing net (#0)
I0219 15:55:20.813094 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7836
I0219 15:55:20.813117 11127 solver.cpp:315]     Test net output #1: loss = 0.635166 (* 1 = 0.635166 loss)
I0219 15:55:20.839404 11127 solver.cpp:189] Iteration 35000, loss = 0.424767
I0219 15:55:20.839417 11127 solver.cpp:204]     Train net output #0: loss = 0.424767 (* 1 = 0.424767 loss)
I0219 15:55:20.839422 11127 solver.cpp:470] Iteration 35000, lr = 0.001
I0219 15:55:32.020025 11127 solver.cpp:189] Iteration 35200, loss = 0.53137
I0219 15:55:32.020054 11127 solver.cpp:204]     Train net output #0: loss = 0.53137 (* 1 = 0.53137 loss)
I0219 15:55:32.020059 11127 solver.cpp:470] Iteration 35200, lr = 0.001
I0219 15:55:43.197418 11127 solver.cpp:189] Iteration 35400, loss = 0.492265
I0219 15:55:43.197505 11127 solver.cpp:204]     Train net output #0: loss = 0.492265 (* 1 = 0.492265 loss)
I0219 15:55:43.197511 11127 solver.cpp:470] Iteration 35400, lr = 0.001
I0219 15:55:54.409067 11127 solver.cpp:189] Iteration 35600, loss = 0.487144
I0219 15:55:54.409107 11127 solver.cpp:204]     Train net output #0: loss = 0.487144 (* 1 = 0.487144 loss)
I0219 15:55:54.409114 11127 solver.cpp:470] Iteration 35600, lr = 0.001
I0219 15:56:05.581593 11127 solver.cpp:189] Iteration 35800, loss = 0.389027
I0219 15:56:05.581632 11127 solver.cpp:204]     Train net output #0: loss = 0.389027 (* 1 = 0.389027 loss)
I0219 15:56:05.581637 11127 solver.cpp:470] Iteration 35800, lr = 0.001
I0219 15:56:16.709470 11127 solver.cpp:266] Iteration 36000, Testing net (#0)
I0219 15:56:18.995219 11127 solver.cpp:315]     Test net output #0: accuracy = 0.784
I0219 15:56:18.995261 11127 solver.cpp:315]     Test net output #1: loss = 0.634829 (* 1 = 0.634829 loss)
I0219 15:56:19.023013 11127 solver.cpp:189] Iteration 36000, loss = 0.421299
I0219 15:56:19.023051 11127 solver.cpp:204]     Train net output #0: loss = 0.421299 (* 1 = 0.421299 loss)
I0219 15:56:19.023057 11127 solver.cpp:470] Iteration 36000, lr = 0.001
I0219 15:56:30.162096 11127 solver.cpp:189] Iteration 36200, loss = 0.529683
I0219 15:56:30.162134 11127 solver.cpp:204]     Train net output #0: loss = 0.529683 (* 1 = 0.529683 loss)
I0219 15:56:30.162142 11127 solver.cpp:470] Iteration 36200, lr = 0.001
I0219 15:56:41.293663 11127 solver.cpp:189] Iteration 36400, loss = 0.484746
I0219 15:56:41.293705 11127 solver.cpp:204]     Train net output #0: loss = 0.484746 (* 1 = 0.484746 loss)
I0219 15:56:41.293710 11127 solver.cpp:470] Iteration 36400, lr = 0.001
I0219 15:56:52.464654 11127 solver.cpp:189] Iteration 36600, loss = 0.487433
I0219 15:56:52.464781 11127 solver.cpp:204]     Train net output #0: loss = 0.487433 (* 1 = 0.487433 loss)
I0219 15:56:52.464797 11127 solver.cpp:470] Iteration 36600, lr = 0.001
I0219 15:57:03.651793 11127 solver.cpp:189] Iteration 36800, loss = 0.385385
I0219 15:57:03.651829 11127 solver.cpp:204]     Train net output #0: loss = 0.385385 (* 1 = 0.385385 loss)
I0219 15:57:03.651835 11127 solver.cpp:470] Iteration 36800, lr = 0.001
I0219 15:57:14.817821 11127 solver.cpp:266] Iteration 37000, Testing net (#0)
I0219 15:57:17.108878 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7841
I0219 15:57:17.108903 11127 solver.cpp:315]     Test net output #1: loss = 0.634337 (* 1 = 0.634337 loss)
I0219 15:57:17.135293 11127 solver.cpp:189] Iteration 37000, loss = 0.41589
I0219 15:57:17.135318 11127 solver.cpp:204]     Train net output #0: loss = 0.41589 (* 1 = 0.41589 loss)
I0219 15:57:17.135323 11127 solver.cpp:470] Iteration 37000, lr = 0.001
I0219 15:57:28.311601 11127 solver.cpp:189] Iteration 37200, loss = 0.528345
I0219 15:57:28.311729 11127 solver.cpp:204]     Train net output #0: loss = 0.528345 (* 1 = 0.528345 loss)
I0219 15:57:28.311735 11127 solver.cpp:470] Iteration 37200, lr = 0.001
I0219 15:57:39.581782 11127 solver.cpp:189] Iteration 37400, loss = 0.477264
I0219 15:57:39.581815 11127 solver.cpp:204]     Train net output #0: loss = 0.477264 (* 1 = 0.477264 loss)
I0219 15:57:39.581818 11127 solver.cpp:470] Iteration 37400, lr = 0.001
I0219 15:57:50.841260 11127 solver.cpp:189] Iteration 37600, loss = 0.487732
I0219 15:57:50.841301 11127 solver.cpp:204]     Train net output #0: loss = 0.487732 (* 1 = 0.487732 loss)
I0219 15:57:50.841306 11127 solver.cpp:470] Iteration 37600, lr = 0.001
I0219 15:58:02.103353 11127 solver.cpp:189] Iteration 37800, loss = 0.383262
I0219 15:58:02.103466 11127 solver.cpp:204]     Train net output #0: loss = 0.383262 (* 1 = 0.383262 loss)
I0219 15:58:02.103472 11127 solver.cpp:470] Iteration 37800, lr = 0.001
I0219 15:58:13.280292 11127 solver.cpp:266] Iteration 38000, Testing net (#0)
I0219 15:58:15.561147 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7839
I0219 15:58:15.561190 11127 solver.cpp:315]     Test net output #1: loss = 0.632618 (* 1 = 0.632618 loss)
I0219 15:58:15.587607 11127 solver.cpp:189] Iteration 38000, loss = 0.411489
I0219 15:58:15.587647 11127 solver.cpp:204]     Train net output #0: loss = 0.411489 (* 1 = 0.411489 loss)
I0219 15:58:15.587653 11127 solver.cpp:470] Iteration 38000, lr = 0.001
I0219 15:58:26.733816 11127 solver.cpp:189] Iteration 38200, loss = 0.522022
I0219 15:58:26.733866 11127 solver.cpp:204]     Train net output #0: loss = 0.522022 (* 1 = 0.522022 loss)
I0219 15:58:26.733871 11127 solver.cpp:470] Iteration 38200, lr = 0.001
I0219 15:58:37.834903 11127 solver.cpp:189] Iteration 38400, loss = 0.473225
I0219 15:58:37.834969 11127 solver.cpp:204]     Train net output #0: loss = 0.473225 (* 1 = 0.473225 loss)
I0219 15:58:37.834975 11127 solver.cpp:470] Iteration 38400, lr = 0.001
I0219 15:58:49.340687 11127 solver.cpp:189] Iteration 38600, loss = 0.486453
I0219 15:58:49.340730 11127 solver.cpp:204]     Train net output #0: loss = 0.486453 (* 1 = 0.486453 loss)
I0219 15:58:49.340736 11127 solver.cpp:470] Iteration 38600, lr = 0.001
I0219 15:59:00.649670 11127 solver.cpp:189] Iteration 38800, loss = 0.378605
I0219 15:59:00.649705 11127 solver.cpp:204]     Train net output #0: loss = 0.378605 (* 1 = 0.378605 loss)
I0219 15:59:00.649709 11127 solver.cpp:470] Iteration 38800, lr = 0.001
I0219 15:59:11.819797 11127 solver.cpp:266] Iteration 39000, Testing net (#0)
I0219 15:59:14.135042 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7844
I0219 15:59:14.135087 11127 solver.cpp:315]     Test net output #1: loss = 0.630033 (* 1 = 0.630033 loss)
I0219 15:59:14.163192 11127 solver.cpp:189] Iteration 39000, loss = 0.405199
I0219 15:59:14.163229 11127 solver.cpp:204]     Train net output #0: loss = 0.405199 (* 1 = 0.405199 loss)
I0219 15:59:14.163235 11127 solver.cpp:470] Iteration 39000, lr = 0.001
I0219 15:59:25.378229 11127 solver.cpp:189] Iteration 39200, loss = 0.519738
I0219 15:59:25.378257 11127 solver.cpp:204]     Train net output #0: loss = 0.519738 (* 1 = 0.519738 loss)
I0219 15:59:25.378262 11127 solver.cpp:470] Iteration 39200, lr = 0.001
I0219 15:59:36.589079 11127 solver.cpp:189] Iteration 39400, loss = 0.46767
I0219 15:59:36.589120 11127 solver.cpp:204]     Train net output #0: loss = 0.46767 (* 1 = 0.46767 loss)
I0219 15:59:36.589125 11127 solver.cpp:470] Iteration 39400, lr = 0.001
I0219 15:59:47.811491 11127 solver.cpp:189] Iteration 39600, loss = 0.483833
I0219 15:59:47.811561 11127 solver.cpp:204]     Train net output #0: loss = 0.483833 (* 1 = 0.483833 loss)
I0219 15:59:47.811568 11127 solver.cpp:470] Iteration 39600, lr = 0.001
I0219 15:59:59.043699 11127 solver.cpp:189] Iteration 39800, loss = 0.37783
I0219 15:59:59.043735 11127 solver.cpp:204]     Train net output #0: loss = 0.37783 (* 1 = 0.37783 loss)
I0219 15:59:59.043740 11127 solver.cpp:470] Iteration 39800, lr = 0.001
I0219 16:00:10.273202 11127 solver.cpp:334] Snapshotting to examples/cifar10/cifar10_full_iter_40000.caffemodel
I0219 16:00:10.273998 11127 solver.cpp:342] Snapshotting solver state to examples/cifar10/cifar10_full_iter_40000.solverstate
I0219 16:00:10.274417 11127 solver.cpp:266] Iteration 40000, Testing net (#0)
I0219 16:00:12.519034 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7844
I0219 16:00:12.519101 11127 solver.cpp:315]     Test net output #1: loss = 0.631732 (* 1 = 0.631732 loss)
I0219 16:00:12.546375 11127 solver.cpp:189] Iteration 40000, loss = 0.40078
I0219 16:00:12.546408 11127 solver.cpp:204]     Train net output #0: loss = 0.40078 (* 1 = 0.40078 loss)
I0219 16:00:12.546414 11127 solver.cpp:470] Iteration 40000, lr = 0.001
I0219 16:00:23.748255 11127 solver.cpp:189] Iteration 40200, loss = 0.5167
I0219 16:00:23.748420 11127 solver.cpp:204]     Train net output #0: loss = 0.5167 (* 1 = 0.5167 loss)
I0219 16:00:23.748426 11127 solver.cpp:470] Iteration 40200, lr = 0.001
I0219 16:00:34.862083 11127 solver.cpp:189] Iteration 40400, loss = 0.463071
I0219 16:00:34.862109 11127 solver.cpp:204]     Train net output #0: loss = 0.463071 (* 1 = 0.463071 loss)
I0219 16:00:34.862113 11127 solver.cpp:470] Iteration 40400, lr = 0.001
I0219 16:00:46.048959 11127 solver.cpp:189] Iteration 40600, loss = 0.481662
I0219 16:00:46.049000 11127 solver.cpp:204]     Train net output #0: loss = 0.481662 (* 1 = 0.481662 loss)
I0219 16:00:46.049006 11127 solver.cpp:470] Iteration 40600, lr = 0.001
I0219 16:00:57.305127 11127 solver.cpp:189] Iteration 40800, loss = 0.376568
I0219 16:00:57.305210 11127 solver.cpp:204]     Train net output #0: loss = 0.376568 (* 1 = 0.376568 loss)
I0219 16:00:57.305217 11127 solver.cpp:470] Iteration 40800, lr = 0.001
I0219 16:01:08.647377 11127 solver.cpp:266] Iteration 41000, Testing net (#0)
I0219 16:01:10.936686 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7842
I0219 16:01:10.936710 11127 solver.cpp:315]     Test net output #1: loss = 0.632 (* 1 = 0.632 loss)
I0219 16:01:10.964356 11127 solver.cpp:189] Iteration 41000, loss = 0.396525
I0219 16:01:10.964380 11127 solver.cpp:204]     Train net output #0: loss = 0.396525 (* 1 = 0.396525 loss)
I0219 16:01:10.964385 11127 solver.cpp:470] Iteration 41000, lr = 0.001
I0219 16:01:22.383582 11127 solver.cpp:189] Iteration 41200, loss = 0.513625
I0219 16:01:22.383608 11127 solver.cpp:204]     Train net output #0: loss = 0.513625 (* 1 = 0.513625 loss)
I0219 16:01:22.383613 11127 solver.cpp:470] Iteration 41200, lr = 0.001
I0219 16:01:33.667402 11127 solver.cpp:189] Iteration 41400, loss = 0.461313
I0219 16:01:33.667498 11127 solver.cpp:204]     Train net output #0: loss = 0.461313 (* 1 = 0.461313 loss)
I0219 16:01:33.667513 11127 solver.cpp:470] Iteration 41400, lr = 0.001
I0219 16:01:45.090510 11127 solver.cpp:189] Iteration 41600, loss = 0.483333
I0219 16:01:45.090548 11127 solver.cpp:204]     Train net output #0: loss = 0.483333 (* 1 = 0.483333 loss)
I0219 16:01:45.090554 11127 solver.cpp:470] Iteration 41600, lr = 0.001
I0219 16:01:56.406975 11127 solver.cpp:189] Iteration 41800, loss = 0.377418
I0219 16:01:56.406999 11127 solver.cpp:204]     Train net output #0: loss = 0.377418 (* 1 = 0.377418 loss)
I0219 16:01:56.407003 11127 solver.cpp:470] Iteration 41800, lr = 0.001
I0219 16:02:07.502629 11127 solver.cpp:266] Iteration 42000, Testing net (#0)
I0219 16:02:09.772202 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7845
I0219 16:02:09.772224 11127 solver.cpp:315]     Test net output #1: loss = 0.631629 (* 1 = 0.631629 loss)
I0219 16:02:09.798660 11127 solver.cpp:189] Iteration 42000, loss = 0.393747
I0219 16:02:09.798677 11127 solver.cpp:204]     Train net output #0: loss = 0.393747 (* 1 = 0.393747 loss)
I0219 16:02:09.798681 11127 solver.cpp:470] Iteration 42000, lr = 0.001
I0219 16:02:20.911824 11127 solver.cpp:189] Iteration 42200, loss = 0.514164
I0219 16:02:20.911857 11127 solver.cpp:204]     Train net output #0: loss = 0.514164 (* 1 = 0.514164 loss)
I0219 16:02:20.911861 11127 solver.cpp:470] Iteration 42200, lr = 0.001
I0219 16:02:32.020606 11127 solver.cpp:189] Iteration 42400, loss = 0.454291
I0219 16:02:32.020629 11127 solver.cpp:204]     Train net output #0: loss = 0.454291 (* 1 = 0.454291 loss)
I0219 16:02:32.020634 11127 solver.cpp:470] Iteration 42400, lr = 0.001
I0219 16:02:43.205126 11127 solver.cpp:189] Iteration 42600, loss = 0.478635
I0219 16:02:43.205225 11127 solver.cpp:204]     Train net output #0: loss = 0.478635 (* 1 = 0.478635 loss)
I0219 16:02:43.205240 11127 solver.cpp:470] Iteration 42600, lr = 0.001
I0219 16:02:54.456161 11127 solver.cpp:189] Iteration 42800, loss = 0.377845
I0219 16:02:54.456185 11127 solver.cpp:204]     Train net output #0: loss = 0.377845 (* 1 = 0.377845 loss)
I0219 16:02:54.456190 11127 solver.cpp:470] Iteration 42800, lr = 0.001
I0219 16:03:05.663547 11127 solver.cpp:266] Iteration 43000, Testing net (#0)
I0219 16:03:07.949374 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7846
I0219 16:03:07.949398 11127 solver.cpp:315]     Test net output #1: loss = 0.631178 (* 1 = 0.631178 loss)
I0219 16:03:07.975728 11127 solver.cpp:189] Iteration 43000, loss = 0.390423
I0219 16:03:07.975749 11127 solver.cpp:204]     Train net output #0: loss = 0.390423 (* 1 = 0.390423 loss)
I0219 16:03:07.975754 11127 solver.cpp:470] Iteration 43000, lr = 0.001
I0219 16:03:19.173827 11127 solver.cpp:189] Iteration 43200, loss = 0.513103
I0219 16:03:19.173916 11127 solver.cpp:204]     Train net output #0: loss = 0.513103 (* 1 = 0.513103 loss)
I0219 16:03:19.173930 11127 solver.cpp:470] Iteration 43200, lr = 0.001
I0219 16:03:30.348640 11127 solver.cpp:189] Iteration 43400, loss = 0.453565
I0219 16:03:30.348683 11127 solver.cpp:204]     Train net output #0: loss = 0.453565 (* 1 = 0.453565 loss)
I0219 16:03:30.348690 11127 solver.cpp:470] Iteration 43400, lr = 0.001
I0219 16:03:41.591351 11127 solver.cpp:189] Iteration 43600, loss = 0.480528
I0219 16:03:41.591390 11127 solver.cpp:204]     Train net output #0: loss = 0.480528 (* 1 = 0.480528 loss)
I0219 16:03:41.591395 11127 solver.cpp:470] Iteration 43600, lr = 0.001
I0219 16:03:52.850195 11127 solver.cpp:189] Iteration 43800, loss = 0.37922
I0219 16:03:52.850302 11127 solver.cpp:204]     Train net output #0: loss = 0.37922 (* 1 = 0.37922 loss)
I0219 16:03:52.850317 11127 solver.cpp:470] Iteration 43800, lr = 0.001
I0219 16:04:04.038239 11127 solver.cpp:266] Iteration 44000, Testing net (#0)
I0219 16:04:06.333106 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7863
I0219 16:04:06.333143 11127 solver.cpp:315]     Test net output #1: loss = 0.629649 (* 1 = 0.629649 loss)
I0219 16:04:06.361589 11127 solver.cpp:189] Iteration 44000, loss = 0.387679
I0219 16:04:06.361615 11127 solver.cpp:204]     Train net output #0: loss = 0.387679 (* 1 = 0.387679 loss)
I0219 16:04:06.361620 11127 solver.cpp:470] Iteration 44000, lr = 0.001
I0219 16:04:17.622272 11127 solver.cpp:189] Iteration 44200, loss = 0.510873
I0219 16:04:17.622319 11127 solver.cpp:204]     Train net output #0: loss = 0.510873 (* 1 = 0.510873 loss)
I0219 16:04:17.622325 11127 solver.cpp:470] Iteration 44200, lr = 0.001
I0219 16:04:28.877552 11127 solver.cpp:189] Iteration 44400, loss = 0.451355
I0219 16:04:28.877616 11127 solver.cpp:204]     Train net output #0: loss = 0.451355 (* 1 = 0.451355 loss)
I0219 16:04:28.877621 11127 solver.cpp:470] Iteration 44400, lr = 0.001
I0219 16:04:40.134948 11127 solver.cpp:189] Iteration 44600, loss = 0.482853
I0219 16:04:40.135025 11127 solver.cpp:204]     Train net output #0: loss = 0.482853 (* 1 = 0.482853 loss)
I0219 16:04:40.135040 11127 solver.cpp:470] Iteration 44600, lr = 0.001
I0219 16:04:51.314244 11127 solver.cpp:189] Iteration 44800, loss = 0.380441
I0219 16:04:51.314290 11127 solver.cpp:204]     Train net output #0: loss = 0.380441 (* 1 = 0.380441 loss)
I0219 16:04:51.314296 11127 solver.cpp:470] Iteration 44800, lr = 0.001
I0219 16:05:02.444152 11127 solver.cpp:266] Iteration 45000, Testing net (#0)
I0219 16:05:04.730078 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7866
I0219 16:05:04.730100 11127 solver.cpp:315]     Test net output #1: loss = 0.629642 (* 1 = 0.629642 loss)
I0219 16:05:04.757596 11127 solver.cpp:189] Iteration 45000, loss = 0.384447
I0219 16:05:04.757618 11127 solver.cpp:204]     Train net output #0: loss = 0.384447 (* 1 = 0.384447 loss)
I0219 16:05:04.757623 11127 solver.cpp:470] Iteration 45000, lr = 0.001
I0219 16:05:15.990214 11127 solver.cpp:189] Iteration 45200, loss = 0.508175
I0219 16:05:15.990252 11127 solver.cpp:204]     Train net output #0: loss = 0.508175 (* 1 = 0.508175 loss)
I0219 16:05:15.990267 11127 solver.cpp:470] Iteration 45200, lr = 0.001
I0219 16:05:27.142344 11127 solver.cpp:189] Iteration 45400, loss = 0.449458
I0219 16:05:27.142369 11127 solver.cpp:204]     Train net output #0: loss = 0.449458 (* 1 = 0.449458 loss)
I0219 16:05:27.142374 11127 solver.cpp:470] Iteration 45400, lr = 0.001
I0219 16:05:38.340888 11127 solver.cpp:189] Iteration 45600, loss = 0.48795
I0219 16:05:38.340970 11127 solver.cpp:204]     Train net output #0: loss = 0.48795 (* 1 = 0.48795 loss)
I0219 16:05:38.340976 11127 solver.cpp:470] Iteration 45600, lr = 0.001
I0219 16:05:49.528486 11127 solver.cpp:189] Iteration 45800, loss = 0.381343
I0219 16:05:49.528512 11127 solver.cpp:204]     Train net output #0: loss = 0.381343 (* 1 = 0.381343 loss)
I0219 16:05:49.528517 11127 solver.cpp:470] Iteration 45800, lr = 0.001
I0219 16:06:00.712841 11127 solver.cpp:266] Iteration 46000, Testing net (#0)
I0219 16:06:03.014123 11127 solver.cpp:315]     Test net output #0: accuracy = 0.787
I0219 16:06:03.014163 11127 solver.cpp:315]     Test net output #1: loss = 0.628368 (* 1 = 0.628368 loss)
I0219 16:06:03.040727 11127 solver.cpp:189] Iteration 46000, loss = 0.381363
I0219 16:06:03.040762 11127 solver.cpp:204]     Train net output #0: loss = 0.381363 (* 1 = 0.381363 loss)
I0219 16:06:03.040767 11127 solver.cpp:470] Iteration 46000, lr = 0.001
I0219 16:06:14.204807 11127 solver.cpp:189] Iteration 46200, loss = 0.51124
I0219 16:06:14.204893 11127 solver.cpp:204]     Train net output #0: loss = 0.51124 (* 1 = 0.51124 loss)
I0219 16:06:14.204901 11127 solver.cpp:470] Iteration 46200, lr = 0.001
I0219 16:06:25.431099 11127 solver.cpp:189] Iteration 46400, loss = 0.44522
I0219 16:06:25.431143 11127 solver.cpp:204]     Train net output #0: loss = 0.44522 (* 1 = 0.44522 loss)
I0219 16:06:25.431148 11127 solver.cpp:470] Iteration 46400, lr = 0.001
I0219 16:06:36.603571 11127 solver.cpp:189] Iteration 46600, loss = 0.488566
I0219 16:06:36.603595 11127 solver.cpp:204]     Train net output #0: loss = 0.488566 (* 1 = 0.488566 loss)
I0219 16:06:36.603600 11127 solver.cpp:470] Iteration 46600, lr = 0.001
I0219 16:06:47.824234 11127 solver.cpp:189] Iteration 46800, loss = 0.381498
I0219 16:06:47.824291 11127 solver.cpp:204]     Train net output #0: loss = 0.381498 (* 1 = 0.381498 loss)
I0219 16:06:47.824295 11127 solver.cpp:470] Iteration 46800, lr = 0.001
I0219 16:06:58.998709 11127 solver.cpp:266] Iteration 47000, Testing net (#0)
I0219 16:07:01.295475 11127 solver.cpp:315]     Test net output #0: accuracy = 0.787
I0219 16:07:01.295502 11127 solver.cpp:315]     Test net output #1: loss = 0.627024 (* 1 = 0.627024 loss)
I0219 16:07:01.322666 11127 solver.cpp:189] Iteration 47000, loss = 0.378817
I0219 16:07:01.322701 11127 solver.cpp:204]     Train net output #0: loss = 0.378817 (* 1 = 0.378817 loss)
I0219 16:07:01.322707 11127 solver.cpp:470] Iteration 47000, lr = 0.001
I0219 16:07:12.587791 11127 solver.cpp:189] Iteration 47200, loss = 0.506262
I0219 16:07:12.587853 11127 solver.cpp:204]     Train net output #0: loss = 0.506262 (* 1 = 0.506262 loss)
I0219 16:07:12.587860 11127 solver.cpp:470] Iteration 47200, lr = 0.001
I0219 16:07:24.068016 11127 solver.cpp:189] Iteration 47400, loss = 0.444781
I0219 16:07:24.068125 11127 solver.cpp:204]     Train net output #0: loss = 0.444781 (* 1 = 0.444781 loss)
I0219 16:07:24.068132 11127 solver.cpp:470] Iteration 47400, lr = 0.001
I0219 16:07:35.316119 11127 solver.cpp:189] Iteration 47600, loss = 0.490468
I0219 16:07:35.316143 11127 solver.cpp:204]     Train net output #0: loss = 0.490468 (* 1 = 0.490468 loss)
I0219 16:07:35.316146 11127 solver.cpp:470] Iteration 47600, lr = 0.001
I0219 16:07:46.479804 11127 solver.cpp:189] Iteration 47800, loss = 0.382675
I0219 16:07:46.479833 11127 solver.cpp:204]     Train net output #0: loss = 0.382675 (* 1 = 0.382675 loss)
I0219 16:07:46.479838 11127 solver.cpp:470] Iteration 47800, lr = 0.001
I0219 16:07:57.513519 11127 solver.cpp:266] Iteration 48000, Testing net (#0)
I0219 16:07:59.774734 11127 solver.cpp:315]     Test net output #0: accuracy = 0.788
I0219 16:07:59.774756 11127 solver.cpp:315]     Test net output #1: loss = 0.624757 (* 1 = 0.624757 loss)
I0219 16:07:59.801600 11127 solver.cpp:189] Iteration 48000, loss = 0.376221
I0219 16:07:59.801628 11127 solver.cpp:204]     Train net output #0: loss = 0.376221 (* 1 = 0.376221 loss)
I0219 16:07:59.801633 11127 solver.cpp:470] Iteration 48000, lr = 0.001
I0219 16:08:10.860040 11127 solver.cpp:189] Iteration 48200, loss = 0.508277
I0219 16:08:10.860062 11127 solver.cpp:204]     Train net output #0: loss = 0.508277 (* 1 = 0.508277 loss)
I0219 16:08:10.860066 11127 solver.cpp:470] Iteration 48200, lr = 0.001
I0219 16:08:21.907925 11127 solver.cpp:189] Iteration 48400, loss = 0.438627
I0219 16:08:21.907958 11127 solver.cpp:204]     Train net output #0: loss = 0.438627 (* 1 = 0.438627 loss)
I0219 16:08:21.907961 11127 solver.cpp:470] Iteration 48400, lr = 0.001
I0219 16:08:32.953610 11127 solver.cpp:189] Iteration 48600, loss = 0.492077
I0219 16:08:32.953671 11127 solver.cpp:204]     Train net output #0: loss = 0.492077 (* 1 = 0.492077 loss)
I0219 16:08:32.953676 11127 solver.cpp:470] Iteration 48600, lr = 0.001
I0219 16:08:44.013375 11127 solver.cpp:189] Iteration 48800, loss = 0.383582
I0219 16:08:44.013414 11127 solver.cpp:204]     Train net output #0: loss = 0.383582 (* 1 = 0.383582 loss)
I0219 16:08:44.013420 11127 solver.cpp:470] Iteration 48800, lr = 0.001
I0219 16:08:55.062450 11127 solver.cpp:266] Iteration 49000, Testing net (#0)
I0219 16:08:57.312207 11127 solver.cpp:315]     Test net output #0: accuracy = 0.789
I0219 16:08:57.312232 11127 solver.cpp:315]     Test net output #1: loss = 0.623279 (* 1 = 0.623279 loss)
I0219 16:08:57.338534 11127 solver.cpp:189] Iteration 49000, loss = 0.372162
I0219 16:08:57.338547 11127 solver.cpp:204]     Train net output #0: loss = 0.372162 (* 1 = 0.372162 loss)
I0219 16:08:57.338551 11127 solver.cpp:470] Iteration 49000, lr = 0.001
I0219 16:09:08.431725 11127 solver.cpp:189] Iteration 49200, loss = 0.506282
I0219 16:09:08.431776 11127 solver.cpp:204]     Train net output #0: loss = 0.506282 (* 1 = 0.506282 loss)
I0219 16:09:08.431779 11127 solver.cpp:470] Iteration 49200, lr = 0.001
I0219 16:09:19.486611 11127 solver.cpp:189] Iteration 49400, loss = 0.432186
I0219 16:09:19.486634 11127 solver.cpp:204]     Train net output #0: loss = 0.432186 (* 1 = 0.432186 loss)
I0219 16:09:19.486637 11127 solver.cpp:470] Iteration 49400, lr = 0.001
I0219 16:09:30.558569 11127 solver.cpp:189] Iteration 49600, loss = 0.494452
I0219 16:09:30.558591 11127 solver.cpp:204]     Train net output #0: loss = 0.494452 (* 1 = 0.494452 loss)
I0219 16:09:30.558596 11127 solver.cpp:470] Iteration 49600, lr = 0.001
I0219 16:09:41.635388 11127 solver.cpp:189] Iteration 49800, loss = 0.386276
I0219 16:09:41.635452 11127 solver.cpp:204]     Train net output #0: loss = 0.386276 (* 1 = 0.386276 loss)
I0219 16:09:41.635457 11127 solver.cpp:470] Iteration 49800, lr = 0.001
I0219 16:09:52.727311 11127 solver.cpp:334] Snapshotting to examples/cifar10/cifar10_full_iter_50000.caffemodel
I0219 16:09:52.728391 11127 solver.cpp:342] Snapshotting solver state to examples/cifar10/cifar10_full_iter_50000.solverstate
I0219 16:09:52.728842 11127 solver.cpp:266] Iteration 50000, Testing net (#0)
I0219 16:09:54.950675 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7892
I0219 16:09:54.950696 11127 solver.cpp:315]     Test net output #1: loss = 0.621947 (* 1 = 0.621947 loss)
I0219 16:09:54.978021 11127 solver.cpp:189] Iteration 50000, loss = 0.370694
I0219 16:09:54.978044 11127 solver.cpp:204]     Train net output #0: loss = 0.370694 (* 1 = 0.370694 loss)
I0219 16:09:54.978049 11127 solver.cpp:470] Iteration 50000, lr = 0.001
I0219 16:10:06.049182 11127 solver.cpp:189] Iteration 50200, loss = 0.504865
I0219 16:10:06.049206 11127 solver.cpp:204]     Train net output #0: loss = 0.504865 (* 1 = 0.504865 loss)
I0219 16:10:06.049211 11127 solver.cpp:470] Iteration 50200, lr = 0.001
I0219 16:10:17.114634 11127 solver.cpp:189] Iteration 50400, loss = 0.431641
I0219 16:10:17.114738 11127 solver.cpp:204]     Train net output #0: loss = 0.431641 (* 1 = 0.431641 loss)
I0219 16:10:17.114753 11127 solver.cpp:470] Iteration 50400, lr = 0.001
I0219 16:10:28.181673 11127 solver.cpp:189] Iteration 50600, loss = 0.490549
I0219 16:10:28.181699 11127 solver.cpp:204]     Train net output #0: loss = 0.490549 (* 1 = 0.490549 loss)
I0219 16:10:28.181704 11127 solver.cpp:470] Iteration 50600, lr = 0.001
I0219 16:10:39.548915 11127 solver.cpp:189] Iteration 50800, loss = 0.384735
I0219 16:10:39.548940 11127 solver.cpp:204]     Train net output #0: loss = 0.384735 (* 1 = 0.384735 loss)
I0219 16:10:39.548945 11127 solver.cpp:470] Iteration 50800, lr = 0.001
I0219 16:10:50.781381 11127 solver.cpp:266] Iteration 51000, Testing net (#0)
I0219 16:10:53.129117 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7885
I0219 16:10:53.129151 11127 solver.cpp:315]     Test net output #1: loss = 0.622879 (* 1 = 0.622879 loss)
I0219 16:10:53.156141 11127 solver.cpp:189] Iteration 51000, loss = 0.366401
I0219 16:10:53.156175 11127 solver.cpp:204]     Train net output #0: loss = 0.366401 (* 1 = 0.366401 loss)
I0219 16:10:53.156180 11127 solver.cpp:470] Iteration 51000, lr = 0.001
I0219 16:11:04.489454 11127 solver.cpp:189] Iteration 51200, loss = 0.501655
I0219 16:11:04.489522 11127 solver.cpp:204]     Train net output #0: loss = 0.501655 (* 1 = 0.501655 loss)
I0219 16:11:04.489537 11127 solver.cpp:470] Iteration 51200, lr = 0.001
I0219 16:11:15.660984 11127 solver.cpp:189] Iteration 51400, loss = 0.427738
I0219 16:11:15.661017 11127 solver.cpp:204]     Train net output #0: loss = 0.427738 (* 1 = 0.427738 loss)
I0219 16:11:15.661021 11127 solver.cpp:470] Iteration 51400, lr = 0.001
I0219 16:11:26.907023 11127 solver.cpp:189] Iteration 51600, loss = 0.48988
I0219 16:11:26.907117 11127 solver.cpp:204]     Train net output #0: loss = 0.48988 (* 1 = 0.48988 loss)
I0219 16:11:26.907135 11127 solver.cpp:470] Iteration 51600, lr = 0.001
I0219 16:11:37.999698 11127 solver.cpp:189] Iteration 51800, loss = 0.382929
I0219 16:11:37.999722 11127 solver.cpp:204]     Train net output #0: loss = 0.382929 (* 1 = 0.382929 loss)
I0219 16:11:37.999725 11127 solver.cpp:470] Iteration 51800, lr = 0.001
I0219 16:11:49.044451 11127 solver.cpp:266] Iteration 52000, Testing net (#0)
I0219 16:11:51.305361 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7892
I0219 16:11:51.305384 11127 solver.cpp:315]     Test net output #1: loss = 0.621143 (* 1 = 0.621143 loss)
I0219 16:11:51.332155 11127 solver.cpp:189] Iteration 52000, loss = 0.364983
I0219 16:11:51.332176 11127 solver.cpp:204]     Train net output #0: loss = 0.364983 (* 1 = 0.364983 loss)
I0219 16:11:51.332180 11127 solver.cpp:470] Iteration 52000, lr = 0.001
I0219 16:12:02.754979 11127 solver.cpp:189] Iteration 52200, loss = 0.501353
I0219 16:12:02.755060 11127 solver.cpp:204]     Train net output #0: loss = 0.501353 (* 1 = 0.501353 loss)
I0219 16:12:02.755065 11127 solver.cpp:470] Iteration 52200, lr = 0.001
I0219 16:12:13.859030 11127 solver.cpp:189] Iteration 52400, loss = 0.426986
I0219 16:12:13.859061 11127 solver.cpp:204]     Train net output #0: loss = 0.426986 (* 1 = 0.426986 loss)
I0219 16:12:13.859066 11127 solver.cpp:470] Iteration 52400, lr = 0.001
I0219 16:12:24.943624 11127 solver.cpp:189] Iteration 52600, loss = 0.491424
I0219 16:12:24.943655 11127 solver.cpp:204]     Train net output #0: loss = 0.491424 (* 1 = 0.491424 loss)
I0219 16:12:24.943660 11127 solver.cpp:470] Iteration 52600, lr = 0.001
I0219 16:12:36.026878 11127 solver.cpp:189] Iteration 52800, loss = 0.381929
I0219 16:12:36.026921 11127 solver.cpp:204]     Train net output #0: loss = 0.381929 (* 1 = 0.381929 loss)
I0219 16:12:36.026926 11127 solver.cpp:470] Iteration 52800, lr = 0.001
I0219 16:12:47.134393 11127 solver.cpp:266] Iteration 53000, Testing net (#0)
I0219 16:12:49.483207 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7896
I0219 16:12:49.483294 11127 solver.cpp:315]     Test net output #1: loss = 0.620095 (* 1 = 0.620095 loss)
I0219 16:12:49.510620 11127 solver.cpp:189] Iteration 53000, loss = 0.359631
I0219 16:12:49.510668 11127 solver.cpp:204]     Train net output #0: loss = 0.359631 (* 1 = 0.359631 loss)
I0219 16:12:49.510675 11127 solver.cpp:470] Iteration 53000, lr = 0.001
I0219 16:13:00.613610 11127 solver.cpp:189] Iteration 53200, loss = 0.500464
I0219 16:13:00.613632 11127 solver.cpp:204]     Train net output #0: loss = 0.500464 (* 1 = 0.500464 loss)
I0219 16:13:00.613636 11127 solver.cpp:470] Iteration 53200, lr = 0.001
I0219 16:13:11.781764 11127 solver.cpp:189] Iteration 53400, loss = 0.424412
I0219 16:13:11.781885 11127 solver.cpp:204]     Train net output #0: loss = 0.424412 (* 1 = 0.424412 loss)
I0219 16:13:11.781905 11127 solver.cpp:470] Iteration 53400, lr = 0.001
I0219 16:13:23.441743 11127 solver.cpp:189] Iteration 53600, loss = 0.489372
I0219 16:13:23.441915 11127 solver.cpp:204]     Train net output #0: loss = 0.489372 (* 1 = 0.489372 loss)
I0219 16:13:23.441974 11127 solver.cpp:470] Iteration 53600, lr = 0.001
I0219 16:13:35.092592 11127 solver.cpp:189] Iteration 53800, loss = 0.381699
I0219 16:13:35.092739 11127 solver.cpp:204]     Train net output #0: loss = 0.381699 (* 1 = 0.381699 loss)
I0219 16:13:35.092805 11127 solver.cpp:470] Iteration 53800, lr = 0.001
I0219 16:13:46.624898 11127 solver.cpp:266] Iteration 54000, Testing net (#0)
I0219 16:13:49.072252 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7897
I0219 16:13:49.072283 11127 solver.cpp:315]     Test net output #1: loss = 0.618934 (* 1 = 0.618934 loss)
I0219 16:13:49.098634 11127 solver.cpp:189] Iteration 54000, loss = 0.357824
I0219 16:13:49.098691 11127 solver.cpp:204]     Train net output #0: loss = 0.357824 (* 1 = 0.357824 loss)
I0219 16:13:49.098708 11127 solver.cpp:470] Iteration 54000, lr = 0.001
I0219 16:14:00.403504 11127 solver.cpp:189] Iteration 54200, loss = 0.497596
I0219 16:14:00.403656 11127 solver.cpp:204]     Train net output #0: loss = 0.497596 (* 1 = 0.497596 loss)
I0219 16:14:00.403722 11127 solver.cpp:470] Iteration 54200, lr = 0.001
I0219 16:14:11.480077 11127 solver.cpp:189] Iteration 54400, loss = 0.424065
I0219 16:14:11.480229 11127 solver.cpp:204]     Train net output #0: loss = 0.424065 (* 1 = 0.424065 loss)
I0219 16:14:11.480289 11127 solver.cpp:470] Iteration 54400, lr = 0.001
I0219 16:14:22.606750 11127 solver.cpp:189] Iteration 54600, loss = 0.489715
I0219 16:14:22.606835 11127 solver.cpp:204]     Train net output #0: loss = 0.489715 (* 1 = 0.489715 loss)
I0219 16:14:22.606853 11127 solver.cpp:470] Iteration 54600, lr = 0.001
I0219 16:14:33.871038 11127 solver.cpp:189] Iteration 54800, loss = 0.382812
I0219 16:14:33.871104 11127 solver.cpp:204]     Train net output #0: loss = 0.382812 (* 1 = 0.382812 loss)
I0219 16:14:33.871124 11127 solver.cpp:470] Iteration 54800, lr = 0.001
I0219 16:14:45.777694 11127 solver.cpp:266] Iteration 55000, Testing net (#0)
I0219 16:14:48.434422 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7883
I0219 16:14:48.434490 11127 solver.cpp:315]     Test net output #1: loss = 0.618836 (* 1 = 0.618836 loss)
I0219 16:14:48.464766 11127 solver.cpp:189] Iteration 55000, loss = 0.353863
I0219 16:14:48.464820 11127 solver.cpp:204]     Train net output #0: loss = 0.353863 (* 1 = 0.353863 loss)
I0219 16:14:48.464834 11127 solver.cpp:470] Iteration 55000, lr = 0.001
I0219 16:15:01.102891 11127 solver.cpp:189] Iteration 55200, loss = 0.496065
I0219 16:15:01.102977 11127 solver.cpp:204]     Train net output #0: loss = 0.496065 (* 1 = 0.496065 loss)
I0219 16:15:01.102991 11127 solver.cpp:470] Iteration 55200, lr = 0.001
I0219 16:15:13.753407 11127 solver.cpp:189] Iteration 55400, loss = 0.424196
I0219 16:15:13.753474 11127 solver.cpp:204]     Train net output #0: loss = 0.424196 (* 1 = 0.424196 loss)
I0219 16:15:13.753489 11127 solver.cpp:470] Iteration 55400, lr = 0.001
I0219 16:15:26.245381 11127 solver.cpp:189] Iteration 55600, loss = 0.489701
I0219 16:15:26.245409 11127 solver.cpp:204]     Train net output #0: loss = 0.489701 (* 1 = 0.489701 loss)
I0219 16:15:26.245414 11127 solver.cpp:470] Iteration 55600, lr = 0.001
I0219 16:15:38.701673 11127 solver.cpp:189] Iteration 55800, loss = 0.381087
I0219 16:15:38.701761 11127 solver.cpp:204]     Train net output #0: loss = 0.381087 (* 1 = 0.381087 loss)
I0219 16:15:38.701767 11127 solver.cpp:470] Iteration 55800, lr = 0.001
I0219 16:15:50.648866 11127 solver.cpp:266] Iteration 56000, Testing net (#0)
I0219 16:15:52.931563 11127 solver.cpp:315]     Test net output #0: accuracy = 0.789
I0219 16:15:52.931587 11127 solver.cpp:315]     Test net output #1: loss = 0.617461 (* 1 = 0.617461 loss)
I0219 16:15:52.958353 11127 solver.cpp:189] Iteration 56000, loss = 0.350922
I0219 16:15:52.958374 11127 solver.cpp:204]     Train net output #0: loss = 0.350922 (* 1 = 0.350922 loss)
I0219 16:15:52.958379 11127 solver.cpp:470] Iteration 56000, lr = 0.001
I0219 16:16:04.326740 11127 solver.cpp:189] Iteration 56200, loss = 0.490921
I0219 16:16:04.326766 11127 solver.cpp:204]     Train net output #0: loss = 0.490921 (* 1 = 0.490921 loss)
I0219 16:16:04.326771 11127 solver.cpp:470] Iteration 56200, lr = 0.001
I0219 16:16:15.680747 11127 solver.cpp:189] Iteration 56400, loss = 0.422114
I0219 16:16:15.680809 11127 solver.cpp:204]     Train net output #0: loss = 0.422114 (* 1 = 0.422114 loss)
I0219 16:16:15.680815 11127 solver.cpp:470] Iteration 56400, lr = 0.001
I0219 16:16:26.754344 11127 solver.cpp:189] Iteration 56600, loss = 0.490373
I0219 16:16:26.754371 11127 solver.cpp:204]     Train net output #0: loss = 0.490373 (* 1 = 0.490373 loss)
I0219 16:16:26.754375 11127 solver.cpp:470] Iteration 56600, lr = 0.001
I0219 16:16:37.948824 11127 solver.cpp:189] Iteration 56800, loss = 0.381069
I0219 16:16:37.948897 11127 solver.cpp:204]     Train net output #0: loss = 0.381069 (* 1 = 0.381069 loss)
I0219 16:16:37.948917 11127 solver.cpp:470] Iteration 56800, lr = 0.001
I0219 16:16:49.079345 11127 solver.cpp:266] Iteration 57000, Testing net (#0)
I0219 16:16:51.399308 11127 solver.cpp:315]     Test net output #0: accuracy = 0.789
I0219 16:16:51.399381 11127 solver.cpp:315]     Test net output #1: loss = 0.61672 (* 1 = 0.61672 loss)
I0219 16:16:51.427850 11127 solver.cpp:189] Iteration 57000, loss = 0.347577
I0219 16:16:51.427919 11127 solver.cpp:204]     Train net output #0: loss = 0.347577 (* 1 = 0.347577 loss)
I0219 16:16:51.427940 11127 solver.cpp:470] Iteration 57000, lr = 0.001
I0219 16:17:02.871044 11127 solver.cpp:189] Iteration 57200, loss = 0.485644
I0219 16:17:02.871069 11127 solver.cpp:204]     Train net output #0: loss = 0.485644 (* 1 = 0.485644 loss)
I0219 16:17:02.871073 11127 solver.cpp:470] Iteration 57200, lr = 0.001
I0219 16:17:14.033761 11127 solver.cpp:189] Iteration 57400, loss = 0.423948
I0219 16:17:14.033787 11127 solver.cpp:204]     Train net output #0: loss = 0.423948 (* 1 = 0.423948 loss)
I0219 16:17:14.033792 11127 solver.cpp:470] Iteration 57400, lr = 0.001
I0219 16:17:25.236196 11127 solver.cpp:189] Iteration 57600, loss = 0.490616
I0219 16:17:25.236274 11127 solver.cpp:204]     Train net output #0: loss = 0.490616 (* 1 = 0.490616 loss)
I0219 16:17:25.236279 11127 solver.cpp:470] Iteration 57600, lr = 0.001
I0219 16:17:36.368868 11127 solver.cpp:189] Iteration 57800, loss = 0.381213
I0219 16:17:36.368895 11127 solver.cpp:204]     Train net output #0: loss = 0.381213 (* 1 = 0.381213 loss)
I0219 16:17:36.368899 11127 solver.cpp:470] Iteration 57800, lr = 0.001
I0219 16:17:47.664715 11127 solver.cpp:266] Iteration 58000, Testing net (#0)
I0219 16:17:49.941733 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7894
I0219 16:17:49.941805 11127 solver.cpp:315]     Test net output #1: loss = 0.615988 (* 1 = 0.615988 loss)
I0219 16:17:49.969225 11127 solver.cpp:189] Iteration 58000, loss = 0.346019
I0219 16:17:49.969264 11127 solver.cpp:204]     Train net output #0: loss = 0.346019 (* 1 = 0.346019 loss)
I0219 16:17:49.969269 11127 solver.cpp:470] Iteration 58000, lr = 0.001
I0219 16:18:01.332087 11127 solver.cpp:189] Iteration 58200, loss = 0.485327
I0219 16:18:01.332198 11127 solver.cpp:204]     Train net output #0: loss = 0.485327 (* 1 = 0.485327 loss)
I0219 16:18:01.332203 11127 solver.cpp:470] Iteration 58200, lr = 0.001
I0219 16:18:12.606325 11127 solver.cpp:189] Iteration 58400, loss = 0.421575
I0219 16:18:12.606348 11127 solver.cpp:204]     Train net output #0: loss = 0.421575 (* 1 = 0.421575 loss)
I0219 16:18:12.606350 11127 solver.cpp:470] Iteration 58400, lr = 0.001
I0219 16:18:23.728390 11127 solver.cpp:189] Iteration 58600, loss = 0.492472
I0219 16:18:23.728416 11127 solver.cpp:204]     Train net output #0: loss = 0.492472 (* 1 = 0.492472 loss)
I0219 16:18:23.728421 11127 solver.cpp:470] Iteration 58600, lr = 0.001
I0219 16:18:35.010748 11127 solver.cpp:189] Iteration 58800, loss = 0.378607
I0219 16:18:35.010807 11127 solver.cpp:204]     Train net output #0: loss = 0.378607 (* 1 = 0.378607 loss)
I0219 16:18:35.010812 11127 solver.cpp:470] Iteration 58800, lr = 0.001
I0219 16:18:46.194460 11127 solver.cpp:266] Iteration 59000, Testing net (#0)
I0219 16:18:48.512891 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7908
I0219 16:18:48.513054 11127 solver.cpp:315]     Test net output #1: loss = 0.615026 (* 1 = 0.615026 loss)
I0219 16:18:48.539489 11127 solver.cpp:189] Iteration 59000, loss = 0.343813
I0219 16:18:48.539535 11127 solver.cpp:204]     Train net output #0: loss = 0.343813 (* 1 = 0.343813 loss)
I0219 16:18:48.539541 11127 solver.cpp:470] Iteration 59000, lr = 0.001
I0219 16:18:59.736289 11127 solver.cpp:189] Iteration 59200, loss = 0.485173
I0219 16:18:59.736322 11127 solver.cpp:204]     Train net output #0: loss = 0.485173 (* 1 = 0.485173 loss)
I0219 16:18:59.736327 11127 solver.cpp:470] Iteration 59200, lr = 0.001
I0219 16:19:10.945466 11127 solver.cpp:189] Iteration 59400, loss = 0.424179
I0219 16:19:10.945547 11127 solver.cpp:204]     Train net output #0: loss = 0.424179 (* 1 = 0.424179 loss)
I0219 16:19:10.945560 11127 solver.cpp:470] Iteration 59400, lr = 0.001
I0219 16:19:22.021006 11127 solver.cpp:189] Iteration 59600, loss = 0.493444
I0219 16:19:22.021028 11127 solver.cpp:204]     Train net output #0: loss = 0.493444 (* 1 = 0.493444 loss)
I0219 16:19:22.021034 11127 solver.cpp:470] Iteration 59600, lr = 0.001
I0219 16:19:33.195597 11127 solver.cpp:189] Iteration 59800, loss = 0.378481
I0219 16:19:33.195632 11127 solver.cpp:204]     Train net output #0: loss = 0.378481 (* 1 = 0.378481 loss)
I0219 16:19:33.195637 11127 solver.cpp:470] Iteration 59800, lr = 0.001
I0219 16:19:44.336415 11127 solver.cpp:334] Snapshotting to examples/cifar10/cifar10_full_iter_60000.caffemodel
I0219 16:19:44.337334 11127 solver.cpp:342] Snapshotting solver state to examples/cifar10/cifar10_full_iter_60000.solverstate
I0219 16:19:44.359895 11127 solver.cpp:248] Iteration 60000, loss = 0.34095
I0219 16:19:44.359925 11127 solver.cpp:266] Iteration 60000, Testing net (#0)
I0219 16:19:46.642313 11127 solver.cpp:315]     Test net output #0: accuracy = 0.7917
I0219 16:19:46.642359 11127 solver.cpp:315]     Test net output #1: loss = 0.613136 (* 1 = 0.613136 loss)
I0219 16:19:46.642364 11127 solver.cpp:253] Optimization Done.
I0219 16:19:46.642366 11127 caffe.cpp:121] Optimization Done.
I0219 16:19:46.688334 20200 caffe.cpp:99] Use GPU with device ID 0
I0219 16:19:46.800045 20200 caffe.cpp:107] Starting Optimization
I0219 16:19:46.800171 20200 solver.cpp:32] Initializing solver from parameters: 
test_iter: 100
test_interval: 1000
base_lr: 0.0001
display: 200
max_iter: 65000
lr_policy: "fixed"
momentum: 0.9
weight_decay: 0.004
snapshot: 5000
snapshot_prefix: "examples/cifar10/cifar10_full"
solver_mode: GPU
net: "examples/cifar10/cifar10_full_train_test.prototxt"
I0219 16:19:46.800204 20200 solver.cpp:70] Creating training net from net file: examples/cifar10/cifar10_full_train_test.prototxt
I0219 16:19:46.800549 20200 net.cpp:256] The NetState phase (0) differed from the phase (1) specified by a rule in layer cifar
I0219 16:19:46.800561 20200 net.cpp:256] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0219 16:19:46.800660 20200 net.cpp:45] Initializing net from parameters: 
name: "CIFAR10_full"
state {
  phase: TRAIN
}
layer {
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mean_file: "examples/cifar10/mean.binaryproto"
  }
  data_param {
    source: "examples/cifar10/cifar10_train_lmdb"
    batch_size: 100
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.0001
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "pool1"
  top: "pool1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "ip1"
  param {
    lr_mult: 1
    decay_mult: 250
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip1"
  bottom: "label"
  top: "loss"
}
I0219 16:19:46.800720 20200 layer_factory.hpp:74] Creating layer cifar
I0219 16:19:46.800771 20200 net.cpp:79] Creating Layer cifar
I0219 16:19:46.800778 20200 net.cpp:337] cifar -> data
I0219 16:19:46.800807 20200 net.cpp:337] cifar -> label
I0219 16:19:46.800817 20200 net.cpp:108] Setting up cifar
I0219 16:19:46.800875 20200 db.cpp:34] Opened lmdb examples/cifar10/cifar10_train_lmdb
I0219 16:19:46.800925 20200 data_layer.cpp:65] output data size: 100,3,32,32
I0219 16:19:46.800930 20200 data_transformer.cpp:59] Loading mean file from: examples/cifar10/mean.binaryproto
I0219 16:19:46.801391 20200 net.cpp:115] Top shape: 100 3 32 32 (307200)
I0219 16:19:46.801398 20200 net.cpp:115] Top shape: 100 1 1 1 (100)
I0219 16:19:46.801410 20200 layer_factory.hpp:74] Creating layer conv1
I0219 16:19:46.801420 20200 net.cpp:79] Creating Layer conv1
I0219 16:19:46.801424 20200 net.cpp:375] conv1 <- data
I0219 16:19:46.801434 20200 net.cpp:337] conv1 -> conv1
I0219 16:19:46.801441 20200 net.cpp:108] Setting up conv1
I0219 16:19:46.801883 20200 net.cpp:115] Top shape: 100 32 32 32 (3276800)
I0219 16:19:46.801904 20200 layer_factory.hpp:74] Creating layer pool1
I0219 16:19:46.801911 20200 net.cpp:79] Creating Layer pool1
I0219 16:19:46.801913 20200 net.cpp:375] pool1 <- conv1
I0219 16:19:46.801918 20200 net.cpp:337] pool1 -> pool1
I0219 16:19:46.801923 20200 net.cpp:108] Setting up pool1
I0219 16:19:46.801930 20200 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:19:46.801933 20200 layer_factory.hpp:74] Creating layer relu1
I0219 16:19:46.801935 20200 net.cpp:79] Creating Layer relu1
I0219 16:19:46.801937 20200 net.cpp:375] relu1 <- pool1
I0219 16:19:46.801940 20200 net.cpp:326] relu1 -> pool1 (in-place)
I0219 16:19:46.801944 20200 net.cpp:108] Setting up relu1
I0219 16:19:46.801945 20200 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:19:46.801947 20200 layer_factory.hpp:74] Creating layer norm1
I0219 16:19:46.801952 20200 net.cpp:79] Creating Layer norm1
I0219 16:19:46.801954 20200 net.cpp:375] norm1 <- pool1
I0219 16:19:46.801959 20200 net.cpp:337] norm1 -> norm1
I0219 16:19:46.801964 20200 net.cpp:108] Setting up norm1
I0219 16:19:46.801987 20200 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:19:46.801990 20200 layer_factory.hpp:74] Creating layer conv2
I0219 16:19:46.801993 20200 net.cpp:79] Creating Layer conv2
I0219 16:19:46.801995 20200 net.cpp:375] conv2 <- norm1
I0219 16:19:46.801998 20200 net.cpp:337] conv2 -> conv2
I0219 16:19:46.802002 20200 net.cpp:108] Setting up conv2
I0219 16:19:46.802573 20200 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:19:46.802584 20200 layer_factory.hpp:74] Creating layer relu2
I0219 16:19:46.802589 20200 net.cpp:79] Creating Layer relu2
I0219 16:19:46.802592 20200 net.cpp:375] relu2 <- conv2
I0219 16:19:46.802594 20200 net.cpp:326] relu2 -> conv2 (in-place)
I0219 16:19:46.802597 20200 net.cpp:108] Setting up relu2
I0219 16:19:46.802599 20200 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:19:46.802602 20200 layer_factory.hpp:74] Creating layer pool2
I0219 16:19:46.802604 20200 net.cpp:79] Creating Layer pool2
I0219 16:19:46.802606 20200 net.cpp:375] pool2 <- conv2
I0219 16:19:46.802613 20200 net.cpp:337] pool2 -> pool2
I0219 16:19:46.802615 20200 net.cpp:108] Setting up pool2
I0219 16:19:46.802618 20200 net.cpp:115] Top shape: 100 32 8 8 (204800)
I0219 16:19:46.802619 20200 layer_factory.hpp:74] Creating layer norm2
I0219 16:19:46.802626 20200 net.cpp:79] Creating Layer norm2
I0219 16:19:46.802628 20200 net.cpp:375] norm2 <- pool2
I0219 16:19:46.802631 20200 net.cpp:337] norm2 -> norm2
I0219 16:19:46.802634 20200 net.cpp:108] Setting up norm2
I0219 16:19:46.802644 20200 net.cpp:115] Top shape: 100 32 8 8 (204800)
I0219 16:19:46.802646 20200 layer_factory.hpp:74] Creating layer conv3
I0219 16:19:46.802651 20200 net.cpp:79] Creating Layer conv3
I0219 16:19:46.802652 20200 net.cpp:375] conv3 <- norm2
I0219 16:19:46.802655 20200 net.cpp:337] conv3 -> conv3
I0219 16:19:46.802660 20200 net.cpp:108] Setting up conv3
I0219 16:19:46.803809 20200 net.cpp:115] Top shape: 100 64 8 8 (409600)
I0219 16:19:46.803822 20200 layer_factory.hpp:74] Creating layer relu3
I0219 16:19:46.803828 20200 net.cpp:79] Creating Layer relu3
I0219 16:19:46.803830 20200 net.cpp:375] relu3 <- conv3
I0219 16:19:46.803834 20200 net.cpp:326] relu3 -> conv3 (in-place)
I0219 16:19:46.803838 20200 net.cpp:108] Setting up relu3
I0219 16:19:46.803840 20200 net.cpp:115] Top shape: 100 64 8 8 (409600)
I0219 16:19:46.803843 20200 layer_factory.hpp:74] Creating layer pool3
I0219 16:19:46.803845 20200 net.cpp:79] Creating Layer pool3
I0219 16:19:46.803865 20200 net.cpp:375] pool3 <- conv3
I0219 16:19:46.803870 20200 net.cpp:337] pool3 -> pool3
I0219 16:19:46.803874 20200 net.cpp:108] Setting up pool3
I0219 16:19:46.803877 20200 net.cpp:115] Top shape: 100 64 4 4 (102400)
I0219 16:19:46.803879 20200 layer_factory.hpp:74] Creating layer ip1
I0219 16:19:46.803885 20200 net.cpp:79] Creating Layer ip1
I0219 16:19:46.803887 20200 net.cpp:375] ip1 <- pool3
I0219 16:19:46.803890 20200 net.cpp:337] ip1 -> ip1
I0219 16:19:46.803895 20200 net.cpp:108] Setting up ip1
I0219 16:19:46.804138 20200 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0219 16:19:46.804146 20200 layer_factory.hpp:74] Creating layer loss
I0219 16:19:46.804150 20200 net.cpp:79] Creating Layer loss
I0219 16:19:46.804152 20200 net.cpp:375] loss <- ip1
I0219 16:19:46.804154 20200 net.cpp:375] loss <- label
I0219 16:19:46.804160 20200 net.cpp:337] loss -> loss
I0219 16:19:46.804164 20200 net.cpp:108] Setting up loss
I0219 16:19:46.804170 20200 layer_factory.hpp:74] Creating layer loss
I0219 16:19:46.804184 20200 net.cpp:115] Top shape: 1 1 1 1 (1)
I0219 16:19:46.804188 20200 net.cpp:121]     with loss weight 1
I0219 16:19:46.804203 20200 net.cpp:166] loss needs backward computation.
I0219 16:19:46.804204 20200 net.cpp:166] ip1 needs backward computation.
I0219 16:19:46.804206 20200 net.cpp:166] pool3 needs backward computation.
I0219 16:19:46.804208 20200 net.cpp:166] relu3 needs backward computation.
I0219 16:19:46.804209 20200 net.cpp:166] conv3 needs backward computation.
I0219 16:19:46.804211 20200 net.cpp:166] norm2 needs backward computation.
I0219 16:19:46.804214 20200 net.cpp:166] pool2 needs backward computation.
I0219 16:19:46.804216 20200 net.cpp:166] relu2 needs backward computation.
I0219 16:19:46.804219 20200 net.cpp:166] conv2 needs backward computation.
I0219 16:19:46.804220 20200 net.cpp:166] norm1 needs backward computation.
I0219 16:19:46.804224 20200 net.cpp:166] relu1 needs backward computation.
I0219 16:19:46.804225 20200 net.cpp:166] pool1 needs backward computation.
I0219 16:19:46.804227 20200 net.cpp:166] conv1 needs backward computation.
I0219 16:19:46.804229 20200 net.cpp:168] cifar does not need backward computation.
I0219 16:19:46.804231 20200 net.cpp:204] This network produces output loss
I0219 16:19:46.804239 20200 net.cpp:449] Collecting Learning Rate and Weight Decay.
I0219 16:19:46.804244 20200 net.cpp:216] Network initialization done.
I0219 16:19:46.804249 20200 net.cpp:217] Memory required for data: 36049204
I0219 16:19:46.804584 20200 solver.cpp:154] Creating test net (#0) specified by net file: examples/cifar10/cifar10_full_train_test.prototxt
I0219 16:19:46.804615 20200 net.cpp:256] The NetState phase (1) differed from the phase (0) specified by a rule in layer cifar
I0219 16:19:46.804708 20200 net.cpp:45] Initializing net from parameters: 
name: "CIFAR10_full"
state {
  phase: TEST
}
layer {
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mean_file: "examples/cifar10/mean.binaryproto"
  }
  data_param {
    source: "examples/cifar10/cifar10_test_lmdb"
    batch_size: 100
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.0001
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "pool1"
  top: "pool1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "ip1"
  param {
    lr_mult: 1
    decay_mult: 250
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "ip1"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip1"
  bottom: "label"
  top: "loss"
}
I0219 16:19:46.804781 20200 layer_factory.hpp:74] Creating layer cifar
I0219 16:19:46.804790 20200 net.cpp:79] Creating Layer cifar
I0219 16:19:46.804793 20200 net.cpp:337] cifar -> data
I0219 16:19:46.804800 20200 net.cpp:337] cifar -> label
I0219 16:19:46.804803 20200 net.cpp:108] Setting up cifar
I0219 16:19:46.804833 20200 db.cpp:34] Opened lmdb examples/cifar10/cifar10_test_lmdb
I0219 16:19:46.804847 20200 data_layer.cpp:65] output data size: 100,3,32,32
I0219 16:19:46.804852 20200 data_transformer.cpp:59] Loading mean file from: examples/cifar10/mean.binaryproto
I0219 16:19:46.805418 20200 net.cpp:115] Top shape: 100 3 32 32 (307200)
I0219 16:19:46.805425 20200 net.cpp:115] Top shape: 100 1 1 1 (100)
I0219 16:19:46.805429 20200 layer_factory.hpp:74] Creating layer label_cifar_1_split
I0219 16:19:46.805451 20200 net.cpp:79] Creating Layer label_cifar_1_split
I0219 16:19:46.805460 20200 net.cpp:375] label_cifar_1_split <- label
I0219 16:19:46.805465 20200 net.cpp:337] label_cifar_1_split -> label_cifar_1_split_0
I0219 16:19:46.805471 20200 net.cpp:337] label_cifar_1_split -> label_cifar_1_split_1
I0219 16:19:46.805475 20200 net.cpp:108] Setting up label_cifar_1_split
I0219 16:19:46.805479 20200 net.cpp:115] Top shape: 100 1 1 1 (100)
I0219 16:19:46.805481 20200 net.cpp:115] Top shape: 100 1 1 1 (100)
I0219 16:19:46.805483 20200 layer_factory.hpp:74] Creating layer conv1
I0219 16:19:46.805488 20200 net.cpp:79] Creating Layer conv1
I0219 16:19:46.805490 20200 net.cpp:375] conv1 <- data
I0219 16:19:46.805493 20200 net.cpp:337] conv1 -> conv1
I0219 16:19:46.805497 20200 net.cpp:108] Setting up conv1
I0219 16:19:46.805572 20200 net.cpp:115] Top shape: 100 32 32 32 (3276800)
I0219 16:19:46.805600 20200 layer_factory.hpp:74] Creating layer pool1
I0219 16:19:46.805606 20200 net.cpp:79] Creating Layer pool1
I0219 16:19:46.805629 20200 net.cpp:375] pool1 <- conv1
I0219 16:19:46.805655 20200 net.cpp:337] pool1 -> pool1
I0219 16:19:46.805660 20200 net.cpp:108] Setting up pool1
I0219 16:19:46.805697 20200 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:19:46.805701 20200 layer_factory.hpp:74] Creating layer relu1
I0219 16:19:46.805728 20200 net.cpp:79] Creating Layer relu1
I0219 16:19:46.805732 20200 net.cpp:375] relu1 <- pool1
I0219 16:19:46.805764 20200 net.cpp:326] relu1 -> pool1 (in-place)
I0219 16:19:46.805769 20200 net.cpp:108] Setting up relu1
I0219 16:19:46.805771 20200 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:19:46.805794 20200 layer_factory.hpp:74] Creating layer norm1
I0219 16:19:46.805799 20200 net.cpp:79] Creating Layer norm1
I0219 16:19:46.805800 20200 net.cpp:375] norm1 <- pool1
I0219 16:19:46.805802 20200 net.cpp:337] norm1 -> norm1
I0219 16:19:46.805806 20200 net.cpp:108] Setting up norm1
I0219 16:19:46.805817 20200 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:19:46.805830 20200 layer_factory.hpp:74] Creating layer conv2
I0219 16:19:46.805833 20200 net.cpp:79] Creating Layer conv2
I0219 16:19:46.805835 20200 net.cpp:375] conv2 <- norm1
I0219 16:19:46.805869 20200 net.cpp:337] conv2 -> conv2
I0219 16:19:46.805876 20200 net.cpp:108] Setting up conv2
I0219 16:19:46.806514 20200 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:19:46.806531 20200 layer_factory.hpp:74] Creating layer relu2
I0219 16:19:46.806535 20200 net.cpp:79] Creating Layer relu2
I0219 16:19:46.806537 20200 net.cpp:375] relu2 <- conv2
I0219 16:19:46.806540 20200 net.cpp:326] relu2 -> conv2 (in-place)
I0219 16:19:46.806545 20200 net.cpp:108] Setting up relu2
I0219 16:19:46.806546 20200 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:19:46.806548 20200 layer_factory.hpp:74] Creating layer pool2
I0219 16:19:46.806553 20200 net.cpp:79] Creating Layer pool2
I0219 16:19:46.806555 20200 net.cpp:375] pool2 <- conv2
I0219 16:19:46.806557 20200 net.cpp:337] pool2 -> pool2
I0219 16:19:46.806561 20200 net.cpp:108] Setting up pool2
I0219 16:19:46.806565 20200 net.cpp:115] Top shape: 100 32 8 8 (204800)
I0219 16:19:46.806566 20200 layer_factory.hpp:74] Creating layer norm2
I0219 16:19:46.806571 20200 net.cpp:79] Creating Layer norm2
I0219 16:19:46.806572 20200 net.cpp:375] norm2 <- pool2
I0219 16:19:46.806576 20200 net.cpp:337] norm2 -> norm2
I0219 16:19:46.806578 20200 net.cpp:108] Setting up norm2
I0219 16:19:46.806587 20200 net.cpp:115] Top shape: 100 32 8 8 (204800)
I0219 16:19:46.806589 20200 layer_factory.hpp:74] Creating layer conv3
I0219 16:19:46.806593 20200 net.cpp:79] Creating Layer conv3
I0219 16:19:46.806596 20200 net.cpp:375] conv3 <- norm2
I0219 16:19:46.806599 20200 net.cpp:337] conv3 -> conv3
I0219 16:19:46.806602 20200 net.cpp:108] Setting up conv3
I0219 16:19:46.807787 20200 net.cpp:115] Top shape: 100 64 8 8 (409600)
I0219 16:19:46.807823 20200 layer_factory.hpp:74] Creating layer relu3
I0219 16:19:46.807845 20200 net.cpp:79] Creating Layer relu3
I0219 16:19:46.807850 20200 net.cpp:375] relu3 <- conv3
I0219 16:19:46.807885 20200 net.cpp:326] relu3 -> conv3 (in-place)
I0219 16:19:46.807890 20200 net.cpp:108] Setting up relu3
I0219 16:19:46.807909 20200 net.cpp:115] Top shape: 100 64 8 8 (409600)
I0219 16:19:46.807930 20200 layer_factory.hpp:74] Creating layer pool3
I0219 16:19:46.807950 20200 net.cpp:79] Creating Layer pool3
I0219 16:19:46.807953 20200 net.cpp:375] pool3 <- conv3
I0219 16:19:46.807978 20200 net.cpp:337] pool3 -> pool3
I0219 16:19:46.807984 20200 net.cpp:108] Setting up pool3
I0219 16:19:46.808002 20200 net.cpp:115] Top shape: 100 64 4 4 (102400)
I0219 16:19:46.808035 20200 layer_factory.hpp:74] Creating layer ip1
I0219 16:19:46.808063 20200 net.cpp:79] Creating Layer ip1
I0219 16:19:46.808068 20200 net.cpp:375] ip1 <- pool3
I0219 16:19:46.808095 20200 net.cpp:337] ip1 -> ip1
I0219 16:19:46.808117 20200 net.cpp:108] Setting up ip1
I0219 16:19:46.808357 20200 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0219 16:19:46.808380 20200 layer_factory.hpp:74] Creating layer ip1_ip1_0_split
I0219 16:19:46.808403 20200 net.cpp:79] Creating Layer ip1_ip1_0_split
I0219 16:19:46.808406 20200 net.cpp:375] ip1_ip1_0_split <- ip1
I0219 16:19:46.808434 20200 net.cpp:337] ip1_ip1_0_split -> ip1_ip1_0_split_0
I0219 16:19:46.808456 20200 net.cpp:337] ip1_ip1_0_split -> ip1_ip1_0_split_1
I0219 16:19:46.808476 20200 net.cpp:108] Setting up ip1_ip1_0_split
I0219 16:19:46.808480 20200 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0219 16:19:46.808497 20200 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0219 16:19:46.808513 20200 layer_factory.hpp:74] Creating layer accuracy
I0219 16:19:46.808552 20200 net.cpp:79] Creating Layer accuracy
I0219 16:19:46.808568 20200 net.cpp:375] accuracy <- ip1_ip1_0_split_0
I0219 16:19:46.808604 20200 net.cpp:375] accuracy <- label_cifar_1_split_0
I0219 16:19:46.808624 20200 net.cpp:337] accuracy -> accuracy
I0219 16:19:46.808645 20200 net.cpp:108] Setting up accuracy
I0219 16:19:46.808665 20200 net.cpp:115] Top shape: 1 1 1 1 (1)
I0219 16:19:46.808668 20200 layer_factory.hpp:74] Creating layer loss
I0219 16:19:46.808696 20200 net.cpp:79] Creating Layer loss
I0219 16:19:46.808699 20200 net.cpp:375] loss <- ip1_ip1_0_split_1
I0219 16:19:46.808702 20200 net.cpp:375] loss <- label_cifar_1_split_1
I0219 16:19:46.808732 20200 net.cpp:337] loss -> loss
I0219 16:19:46.808753 20200 net.cpp:108] Setting up loss
I0219 16:19:46.808773 20200 layer_factory.hpp:74] Creating layer loss
I0219 16:19:46.808802 20200 net.cpp:115] Top shape: 1 1 1 1 (1)
I0219 16:19:46.808805 20200 net.cpp:121]     with loss weight 1
I0219 16:19:46.808828 20200 net.cpp:166] loss needs backward computation.
I0219 16:19:46.808845 20200 net.cpp:168] accuracy does not need backward computation.
I0219 16:19:46.808861 20200 net.cpp:166] ip1_ip1_0_split needs backward computation.
I0219 16:19:46.808863 20200 net.cpp:166] ip1 needs backward computation.
I0219 16:19:46.808864 20200 net.cpp:166] pool3 needs backward computation.
I0219 16:19:46.808866 20200 net.cpp:166] relu3 needs backward computation.
I0219 16:19:46.808903 20200 net.cpp:166] conv3 needs backward computation.
I0219 16:19:46.808926 20200 net.cpp:166] norm2 needs backward computation.
I0219 16:19:46.808929 20200 net.cpp:166] pool2 needs backward computation.
I0219 16:19:46.808950 20200 net.cpp:166] relu2 needs backward computation.
I0219 16:19:46.808954 20200 net.cpp:166] conv2 needs backward computation.
I0219 16:19:46.808976 20200 net.cpp:166] norm1 needs backward computation.
I0219 16:19:46.808980 20200 net.cpp:166] relu1 needs backward computation.
I0219 16:19:46.809000 20200 net.cpp:166] pool1 needs backward computation.
I0219 16:19:46.809003 20200 net.cpp:166] conv1 needs backward computation.
I0219 16:19:46.809026 20200 net.cpp:168] label_cifar_1_split does not need backward computation.
I0219 16:19:46.809028 20200 net.cpp:168] cifar does not need backward computation.
I0219 16:19:46.809031 20200 net.cpp:204] This network produces output accuracy
I0219 16:19:46.809032 20200 net.cpp:204] This network produces output loss
I0219 16:19:46.809069 20200 net.cpp:449] Collecting Learning Rate and Weight Decay.
I0219 16:19:46.809074 20200 net.cpp:216] Network initialization done.
I0219 16:19:46.809075 20200 net.cpp:217] Memory required for data: 36058008
I0219 16:19:46.809155 20200 solver.cpp:42] Solver scaffolding done.
I0219 16:19:46.809191 20200 caffe.cpp:112] Resuming from examples/cifar10/cifar10_full_iter_60000.solverstate
I0219 16:19:46.809216 20200 solver.cpp:222] Solving CIFAR10_full
I0219 16:19:46.809221 20200 solver.cpp:223] Learning Rate Policy: fixed
I0219 16:19:46.809247 20200 solver.cpp:226] Restoring previous solver status from examples/cifar10/cifar10_full_iter_60000.solverstate
I0219 16:19:46.810359 20200 solver.cpp:570] SGDSolver: restoring history
I0219 16:19:46.810565 20200 solver.cpp:266] Iteration 60000, Testing net (#0)
I0219 16:19:49.114650 20200 solver.cpp:315]     Test net output #0: accuracy = 0.7917
I0219 16:19:49.114692 20200 solver.cpp:315]     Test net output #1: loss = 0.613136 (* 1 = 0.613136 loss)
I0219 16:19:49.142807 20200 solver.cpp:189] Iteration 60000, loss = 0.34095
I0219 16:19:49.142889 20200 solver.cpp:204]     Train net output #0: loss = 0.34095 (* 1 = 0.34095 loss)
I0219 16:19:49.142906 20200 solver.cpp:470] Iteration 60000, lr = 0.0001
I0219 16:20:00.303728 20200 solver.cpp:189] Iteration 60200, loss = 0.385106
I0219 16:20:00.303763 20200 solver.cpp:204]     Train net output #0: loss = 0.385106 (* 1 = 0.385106 loss)
I0219 16:20:00.303769 20200 solver.cpp:470] Iteration 60200, lr = 0.0001
I0219 16:20:11.471721 20200 solver.cpp:189] Iteration 60400, loss = 0.338074
I0219 16:20:11.471743 20200 solver.cpp:204]     Train net output #0: loss = 0.338074 (* 1 = 0.338074 loss)
I0219 16:20:11.471791 20200 solver.cpp:470] Iteration 60400, lr = 0.0001
I0219 16:20:22.698717 20200 solver.cpp:189] Iteration 60600, loss = 0.417737
I0219 16:20:22.698782 20200 solver.cpp:204]     Train net output #0: loss = 0.417737 (* 1 = 0.417737 loss)
I0219 16:20:22.698787 20200 solver.cpp:470] Iteration 60600, lr = 0.0001
I0219 16:20:33.919265 20200 solver.cpp:189] Iteration 60800, loss = 0.333873
I0219 16:20:33.919286 20200 solver.cpp:204]     Train net output #0: loss = 0.333873 (* 1 = 0.333873 loss)
I0219 16:20:33.919289 20200 solver.cpp:470] Iteration 60800, lr = 0.0001
I0219 16:20:45.191696 20200 solver.cpp:266] Iteration 61000, Testing net (#0)
I0219 16:20:47.514690 20200 solver.cpp:315]     Test net output #0: accuracy = 0.8144
I0219 16:20:47.514874 20200 solver.cpp:315]     Test net output #1: loss = 0.544071 (* 1 = 0.544071 loss)
I0219 16:20:47.542877 20200 solver.cpp:189] Iteration 61000, loss = 0.323942
I0219 16:20:47.543022 20200 solver.cpp:204]     Train net output #0: loss = 0.323942 (* 1 = 0.323942 loss)
I0219 16:20:47.543072 20200 solver.cpp:470] Iteration 61000, lr = 0.0001
I0219 16:20:58.883936 20200 solver.cpp:189] Iteration 61200, loss = 0.386508
I0219 16:20:58.883997 20200 solver.cpp:204]     Train net output #0: loss = 0.386508 (* 1 = 0.386508 loss)
I0219 16:20:58.884003 20200 solver.cpp:470] Iteration 61200, lr = 0.0001
I0219 16:21:10.075959 20200 solver.cpp:189] Iteration 61400, loss = 0.330459
I0219 16:21:10.075981 20200 solver.cpp:204]     Train net output #0: loss = 0.330459 (* 1 = 0.330459 loss)
I0219 16:21:10.075986 20200 solver.cpp:470] Iteration 61400, lr = 0.0001
I0219 16:21:21.397155 20200 solver.cpp:189] Iteration 61600, loss = 0.411569
I0219 16:21:21.397177 20200 solver.cpp:204]     Train net output #0: loss = 0.411569 (* 1 = 0.411569 loss)
I0219 16:21:21.397181 20200 solver.cpp:470] Iteration 61600, lr = 0.0001
I0219 16:21:32.667414 20200 solver.cpp:189] Iteration 61800, loss = 0.329293
I0219 16:21:32.667474 20200 solver.cpp:204]     Train net output #0: loss = 0.329293 (* 1 = 0.329293 loss)
I0219 16:21:32.667479 20200 solver.cpp:470] Iteration 61800, lr = 0.0001
I0219 16:21:43.817139 20200 solver.cpp:266] Iteration 62000, Testing net (#0)
I0219 16:21:46.145761 20200 solver.cpp:315]     Test net output #0: accuracy = 0.8122
I0219 16:21:46.145803 20200 solver.cpp:315]     Test net output #1: loss = 0.544012 (* 1 = 0.544012 loss)
I0219 16:21:46.173409 20200 solver.cpp:189] Iteration 62000, loss = 0.319571
I0219 16:21:46.173455 20200 solver.cpp:204]     Train net output #0: loss = 0.319571 (* 1 = 0.319571 loss)
I0219 16:21:46.173461 20200 solver.cpp:470] Iteration 62000, lr = 0.0001
I0219 16:21:57.445032 20200 solver.cpp:189] Iteration 62200, loss = 0.383066
I0219 16:21:57.445057 20200 solver.cpp:204]     Train net output #0: loss = 0.383066 (* 1 = 0.383066 loss)
I0219 16:21:57.445061 20200 solver.cpp:470] Iteration 62200, lr = 0.0001
I0219 16:22:08.636569 20200 solver.cpp:189] Iteration 62400, loss = 0.327755
I0219 16:22:08.636615 20200 solver.cpp:204]     Train net output #0: loss = 0.327755 (* 1 = 0.327755 loss)
I0219 16:22:08.636620 20200 solver.cpp:470] Iteration 62400, lr = 0.0001
I0219 16:22:19.784279 20200 solver.cpp:189] Iteration 62600, loss = 0.4112
I0219 16:22:19.784301 20200 solver.cpp:204]     Train net output #0: loss = 0.4112 (* 1 = 0.4112 loss)
I0219 16:22:19.784306 20200 solver.cpp:470] Iteration 62600, lr = 0.0001
I0219 16:22:30.963759 20200 solver.cpp:189] Iteration 62800, loss = 0.32735
I0219 16:22:30.963783 20200 solver.cpp:204]     Train net output #0: loss = 0.32735 (* 1 = 0.32735 loss)
I0219 16:22:30.963788 20200 solver.cpp:470] Iteration 62800, lr = 0.0001
I0219 16:22:42.191521 20200 solver.cpp:266] Iteration 63000, Testing net (#0)
I0219 16:22:44.476992 20200 solver.cpp:315]     Test net output #0: accuracy = 0.8128
I0219 16:22:44.477030 20200 solver.cpp:315]     Test net output #1: loss = 0.543833 (* 1 = 0.543833 loss)
I0219 16:22:44.503412 20200 solver.cpp:189] Iteration 63000, loss = 0.316431
I0219 16:22:44.503449 20200 solver.cpp:204]     Train net output #0: loss = 0.316431 (* 1 = 0.316431 loss)
I0219 16:22:44.503454 20200 solver.cpp:470] Iteration 63000, lr = 0.0001
I0219 16:22:55.687024 20200 solver.cpp:189] Iteration 63200, loss = 0.379511
I0219 16:22:55.687054 20200 solver.cpp:204]     Train net output #0: loss = 0.379511 (* 1 = 0.379511 loss)
I0219 16:22:55.687058 20200 solver.cpp:470] Iteration 63200, lr = 0.0001
I0219 16:23:06.835247 20200 solver.cpp:189] Iteration 63400, loss = 0.325739
I0219 16:23:06.835269 20200 solver.cpp:204]     Train net output #0: loss = 0.325739 (* 1 = 0.325739 loss)
I0219 16:23:06.835273 20200 solver.cpp:470] Iteration 63400, lr = 0.0001
I0219 16:23:18.096074 20200 solver.cpp:189] Iteration 63600, loss = 0.40966
I0219 16:23:18.096174 20200 solver.cpp:204]     Train net output #0: loss = 0.40966 (* 1 = 0.40966 loss)
I0219 16:23:18.096189 20200 solver.cpp:470] Iteration 63600, lr = 0.0001
I0219 16:23:29.341202 20200 solver.cpp:189] Iteration 63800, loss = 0.325166
I0219 16:23:29.341224 20200 solver.cpp:204]     Train net output #0: loss = 0.325166 (* 1 = 0.325166 loss)
I0219 16:23:29.341229 20200 solver.cpp:470] Iteration 63800, lr = 0.0001
I0219 16:23:40.553455 20200 solver.cpp:266] Iteration 64000, Testing net (#0)
I0219 16:23:42.829855 20200 solver.cpp:315]     Test net output #0: accuracy = 0.8129
I0219 16:23:42.829890 20200 solver.cpp:315]     Test net output #1: loss = 0.543573 (* 1 = 0.543573 loss)
I0219 16:23:42.856636 20200 solver.cpp:189] Iteration 64000, loss = 0.314639
I0219 16:23:42.856657 20200 solver.cpp:204]     Train net output #0: loss = 0.314639 (* 1 = 0.314639 loss)
I0219 16:23:42.856662 20200 solver.cpp:470] Iteration 64000, lr = 0.0001
I0219 16:23:54.230015 20200 solver.cpp:189] Iteration 64200, loss = 0.376256
I0219 16:23:54.230077 20200 solver.cpp:204]     Train net output #0: loss = 0.376256 (* 1 = 0.376256 loss)
I0219 16:23:54.230083 20200 solver.cpp:470] Iteration 64200, lr = 0.0001
I0219 16:24:05.417397 20200 solver.cpp:189] Iteration 64400, loss = 0.323804
I0219 16:24:05.417433 20200 solver.cpp:204]     Train net output #0: loss = 0.323804 (* 1 = 0.323804 loss)
I0219 16:24:05.417439 20200 solver.cpp:470] Iteration 64400, lr = 0.0001
I0219 16:24:16.564348 20200 solver.cpp:189] Iteration 64600, loss = 0.408412
I0219 16:24:16.564380 20200 solver.cpp:204]     Train net output #0: loss = 0.408412 (* 1 = 0.408412 loss)
I0219 16:24:16.564385 20200 solver.cpp:470] Iteration 64600, lr = 0.0001
I0219 16:24:27.681190 20200 solver.cpp:189] Iteration 64800, loss = 0.322994
I0219 16:24:27.681248 20200 solver.cpp:204]     Train net output #0: loss = 0.322994 (* 1 = 0.322994 loss)
I0219 16:24:27.681253 20200 solver.cpp:470] Iteration 64800, lr = 0.0001
I0219 16:24:38.768090 20200 solver.cpp:334] Snapshotting to examples/cifar10/cifar10_full_iter_65000.caffemodel
I0219 16:24:38.768936 20200 solver.cpp:342] Snapshotting solver state to examples/cifar10/cifar10_full_iter_65000.solverstate
I0219 16:24:38.791339 20200 solver.cpp:248] Iteration 65000, loss = 0.313385
I0219 16:24:38.791353 20200 solver.cpp:266] Iteration 65000, Testing net (#0)
I0219 16:24:41.020370 20200 solver.cpp:315]     Test net output #0: accuracy = 0.8127
I0219 16:24:41.020390 20200 solver.cpp:315]     Test net output #1: loss = 0.543179 (* 1 = 0.543179 loss)
I0219 16:24:41.020395 20200 solver.cpp:253] Optimization Done.
I0219 16:24:41.020396 20200 caffe.cpp:121] Optimization Done.
I0219 16:24:41.065583 26813 caffe.cpp:99] Use GPU with device ID 0
I0219 16:24:41.174883 26813 caffe.cpp:107] Starting Optimization
I0219 16:24:41.175029 26813 solver.cpp:32] Initializing solver from parameters: 
test_iter: 100
test_interval: 1000
base_lr: 1e-05
display: 200
max_iter: 70000
lr_policy: "fixed"
momentum: 0.9
weight_decay: 0.004
snapshot: 5000
snapshot_prefix: "examples/cifar10/cifar10_full"
solver_mode: GPU
net: "examples/cifar10/cifar10_full_train_test.prototxt"
I0219 16:24:41.175060 26813 solver.cpp:70] Creating training net from net file: examples/cifar10/cifar10_full_train_test.prototxt
I0219 16:24:41.175364 26813 net.cpp:256] The NetState phase (0) differed from the phase (1) specified by a rule in layer cifar
I0219 16:24:41.175375 26813 net.cpp:256] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0219 16:24:41.175484 26813 net.cpp:45] Initializing net from parameters: 
name: "CIFAR10_full"
state {
  phase: TRAIN
}
layer {
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mean_file: "examples/cifar10/mean.binaryproto"
  }
  data_param {
    source: "examples/cifar10/cifar10_train_lmdb"
    batch_size: 100
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.0001
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "pool1"
  top: "pool1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "ip1"
  param {
    lr_mult: 1
    decay_mult: 250
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip1"
  bottom: "label"
  top: "loss"
}
I0219 16:24:41.175544 26813 layer_factory.hpp:74] Creating layer cifar
I0219 16:24:41.175556 26813 net.cpp:79] Creating Layer cifar
I0219 16:24:41.175561 26813 net.cpp:337] cifar -> data
I0219 16:24:41.175578 26813 net.cpp:337] cifar -> label
I0219 16:24:41.175585 26813 net.cpp:108] Setting up cifar
I0219 16:24:41.175636 26813 db.cpp:34] Opened lmdb examples/cifar10/cifar10_train_lmdb
I0219 16:24:41.175662 26813 data_layer.cpp:65] output data size: 100,3,32,32
I0219 16:24:41.175667 26813 data_transformer.cpp:59] Loading mean file from: examples/cifar10/mean.binaryproto
I0219 16:24:41.176096 26813 net.cpp:115] Top shape: 100 3 32 32 (307200)
I0219 16:24:41.176101 26813 net.cpp:115] Top shape: 100 1 1 1 (100)
I0219 16:24:41.176105 26813 layer_factory.hpp:74] Creating layer conv1
I0219 16:24:41.176112 26813 net.cpp:79] Creating Layer conv1
I0219 16:24:41.176126 26813 net.cpp:375] conv1 <- data
I0219 16:24:41.176141 26813 net.cpp:337] conv1 -> conv1
I0219 16:24:41.176147 26813 net.cpp:108] Setting up conv1
I0219 16:24:41.176617 26813 net.cpp:115] Top shape: 100 32 32 32 (3276800)
I0219 16:24:41.176646 26813 layer_factory.hpp:74] Creating layer pool1
I0219 16:24:41.176661 26813 net.cpp:79] Creating Layer pool1
I0219 16:24:41.176662 26813 net.cpp:375] pool1 <- conv1
I0219 16:24:41.176666 26813 net.cpp:337] pool1 -> pool1
I0219 16:24:41.176669 26813 net.cpp:108] Setting up pool1
I0219 16:24:41.176677 26813 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:24:41.176681 26813 layer_factory.hpp:74] Creating layer relu1
I0219 16:24:41.176686 26813 net.cpp:79] Creating Layer relu1
I0219 16:24:41.176687 26813 net.cpp:375] relu1 <- pool1
I0219 16:24:41.176690 26813 net.cpp:326] relu1 -> pool1 (in-place)
I0219 16:24:41.176692 26813 net.cpp:108] Setting up relu1
I0219 16:24:41.176695 26813 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:24:41.176697 26813 layer_factory.hpp:74] Creating layer norm1
I0219 16:24:41.176702 26813 net.cpp:79] Creating Layer norm1
I0219 16:24:41.176702 26813 net.cpp:375] norm1 <- pool1
I0219 16:24:41.176705 26813 net.cpp:337] norm1 -> norm1
I0219 16:24:41.176709 26813 net.cpp:108] Setting up norm1
I0219 16:24:41.176730 26813 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:24:41.176733 26813 layer_factory.hpp:74] Creating layer conv2
I0219 16:24:41.176746 26813 net.cpp:79] Creating Layer conv2
I0219 16:24:41.176748 26813 net.cpp:375] conv2 <- norm1
I0219 16:24:41.176750 26813 net.cpp:337] conv2 -> conv2
I0219 16:24:41.176764 26813 net.cpp:108] Setting up conv2
I0219 16:24:41.177386 26813 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:24:41.177392 26813 layer_factory.hpp:74] Creating layer relu2
I0219 16:24:41.177404 26813 net.cpp:79] Creating Layer relu2
I0219 16:24:41.177407 26813 net.cpp:375] relu2 <- conv2
I0219 16:24:41.177408 26813 net.cpp:326] relu2 -> conv2 (in-place)
I0219 16:24:41.177412 26813 net.cpp:108] Setting up relu2
I0219 16:24:41.177413 26813 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:24:41.177415 26813 layer_factory.hpp:74] Creating layer pool2
I0219 16:24:41.177418 26813 net.cpp:79] Creating Layer pool2
I0219 16:24:41.177420 26813 net.cpp:375] pool2 <- conv2
I0219 16:24:41.177423 26813 net.cpp:337] pool2 -> pool2
I0219 16:24:41.177425 26813 net.cpp:108] Setting up pool2
I0219 16:24:41.177428 26813 net.cpp:115] Top shape: 100 32 8 8 (204800)
I0219 16:24:41.177429 26813 layer_factory.hpp:74] Creating layer norm2
I0219 16:24:41.177433 26813 net.cpp:79] Creating Layer norm2
I0219 16:24:41.177435 26813 net.cpp:375] norm2 <- pool2
I0219 16:24:41.177438 26813 net.cpp:337] norm2 -> norm2
I0219 16:24:41.177440 26813 net.cpp:108] Setting up norm2
I0219 16:24:41.177448 26813 net.cpp:115] Top shape: 100 32 8 8 (204800)
I0219 16:24:41.177450 26813 layer_factory.hpp:74] Creating layer conv3
I0219 16:24:41.177453 26813 net.cpp:79] Creating Layer conv3
I0219 16:24:41.177455 26813 net.cpp:375] conv3 <- norm2
I0219 16:24:41.177458 26813 net.cpp:337] conv3 -> conv3
I0219 16:24:41.177460 26813 net.cpp:108] Setting up conv3
I0219 16:24:41.178550 26813 net.cpp:115] Top shape: 100 64 8 8 (409600)
I0219 16:24:41.178565 26813 layer_factory.hpp:74] Creating layer relu3
I0219 16:24:41.178568 26813 net.cpp:79] Creating Layer relu3
I0219 16:24:41.178570 26813 net.cpp:375] relu3 <- conv3
I0219 16:24:41.178572 26813 net.cpp:326] relu3 -> conv3 (in-place)
I0219 16:24:41.178585 26813 net.cpp:108] Setting up relu3
I0219 16:24:41.178586 26813 net.cpp:115] Top shape: 100 64 8 8 (409600)
I0219 16:24:41.178588 26813 layer_factory.hpp:74] Creating layer pool3
I0219 16:24:41.178591 26813 net.cpp:79] Creating Layer pool3
I0219 16:24:41.178603 26813 net.cpp:375] pool3 <- conv3
I0219 16:24:41.178608 26813 net.cpp:337] pool3 -> pool3
I0219 16:24:41.178611 26813 net.cpp:108] Setting up pool3
I0219 16:24:41.178613 26813 net.cpp:115] Top shape: 100 64 4 4 (102400)
I0219 16:24:41.178614 26813 layer_factory.hpp:74] Creating layer ip1
I0219 16:24:41.178619 26813 net.cpp:79] Creating Layer ip1
I0219 16:24:41.178622 26813 net.cpp:375] ip1 <- pool3
I0219 16:24:41.178624 26813 net.cpp:337] ip1 -> ip1
I0219 16:24:41.178629 26813 net.cpp:108] Setting up ip1
I0219 16:24:41.178890 26813 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0219 16:24:41.178895 26813 layer_factory.hpp:74] Creating layer loss
I0219 16:24:41.178910 26813 net.cpp:79] Creating Layer loss
I0219 16:24:41.178911 26813 net.cpp:375] loss <- ip1
I0219 16:24:41.178913 26813 net.cpp:375] loss <- label
I0219 16:24:41.178926 26813 net.cpp:337] loss -> loss
I0219 16:24:41.178930 26813 net.cpp:108] Setting up loss
I0219 16:24:41.178935 26813 layer_factory.hpp:74] Creating layer loss
I0219 16:24:41.178947 26813 net.cpp:115] Top shape: 1 1 1 1 (1)
I0219 16:24:41.178951 26813 net.cpp:121]     with loss weight 1
I0219 16:24:41.178971 26813 net.cpp:166] loss needs backward computation.
I0219 16:24:41.178982 26813 net.cpp:166] ip1 needs backward computation.
I0219 16:24:41.178984 26813 net.cpp:166] pool3 needs backward computation.
I0219 16:24:41.178985 26813 net.cpp:166] relu3 needs backward computation.
I0219 16:24:41.178987 26813 net.cpp:166] conv3 needs backward computation.
I0219 16:24:41.178988 26813 net.cpp:166] norm2 needs backward computation.
I0219 16:24:41.178990 26813 net.cpp:166] pool2 needs backward computation.
I0219 16:24:41.178992 26813 net.cpp:166] relu2 needs backward computation.
I0219 16:24:41.178993 26813 net.cpp:166] conv2 needs backward computation.
I0219 16:24:41.178995 26813 net.cpp:166] norm1 needs backward computation.
I0219 16:24:41.178997 26813 net.cpp:166] relu1 needs backward computation.
I0219 16:24:41.178998 26813 net.cpp:166] pool1 needs backward computation.
I0219 16:24:41.178999 26813 net.cpp:166] conv1 needs backward computation.
I0219 16:24:41.179002 26813 net.cpp:168] cifar does not need backward computation.
I0219 16:24:41.179003 26813 net.cpp:204] This network produces output loss
I0219 16:24:41.179010 26813 net.cpp:449] Collecting Learning Rate and Weight Decay.
I0219 16:24:41.179014 26813 net.cpp:216] Network initialization done.
I0219 16:24:41.179018 26813 net.cpp:217] Memory required for data: 36049204
I0219 16:24:41.179311 26813 solver.cpp:154] Creating test net (#0) specified by net file: examples/cifar10/cifar10_full_train_test.prototxt
I0219 16:24:41.179342 26813 net.cpp:256] The NetState phase (1) differed from the phase (0) specified by a rule in layer cifar
I0219 16:24:41.179440 26813 net.cpp:45] Initializing net from parameters: 
name: "CIFAR10_full"
state {
  phase: TEST
}
layer {
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mean_file: "examples/cifar10/mean.binaryproto"
  }
  data_param {
    source: "examples/cifar10/cifar10_test_lmdb"
    batch_size: 100
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.0001
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "pool1"
  top: "pool1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 3
    alpha: 5e-05
    beta: 0.75
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "ip1"
  param {
    lr_mult: 1
    decay_mult: 250
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "ip1"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip1"
  bottom: "label"
  top: "loss"
}
I0219 16:24:41.179514 26813 layer_factory.hpp:74] Creating layer cifar
I0219 16:24:41.179520 26813 net.cpp:79] Creating Layer cifar
I0219 16:24:41.179523 26813 net.cpp:337] cifar -> data
I0219 16:24:41.179527 26813 net.cpp:337] cifar -> label
I0219 16:24:41.179532 26813 net.cpp:108] Setting up cifar
I0219 16:24:41.179571 26813 db.cpp:34] Opened lmdb examples/cifar10/cifar10_test_lmdb
I0219 16:24:41.179587 26813 data_layer.cpp:65] output data size: 100,3,32,32
I0219 16:24:41.179590 26813 data_transformer.cpp:59] Loading mean file from: examples/cifar10/mean.binaryproto
I0219 16:24:41.179932 26813 net.cpp:115] Top shape: 100 3 32 32 (307200)
I0219 16:24:41.179937 26813 net.cpp:115] Top shape: 100 1 1 1 (100)
I0219 16:24:41.179940 26813 layer_factory.hpp:74] Creating layer label_cifar_1_split
I0219 16:24:41.179960 26813 net.cpp:79] Creating Layer label_cifar_1_split
I0219 16:24:41.179961 26813 net.cpp:375] label_cifar_1_split <- label
I0219 16:24:41.179965 26813 net.cpp:337] label_cifar_1_split -> label_cifar_1_split_0
I0219 16:24:41.179968 26813 net.cpp:337] label_cifar_1_split -> label_cifar_1_split_1
I0219 16:24:41.179975 26813 net.cpp:108] Setting up label_cifar_1_split
I0219 16:24:41.179976 26813 net.cpp:115] Top shape: 100 1 1 1 (100)
I0219 16:24:41.179978 26813 net.cpp:115] Top shape: 100 1 1 1 (100)
I0219 16:24:41.179980 26813 layer_factory.hpp:74] Creating layer conv1
I0219 16:24:41.179987 26813 net.cpp:79] Creating Layer conv1
I0219 16:24:41.179990 26813 net.cpp:375] conv1 <- data
I0219 16:24:41.179992 26813 net.cpp:337] conv1 -> conv1
I0219 16:24:41.179996 26813 net.cpp:108] Setting up conv1
I0219 16:24:41.180071 26813 net.cpp:115] Top shape: 100 32 32 32 (3276800)
I0219 16:24:41.180078 26813 layer_factory.hpp:74] Creating layer pool1
I0219 16:24:41.180093 26813 net.cpp:79] Creating Layer pool1
I0219 16:24:41.180094 26813 net.cpp:375] pool1 <- conv1
I0219 16:24:41.180097 26813 net.cpp:337] pool1 -> pool1
I0219 16:24:41.180100 26813 net.cpp:108] Setting up pool1
I0219 16:24:41.180104 26813 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:24:41.180105 26813 layer_factory.hpp:74] Creating layer relu1
I0219 16:24:41.180107 26813 net.cpp:79] Creating Layer relu1
I0219 16:24:41.180109 26813 net.cpp:375] relu1 <- pool1
I0219 16:24:41.180111 26813 net.cpp:326] relu1 -> pool1 (in-place)
I0219 16:24:41.180114 26813 net.cpp:108] Setting up relu1
I0219 16:24:41.180116 26813 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:24:41.180127 26813 layer_factory.hpp:74] Creating layer norm1
I0219 16:24:41.180131 26813 net.cpp:79] Creating Layer norm1
I0219 16:24:41.180133 26813 net.cpp:375] norm1 <- pool1
I0219 16:24:41.180135 26813 net.cpp:337] norm1 -> norm1
I0219 16:24:41.180138 26813 net.cpp:108] Setting up norm1
I0219 16:24:41.180147 26813 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:24:41.180150 26813 layer_factory.hpp:74] Creating layer conv2
I0219 16:24:41.180152 26813 net.cpp:79] Creating Layer conv2
I0219 16:24:41.180153 26813 net.cpp:375] conv2 <- norm1
I0219 16:24:41.180157 26813 net.cpp:337] conv2 -> conv2
I0219 16:24:41.180160 26813 net.cpp:108] Setting up conv2
I0219 16:24:41.180699 26813 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:24:41.180704 26813 layer_factory.hpp:74] Creating layer relu2
I0219 16:24:41.180707 26813 net.cpp:79] Creating Layer relu2
I0219 16:24:41.180718 26813 net.cpp:375] relu2 <- conv2
I0219 16:24:41.180722 26813 net.cpp:326] relu2 -> conv2 (in-place)
I0219 16:24:41.180726 26813 net.cpp:108] Setting up relu2
I0219 16:24:41.180727 26813 net.cpp:115] Top shape: 100 32 16 16 (819200)
I0219 16:24:41.180728 26813 layer_factory.hpp:74] Creating layer pool2
I0219 16:24:41.180732 26813 net.cpp:79] Creating Layer pool2
I0219 16:24:41.180733 26813 net.cpp:375] pool2 <- conv2
I0219 16:24:41.180735 26813 net.cpp:337] pool2 -> pool2
I0219 16:24:41.180739 26813 net.cpp:108] Setting up pool2
I0219 16:24:41.180742 26813 net.cpp:115] Top shape: 100 32 8 8 (204800)
I0219 16:24:41.180743 26813 layer_factory.hpp:74] Creating layer norm2
I0219 16:24:41.180747 26813 net.cpp:79] Creating Layer norm2
I0219 16:24:41.180747 26813 net.cpp:375] norm2 <- pool2
I0219 16:24:41.180750 26813 net.cpp:337] norm2 -> norm2
I0219 16:24:41.180753 26813 net.cpp:108] Setting up norm2
I0219 16:24:41.180763 26813 net.cpp:115] Top shape: 100 32 8 8 (204800)
I0219 16:24:41.180768 26813 layer_factory.hpp:74] Creating layer conv3
I0219 16:24:41.180769 26813 net.cpp:79] Creating Layer conv3
I0219 16:24:41.180773 26813 net.cpp:375] conv3 <- norm2
I0219 16:24:41.180774 26813 net.cpp:337] conv3 -> conv3
I0219 16:24:41.180778 26813 net.cpp:108] Setting up conv3
I0219 16:24:41.181851 26813 net.cpp:115] Top shape: 100 64 8 8 (409600)
I0219 16:24:41.181866 26813 layer_factory.hpp:74] Creating layer relu3
I0219 16:24:41.181869 26813 net.cpp:79] Creating Layer relu3
I0219 16:24:41.181871 26813 net.cpp:375] relu3 <- conv3
I0219 16:24:41.181874 26813 net.cpp:326] relu3 -> conv3 (in-place)
I0219 16:24:41.181875 26813 net.cpp:108] Setting up relu3
I0219 16:24:41.181887 26813 net.cpp:115] Top shape: 100 64 8 8 (409600)
I0219 16:24:41.181890 26813 layer_factory.hpp:74] Creating layer pool3
I0219 16:24:41.181892 26813 net.cpp:79] Creating Layer pool3
I0219 16:24:41.181895 26813 net.cpp:375] pool3 <- conv3
I0219 16:24:41.181897 26813 net.cpp:337] pool3 -> pool3
I0219 16:24:41.181900 26813 net.cpp:108] Setting up pool3
I0219 16:24:41.181901 26813 net.cpp:115] Top shape: 100 64 4 4 (102400)
I0219 16:24:41.181903 26813 layer_factory.hpp:74] Creating layer ip1
I0219 16:24:41.181906 26813 net.cpp:79] Creating Layer ip1
I0219 16:24:41.181908 26813 net.cpp:375] ip1 <- pool3
I0219 16:24:41.181912 26813 net.cpp:337] ip1 -> ip1
I0219 16:24:41.181915 26813 net.cpp:108] Setting up ip1
I0219 16:24:41.182162 26813 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0219 16:24:41.182173 26813 layer_factory.hpp:74] Creating layer ip1_ip1_0_split
I0219 16:24:41.182184 26813 net.cpp:79] Creating Layer ip1_ip1_0_split
I0219 16:24:41.182186 26813 net.cpp:375] ip1_ip1_0_split <- ip1
I0219 16:24:41.182189 26813 net.cpp:337] ip1_ip1_0_split -> ip1_ip1_0_split_0
I0219 16:24:41.182193 26813 net.cpp:337] ip1_ip1_0_split -> ip1_ip1_0_split_1
I0219 16:24:41.182205 26813 net.cpp:108] Setting up ip1_ip1_0_split
I0219 16:24:41.182207 26813 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0219 16:24:41.182209 26813 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0219 16:24:41.182210 26813 layer_factory.hpp:74] Creating layer accuracy
I0219 16:24:41.182219 26813 net.cpp:79] Creating Layer accuracy
I0219 16:24:41.182227 26813 net.cpp:375] accuracy <- ip1_ip1_0_split_0
I0219 16:24:41.182229 26813 net.cpp:375] accuracy <- label_cifar_1_split_0
I0219 16:24:41.182232 26813 net.cpp:337] accuracy -> accuracy
I0219 16:24:41.182235 26813 net.cpp:108] Setting up accuracy
I0219 16:24:41.182240 26813 net.cpp:115] Top shape: 1 1 1 1 (1)
I0219 16:24:41.182241 26813 layer_factory.hpp:74] Creating layer loss
I0219 16:24:41.182245 26813 net.cpp:79] Creating Layer loss
I0219 16:24:41.182247 26813 net.cpp:375] loss <- ip1_ip1_0_split_1
I0219 16:24:41.182250 26813 net.cpp:375] loss <- label_cifar_1_split_1
I0219 16:24:41.182251 26813 net.cpp:337] loss -> loss
I0219 16:24:41.182255 26813 net.cpp:108] Setting up loss
I0219 16:24:41.182257 26813 layer_factory.hpp:74] Creating layer loss
I0219 16:24:41.182265 26813 net.cpp:115] Top shape: 1 1 1 1 (1)
I0219 16:24:41.182266 26813 net.cpp:121]     with loss weight 1
I0219 16:24:41.182271 26813 net.cpp:166] loss needs backward computation.
I0219 16:24:41.182273 26813 net.cpp:168] accuracy does not need backward computation.
I0219 16:24:41.182276 26813 net.cpp:166] ip1_ip1_0_split needs backward computation.
I0219 16:24:41.182276 26813 net.cpp:166] ip1 needs backward computation.
I0219 16:24:41.182278 26813 net.cpp:166] pool3 needs backward computation.
I0219 16:24:41.182281 26813 net.cpp:166] relu3 needs backward computation.
I0219 16:24:41.182281 26813 net.cpp:166] conv3 needs backward computation.
I0219 16:24:41.182283 26813 net.cpp:166] norm2 needs backward computation.
I0219 16:24:41.182286 26813 net.cpp:166] pool2 needs backward computation.
I0219 16:24:41.182287 26813 net.cpp:166] relu2 needs backward computation.
I0219 16:24:41.182288 26813 net.cpp:166] conv2 needs backward computation.
I0219 16:24:41.182291 26813 net.cpp:166] norm1 needs backward computation.
I0219 16:24:41.182291 26813 net.cpp:166] relu1 needs backward computation.
I0219 16:24:41.182293 26813 net.cpp:166] pool1 needs backward computation.
I0219 16:24:41.182296 26813 net.cpp:166] conv1 needs backward computation.
I0219 16:24:41.182296 26813 net.cpp:168] label_cifar_1_split does not need backward computation.
I0219 16:24:41.182298 26813 net.cpp:168] cifar does not need backward computation.
I0219 16:24:41.182301 26813 net.cpp:204] This network produces output accuracy
I0219 16:24:41.182301 26813 net.cpp:204] This network produces output loss
I0219 16:24:41.182312 26813 net.cpp:449] Collecting Learning Rate and Weight Decay.
I0219 16:24:41.182314 26813 net.cpp:216] Network initialization done.
I0219 16:24:41.182317 26813 net.cpp:217] Memory required for data: 36058008
I0219 16:24:41.182358 26813 solver.cpp:42] Solver scaffolding done.
I0219 16:24:41.182384 26813 caffe.cpp:112] Resuming from examples/cifar10/cifar10_full_iter_65000.solverstate
I0219 16:24:41.182397 26813 solver.cpp:222] Solving CIFAR10_full
I0219 16:24:41.182399 26813 solver.cpp:223] Learning Rate Policy: fixed
I0219 16:24:41.182400 26813 solver.cpp:226] Restoring previous solver status from examples/cifar10/cifar10_full_iter_65000.solverstate
I0219 16:24:41.183452 26813 solver.cpp:570] SGDSolver: restoring history
I0219 16:24:41.183595 26813 solver.cpp:266] Iteration 65000, Testing net (#0)
I0219 16:24:43.413979 26813 solver.cpp:315]     Test net output #0: accuracy = 0.8127
I0219 16:24:43.414011 26813 solver.cpp:315]     Test net output #1: loss = 0.543179 (* 1 = 0.543179 loss)
I0219 16:24:43.441825 26813 solver.cpp:189] Iteration 65000, loss = 0.313385
I0219 16:24:43.441858 26813 solver.cpp:204]     Train net output #0: loss = 0.313385 (* 1 = 0.313385 loss)
I0219 16:24:43.441864 26813 solver.cpp:470] Iteration 65000, lr = 1e-05
I0219 16:24:54.605008 26813 solver.cpp:189] Iteration 65200, loss = 0.359124
I0219 16:24:54.605073 26813 solver.cpp:204]     Train net output #0: loss = 0.359124 (* 1 = 0.359124 loss)
I0219 16:24:54.605087 26813 solver.cpp:470] Iteration 65200, lr = 1e-05
I0219 16:25:05.990077 26813 solver.cpp:189] Iteration 65400, loss = 0.303015
I0219 16:25:05.990099 26813 solver.cpp:204]     Train net output #0: loss = 0.303015 (* 1 = 0.303015 loss)
I0219 16:25:05.990136 26813 solver.cpp:470] Iteration 65400, lr = 1e-05
I0219 16:25:17.553380 26813 solver.cpp:189] Iteration 65600, loss = 0.369056
I0219 16:25:17.553586 26813 solver.cpp:204]     Train net output #0: loss = 0.369056 (* 1 = 0.369056 loss)
I0219 16:25:17.553654 26813 solver.cpp:470] Iteration 65600, lr = 1e-05
I0219 16:25:28.771941 26813 solver.cpp:189] Iteration 65800, loss = 0.32488
I0219 16:25:28.772094 26813 solver.cpp:204]     Train net output #0: loss = 0.32488 (* 1 = 0.32488 loss)
I0219 16:25:28.772145 26813 solver.cpp:470] Iteration 65800, lr = 1e-05
I0219 16:25:40.208818 26813 solver.cpp:266] Iteration 66000, Testing net (#0)
I0219 16:25:42.656963 26813 solver.cpp:315]     Test net output #0: accuracy = 0.8154
I0219 16:25:42.657021 26813 solver.cpp:315]     Test net output #1: loss = 0.534387 (* 1 = 0.534387 loss)
I0219 16:25:42.686127 26813 solver.cpp:189] Iteration 66000, loss = 0.317802
I0219 16:25:42.686187 26813 solver.cpp:204]     Train net output #0: loss = 0.317802 (* 1 = 0.317802 loss)
I0219 16:25:42.686203 26813 solver.cpp:470] Iteration 66000, lr = 1e-05
I0219 16:25:54.106515 26813 solver.cpp:189] Iteration 66200, loss = 0.356099
I0219 16:25:54.106714 26813 solver.cpp:204]     Train net output #0: loss = 0.356099 (* 1 = 0.356099 loss)
I0219 16:25:54.106786 26813 solver.cpp:470] Iteration 66200, lr = 1e-05
I0219 16:26:05.305729 26813 solver.cpp:189] Iteration 66400, loss = 0.304069
I0219 16:26:05.305809 26813 solver.cpp:204]     Train net output #0: loss = 0.304069 (* 1 = 0.304069 loss)
I0219 16:26:05.305829 26813 solver.cpp:470] Iteration 66400, lr = 1e-05
I0219 16:26:16.462848 26813 solver.cpp:189] Iteration 66600, loss = 0.365986
I0219 16:26:16.462918 26813 solver.cpp:204]     Train net output #0: loss = 0.365986 (* 1 = 0.365986 loss)
I0219 16:26:16.462931 26813 solver.cpp:470] Iteration 66600, lr = 1e-05
I0219 16:26:27.743458 26813 solver.cpp:189] Iteration 66800, loss = 0.322575
I0219 16:26:27.745216 26813 solver.cpp:204]     Train net output #0: loss = 0.322575 (* 1 = 0.322575 loss)
I0219 16:26:27.745236 26813 solver.cpp:470] Iteration 66800, lr = 1e-05
I0219 16:26:38.840087 26813 solver.cpp:266] Iteration 67000, Testing net (#0)
I0219 16:26:41.286458 26813 solver.cpp:315]     Test net output #0: accuracy = 0.8146
I0219 16:26:41.286532 26813 solver.cpp:315]     Test net output #1: loss = 0.534173 (* 1 = 0.534173 loss)
I0219 16:26:41.312939 26813 solver.cpp:189] Iteration 67000, loss = 0.317592
I0219 16:26:41.313009 26813 solver.cpp:204]     Train net output #0: loss = 0.317592 (* 1 = 0.317592 loss)
I0219 16:26:41.313030 26813 solver.cpp:470] Iteration 67000, lr = 1e-05
I0219 16:26:52.560382 26813 solver.cpp:189] Iteration 67200, loss = 0.355667
I0219 16:26:52.560443 26813 solver.cpp:204]     Train net output #0: loss = 0.355667 (* 1 = 0.355667 loss)
I0219 16:26:52.560457 26813 solver.cpp:470] Iteration 67200, lr = 1e-05
I0219 16:27:03.911736 26813 solver.cpp:189] Iteration 67400, loss = 0.305104
I0219 16:27:03.911850 26813 solver.cpp:204]     Train net output #0: loss = 0.305104 (* 1 = 0.305104 loss)
I0219 16:27:03.911866 26813 solver.cpp:470] Iteration 67400, lr = 1e-05
I0219 16:27:15.104290 26813 solver.cpp:189] Iteration 67600, loss = 0.364202
I0219 16:27:15.104313 26813 solver.cpp:204]     Train net output #0: loss = 0.364202 (* 1 = 0.364202 loss)
I0219 16:27:15.104317 26813 solver.cpp:470] Iteration 67600, lr = 1e-05
I0219 16:27:26.369868 26813 solver.cpp:189] Iteration 67800, loss = 0.322271
I0219 16:27:26.369894 26813 solver.cpp:204]     Train net output #0: loss = 0.322271 (* 1 = 0.322271 loss)
I0219 16:27:26.369897 26813 solver.cpp:470] Iteration 67800, lr = 1e-05
I0219 16:27:37.666800 26813 solver.cpp:266] Iteration 68000, Testing net (#0)
I0219 16:27:39.999320 26813 solver.cpp:315]     Test net output #0: accuracy = 0.8147
I0219 16:27:39.999388 26813 solver.cpp:315]     Test net output #1: loss = 0.534085 (* 1 = 0.534085 loss)
I0219 16:27:40.026810 26813 solver.cpp:189] Iteration 68000, loss = 0.317075
I0219 16:27:40.026834 26813 solver.cpp:204]     Train net output #0: loss = 0.317075 (* 1 = 0.317075 loss)
I0219 16:27:40.026839 26813 solver.cpp:470] Iteration 68000, lr = 1e-05
I0219 16:27:51.222507 26813 solver.cpp:189] Iteration 68200, loss = 0.355715
I0219 16:27:51.222542 26813 solver.cpp:204]     Train net output #0: loss = 0.355715 (* 1 = 0.355715 loss)
I0219 16:27:51.222547 26813 solver.cpp:470] Iteration 68200, lr = 1e-05
I0219 16:28:02.308022 26813 solver.cpp:189] Iteration 68400, loss = 0.305655
I0219 16:28:02.308053 26813 solver.cpp:204]     Train net output #0: loss = 0.305655 (* 1 = 0.305655 loss)
I0219 16:28:02.308058 26813 solver.cpp:470] Iteration 68400, lr = 1e-05
I0219 16:28:13.385090 26813 solver.cpp:189] Iteration 68600, loss = 0.363122
I0219 16:28:13.385169 26813 solver.cpp:204]     Train net output #0: loss = 0.363122 (* 1 = 0.363122 loss)
I0219 16:28:13.385175 26813 solver.cpp:470] Iteration 68600, lr = 1e-05
I0219 16:28:24.765671 26813 solver.cpp:189] Iteration 68800, loss = 0.322381
I0219 16:28:24.765714 26813 solver.cpp:204]     Train net output #0: loss = 0.322381 (* 1 = 0.322381 loss)
I0219 16:28:24.765719 26813 solver.cpp:470] Iteration 68800, lr = 1e-05
I0219 16:28:35.943213 26813 solver.cpp:266] Iteration 69000, Testing net (#0)
I0219 16:28:38.237635 26813 solver.cpp:315]     Test net output #0: accuracy = 0.8152
I0219 16:28:38.237720 26813 solver.cpp:315]     Test net output #1: loss = 0.534047 (* 1 = 0.534047 loss)
I0219 16:28:38.264847 26813 solver.cpp:189] Iteration 69000, loss = 0.316688
I0219 16:28:38.264909 26813 solver.cpp:204]     Train net output #0: loss = 0.316688 (* 1 = 0.316688 loss)
I0219 16:28:38.264924 26813 solver.cpp:470] Iteration 69000, lr = 1e-05
I0219 16:28:49.495754 26813 solver.cpp:189] Iteration 69200, loss = 0.355948
I0219 16:28:49.495852 26813 solver.cpp:204]     Train net output #0: loss = 0.355948 (* 1 = 0.355948 loss)
I0219 16:28:49.495867 26813 solver.cpp:470] Iteration 69200, lr = 1e-05
I0219 16:29:00.759639 26813 solver.cpp:189] Iteration 69400, loss = 0.305905
I0219 16:29:00.759677 26813 solver.cpp:204]     Train net output #0: loss = 0.305905 (* 1 = 0.305905 loss)
I0219 16:29:00.759682 26813 solver.cpp:470] Iteration 69400, lr = 1e-05
I0219 16:29:11.968766 26813 solver.cpp:189] Iteration 69600, loss = 0.362566
I0219 16:29:11.968839 26813 solver.cpp:204]     Train net output #0: loss = 0.362566 (* 1 = 0.362566 loss)
I0219 16:29:11.968854 26813 solver.cpp:470] Iteration 69600, lr = 1e-05
I0219 16:29:23.221169 26813 solver.cpp:189] Iteration 69800, loss = 0.322597
I0219 16:29:23.221267 26813 solver.cpp:204]     Train net output #0: loss = 0.322597 (* 1 = 0.322597 loss)
I0219 16:29:23.221282 26813 solver.cpp:470] Iteration 69800, lr = 1e-05
I0219 16:29:34.393460 26813 solver.cpp:334] Snapshotting to examples/cifar10/cifar10_full_iter_70000.caffemodel
I0219 16:29:34.394340 26813 solver.cpp:342] Snapshotting solver state to examples/cifar10/cifar10_full_iter_70000.solverstate
I0219 16:29:34.416803 26813 solver.cpp:248] Iteration 70000, loss = 0.316409
I0219 16:29:34.416817 26813 solver.cpp:266] Iteration 70000, Testing net (#0)
I0219 16:29:36.639989 26813 solver.cpp:315]     Test net output #0: accuracy = 0.8155
I0219 16:29:36.640017 26813 solver.cpp:315]     Test net output #1: loss = 0.534017 (* 1 = 0.534017 loss)
I0219 16:29:36.640019 26813 solver.cpp:253] Optimization Done.
I0219 16:29:36.640022 26813 caffe.cpp:121] Optimization Done.

cifar_vgg16_bn_baselr=0.001.log

I0224 19:35:51.926918 29812 caffe.cpp:99] Use GPU with device ID 0
I0224 19:35:52.028787 29812 caffe.cpp:107] Starting Optimization
I0224 19:35:52.028867 29812 solver.cpp:32] Initializing solver from parameters: 
test_iter: 100
test_interval: 1000
base_lr: 0.001
display: 50
max_iter: 300000
lr_policy: "step"
gamma: 0.7
momentum: 0.9
weight_decay: 0.004
stepsize: 20000
snapshot: 1000
snapshot_prefix: "examples/cifar10_vgg/vgg16-msr_bn"
solver_mode: GPU
net: "examples/cifar10_vgg/vgg16-msr_bn.prototxt"
I0224 19:35:52.028908 29812 solver.cpp:70] Creating training net from net file: examples/cifar10_vgg/vgg16-msr_bn.prototxt
I0224 19:35:52.029892 29812 net.cpp:256] The NetState phase (0) differed from the phase (1) specified by a rule in layer cifar
I0224 19:35:52.029920 29812 net.cpp:256] The NetState phase (0) differed from the phase (1) specified by a rule in layer fc7/acc
I0224 19:35:52.030184 29812 net.cpp:45] Initializing net from parameters: 
name: "VGG_ILSVRC_16_layers_bn"
state {
  phase: TRAIN
}
layer {
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    mean_file: "examples/cifar10/mean.binaryproto"
    vertical_mirror: true
    random_90_deg_rot: true
  }
  data_param {
    source: "examples/cifar10/cifar10_train_lmdb"
    batch_size: 100
    backend: LMDB
    shuffle_buffer_size: 500
  }
}
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn1"
  type: "BN"
  bottom: "conv1_1"
  top: "bn1"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu1_1"
  type: "PReLU"
  bottom: "bn1"
  top: "relu1_1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv1_1"
  type: "Dropout"
  bottom: "relu1_1"
  top: "do1_1"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "do1_1"
  top: "conv1_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn1_2"
  type: "BN"
  bottom: "conv1_2"
  top: "bn1_2"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu1_2"
  type: "PReLU"
  bottom: "bn1_2"
  top: "relu1_2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "relu1_2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    engine: DEFAULT
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn2_1"
  type: "BN"
  bottom: "conv2_1"
  top: "bn2_1"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu2_1"
  type: "PReLU"
  bottom: "bn2_1"
  top: "relu2_1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv2_1"
  type: "Dropout"
  bottom: "relu2_1"
  top: "do2_1"
  dropout_param {
    dropout_ratio: 0.3
  }
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "do2_1"
  top: "conv2_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn2_2"
  type: "BN"
  bottom: "conv2_2"
  top: "bn2_2"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu2_2"
  type: "PReLU"
  bottom: "bn2_2"
  top: "relu2_2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "relu2_2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    engine: DEFAULT
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn3_1"
  type: "BN"
  bottom: "conv3_1"
  top: "bn3_1"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu3_1"
  type: "PReLU"
  bottom: "bn3_1"
  top: "relu3_1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv3_1"
  type: "Dropout"
  bottom: "relu3_1"
  top: "do3_1"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "do3_1"
  top: "conv3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn3_2"
  type: "BN"
  bottom: "conv3_2"
  top: "bn3_2"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu3_2"
  type: "PReLU"
  bottom: "bn3_2"
  top: "relu3_2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "relu3_2"
  top: "conv3_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn3_3"
  type: "BN"
  bottom: "conv3_3"
  top: "bn3_3"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu3_3"
  type: "PReLU"
  bottom: "bn3_3"
  top: "relu3_3"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv3_2"
  type: "Dropout"
  bottom: "relu3_3"
  top: "relu3_3"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "relu3_3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    engine: DEFAULT
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn4_1"
  type: "BN"
  bottom: "conv4_1"
  top: "bn4_1"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu4_1"
  type: "PReLU"
  bottom: "bn4_1"
  top: "relu4_1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv4_1"
  type: "Dropout"
  bottom: "relu4_1"
  top: "do4_1"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "do4_1"
  top: "conv4_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn4_2"
  type: "BN"
  bottom: "conv4_2"
  top: "bn4_2"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu4_2"
  type: "PReLU"
  bottom: "bn4_2"
  top: "relu4_2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv4_2"
  type: "Dropout"
  bottom: "relu4_2"
  top: "do4_2"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "do4_2"
  top: "conv4_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn4_3"
  type: "BN"
  bottom: "conv4_3"
  top: "bn4_3"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu4_3"
  type: "PReLU"
  bottom: "bn4_3"
  top: "relu4_3"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "relu4_3"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    engine: DEFAULT
  }
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn5_1"
  type: "BN"
  bottom: "conv5_1"
  top: "bn5_1"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu5_1"
  type: "PReLU"
  bottom: "bn5_1"
  top: "relu5_1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv5_1"
  type: "Dropout"
  bottom: "relu5_1"
  top: "do5_1"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "do5_1"
  top: "conv5_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn5_2"
  type: "BN"
  bottom: "conv5_2"
  top: "bn5_2"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu5_2"
  type: "PReLU"
  bottom: "bn5_2"
  top: "relu5_2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv5_2"
  type: "Dropout"
  bottom: "relu5_2"
  top: "do5_2"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "do5_2"
  top: "conv5_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn5_3"
  type: "BN"
  bottom: "conv5_3"
  top: "bn5_3"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu5_3"
  type: "PReLU"
  bottom: "bn5_3"
  top: "relu5_3"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "relu5_3"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    engine: DEFAULT
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 100
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "bn"
  type: "BN"
  bottom: "fc6"
  top: "bn6"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu6"
  type: "PReLU"
  bottom: "bn6"
  top: "relu6"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "relu6"
  top: "do6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "do6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "fc7/loss3"
  type: "SoftmaxWithLoss"
  bottom: "fc7"
  bottom: "label"
  top: "fc7/loss3"
  loss_weight: 1
}
I0224 19:35:52.030345 29812 layer_factory.hpp:74] Creating layer cifar
I0224 19:35:52.030359 29812 net.cpp:79] Creating Layer cifar
I0224 19:35:52.030364 29812 net.cpp:337] cifar -> data
I0224 19:35:52.030382 29812 net.cpp:337] cifar -> label
I0224 19:35:52.030390 29812 net.cpp:108] Setting up cifar
I0224 19:35:52.030444 29812 db.cpp:34] Opened lmdb examples/cifar10/cifar10_train_lmdb
I0224 19:35:52.030478 29812 data_layer.cpp:79] output data size: 100,3,32,32
I0224 19:35:52.030485 29812 data_transformer.cpp:59] Loading mean file from: examples/cifar10/mean.binaryproto
I0224 19:35:52.031218 29812 net.cpp:115] Top shape: 100 3 32 32 (307200)
I0224 19:35:52.031224 29812 net.cpp:115] Top shape: 100 1 1 1 (100)
I0224 19:35:52.031229 29812 layer_factory.hpp:74] Creating layer conv1_1
I0224 19:35:52.031236 29812 net.cpp:79] Creating Layer conv1_1
I0224 19:35:52.031240 29812 net.cpp:375] conv1_1 <- data
I0224 19:35:52.031250 29812 net.cpp:337] conv1_1 -> conv1_1
I0224 19:35:52.031256 29812 net.cpp:108] Setting up conv1_1
I0224 19:35:52.051120 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.051195 29812 layer_factory.hpp:74] Creating layer bn1
I0224 19:35:52.051225 29812 net.cpp:79] Creating Layer bn1
I0224 19:35:52.051230 29812 net.cpp:375] bn1 <- conv1_1
I0224 19:35:52.051239 29812 net.cpp:337] bn1 -> bn1
I0224 19:35:52.051250 29812 net.cpp:108] Setting up bn1
I0224 19:35:52.051275 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.051281 29812 layer_factory.hpp:74] Creating layer relu1_1
I0224 19:35:52.051291 29812 net.cpp:79] Creating Layer relu1_1
I0224 19:35:52.051295 29812 net.cpp:375] relu1_1 <- bn1
I0224 19:35:52.051297 29812 net.cpp:337] relu1_1 -> relu1_1
I0224 19:35:52.051301 29812 net.cpp:108] Setting up relu1_1
I0224 19:35:52.051475 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.051499 29812 layer_factory.hpp:74] Creating layer drop_conv1_1
I0224 19:35:52.051508 29812 net.cpp:79] Creating Layer drop_conv1_1
I0224 19:35:52.051512 29812 net.cpp:375] drop_conv1_1 <- relu1_1
I0224 19:35:52.051524 29812 net.cpp:337] drop_conv1_1 -> do1_1
I0224 19:35:52.051532 29812 net.cpp:108] Setting up drop_conv1_1
I0224 19:35:52.051539 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.051543 29812 layer_factory.hpp:74] Creating layer conv1_2
I0224 19:35:52.051550 29812 net.cpp:79] Creating Layer conv1_2
I0224 19:35:52.051554 29812 net.cpp:375] conv1_2 <- do1_1
I0224 19:35:52.051558 29812 net.cpp:337] conv1_2 -> conv1_2
I0224 19:35:52.051564 29812 net.cpp:108] Setting up conv1_2
I0224 19:35:52.052619 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.052667 29812 layer_factory.hpp:74] Creating layer bn1_2
I0224 19:35:52.052685 29812 net.cpp:79] Creating Layer bn1_2
I0224 19:35:52.052688 29812 net.cpp:375] bn1_2 <- conv1_2
I0224 19:35:52.052701 29812 net.cpp:337] bn1_2 -> bn1_2
I0224 19:35:52.052722 29812 net.cpp:108] Setting up bn1_2
I0224 19:35:52.052736 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.052743 29812 layer_factory.hpp:74] Creating layer relu1_2
I0224 19:35:52.052752 29812 net.cpp:79] Creating Layer relu1_2
I0224 19:35:52.052763 29812 net.cpp:375] relu1_2 <- bn1_2
I0224 19:35:52.052767 29812 net.cpp:337] relu1_2 -> relu1_2
I0224 19:35:52.052772 29812 net.cpp:108] Setting up relu1_2
I0224 19:35:52.052975 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.053004 29812 layer_factory.hpp:74] Creating layer pool1
I0224 19:35:52.053024 29812 net.cpp:79] Creating Layer pool1
I0224 19:35:52.053027 29812 net.cpp:375] pool1 <- relu1_2
I0224 19:35:52.053030 29812 net.cpp:337] pool1 -> pool1
I0224 19:35:52.053037 29812 net.cpp:108] Setting up pool1
I0224 19:35:52.053091 29812 net.cpp:115] Top shape: 100 64 16 16 (1638400)
I0224 19:35:52.053094 29812 layer_factory.hpp:74] Creating layer conv2_1
I0224 19:35:52.053103 29812 net.cpp:79] Creating Layer conv2_1
I0224 19:35:52.053105 29812 net.cpp:375] conv2_1 <- pool1
I0224 19:35:52.053109 29812 net.cpp:337] conv2_1 -> conv2_1
I0224 19:35:52.053114 29812 net.cpp:108] Setting up conv2_1
I0224 19:35:52.054965 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.055009 29812 layer_factory.hpp:74] Creating layer bn2_1
I0224 19:35:52.055019 29812 net.cpp:79] Creating Layer bn2_1
I0224 19:35:52.055022 29812 net.cpp:375] bn2_1 <- conv2_1
I0224 19:35:52.055028 29812 net.cpp:337] bn2_1 -> bn2_1
I0224 19:35:52.055065 29812 net.cpp:108] Setting up bn2_1
I0224 19:35:52.055076 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.055083 29812 layer_factory.hpp:74] Creating layer relu2_1
I0224 19:35:52.055088 29812 net.cpp:79] Creating Layer relu2_1
I0224 19:35:52.055093 29812 net.cpp:375] relu2_1 <- bn2_1
I0224 19:35:52.055099 29812 net.cpp:337] relu2_1 -> relu2_1
I0224 19:35:52.055101 29812 net.cpp:108] Setting up relu2_1
I0224 19:35:52.055294 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.055315 29812 layer_factory.hpp:74] Creating layer drop_conv2_1
I0224 19:35:52.055325 29812 net.cpp:79] Creating Layer drop_conv2_1
I0224 19:35:52.055327 29812 net.cpp:375] drop_conv2_1 <- relu2_1
I0224 19:35:52.055341 29812 net.cpp:337] drop_conv2_1 -> do2_1
I0224 19:35:52.055346 29812 net.cpp:108] Setting up drop_conv2_1
I0224 19:35:52.055351 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.055352 29812 layer_factory.hpp:74] Creating layer conv2_2
I0224 19:35:52.055359 29812 net.cpp:79] Creating Layer conv2_2
I0224 19:35:52.055361 29812 net.cpp:375] conv2_2 <- do2_1
I0224 19:35:52.055366 29812 net.cpp:337] conv2_2 -> conv2_2
I0224 19:35:52.055371 29812 net.cpp:108] Setting up conv2_2
I0224 19:35:52.059171 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.059206 29812 layer_factory.hpp:74] Creating layer bn2_2
I0224 19:35:52.059218 29812 net.cpp:79] Creating Layer bn2_2
I0224 19:35:52.059222 29812 net.cpp:375] bn2_2 <- conv2_2
I0224 19:35:52.059227 29812 net.cpp:337] bn2_2 -> bn2_2
I0224 19:35:52.059247 29812 net.cpp:108] Setting up bn2_2
I0224 19:35:52.059259 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.059264 29812 layer_factory.hpp:74] Creating layer relu2_2
I0224 19:35:52.059269 29812 net.cpp:79] Creating Layer relu2_2
I0224 19:35:52.059272 29812 net.cpp:375] relu2_2 <- bn2_2
I0224 19:35:52.059276 29812 net.cpp:337] relu2_2 -> relu2_2
I0224 19:35:52.059281 29812 net.cpp:108] Setting up relu2_2
I0224 19:35:52.059470 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.059491 29812 layer_factory.hpp:74] Creating layer pool2
I0224 19:35:52.059500 29812 net.cpp:79] Creating Layer pool2
I0224 19:35:52.059502 29812 net.cpp:375] pool2 <- relu2_2
I0224 19:35:52.059516 29812 net.cpp:337] pool2 -> pool2
I0224 19:35:52.059521 29812 net.cpp:108] Setting up pool2
I0224 19:35:52.059533 29812 net.cpp:115] Top shape: 100 128 8 8 (819200)
I0224 19:35:52.059537 29812 layer_factory.hpp:74] Creating layer conv3_1
I0224 19:35:52.059546 29812 net.cpp:79] Creating Layer conv3_1
I0224 19:35:52.059550 29812 net.cpp:375] conv3_1 <- pool2
I0224 19:35:52.059555 29812 net.cpp:337] conv3_1 -> conv3_1
I0224 19:35:52.059558 29812 net.cpp:108] Setting up conv3_1
I0224 19:35:52.066746 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.066787 29812 layer_factory.hpp:74] Creating layer bn3_1
I0224 19:35:52.066799 29812 net.cpp:79] Creating Layer bn3_1
I0224 19:35:52.066804 29812 net.cpp:375] bn3_1 <- conv3_1
I0224 19:35:52.066809 29812 net.cpp:337] bn3_1 -> bn3_1
I0224 19:35:52.066828 29812 net.cpp:108] Setting up bn3_1
I0224 19:35:52.066843 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.066846 29812 layer_factory.hpp:74] Creating layer relu3_1
I0224 19:35:52.066854 29812 net.cpp:79] Creating Layer relu3_1
I0224 19:35:52.066855 29812 net.cpp:375] relu3_1 <- bn3_1
I0224 19:35:52.066859 29812 net.cpp:337] relu3_1 -> relu3_1
I0224 19:35:52.066864 29812 net.cpp:108] Setting up relu3_1
I0224 19:35:52.066898 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.066911 29812 layer_factory.hpp:74] Creating layer drop_conv3_1
I0224 19:35:52.066928 29812 net.cpp:79] Creating Layer drop_conv3_1
I0224 19:35:52.066931 29812 net.cpp:375] drop_conv3_1 <- relu3_1
I0224 19:35:52.066936 29812 net.cpp:337] drop_conv3_1 -> do3_1
I0224 19:35:52.066939 29812 net.cpp:108] Setting up drop_conv3_1
I0224 19:35:52.066942 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.066944 29812 layer_factory.hpp:74] Creating layer conv3_2
I0224 19:35:52.066978 29812 net.cpp:79] Creating Layer conv3_2
I0224 19:35:52.066982 29812 net.cpp:375] conv3_2 <- do3_1
I0224 19:35:52.066984 29812 net.cpp:337] conv3_2 -> conv3_2
I0224 19:35:52.066989 29812 net.cpp:108] Setting up conv3_2
I0224 19:35:52.081329 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.081370 29812 layer_factory.hpp:74] Creating layer bn3_2
I0224 19:35:52.081383 29812 net.cpp:79] Creating Layer bn3_2
I0224 19:35:52.081390 29812 net.cpp:375] bn3_2 <- conv3_2
I0224 19:35:52.081400 29812 net.cpp:337] bn3_2 -> bn3_2
I0224 19:35:52.081420 29812 net.cpp:108] Setting up bn3_2
I0224 19:35:52.081435 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.081444 29812 layer_factory.hpp:74] Creating layer relu3_2
I0224 19:35:52.081449 29812 net.cpp:79] Creating Layer relu3_2
I0224 19:35:52.081451 29812 net.cpp:375] relu3_2 <- bn3_2
I0224 19:35:52.081455 29812 net.cpp:337] relu3_2 -> relu3_2
I0224 19:35:52.081459 29812 net.cpp:108] Setting up relu3_2
I0224 19:35:52.081495 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.081507 29812 layer_factory.hpp:74] Creating layer conv3_3
I0224 19:35:52.081526 29812 net.cpp:79] Creating Layer conv3_3
I0224 19:35:52.081528 29812 net.cpp:375] conv3_3 <- relu3_2
I0224 19:35:52.081532 29812 net.cpp:337] conv3_3 -> conv3_3
I0224 19:35:52.081537 29812 net.cpp:108] Setting up conv3_3
I0224 19:35:52.095676 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.095743 29812 layer_factory.hpp:74] Creating layer bn3_3
I0224 19:35:52.095760 29812 net.cpp:79] Creating Layer bn3_3
I0224 19:35:52.095765 29812 net.cpp:375] bn3_3 <- conv3_3
I0224 19:35:52.095779 29812 net.cpp:337] bn3_3 -> bn3_3
I0224 19:35:52.095799 29812 net.cpp:108] Setting up bn3_3
I0224 19:35:52.095813 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.095821 29812 layer_factory.hpp:74] Creating layer relu3_3
I0224 19:35:52.095827 29812 net.cpp:79] Creating Layer relu3_3
I0224 19:35:52.095830 29812 net.cpp:375] relu3_3 <- bn3_3
I0224 19:35:52.095834 29812 net.cpp:337] relu3_3 -> relu3_3
I0224 19:35:52.095839 29812 net.cpp:108] Setting up relu3_3
I0224 19:35:52.095877 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.095891 29812 layer_factory.hpp:74] Creating layer drop_conv3_2
I0224 19:35:52.095908 29812 net.cpp:79] Creating Layer drop_conv3_2
I0224 19:35:52.095911 29812 net.cpp:375] drop_conv3_2 <- relu3_3
I0224 19:35:52.095913 29812 net.cpp:326] drop_conv3_2 -> relu3_3 (in-place)
I0224 19:35:52.095917 29812 net.cpp:108] Setting up drop_conv3_2
I0224 19:35:52.095921 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.095922 29812 layer_factory.hpp:74] Creating layer pool3
I0224 19:35:52.095931 29812 net.cpp:79] Creating Layer pool3
I0224 19:35:52.095933 29812 net.cpp:375] pool3 <- relu3_3
I0224 19:35:52.095937 29812 net.cpp:337] pool3 -> pool3
I0224 19:35:52.095940 29812 net.cpp:108] Setting up pool3
I0224 19:35:52.095948 29812 net.cpp:115] Top shape: 100 256 4 4 (409600)
I0224 19:35:52.095950 29812 layer_factory.hpp:74] Creating layer conv4_1
I0224 19:35:52.095959 29812 net.cpp:79] Creating Layer conv4_1
I0224 19:35:52.095963 29812 net.cpp:375] conv4_1 <- pool3
I0224 19:35:52.095967 29812 net.cpp:337] conv4_1 -> conv4_1
I0224 19:35:52.095970 29812 net.cpp:108] Setting up conv4_1
I0224 19:35:52.122608 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.122639 29812 layer_factory.hpp:74] Creating layer bn4_1
I0224 19:35:52.122649 29812 net.cpp:79] Creating Layer bn4_1
I0224 19:35:52.122653 29812 net.cpp:375] bn4_1 <- conv4_1
I0224 19:35:52.122671 29812 net.cpp:337] bn4_1 -> bn4_1
I0224 19:35:52.122679 29812 net.cpp:108] Setting up bn4_1
I0224 19:35:52.122689 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.122692 29812 layer_factory.hpp:74] Creating layer relu4_1
I0224 19:35:52.122696 29812 net.cpp:79] Creating Layer relu4_1
I0224 19:35:52.122699 29812 net.cpp:375] relu4_1 <- bn4_1
I0224 19:35:52.122702 29812 net.cpp:337] relu4_1 -> relu4_1
I0224 19:35:52.122721 29812 net.cpp:108] Setting up relu4_1
I0224 19:35:52.122733 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.122737 29812 layer_factory.hpp:74] Creating layer drop_conv4_1
I0224 19:35:52.122745 29812 net.cpp:79] Creating Layer drop_conv4_1
I0224 19:35:52.122746 29812 net.cpp:375] drop_conv4_1 <- relu4_1
I0224 19:35:52.122750 29812 net.cpp:337] drop_conv4_1 -> do4_1
I0224 19:35:52.122755 29812 net.cpp:108] Setting up drop_conv4_1
I0224 19:35:52.122758 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.122761 29812 layer_factory.hpp:74] Creating layer conv4_2
I0224 19:35:52.122767 29812 net.cpp:79] Creating Layer conv4_2
I0224 19:35:52.122771 29812 net.cpp:375] conv4_2 <- do4_1
I0224 19:35:52.122773 29812 net.cpp:337] conv4_2 -> conv4_2
I0224 19:35:52.122777 29812 net.cpp:108] Setting up conv4_2
I0224 19:35:52.175540 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.175562 29812 layer_factory.hpp:74] Creating layer bn4_2
I0224 19:35:52.175571 29812 net.cpp:79] Creating Layer bn4_2
I0224 19:35:52.175575 29812 net.cpp:375] bn4_2 <- conv4_2
I0224 19:35:52.175581 29812 net.cpp:337] bn4_2 -> bn4_2
I0224 19:35:52.175598 29812 net.cpp:108] Setting up bn4_2
I0224 19:35:52.175609 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.175613 29812 layer_factory.hpp:74] Creating layer relu4_2
I0224 19:35:52.175617 29812 net.cpp:79] Creating Layer relu4_2
I0224 19:35:52.175619 29812 net.cpp:375] relu4_2 <- bn4_2
I0224 19:35:52.175623 29812 net.cpp:337] relu4_2 -> relu4_2
I0224 19:35:52.175626 29812 net.cpp:108] Setting up relu4_2
I0224 19:35:52.175643 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.175647 29812 layer_factory.hpp:74] Creating layer drop_conv4_2
I0224 19:35:52.175652 29812 net.cpp:79] Creating Layer drop_conv4_2
I0224 19:35:52.175654 29812 net.cpp:375] drop_conv4_2 <- relu4_2
I0224 19:35:52.175658 29812 net.cpp:337] drop_conv4_2 -> do4_2
I0224 19:35:52.175662 29812 net.cpp:108] Setting up drop_conv4_2
I0224 19:35:52.175664 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.175667 29812 layer_factory.hpp:74] Creating layer conv4_3
I0224 19:35:52.175672 29812 net.cpp:79] Creating Layer conv4_3
I0224 19:35:52.175673 29812 net.cpp:375] conv4_3 <- do4_2
I0224 19:35:52.175678 29812 net.cpp:337] conv4_3 -> conv4_3
I0224 19:35:52.175681 29812 net.cpp:108] Setting up conv4_3
I0224 19:35:52.228620 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.228657 29812 layer_factory.hpp:74] Creating layer bn4_3
I0224 19:35:52.228668 29812 net.cpp:79] Creating Layer bn4_3
I0224 19:35:52.228672 29812 net.cpp:375] bn4_3 <- conv4_3
I0224 19:35:52.228677 29812 net.cpp:337] bn4_3 -> bn4_3
I0224 19:35:52.228696 29812 net.cpp:108] Setting up bn4_3
I0224 19:35:52.228706 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.228710 29812 layer_factory.hpp:74] Creating layer relu4_3
I0224 19:35:52.228714 29812 net.cpp:79] Creating Layer relu4_3
I0224 19:35:52.228718 29812 net.cpp:375] relu4_3 <- bn4_3
I0224 19:35:52.228720 29812 net.cpp:337] relu4_3 -> relu4_3
I0224 19:35:52.228724 29812 net.cpp:108] Setting up relu4_3
I0224 19:35:52.228737 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.228741 29812 layer_factory.hpp:74] Creating layer pool4
I0224 19:35:52.228746 29812 net.cpp:79] Creating Layer pool4
I0224 19:35:52.228749 29812 net.cpp:375] pool4 <- relu4_3
I0224 19:35:52.228752 29812 net.cpp:337] pool4 -> pool4
I0224 19:35:52.228755 29812 net.cpp:108] Setting up pool4
I0224 19:35:52.228761 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.228765 29812 layer_factory.hpp:74] Creating layer conv5_1
I0224 19:35:52.228770 29812 net.cpp:79] Creating Layer conv5_1
I0224 19:35:52.228772 29812 net.cpp:375] conv5_1 <- pool4
I0224 19:35:52.228775 29812 net.cpp:337] conv5_1 -> conv5_1
I0224 19:35:52.228780 29812 net.cpp:108] Setting up conv5_1
I0224 19:35:52.281718 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.281751 29812 layer_factory.hpp:74] Creating layer bn5_1
I0224 19:35:52.281791 29812 net.cpp:79] Creating Layer bn5_1
I0224 19:35:52.281795 29812 net.cpp:375] bn5_1 <- conv5_1
I0224 19:35:52.281801 29812 net.cpp:337] bn5_1 -> bn5_1
I0224 19:35:52.281808 29812 net.cpp:108] Setting up bn5_1
I0224 19:35:52.281821 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.281826 29812 layer_factory.hpp:74] Creating layer relu5_1
I0224 19:35:52.281831 29812 net.cpp:79] Creating Layer relu5_1
I0224 19:35:52.281833 29812 net.cpp:375] relu5_1 <- bn5_1
I0224 19:35:52.281836 29812 net.cpp:337] relu5_1 -> relu5_1
I0224 19:35:52.281839 29812 net.cpp:108] Setting up relu5_1
I0224 19:35:52.281847 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.281851 29812 layer_factory.hpp:74] Creating layer drop_conv5_1
I0224 19:35:52.281855 29812 net.cpp:79] Creating Layer drop_conv5_1
I0224 19:35:52.281857 29812 net.cpp:375] drop_conv5_1 <- relu5_1
I0224 19:35:52.281862 29812 net.cpp:337] drop_conv5_1 -> do5_1
I0224 19:35:52.281864 29812 net.cpp:108] Setting up drop_conv5_1
I0224 19:35:52.281867 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.281869 29812 layer_factory.hpp:74] Creating layer conv5_2
I0224 19:35:52.281875 29812 net.cpp:79] Creating Layer conv5_2
I0224 19:35:52.281877 29812 net.cpp:375] conv5_2 <- do5_1
I0224 19:35:52.281882 29812 net.cpp:337] conv5_2 -> conv5_2
I0224 19:35:52.281885 29812 net.cpp:108] Setting up conv5_2
I0224 19:35:52.334892 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.334926 29812 layer_factory.hpp:74] Creating layer bn5_2
I0224 19:35:52.334935 29812 net.cpp:79] Creating Layer bn5_2
I0224 19:35:52.334939 29812 net.cpp:375] bn5_2 <- conv5_2
I0224 19:35:52.334945 29812 net.cpp:337] bn5_2 -> bn5_2
I0224 19:35:52.334964 29812 net.cpp:108] Setting up bn5_2
I0224 19:35:52.334975 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.334978 29812 layer_factory.hpp:74] Creating layer relu5_2
I0224 19:35:52.334982 29812 net.cpp:79] Creating Layer relu5_2
I0224 19:35:52.334985 29812 net.cpp:375] relu5_2 <- bn5_2
I0224 19:35:52.334990 29812 net.cpp:337] relu5_2 -> relu5_2
I0224 19:35:52.334992 29812 net.cpp:108] Setting up relu5_2
I0224 19:35:52.335005 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.335008 29812 layer_factory.hpp:74] Creating layer drop_conv5_2
I0224 19:35:52.335013 29812 net.cpp:79] Creating Layer drop_conv5_2
I0224 19:35:52.335016 29812 net.cpp:375] drop_conv5_2 <- relu5_2
I0224 19:35:52.335018 29812 net.cpp:337] drop_conv5_2 -> do5_2
I0224 19:35:52.335021 29812 net.cpp:108] Setting up drop_conv5_2
I0224 19:35:52.335026 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.335027 29812 layer_factory.hpp:74] Creating layer conv5_3
I0224 19:35:52.335032 29812 net.cpp:79] Creating Layer conv5_3
I0224 19:35:52.335036 29812 net.cpp:375] conv5_3 <- do5_2
I0224 19:35:52.335038 29812 net.cpp:337] conv5_3 -> conv5_3
I0224 19:35:52.335042 29812 net.cpp:108] Setting up conv5_3
I0224 19:35:52.387712 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.387733 29812 layer_factory.hpp:74] Creating layer bn5_3
I0224 19:35:52.387742 29812 net.cpp:79] Creating Layer bn5_3
I0224 19:35:52.387745 29812 net.cpp:375] bn5_3 <- conv5_3
I0224 19:35:52.387751 29812 net.cpp:337] bn5_3 -> bn5_3
I0224 19:35:52.387758 29812 net.cpp:108] Setting up bn5_3
I0224 19:35:52.387778 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.387781 29812 layer_factory.hpp:74] Creating layer relu5_3
I0224 19:35:52.387785 29812 net.cpp:79] Creating Layer relu5_3
I0224 19:35:52.387787 29812 net.cpp:375] relu5_3 <- bn5_3
I0224 19:35:52.387790 29812 net.cpp:337] relu5_3 -> relu5_3
I0224 19:35:52.387794 29812 net.cpp:108] Setting up relu5_3
I0224 19:35:52.387801 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.387807 29812 layer_factory.hpp:74] Creating layer pool5
I0224 19:35:52.387816 29812 net.cpp:79] Creating Layer pool5
I0224 19:35:52.387820 29812 net.cpp:375] pool5 <- relu5_3
I0224 19:35:52.387823 29812 net.cpp:337] pool5 -> pool5
I0224 19:35:52.387826 29812 net.cpp:108] Setting up pool5
I0224 19:35:52.387846 29812 net.cpp:115] Top shape: 100 512 1 1 (51200)
I0224 19:35:52.387850 29812 layer_factory.hpp:74] Creating layer fc6
I0224 19:35:52.387858 29812 net.cpp:79] Creating Layer fc6
I0224 19:35:52.387861 29812 net.cpp:375] fc6 <- pool5
I0224 19:35:52.387866 29812 net.cpp:337] fc6 -> fc6
I0224 19:35:52.387872 29812 net.cpp:108] Setting up fc6
I0224 19:35:52.388203 29812 net.cpp:115] Top shape: 100 100 1 1 (10000)
I0224 19:35:52.388209 29812 layer_factory.hpp:74] Creating layer bn
I0224 19:35:52.388223 29812 net.cpp:79] Creating Layer bn
I0224 19:35:52.388226 29812 net.cpp:375] bn <- fc6
I0224 19:35:52.388231 29812 net.cpp:337] bn -> bn6
I0224 19:35:52.388234 29812 net.cpp:108] Setting up bn
I0224 19:35:52.388239 29812 net.cpp:115] Top shape: 100 100 1 1 (10000)
I0224 19:35:52.388243 29812 layer_factory.hpp:74] Creating layer relu6
I0224 19:35:52.388247 29812 net.cpp:79] Creating Layer relu6
I0224 19:35:52.388258 29812 net.cpp:375] relu6 <- bn6
I0224 19:35:52.388262 29812 net.cpp:337] relu6 -> relu6
I0224 19:35:52.388264 29812 net.cpp:108] Setting up relu6
I0224 19:35:52.388268 29812 net.cpp:115] Top shape: 100 100 1 1 (10000)
I0224 19:35:52.388272 29812 layer_factory.hpp:74] Creating layer drop6
I0224 19:35:52.388275 29812 net.cpp:79] Creating Layer drop6
I0224 19:35:52.388278 29812 net.cpp:375] drop6 <- relu6
I0224 19:35:52.388283 29812 net.cpp:337] drop6 -> do6
I0224 19:35:52.388286 29812 net.cpp:108] Setting up drop6
I0224 19:35:52.388288 29812 net.cpp:115] Top shape: 100 100 1 1 (10000)
I0224 19:35:52.388290 29812 layer_factory.hpp:74] Creating layer fc7
I0224 19:35:52.388294 29812 net.cpp:79] Creating Layer fc7
I0224 19:35:52.388296 29812 net.cpp:375] fc7 <- do6
I0224 19:35:52.388300 29812 net.cpp:337] fc7 -> fc7
I0224 19:35:52.388303 29812 net.cpp:108] Setting up fc7
I0224 19:35:52.388315 29812 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0224 19:35:52.388319 29812 layer_factory.hpp:74] Creating layer fc7/loss3
I0224 19:35:52.388325 29812 net.cpp:79] Creating Layer fc7/loss3
I0224 19:35:52.388327 29812 net.cpp:375] fc7/loss3 <- fc7
I0224 19:35:52.388330 29812 net.cpp:375] fc7/loss3 <- label
I0224 19:35:52.388334 29812 net.cpp:337] fc7/loss3 -> fc7/loss3
I0224 19:35:52.388339 29812 net.cpp:108] Setting up fc7/loss3
I0224 19:35:52.388345 29812 layer_factory.hpp:74] Creating layer fc7/loss3
I0224 19:35:52.388362 29812 net.cpp:115] Top shape: 1 1 1 1 (1)
I0224 19:35:52.388365 29812 net.cpp:121]     with loss weight 1
I0224 19:35:52.388381 29812 net.cpp:166] fc7/loss3 needs backward computation.
I0224 19:35:52.388384 29812 net.cpp:166] fc7 needs backward computation.
I0224 19:35:52.388386 29812 net.cpp:166] drop6 needs backward computation.
I0224 19:35:52.388387 29812 net.cpp:166] relu6 needs backward computation.
I0224 19:35:52.388389 29812 net.cpp:166] bn needs backward computation.
I0224 19:35:52.388391 29812 net.cpp:166] fc6 needs backward computation.
I0224 19:35:52.388393 29812 net.cpp:166] pool5 needs backward computation.
I0224 19:35:52.388396 29812 net.cpp:166] relu5_3 needs backward computation.
I0224 19:35:52.388397 29812 net.cpp:166] bn5_3 needs backward computation.
I0224 19:35:52.388398 29812 net.cpp:166] conv5_3 needs backward computation.
I0224 19:35:52.388401 29812 net.cpp:166] drop_conv5_2 needs backward computation.
I0224 19:35:52.388403 29812 net.cpp:166] relu5_2 needs backward computation.
I0224 19:35:52.388406 29812 net.cpp:166] bn5_2 needs backward computation.
I0224 19:35:52.388407 29812 net.cpp:166] conv5_2 needs backward computation.
I0224 19:35:52.388409 29812 net.cpp:166] drop_conv5_1 needs backward computation.
I0224 19:35:52.388411 29812 net.cpp:166] relu5_1 needs backward computation.
I0224 19:35:52.388414 29812 net.cpp:166] bn5_1 needs backward computation.
I0224 19:35:52.388417 29812 net.cpp:166] conv5_1 needs backward computation.
I0224 19:35:52.388418 29812 net.cpp:166] pool4 needs backward computation.
I0224 19:35:52.388420 29812 net.cpp:166] relu4_3 needs backward computation.
I0224 19:35:52.388422 29812 net.cpp:166] bn4_3 needs backward computation.
I0224 19:35:52.388432 29812 net.cpp:166] conv4_3 needs backward computation.
I0224 19:35:52.388434 29812 net.cpp:166] drop_conv4_2 needs backward computation.
I0224 19:35:52.388437 29812 net.cpp:166] relu4_2 needs backward computation.
I0224 19:35:52.388438 29812 net.cpp:166] bn4_2 needs backward computation.
I0224 19:35:52.388440 29812 net.cpp:166] conv4_2 needs backward computation.
I0224 19:35:52.388442 29812 net.cpp:166] drop_conv4_1 needs backward computation.
I0224 19:35:52.388445 29812 net.cpp:166] relu4_1 needs backward computation.
I0224 19:35:52.388447 29812 net.cpp:166] bn4_1 needs backward computation.
I0224 19:35:52.388449 29812 net.cpp:166] conv4_1 needs backward computation.
I0224 19:35:52.388452 29812 net.cpp:166] pool3 needs backward computation.
I0224 19:35:52.388454 29812 net.cpp:166] drop_conv3_2 needs backward computation.
I0224 19:35:52.388456 29812 net.cpp:166] relu3_3 needs backward computation.
I0224 19:35:52.388458 29812 net.cpp:166] bn3_3 needs backward computation.
I0224 19:35:52.388461 29812 net.cpp:166] conv3_3 needs backward computation.
I0224 19:35:52.388463 29812 net.cpp:166] relu3_2 needs backward computation.
I0224 19:35:52.388466 29812 net.cpp:166] bn3_2 needs backward computation.
I0224 19:35:52.388468 29812 net.cpp:166] conv3_2 needs backward computation.
I0224 19:35:52.388471 29812 net.cpp:166] drop_conv3_1 needs backward computation.
I0224 19:35:52.388473 29812 net.cpp:166] relu3_1 needs backward computation.
I0224 19:35:52.388475 29812 net.cpp:166] bn3_1 needs backward computation.
I0224 19:35:52.388478 29812 net.cpp:166] conv3_1 needs backward computation.
I0224 19:35:52.388479 29812 net.cpp:166] pool2 needs backward computation.
I0224 19:35:52.388481 29812 net.cpp:166] relu2_2 needs backward computation.
I0224 19:35:52.388484 29812 net.cpp:166] bn2_2 needs backward computation.
I0224 19:35:52.388486 29812 net.cpp:166] conv2_2 needs backward computation.
I0224 19:35:52.388489 29812 net.cpp:166] drop_conv2_1 needs backward computation.
I0224 19:35:52.388490 29812 net.cpp:166] relu2_1 needs backward computation.
I0224 19:35:52.388494 29812 net.cpp:166] bn2_1 needs backward computation.
I0224 19:35:52.388495 29812 net.cpp:166] conv2_1 needs backward computation.
I0224 19:35:52.388497 29812 net.cpp:166] pool1 needs backward computation.
I0224 19:35:52.388499 29812 net.cpp:166] relu1_2 needs backward computation.
I0224 19:35:52.388502 29812 net.cpp:166] bn1_2 needs backward computation.
I0224 19:35:52.388504 29812 net.cpp:166] conv1_2 needs backward computation.
I0224 19:35:52.388506 29812 net.cpp:166] drop_conv1_1 needs backward computation.
I0224 19:35:52.388509 29812 net.cpp:166] relu1_1 needs backward computation.
I0224 19:35:52.388511 29812 net.cpp:166] bn1 needs backward computation.
I0224 19:35:52.388514 29812 net.cpp:166] conv1_1 needs backward computation.
I0224 19:35:52.388515 29812 net.cpp:168] cifar does not need backward computation.
I0224 19:35:52.388517 29812 net.cpp:204] This network produces output fc7/loss3
I0224 19:35:52.388540 29812 net.cpp:449] Collecting Learning Rate and Weight Decay.
I0224 19:35:52.388553 29812 net.cpp:216] Network initialization done.
I0224 19:35:52.388557 29812 net.cpp:217] Memory required for data: 406282804
I0224 19:35:52.389587 29812 solver.cpp:154] Creating test net (#0) specified by net file: examples/cifar10_vgg/vgg16-msr_bn.prototxt
I0224 19:35:52.389647 29812 net.cpp:256] The NetState phase (1) differed from the phase (0) specified by a rule in layer cifar
I0224 19:35:52.389960 29812 net.cpp:45] Initializing net from parameters: 
name: "VGG_ILSVRC_16_layers_bn"
state {
  phase: TEST
}
layer {
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mean_file: "examples/cifar10/mean.binaryproto"
  }
  data_param {
    source: "examples/cifar10/cifar10_test_lmdb"
    batch_size: 100
    backend: LMDB
    shuffle_buffer_size: 100
  }
}
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn1"
  type: "BN"
  bottom: "conv1_1"
  top: "bn1"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu1_1"
  type: "PReLU"
  bottom: "bn1"
  top: "relu1_1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv1_1"
  type: "Dropout"
  bottom: "relu1_1"
  top: "do1_1"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "do1_1"
  top: "conv1_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn1_2"
  type: "BN"
  bottom: "conv1_2"
  top: "bn1_2"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu1_2"
  type: "PReLU"
  bottom: "bn1_2"
  top: "relu1_2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "relu1_2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    engine: DEFAULT
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn2_1"
  type: "BN"
  bottom: "conv2_1"
  top: "bn2_1"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu2_1"
  type: "PReLU"
  bottom: "bn2_1"
  top: "relu2_1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv2_1"
  type: "Dropout"
  bottom: "relu2_1"
  top: "do2_1"
  dropout_param {
    dropout_ratio: 0.3
  }
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "do2_1"
  top: "conv2_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn2_2"
  type: "BN"
  bottom: "conv2_2"
  top: "bn2_2"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu2_2"
  type: "PReLU"
  bottom: "bn2_2"
  top: "relu2_2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "relu2_2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    engine: DEFAULT
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn3_1"
  type: "BN"
  bottom: "conv3_1"
  top: "bn3_1"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu3_1"
  type: "PReLU"
  bottom: "bn3_1"
  top: "relu3_1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv3_1"
  type: "Dropout"
  bottom: "relu3_1"
  top: "do3_1"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "do3_1"
  top: "conv3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn3_2"
  type: "BN"
  bottom: "conv3_2"
  top: "bn3_2"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu3_2"
  type: "PReLU"
  bottom: "bn3_2"
  top: "relu3_2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "relu3_2"
  top: "conv3_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn3_3"
  type: "BN"
  bottom: "conv3_3"
  top: "bn3_3"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu3_3"
  type: "PReLU"
  bottom: "bn3_3"
  top: "relu3_3"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv3_2"
  type: "Dropout"
  bottom: "relu3_3"
  top: "relu3_3"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "relu3_3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    engine: DEFAULT
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn4_1"
  type: "BN"
  bottom: "conv4_1"
  top: "bn4_1"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu4_1"
  type: "PReLU"
  bottom: "bn4_1"
  top: "relu4_1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv4_1"
  type: "Dropout"
  bottom: "relu4_1"
  top: "do4_1"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "do4_1"
  top: "conv4_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn4_2"
  type: "BN"
  bottom: "conv4_2"
  top: "bn4_2"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu4_2"
  type: "PReLU"
  bottom: "bn4_2"
  top: "relu4_2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv4_2"
  type: "Dropout"
  bottom: "relu4_2"
  top: "do4_2"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "do4_2"
  top: "conv4_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn4_3"
  type: "BN"
  bottom: "conv4_3"
  top: "bn4_3"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu4_3"
  type: "PReLU"
  bottom: "bn4_3"
  top: "relu4_3"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "relu4_3"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    engine: DEFAULT
  }
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn5_1"
  type: "BN"
  bottom: "conv5_1"
  top: "bn5_1"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu5_1"
  type: "PReLU"
  bottom: "bn5_1"
  top: "relu5_1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv5_1"
  type: "Dropout"
  bottom: "relu5_1"
  top: "do5_1"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "do5_1"
  top: "conv5_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn5_2"
  type: "BN"
  bottom: "conv5_2"
  top: "bn5_2"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu5_2"
  type: "PReLU"
  bottom: "bn5_2"
  top: "relu5_2"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop_conv5_2"
  type: "Dropout"
  bottom: "relu5_2"
  top: "do5_2"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "do5_2"
  top: "conv5_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msr"
      negative_slope: 0.25
      layer_type: CONVOLUTION
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: DEFAULT
  }
}
layer {
  name: "bn5_3"
  type: "BN"
  bottom: "conv5_3"
  top: "bn5_3"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu5_3"
  type: "PReLU"
  bottom: "bn5_3"
  top: "relu5_3"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "relu5_3"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    engine: DEFAULT
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 100
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "bn"
  type: "BN"
  bottom: "fc6"
  top: "bn6"
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  param {
    lr_mult: 1.00001
    decay_mult: 0
  }
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0.001
    }
  }
}
layer {
  name: "relu6"
  type: "PReLU"
  bottom: "bn6"
  top: "relu6"
  param {
    lr_mult: 1
    decay_mult: 0
  }
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "relu6"
  top: "do6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "do6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "fc7/loss3"
  type: "SoftmaxWithLoss"
  bottom: "fc7"
  bottom: "label"
  top: "fc7/loss3"
  loss_weight: 1
}
layer {
  name: "fc7/acc"
  type: "Accuracy"
  bottom: "fc7"
  bottom: "label"
  top: "fc7/acc"
  include {
    phase: TEST
  }
}
I0224 19:35:52.390137 29812 layer_factory.hpp:74] Creating layer cifar
I0224 19:35:52.390144 29812 net.cpp:79] Creating Layer cifar
I0224 19:35:52.390148 29812 net.cpp:337] cifar -> data
I0224 19:35:52.390153 29812 net.cpp:337] cifar -> label
I0224 19:35:52.390157 29812 net.cpp:108] Setting up cifar
I0224 19:35:52.390188 29812 db.cpp:34] Opened lmdb examples/cifar10/cifar10_test_lmdb
I0224 19:35:52.390203 29812 data_layer.cpp:79] output data size: 100,3,32,32
I0224 19:35:52.390207 29812 data_transformer.cpp:59] Loading mean file from: examples/cifar10/mean.binaryproto
I0224 19:35:52.390511 29812 net.cpp:115] Top shape: 100 3 32 32 (307200)
I0224 19:35:52.390522 29812 net.cpp:115] Top shape: 100 1 1 1 (100)
I0224 19:35:52.390524 29812 layer_factory.hpp:74] Creating layer label_cifar_1_split
I0224 19:35:52.390532 29812 net.cpp:79] Creating Layer label_cifar_1_split
I0224 19:35:52.390533 29812 net.cpp:375] label_cifar_1_split <- label
I0224 19:35:52.390537 29812 net.cpp:337] label_cifar_1_split -> label_cifar_1_split_0
I0224 19:35:52.390542 29812 net.cpp:337] label_cifar_1_split -> label_cifar_1_split_1
I0224 19:35:52.390545 29812 net.cpp:108] Setting up label_cifar_1_split
I0224 19:35:52.390548 29812 net.cpp:115] Top shape: 100 1 1 1 (100)
I0224 19:35:52.390550 29812 net.cpp:115] Top shape: 100 1 1 1 (100)
I0224 19:35:52.390552 29812 layer_factory.hpp:74] Creating layer conv1_1
I0224 19:35:52.390557 29812 net.cpp:79] Creating Layer conv1_1
I0224 19:35:52.390558 29812 net.cpp:375] conv1_1 <- data
I0224 19:35:52.390563 29812 net.cpp:337] conv1_1 -> conv1_1
I0224 19:35:52.390566 29812 net.cpp:108] Setting up conv1_1
I0224 19:35:52.390667 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.390681 29812 layer_factory.hpp:74] Creating layer bn1
I0224 19:35:52.390687 29812 net.cpp:79] Creating Layer bn1
I0224 19:35:52.390691 29812 net.cpp:375] bn1 <- conv1_1
I0224 19:35:52.390693 29812 net.cpp:337] bn1 -> bn1
I0224 19:35:52.390697 29812 net.cpp:108] Setting up bn1
I0224 19:35:52.390705 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.390709 29812 layer_factory.hpp:74] Creating layer relu1_1
I0224 19:35:52.390712 29812 net.cpp:79] Creating Layer relu1_1
I0224 19:35:52.390714 29812 net.cpp:375] relu1_1 <- bn1
I0224 19:35:52.390719 29812 net.cpp:337] relu1_1 -> relu1_1
I0224 19:35:52.390722 29812 net.cpp:108] Setting up relu1_1
I0224 19:35:52.390816 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.390821 29812 layer_factory.hpp:74] Creating layer drop_conv1_1
I0224 19:35:52.390825 29812 net.cpp:79] Creating Layer drop_conv1_1
I0224 19:35:52.390827 29812 net.cpp:375] drop_conv1_1 <- relu1_1
I0224 19:35:52.390830 29812 net.cpp:337] drop_conv1_1 -> do1_1
I0224 19:35:52.390835 29812 net.cpp:108] Setting up drop_conv1_1
I0224 19:35:52.390836 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.390838 29812 layer_factory.hpp:74] Creating layer conv1_2
I0224 19:35:52.390842 29812 net.cpp:79] Creating Layer conv1_2
I0224 19:35:52.390846 29812 net.cpp:375] conv1_2 <- do1_1
I0224 19:35:52.390848 29812 net.cpp:337] conv1_2 -> conv1_2
I0224 19:35:52.390851 29812 net.cpp:108] Setting up conv1_2
I0224 19:35:52.391765 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.391772 29812 layer_factory.hpp:74] Creating layer bn1_2
I0224 19:35:52.391777 29812 net.cpp:79] Creating Layer bn1_2
I0224 19:35:52.391778 29812 net.cpp:375] bn1_2 <- conv1_2
I0224 19:35:52.391782 29812 net.cpp:337] bn1_2 -> bn1_2
I0224 19:35:52.391787 29812 net.cpp:108] Setting up bn1_2
I0224 19:35:52.391794 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.391799 29812 layer_factory.hpp:74] Creating layer relu1_2
I0224 19:35:52.391803 29812 net.cpp:79] Creating Layer relu1_2
I0224 19:35:52.391805 29812 net.cpp:375] relu1_2 <- bn1_2
I0224 19:35:52.391808 29812 net.cpp:337] relu1_2 -> relu1_2
I0224 19:35:52.391811 29812 net.cpp:108] Setting up relu1_2
I0224 19:35:52.391887 29812 net.cpp:115] Top shape: 100 64 32 32 (6553600)
I0224 19:35:52.391891 29812 layer_factory.hpp:74] Creating layer pool1
I0224 19:35:52.391896 29812 net.cpp:79] Creating Layer pool1
I0224 19:35:52.391898 29812 net.cpp:375] pool1 <- relu1_2
I0224 19:35:52.391901 29812 net.cpp:337] pool1 -> pool1
I0224 19:35:52.391904 29812 net.cpp:108] Setting up pool1
I0224 19:35:52.391909 29812 net.cpp:115] Top shape: 100 64 16 16 (1638400)
I0224 19:35:52.391911 29812 layer_factory.hpp:74] Creating layer conv2_1
I0224 19:35:52.391916 29812 net.cpp:79] Creating Layer conv2_1
I0224 19:35:52.391917 29812 net.cpp:375] conv2_1 <- pool1
I0224 19:35:52.391922 29812 net.cpp:337] conv2_1 -> conv2_1
I0224 19:35:52.391926 29812 net.cpp:108] Setting up conv2_1
I0224 19:35:52.393867 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.393887 29812 layer_factory.hpp:74] Creating layer bn2_1
I0224 19:35:52.393893 29812 net.cpp:79] Creating Layer bn2_1
I0224 19:35:52.393895 29812 net.cpp:375] bn2_1 <- conv2_1
I0224 19:35:52.393909 29812 net.cpp:337] bn2_1 -> bn2_1
I0224 19:35:52.393913 29812 net.cpp:108] Setting up bn2_1
I0224 19:35:52.393923 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.393928 29812 layer_factory.hpp:74] Creating layer relu2_1
I0224 19:35:52.393931 29812 net.cpp:79] Creating Layer relu2_1
I0224 19:35:52.393934 29812 net.cpp:375] relu2_1 <- bn2_1
I0224 19:35:52.393935 29812 net.cpp:337] relu2_1 -> relu2_1
I0224 19:35:52.393939 29812 net.cpp:108] Setting up relu2_1
I0224 19:35:52.393986 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.394001 29812 layer_factory.hpp:74] Creating layer drop_conv2_1
I0224 19:35:52.394004 29812 net.cpp:79] Creating Layer drop_conv2_1
I0224 19:35:52.394006 29812 net.cpp:375] drop_conv2_1 <- relu2_1
I0224 19:35:52.394026 29812 net.cpp:337] drop_conv2_1 -> do2_1
I0224 19:35:52.394039 29812 net.cpp:108] Setting up drop_conv2_1
I0224 19:35:52.394042 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.394044 29812 layer_factory.hpp:74] Creating layer conv2_2
I0224 19:35:52.394048 29812 net.cpp:79] Creating Layer conv2_2
I0224 19:35:52.394052 29812 net.cpp:375] conv2_2 <- do2_1
I0224 19:35:52.394054 29812 net.cpp:337] conv2_2 -> conv2_2
I0224 19:35:52.394058 29812 net.cpp:108] Setting up conv2_2
I0224 19:35:52.397594 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.397622 29812 layer_factory.hpp:74] Creating layer bn2_2
I0224 19:35:52.397629 29812 net.cpp:79] Creating Layer bn2_2
I0224 19:35:52.397632 29812 net.cpp:375] bn2_2 <- conv2_2
I0224 19:35:52.397636 29812 net.cpp:337] bn2_2 -> bn2_2
I0224 19:35:52.397641 29812 net.cpp:108] Setting up bn2_2
I0224 19:35:52.397650 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.397653 29812 layer_factory.hpp:74] Creating layer relu2_2
I0224 19:35:52.397658 29812 net.cpp:79] Creating Layer relu2_2
I0224 19:35:52.397661 29812 net.cpp:375] relu2_2 <- bn2_2
I0224 19:35:52.397666 29812 net.cpp:337] relu2_2 -> relu2_2
I0224 19:35:52.397670 29812 net.cpp:108] Setting up relu2_2
I0224 19:35:52.397722 29812 net.cpp:115] Top shape: 100 128 16 16 (3276800)
I0224 19:35:52.397725 29812 layer_factory.hpp:74] Creating layer pool2
I0224 19:35:52.397729 29812 net.cpp:79] Creating Layer pool2
I0224 19:35:52.397732 29812 net.cpp:375] pool2 <- relu2_2
I0224 19:35:52.397735 29812 net.cpp:337] pool2 -> pool2
I0224 19:35:52.397738 29812 net.cpp:108] Setting up pool2
I0224 19:35:52.397744 29812 net.cpp:115] Top shape: 100 128 8 8 (819200)
I0224 19:35:52.397747 29812 layer_factory.hpp:74] Creating layer conv3_1
I0224 19:35:52.397754 29812 net.cpp:79] Creating Layer conv3_1
I0224 19:35:52.397758 29812 net.cpp:375] conv3_1 <- pool2
I0224 19:35:52.397760 29812 net.cpp:337] conv3_1 -> conv3_1
I0224 19:35:52.397764 29812 net.cpp:108] Setting up conv3_1
I0224 19:35:52.405275 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.405352 29812 layer_factory.hpp:74] Creating layer bn3_1
I0224 19:35:52.405380 29812 net.cpp:79] Creating Layer bn3_1
I0224 19:35:52.405552 29812 net.cpp:375] bn3_1 <- conv3_1
I0224 19:35:52.405586 29812 net.cpp:337] bn3_1 -> bn3_1
I0224 19:35:52.405745 29812 net.cpp:108] Setting up bn3_1
I0224 19:35:52.405858 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.405884 29812 layer_factory.hpp:74] Creating layer relu3_1
I0224 19:35:52.405892 29812 net.cpp:79] Creating Layer relu3_1
I0224 19:35:52.406007 29812 net.cpp:375] relu3_1 <- bn3_1
I0224 19:35:52.406103 29812 net.cpp:337] relu3_1 -> relu3_1
I0224 19:35:52.406169 29812 net.cpp:108] Setting up relu3_1
I0224 19:35:52.406219 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.406249 29812 layer_factory.hpp:74] Creating layer drop_conv3_1
I0224 19:35:52.406296 29812 net.cpp:79] Creating Layer drop_conv3_1
I0224 19:35:52.406304 29812 net.cpp:375] drop_conv3_1 <- relu3_1
I0224 19:35:52.406363 29812 net.cpp:337] drop_conv3_1 -> do3_1
I0224 19:35:52.406373 29812 net.cpp:108] Setting up drop_conv3_1
I0224 19:35:52.406399 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.406421 29812 layer_factory.hpp:74] Creating layer conv3_2
I0224 19:35:52.406469 29812 net.cpp:79] Creating Layer conv3_2
I0224 19:35:52.406477 29812 net.cpp:375] conv3_2 <- do3_1
I0224 19:35:52.406486 29812 net.cpp:337] conv3_2 -> conv3_2
I0224 19:35:52.406491 29812 net.cpp:108] Setting up conv3_2
I0224 19:35:52.421201 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.421229 29812 layer_factory.hpp:74] Creating layer bn3_2
I0224 19:35:52.421329 29812 net.cpp:79] Creating Layer bn3_2
I0224 19:35:52.421334 29812 net.cpp:375] bn3_2 <- conv3_2
I0224 19:35:52.421341 29812 net.cpp:337] bn3_2 -> bn3_2
I0224 19:35:52.421427 29812 net.cpp:108] Setting up bn3_2
I0224 19:35:52.421486 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.421516 29812 layer_factory.hpp:74] Creating layer relu3_2
I0224 19:35:52.421524 29812 net.cpp:79] Creating Layer relu3_2
I0224 19:35:52.421526 29812 net.cpp:375] relu3_2 <- bn3_2
I0224 19:35:52.421607 29812 net.cpp:337] relu3_2 -> relu3_2
I0224 19:35:52.421623 29812 net.cpp:108] Setting up relu3_2
I0224 19:35:52.421696 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.421716 29812 layer_factory.hpp:74] Creating layer conv3_3
I0224 19:35:52.421725 29812 net.cpp:79] Creating Layer conv3_3
I0224 19:35:52.421773 29812 net.cpp:375] conv3_3 <- relu3_2
I0224 19:35:52.421790 29812 net.cpp:337] conv3_3 -> conv3_3
I0224 19:35:52.421797 29812 net.cpp:108] Setting up conv3_3
I0224 19:35:52.435964 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.435992 29812 layer_factory.hpp:74] Creating layer bn3_3
I0224 19:35:52.436085 29812 net.cpp:79] Creating Layer bn3_3
I0224 19:35:52.436092 29812 net.cpp:375] bn3_3 <- conv3_3
I0224 19:35:52.436110 29812 net.cpp:337] bn3_3 -> bn3_3
I0224 19:35:52.436172 29812 net.cpp:108] Setting up bn3_3
I0224 19:35:52.436233 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.436252 29812 layer_factory.hpp:74] Creating layer relu3_3
I0224 19:35:52.436290 29812 net.cpp:79] Creating Layer relu3_3
I0224 19:35:52.436295 29812 net.cpp:375] relu3_3 <- bn3_3
I0224 19:35:52.436326 29812 net.cpp:337] relu3_3 -> relu3_3
I0224 19:35:52.436348 29812 net.cpp:108] Setting up relu3_3
I0224 19:35:52.436395 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.436416 29812 layer_factory.hpp:74] Creating layer drop_conv3_2
I0224 19:35:52.436444 29812 net.cpp:79] Creating Layer drop_conv3_2
I0224 19:35:52.436449 29812 net.cpp:375] drop_conv3_2 <- relu3_3
I0224 19:35:52.436476 29812 net.cpp:326] drop_conv3_2 -> relu3_3 (in-place)
I0224 19:35:52.436481 29812 net.cpp:108] Setting up drop_conv3_2
I0224 19:35:52.436506 29812 net.cpp:115] Top shape: 100 256 8 8 (1638400)
I0224 19:35:52.436525 29812 layer_factory.hpp:74] Creating layer pool3
I0224 19:35:52.436559 29812 net.cpp:79] Creating Layer pool3
I0224 19:35:52.436566 29812 net.cpp:375] pool3 <- relu3_3
I0224 19:35:52.436594 29812 net.cpp:337] pool3 -> pool3
I0224 19:35:52.436616 29812 net.cpp:108] Setting up pool3
I0224 19:35:52.436641 29812 net.cpp:115] Top shape: 100 256 4 4 (409600)
I0224 19:35:52.436650 29812 layer_factory.hpp:74] Creating layer conv4_1
I0224 19:35:52.436688 29812 net.cpp:79] Creating Layer conv4_1
I0224 19:35:52.436692 29812 net.cpp:375] conv4_1 <- pool3
I0224 19:35:52.436724 29812 net.cpp:337] conv4_1 -> conv4_1
I0224 19:35:52.436748 29812 net.cpp:108] Setting up conv4_1
I0224 19:35:52.464571 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.464612 29812 layer_factory.hpp:74] Creating layer bn4_1
I0224 19:35:52.464623 29812 net.cpp:79] Creating Layer bn4_1
I0224 19:35:52.464727 29812 net.cpp:375] bn4_1 <- conv4_1
I0224 19:35:52.464748 29812 net.cpp:337] bn4_1 -> bn4_1
I0224 19:35:52.464758 29812 net.cpp:108] Setting up bn4_1
I0224 19:35:52.464818 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.464825 29812 layer_factory.hpp:74] Creating layer relu4_1
I0224 19:35:52.464843 29812 net.cpp:79] Creating Layer relu4_1
I0224 19:35:52.464844 29812 net.cpp:375] relu4_1 <- bn4_1
I0224 19:35:52.464895 29812 net.cpp:337] relu4_1 -> relu4_1
I0224 19:35:52.464901 29812 net.cpp:108] Setting up relu4_1
I0224 19:35:52.464962 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.464969 29812 layer_factory.hpp:74] Creating layer drop_conv4_1
I0224 19:35:52.464984 29812 net.cpp:79] Creating Layer drop_conv4_1
I0224 19:35:52.464987 29812 net.cpp:375] drop_conv4_1 <- relu4_1
I0224 19:35:52.465035 29812 net.cpp:337] drop_conv4_1 -> do4_1
I0224 19:35:52.465041 29812 net.cpp:108] Setting up drop_conv4_1
I0224 19:35:52.465054 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.465056 29812 layer_factory.hpp:74] Creating layer conv4_2
I0224 19:35:52.465107 29812 net.cpp:79] Creating Layer conv4_2
I0224 19:35:52.465112 29812 net.cpp:375] conv4_2 <- do4_1
I0224 19:35:52.465167 29812 net.cpp:337] conv4_2 -> conv4_2
I0224 19:35:52.465190 29812 net.cpp:108] Setting up conv4_2
I0224 19:35:52.520339 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.520380 29812 layer_factory.hpp:74] Creating layer bn4_2
I0224 19:35:52.520390 29812 net.cpp:79] Creating Layer bn4_2
I0224 19:35:52.520395 29812 net.cpp:375] bn4_2 <- conv4_2
I0224 19:35:52.520503 29812 net.cpp:337] bn4_2 -> bn4_2
I0224 19:35:52.520555 29812 net.cpp:108] Setting up bn4_2
I0224 19:35:52.520578 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.520620 29812 layer_factory.hpp:74] Creating layer relu4_2
I0224 19:35:52.520627 29812 net.cpp:79] Creating Layer relu4_2
I0224 19:35:52.520639 29812 net.cpp:375] relu4_2 <- bn4_2
I0224 19:35:52.520643 29812 net.cpp:337] relu4_2 -> relu4_2
I0224 19:35:52.520648 29812 net.cpp:108] Setting up relu4_2
I0224 19:35:52.520751 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.520768 29812 layer_factory.hpp:74] Creating layer drop_conv4_2
I0224 19:35:52.520783 29812 net.cpp:79] Creating Layer drop_conv4_2
I0224 19:35:52.520787 29812 net.cpp:375] drop_conv4_2 <- relu4_2
I0224 19:35:52.520791 29812 net.cpp:337] drop_conv4_2 -> do4_2
I0224 19:35:52.520859 29812 net.cpp:108] Setting up drop_conv4_2
I0224 19:35:52.520864 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.520865 29812 layer_factory.hpp:74] Creating layer conv4_3
I0224 19:35:52.520911 29812 net.cpp:79] Creating Layer conv4_3
I0224 19:35:52.520915 29812 net.cpp:375] conv4_3 <- do4_2
I0224 19:35:52.520947 29812 net.cpp:337] conv4_3 -> conv4_3
I0224 19:35:52.520970 29812 net.cpp:108] Setting up conv4_3
I0224 19:35:52.575500 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.575556 29812 layer_factory.hpp:74] Creating layer bn4_3
I0224 19:35:52.575573 29812 net.cpp:79] Creating Layer bn4_3
I0224 19:35:52.575695 29812 net.cpp:375] bn4_3 <- conv4_3
I0224 19:35:52.575719 29812 net.cpp:337] bn4_3 -> bn4_3
I0224 19:35:52.575774 29812 net.cpp:108] Setting up bn4_3
I0224 19:35:52.575799 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.575844 29812 layer_factory.hpp:74] Creating layer relu4_3
I0224 19:35:52.575851 29812 net.cpp:79] Creating Layer relu4_3
I0224 19:35:52.575863 29812 net.cpp:375] relu4_3 <- bn4_3
I0224 19:35:52.575868 29812 net.cpp:337] relu4_3 -> relu4_3
I0224 19:35:52.575872 29812 net.cpp:108] Setting up relu4_3
I0224 19:35:52.575966 29812 net.cpp:115] Top shape: 100 512 4 4 (819200)
I0224 19:35:52.575984 29812 layer_factory.hpp:74] Creating layer pool4
I0224 19:35:52.575989 29812 net.cpp:79] Creating Layer pool4
I0224 19:35:52.576001 29812 net.cpp:375] pool4 <- relu4_3
I0224 19:35:52.576005 29812 net.cpp:337] pool4 -> pool4
I0224 19:35:52.576009 29812 net.cpp:108] Setting up pool4
I0224 19:35:52.576081 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.576087 29812 layer_factory.hpp:74] Creating layer conv5_1
I0224 19:35:52.576125 29812 net.cpp:79] Creating Layer conv5_1
I0224 19:35:52.576129 29812 net.cpp:375] conv5_1 <- pool4
I0224 19:35:52.576159 29812 net.cpp:337] conv5_1 -> conv5_1
I0224 19:35:52.576189 29812 net.cpp:108] Setting up conv5_1
I0224 19:35:52.631119 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.631150 29812 layer_factory.hpp:74] Creating layer bn5_1
I0224 19:35:52.631160 29812 net.cpp:79] Creating Layer bn5_1
I0224 19:35:52.631165 29812 net.cpp:375] bn5_1 <- conv5_1
I0224 19:35:52.631181 29812 net.cpp:337] bn5_1 -> bn5_1
I0224 19:35:52.631189 29812 net.cpp:108] Setting up bn5_1
I0224 19:35:52.631201 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.631206 29812 layer_factory.hpp:74] Creating layer relu5_1
I0224 19:35:52.631211 29812 net.cpp:79] Creating Layer relu5_1
I0224 19:35:52.631216 29812 net.cpp:375] relu5_1 <- bn5_1
I0224 19:35:52.631219 29812 net.cpp:337] relu5_1 -> relu5_1
I0224 19:35:52.631222 29812 net.cpp:108] Setting up relu5_1
I0224 19:35:52.631232 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.631235 29812 layer_factory.hpp:74] Creating layer drop_conv5_1
I0224 19:35:52.631254 29812 net.cpp:79] Creating Layer drop_conv5_1
I0224 19:35:52.631258 29812 net.cpp:375] drop_conv5_1 <- relu5_1
I0224 19:35:52.631263 29812 net.cpp:337] drop_conv5_1 -> do5_1
I0224 19:35:52.631266 29812 net.cpp:108] Setting up drop_conv5_1
I0224 19:35:52.631269 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.631273 29812 layer_factory.hpp:74] Creating layer conv5_2
I0224 19:35:52.631278 29812 net.cpp:79] Creating Layer conv5_2
I0224 19:35:52.631280 29812 net.cpp:375] conv5_2 <- do5_1
I0224 19:35:52.631285 29812 net.cpp:337] conv5_2 -> conv5_2
I0224 19:35:52.631289 29812 net.cpp:108] Setting up conv5_2
I0224 19:35:52.685111 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.685132 29812 layer_factory.hpp:74] Creating layer bn5_2
I0224 19:35:52.685142 29812 net.cpp:79] Creating Layer bn5_2
I0224 19:35:52.685147 29812 net.cpp:375] bn5_2 <- conv5_2
I0224 19:35:52.685153 29812 net.cpp:337] bn5_2 -> bn5_2
I0224 19:35:52.685171 29812 net.cpp:108] Setting up bn5_2
I0224 19:35:52.685183 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.685187 29812 layer_factory.hpp:74] Creating layer relu5_2
I0224 19:35:52.685192 29812 net.cpp:79] Creating Layer relu5_2
I0224 19:35:52.685194 29812 net.cpp:375] relu5_2 <- bn5_2
I0224 19:35:52.685199 29812 net.cpp:337] relu5_2 -> relu5_2
I0224 19:35:52.685204 29812 net.cpp:108] Setting up relu5_2
I0224 19:35:52.685215 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.685220 29812 layer_factory.hpp:74] Creating layer drop_conv5_2
I0224 19:35:52.685225 29812 net.cpp:79] Creating Layer drop_conv5_2
I0224 19:35:52.685228 29812 net.cpp:375] drop_conv5_2 <- relu5_2
I0224 19:35:52.685232 29812 net.cpp:337] drop_conv5_2 -> do5_2
I0224 19:35:52.685236 29812 net.cpp:108] Setting up drop_conv5_2
I0224 19:35:52.685240 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.685241 29812 layer_factory.hpp:74] Creating layer conv5_3
I0224 19:35:52.685250 29812 net.cpp:79] Creating Layer conv5_3
I0224 19:35:52.685253 29812 net.cpp:375] conv5_3 <- do5_2
I0224 19:35:52.685256 29812 net.cpp:337] conv5_3 -> conv5_3
I0224 19:35:52.685261 29812 net.cpp:108] Setting up conv5_3
I0224 19:35:52.739284 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.739315 29812 layer_factory.hpp:74] Creating layer bn5_3
I0224 19:35:52.739330 29812 net.cpp:79] Creating Layer bn5_3
I0224 19:35:52.739337 29812 net.cpp:375] bn5_3 <- conv5_3
I0224 19:35:52.739348 29812 net.cpp:337] bn5_3 -> bn5_3
I0224 19:35:52.739370 29812 net.cpp:108] Setting up bn5_3
I0224 19:35:52.739392 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.739398 29812 layer_factory.hpp:74] Creating layer relu5_3
I0224 19:35:52.739403 29812 net.cpp:79] Creating Layer relu5_3
I0224 19:35:52.739404 29812 net.cpp:375] relu5_3 <- bn5_3
I0224 19:35:52.739408 29812 net.cpp:337] relu5_3 -> relu5_3
I0224 19:35:52.739413 29812 net.cpp:108] Setting up relu5_3
I0224 19:35:52.739421 29812 net.cpp:115] Top shape: 100 512 2 2 (204800)
I0224 19:35:52.739428 29812 layer_factory.hpp:74] Creating layer pool5
I0224 19:35:52.739434 29812 net.cpp:79] Creating Layer pool5
I0224 19:35:52.739436 29812 net.cpp:375] pool5 <- relu5_3
I0224 19:35:52.739440 29812 net.cpp:337] pool5 -> pool5
I0224 19:35:52.739444 29812 net.cpp:108] Setting up pool5
I0224 19:35:52.739451 29812 net.cpp:115] Top shape: 100 512 1 1 (51200)
I0224 19:35:52.739454 29812 layer_factory.hpp:74] Creating layer fc6
I0224 19:35:52.739459 29812 net.cpp:79] Creating Layer fc6
I0224 19:35:52.739462 29812 net.cpp:375] fc6 <- pool5
I0224 19:35:52.739465 29812 net.cpp:337] fc6 -> fc6
I0224 19:35:52.739470 29812 net.cpp:108] Setting up fc6
I0224 19:35:52.739837 29812 net.cpp:115] Top shape: 100 100 1 1 (10000)
I0224 19:35:52.739845 29812 layer_factory.hpp:74] Creating layer bn
I0224 19:35:52.739850 29812 net.cpp:79] Creating Layer bn
I0224 19:35:52.739852 29812 net.cpp:375] bn <- fc6
I0224 19:35:52.739857 29812 net.cpp:337] bn -> bn6
I0224 19:35:52.739861 29812 net.cpp:108] Setting up bn
I0224 19:35:52.739877 29812 net.cpp:115] Top shape: 100 100 1 1 (10000)
I0224 19:35:52.739893 29812 layer_factory.hpp:74] Creating layer relu6
I0224 19:35:52.739898 29812 net.cpp:79] Creating Layer relu6
I0224 19:35:52.739902 29812 net.cpp:375] relu6 <- bn6
I0224 19:35:52.739904 29812 net.cpp:337] relu6 -> relu6
I0224 19:35:52.739908 29812 net.cpp:108] Setting up relu6
I0224 19:35:52.739915 29812 net.cpp:115] Top shape: 100 100 1 1 (10000)
I0224 19:35:52.739919 29812 layer_factory.hpp:74] Creating layer drop6
I0224 19:35:52.739923 29812 net.cpp:79] Creating Layer drop6
I0224 19:35:52.739925 29812 net.cpp:375] drop6 <- relu6
I0224 19:35:52.739929 29812 net.cpp:337] drop6 -> do6
I0224 19:35:52.739933 29812 net.cpp:108] Setting up drop6
I0224 19:35:52.739938 29812 net.cpp:115] Top shape: 100 100 1 1 (10000)
I0224 19:35:52.739940 29812 layer_factory.hpp:74] Creating layer fc7
I0224 19:35:52.739944 29812 net.cpp:79] Creating Layer fc7
I0224 19:35:52.739948 29812 net.cpp:375] fc7 <- do6
I0224 19:35:52.739951 29812 net.cpp:337] fc7 -> fc7
I0224 19:35:52.739955 29812 net.cpp:108] Setting up fc7
I0224 19:35:52.739969 29812 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0224 19:35:52.739972 29812 layer_factory.hpp:74] Creating layer fc7_fc7_0_split
I0224 19:35:52.739985 29812 net.cpp:79] Creating Layer fc7_fc7_0_split
I0224 19:35:52.739987 29812 net.cpp:375] fc7_fc7_0_split <- fc7
I0224 19:35:52.739990 29812 net.cpp:337] fc7_fc7_0_split -> fc7_fc7_0_split_0
I0224 19:35:52.740003 29812 net.cpp:337] fc7_fc7_0_split -> fc7_fc7_0_split_1
I0224 19:35:52.740006 29812 net.cpp:108] Setting up fc7_fc7_0_split
I0224 19:35:52.740010 29812 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0224 19:35:52.740011 29812 net.cpp:115] Top shape: 100 10 1 1 (1000)
I0224 19:35:52.740013 29812 layer_factory.hpp:74] Creating layer fc7/loss3
I0224 19:35:52.740017 29812 net.cpp:79] Creating Layer fc7/loss3
I0224 19:35:52.740031 29812 net.cpp:375] fc7/loss3 <- fc7_fc7_0_split_0
I0224 19:35:52.740032 29812 net.cpp:375] fc7/loss3 <- label_cifar_1_split_0
I0224 19:35:52.740036 29812 net.cpp:337] fc7/loss3 -> fc7/loss3
I0224 19:35:52.740052 29812 net.cpp:108] Setting up fc7/loss3
I0224 19:35:52.740056 29812 layer_factory.hpp:74] Creating layer fc7/loss3
I0224 19:35:52.740077 29812 net.cpp:115] Top shape: 1 1 1 1 (1)
I0224 19:35:52.740079 29812 net.cpp:121]     with loss weight 1
I0224 19:35:52.740087 29812 layer_factory.hpp:74] Creating layer fc7/acc
I0224 19:35:52.740104 29812 net.cpp:79] Creating Layer fc7/acc
I0224 19:35:52.740108 29812 net.cpp:375] fc7/acc <- fc7_fc7_0_split_1
I0224 19:35:52.740109 29812 net.cpp:375] fc7/acc <- label_cifar_1_split_1
I0224 19:35:52.740113 29812 net.cpp:337] fc7/acc -> fc7/acc
I0224 19:35:52.740118 29812 net.cpp:108] Setting up fc7/acc
I0224 19:35:52.740120 29812 net.cpp:115] Top shape: 1 1 1 1 (1)
I0224 19:35:52.740131 29812 net.cpp:168] fc7/acc does not need backward computation.
I0224 19:35:52.740134 29812 net.cpp:166] fc7/loss3 needs backward computation.
I0224 19:35:52.740136 29812 net.cpp:166] fc7_fc7_0_split needs backward computation.
I0224 19:35:52.740139 29812 net.cpp:166] fc7 needs backward computation.
I0224 19:35:52.740142 29812 net.cpp:166] drop6 needs backward computation.
I0224 19:35:52.740144 29812 net.cpp:166] relu6 needs backward computation.
I0224 19:35:52.740155 29812 net.cpp:166] bn needs backward computation.
I0224 19:35:52.740157 29812 net.cpp:166] fc6 needs backward computation.
I0224 19:35:52.740159 29812 net.cpp:166] pool5 needs backward computation.
I0224 19:35:52.740161 29812 net.cpp:166] relu5_3 needs backward computation.
I0224 19:35:52.740164 29812 net.cpp:166] bn5_3 needs backward computation.
I0224 19:35:52.740165 29812 net.cpp:166] conv5_3 needs backward computation.
I0224 19:35:52.740169 29812 net.cpp:166] drop_conv5_2 needs backward computation.
I0224 19:35:52.740170 29812 net.cpp:166] relu5_2 needs backward computation.
I0224 19:35:52.740172 29812 net.cpp:166] bn5_2 needs backward computation.
I0224 19:35:52.740175 29812 net.cpp:166] conv5_2 needs backward computation.
I0224 19:35:52.740190 29812 net.cpp:166] drop_conv5_1 needs backward computation.
I0224 19:35:52.740192 29812 net.cpp:166] relu5_1 needs backward computation.
I0224 19:35:52.740195 29812 net.cpp:166] bn5_1 needs backward computation.
I0224 19:35:52.740196 29812 net.cpp:166] conv5_1 needs backward computation.
I0224 19:35:52.740200 29812 net.cpp:166] pool4 needs backward computation.
I0224 19:35:52.740201 29812 net.cpp:166] relu4_3 needs backward computation.
I0224 19:35:52.740205 29812 net.cpp:166] bn4_3 needs backward computation.
I0224 19:35:52.740207 29812 net.cpp:166] conv4_3 needs backward computation.
I0224 19:35:52.740209 29812 net.cpp:166] drop_conv4_2 needs backward computation.
I0224 19:35:52.740212 29812 net.cpp:166] relu4_2 needs backward computation.
I0224 19:35:52.740214 29812 net.cpp:166] bn4_2 needs backward computation.
I0224 19:35:52.740217 29812 net.cpp:166] conv4_2 needs backward computation.
I0224 19:35:52.740221 29812 net.cpp:166] drop_conv4_1 needs backward computation.
I0224 19:35:52.740222 29812 net.cpp:166] relu4_1 needs backward computation.
I0224 19:35:52.740224 29812 net.cpp:166] bn4_1 needs backward computation.
I0224 19:35:52.740227 29812 net.cpp:166] conv4_1 needs backward computation.
I0224 19:35:52.740229 29812 net.cpp:166] pool3 needs backward computation.
I0224 19:35:52.740233 29812 net.cpp:166] drop_conv3_2 needs backward computation.
I0224 19:35:52.740236 29812 net.cpp:166] relu3_3 needs backward computation.
I0224 19:35:52.740238 29812 net.cpp:166] bn3_3 needs backward computation.
I0224 19:35:52.740241 29812 net.cpp:166] conv3_3 needs backward computation.
I0224 19:35:52.740243 29812 net.cpp:166] relu3_2 needs backward computation.
I0224 19:35:52.740245 29812 net.cpp:166] bn3_2 needs backward computation.
I0224 19:35:52.740247 29812 net.cpp:166] conv3_2 needs backward computation.
I0224 19:35:52.740250 29812 net.cpp:166] drop_conv3_1 needs backward computation.
I0224 19:35:52.740253 29812 net.cpp:166] relu3_1 needs backward computation.
I0224 19:35:52.740255 29812 net.cpp:166] bn3_1 needs backward computation.
I0224 19:35:52.740257 29812 net.cpp:166] conv3_1 needs backward computation.
I0224 19:35:52.740259 29812 net.cpp:166] pool2 needs backward computation.
I0224 19:35:52.740262 29812 net.cpp:166] relu2_2 needs backward computation.
I0224 19:35:52.740264 29812 net.cpp:166] bn2_2 needs backward computation.
I0224 19:35:52.740267 29812 net.cpp:166] conv2_2 needs backward computation.
I0224 19:35:52.740270 29812 net.cpp:166] drop_conv2_1 needs backward computation.
I0224 19:35:52.740272 29812 net.cpp:166] relu2_1 needs backward computation.
I0224 19:35:52.740275 29812 net.cpp:166] bn2_1 needs backward computation.
I0224 19:35:52.740278 29812 net.cpp:166] conv2_1 needs backward computation.
I0224 19:35:52.740280 29812 net.cpp:166] pool1 needs backward computation.
I0224 19:35:52.740283 29812 net.cpp:166] relu1_2 needs backward computation.
I0224 19:35:52.740284 29812 net.cpp:166] bn1_2 needs backward computation.
I0224 19:35:52.740286 29812 net.cpp:166] conv1_2 needs backward computation.
I0224 19:35:52.740289 29812 net.cpp:166] drop_conv1_1 needs backward computation.
I0224 19:35:52.740291 29812 net.cpp:166] relu1_1 needs backward computation.
I0224 19:35:52.740293 29812 net.cpp:166] bn1 needs backward computation.
I0224 19:35:52.740295 29812 net.cpp:166] conv1_1 needs backward computation.
I0224 19:35:52.740299 29812 net.cpp:168] label_cifar_1_split does not need backward computation.
I0224 19:35:52.740301 29812 net.cpp:168] cifar does not need backward computation.
I0224 19:35:52.740303 29812 net.cpp:204] This network produces output fc7/acc
I0224 19:35:52.740306 29812 net.cpp:204] This network produces output fc7/loss3
I0224 19:35:52.740334 29812 net.cpp:449] Collecting Learning Rate and Weight Decay.
I0224 19:35:52.740344 29812 net.cpp:216] Network initialization done.
I0224 19:35:52.740346 29812 net.cpp:217] Memory required for data: 406291608
I0224 19:35:52.740526 29812 solver.cpp:42] Solver scaffolding done.
I0224 19:35:52.740617 29812 solver.cpp:222] Solving VGG_ILSVRC_16_layers_bn
I0224 19:35:52.740624 29812 solver.cpp:223] Learning Rate Policy: step
I0224 19:35:52.740640 29812 solver.cpp:266] Iteration 0, Testing net (#0)
I0224 19:36:00.437657 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.1085
I0224 19:36:00.437695 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 2.32708 (* 1 = 2.32708 loss)
I0224 19:36:00.818984 29812 solver.cpp:189] Iteration 0, loss = 2.34866
I0224 19:36:00.819010 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 2.34866 (* 1 = 2.34866 loss)
I0224 19:36:00.819028 29812 solver.cpp:470] Iteration 0, lr = 0.001
I0224 19:36:20.217669 29812 solver.cpp:189] Iteration 50, loss = 2.15998
I0224 19:36:20.217691 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 2.15998 (* 1 = 2.15998 loss)
I0224 19:36:20.217696 29812 solver.cpp:470] Iteration 50, lr = 0.001
I0224 19:36:39.888532 29812 solver.cpp:189] Iteration 100, loss = 2.09897
I0224 19:36:39.888608 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 2.09897 (* 1 = 2.09897 loss)
I0224 19:36:39.888615 29812 solver.cpp:470] Iteration 100, lr = 0.001
I0224 19:36:59.764444 29812 solver.cpp:189] Iteration 150, loss = 2.11049
I0224 19:36:59.764489 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 2.11049 (* 1 = 2.11049 loss)
I0224 19:36:59.764495 29812 solver.cpp:470] Iteration 150, lr = 0.001
I0224 19:37:19.294481 29812 solver.cpp:189] Iteration 200, loss = 1.94521
I0224 19:37:19.294574 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.94521 (* 1 = 1.94521 loss)
I0224 19:37:19.294590 29812 solver.cpp:470] Iteration 200, lr = 0.001
I0224 19:37:39.091683 29812 solver.cpp:189] Iteration 250, loss = 1.95751
I0224 19:37:39.091707 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.95751 (* 1 = 1.95751 loss)
I0224 19:37:39.091713 29812 solver.cpp:470] Iteration 250, lr = 0.001
I0224 19:37:58.617707 29812 solver.cpp:189] Iteration 300, loss = 1.95041
I0224 19:37:58.617769 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.95041 (* 1 = 1.95041 loss)
I0224 19:37:58.617776 29812 solver.cpp:470] Iteration 300, lr = 0.001
I0224 19:38:18.043181 29812 solver.cpp:189] Iteration 350, loss = 1.87508
I0224 19:38:18.043208 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.87508 (* 1 = 1.87508 loss)
I0224 19:38:18.043215 29812 solver.cpp:470] Iteration 350, lr = 0.001
I0224 19:38:37.579059 29812 solver.cpp:189] Iteration 400, loss = 1.82433
I0224 19:38:37.579134 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.82433 (* 1 = 1.82433 loss)
I0224 19:38:37.579149 29812 solver.cpp:470] Iteration 400, lr = 0.001
I0224 19:38:57.254806 29812 solver.cpp:189] Iteration 450, loss = 1.86952
I0224 19:38:57.254871 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.86952 (* 1 = 1.86952 loss)
I0224 19:38:57.254887 29812 solver.cpp:470] Iteration 450, lr = 0.001
I0224 19:39:16.749193 29812 solver.cpp:189] Iteration 500, loss = 1.86236
I0224 19:39:16.749270 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.86236 (* 1 = 1.86236 loss)
I0224 19:39:16.749285 29812 solver.cpp:470] Iteration 500, lr = 0.001
I0224 19:39:36.189875 29812 solver.cpp:189] Iteration 550, loss = 1.80446
I0224 19:39:36.189915 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.80446 (* 1 = 1.80446 loss)
I0224 19:39:36.189923 29812 solver.cpp:470] Iteration 550, lr = 0.001
I0224 19:39:55.769502 29812 solver.cpp:189] Iteration 600, loss = 1.74249
I0224 19:39:55.769587 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.74249 (* 1 = 1.74249 loss)
I0224 19:39:55.769603 29812 solver.cpp:470] Iteration 600, lr = 0.001
I0224 19:40:15.413545 29812 solver.cpp:189] Iteration 650, loss = 1.65007
I0224 19:40:15.413584 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.65007 (* 1 = 1.65007 loss)
I0224 19:40:15.413589 29812 solver.cpp:470] Iteration 650, lr = 0.001
I0224 19:40:35.069248 29812 solver.cpp:189] Iteration 700, loss = 1.82351
I0224 19:40:35.069342 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.82351 (* 1 = 1.82351 loss)
I0224 19:40:35.069357 29812 solver.cpp:470] Iteration 700, lr = 0.001
I0224 19:40:54.761307 29812 solver.cpp:189] Iteration 750, loss = 1.71093
I0224 19:40:54.761349 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.71093 (* 1 = 1.71093 loss)
I0224 19:40:54.761365 29812 solver.cpp:470] Iteration 750, lr = 0.001
I0224 19:41:14.539510 29812 solver.cpp:189] Iteration 800, loss = 1.84651
I0224 19:41:14.539590 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.84651 (* 1 = 1.84651 loss)
I0224 19:41:14.539597 29812 solver.cpp:470] Iteration 800, lr = 0.001
I0224 19:41:34.155346 29812 solver.cpp:189] Iteration 850, loss = 1.72128
I0224 19:41:34.155374 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.72128 (* 1 = 1.72128 loss)
I0224 19:41:34.155380 29812 solver.cpp:470] Iteration 850, lr = 0.001
I0224 19:41:53.642631 29812 solver.cpp:189] Iteration 900, loss = 1.70726
I0224 19:41:53.642712 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.70726 (* 1 = 1.70726 loss)
I0224 19:41:53.642731 29812 solver.cpp:470] Iteration 900, lr = 0.001
I0224 19:42:13.086704 29812 solver.cpp:189] Iteration 950, loss = 1.80332
I0224 19:42:13.086730 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.80332 (* 1 = 1.80332 loss)
I0224 19:42:13.086735 29812 solver.cpp:470] Iteration 950, lr = 0.001
I0224 19:42:32.362934 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_1000.caffemodel
I0224 19:42:32.488400 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_1000.solverstate
I0224 19:42:32.550237 29812 solver.cpp:266] Iteration 1000, Testing net (#0)
I0224 19:42:40.204702 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.3741
I0224 19:42:40.204788 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 1.62373 (* 1 = 1.62373 loss)
I0224 19:42:40.493381 29812 solver.cpp:189] Iteration 1000, loss = 1.54851
I0224 19:42:40.493454 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.54851 (* 1 = 1.54851 loss)
I0224 19:42:40.493471 29812 solver.cpp:470] Iteration 1000, lr = 0.001
I0224 19:42:59.930662 29812 solver.cpp:189] Iteration 1050, loss = 1.6967
I0224 19:42:59.930685 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.6967 (* 1 = 1.6967 loss)
I0224 19:42:59.930691 29812 solver.cpp:470] Iteration 1050, lr = 0.001
I0224 19:43:19.347350 29812 solver.cpp:189] Iteration 1100, loss = 1.60625
I0224 19:43:19.347393 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.60625 (* 1 = 1.60625 loss)
I0224 19:43:19.347398 29812 solver.cpp:470] Iteration 1100, lr = 0.001
I0224 19:43:38.753686 29812 solver.cpp:189] Iteration 1150, loss = 1.65546
I0224 19:43:38.753711 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.65546 (* 1 = 1.65546 loss)
I0224 19:43:38.753717 29812 solver.cpp:470] Iteration 1150, lr = 0.001
I0224 19:43:58.402479 29812 solver.cpp:189] Iteration 1200, loss = 1.77102
I0224 19:43:58.402523 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.77102 (* 1 = 1.77102 loss)
I0224 19:43:58.402529 29812 solver.cpp:470] Iteration 1200, lr = 0.001
I0224 19:44:17.867128 29812 solver.cpp:189] Iteration 1250, loss = 1.64384
I0224 19:44:17.867153 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.64384 (* 1 = 1.64384 loss)
I0224 19:44:17.867158 29812 solver.cpp:470] Iteration 1250, lr = 0.001
I0224 19:44:37.549705 29812 solver.cpp:189] Iteration 1300, loss = 1.53313
I0224 19:44:37.549794 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.53313 (* 1 = 1.53313 loss)
I0224 19:44:37.549811 29812 solver.cpp:470] Iteration 1300, lr = 0.001
I0224 19:44:57.033506 29812 solver.cpp:189] Iteration 1350, loss = 1.55673
I0224 19:44:57.033531 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.55673 (* 1 = 1.55673 loss)
I0224 19:44:57.033537 29812 solver.cpp:470] Iteration 1350, lr = 0.001
I0224 19:45:16.636850 29812 solver.cpp:189] Iteration 1400, loss = 1.57198
I0224 19:45:16.636956 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.57198 (* 1 = 1.57198 loss)
I0224 19:45:16.636965 29812 solver.cpp:470] Iteration 1400, lr = 0.001
I0224 19:45:36.015007 29812 solver.cpp:189] Iteration 1450, loss = 1.65208
I0224 19:45:36.015030 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.65208 (* 1 = 1.65208 loss)
I0224 19:45:36.015035 29812 solver.cpp:470] Iteration 1450, lr = 0.001
I0224 19:45:55.384876 29812 solver.cpp:189] Iteration 1500, loss = 1.60831
I0224 19:45:55.384922 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.60831 (* 1 = 1.60831 loss)
I0224 19:45:55.384927 29812 solver.cpp:470] Iteration 1500, lr = 0.001
I0224 19:46:14.821041 29812 solver.cpp:189] Iteration 1550, loss = 1.57507
I0224 19:46:14.821066 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.57507 (* 1 = 1.57507 loss)
I0224 19:46:14.821072 29812 solver.cpp:470] Iteration 1550, lr = 0.001
I0224 19:46:34.228653 29812 solver.cpp:189] Iteration 1600, loss = 1.66386
I0224 19:46:34.228690 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.66386 (* 1 = 1.66386 loss)
I0224 19:46:34.228696 29812 solver.cpp:470] Iteration 1600, lr = 0.001
I0224 19:46:53.599665 29812 solver.cpp:189] Iteration 1650, loss = 1.57169
I0224 19:46:53.599688 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.57169 (* 1 = 1.57169 loss)
I0224 19:46:53.599694 29812 solver.cpp:470] Iteration 1650, lr = 0.001
I0224 19:47:13.004421 29812 solver.cpp:189] Iteration 1700, loss = 1.49799
I0224 19:47:13.004508 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.49799 (* 1 = 1.49799 loss)
I0224 19:47:13.004513 29812 solver.cpp:470] Iteration 1700, lr = 0.001
I0224 19:47:32.532891 29812 solver.cpp:189] Iteration 1750, loss = 1.4569
I0224 19:47:32.532915 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.4569 (* 1 = 1.4569 loss)
I0224 19:47:32.532922 29812 solver.cpp:470] Iteration 1750, lr = 0.001
I0224 19:47:51.944872 29812 solver.cpp:189] Iteration 1800, loss = 1.47209
I0224 19:47:51.944943 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.47209 (* 1 = 1.47209 loss)
I0224 19:47:51.944950 29812 solver.cpp:470] Iteration 1800, lr = 0.001
I0224 19:48:11.624477 29812 solver.cpp:189] Iteration 1850, loss = 1.49487
I0224 19:48:11.624505 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.49487 (* 1 = 1.49487 loss)
I0224 19:48:11.624511 29812 solver.cpp:470] Iteration 1850, lr = 0.001
I0224 19:48:31.572814 29812 solver.cpp:189] Iteration 1900, loss = 1.3175
I0224 19:48:31.572852 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.3175 (* 1 = 1.3175 loss)
I0224 19:48:31.572857 29812 solver.cpp:470] Iteration 1900, lr = 0.001
I0224 19:48:51.029847 29812 solver.cpp:189] Iteration 1950, loss = 1.42649
I0224 19:48:51.029870 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.42649 (* 1 = 1.42649 loss)
I0224 19:48:51.029876 29812 solver.cpp:470] Iteration 1950, lr = 0.001
I0224 19:49:10.459483 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_2000.caffemodel
I0224 19:49:10.570107 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_2000.solverstate
I0224 19:49:10.631156 29812 solver.cpp:266] Iteration 2000, Testing net (#0)
I0224 19:49:18.277355 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.4788
I0224 19:49:18.277395 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 1.39028 (* 1 = 1.39028 loss)
I0224 19:49:18.565680 29812 solver.cpp:189] Iteration 2000, loss = 1.43889
I0224 19:49:18.565706 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.43889 (* 1 = 1.43889 loss)
I0224 19:49:18.565711 29812 solver.cpp:470] Iteration 2000, lr = 0.001
I0224 19:49:38.430197 29812 solver.cpp:189] Iteration 2050, loss = 1.49378
I0224 19:49:38.430222 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.49378 (* 1 = 1.49378 loss)
I0224 19:49:38.430227 29812 solver.cpp:470] Iteration 2050, lr = 0.001
I0224 19:49:58.264863 29812 solver.cpp:189] Iteration 2100, loss = 1.44711
I0224 19:49:58.264956 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.44711 (* 1 = 1.44711 loss)
I0224 19:49:58.264972 29812 solver.cpp:470] Iteration 2100, lr = 0.001
I0224 19:50:18.120060 29812 solver.cpp:189] Iteration 2150, loss = 1.45181
I0224 19:50:18.120085 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.45181 (* 1 = 1.45181 loss)
I0224 19:50:18.120091 29812 solver.cpp:470] Iteration 2150, lr = 0.001
I0224 19:50:37.937237 29812 solver.cpp:189] Iteration 2200, loss = 1.61164
I0224 19:50:37.937281 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.61164 (* 1 = 1.61164 loss)
I0224 19:50:37.937288 29812 solver.cpp:470] Iteration 2200, lr = 0.001
I0224 19:50:57.805121 29812 solver.cpp:189] Iteration 2250, loss = 1.28845
I0224 19:50:57.805160 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.28845 (* 1 = 1.28845 loss)
I0224 19:50:57.805166 29812 solver.cpp:470] Iteration 2250, lr = 0.001
I0224 19:51:17.790675 29812 solver.cpp:189] Iteration 2300, loss = 1.32073
I0224 19:51:17.790776 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.32073 (* 1 = 1.32073 loss)
I0224 19:51:17.790792 29812 solver.cpp:470] Iteration 2300, lr = 0.001
I0224 19:51:37.771152 29812 solver.cpp:189] Iteration 2350, loss = 1.34948
I0224 19:51:37.771217 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.34948 (* 1 = 1.34948 loss)
I0224 19:51:37.771234 29812 solver.cpp:470] Iteration 2350, lr = 0.001
I0224 19:51:57.269717 29812 solver.cpp:189] Iteration 2400, loss = 1.44763
I0224 19:51:57.269793 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.44763 (* 1 = 1.44763 loss)
I0224 19:51:57.269808 29812 solver.cpp:470] Iteration 2400, lr = 0.001
I0224 19:52:16.788600 29812 solver.cpp:189] Iteration 2450, loss = 1.36794
I0224 19:52:16.788625 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.36794 (* 1 = 1.36794 loss)
I0224 19:52:16.788630 29812 solver.cpp:470] Iteration 2450, lr = 0.001
I0224 19:52:36.196056 29812 solver.cpp:189] Iteration 2500, loss = 1.41476
I0224 19:52:36.196099 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.41476 (* 1 = 1.41476 loss)
I0224 19:52:36.196105 29812 solver.cpp:470] Iteration 2500, lr = 0.001
I0224 19:52:55.579948 29812 solver.cpp:189] Iteration 2550, loss = 1.49448
I0224 19:52:55.579973 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.49448 (* 1 = 1.49448 loss)
I0224 19:52:55.579979 29812 solver.cpp:470] Iteration 2550, lr = 0.001
I0224 19:53:14.992722 29812 solver.cpp:189] Iteration 2600, loss = 1.31682
I0224 19:53:14.992810 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.31682 (* 1 = 1.31682 loss)
I0224 19:53:14.992825 29812 solver.cpp:470] Iteration 2600, lr = 0.001
I0224 19:53:34.407721 29812 solver.cpp:189] Iteration 2650, loss = 1.45477
I0224 19:53:34.407747 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.45477 (* 1 = 1.45477 loss)
I0224 19:53:34.407752 29812 solver.cpp:470] Iteration 2650, lr = 0.001
I0224 19:53:54.044905 29812 solver.cpp:189] Iteration 2700, loss = 1.15945
I0224 19:53:54.044983 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.15945 (* 1 = 1.15945 loss)
I0224 19:53:54.044998 29812 solver.cpp:470] Iteration 2700, lr = 0.001
I0224 19:54:13.509135 29812 solver.cpp:189] Iteration 2750, loss = 1.35155
I0224 19:54:13.509174 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.35155 (* 1 = 1.35155 loss)
I0224 19:54:13.509181 29812 solver.cpp:470] Iteration 2750, lr = 0.001
I0224 19:54:33.086626 29812 solver.cpp:189] Iteration 2800, loss = 1.37515
I0224 19:54:33.086727 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.37515 (* 1 = 1.37515 loss)
I0224 19:54:33.086735 29812 solver.cpp:470] Iteration 2800, lr = 0.001
I0224 19:54:52.710031 29812 solver.cpp:189] Iteration 2850, loss = 1.34777
I0224 19:54:52.710067 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.34777 (* 1 = 1.34777 loss)
I0224 19:54:52.710072 29812 solver.cpp:470] Iteration 2850, lr = 0.001
I0224 19:55:12.141823 29812 solver.cpp:189] Iteration 2900, loss = 1.38012
I0224 19:55:12.141918 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.38012 (* 1 = 1.38012 loss)
I0224 19:55:12.141938 29812 solver.cpp:470] Iteration 2900, lr = 0.001
I0224 19:55:31.865211 29812 solver.cpp:189] Iteration 2950, loss = 1.44562
I0224 19:55:31.865247 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.44562 (* 1 = 1.44562 loss)
I0224 19:55:31.865254 29812 solver.cpp:470] Iteration 2950, lr = 0.001
I0224 19:55:51.537204 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_3000.caffemodel
I0224 19:55:51.639981 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_3000.solverstate
I0224 19:55:51.698603 29812 solver.cpp:266] Iteration 3000, Testing net (#0)
I0224 19:55:59.421478 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.54
I0224 19:55:59.421514 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 1.22539 (* 1 = 1.22539 loss)
I0224 19:55:59.709409 29812 solver.cpp:189] Iteration 3000, loss = 1.36923
I0224 19:55:59.709434 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.36923 (* 1 = 1.36923 loss)
I0224 19:55:59.709439 29812 solver.cpp:470] Iteration 3000, lr = 0.001
I0224 19:56:19.219020 29812 solver.cpp:189] Iteration 3050, loss = 1.19403
I0224 19:56:19.219045 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.19403 (* 1 = 1.19403 loss)
I0224 19:56:19.219051 29812 solver.cpp:470] Iteration 3050, lr = 0.001
I0224 19:56:38.650670 29812 solver.cpp:189] Iteration 3100, loss = 1.40754
I0224 19:56:38.650760 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.40754 (* 1 = 1.40754 loss)
I0224 19:56:38.650776 29812 solver.cpp:470] Iteration 3100, lr = 0.001
I0224 19:56:58.075084 29812 solver.cpp:189] Iteration 3150, loss = 1.19031
I0224 19:56:58.075165 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.19031 (* 1 = 1.19031 loss)
I0224 19:56:58.075181 29812 solver.cpp:470] Iteration 3150, lr = 0.001
I0224 19:57:17.673040 29812 solver.cpp:189] Iteration 3200, loss = 1.27106
I0224 19:57:17.673110 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.27106 (* 1 = 1.27106 loss)
I0224 19:57:17.673125 29812 solver.cpp:470] Iteration 3200, lr = 0.001
I0224 19:57:37.292316 29812 solver.cpp:189] Iteration 3250, loss = 1.18285
I0224 19:57:37.292340 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.18285 (* 1 = 1.18285 loss)
I0224 19:57:37.292346 29812 solver.cpp:470] Iteration 3250, lr = 0.001
I0224 19:57:56.705517 29812 solver.cpp:189] Iteration 3300, loss = 1.41536
I0224 19:57:56.705585 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.41536 (* 1 = 1.41536 loss)
I0224 19:57:56.705592 29812 solver.cpp:470] Iteration 3300, lr = 0.001
I0224 19:58:16.276315 29812 solver.cpp:189] Iteration 3350, loss = 1.29484
I0224 19:58:16.276382 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.29484 (* 1 = 1.29484 loss)
I0224 19:58:16.276406 29812 solver.cpp:470] Iteration 3350, lr = 0.001
I0224 19:58:35.713008 29812 solver.cpp:189] Iteration 3400, loss = 1.29888
I0224 19:58:35.713083 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.29888 (* 1 = 1.29888 loss)
I0224 19:58:35.713098 29812 solver.cpp:470] Iteration 3400, lr = 0.001
I0224 19:58:55.319454 29812 solver.cpp:189] Iteration 3450, loss = 1.14148
I0224 19:58:55.319478 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.14148 (* 1 = 1.14148 loss)
I0224 19:58:55.319483 29812 solver.cpp:470] Iteration 3450, lr = 0.001
I0224 19:59:14.824868 29812 solver.cpp:189] Iteration 3500, loss = 1.33968
I0224 19:59:14.824962 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.33968 (* 1 = 1.33968 loss)
I0224 19:59:14.824970 29812 solver.cpp:470] Iteration 3500, lr = 0.001
I0224 19:59:34.424340 29812 solver.cpp:189] Iteration 3550, loss = 1.56959
I0224 19:59:34.424367 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.56959 (* 1 = 1.56959 loss)
I0224 19:59:34.424373 29812 solver.cpp:470] Iteration 3550, lr = 0.001
I0224 19:59:53.920043 29812 solver.cpp:189] Iteration 3600, loss = 1.41999
I0224 19:59:53.920120 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.41999 (* 1 = 1.41999 loss)
I0224 19:59:53.920127 29812 solver.cpp:470] Iteration 3600, lr = 0.001
I0224 20:00:13.446137 29812 solver.cpp:189] Iteration 3650, loss = 1.04625
I0224 20:00:13.446161 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.04625 (* 1 = 1.04625 loss)
I0224 20:00:13.446167 29812 solver.cpp:470] Iteration 3650, lr = 0.001
I0224 20:00:32.983383 29812 solver.cpp:189] Iteration 3700, loss = 1.19045
I0224 20:00:32.983484 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.19045 (* 1 = 1.19045 loss)
I0224 20:00:32.983501 29812 solver.cpp:470] Iteration 3700, lr = 0.001
I0224 20:00:52.579711 29812 solver.cpp:189] Iteration 3750, loss = 1.27983
I0224 20:00:52.579784 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.27983 (* 1 = 1.27983 loss)
I0224 20:00:52.579802 29812 solver.cpp:470] Iteration 3750, lr = 0.001
I0224 20:01:12.191714 29812 solver.cpp:189] Iteration 3800, loss = 1.17473
I0224 20:01:12.191825 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.17473 (* 1 = 1.17473 loss)
I0224 20:01:12.191833 29812 solver.cpp:470] Iteration 3800, lr = 0.001
I0224 20:01:31.658331 29812 solver.cpp:189] Iteration 3850, loss = 1.20663
I0224 20:01:31.658359 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.20663 (* 1 = 1.20663 loss)
I0224 20:01:31.658363 29812 solver.cpp:470] Iteration 3850, lr = 0.001
I0224 20:01:51.180387 29812 solver.cpp:189] Iteration 3900, loss = 1.10258
I0224 20:01:51.180428 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.10258 (* 1 = 1.10258 loss)
I0224 20:01:51.180434 29812 solver.cpp:470] Iteration 3900, lr = 0.001
I0224 20:02:10.715620 29812 solver.cpp:189] Iteration 3950, loss = 1.18994
I0224 20:02:10.715647 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.18994 (* 1 = 1.18994 loss)
I0224 20:02:10.715653 29812 solver.cpp:470] Iteration 3950, lr = 0.001
I0224 20:02:30.037502 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_4000.caffemodel
I0224 20:02:30.144793 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_4000.solverstate
I0224 20:02:30.204483 29812 solver.cpp:266] Iteration 4000, Testing net (#0)
I0224 20:02:37.885015 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.6052
I0224 20:02:37.885124 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 1.07827 (* 1 = 1.07827 loss)
I0224 20:02:38.177518 29812 solver.cpp:189] Iteration 4000, loss = 1.36822
I0224 20:02:38.177567 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.36822 (* 1 = 1.36822 loss)
I0224 20:02:38.177573 29812 solver.cpp:470] Iteration 4000, lr = 0.001
I0224 20:02:57.720604 29812 solver.cpp:189] Iteration 4050, loss = 1.15214
I0224 20:02:57.720630 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.15214 (* 1 = 1.15214 loss)
I0224 20:02:57.720636 29812 solver.cpp:470] Iteration 4050, lr = 0.001
I0224 20:03:17.252470 29812 solver.cpp:189] Iteration 4100, loss = 0.985424
I0224 20:03:17.252557 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.985424 (* 1 = 0.985424 loss)
I0224 20:03:17.252573 29812 solver.cpp:470] Iteration 4100, lr = 0.001
I0224 20:03:36.974148 29812 solver.cpp:189] Iteration 4150, loss = 1.23791
I0224 20:03:36.974172 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.23791 (* 1 = 1.23791 loss)
I0224 20:03:36.974177 29812 solver.cpp:470] Iteration 4150, lr = 0.001
I0224 20:03:56.423684 29812 solver.cpp:189] Iteration 4200, loss = 1.17509
I0224 20:03:56.423725 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.17509 (* 1 = 1.17509 loss)
I0224 20:03:56.423732 29812 solver.cpp:470] Iteration 4200, lr = 0.001
I0224 20:04:15.956682 29812 solver.cpp:189] Iteration 4250, loss = 1.03214
I0224 20:04:15.956707 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.03214 (* 1 = 1.03214 loss)
I0224 20:04:15.956712 29812 solver.cpp:470] Iteration 4250, lr = 0.001
I0224 20:04:35.436352 29812 solver.cpp:189] Iteration 4300, loss = 1.02365
I0224 20:04:35.436446 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.02365 (* 1 = 1.02365 loss)
I0224 20:04:35.436453 29812 solver.cpp:470] Iteration 4300, lr = 0.001
I0224 20:04:55.223825 29812 solver.cpp:189] Iteration 4350, loss = 1.03277
I0224 20:04:55.223850 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.03277 (* 1 = 1.03277 loss)
I0224 20:04:55.223855 29812 solver.cpp:470] Iteration 4350, lr = 0.001
I0224 20:05:14.634371 29812 solver.cpp:189] Iteration 4400, loss = 1.24931
I0224 20:05:14.634413 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.24931 (* 1 = 1.24931 loss)
I0224 20:05:14.634419 29812 solver.cpp:470] Iteration 4400, lr = 0.001
I0224 20:05:34.111907 29812 solver.cpp:189] Iteration 4450, loss = 1.12784
I0224 20:05:34.111948 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.12784 (* 1 = 1.12784 loss)
I0224 20:05:34.111955 29812 solver.cpp:470] Iteration 4450, lr = 0.001
I0224 20:05:53.686640 29812 solver.cpp:189] Iteration 4500, loss = 1.12362
I0224 20:05:53.686717 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.12362 (* 1 = 1.12362 loss)
I0224 20:05:53.686724 29812 solver.cpp:470] Iteration 4500, lr = 0.001
I0224 20:06:13.167404 29812 solver.cpp:189] Iteration 4550, loss = 1.31873
I0224 20:06:13.167428 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.31873 (* 1 = 1.31873 loss)
I0224 20:06:13.167433 29812 solver.cpp:470] Iteration 4550, lr = 0.001
I0224 20:06:32.556124 29812 solver.cpp:189] Iteration 4600, loss = 1.12955
I0224 20:06:32.556164 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.12955 (* 1 = 1.12955 loss)
I0224 20:06:32.556169 29812 solver.cpp:470] Iteration 4600, lr = 0.001
I0224 20:06:52.023155 29812 solver.cpp:189] Iteration 4650, loss = 1.22746
I0224 20:06:52.023180 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.22746 (* 1 = 1.22746 loss)
I0224 20:06:52.023185 29812 solver.cpp:470] Iteration 4650, lr = 0.001
I0224 20:07:11.493423 29812 solver.cpp:189] Iteration 4700, loss = 1.41789
I0224 20:07:11.493494 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.41789 (* 1 = 1.41789 loss)
I0224 20:07:11.493510 29812 solver.cpp:470] Iteration 4700, lr = 0.001
I0224 20:07:30.952581 29812 solver.cpp:189] Iteration 4750, loss = 0.998123
I0224 20:07:30.952605 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.998123 (* 1 = 0.998123 loss)
I0224 20:07:30.952610 29812 solver.cpp:470] Iteration 4750, lr = 0.001
I0224 20:07:50.351421 29812 solver.cpp:189] Iteration 4800, loss = 1.0319
I0224 20:07:50.351500 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.0319 (* 1 = 1.0319 loss)
I0224 20:07:50.351507 29812 solver.cpp:470] Iteration 4800, lr = 0.001
I0224 20:08:09.793963 29812 solver.cpp:189] Iteration 4850, loss = 1.15831
I0224 20:08:09.793987 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.15831 (* 1 = 1.15831 loss)
I0224 20:08:09.793993 29812 solver.cpp:470] Iteration 4850, lr = 0.001
I0224 20:08:29.207614 29812 solver.cpp:189] Iteration 4900, loss = 1.15185
I0224 20:08:29.207675 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.15185 (* 1 = 1.15185 loss)
I0224 20:08:29.207681 29812 solver.cpp:470] Iteration 4900, lr = 0.001
I0224 20:08:48.704257 29812 solver.cpp:189] Iteration 4950, loss = 1.16467
I0224 20:08:48.704283 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.16467 (* 1 = 1.16467 loss)
I0224 20:08:48.704289 29812 solver.cpp:470] Iteration 4950, lr = 0.001
I0224 20:09:08.081472 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_5000.caffemodel
I0224 20:09:08.185881 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_5000.solverstate
I0224 20:09:08.243727 29812 solver.cpp:266] Iteration 5000, Testing net (#0)
I0224 20:09:15.897745 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.6456
I0224 20:09:15.897784 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.973926 (* 1 = 0.973926 loss)
I0224 20:09:16.185664 29812 solver.cpp:189] Iteration 5000, loss = 1.22087
I0224 20:09:16.185688 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.22087 (* 1 = 1.22087 loss)
I0224 20:09:16.185694 29812 solver.cpp:470] Iteration 5000, lr = 0.001
I0224 20:09:35.586833 29812 solver.cpp:189] Iteration 5050, loss = 1.32362
I0224 20:09:35.586858 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.32362 (* 1 = 1.32362 loss)
I0224 20:09:35.586863 29812 solver.cpp:470] Iteration 5050, lr = 0.001
I0224 20:09:55.085371 29812 solver.cpp:189] Iteration 5100, loss = 1.08125
I0224 20:09:55.085469 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.08125 (* 1 = 1.08125 loss)
I0224 20:09:55.085485 29812 solver.cpp:470] Iteration 5100, lr = 0.001
I0224 20:10:14.621201 29812 solver.cpp:189] Iteration 5150, loss = 0.947766
I0224 20:10:14.621223 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.947766 (* 1 = 0.947766 loss)
I0224 20:10:14.621229 29812 solver.cpp:470] Iteration 5150, lr = 0.001
I0224 20:10:34.000062 29812 solver.cpp:189] Iteration 5200, loss = 1.17172
I0224 20:10:34.000154 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.17172 (* 1 = 1.17172 loss)
I0224 20:10:34.000159 29812 solver.cpp:470] Iteration 5200, lr = 0.001
I0224 20:10:53.376924 29812 solver.cpp:189] Iteration 5250, loss = 1.06962
I0224 20:10:53.376950 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.06962 (* 1 = 1.06962 loss)
I0224 20:10:53.376955 29812 solver.cpp:470] Iteration 5250, lr = 0.001
I0224 20:11:12.747220 29812 solver.cpp:189] Iteration 5300, loss = 0.903167
I0224 20:11:12.747308 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.903167 (* 1 = 0.903167 loss)
I0224 20:11:12.747324 29812 solver.cpp:470] Iteration 5300, lr = 0.001
I0224 20:11:32.123250 29812 solver.cpp:189] Iteration 5350, loss = 1.06183
I0224 20:11:32.123283 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.06183 (* 1 = 1.06183 loss)
I0224 20:11:32.123291 29812 solver.cpp:470] Iteration 5350, lr = 0.001
I0224 20:11:51.501335 29812 solver.cpp:189] Iteration 5400, loss = 1.21438
I0224 20:11:51.501375 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.21438 (* 1 = 1.21438 loss)
I0224 20:11:51.501381 29812 solver.cpp:470] Iteration 5400, lr = 0.001
I0224 20:12:11.541656 29812 solver.cpp:189] Iteration 5450, loss = 1.03892
I0224 20:12:11.541681 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.03892 (* 1 = 1.03892 loss)
I0224 20:12:11.541685 29812 solver.cpp:470] Iteration 5450, lr = 0.001
I0224 20:12:30.933465 29812 solver.cpp:189] Iteration 5500, loss = 0.907517
I0224 20:12:30.933524 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.907517 (* 1 = 0.907517 loss)
I0224 20:12:30.933531 29812 solver.cpp:470] Iteration 5500, lr = 0.001
I0224 20:12:50.327321 29812 solver.cpp:189] Iteration 5550, loss = 1.31267
I0224 20:12:50.327345 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.31267 (* 1 = 1.31267 loss)
I0224 20:12:50.327352 29812 solver.cpp:470] Iteration 5550, lr = 0.001
I0224 20:13:09.720969 29812 solver.cpp:189] Iteration 5600, loss = 1.05422
I0224 20:13:09.721027 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.05422 (* 1 = 1.05422 loss)
I0224 20:13:09.721034 29812 solver.cpp:470] Iteration 5600, lr = 0.001
I0224 20:13:29.105831 29812 solver.cpp:189] Iteration 5650, loss = 1.00607
I0224 20:13:29.105854 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.00607 (* 1 = 1.00607 loss)
I0224 20:13:29.105860 29812 solver.cpp:470] Iteration 5650, lr = 0.001
I0224 20:13:48.494592 29812 solver.cpp:189] Iteration 5700, loss = 1.18321
I0224 20:13:48.494663 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.18321 (* 1 = 1.18321 loss)
I0224 20:13:48.494678 29812 solver.cpp:470] Iteration 5700, lr = 0.001
I0224 20:14:07.893947 29812 solver.cpp:189] Iteration 5750, loss = 0.97778
I0224 20:14:07.893970 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.97778 (* 1 = 0.97778 loss)
I0224 20:14:07.893976 29812 solver.cpp:470] Iteration 5750, lr = 0.001
I0224 20:14:27.290246 29812 solver.cpp:189] Iteration 5800, loss = 0.952928
I0224 20:14:27.290308 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.952928 (* 1 = 0.952928 loss)
I0224 20:14:27.290314 29812 solver.cpp:470] Iteration 5800, lr = 0.001
I0224 20:14:46.684859 29812 solver.cpp:189] Iteration 5850, loss = 1.02208
I0224 20:14:46.684886 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.02208 (* 1 = 1.02208 loss)
I0224 20:14:46.684892 29812 solver.cpp:470] Iteration 5850, lr = 0.001
I0224 20:15:06.083730 29812 solver.cpp:189] Iteration 5900, loss = 1.01665
I0224 20:15:06.083822 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.01665 (* 1 = 1.01665 loss)
I0224 20:15:06.083828 29812 solver.cpp:470] Iteration 5900, lr = 0.001
I0224 20:15:25.477154 29812 solver.cpp:189] Iteration 5950, loss = 1.08651
I0224 20:15:25.477177 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.08651 (* 1 = 1.08651 loss)
I0224 20:15:25.477183 29812 solver.cpp:470] Iteration 5950, lr = 0.001
I0224 20:15:44.621664 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_6000.caffemodel
I0224 20:15:44.724465 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_6000.solverstate
I0224 20:15:44.782568 29812 solver.cpp:266] Iteration 6000, Testing net (#0)
I0224 20:15:52.435541 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.669
I0224 20:15:52.435577 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.926877 (* 1 = 0.926877 loss)
I0224 20:15:52.722959 29812 solver.cpp:189] Iteration 6000, loss = 1.03523
I0224 20:15:52.722983 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.03523 (* 1 = 1.03523 loss)
I0224 20:15:52.722990 29812 solver.cpp:470] Iteration 6000, lr = 0.001
I0224 20:16:12.119329 29812 solver.cpp:189] Iteration 6050, loss = 0.965946
I0224 20:16:12.119355 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.965946 (* 1 = 0.965946 loss)
I0224 20:16:12.119360 29812 solver.cpp:470] Iteration 6050, lr = 0.001
I0224 20:16:31.508929 29812 solver.cpp:189] Iteration 6100, loss = 1.00753
I0224 20:16:31.509001 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.00753 (* 1 = 1.00753 loss)
I0224 20:16:31.509016 29812 solver.cpp:470] Iteration 6100, lr = 0.001
I0224 20:16:50.900614 29812 solver.cpp:189] Iteration 6150, loss = 0.945861
I0224 20:16:50.900640 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.945861 (* 1 = 0.945861 loss)
I0224 20:16:50.900645 29812 solver.cpp:470] Iteration 6150, lr = 0.001
I0224 20:17:10.300307 29812 solver.cpp:189] Iteration 6200, loss = 1.08995
I0224 20:17:10.300400 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.08995 (* 1 = 1.08995 loss)
I0224 20:17:10.300415 29812 solver.cpp:470] Iteration 6200, lr = 0.001
I0224 20:17:29.690628 29812 solver.cpp:189] Iteration 6250, loss = 0.96971
I0224 20:17:29.690654 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.96971 (* 1 = 0.96971 loss)
I0224 20:17:29.690659 29812 solver.cpp:470] Iteration 6250, lr = 0.001
I0224 20:17:49.078029 29812 solver.cpp:189] Iteration 6300, loss = 0.986484
I0224 20:17:49.078099 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.986484 (* 1 = 0.986484 loss)
I0224 20:17:49.078114 29812 solver.cpp:470] Iteration 6300, lr = 0.001
I0224 20:18:08.480300 29812 solver.cpp:189] Iteration 6350, loss = 1.0674
I0224 20:18:08.480322 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.0674 (* 1 = 1.0674 loss)
I0224 20:18:08.480329 29812 solver.cpp:470] Iteration 6350, lr = 0.001
I0224 20:18:27.875259 29812 solver.cpp:189] Iteration 6400, loss = 1.1485
I0224 20:18:27.875326 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.1485 (* 1 = 1.1485 loss)
I0224 20:18:27.875341 29812 solver.cpp:470] Iteration 6400, lr = 0.001
I0224 20:18:47.269472 29812 solver.cpp:189] Iteration 6450, loss = 0.865213
I0224 20:18:47.269497 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.865213 (* 1 = 0.865213 loss)
I0224 20:18:47.269502 29812 solver.cpp:470] Iteration 6450, lr = 0.001
I0224 20:19:06.667474 29812 solver.cpp:189] Iteration 6500, loss = 1.04364
I0224 20:19:06.667575 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.04364 (* 1 = 1.04364 loss)
I0224 20:19:06.667590 29812 solver.cpp:470] Iteration 6500, lr = 0.001
I0224 20:19:26.070081 29812 solver.cpp:189] Iteration 6550, loss = 0.941985
I0224 20:19:26.070117 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.941985 (* 1 = 0.941985 loss)
I0224 20:19:26.070123 29812 solver.cpp:470] Iteration 6550, lr = 0.001
I0224 20:19:45.465637 29812 solver.cpp:189] Iteration 6600, loss = 1.04184
I0224 20:19:45.465680 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.04184 (* 1 = 1.04184 loss)
I0224 20:19:45.465687 29812 solver.cpp:470] Iteration 6600, lr = 0.001
I0224 20:20:04.857846 29812 solver.cpp:189] Iteration 6650, loss = 1.05558
I0224 20:20:04.857869 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.05558 (* 1 = 1.05558 loss)
I0224 20:20:04.857875 29812 solver.cpp:470] Iteration 6650, lr = 0.001
I0224 20:20:24.248344 29812 solver.cpp:189] Iteration 6700, loss = 1.03584
I0224 20:20:24.248384 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.03584 (* 1 = 1.03584 loss)
I0224 20:20:24.248389 29812 solver.cpp:470] Iteration 6700, lr = 0.001
I0224 20:20:43.640974 29812 solver.cpp:189] Iteration 6750, loss = 0.791845
I0224 20:20:43.641042 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.791845 (* 1 = 0.791845 loss)
I0224 20:20:43.641051 29812 solver.cpp:470] Iteration 6750, lr = 0.001
I0224 20:21:03.031824 29812 solver.cpp:189] Iteration 6800, loss = 0.989689
I0224 20:21:03.031893 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.989689 (* 1 = 0.989689 loss)
I0224 20:21:03.031908 29812 solver.cpp:470] Iteration 6800, lr = 0.001
I0224 20:21:22.419386 29812 solver.cpp:189] Iteration 6850, loss = 1.05824
I0224 20:21:22.419410 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.05824 (* 1 = 1.05824 loss)
I0224 20:21:22.419415 29812 solver.cpp:470] Iteration 6850, lr = 0.001
I0224 20:21:41.825889 29812 solver.cpp:189] Iteration 6900, loss = 0.859343
I0224 20:21:41.825958 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.859343 (* 1 = 0.859343 loss)
I0224 20:21:41.825973 29812 solver.cpp:470] Iteration 6900, lr = 0.001
I0224 20:22:01.213660 29812 solver.cpp:189] Iteration 6950, loss = 0.890605
I0224 20:22:01.213685 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.890605 (* 1 = 0.890605 loss)
I0224 20:22:01.213690 29812 solver.cpp:470] Iteration 6950, lr = 0.001
I0224 20:22:20.358135 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_7000.caffemodel
I0224 20:22:20.461361 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_7000.solverstate
I0224 20:22:20.519311 29812 solver.cpp:266] Iteration 7000, Testing net (#0)
I0224 20:22:28.178390 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.6969
I0224 20:22:28.178427 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.843556 (* 1 = 0.843556 loss)
I0224 20:22:28.465386 29812 solver.cpp:189] Iteration 7000, loss = 1.01821
I0224 20:22:28.465409 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.01821 (* 1 = 1.01821 loss)
I0224 20:22:28.465415 29812 solver.cpp:470] Iteration 7000, lr = 0.001
I0224 20:22:47.851533 29812 solver.cpp:189] Iteration 7050, loss = 1.09505
I0224 20:22:47.851557 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.09505 (* 1 = 1.09505 loss)
I0224 20:22:47.851562 29812 solver.cpp:470] Iteration 7050, lr = 0.001
I0224 20:23:07.235430 29812 solver.cpp:189] Iteration 7100, loss = 1.18137
I0224 20:23:07.235469 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.18137 (* 1 = 1.18137 loss)
I0224 20:23:07.235476 29812 solver.cpp:470] Iteration 7100, lr = 0.001
I0224 20:23:26.617000 29812 solver.cpp:189] Iteration 7150, loss = 0.821046
I0224 20:23:26.617024 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.821046 (* 1 = 0.821046 loss)
I0224 20:23:26.617029 29812 solver.cpp:470] Iteration 7150, lr = 0.001
I0224 20:23:45.999295 29812 solver.cpp:189] Iteration 7200, loss = 0.997876
I0224 20:23:45.999397 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.997876 (* 1 = 0.997876 loss)
I0224 20:23:45.999403 29812 solver.cpp:470] Iteration 7200, lr = 0.001
I0224 20:24:05.390640 29812 solver.cpp:189] Iteration 7250, loss = 0.86212
I0224 20:24:05.390665 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.86212 (* 1 = 0.86212 loss)
I0224 20:24:05.390669 29812 solver.cpp:470] Iteration 7250, lr = 0.001
I0224 20:24:24.769765 29812 solver.cpp:189] Iteration 7300, loss = 0.936959
I0224 20:24:24.769860 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.936959 (* 1 = 0.936959 loss)
I0224 20:24:24.769876 29812 solver.cpp:470] Iteration 7300, lr = 0.001
I0224 20:24:44.146409 29812 solver.cpp:189] Iteration 7350, loss = 0.97403
I0224 20:24:44.146432 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.97403 (* 1 = 0.97403 loss)
I0224 20:24:44.146437 29812 solver.cpp:470] Iteration 7350, lr = 0.001
I0224 20:25:03.530218 29812 solver.cpp:189] Iteration 7400, loss = 0.995985
I0224 20:25:03.530289 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.995985 (* 1 = 0.995985 loss)
I0224 20:25:03.530304 29812 solver.cpp:470] Iteration 7400, lr = 0.001
I0224 20:25:22.921600 29812 solver.cpp:189] Iteration 7450, loss = 0.896334
I0224 20:25:22.921625 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.896334 (* 1 = 0.896334 loss)
I0224 20:25:22.921632 29812 solver.cpp:470] Iteration 7450, lr = 0.001
I0224 20:25:42.309331 29812 solver.cpp:189] Iteration 7500, loss = 0.986725
I0224 20:25:42.309386 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.986725 (* 1 = 0.986725 loss)
I0224 20:25:42.309391 29812 solver.cpp:470] Iteration 7500, lr = 0.001
I0224 20:26:01.704769 29812 solver.cpp:189] Iteration 7550, loss = 0.819363
I0224 20:26:01.704794 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.819363 (* 1 = 0.819363 loss)
I0224 20:26:01.704799 29812 solver.cpp:470] Iteration 7550, lr = 0.001
I0224 20:26:21.087785 29812 solver.cpp:189] Iteration 7600, loss = 0.909218
I0224 20:26:21.087877 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.909218 (* 1 = 0.909218 loss)
I0224 20:26:21.087883 29812 solver.cpp:470] Iteration 7600, lr = 0.001
I0224 20:26:40.466545 29812 solver.cpp:189] Iteration 7650, loss = 1.03835
I0224 20:26:40.466569 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.03835 (* 1 = 1.03835 loss)
I0224 20:26:40.466574 29812 solver.cpp:470] Iteration 7650, lr = 0.001
I0224 20:26:59.851666 29812 solver.cpp:189] Iteration 7700, loss = 0.911917
I0224 20:26:59.851706 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.911917 (* 1 = 0.911917 loss)
I0224 20:26:59.851712 29812 solver.cpp:470] Iteration 7700, lr = 0.001
I0224 20:27:19.233330 29812 solver.cpp:189] Iteration 7750, loss = 0.824271
I0224 20:27:19.233363 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.824271 (* 1 = 0.824271 loss)
I0224 20:27:19.233369 29812 solver.cpp:470] Iteration 7750, lr = 0.001
I0224 20:27:38.602542 29812 solver.cpp:189] Iteration 7800, loss = 0.836044
I0224 20:27:38.602612 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.836044 (* 1 = 0.836044 loss)
I0224 20:27:38.602627 29812 solver.cpp:470] Iteration 7800, lr = 0.001
I0224 20:27:57.990604 29812 solver.cpp:189] Iteration 7850, loss = 0.877774
I0224 20:27:57.990628 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.877774 (* 1 = 0.877774 loss)
I0224 20:27:57.990633 29812 solver.cpp:470] Iteration 7850, lr = 0.001
I0224 20:28:17.381778 29812 solver.cpp:189] Iteration 7900, loss = 0.727167
I0224 20:28:17.381844 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.727167 (* 1 = 0.727167 loss)
I0224 20:28:17.381860 29812 solver.cpp:470] Iteration 7900, lr = 0.001
I0224 20:28:36.767642 29812 solver.cpp:189] Iteration 7950, loss = 0.826932
I0224 20:28:36.767668 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.826932 (* 1 = 0.826932 loss)
I0224 20:28:36.767674 29812 solver.cpp:470] Iteration 7950, lr = 0.001
I0224 20:28:55.900223 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_8000.caffemodel
I0224 20:28:56.002408 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_8000.solverstate
I0224 20:28:56.059703 29812 solver.cpp:266] Iteration 8000, Testing net (#0)
I0224 20:29:03.714751 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.7272
I0224 20:29:03.714789 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.775842 (* 1 = 0.775842 loss)
I0224 20:29:04.001282 29812 solver.cpp:189] Iteration 8000, loss = 0.835412
I0224 20:29:04.001302 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.835412 (* 1 = 0.835412 loss)
I0224 20:29:04.001308 29812 solver.cpp:470] Iteration 8000, lr = 0.001
I0224 20:29:23.397316 29812 solver.cpp:189] Iteration 8050, loss = 0.746636
I0224 20:29:23.397341 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.746636 (* 1 = 0.746636 loss)
I0224 20:29:23.397346 29812 solver.cpp:470] Iteration 8050, lr = 0.001
I0224 20:29:42.795534 29812 solver.cpp:189] Iteration 8100, loss = 1.00089
I0224 20:29:42.795629 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.00089 (* 1 = 1.00089 loss)
I0224 20:29:42.795644 29812 solver.cpp:470] Iteration 8100, lr = 0.001
I0224 20:30:02.192759 29812 solver.cpp:189] Iteration 8150, loss = 0.899751
I0224 20:30:02.192783 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.899751 (* 1 = 0.899751 loss)
I0224 20:30:02.192788 29812 solver.cpp:470] Iteration 8150, lr = 0.001
I0224 20:30:21.582114 29812 solver.cpp:189] Iteration 8200, loss = 0.774768
I0224 20:30:21.582173 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.774768 (* 1 = 0.774768 loss)
I0224 20:30:21.582180 29812 solver.cpp:470] Iteration 8200, lr = 0.001
I0224 20:30:40.979099 29812 solver.cpp:189] Iteration 8250, loss = 0.886315
I0224 20:30:40.979122 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.886315 (* 1 = 0.886315 loss)
I0224 20:30:40.979128 29812 solver.cpp:470] Iteration 8250, lr = 0.001
I0224 20:31:00.382130 29812 solver.cpp:189] Iteration 8300, loss = 0.70928
I0224 20:31:00.382201 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.70928 (* 1 = 0.70928 loss)
I0224 20:31:00.382207 29812 solver.cpp:470] Iteration 8300, lr = 0.001
I0224 20:31:19.776545 29812 solver.cpp:189] Iteration 8350, loss = 0.776611
I0224 20:31:19.776571 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.776611 (* 1 = 0.776611 loss)
I0224 20:31:19.776576 29812 solver.cpp:470] Iteration 8350, lr = 0.001
I0224 20:31:39.172372 29812 solver.cpp:189] Iteration 8400, loss = 0.944658
I0224 20:31:39.172425 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.944658 (* 1 = 0.944658 loss)
I0224 20:31:39.172430 29812 solver.cpp:470] Iteration 8400, lr = 0.001
I0224 20:31:58.566213 29812 solver.cpp:189] Iteration 8450, loss = 0.770837
I0224 20:31:58.566236 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.770837 (* 1 = 0.770837 loss)
I0224 20:31:58.566242 29812 solver.cpp:470] Iteration 8450, lr = 0.001
I0224 20:32:17.959933 29812 solver.cpp:189] Iteration 8500, loss = 0.949265
I0224 20:32:17.960019 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.949265 (* 1 = 0.949265 loss)
I0224 20:32:17.960026 29812 solver.cpp:470] Iteration 8500, lr = 0.001
I0224 20:32:37.349678 29812 solver.cpp:189] Iteration 8550, loss = 1.02435
I0224 20:32:37.349704 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 1.02435 (* 1 = 1.02435 loss)
I0224 20:32:37.349709 29812 solver.cpp:470] Iteration 8550, lr = 0.001
I0224 20:32:56.728970 29812 solver.cpp:189] Iteration 8600, loss = 0.848533
I0224 20:32:56.729032 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.848533 (* 1 = 0.848533 loss)
I0224 20:32:56.729038 29812 solver.cpp:470] Iteration 8600, lr = 0.001
I0224 20:33:16.117856 29812 solver.cpp:189] Iteration 8650, loss = 0.839277
I0224 20:33:16.117882 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.839277 (* 1 = 0.839277 loss)
I0224 20:33:16.117887 29812 solver.cpp:470] Iteration 8650, lr = 0.001
I0224 20:33:35.514783 29812 solver.cpp:189] Iteration 8700, loss = 0.981964
I0224 20:33:35.514873 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.981964 (* 1 = 0.981964 loss)
I0224 20:33:35.514889 29812 solver.cpp:470] Iteration 8700, lr = 0.001
I0224 20:33:54.910374 29812 solver.cpp:189] Iteration 8750, loss = 0.98582
I0224 20:33:54.910399 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.98582 (* 1 = 0.98582 loss)
I0224 20:33:54.910404 29812 solver.cpp:470] Iteration 8750, lr = 0.001
I0224 20:34:14.313211 29812 solver.cpp:189] Iteration 8800, loss = 0.721142
I0224 20:34:14.313271 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.721142 (* 1 = 0.721142 loss)
I0224 20:34:14.313285 29812 solver.cpp:470] Iteration 8800, lr = 0.001
I0224 20:34:33.709033 29812 solver.cpp:189] Iteration 8850, loss = 0.941437
I0224 20:34:33.709059 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.941437 (* 1 = 0.941437 loss)
I0224 20:34:33.709065 29812 solver.cpp:470] Iteration 8850, lr = 0.001
I0224 20:34:53.108866 29812 solver.cpp:189] Iteration 8900, loss = 0.815409
I0224 20:34:53.108922 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.815409 (* 1 = 0.815409 loss)
I0224 20:34:53.108928 29812 solver.cpp:470] Iteration 8900, lr = 0.001
I0224 20:35:12.502964 29812 solver.cpp:189] Iteration 8950, loss = 0.623294
I0224 20:35:12.502987 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.623294 (* 1 = 0.623294 loss)
I0224 20:35:12.502992 29812 solver.cpp:470] Iteration 8950, lr = 0.001
I0224 20:35:31.650910 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_9000.caffemodel
I0224 20:35:31.752553 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_9000.solverstate
I0224 20:35:31.809974 29812 solver.cpp:266] Iteration 9000, Testing net (#0)
I0224 20:35:39.459661 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.7368
I0224 20:35:39.459697 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.747877 (* 1 = 0.747877 loss)
I0224 20:35:39.747022 29812 solver.cpp:189] Iteration 9000, loss = 0.613351
I0224 20:35:39.747047 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.613351 (* 1 = 0.613351 loss)
I0224 20:35:39.747052 29812 solver.cpp:470] Iteration 9000, lr = 0.001
I0224 20:35:59.145189 29812 solver.cpp:189] Iteration 9050, loss = 0.627471
I0224 20:35:59.145211 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.627471 (* 1 = 0.627471 loss)
I0224 20:35:59.145216 29812 solver.cpp:470] Iteration 9050, lr = 0.001
I0224 20:36:18.532562 29812 solver.cpp:189] Iteration 9100, loss = 0.682083
I0224 20:36:18.532649 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.682083 (* 1 = 0.682083 loss)
I0224 20:36:18.532665 29812 solver.cpp:470] Iteration 9100, lr = 0.001
I0224 20:36:37.919858 29812 solver.cpp:189] Iteration 9150, loss = 0.686686
I0224 20:36:37.919883 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.686686 (* 1 = 0.686686 loss)
I0224 20:36:37.919889 29812 solver.cpp:470] Iteration 9150, lr = 0.001
I0224 20:36:57.310960 29812 solver.cpp:189] Iteration 9200, loss = 0.963501
I0224 20:36:57.311000 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.963501 (* 1 = 0.963501 loss)
I0224 20:36:57.311007 29812 solver.cpp:470] Iteration 9200, lr = 0.001
I0224 20:37:16.704052 29812 solver.cpp:189] Iteration 9250, loss = 0.856756
I0224 20:37:16.704077 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.856756 (* 1 = 0.856756 loss)
I0224 20:37:16.704083 29812 solver.cpp:470] Iteration 9250, lr = 0.001
I0224 20:37:36.096166 29812 solver.cpp:189] Iteration 9300, loss = 0.809878
I0224 20:37:36.096240 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.809878 (* 1 = 0.809878 loss)
I0224 20:37:36.096246 29812 solver.cpp:470] Iteration 9300, lr = 0.001
I0224 20:37:55.485771 29812 solver.cpp:189] Iteration 9350, loss = 0.746335
I0224 20:37:55.485796 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.746335 (* 1 = 0.746335 loss)
I0224 20:37:55.485801 29812 solver.cpp:470] Iteration 9350, lr = 0.001
I0224 20:38:14.872931 29812 solver.cpp:189] Iteration 9400, loss = 0.672152
I0224 20:38:14.873020 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.672152 (* 1 = 0.672152 loss)
I0224 20:38:14.873035 29812 solver.cpp:470] Iteration 9400, lr = 0.001
I0224 20:38:34.263429 29812 solver.cpp:189] Iteration 9450, loss = 0.820835
I0224 20:38:34.263454 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.820835 (* 1 = 0.820835 loss)
I0224 20:38:34.263459 29812 solver.cpp:470] Iteration 9450, lr = 0.001
I0224 20:38:53.653480 29812 solver.cpp:189] Iteration 9500, loss = 0.819734
I0224 20:38:53.653573 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.819734 (* 1 = 0.819734 loss)
I0224 20:38:53.653579 29812 solver.cpp:470] Iteration 9500, lr = 0.001
I0224 20:39:13.056531 29812 solver.cpp:189] Iteration 9550, loss = 0.787224
I0224 20:39:13.056555 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.787224 (* 1 = 0.787224 loss)
I0224 20:39:13.056560 29812 solver.cpp:470] Iteration 9550, lr = 0.001
I0224 20:39:32.447494 29812 solver.cpp:189] Iteration 9600, loss = 0.818946
I0224 20:39:32.447556 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.818946 (* 1 = 0.818946 loss)
I0224 20:39:32.447562 29812 solver.cpp:470] Iteration 9600, lr = 0.001
I0224 20:39:51.835649 29812 solver.cpp:189] Iteration 9650, loss = 0.927735
I0224 20:39:51.835672 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.927735 (* 1 = 0.927735 loss)
I0224 20:39:51.835677 29812 solver.cpp:470] Iteration 9650, lr = 0.001
I0224 20:40:11.228705 29812 solver.cpp:189] Iteration 9700, loss = 0.62472
I0224 20:40:11.228765 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.62472 (* 1 = 0.62472 loss)
I0224 20:40:11.228770 29812 solver.cpp:470] Iteration 9700, lr = 0.001
I0224 20:40:30.612848 29812 solver.cpp:189] Iteration 9750, loss = 0.715426
I0224 20:40:30.612871 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.715426 (* 1 = 0.715426 loss)
I0224 20:40:30.612877 29812 solver.cpp:470] Iteration 9750, lr = 0.001
I0224 20:40:50.001404 29812 solver.cpp:189] Iteration 9800, loss = 0.83954
I0224 20:40:50.001487 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.83954 (* 1 = 0.83954 loss)
I0224 20:40:50.001492 29812 solver.cpp:470] Iteration 9800, lr = 0.001
I0224 20:41:09.397199 29812 solver.cpp:189] Iteration 9850, loss = 0.783041
I0224 20:41:09.397223 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.783041 (* 1 = 0.783041 loss)
I0224 20:41:09.397229 29812 solver.cpp:470] Iteration 9850, lr = 0.001
I0224 20:41:28.791189 29812 solver.cpp:189] Iteration 9900, loss = 0.748743
I0224 20:41:28.791249 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.748743 (* 1 = 0.748743 loss)
I0224 20:41:28.791255 29812 solver.cpp:470] Iteration 9900, lr = 0.001
I0224 20:41:48.186318 29812 solver.cpp:189] Iteration 9950, loss = 0.680486
I0224 20:41:48.186342 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.680486 (* 1 = 0.680486 loss)
I0224 20:41:48.186347 29812 solver.cpp:470] Iteration 9950, lr = 0.001
I0224 20:42:07.334300 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_10000.caffemodel
I0224 20:42:07.436264 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_10000.solverstate
I0224 20:42:07.493378 29812 solver.cpp:266] Iteration 10000, Testing net (#0)
I0224 20:42:15.149345 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.753
I0224 20:42:15.149380 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.715231 (* 1 = 0.715231 loss)
I0224 20:42:15.437330 29812 solver.cpp:189] Iteration 10000, loss = 0.79421
I0224 20:42:15.437355 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.79421 (* 1 = 0.79421 loss)
I0224 20:42:15.437361 29812 solver.cpp:470] Iteration 10000, lr = 0.001
I0224 20:42:34.826899 29812 solver.cpp:189] Iteration 10050, loss = 0.799527
I0224 20:42:34.826922 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.799527 (* 1 = 0.799527 loss)
I0224 20:42:34.826927 29812 solver.cpp:470] Iteration 10050, lr = 0.001
I0224 20:42:54.216822 29812 solver.cpp:189] Iteration 10100, loss = 0.683295
I0224 20:42:54.216917 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.683295 (* 1 = 0.683295 loss)
I0224 20:42:54.216931 29812 solver.cpp:470] Iteration 10100, lr = 0.001
I0224 20:43:13.613941 29812 solver.cpp:189] Iteration 10150, loss = 0.74897
I0224 20:43:13.613965 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.74897 (* 1 = 0.74897 loss)
I0224 20:43:13.613971 29812 solver.cpp:470] Iteration 10150, lr = 0.001
I0224 20:43:33.007710 29812 solver.cpp:189] Iteration 10200, loss = 0.759494
I0224 20:43:33.007799 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.759494 (* 1 = 0.759494 loss)
I0224 20:43:33.007805 29812 solver.cpp:470] Iteration 10200, lr = 0.001
I0224 20:43:52.399759 29812 solver.cpp:189] Iteration 10250, loss = 0.535678
I0224 20:43:52.399782 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.535678 (* 1 = 0.535678 loss)
I0224 20:43:52.399788 29812 solver.cpp:470] Iteration 10250, lr = 0.001
I0224 20:44:11.796025 29812 solver.cpp:189] Iteration 10300, loss = 0.658428
I0224 20:44:11.796111 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.658428 (* 1 = 0.658428 loss)
I0224 20:44:11.796118 29812 solver.cpp:470] Iteration 10300, lr = 0.001
I0224 20:44:31.192260 29812 solver.cpp:189] Iteration 10350, loss = 0.652736
I0224 20:44:31.192283 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.652736 (* 1 = 0.652736 loss)
I0224 20:44:31.192289 29812 solver.cpp:470] Iteration 10350, lr = 0.001
I0224 20:44:50.582362 29812 solver.cpp:189] Iteration 10400, loss = 0.818248
I0224 20:44:50.582422 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.818248 (* 1 = 0.818248 loss)
I0224 20:44:50.582428 29812 solver.cpp:470] Iteration 10400, lr = 0.001
I0224 20:45:09.976639 29812 solver.cpp:189] Iteration 10450, loss = 0.732605
I0224 20:45:09.976663 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.732605 (* 1 = 0.732605 loss)
I0224 20:45:09.976670 29812 solver.cpp:470] Iteration 10450, lr = 0.001
I0224 20:45:29.359117 29812 solver.cpp:189] Iteration 10500, loss = 0.787093
I0224 20:45:29.359158 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.787093 (* 1 = 0.787093 loss)
I0224 20:45:29.359163 29812 solver.cpp:470] Iteration 10500, lr = 0.001
I0224 20:45:48.751147 29812 solver.cpp:189] Iteration 10550, loss = 0.673181
I0224 20:45:48.751173 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.673181 (* 1 = 0.673181 loss)
I0224 20:45:48.751178 29812 solver.cpp:470] Iteration 10550, lr = 0.001
I0224 20:46:08.148668 29812 solver.cpp:189] Iteration 10600, loss = 0.898778
I0224 20:46:08.148753 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.898778 (* 1 = 0.898778 loss)
I0224 20:46:08.148759 29812 solver.cpp:470] Iteration 10600, lr = 0.001
I0224 20:46:27.537956 29812 solver.cpp:189] Iteration 10650, loss = 0.621363
I0224 20:46:27.537981 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.621363 (* 1 = 0.621363 loss)
I0224 20:46:27.537987 29812 solver.cpp:470] Iteration 10650, lr = 0.001
I0224 20:46:46.917353 29812 solver.cpp:189] Iteration 10700, loss = 0.69952
I0224 20:46:46.917392 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.69952 (* 1 = 0.69952 loss)
I0224 20:46:46.917398 29812 solver.cpp:470] Iteration 10700, lr = 0.001
I0224 20:47:06.311913 29812 solver.cpp:189] Iteration 10750, loss = 0.59605
I0224 20:47:06.311938 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.59605 (* 1 = 0.59605 loss)
I0224 20:47:06.311943 29812 solver.cpp:470] Iteration 10750, lr = 0.001
I0224 20:47:25.708477 29812 solver.cpp:189] Iteration 10800, loss = 0.889645
I0224 20:47:25.708537 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.889645 (* 1 = 0.889645 loss)
I0224 20:47:25.708544 29812 solver.cpp:470] Iteration 10800, lr = 0.001
I0224 20:47:45.099225 29812 solver.cpp:189] Iteration 10850, loss = 0.642706
I0224 20:47:45.099247 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.642706 (* 1 = 0.642706 loss)
I0224 20:47:45.099252 29812 solver.cpp:470] Iteration 10850, lr = 0.001
I0224 20:48:04.490882 29812 solver.cpp:189] Iteration 10900, loss = 0.671132
I0224 20:48:04.490944 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.671132 (* 1 = 0.671132 loss)
I0224 20:48:04.490952 29812 solver.cpp:470] Iteration 10900, lr = 0.001
I0224 20:48:23.879974 29812 solver.cpp:189] Iteration 10950, loss = 0.636367
I0224 20:48:23.879999 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.636367 (* 1 = 0.636367 loss)
I0224 20:48:23.880004 29812 solver.cpp:470] Iteration 10950, lr = 0.001
I0224 20:48:43.028126 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_11000.caffemodel
I0224 20:48:43.131929 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_11000.solverstate
I0224 20:48:43.190186 29812 solver.cpp:266] Iteration 11000, Testing net (#0)
I0224 20:48:50.847836 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.7721
I0224 20:48:50.847872 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.659633 (* 1 = 0.659633 loss)
I0224 20:48:51.135155 29812 solver.cpp:189] Iteration 11000, loss = 0.76343
I0224 20:48:51.135176 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.76343 (* 1 = 0.76343 loss)
I0224 20:48:51.135181 29812 solver.cpp:470] Iteration 11000, lr = 0.001
I0224 20:49:10.515275 29812 solver.cpp:189] Iteration 11050, loss = 0.847978
I0224 20:49:10.515298 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.847978 (* 1 = 0.847978 loss)
I0224 20:49:10.515305 29812 solver.cpp:470] Iteration 11050, lr = 0.001
I0224 20:49:29.896937 29812 solver.cpp:189] Iteration 11100, loss = 0.575519
I0224 20:49:29.896978 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.575519 (* 1 = 0.575519 loss)
I0224 20:49:29.896985 29812 solver.cpp:470] Iteration 11100, lr = 0.001
I0224 20:49:49.275132 29812 solver.cpp:189] Iteration 11150, loss = 0.881374
I0224 20:49:49.275156 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.881374 (* 1 = 0.881374 loss)
I0224 20:49:49.275161 29812 solver.cpp:470] Iteration 11150, lr = 0.001
I0224 20:50:08.663908 29812 solver.cpp:189] Iteration 11200, loss = 0.677927
I0224 20:50:08.663980 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.677927 (* 1 = 0.677927 loss)
I0224 20:50:08.663995 29812 solver.cpp:470] Iteration 11200, lr = 0.001
I0224 20:50:28.049067 29812 solver.cpp:189] Iteration 11250, loss = 0.8007
I0224 20:50:28.049092 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.8007 (* 1 = 0.8007 loss)
I0224 20:50:28.049096 29812 solver.cpp:470] Iteration 11250, lr = 0.001
I0224 20:50:47.437885 29812 solver.cpp:189] Iteration 11300, loss = 0.64593
I0224 20:50:47.437937 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.64593 (* 1 = 0.64593 loss)
I0224 20:50:47.437943 29812 solver.cpp:470] Iteration 11300, lr = 0.001
I0224 20:51:06.823385 29812 solver.cpp:189] Iteration 11350, loss = 0.610579
I0224 20:51:06.823408 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.610579 (* 1 = 0.610579 loss)
I0224 20:51:06.823415 29812 solver.cpp:470] Iteration 11350, lr = 0.001
I0224 20:51:26.205143 29812 solver.cpp:189] Iteration 11400, loss = 0.801077
I0224 20:51:26.205204 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.801077 (* 1 = 0.801077 loss)
I0224 20:51:26.205209 29812 solver.cpp:470] Iteration 11400, lr = 0.001
I0224 20:51:45.585707 29812 solver.cpp:189] Iteration 11450, loss = 0.596426
I0224 20:51:45.585731 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.596426 (* 1 = 0.596426 loss)
I0224 20:51:45.585736 29812 solver.cpp:470] Iteration 11450, lr = 0.001
I0224 20:52:04.971752 29812 solver.cpp:189] Iteration 11500, loss = 0.725548
I0224 20:52:04.971837 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.725548 (* 1 = 0.725548 loss)
I0224 20:52:04.971843 29812 solver.cpp:470] Iteration 11500, lr = 0.001
I0224 20:52:24.359176 29812 solver.cpp:189] Iteration 11550, loss = 0.574593
I0224 20:52:24.359200 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.574593 (* 1 = 0.574593 loss)
I0224 20:52:24.359205 29812 solver.cpp:470] Iteration 11550, lr = 0.001
I0224 20:52:43.753533 29812 solver.cpp:189] Iteration 11600, loss = 0.758378
I0224 20:52:43.753618 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.758378 (* 1 = 0.758378 loss)
I0224 20:52:43.753623 29812 solver.cpp:470] Iteration 11600, lr = 0.001
I0224 20:53:03.142185 29812 solver.cpp:189] Iteration 11650, loss = 0.770234
I0224 20:53:03.142209 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.770234 (* 1 = 0.770234 loss)
I0224 20:53:03.142212 29812 solver.cpp:470] Iteration 11650, lr = 0.001
I0224 20:53:22.526849 29812 solver.cpp:189] Iteration 11700, loss = 0.679587
I0224 20:53:22.526939 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.679587 (* 1 = 0.679587 loss)
I0224 20:53:22.526945 29812 solver.cpp:470] Iteration 11700, lr = 0.001
I0224 20:53:41.913094 29812 solver.cpp:189] Iteration 11750, loss = 0.729426
I0224 20:53:41.913117 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.729426 (* 1 = 0.729426 loss)
I0224 20:53:41.913123 29812 solver.cpp:470] Iteration 11750, lr = 0.001
I0224 20:54:01.296026 29812 solver.cpp:189] Iteration 11800, loss = 0.825804
I0224 20:54:01.296066 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.825804 (* 1 = 0.825804 loss)
I0224 20:54:01.296071 29812 solver.cpp:470] Iteration 11800, lr = 0.001
I0224 20:54:20.675348 29812 solver.cpp:189] Iteration 11850, loss = 0.640494
I0224 20:54:20.675374 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.640494 (* 1 = 0.640494 loss)
I0224 20:54:20.675379 29812 solver.cpp:470] Iteration 11850, lr = 0.001
I0224 20:54:40.070566 29812 solver.cpp:189] Iteration 11900, loss = 0.842183
I0224 20:54:40.070624 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.842183 (* 1 = 0.842183 loss)
I0224 20:54:40.070631 29812 solver.cpp:470] Iteration 11900, lr = 0.001
I0224 20:54:59.456851 29812 solver.cpp:189] Iteration 11950, loss = 0.620875
I0224 20:54:59.456884 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.620875 (* 1 = 0.620875 loss)
I0224 20:54:59.456889 29812 solver.cpp:470] Iteration 11950, lr = 0.001
I0224 20:55:18.603746 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_12000.caffemodel
I0224 20:55:18.705312 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_12000.solverstate
I0224 20:55:18.762872 29812 solver.cpp:266] Iteration 12000, Testing net (#0)
I0224 20:55:26.417495 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.7823
I0224 20:55:26.417532 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.619399 (* 1 = 0.619399 loss)
I0224 20:55:26.704197 29812 solver.cpp:189] Iteration 12000, loss = 0.703676
I0224 20:55:26.704222 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.703676 (* 1 = 0.703676 loss)
I0224 20:55:26.704228 29812 solver.cpp:470] Iteration 12000, lr = 0.001
I0224 20:55:46.097374 29812 solver.cpp:189] Iteration 12050, loss = 0.686852
I0224 20:55:46.097398 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.686852 (* 1 = 0.686852 loss)
I0224 20:55:46.097404 29812 solver.cpp:470] Iteration 12050, lr = 0.001
I0224 20:56:05.486767 29812 solver.cpp:189] Iteration 12100, loss = 0.835702
I0224 20:56:05.486809 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.835702 (* 1 = 0.835702 loss)
I0224 20:56:05.486814 29812 solver.cpp:470] Iteration 12100, lr = 0.001
I0224 20:56:24.872110 29812 solver.cpp:189] Iteration 12150, loss = 0.737875
I0224 20:56:24.872135 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.737875 (* 1 = 0.737875 loss)
I0224 20:56:24.872141 29812 solver.cpp:470] Iteration 12150, lr = 0.001
I0224 20:56:44.273286 29812 solver.cpp:189] Iteration 12200, loss = 0.623427
I0224 20:56:44.273341 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.623427 (* 1 = 0.623427 loss)
I0224 20:56:44.273346 29812 solver.cpp:470] Iteration 12200, lr = 0.001
I0224 20:57:03.664572 29812 solver.cpp:189] Iteration 12250, loss = 0.647062
I0224 20:57:03.664597 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.647062 (* 1 = 0.647062 loss)
I0224 20:57:03.664602 29812 solver.cpp:470] Iteration 12250, lr = 0.001
I0224 20:57:23.049834 29812 solver.cpp:189] Iteration 12300, loss = 0.673076
I0224 20:57:23.049942 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.673076 (* 1 = 0.673076 loss)
I0224 20:57:23.049947 29812 solver.cpp:470] Iteration 12300, lr = 0.001
I0224 20:57:42.446964 29812 solver.cpp:189] Iteration 12350, loss = 0.517083
I0224 20:57:42.446988 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.517083 (* 1 = 0.517083 loss)
I0224 20:57:42.446993 29812 solver.cpp:470] Iteration 12350, lr = 0.001
I0224 20:58:01.843513 29812 solver.cpp:189] Iteration 12400, loss = 0.758672
I0224 20:58:01.843605 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.758672 (* 1 = 0.758672 loss)
I0224 20:58:01.843611 29812 solver.cpp:470] Iteration 12400, lr = 0.001
I0224 20:58:21.233072 29812 solver.cpp:189] Iteration 12450, loss = 0.653546
I0224 20:58:21.233095 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.653546 (* 1 = 0.653546 loss)
I0224 20:58:21.233099 29812 solver.cpp:470] Iteration 12450, lr = 0.001
I0224 20:58:40.629293 29812 solver.cpp:189] Iteration 12500, loss = 0.933418
I0224 20:58:40.629362 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.933418 (* 1 = 0.933418 loss)
I0224 20:58:40.629377 29812 solver.cpp:470] Iteration 12500, lr = 0.001
I0224 20:59:00.016469 29812 solver.cpp:189] Iteration 12550, loss = 0.598942
I0224 20:59:00.016492 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.598942 (* 1 = 0.598942 loss)
I0224 20:59:00.016499 29812 solver.cpp:470] Iteration 12550, lr = 0.001
I0224 20:59:19.411669 29812 solver.cpp:189] Iteration 12600, loss = 0.765213
I0224 20:59:19.411726 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.765213 (* 1 = 0.765213 loss)
I0224 20:59:19.411731 29812 solver.cpp:470] Iteration 12600, lr = 0.001
I0224 20:59:38.805266 29812 solver.cpp:189] Iteration 12650, loss = 0.722777
I0224 20:59:38.805289 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.722777 (* 1 = 0.722777 loss)
I0224 20:59:38.805295 29812 solver.cpp:470] Iteration 12650, lr = 0.001
I0224 20:59:58.195677 29812 solver.cpp:189] Iteration 12700, loss = 0.622722
I0224 20:59:58.195734 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.622722 (* 1 = 0.622722 loss)
I0224 20:59:58.195740 29812 solver.cpp:470] Iteration 12700, lr = 0.001
I0224 21:00:17.595989 29812 solver.cpp:189] Iteration 12750, loss = 0.736857
I0224 21:00:17.596014 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.736857 (* 1 = 0.736857 loss)
I0224 21:00:17.596020 29812 solver.cpp:470] Iteration 12750, lr = 0.001
I0224 21:00:36.989521 29812 solver.cpp:189] Iteration 12800, loss = 0.542265
I0224 21:00:36.989589 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.542265 (* 1 = 0.542265 loss)
I0224 21:00:36.989604 29812 solver.cpp:470] Iteration 12800, lr = 0.001
I0224 21:00:56.383963 29812 solver.cpp:189] Iteration 12850, loss = 0.648436
I0224 21:00:56.383987 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.648436 (* 1 = 0.648436 loss)
I0224 21:00:56.383992 29812 solver.cpp:470] Iteration 12850, lr = 0.001
I0224 21:01:15.777945 29812 solver.cpp:189] Iteration 12900, loss = 0.693251
I0224 21:01:15.778034 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.693251 (* 1 = 0.693251 loss)
I0224 21:01:15.778041 29812 solver.cpp:470] Iteration 12900, lr = 0.001
I0224 21:01:35.166793 29812 solver.cpp:189] Iteration 12950, loss = 0.461258
I0224 21:01:35.166817 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.461258 (* 1 = 0.461258 loss)
I0224 21:01:35.166822 29812 solver.cpp:470] Iteration 12950, lr = 0.001
I0224 21:01:54.319424 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_13000.caffemodel
I0224 21:01:54.420891 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_13000.solverstate
I0224 21:01:54.477959 29812 solver.cpp:266] Iteration 13000, Testing net (#0)
I0224 21:02:02.135684 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.7881
I0224 21:02:02.135720 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.611772 (* 1 = 0.611772 loss)
I0224 21:02:02.422096 29812 solver.cpp:189] Iteration 13000, loss = 0.625767
I0224 21:02:02.422116 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.625767 (* 1 = 0.625767 loss)
I0224 21:02:02.422122 29812 solver.cpp:470] Iteration 13000, lr = 0.001
I0224 21:02:21.805977 29812 solver.cpp:189] Iteration 13050, loss = 0.483937
I0224 21:02:21.806002 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.483937 (* 1 = 0.483937 loss)
I0224 21:02:21.806009 29812 solver.cpp:470] Iteration 13050, lr = 0.001
I0224 21:02:41.193251 29812 solver.cpp:189] Iteration 13100, loss = 0.566528
I0224 21:02:41.193313 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.566528 (* 1 = 0.566528 loss)
I0224 21:02:41.193318 29812 solver.cpp:470] Iteration 13100, lr = 0.001
I0224 21:03:00.588547 29812 solver.cpp:189] Iteration 13150, loss = 0.800548
I0224 21:03:00.588572 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.800548 (* 1 = 0.800548 loss)
I0224 21:03:00.588577 29812 solver.cpp:470] Iteration 13150, lr = 0.001
I0224 21:03:19.988181 29812 solver.cpp:189] Iteration 13200, loss = 0.885104
I0224 21:03:19.988253 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.885104 (* 1 = 0.885104 loss)
I0224 21:03:19.988268 29812 solver.cpp:470] Iteration 13200, lr = 0.001
I0224 21:03:39.379328 29812 solver.cpp:189] Iteration 13250, loss = 0.568165
I0224 21:03:39.379351 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.568165 (* 1 = 0.568165 loss)
I0224 21:03:39.379356 29812 solver.cpp:470] Iteration 13250, lr = 0.001
I0224 21:03:58.770030 29812 solver.cpp:189] Iteration 13300, loss = 0.625028
I0224 21:03:58.770102 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.625028 (* 1 = 0.625028 loss)
I0224 21:03:58.770117 29812 solver.cpp:470] Iteration 13300, lr = 0.001
I0224 21:04:18.167657 29812 solver.cpp:189] Iteration 13350, loss = 0.668065
I0224 21:04:18.167680 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.668065 (* 1 = 0.668065 loss)
I0224 21:04:18.167685 29812 solver.cpp:470] Iteration 13350, lr = 0.001
I0224 21:04:37.566534 29812 solver.cpp:189] Iteration 13400, loss = 0.649063
I0224 21:04:37.566598 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.649063 (* 1 = 0.649063 loss)
I0224 21:04:37.566604 29812 solver.cpp:470] Iteration 13400, lr = 0.001
I0224 21:04:56.959091 29812 solver.cpp:189] Iteration 13450, loss = 0.549087
I0224 21:04:56.959115 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.549087 (* 1 = 0.549087 loss)
I0224 21:04:56.959120 29812 solver.cpp:470] Iteration 13450, lr = 0.001
I0224 21:05:16.344492 29812 solver.cpp:189] Iteration 13500, loss = 0.716367
I0224 21:05:16.344550 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.716367 (* 1 = 0.716367 loss)
I0224 21:05:16.344557 29812 solver.cpp:470] Iteration 13500, lr = 0.001
I0224 21:05:35.731247 29812 solver.cpp:189] Iteration 13550, loss = 0.57289
I0224 21:05:35.731272 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.57289 (* 1 = 0.57289 loss)
I0224 21:05:35.731277 29812 solver.cpp:470] Iteration 13550, lr = 0.001
I0224 21:05:55.113737 29812 solver.cpp:189] Iteration 13600, loss = 0.699254
I0224 21:05:55.113776 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.699254 (* 1 = 0.699254 loss)
I0224 21:05:55.113781 29812 solver.cpp:470] Iteration 13600, lr = 0.001
I0224 21:06:14.503125 29812 solver.cpp:189] Iteration 13650, loss = 0.647749
I0224 21:06:14.503149 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.647749 (* 1 = 0.647749 loss)
I0224 21:06:14.503154 29812 solver.cpp:470] Iteration 13650, lr = 0.001
I0224 21:06:33.896174 29812 solver.cpp:189] Iteration 13700, loss = 0.630268
I0224 21:06:33.896239 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.630268 (* 1 = 0.630268 loss)
I0224 21:06:33.896246 29812 solver.cpp:470] Iteration 13700, lr = 0.001
I0224 21:06:53.293378 29812 solver.cpp:189] Iteration 13750, loss = 0.813409
I0224 21:06:53.293402 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.813409 (* 1 = 0.813409 loss)
I0224 21:06:53.293406 29812 solver.cpp:470] Iteration 13750, lr = 0.001
I0224 21:07:12.689637 29812 solver.cpp:189] Iteration 13800, loss = 0.545726
I0224 21:07:12.689699 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.545726 (* 1 = 0.545726 loss)
I0224 21:07:12.689707 29812 solver.cpp:470] Iteration 13800, lr = 0.001
I0224 21:07:32.064833 29812 solver.cpp:189] Iteration 13850, loss = 0.588831
I0224 21:07:32.064857 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.588831 (* 1 = 0.588831 loss)
I0224 21:07:32.064860 29812 solver.cpp:470] Iteration 13850, lr = 0.001
I0224 21:07:51.457564 29812 solver.cpp:189] Iteration 13900, loss = 0.585342
I0224 21:07:51.457602 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.585342 (* 1 = 0.585342 loss)
I0224 21:07:51.457607 29812 solver.cpp:470] Iteration 13900, lr = 0.001
I0224 21:08:10.843785 29812 solver.cpp:189] Iteration 13950, loss = 0.658581
I0224 21:08:10.843809 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.658581 (* 1 = 0.658581 loss)
I0224 21:08:10.843814 29812 solver.cpp:470] Iteration 13950, lr = 0.001
I0224 21:08:29.989761 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_14000.caffemodel
I0224 21:08:30.091138 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_14000.solverstate
I0224 21:08:30.148516 29812 solver.cpp:266] Iteration 14000, Testing net (#0)
I0224 21:08:37.792683 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.7979
I0224 21:08:37.792719 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.577098 (* 1 = 0.577098 loss)
I0224 21:08:38.080584 29812 solver.cpp:189] Iteration 14000, loss = 0.667658
I0224 21:08:38.080607 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.667658 (* 1 = 0.667658 loss)
I0224 21:08:38.080613 29812 solver.cpp:470] Iteration 14000, lr = 0.001
I0224 21:08:57.478158 29812 solver.cpp:189] Iteration 14050, loss = 0.61659
I0224 21:08:57.478183 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.61659 (* 1 = 0.61659 loss)
I0224 21:08:57.478188 29812 solver.cpp:470] Iteration 14050, lr = 0.001
I0224 21:09:16.868574 29812 solver.cpp:189] Iteration 14100, loss = 0.480788
I0224 21:09:16.868616 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.480788 (* 1 = 0.480788 loss)
I0224 21:09:16.868623 29812 solver.cpp:470] Iteration 14100, lr = 0.001
I0224 21:09:36.258466 29812 solver.cpp:189] Iteration 14150, loss = 0.681001
I0224 21:09:36.258489 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.681001 (* 1 = 0.681001 loss)
I0224 21:09:36.258493 29812 solver.cpp:470] Iteration 14150, lr = 0.001
I0224 21:09:55.658493 29812 solver.cpp:189] Iteration 14200, loss = 0.524719
I0224 21:09:55.658566 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.524719 (* 1 = 0.524719 loss)
I0224 21:09:55.658581 29812 solver.cpp:470] Iteration 14200, lr = 0.001
I0224 21:10:15.060391 29812 solver.cpp:189] Iteration 14250, loss = 0.598442
I0224 21:10:15.060415 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.598442 (* 1 = 0.598442 loss)
I0224 21:10:15.060420 29812 solver.cpp:470] Iteration 14250, lr = 0.001
I0224 21:10:34.447957 29812 solver.cpp:189] Iteration 14300, loss = 0.454615
I0224 21:10:34.448024 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.454615 (* 1 = 0.454615 loss)
I0224 21:10:34.448029 29812 solver.cpp:470] Iteration 14300, lr = 0.001
I0224 21:10:53.851058 29812 solver.cpp:189] Iteration 14350, loss = 0.541303
I0224 21:10:53.851081 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.541303 (* 1 = 0.541303 loss)
I0224 21:10:53.851088 29812 solver.cpp:470] Iteration 14350, lr = 0.001
I0224 21:11:13.247642 29812 solver.cpp:189] Iteration 14400, loss = 0.681757
I0224 21:11:13.247694 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.681757 (* 1 = 0.681757 loss)
I0224 21:11:13.247700 29812 solver.cpp:470] Iteration 14400, lr = 0.001
I0224 21:11:32.652079 29812 solver.cpp:189] Iteration 14450, loss = 0.6411
I0224 21:11:32.652106 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.6411 (* 1 = 0.6411 loss)
I0224 21:11:32.652111 29812 solver.cpp:470] Iteration 14450, lr = 0.001
I0224 21:11:52.055481 29812 solver.cpp:189] Iteration 14500, loss = 0.492202
I0224 21:11:52.055558 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.492202 (* 1 = 0.492202 loss)
I0224 21:11:52.055573 29812 solver.cpp:470] Iteration 14500, lr = 0.001
I0224 21:12:11.454809 29812 solver.cpp:189] Iteration 14550, loss = 0.764313
I0224 21:12:11.454833 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.764313 (* 1 = 0.764313 loss)
I0224 21:12:11.454836 29812 solver.cpp:470] Iteration 14550, lr = 0.001
I0224 21:12:30.850039 29812 solver.cpp:189] Iteration 14600, loss = 0.628578
I0224 21:12:30.850111 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.628578 (* 1 = 0.628578 loss)
I0224 21:12:30.850126 29812 solver.cpp:470] Iteration 14600, lr = 0.001
I0224 21:12:50.235819 29812 solver.cpp:189] Iteration 14650, loss = 0.546687
I0224 21:12:50.235842 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.546687 (* 1 = 0.546687 loss)
I0224 21:12:50.235847 29812 solver.cpp:470] Iteration 14650, lr = 0.001
I0224 21:13:09.623920 29812 solver.cpp:189] Iteration 14700, loss = 0.497403
I0224 21:13:09.623992 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.497403 (* 1 = 0.497403 loss)
I0224 21:13:09.624007 29812 solver.cpp:470] Iteration 14700, lr = 0.001
I0224 21:13:29.019464 29812 solver.cpp:189] Iteration 14750, loss = 0.606156
I0224 21:13:29.019487 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.606156 (* 1 = 0.606156 loss)
I0224 21:13:29.019493 29812 solver.cpp:470] Iteration 14750, lr = 0.001
I0224 21:13:48.415889 29812 solver.cpp:189] Iteration 14800, loss = 0.733231
I0224 21:13:48.415958 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.733231 (* 1 = 0.733231 loss)
I0224 21:13:48.415963 29812 solver.cpp:470] Iteration 14800, lr = 0.001
I0224 21:14:07.809864 29812 solver.cpp:189] Iteration 14850, loss = 0.540187
I0224 21:14:07.809888 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.540187 (* 1 = 0.540187 loss)
I0224 21:14:07.809893 29812 solver.cpp:470] Iteration 14850, lr = 0.001
I0224 21:14:27.209276 29812 solver.cpp:189] Iteration 14900, loss = 0.542143
I0224 21:14:27.209316 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.542143 (* 1 = 0.542143 loss)
I0224 21:14:27.209321 29812 solver.cpp:470] Iteration 14900, lr = 0.001
I0224 21:14:46.605439 29812 solver.cpp:189] Iteration 14950, loss = 0.770297
I0224 21:14:46.605463 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.770297 (* 1 = 0.770297 loss)
I0224 21:14:46.605468 29812 solver.cpp:470] Iteration 14950, lr = 0.001
I0224 21:15:05.759382 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_15000.caffemodel
I0224 21:15:05.874568 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_15000.solverstate
I0224 21:15:05.934123 29812 solver.cpp:266] Iteration 15000, Testing net (#0)
I0224 21:15:13.587666 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8118
I0224 21:15:13.587702 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.554458 (* 1 = 0.554458 loss)
I0224 21:15:13.874855 29812 solver.cpp:189] Iteration 15000, loss = 0.65278
I0224 21:15:13.874881 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.65278 (* 1 = 0.65278 loss)
I0224 21:15:13.874887 29812 solver.cpp:470] Iteration 15000, lr = 0.001
I0224 21:15:33.267225 29812 solver.cpp:189] Iteration 15050, loss = 0.450703
I0224 21:15:33.267247 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.450703 (* 1 = 0.450703 loss)
I0224 21:15:33.267252 29812 solver.cpp:470] Iteration 15050, lr = 0.001
I0224 21:15:52.650897 29812 solver.cpp:189] Iteration 15100, loss = 0.477407
I0224 21:15:52.650971 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.477407 (* 1 = 0.477407 loss)
I0224 21:15:52.650977 29812 solver.cpp:470] Iteration 15100, lr = 0.001
I0224 21:16:12.034081 29812 solver.cpp:189] Iteration 15150, loss = 0.538277
I0224 21:16:12.034104 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.538277 (* 1 = 0.538277 loss)
I0224 21:16:12.034111 29812 solver.cpp:470] Iteration 15150, lr = 0.001
I0224 21:16:31.421782 29812 solver.cpp:189] Iteration 15200, loss = 0.521647
I0224 21:16:31.421824 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.521647 (* 1 = 0.521647 loss)
I0224 21:16:31.421829 29812 solver.cpp:470] Iteration 15200, lr = 0.001
I0224 21:16:50.808594 29812 solver.cpp:189] Iteration 15250, loss = 0.619051
I0224 21:16:50.808617 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.619051 (* 1 = 0.619051 loss)
I0224 21:16:50.808624 29812 solver.cpp:470] Iteration 15250, lr = 0.001
I0224 21:17:10.192369 29812 solver.cpp:189] Iteration 15300, loss = 0.444377
I0224 21:17:10.192440 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.444377 (* 1 = 0.444377 loss)
I0224 21:17:10.192455 29812 solver.cpp:470] Iteration 15300, lr = 0.001
I0224 21:17:29.579052 29812 solver.cpp:189] Iteration 15350, loss = 0.571238
I0224 21:17:29.579077 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.571238 (* 1 = 0.571238 loss)
I0224 21:17:29.579080 29812 solver.cpp:470] Iteration 15350, lr = 0.001
I0224 21:17:48.963721 29812 solver.cpp:189] Iteration 15400, loss = 0.482424
I0224 21:17:48.963827 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.482424 (* 1 = 0.482424 loss)
I0224 21:17:48.963842 29812 solver.cpp:470] Iteration 15400, lr = 0.001
I0224 21:18:08.354404 29812 solver.cpp:189] Iteration 15450, loss = 0.63214
I0224 21:18:08.354431 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.63214 (* 1 = 0.63214 loss)
I0224 21:18:08.354436 29812 solver.cpp:470] Iteration 15450, lr = 0.001
I0224 21:18:27.747910 29812 solver.cpp:189] Iteration 15500, loss = 0.525195
I0224 21:18:27.747980 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.525195 (* 1 = 0.525195 loss)
I0224 21:18:27.747995 29812 solver.cpp:470] Iteration 15500, lr = 0.001
I0224 21:18:47.136986 29812 solver.cpp:189] Iteration 15550, loss = 0.723782
I0224 21:18:47.137009 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.723782 (* 1 = 0.723782 loss)
I0224 21:18:47.137014 29812 solver.cpp:470] Iteration 15550, lr = 0.001
I0224 21:19:06.522033 29812 solver.cpp:189] Iteration 15600, loss = 0.71755
I0224 21:19:06.522104 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.71755 (* 1 = 0.71755 loss)
I0224 21:19:06.522109 29812 solver.cpp:470] Iteration 15600, lr = 0.001
I0224 21:19:25.905728 29812 solver.cpp:189] Iteration 15650, loss = 0.792256
I0224 21:19:25.905753 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.792256 (* 1 = 0.792256 loss)
I0224 21:19:25.905760 29812 solver.cpp:470] Iteration 15650, lr = 0.001
I0224 21:19:45.292631 29812 solver.cpp:189] Iteration 15700, loss = 0.529651
I0224 21:19:45.292670 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.529651 (* 1 = 0.529651 loss)
I0224 21:19:45.292675 29812 solver.cpp:470] Iteration 15700, lr = 0.001
I0224 21:20:04.673665 29812 solver.cpp:189] Iteration 15750, loss = 0.503399
I0224 21:20:04.673691 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.503399 (* 1 = 0.503399 loss)
I0224 21:20:04.673696 29812 solver.cpp:470] Iteration 15750, lr = 0.001
I0224 21:20:24.057736 29812 solver.cpp:189] Iteration 15800, loss = 0.550568
I0224 21:20:24.057775 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.550568 (* 1 = 0.550568 loss)
I0224 21:20:24.057780 29812 solver.cpp:470] Iteration 15800, lr = 0.001
I0224 21:20:43.445549 29812 solver.cpp:189] Iteration 15850, loss = 0.827165
I0224 21:20:43.445571 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.827165 (* 1 = 0.827165 loss)
I0224 21:20:43.445576 29812 solver.cpp:470] Iteration 15850, lr = 0.001
I0224 21:21:02.839737 29812 solver.cpp:189] Iteration 15900, loss = 0.482329
I0224 21:21:02.839848 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.482329 (* 1 = 0.482329 loss)
I0224 21:21:02.839854 29812 solver.cpp:470] Iteration 15900, lr = 0.001
I0224 21:21:22.216209 29812 solver.cpp:189] Iteration 15950, loss = 0.629739
I0224 21:21:22.216234 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.629739 (* 1 = 0.629739 loss)
I0224 21:21:22.216239 29812 solver.cpp:470] Iteration 15950, lr = 0.001
I0224 21:21:41.359194 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_16000.caffemodel
I0224 21:21:41.483259 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_16000.solverstate
I0224 21:21:41.542083 29812 solver.cpp:266] Iteration 16000, Testing net (#0)
I0224 21:21:49.183675 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8226
I0224 21:21:49.183711 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.518218 (* 1 = 0.518218 loss)
I0224 21:21:49.470010 29812 solver.cpp:189] Iteration 16000, loss = 0.580826
I0224 21:21:49.470031 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.580826 (* 1 = 0.580826 loss)
I0224 21:21:49.470036 29812 solver.cpp:470] Iteration 16000, lr = 0.001
I0224 21:22:08.868963 29812 solver.cpp:189] Iteration 16050, loss = 0.60108
I0224 21:22:08.868989 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.60108 (* 1 = 0.60108 loss)
I0224 21:22:08.868995 29812 solver.cpp:470] Iteration 16050, lr = 0.001
I0224 21:22:28.254350 29812 solver.cpp:189] Iteration 16100, loss = 0.524705
I0224 21:22:28.254420 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.524705 (* 1 = 0.524705 loss)
I0224 21:22:28.254434 29812 solver.cpp:470] Iteration 16100, lr = 0.001
I0224 21:22:47.648339 29812 solver.cpp:189] Iteration 16150, loss = 0.591906
I0224 21:22:47.648365 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.591906 (* 1 = 0.591906 loss)
I0224 21:22:47.648370 29812 solver.cpp:470] Iteration 16150, lr = 0.001
I0224 21:23:07.042901 29812 solver.cpp:189] Iteration 16200, loss = 0.474302
I0224 21:23:07.042958 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.474302 (* 1 = 0.474302 loss)
I0224 21:23:07.042964 29812 solver.cpp:470] Iteration 16200, lr = 0.001
I0224 21:23:26.430907 29812 solver.cpp:189] Iteration 16250, loss = 0.429075
I0224 21:23:26.430932 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.429075 (* 1 = 0.429075 loss)
I0224 21:23:26.430937 29812 solver.cpp:470] Iteration 16250, lr = 0.001
I0224 21:23:45.819089 29812 solver.cpp:189] Iteration 16300, loss = 0.534192
I0224 21:23:45.819149 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.534192 (* 1 = 0.534192 loss)
I0224 21:23:45.819155 29812 solver.cpp:470] Iteration 16300, lr = 0.001
I0224 21:24:05.216469 29812 solver.cpp:189] Iteration 16350, loss = 0.536045
I0224 21:24:05.216491 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.536045 (* 1 = 0.536045 loss)
I0224 21:24:05.216496 29812 solver.cpp:470] Iteration 16350, lr = 0.001
I0224 21:24:24.610491 29812 solver.cpp:189] Iteration 16400, loss = 0.620052
I0224 21:24:24.610559 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.620052 (* 1 = 0.620052 loss)
I0224 21:24:24.610564 29812 solver.cpp:470] Iteration 16400, lr = 0.001
I0224 21:24:44.012344 29812 solver.cpp:189] Iteration 16450, loss = 0.580237
I0224 21:24:44.012368 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.580237 (* 1 = 0.580237 loss)
I0224 21:24:44.012373 29812 solver.cpp:470] Iteration 16450, lr = 0.001
I0224 21:25:03.417959 29812 solver.cpp:189] Iteration 16500, loss = 0.491204
I0224 21:25:03.417999 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.491204 (* 1 = 0.491204 loss)
I0224 21:25:03.418004 29812 solver.cpp:470] Iteration 16500, lr = 0.001
I0224 21:25:22.807540 29812 solver.cpp:189] Iteration 16550, loss = 0.595271
I0224 21:25:22.807565 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.59527 (* 1 = 0.59527 loss)
I0224 21:25:22.807570 29812 solver.cpp:470] Iteration 16550, lr = 0.001
I0224 21:25:42.196146 29812 solver.cpp:189] Iteration 16600, loss = 0.483203
I0224 21:25:42.196244 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.483203 (* 1 = 0.483203 loss)
I0224 21:25:42.196259 29812 solver.cpp:470] Iteration 16600, lr = 0.001
I0224 21:26:01.574661 29812 solver.cpp:189] Iteration 16650, loss = 0.731299
I0224 21:26:01.574686 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.731299 (* 1 = 0.731299 loss)
I0224 21:26:01.574689 29812 solver.cpp:470] Iteration 16650, lr = 0.001
I0224 21:26:20.959573 29812 solver.cpp:189] Iteration 16700, loss = 0.589437
I0224 21:26:20.959616 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.589437 (* 1 = 0.589437 loss)
I0224 21:26:20.959622 29812 solver.cpp:470] Iteration 16700, lr = 0.001
I0224 21:26:40.344418 29812 solver.cpp:189] Iteration 16750, loss = 0.596592
I0224 21:26:40.344441 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.596592 (* 1 = 0.596592 loss)
I0224 21:26:40.344445 29812 solver.cpp:470] Iteration 16750, lr = 0.001
I0224 21:26:59.726788 29812 solver.cpp:189] Iteration 16800, loss = 0.386306
I0224 21:26:59.726830 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.386306 (* 1 = 0.386306 loss)
I0224 21:26:59.726835 29812 solver.cpp:470] Iteration 16800, lr = 0.001
I0224 21:27:19.127826 29812 solver.cpp:189] Iteration 16850, loss = 0.641444
I0224 21:27:19.127851 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.641444 (* 1 = 0.641444 loss)
I0224 21:27:19.127856 29812 solver.cpp:470] Iteration 16850, lr = 0.001
I0224 21:27:38.516721 29812 solver.cpp:189] Iteration 16900, loss = 0.605681
I0224 21:27:38.516759 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.605681 (* 1 = 0.605681 loss)
I0224 21:27:38.516764 29812 solver.cpp:470] Iteration 16900, lr = 0.001
I0224 21:27:57.907749 29812 solver.cpp:189] Iteration 16950, loss = 0.492617
I0224 21:27:57.907774 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.492617 (* 1 = 0.492617 loss)
I0224 21:27:57.907779 29812 solver.cpp:470] Iteration 16950, lr = 0.001
I0224 21:28:17.062430 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_17000.caffemodel
I0224 21:28:17.199609 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_17000.solverstate
I0224 21:28:17.259021 29812 solver.cpp:266] Iteration 17000, Testing net (#0)
I0224 21:28:24.902499 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8229
I0224 21:28:24.902535 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.525874 (* 1 = 0.525874 loss)
I0224 21:28:25.189111 29812 solver.cpp:189] Iteration 17000, loss = 0.433392
I0224 21:28:25.189132 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.433392 (* 1 = 0.433392 loss)
I0224 21:28:25.189138 29812 solver.cpp:470] Iteration 17000, lr = 0.001
I0224 21:28:44.580607 29812 solver.cpp:189] Iteration 17050, loss = 0.544433
I0224 21:28:44.580634 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.544433 (* 1 = 0.544433 loss)
I0224 21:28:44.580641 29812 solver.cpp:470] Iteration 17050, lr = 0.001
I0224 21:29:03.968463 29812 solver.cpp:189] Iteration 17100, loss = 0.518793
I0224 21:29:03.968502 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.518793 (* 1 = 0.518793 loss)
I0224 21:29:03.968508 29812 solver.cpp:470] Iteration 17100, lr = 0.001
I0224 21:29:23.356153 29812 solver.cpp:189] Iteration 17150, loss = 0.410528
I0224 21:29:23.356184 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.410528 (* 1 = 0.410528 loss)
I0224 21:29:23.356191 29812 solver.cpp:470] Iteration 17150, lr = 0.001
I0224 21:29:42.749598 29812 solver.cpp:189] Iteration 17200, loss = 0.608754
I0224 21:29:42.749689 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.608754 (* 1 = 0.608754 loss)
I0224 21:29:42.749696 29812 solver.cpp:470] Iteration 17200, lr = 0.001
I0224 21:30:02.145273 29812 solver.cpp:189] Iteration 17250, loss = 0.609707
I0224 21:30:02.145301 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.609707 (* 1 = 0.609707 loss)
I0224 21:30:02.145308 29812 solver.cpp:470] Iteration 17250, lr = 0.001
I0224 21:30:21.532781 29812 solver.cpp:189] Iteration 17300, loss = 0.431831
I0224 21:30:21.532842 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.431831 (* 1 = 0.431831 loss)
I0224 21:30:21.532848 29812 solver.cpp:470] Iteration 17300, lr = 0.001
I0224 21:30:40.929790 29812 solver.cpp:189] Iteration 17350, loss = 0.487061
I0224 21:30:40.929816 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.487061 (* 1 = 0.487061 loss)
I0224 21:30:40.929821 29812 solver.cpp:470] Iteration 17350, lr = 0.001
I0224 21:31:00.334383 29812 solver.cpp:189] Iteration 17400, loss = 0.480432
I0224 21:31:00.334465 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.480432 (* 1 = 0.480432 loss)
I0224 21:31:00.334471 29812 solver.cpp:470] Iteration 17400, lr = 0.001
I0224 21:31:19.721848 29812 solver.cpp:189] Iteration 17450, loss = 0.426062
I0224 21:31:19.721873 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.426062 (* 1 = 0.426062 loss)
I0224 21:31:19.721879 29812 solver.cpp:470] Iteration 17450, lr = 0.001
I0224 21:31:39.102198 29812 solver.cpp:189] Iteration 17500, loss = 0.428645
I0224 21:31:39.102269 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.428645 (* 1 = 0.428645 loss)
I0224 21:31:39.102284 29812 solver.cpp:470] Iteration 17500, lr = 0.001
I0224 21:31:58.499297 29812 solver.cpp:189] Iteration 17550, loss = 0.473656
I0224 21:31:58.499321 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.473656 (* 1 = 0.473656 loss)
I0224 21:31:58.499326 29812 solver.cpp:470] Iteration 17550, lr = 0.001
I0224 21:32:17.887687 29812 solver.cpp:189] Iteration 17600, loss = 0.471718
I0224 21:32:17.887758 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.471718 (* 1 = 0.471718 loss)
I0224 21:32:17.887773 29812 solver.cpp:470] Iteration 17600, lr = 0.001
I0224 21:32:37.271126 29812 solver.cpp:189] Iteration 17650, loss = 0.570976
I0224 21:32:37.271150 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.570976 (* 1 = 0.570976 loss)
I0224 21:32:37.271155 29812 solver.cpp:470] Iteration 17650, lr = 0.001
I0224 21:32:56.660897 29812 solver.cpp:189] Iteration 17700, loss = 0.471915
I0224 21:32:56.660959 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.471915 (* 1 = 0.471915 loss)
I0224 21:32:56.660966 29812 solver.cpp:470] Iteration 17700, lr = 0.001
I0224 21:33:16.047968 29812 solver.cpp:189] Iteration 17750, loss = 0.646676
I0224 21:33:16.047992 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.646676 (* 1 = 0.646676 loss)
I0224 21:33:16.047998 29812 solver.cpp:470] Iteration 17750, lr = 0.001
I0224 21:33:35.441771 29812 solver.cpp:189] Iteration 17800, loss = 0.480627
I0224 21:33:35.441804 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.480627 (* 1 = 0.480627 loss)
I0224 21:33:35.441809 29812 solver.cpp:470] Iteration 17800, lr = 0.001
I0224 21:33:54.831271 29812 solver.cpp:189] Iteration 17850, loss = 0.659208
I0224 21:33:54.831296 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.659208 (* 1 = 0.659208 loss)
I0224 21:33:54.831301 29812 solver.cpp:470] Iteration 17850, lr = 0.001
I0224 21:34:14.224026 29812 solver.cpp:189] Iteration 17900, loss = 0.447604
I0224 21:34:14.224097 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.447604 (* 1 = 0.447604 loss)
I0224 21:34:14.224112 29812 solver.cpp:470] Iteration 17900, lr = 0.001
I0224 21:34:33.610853 29812 solver.cpp:189] Iteration 17950, loss = 0.465876
I0224 21:34:33.610877 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.465876 (* 1 = 0.465876 loss)
I0224 21:34:33.610882 29812 solver.cpp:470] Iteration 17950, lr = 0.001
I0224 21:34:52.770117 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_18000.caffemodel
I0224 21:34:52.895781 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_18000.solverstate
I0224 21:34:52.953814 29812 solver.cpp:266] Iteration 18000, Testing net (#0)
I0224 21:35:00.619411 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8262
I0224 21:35:00.619448 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.505655 (* 1 = 0.505655 loss)
I0224 21:35:00.908275 29812 solver.cpp:189] Iteration 18000, loss = 0.56951
I0224 21:35:00.908298 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.56951 (* 1 = 0.56951 loss)
I0224 21:35:00.908304 29812 solver.cpp:470] Iteration 18000, lr = 0.001
I0224 21:35:20.306041 29812 solver.cpp:189] Iteration 18050, loss = 0.55465
I0224 21:35:20.306063 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.55465 (* 1 = 0.55465 loss)
I0224 21:35:20.306068 29812 solver.cpp:470] Iteration 18050, lr = 0.001
I0224 21:35:39.707815 29812 solver.cpp:189] Iteration 18100, loss = 0.693626
I0224 21:35:39.707905 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.693626 (* 1 = 0.693626 loss)
I0224 21:35:39.707911 29812 solver.cpp:470] Iteration 18100, lr = 0.001
I0224 21:35:59.102088 29812 solver.cpp:189] Iteration 18150, loss = 0.570128
I0224 21:35:59.102111 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.570128 (* 1 = 0.570128 loss)
I0224 21:35:59.102118 29812 solver.cpp:470] Iteration 18150, lr = 0.001
I0224 21:36:18.491569 29812 solver.cpp:189] Iteration 18200, loss = 0.458272
I0224 21:36:18.491602 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.458272 (* 1 = 0.458272 loss)
I0224 21:36:18.491607 29812 solver.cpp:470] Iteration 18200, lr = 0.001
I0224 21:36:37.879245 29812 solver.cpp:189] Iteration 18250, loss = 0.519167
I0224 21:36:37.879271 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.519167 (* 1 = 0.519167 loss)
I0224 21:36:37.879276 29812 solver.cpp:470] Iteration 18250, lr = 0.001
I0224 21:36:57.277176 29812 solver.cpp:189] Iteration 18300, loss = 0.42943
I0224 21:36:57.277215 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.42943 (* 1 = 0.42943 loss)
I0224 21:36:57.277221 29812 solver.cpp:470] Iteration 18300, lr = 0.001
I0224 21:37:16.670578 29812 solver.cpp:189] Iteration 18350, loss = 0.386919
I0224 21:37:16.670605 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.386919 (* 1 = 0.386919 loss)
I0224 21:37:16.670610 29812 solver.cpp:470] Iteration 18350, lr = 0.001
I0224 21:37:36.060021 29812 solver.cpp:189] Iteration 18400, loss = 0.508105
I0224 21:37:36.060061 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.508105 (* 1 = 0.508105 loss)
I0224 21:37:36.060067 29812 solver.cpp:470] Iteration 18400, lr = 0.001
I0224 21:37:55.448056 29812 solver.cpp:189] Iteration 18450, loss = 0.504519
I0224 21:37:55.448079 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.504519 (* 1 = 0.504519 loss)
I0224 21:37:55.448084 29812 solver.cpp:470] Iteration 18450, lr = 0.001
I0224 21:38:14.845196 29812 solver.cpp:189] Iteration 18500, loss = 0.503722
I0224 21:38:14.845268 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.503722 (* 1 = 0.503722 loss)
I0224 21:38:14.845283 29812 solver.cpp:470] Iteration 18500, lr = 0.001
I0224 21:38:34.238832 29812 solver.cpp:189] Iteration 18550, loss = 0.493131
I0224 21:38:34.238855 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.493131 (* 1 = 0.493131 loss)
I0224 21:38:34.238860 29812 solver.cpp:470] Iteration 18550, lr = 0.001
I0224 21:38:53.619335 29812 solver.cpp:189] Iteration 18600, loss = 0.564456
I0224 21:38:53.619423 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.564456 (* 1 = 0.564456 loss)
I0224 21:38:53.619429 29812 solver.cpp:470] Iteration 18600, lr = 0.001
I0224 21:39:13.017240 29812 solver.cpp:189] Iteration 18650, loss = 0.514331
I0224 21:39:13.017264 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.514331 (* 1 = 0.514331 loss)
I0224 21:39:13.017271 29812 solver.cpp:470] Iteration 18650, lr = 0.001
I0224 21:39:32.415102 29812 solver.cpp:189] Iteration 18700, loss = 0.631847
I0224 21:39:32.415165 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.631847 (* 1 = 0.631847 loss)
I0224 21:39:32.415171 29812 solver.cpp:470] Iteration 18700, lr = 0.001
I0224 21:39:51.807306 29812 solver.cpp:189] Iteration 18750, loss = 0.712264
I0224 21:39:51.807330 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.712264 (* 1 = 0.712264 loss)
I0224 21:39:51.807335 29812 solver.cpp:470] Iteration 18750, lr = 0.001
I0224 21:40:11.205672 29812 solver.cpp:189] Iteration 18800, loss = 0.484722
I0224 21:40:11.205732 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.484722 (* 1 = 0.484722 loss)
I0224 21:40:11.205739 29812 solver.cpp:470] Iteration 18800, lr = 0.001
I0224 21:40:30.599545 29812 solver.cpp:189] Iteration 18850, loss = 0.503801
I0224 21:40:30.599568 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.5038 (* 1 = 0.5038 loss)
I0224 21:40:30.599573 29812 solver.cpp:470] Iteration 18850, lr = 0.001
I0224 21:40:49.993037 29812 solver.cpp:189] Iteration 18900, loss = 0.585517
I0224 21:40:49.993109 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.585517 (* 1 = 0.585517 loss)
I0224 21:40:49.993124 29812 solver.cpp:470] Iteration 18900, lr = 0.001
I0224 21:41:09.382967 29812 solver.cpp:189] Iteration 18950, loss = 0.538467
I0224 21:41:09.382990 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.538467 (* 1 = 0.538467 loss)
I0224 21:41:09.382995 29812 solver.cpp:470] Iteration 18950, lr = 0.001
I0224 21:41:28.534782 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_19000.caffemodel
I0224 21:41:28.641973 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_19000.solverstate
I0224 21:41:28.699609 29812 solver.cpp:266] Iteration 19000, Testing net (#0)
I0224 21:41:36.351564 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8374
I0224 21:41:36.351603 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.484497 (* 1 = 0.484497 loss)
I0224 21:41:36.637924 29812 solver.cpp:189] Iteration 19000, loss = 0.543471
I0224 21:41:36.637959 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.543471 (* 1 = 0.543471 loss)
I0224 21:41:36.637964 29812 solver.cpp:470] Iteration 19000, lr = 0.001
I0224 21:41:56.026028 29812 solver.cpp:189] Iteration 19050, loss = 0.382942
I0224 21:41:56.026052 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.382942 (* 1 = 0.382942 loss)
I0224 21:41:56.026058 29812 solver.cpp:470] Iteration 19050, lr = 0.001
I0224 21:42:15.415243 29812 solver.cpp:189] Iteration 19100, loss = 0.463141
I0224 21:42:15.415295 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.463141 (* 1 = 0.463141 loss)
I0224 21:42:15.415302 29812 solver.cpp:470] Iteration 19100, lr = 0.001
I0224 21:42:34.800886 29812 solver.cpp:189] Iteration 19150, loss = 0.465457
I0224 21:42:34.800914 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.465457 (* 1 = 0.465457 loss)
I0224 21:42:34.800918 29812 solver.cpp:470] Iteration 19150, lr = 0.001
I0224 21:42:54.180990 29812 solver.cpp:189] Iteration 19200, loss = 0.505281
I0224 21:42:54.181062 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.505281 (* 1 = 0.505281 loss)
I0224 21:42:54.181077 29812 solver.cpp:470] Iteration 19200, lr = 0.001
I0224 21:43:13.563773 29812 solver.cpp:189] Iteration 19250, loss = 0.508687
I0224 21:43:13.563796 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.508687 (* 1 = 0.508687 loss)
I0224 21:43:13.563802 29812 solver.cpp:470] Iteration 19250, lr = 0.001
I0224 21:43:32.952191 29812 solver.cpp:189] Iteration 19300, loss = 0.659676
I0224 21:43:32.952229 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.659676 (* 1 = 0.659676 loss)
I0224 21:43:32.952235 29812 solver.cpp:470] Iteration 19300, lr = 0.001
I0224 21:43:52.340698 29812 solver.cpp:189] Iteration 19350, loss = 0.47791
I0224 21:43:52.340723 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.47791 (* 1 = 0.47791 loss)
I0224 21:43:52.340728 29812 solver.cpp:470] Iteration 19350, lr = 0.001
I0224 21:44:11.732059 29812 solver.cpp:189] Iteration 19400, loss = 0.529598
I0224 21:44:11.732152 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.529598 (* 1 = 0.529598 loss)
I0224 21:44:11.732167 29812 solver.cpp:470] Iteration 19400, lr = 0.001
I0224 21:44:31.111490 29812 solver.cpp:189] Iteration 19450, loss = 0.477215
I0224 21:44:31.111512 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.477215 (* 1 = 0.477215 loss)
I0224 21:44:31.111518 29812 solver.cpp:470] Iteration 19450, lr = 0.001
I0224 21:44:50.498698 29812 solver.cpp:189] Iteration 19500, loss = 0.588022
I0224 21:44:50.498742 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.588022 (* 1 = 0.588022 loss)
I0224 21:44:50.498749 29812 solver.cpp:470] Iteration 19500, lr = 0.001
I0224 21:45:09.889262 29812 solver.cpp:189] Iteration 19550, loss = 0.524536
I0224 21:45:09.889287 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.524535 (* 1 = 0.524535 loss)
I0224 21:45:09.889293 29812 solver.cpp:470] Iteration 19550, lr = 0.001
I0224 21:45:29.266916 29812 solver.cpp:189] Iteration 19600, loss = 0.380876
I0224 21:45:29.267004 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.380876 (* 1 = 0.380876 loss)
I0224 21:45:29.267009 29812 solver.cpp:470] Iteration 19600, lr = 0.001
I0224 21:45:48.652392 29812 solver.cpp:189] Iteration 19650, loss = 0.570489
I0224 21:45:48.652420 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.570489 (* 1 = 0.570489 loss)
I0224 21:45:48.652426 29812 solver.cpp:470] Iteration 19650, lr = 0.001
I0224 21:46:08.045110 29812 solver.cpp:189] Iteration 19700, loss = 0.570346
I0224 21:46:08.045151 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.570346 (* 1 = 0.570346 loss)
I0224 21:46:08.045157 29812 solver.cpp:470] Iteration 19700, lr = 0.001
I0224 21:46:27.430863 29812 solver.cpp:189] Iteration 19750, loss = 0.463178
I0224 21:46:27.430887 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.463178 (* 1 = 0.463178 loss)
I0224 21:46:27.430893 29812 solver.cpp:470] Iteration 19750, lr = 0.001
I0224 21:46:46.811259 29812 solver.cpp:189] Iteration 19800, loss = 0.475966
I0224 21:46:46.811350 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.475966 (* 1 = 0.475966 loss)
I0224 21:46:46.811357 29812 solver.cpp:470] Iteration 19800, lr = 0.001
I0224 21:47:06.193603 29812 solver.cpp:189] Iteration 19850, loss = 0.536067
I0224 21:47:06.193627 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.536067 (* 1 = 0.536067 loss)
I0224 21:47:06.193634 29812 solver.cpp:470] Iteration 19850, lr = 0.001
I0224 21:47:25.575562 29812 solver.cpp:189] Iteration 19900, loss = 0.56477
I0224 21:47:25.575600 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.56477 (* 1 = 0.56477 loss)
I0224 21:47:25.575605 29812 solver.cpp:470] Iteration 19900, lr = 0.001
I0224 21:47:44.965200 29812 solver.cpp:189] Iteration 19950, loss = 0.413026
I0224 21:47:44.965225 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.413025 (* 1 = 0.413025 loss)
I0224 21:47:44.965231 29812 solver.cpp:470] Iteration 19950, lr = 0.001
I0224 21:48:04.120429 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_20000.caffemodel
I0224 21:48:04.236531 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_20000.solverstate
I0224 21:48:04.293897 29812 solver.cpp:266] Iteration 20000, Testing net (#0)
I0224 21:48:11.950450 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8412
I0224 21:48:11.950489 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.46501 (* 1 = 0.46501 loss)
I0224 21:48:12.237810 29812 solver.cpp:189] Iteration 20000, loss = 0.43285
I0224 21:48:12.237833 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.43285 (* 1 = 0.43285 loss)
I0224 21:48:12.237838 29812 solver.cpp:470] Iteration 20000, lr = 0.0007
I0224 21:48:31.622714 29812 solver.cpp:189] Iteration 20050, loss = 0.504297
I0224 21:48:31.622738 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.504297 (* 1 = 0.504297 loss)
I0224 21:48:31.622742 29812 solver.cpp:470] Iteration 20050, lr = 0.0007
I0224 21:48:51.012617 29812 solver.cpp:189] Iteration 20100, loss = 0.504668
I0224 21:48:51.012748 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.504668 (* 1 = 0.504668 loss)
I0224 21:48:51.012766 29812 solver.cpp:470] Iteration 20100, lr = 0.0007
I0224 21:49:10.404747 29812 solver.cpp:189] Iteration 20150, loss = 0.48919
I0224 21:49:10.404772 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.48919 (* 1 = 0.48919 loss)
I0224 21:49:10.404777 29812 solver.cpp:470] Iteration 20150, lr = 0.0007
I0224 21:49:29.787611 29812 solver.cpp:189] Iteration 20200, loss = 0.519746
I0224 21:49:29.787686 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.519746 (* 1 = 0.519746 loss)
I0224 21:49:29.787701 29812 solver.cpp:470] Iteration 20200, lr = 0.0007
I0224 21:49:49.171777 29812 solver.cpp:189] Iteration 20250, loss = 0.377272
I0224 21:49:49.171802 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.377272 (* 1 = 0.377272 loss)
I0224 21:49:49.171807 29812 solver.cpp:470] Iteration 20250, lr = 0.0007
I0224 21:50:08.561278 29812 solver.cpp:189] Iteration 20300, loss = 0.464592
I0224 21:50:08.561347 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.464592 (* 1 = 0.464592 loss)
I0224 21:50:08.561353 29812 solver.cpp:470] Iteration 20300, lr = 0.0007
I0224 21:50:27.952877 29812 solver.cpp:189] Iteration 20350, loss = 0.531847
I0224 21:50:27.952903 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.531847 (* 1 = 0.531847 loss)
I0224 21:50:27.952908 29812 solver.cpp:470] Iteration 20350, lr = 0.0007
I0224 21:50:47.339972 29812 solver.cpp:189] Iteration 20400, loss = 0.447263
I0224 21:50:47.340042 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.447263 (* 1 = 0.447263 loss)
I0224 21:50:47.340056 29812 solver.cpp:470] Iteration 20400, lr = 0.0007
I0224 21:51:06.717031 29812 solver.cpp:189] Iteration 20450, loss = 0.511128
I0224 21:51:06.717058 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.511128 (* 1 = 0.511128 loss)
I0224 21:51:06.717064 29812 solver.cpp:470] Iteration 20450, lr = 0.0007
I0224 21:51:26.101824 29812 solver.cpp:189] Iteration 20500, loss = 0.472198
I0224 21:51:26.101866 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.472198 (* 1 = 0.472198 loss)
I0224 21:51:26.101872 29812 solver.cpp:470] Iteration 20500, lr = 0.0007
I0224 21:51:45.491178 29812 solver.cpp:189] Iteration 20550, loss = 0.492596
I0224 21:51:45.491202 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.492596 (* 1 = 0.492596 loss)
I0224 21:51:45.491207 29812 solver.cpp:470] Iteration 20550, lr = 0.0007
I0224 21:52:04.874092 29812 solver.cpp:189] Iteration 20600, loss = 0.299993
I0224 21:52:04.874163 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.299993 (* 1 = 0.299993 loss)
I0224 21:52:04.874177 29812 solver.cpp:470] Iteration 20600, lr = 0.0007
I0224 21:52:24.260673 29812 solver.cpp:189] Iteration 20650, loss = 0.550183
I0224 21:52:24.260696 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.550183 (* 1 = 0.550183 loss)
I0224 21:52:24.260702 29812 solver.cpp:470] Iteration 20650, lr = 0.0007
I0224 21:52:43.647817 29812 solver.cpp:189] Iteration 20700, loss = 0.439438
I0224 21:52:43.647904 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.439438 (* 1 = 0.439438 loss)
I0224 21:52:43.647910 29812 solver.cpp:470] Iteration 20700, lr = 0.0007
I0224 21:53:03.041570 29812 solver.cpp:189] Iteration 20750, loss = 0.462342
I0224 21:53:03.041596 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.462342 (* 1 = 0.462342 loss)
I0224 21:53:03.041601 29812 solver.cpp:470] Iteration 20750, lr = 0.0007
I0224 21:53:22.437898 29812 solver.cpp:189] Iteration 20800, loss = 0.318734
I0224 21:53:22.437937 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.318734 (* 1 = 0.318734 loss)
I0224 21:53:22.437943 29812 solver.cpp:470] Iteration 20800, lr = 0.0007
I0224 21:53:41.827599 29812 solver.cpp:189] Iteration 20850, loss = 0.642159
I0224 21:53:41.827622 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.642159 (* 1 = 0.642159 loss)
I0224 21:53:41.827627 29812 solver.cpp:470] Iteration 20850, lr = 0.0007
I0224 21:54:01.219348 29812 solver.cpp:189] Iteration 20900, loss = 0.341436
I0224 21:54:01.219439 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.341436 (* 1 = 0.341436 loss)
I0224 21:54:01.219454 29812 solver.cpp:470] Iteration 20900, lr = 0.0007
I0224 21:54:20.612871 29812 solver.cpp:189] Iteration 20950, loss = 0.495965
I0224 21:54:20.612895 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.495965 (* 1 = 0.495965 loss)
I0224 21:54:20.612900 29812 solver.cpp:470] Iteration 20950, lr = 0.0007
I0224 21:54:39.751454 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_21000.caffemodel
I0224 21:54:39.857547 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_21000.solverstate
I0224 21:54:39.915511 29812 solver.cpp:266] Iteration 21000, Testing net (#0)
I0224 21:54:47.582325 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8509
I0224 21:54:47.582361 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.43898 (* 1 = 0.43898 loss)
I0224 21:54:47.868559 29812 solver.cpp:189] Iteration 21000, loss = 0.598923
I0224 21:54:47.868582 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.598923 (* 1 = 0.598923 loss)
I0224 21:54:47.868587 29812 solver.cpp:470] Iteration 21000, lr = 0.0007
I0224 21:55:07.260072 29812 solver.cpp:189] Iteration 21050, loss = 0.402304
I0224 21:55:07.260097 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.402304 (* 1 = 0.402304 loss)
I0224 21:55:07.260102 29812 solver.cpp:470] Iteration 21050, lr = 0.0007
I0224 21:55:26.653985 29812 solver.cpp:189] Iteration 21100, loss = 0.51332
I0224 21:55:26.654044 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.51332 (* 1 = 0.51332 loss)
I0224 21:55:26.654050 29812 solver.cpp:470] Iteration 21100, lr = 0.0007
I0224 21:55:46.040565 29812 solver.cpp:189] Iteration 21150, loss = 0.418948
I0224 21:55:46.040591 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.418948 (* 1 = 0.418948 loss)
I0224 21:55:46.040596 29812 solver.cpp:470] Iteration 21150, lr = 0.0007
I0224 21:56:05.439735 29812 solver.cpp:189] Iteration 21200, loss = 0.375775
I0224 21:56:05.439818 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.375775 (* 1 = 0.375775 loss)
I0224 21:56:05.439824 29812 solver.cpp:470] Iteration 21200, lr = 0.0007
I0224 21:56:24.841706 29812 solver.cpp:189] Iteration 21250, loss = 0.484112
I0224 21:56:24.841733 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.484112 (* 1 = 0.484112 loss)
I0224 21:56:24.841738 29812 solver.cpp:470] Iteration 21250, lr = 0.0007
I0224 21:56:44.238126 29812 solver.cpp:189] Iteration 21300, loss = 0.392712
I0224 21:56:44.238196 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.392712 (* 1 = 0.392712 loss)
I0224 21:56:44.238211 29812 solver.cpp:470] Iteration 21300, lr = 0.0007
I0224 21:57:03.627467 29812 solver.cpp:189] Iteration 21350, loss = 0.45337
I0224 21:57:03.627490 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.45337 (* 1 = 0.45337 loss)
I0224 21:57:03.627496 29812 solver.cpp:470] Iteration 21350, lr = 0.0007
I0224 21:57:23.022629 29812 solver.cpp:189] Iteration 21400, loss = 0.549251
I0224 21:57:23.022717 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.549251 (* 1 = 0.549251 loss)
I0224 21:57:23.022733 29812 solver.cpp:470] Iteration 21400, lr = 0.0007
I0224 21:57:42.430239 29812 solver.cpp:189] Iteration 21450, loss = 0.389425
I0224 21:57:42.430264 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.389425 (* 1 = 0.389425 loss)
I0224 21:57:42.430269 29812 solver.cpp:470] Iteration 21450, lr = 0.0007
I0224 21:58:01.822273 29812 solver.cpp:189] Iteration 21500, loss = 0.600063
I0224 21:58:01.822356 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.600063 (* 1 = 0.600063 loss)
I0224 21:58:01.822371 29812 solver.cpp:470] Iteration 21500, lr = 0.0007
I0224 21:58:21.225152 29812 solver.cpp:189] Iteration 21550, loss = 0.503829
I0224 21:58:21.225175 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.503829 (* 1 = 0.503829 loss)
I0224 21:58:21.225180 29812 solver.cpp:470] Iteration 21550, lr = 0.0007
I0224 21:58:40.625567 29812 solver.cpp:189] Iteration 21600, loss = 0.316774
I0224 21:58:40.625629 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.316774 (* 1 = 0.316774 loss)
I0224 21:58:40.625634 29812 solver.cpp:470] Iteration 21600, lr = 0.0007
I0224 21:59:00.017995 29812 solver.cpp:189] Iteration 21650, loss = 0.529077
I0224 21:59:00.018020 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.529077 (* 1 = 0.529077 loss)
I0224 21:59:00.018026 29812 solver.cpp:470] Iteration 21650, lr = 0.0007
I0224 21:59:19.417907 29812 solver.cpp:189] Iteration 21700, loss = 0.464716
I0224 21:59:19.417979 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.464716 (* 1 = 0.464716 loss)
I0224 21:59:19.417994 29812 solver.cpp:470] Iteration 21700, lr = 0.0007
I0224 21:59:38.806030 29812 solver.cpp:189] Iteration 21750, loss = 0.451816
I0224 21:59:38.806056 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.451816 (* 1 = 0.451816 loss)
I0224 21:59:38.806061 29812 solver.cpp:470] Iteration 21750, lr = 0.0007
I0224 21:59:58.192760 29812 solver.cpp:189] Iteration 21800, loss = 0.345915
I0224 21:59:58.192819 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.345915 (* 1 = 0.345915 loss)
I0224 21:59:58.192826 29812 solver.cpp:470] Iteration 21800, lr = 0.0007
I0224 22:00:17.591699 29812 solver.cpp:189] Iteration 21850, loss = 0.381865
I0224 22:00:17.591723 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.381865 (* 1 = 0.381865 loss)
I0224 22:00:17.591728 29812 solver.cpp:470] Iteration 21850, lr = 0.0007
I0224 22:00:36.984526 29812 solver.cpp:189] Iteration 21900, loss = 0.463765
I0224 22:00:36.984586 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.463765 (* 1 = 0.463765 loss)
I0224 22:00:36.984592 29812 solver.cpp:470] Iteration 21900, lr = 0.0007
I0224 22:00:56.385874 29812 solver.cpp:189] Iteration 21950, loss = 0.346963
I0224 22:00:56.385898 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.346963 (* 1 = 0.346963 loss)
I0224 22:00:56.385903 29812 solver.cpp:470] Iteration 21950, lr = 0.0007
I0224 22:01:15.535174 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_22000.caffemodel
I0224 22:01:15.654942 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_22000.solverstate
I0224 22:01:15.713002 29812 solver.cpp:266] Iteration 22000, Testing net (#0)
I0224 22:01:23.363200 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8488
I0224 22:01:23.363236 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.454697 (* 1 = 0.454697 loss)
I0224 22:01:23.650382 29812 solver.cpp:189] Iteration 22000, loss = 0.43088
I0224 22:01:23.650404 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.43088 (* 1 = 0.43088 loss)
I0224 22:01:23.650409 29812 solver.cpp:470] Iteration 22000, lr = 0.0007
I0224 22:01:43.051986 29812 solver.cpp:189] Iteration 22050, loss = 0.34062
I0224 22:01:43.052011 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.34062 (* 1 = 0.34062 loss)
I0224 22:01:43.052016 29812 solver.cpp:470] Iteration 22050, lr = 0.0007
I0224 22:02:02.451349 29812 solver.cpp:189] Iteration 22100, loss = 0.454663
I0224 22:02:02.451387 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.454663 (* 1 = 0.454663 loss)
I0224 22:02:02.451393 29812 solver.cpp:470] Iteration 22100, lr = 0.0007
I0224 22:02:21.842248 29812 solver.cpp:189] Iteration 22150, loss = 0.563719
I0224 22:02:21.842273 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.563719 (* 1 = 0.563719 loss)
I0224 22:02:21.842279 29812 solver.cpp:470] Iteration 22150, lr = 0.0007
I0224 22:02:41.235935 29812 solver.cpp:189] Iteration 22200, loss = 0.407066
I0224 22:02:41.236008 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.407066 (* 1 = 0.407066 loss)
I0224 22:02:41.236014 29812 solver.cpp:470] Iteration 22200, lr = 0.0007
I0224 22:03:00.628499 29812 solver.cpp:189] Iteration 22250, loss = 0.369269
I0224 22:03:00.628523 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.369269 (* 1 = 0.369269 loss)
I0224 22:03:00.628527 29812 solver.cpp:470] Iteration 22250, lr = 0.0007
I0224 22:03:20.025341 29812 solver.cpp:189] Iteration 22300, loss = 0.43444
I0224 22:03:20.025406 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.43444 (* 1 = 0.43444 loss)
I0224 22:03:20.025413 29812 solver.cpp:470] Iteration 22300, lr = 0.0007
I0224 22:03:39.413167 29812 solver.cpp:189] Iteration 22350, loss = 0.477328
I0224 22:03:39.413190 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.477328 (* 1 = 0.477328 loss)
I0224 22:03:39.413195 29812 solver.cpp:470] Iteration 22350, lr = 0.0007
I0224 22:03:58.807492 29812 solver.cpp:189] Iteration 22400, loss = 0.593448
I0224 22:03:58.807584 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.593448 (* 1 = 0.593448 loss)
I0224 22:03:58.807598 29812 solver.cpp:470] Iteration 22400, lr = 0.0007
I0224 22:04:18.213785 29812 solver.cpp:189] Iteration 22450, loss = 0.34585
I0224 22:04:18.213810 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.34585 (* 1 = 0.34585 loss)
I0224 22:04:18.213815 29812 solver.cpp:470] Iteration 22450, lr = 0.0007
I0224 22:04:37.619242 29812 solver.cpp:189] Iteration 22500, loss = 0.414298
I0224 22:04:37.619300 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.414298 (* 1 = 0.414298 loss)
I0224 22:04:37.619305 29812 solver.cpp:470] Iteration 22500, lr = 0.0007
I0224 22:04:57.013046 29812 solver.cpp:189] Iteration 22550, loss = 0.435285
I0224 22:04:57.013072 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.435285 (* 1 = 0.435285 loss)
I0224 22:04:57.013077 29812 solver.cpp:470] Iteration 22550, lr = 0.0007
I0224 22:05:16.409636 29812 solver.cpp:189] Iteration 22600, loss = 0.421003
I0224 22:05:16.409694 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.421003 (* 1 = 0.421003 loss)
I0224 22:05:16.409700 29812 solver.cpp:470] Iteration 22600, lr = 0.0007
I0224 22:05:35.798661 29812 solver.cpp:189] Iteration 22650, loss = 0.329833
I0224 22:05:35.798687 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.329832 (* 1 = 0.329832 loss)
I0224 22:05:35.798692 29812 solver.cpp:470] Iteration 22650, lr = 0.0007
I0224 22:05:55.190660 29812 solver.cpp:189] Iteration 22700, loss = 0.449583
I0224 22:05:55.190701 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.449583 (* 1 = 0.449583 loss)
I0224 22:05:55.190707 29812 solver.cpp:470] Iteration 22700, lr = 0.0007
I0224 22:06:14.589627 29812 solver.cpp:189] Iteration 22750, loss = 0.353113
I0224 22:06:14.589651 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.353113 (* 1 = 0.353113 loss)
I0224 22:06:14.589655 29812 solver.cpp:470] Iteration 22750, lr = 0.0007
I0224 22:06:33.987905 29812 solver.cpp:189] Iteration 22800, loss = 0.394432
I0224 22:06:33.987995 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.394431 (* 1 = 0.394431 loss)
I0224 22:06:33.988001 29812 solver.cpp:470] Iteration 22800, lr = 0.0007
I0224 22:06:53.377578 29812 solver.cpp:189] Iteration 22850, loss = 0.321543
I0224 22:06:53.377601 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.321543 (* 1 = 0.321543 loss)
I0224 22:06:53.377606 29812 solver.cpp:470] Iteration 22850, lr = 0.0007
I0224 22:07:12.769668 29812 solver.cpp:189] Iteration 22900, loss = 0.362455
I0224 22:07:12.769757 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.362455 (* 1 = 0.362455 loss)
I0224 22:07:12.769763 29812 solver.cpp:470] Iteration 22900, lr = 0.0007
I0224 22:07:32.160693 29812 solver.cpp:189] Iteration 22950, loss = 0.480717
I0224 22:07:32.160719 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.480717 (* 1 = 0.480717 loss)
I0224 22:07:32.160724 29812 solver.cpp:470] Iteration 22950, lr = 0.0007
I0224 22:07:51.316400 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_23000.caffemodel
I0224 22:07:51.444001 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_23000.solverstate
I0224 22:07:51.502450 29812 solver.cpp:266] Iteration 23000, Testing net (#0)
I0224 22:07:59.154755 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8544
I0224 22:07:59.154790 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.441343 (* 1 = 0.441343 loss)
I0224 22:07:59.441437 29812 solver.cpp:189] Iteration 23000, loss = 0.498348
I0224 22:07:59.441459 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.498348 (* 1 = 0.498348 loss)
I0224 22:07:59.441464 29812 solver.cpp:470] Iteration 23000, lr = 0.0007
I0224 22:08:18.827339 29812 solver.cpp:189] Iteration 23050, loss = 0.420635
I0224 22:08:18.827363 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.420635 (* 1 = 0.420635 loss)
I0224 22:08:18.827369 29812 solver.cpp:470] Iteration 23050, lr = 0.0007
I0224 22:08:38.212978 29812 solver.cpp:189] Iteration 23100, loss = 0.692331
I0224 22:08:38.213023 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.692331 (* 1 = 0.692331 loss)
I0224 22:08:38.213029 29812 solver.cpp:470] Iteration 23100, lr = 0.0007
I0224 22:08:57.597997 29812 solver.cpp:189] Iteration 23150, loss = 0.386574
I0224 22:08:57.598021 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.386574 (* 1 = 0.386574 loss)
I0224 22:08:57.598026 29812 solver.cpp:470] Iteration 23150, lr = 0.0007
I0224 22:09:16.982576 29812 solver.cpp:189] Iteration 23200, loss = 0.393599
I0224 22:09:16.982645 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.393599 (* 1 = 0.393599 loss)
I0224 22:09:16.982652 29812 solver.cpp:470] Iteration 23200, lr = 0.0007
I0224 22:09:36.369292 29812 solver.cpp:189] Iteration 23250, loss = 0.543485
I0224 22:09:36.369315 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.543485 (* 1 = 0.543485 loss)
I0224 22:09:36.369321 29812 solver.cpp:470] Iteration 23250, lr = 0.0007
I0224 22:09:55.758802 29812 solver.cpp:189] Iteration 23300, loss = 0.43698
I0224 22:09:55.758894 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.43698 (* 1 = 0.43698 loss)
I0224 22:09:55.758908 29812 solver.cpp:470] Iteration 23300, lr = 0.0007
I0224 22:10:15.140722 29812 solver.cpp:189] Iteration 23350, loss = 0.478095
I0224 22:10:15.140746 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.478095 (* 1 = 0.478095 loss)
I0224 22:10:15.140751 29812 solver.cpp:470] Iteration 23350, lr = 0.0007
I0224 22:10:34.526271 29812 solver.cpp:189] Iteration 23400, loss = 0.510298
I0224 22:10:34.526331 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.510298 (* 1 = 0.510298 loss)
I0224 22:10:34.526336 29812 solver.cpp:470] Iteration 23400, lr = 0.0007
I0224 22:10:53.911165 29812 solver.cpp:189] Iteration 23450, loss = 0.308656
I0224 22:10:53.911190 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.308656 (* 1 = 0.308656 loss)
I0224 22:10:53.911195 29812 solver.cpp:470] Iteration 23450, lr = 0.0007
I0224 22:11:13.292311 29812 solver.cpp:189] Iteration 23500, loss = 0.547943
I0224 22:11:13.292351 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.547943 (* 1 = 0.547943 loss)
I0224 22:11:13.292356 29812 solver.cpp:470] Iteration 23500, lr = 0.0007
I0224 22:11:32.675014 29812 solver.cpp:189] Iteration 23550, loss = 0.354279
I0224 22:11:32.675040 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.354279 (* 1 = 0.354279 loss)
I0224 22:11:32.675045 29812 solver.cpp:470] Iteration 23550, lr = 0.0007
I0224 22:11:52.063562 29812 solver.cpp:189] Iteration 23600, loss = 0.44319
I0224 22:11:52.063648 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.44319 (* 1 = 0.44319 loss)
I0224 22:11:52.063653 29812 solver.cpp:470] Iteration 23600, lr = 0.0007
I0224 22:12:11.450680 29812 solver.cpp:189] Iteration 23650, loss = 0.453788
I0224 22:12:11.450703 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.453788 (* 1 = 0.453788 loss)
I0224 22:12:11.450708 29812 solver.cpp:470] Iteration 23650, lr = 0.0007
I0224 22:12:30.837271 29812 solver.cpp:189] Iteration 23700, loss = 0.554544
I0224 22:12:30.837362 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.554544 (* 1 = 0.554544 loss)
I0224 22:12:30.837376 29812 solver.cpp:470] Iteration 23700, lr = 0.0007
I0224 22:12:50.232254 29812 solver.cpp:189] Iteration 23750, loss = 0.374067
I0224 22:12:50.232280 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.374067 (* 1 = 0.374067 loss)
I0224 22:12:50.232285 29812 solver.cpp:470] Iteration 23750, lr = 0.0007
I0224 22:13:09.618221 29812 solver.cpp:189] Iteration 23800, loss = 0.371322
I0224 22:13:09.618281 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.371322 (* 1 = 0.371322 loss)
I0224 22:13:09.618288 29812 solver.cpp:470] Iteration 23800, lr = 0.0007
I0224 22:13:29.008929 29812 solver.cpp:189] Iteration 23850, loss = 0.427328
I0224 22:13:29.008954 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.427328 (* 1 = 0.427328 loss)
I0224 22:13:29.008960 29812 solver.cpp:470] Iteration 23850, lr = 0.0007
I0224 22:13:48.401015 29812 solver.cpp:189] Iteration 23900, loss = 0.469898
I0224 22:13:48.401053 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.469898 (* 1 = 0.469898 loss)
I0224 22:13:48.401059 29812 solver.cpp:470] Iteration 23900, lr = 0.0007
I0224 22:14:07.791934 29812 solver.cpp:189] Iteration 23950, loss = 0.33663
I0224 22:14:07.791960 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.33663 (* 1 = 0.33663 loss)
I0224 22:14:07.791965 29812 solver.cpp:470] Iteration 23950, lr = 0.0007
I0224 22:14:26.927378 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_24000.caffemodel
I0224 22:14:27.052853 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_24000.solverstate
I0224 22:14:27.111188 29812 solver.cpp:266] Iteration 24000, Testing net (#0)
I0224 22:14:34.756904 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8576
I0224 22:14:34.756939 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.432207 (* 1 = 0.432207 loss)
I0224 22:14:35.044713 29812 solver.cpp:189] Iteration 24000, loss = 0.590256
I0224 22:14:35.044735 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.590256 (* 1 = 0.590256 loss)
I0224 22:14:35.044741 29812 solver.cpp:470] Iteration 24000, lr = 0.0007
I0224 22:14:54.434319 29812 solver.cpp:189] Iteration 24050, loss = 0.288495
I0224 22:14:54.434342 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.288495 (* 1 = 0.288495 loss)
I0224 22:14:54.434347 29812 solver.cpp:470] Iteration 24050, lr = 0.0007
I0224 22:15:13.831308 29812 solver.cpp:189] Iteration 24100, loss = 0.389486
I0224 22:15:13.831401 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.389486 (* 1 = 0.389486 loss)
I0224 22:15:13.831408 29812 solver.cpp:470] Iteration 24100, lr = 0.0007
I0224 22:15:33.218154 29812 solver.cpp:189] Iteration 24150, loss = 0.330841
I0224 22:15:33.218178 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.330841 (* 1 = 0.330841 loss)
I0224 22:15:33.218183 29812 solver.cpp:470] Iteration 24150, lr = 0.0007
I0224 22:15:52.600581 29812 solver.cpp:189] Iteration 24200, loss = 0.335565
I0224 22:15:52.600664 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.335564 (* 1 = 0.335564 loss)
I0224 22:15:52.600671 29812 solver.cpp:470] Iteration 24200, lr = 0.0007
I0224 22:16:11.995177 29812 solver.cpp:189] Iteration 24250, loss = 0.441615
I0224 22:16:11.995200 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.441615 (* 1 = 0.441615 loss)
I0224 22:16:11.995206 29812 solver.cpp:470] Iteration 24250, lr = 0.0007
I0224 22:16:31.385006 29812 solver.cpp:189] Iteration 24300, loss = 0.374824
I0224 22:16:31.385045 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.374824 (* 1 = 0.374824 loss)
I0224 22:16:31.385051 29812 solver.cpp:470] Iteration 24300, lr = 0.0007
I0224 22:16:50.775576 29812 solver.cpp:189] Iteration 24350, loss = 0.373993
I0224 22:16:50.775601 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.373993 (* 1 = 0.373993 loss)
I0224 22:16:50.775606 29812 solver.cpp:470] Iteration 24350, lr = 0.0007
I0224 22:17:10.165715 29812 solver.cpp:189] Iteration 24400, loss = 0.493997
I0224 22:17:10.165779 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.493997 (* 1 = 0.493997 loss)
I0224 22:17:10.165786 29812 solver.cpp:470] Iteration 24400, lr = 0.0007
I0224 22:17:29.552080 29812 solver.cpp:189] Iteration 24450, loss = 0.390929
I0224 22:17:29.552105 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.390928 (* 1 = 0.390928 loss)
I0224 22:17:29.552110 29812 solver.cpp:470] Iteration 24450, lr = 0.0007
I0224 22:17:48.944600 29812 solver.cpp:189] Iteration 24500, loss = 0.214471
I0224 22:17:48.944643 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.214471 (* 1 = 0.214471 loss)
I0224 22:17:48.944649 29812 solver.cpp:470] Iteration 24500, lr = 0.0007
I0224 22:18:08.337435 29812 solver.cpp:189] Iteration 24550, loss = 0.431709
I0224 22:18:08.337460 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.431709 (* 1 = 0.431709 loss)
I0224 22:18:08.337466 29812 solver.cpp:470] Iteration 24550, lr = 0.0007
I0224 22:18:27.712884 29812 solver.cpp:189] Iteration 24600, loss = 0.492838
I0224 22:18:27.712923 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.492838 (* 1 = 0.492838 loss)
I0224 22:18:27.712929 29812 solver.cpp:470] Iteration 24600, lr = 0.0007
I0224 22:18:47.107527 29812 solver.cpp:189] Iteration 24650, loss = 0.370574
I0224 22:18:47.107552 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.370573 (* 1 = 0.370573 loss)
I0224 22:18:47.107556 29812 solver.cpp:470] Iteration 24650, lr = 0.0007
I0224 22:19:06.495307 29812 solver.cpp:189] Iteration 24700, loss = 0.421542
I0224 22:19:06.495345 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.421542 (* 1 = 0.421542 loss)
I0224 22:19:06.495352 29812 solver.cpp:470] Iteration 24700, lr = 0.0007
I0224 22:19:25.890602 29812 solver.cpp:189] Iteration 24750, loss = 0.427619
I0224 22:19:25.890626 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.427619 (* 1 = 0.427619 loss)
I0224 22:19:25.890631 29812 solver.cpp:470] Iteration 24750, lr = 0.0007
I0224 22:19:45.284167 29812 solver.cpp:189] Iteration 24800, loss = 0.474258
I0224 22:19:45.284219 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.474258 (* 1 = 0.474258 loss)
I0224 22:19:45.284225 29812 solver.cpp:470] Iteration 24800, lr = 0.0007
I0224 22:20:04.672065 29812 solver.cpp:189] Iteration 24850, loss = 0.327722
I0224 22:20:04.672092 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.327721 (* 1 = 0.327721 loss)
I0224 22:20:04.672097 29812 solver.cpp:470] Iteration 24850, lr = 0.0007
I0224 22:20:24.070561 29812 solver.cpp:189] Iteration 24900, loss = 0.45698
I0224 22:20:24.070616 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.45698 (* 1 = 0.45698 loss)
I0224 22:20:24.070622 29812 solver.cpp:470] Iteration 24900, lr = 0.0007
I0224 22:20:43.455174 29812 solver.cpp:189] Iteration 24950, loss = 0.318402
I0224 22:20:43.455199 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.318402 (* 1 = 0.318402 loss)
I0224 22:20:43.455204 29812 solver.cpp:470] Iteration 24950, lr = 0.0007
I0224 22:21:02.590353 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_25000.caffemodel
I0224 22:21:02.712535 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_25000.solverstate
I0224 22:21:02.771033 29812 solver.cpp:266] Iteration 25000, Testing net (#0)
I0224 22:21:10.413902 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8598
I0224 22:21:10.413939 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.42184 (* 1 = 0.42184 loss)
I0224 22:21:10.701598 29812 solver.cpp:189] Iteration 25000, loss = 0.497102
I0224 22:21:10.701622 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.497102 (* 1 = 0.497102 loss)
I0224 22:21:10.701628 29812 solver.cpp:470] Iteration 25000, lr = 0.0007
I0224 22:21:30.094355 29812 solver.cpp:189] Iteration 25050, loss = 0.388121
I0224 22:21:30.094380 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.388121 (* 1 = 0.388121 loss)
I0224 22:21:30.094385 29812 solver.cpp:470] Iteration 25050, lr = 0.0007
I0224 22:21:49.491878 29812 solver.cpp:189] Iteration 25100, loss = 0.37936
I0224 22:21:49.491946 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.37936 (* 1 = 0.37936 loss)
I0224 22:21:49.491952 29812 solver.cpp:470] Iteration 25100, lr = 0.0007
I0224 22:22:08.895750 29812 solver.cpp:189] Iteration 25150, loss = 0.469295
I0224 22:22:08.895776 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.469295 (* 1 = 0.469295 loss)
I0224 22:22:08.895781 29812 solver.cpp:470] Iteration 25150, lr = 0.0007
I0224 22:22:28.301074 29812 solver.cpp:189] Iteration 25200, loss = 0.280539
I0224 22:22:28.301137 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.280539 (* 1 = 0.280539 loss)
I0224 22:22:28.301143 29812 solver.cpp:470] Iteration 25200, lr = 0.0007
I0224 22:22:47.699563 29812 solver.cpp:189] Iteration 25250, loss = 0.386822
I0224 22:22:47.699586 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.386822 (* 1 = 0.386822 loss)
I0224 22:22:47.699592 29812 solver.cpp:470] Iteration 25250, lr = 0.0007
I0224 22:23:07.096199 29812 solver.cpp:189] Iteration 25300, loss = 0.31266
I0224 22:23:07.096271 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.31266 (* 1 = 0.31266 loss)
I0224 22:23:07.096285 29812 solver.cpp:470] Iteration 25300, lr = 0.0007
I0224 22:23:26.492061 29812 solver.cpp:189] Iteration 25350, loss = 0.502768
I0224 22:23:26.492085 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.502768 (* 1 = 0.502768 loss)
I0224 22:23:26.492091 29812 solver.cpp:470] Iteration 25350, lr = 0.0007
I0224 22:23:45.884196 29812 solver.cpp:189] Iteration 25400, loss = 0.306394
I0224 22:23:45.884269 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.306394 (* 1 = 0.306394 loss)
I0224 22:23:45.884284 29812 solver.cpp:470] Iteration 25400, lr = 0.0007
I0224 22:24:05.282203 29812 solver.cpp:189] Iteration 25450, loss = 0.327824
I0224 22:24:05.282227 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.327824 (* 1 = 0.327824 loss)
I0224 22:24:05.282232 29812 solver.cpp:470] Iteration 25450, lr = 0.0007
I0224 22:24:24.680501 29812 solver.cpp:189] Iteration 25500, loss = 0.332507
I0224 22:24:24.680593 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.332507 (* 1 = 0.332507 loss)
I0224 22:24:24.680599 29812 solver.cpp:470] Iteration 25500, lr = 0.0007
I0224 22:24:44.067284 29812 solver.cpp:189] Iteration 25550, loss = 0.372233
I0224 22:24:44.067308 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.372233 (* 1 = 0.372233 loss)
I0224 22:24:44.067313 29812 solver.cpp:470] Iteration 25550, lr = 0.0007
I0224 22:25:03.463734 29812 solver.cpp:189] Iteration 25600, loss = 0.542801
I0224 22:25:03.463773 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.542801 (* 1 = 0.542801 loss)
I0224 22:25:03.463779 29812 solver.cpp:470] Iteration 25600, lr = 0.0007
I0224 22:25:22.862818 29812 solver.cpp:189] Iteration 25650, loss = 0.471158
I0224 22:25:22.862841 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.471158 (* 1 = 0.471158 loss)
I0224 22:25:22.862846 29812 solver.cpp:470] Iteration 25650, lr = 0.0007
I0224 22:25:42.255707 29812 solver.cpp:189] Iteration 25700, loss = 0.408653
I0224 22:25:42.255780 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.408653 (* 1 = 0.408653 loss)
I0224 22:25:42.255795 29812 solver.cpp:470] Iteration 25700, lr = 0.0007
I0224 22:26:01.661984 29812 solver.cpp:189] Iteration 25750, loss = 0.317602
I0224 22:26:01.662010 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.317602 (* 1 = 0.317602 loss)
I0224 22:26:01.662015 29812 solver.cpp:470] Iteration 25750, lr = 0.0007
I0224 22:26:21.061889 29812 solver.cpp:189] Iteration 25800, loss = 0.441575
I0224 22:26:21.061949 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.441574 (* 1 = 0.441574 loss)
I0224 22:26:21.061954 29812 solver.cpp:470] Iteration 25800, lr = 0.0007
I0224 22:26:40.454167 29812 solver.cpp:189] Iteration 25850, loss = 0.392515
I0224 22:26:40.454191 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.392515 (* 1 = 0.392515 loss)
I0224 22:26:40.454197 29812 solver.cpp:470] Iteration 25850, lr = 0.0007
I0224 22:26:59.849552 29812 solver.cpp:189] Iteration 25900, loss = 0.476694
I0224 22:26:59.849644 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.476694 (* 1 = 0.476694 loss)
I0224 22:26:59.849659 29812 solver.cpp:470] Iteration 25900, lr = 0.0007
I0224 22:27:19.244621 29812 solver.cpp:189] Iteration 25950, loss = 0.384434
I0224 22:27:19.244645 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.384434 (* 1 = 0.384434 loss)
I0224 22:27:19.244652 29812 solver.cpp:470] Iteration 25950, lr = 0.0007
I0224 22:27:38.394851 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_26000.caffemodel
I0224 22:27:38.521107 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_26000.solverstate
I0224 22:27:38.578564 29812 solver.cpp:266] Iteration 26000, Testing net (#0)
I0224 22:27:46.238817 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8698
I0224 22:27:46.238857 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.402365 (* 1 = 0.402365 loss)
I0224 22:27:46.524255 29812 solver.cpp:189] Iteration 26000, loss = 0.314713
I0224 22:27:46.524274 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.314712 (* 1 = 0.314712 loss)
I0224 22:27:46.524281 29812 solver.cpp:470] Iteration 26000, lr = 0.0007
I0224 22:28:05.911403 29812 solver.cpp:189] Iteration 26050, loss = 0.350043
I0224 22:28:05.911427 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.350043 (* 1 = 0.350043 loss)
I0224 22:28:05.911432 29812 solver.cpp:470] Iteration 26050, lr = 0.0007
I0224 22:28:25.301738 29812 solver.cpp:189] Iteration 26100, loss = 0.288211
I0224 22:28:25.301780 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.288211 (* 1 = 0.288211 loss)
I0224 22:28:25.301786 29812 solver.cpp:470] Iteration 26100, lr = 0.0007
I0224 22:28:44.691848 29812 solver.cpp:189] Iteration 26150, loss = 0.487191
I0224 22:28:44.691874 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.487191 (* 1 = 0.487191 loss)
I0224 22:28:44.691879 29812 solver.cpp:470] Iteration 26150, lr = 0.0007
I0224 22:29:04.083009 29812 solver.cpp:189] Iteration 26200, loss = 0.372654
I0224 22:29:04.083096 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.372654 (* 1 = 0.372654 loss)
I0224 22:29:04.083101 29812 solver.cpp:470] Iteration 26200, lr = 0.0007
I0224 22:29:23.475159 29812 solver.cpp:189] Iteration 26250, loss = 0.368015
I0224 22:29:23.475183 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.368015 (* 1 = 0.368015 loss)
I0224 22:29:23.475188 29812 solver.cpp:470] Iteration 26250, lr = 0.0007
I0224 22:29:42.876734 29812 solver.cpp:189] Iteration 26300, loss = 0.307203
I0224 22:29:42.876816 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.307203 (* 1 = 0.307203 loss)
I0224 22:29:42.876822 29812 solver.cpp:470] Iteration 26300, lr = 0.0007
I0224 22:30:02.271358 29812 solver.cpp:189] Iteration 26350, loss = 0.377844
I0224 22:30:02.271383 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.377844 (* 1 = 0.377844 loss)
I0224 22:30:02.271388 29812 solver.cpp:470] Iteration 26350, lr = 0.0007
I0224 22:30:21.659950 29812 solver.cpp:189] Iteration 26400, loss = 0.434256
I0224 22:30:21.660023 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.434256 (* 1 = 0.434256 loss)
I0224 22:30:21.660038 29812 solver.cpp:470] Iteration 26400, lr = 0.0007
I0224 22:30:41.056200 29812 solver.cpp:189] Iteration 26450, loss = 0.435105
I0224 22:30:41.056223 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.435105 (* 1 = 0.435105 loss)
I0224 22:30:41.056228 29812 solver.cpp:470] Iteration 26450, lr = 0.0007
I0224 22:31:00.459997 29812 solver.cpp:189] Iteration 26500, loss = 0.213202
I0224 22:31:00.460078 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.213202 (* 1 = 0.213202 loss)
I0224 22:31:00.460084 29812 solver.cpp:470] Iteration 26500, lr = 0.0007
I0224 22:31:19.857435 29812 solver.cpp:189] Iteration 26550, loss = 0.340101
I0224 22:31:19.857460 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.3401 (* 1 = 0.3401 loss)
I0224 22:31:19.857465 29812 solver.cpp:470] Iteration 26550, lr = 0.0007
I0224 22:31:39.251664 29812 solver.cpp:189] Iteration 26600, loss = 0.365201
I0224 22:31:39.251708 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.365201 (* 1 = 0.365201 loss)
I0224 22:31:39.251713 29812 solver.cpp:470] Iteration 26600, lr = 0.0007
I0224 22:31:58.644183 29812 solver.cpp:189] Iteration 26650, loss = 0.374257
I0224 22:31:58.644208 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.374256 (* 1 = 0.374256 loss)
I0224 22:31:58.644214 29812 solver.cpp:470] Iteration 26650, lr = 0.0007
I0224 22:32:18.042112 29812 solver.cpp:189] Iteration 26700, loss = 0.460578
I0224 22:32:18.042171 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.460577 (* 1 = 0.460577 loss)
I0224 22:32:18.042178 29812 solver.cpp:470] Iteration 26700, lr = 0.0007
I0224 22:32:37.436895 29812 solver.cpp:189] Iteration 26750, loss = 0.413381
I0224 22:32:37.436920 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.413381 (* 1 = 0.413381 loss)
I0224 22:32:37.436925 29812 solver.cpp:470] Iteration 26750, lr = 0.0007
I0224 22:32:56.827947 29812 solver.cpp:189] Iteration 26800, loss = 0.327501
I0224 22:32:56.828035 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.327501 (* 1 = 0.327501 loss)
I0224 22:32:56.828042 29812 solver.cpp:470] Iteration 26800, lr = 0.0007
I0224 22:33:16.213007 29812 solver.cpp:189] Iteration 26850, loss = 0.312708
I0224 22:33:16.213032 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.312708 (* 1 = 0.312708 loss)
I0224 22:33:16.213037 29812 solver.cpp:470] Iteration 26850, lr = 0.0007
I0224 22:33:35.616827 29812 solver.cpp:189] Iteration 26900, loss = 0.421967
I0224 22:33:35.616885 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.421967 (* 1 = 0.421967 loss)
I0224 22:33:35.616891 29812 solver.cpp:470] Iteration 26900, lr = 0.0007
I0224 22:33:55.006880 29812 solver.cpp:189] Iteration 26950, loss = 0.394704
I0224 22:33:55.006902 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.394704 (* 1 = 0.394704 loss)
I0224 22:33:55.006907 29812 solver.cpp:470] Iteration 26950, lr = 0.0007
I0224 22:34:14.158089 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_27000.caffemodel
I0224 22:34:14.265413 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_27000.solverstate
I0224 22:34:14.323837 29812 solver.cpp:266] Iteration 27000, Testing net (#0)
I0224 22:34:21.983304 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8657
I0224 22:34:21.983342 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.397817 (* 1 = 0.397817 loss)
I0224 22:34:22.270756 29812 solver.cpp:189] Iteration 27000, loss = 0.347705
I0224 22:34:22.270781 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.347705 (* 1 = 0.347705 loss)
I0224 22:34:22.270787 29812 solver.cpp:470] Iteration 27000, lr = 0.0007
I0224 22:34:41.644192 29812 solver.cpp:189] Iteration 27050, loss = 0.388868
I0224 22:34:41.644217 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.388867 (* 1 = 0.388867 loss)
I0224 22:34:41.644222 29812 solver.cpp:470] Iteration 27050, lr = 0.0007
I0224 22:35:01.032575 29812 solver.cpp:189] Iteration 27100, loss = 0.430417
I0224 22:35:01.032616 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.430416 (* 1 = 0.430416 loss)
I0224 22:35:01.032623 29812 solver.cpp:470] Iteration 27100, lr = 0.0007
I0224 22:35:20.414675 29812 solver.cpp:189] Iteration 27150, loss = 0.469832
I0224 22:35:20.414700 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.469832 (* 1 = 0.469832 loss)
I0224 22:35:20.414705 29812 solver.cpp:470] Iteration 27150, lr = 0.0007
I0224 22:35:39.808511 29812 solver.cpp:189] Iteration 27200, loss = 0.281602
I0224 22:35:39.808607 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.281601 (* 1 = 0.281601 loss)
I0224 22:35:39.808622 29812 solver.cpp:470] Iteration 27200, lr = 0.0007
I0224 22:35:59.190111 29812 solver.cpp:189] Iteration 27250, loss = 0.370811
I0224 22:35:59.190136 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.370811 (* 1 = 0.370811 loss)
I0224 22:35:59.190142 29812 solver.cpp:470] Iteration 27250, lr = 0.0007
I0224 22:36:18.578934 29812 solver.cpp:189] Iteration 27300, loss = 0.346296
I0224 22:36:18.578977 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.346296 (* 1 = 0.346296 loss)
I0224 22:36:18.578984 29812 solver.cpp:470] Iteration 27300, lr = 0.0007
I0224 22:36:37.963778 29812 solver.cpp:189] Iteration 27350, loss = 0.285192
I0224 22:36:37.963800 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.285192 (* 1 = 0.285192 loss)
I0224 22:36:37.963805 29812 solver.cpp:470] Iteration 27350, lr = 0.0007
I0224 22:36:57.352063 29812 solver.cpp:189] Iteration 27400, loss = 0.370003
I0224 22:36:57.352104 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.370003 (* 1 = 0.370003 loss)
I0224 22:36:57.352109 29812 solver.cpp:470] Iteration 27400, lr = 0.0007
I0224 22:37:16.741123 29812 solver.cpp:189] Iteration 27450, loss = 0.371603
I0224 22:37:16.741149 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.371603 (* 1 = 0.371603 loss)
I0224 22:37:16.741154 29812 solver.cpp:470] Iteration 27450, lr = 0.0007
I0224 22:37:36.118705 29812 solver.cpp:189] Iteration 27500, loss = 0.46749
I0224 22:37:36.118746 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.467489 (* 1 = 0.467489 loss)
I0224 22:37:36.118752 29812 solver.cpp:470] Iteration 27500, lr = 0.0007
I0224 22:37:55.514324 29812 solver.cpp:189] Iteration 27550, loss = 0.407119
I0224 22:37:55.514348 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.407119 (* 1 = 0.407119 loss)
I0224 22:37:55.514353 29812 solver.cpp:470] Iteration 27550, lr = 0.0007
I0224 22:38:14.898653 29812 solver.cpp:189] Iteration 27600, loss = 0.220577
I0224 22:38:14.898723 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.220577 (* 1 = 0.220577 loss)
I0224 22:38:14.898738 29812 solver.cpp:470] Iteration 27600, lr = 0.0007
I0224 22:38:34.288480 29812 solver.cpp:189] Iteration 27650, loss = 0.227346
I0224 22:38:34.288506 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.227346 (* 1 = 0.227346 loss)
I0224 22:38:34.288511 29812 solver.cpp:470] Iteration 27650, lr = 0.0007
I0224 22:38:53.671957 29812 solver.cpp:189] Iteration 27700, loss = 0.314784
I0224 22:38:53.672027 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.314784 (* 1 = 0.314784 loss)
I0224 22:38:53.672041 29812 solver.cpp:470] Iteration 27700, lr = 0.0007
I0224 22:39:13.057107 29812 solver.cpp:189] Iteration 27750, loss = 0.455991
I0224 22:39:13.057132 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.45599 (* 1 = 0.45599 loss)
I0224 22:39:13.057137 29812 solver.cpp:470] Iteration 27750, lr = 0.0007
I0224 22:39:32.448860 29812 solver.cpp:189] Iteration 27800, loss = 0.382272
I0224 22:39:32.448945 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.382271 (* 1 = 0.382271 loss)
I0224 22:39:32.448951 29812 solver.cpp:470] Iteration 27800, lr = 0.0007
I0224 22:39:51.831274 29812 solver.cpp:189] Iteration 27850, loss = 0.273217
I0224 22:39:51.831300 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.273217 (* 1 = 0.273217 loss)
I0224 22:39:51.831305 29812 solver.cpp:470] Iteration 27850, lr = 0.0007
I0224 22:40:11.225113 29812 solver.cpp:189] Iteration 27900, loss = 0.527257
I0224 22:40:11.225172 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.527257 (* 1 = 0.527257 loss)
I0224 22:40:11.225178 29812 solver.cpp:470] Iteration 27900, lr = 0.0007
I0224 22:40:30.613829 29812 solver.cpp:189] Iteration 27950, loss = 0.229178
I0224 22:40:30.613852 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.229177 (* 1 = 0.229177 loss)
I0224 22:40:30.613857 29812 solver.cpp:470] Iteration 27950, lr = 0.0007
I0224 22:40:49.751106 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_28000.caffemodel
I0224 22:40:49.873167 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_28000.solverstate
I0224 22:40:49.931067 29812 solver.cpp:266] Iteration 28000, Testing net (#0)
I0224 22:40:57.575989 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8681
I0224 22:40:57.576025 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.405768 (* 1 = 0.405768 loss)
I0224 22:40:57.863456 29812 solver.cpp:189] Iteration 28000, loss = 0.304454
I0224 22:40:57.863478 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.304454 (* 1 = 0.304454 loss)
I0224 22:40:57.863484 29812 solver.cpp:470] Iteration 28000, lr = 0.0007
I0224 22:41:17.247041 29812 solver.cpp:189] Iteration 28050, loss = 0.320529
I0224 22:41:17.247064 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.320529 (* 1 = 0.320529 loss)
I0224 22:41:17.247069 29812 solver.cpp:470] Iteration 28050, lr = 0.0007
I0224 22:41:36.626798 29812 solver.cpp:189] Iteration 28100, loss = 0.30935
I0224 22:41:36.626871 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.30935 (* 1 = 0.30935 loss)
I0224 22:41:36.626878 29812 solver.cpp:470] Iteration 28100, lr = 0.0007
I0224 22:41:56.012687 29812 solver.cpp:189] Iteration 28150, loss = 0.42298
I0224 22:41:56.012712 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.42298 (* 1 = 0.42298 loss)
I0224 22:41:56.012717 29812 solver.cpp:470] Iteration 28150, lr = 0.0007
I0224 22:42:15.403399 29812 solver.cpp:189] Iteration 28200, loss = 0.349428
I0224 22:42:15.403439 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.349428 (* 1 = 0.349428 loss)
I0224 22:42:15.403445 29812 solver.cpp:470] Iteration 28200, lr = 0.0007
I0224 22:42:34.798130 29812 solver.cpp:189] Iteration 28250, loss = 0.284225
I0224 22:42:34.798156 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.284225 (* 1 = 0.284225 loss)
I0224 22:42:34.798161 29812 solver.cpp:470] Iteration 28250, lr = 0.0007
I0224 22:42:54.189038 29812 solver.cpp:189] Iteration 28300, loss = 0.481164
I0224 22:42:54.189110 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.481163 (* 1 = 0.481163 loss)
I0224 22:42:54.189124 29812 solver.cpp:470] Iteration 28300, lr = 0.0007
I0224 22:43:13.573760 29812 solver.cpp:189] Iteration 28350, loss = 0.249346
I0224 22:43:13.573783 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.249345 (* 1 = 0.249345 loss)
I0224 22:43:13.573789 29812 solver.cpp:470] Iteration 28350, lr = 0.0007
I0224 22:43:32.963811 29812 solver.cpp:189] Iteration 28400, loss = 0.371936
I0224 22:43:32.963894 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.371936 (* 1 = 0.371936 loss)
I0224 22:43:32.963901 29812 solver.cpp:470] Iteration 28400, lr = 0.0007
I0224 22:43:52.359158 29812 solver.cpp:189] Iteration 28450, loss = 0.35137
I0224 22:43:52.359182 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.35137 (* 1 = 0.35137 loss)
I0224 22:43:52.359189 29812 solver.cpp:470] Iteration 28450, lr = 0.0007
I0224 22:44:11.749531 29812 solver.cpp:189] Iteration 28500, loss = 0.391262
I0224 22:44:11.749600 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.391262 (* 1 = 0.391262 loss)
I0224 22:44:11.749614 29812 solver.cpp:470] Iteration 28500, lr = 0.0007
I0224 22:44:31.132769 29812 solver.cpp:189] Iteration 28550, loss = 0.361035
I0224 22:44:31.132796 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.361035 (* 1 = 0.361035 loss)
I0224 22:44:31.132800 29812 solver.cpp:470] Iteration 28550, lr = 0.0007
I0224 22:44:50.515396 29812 solver.cpp:189] Iteration 28600, loss = 0.387395
I0224 22:44:50.515436 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.387395 (* 1 = 0.387395 loss)
I0224 22:44:50.515442 29812 solver.cpp:470] Iteration 28600, lr = 0.0007
I0224 22:45:09.905803 29812 solver.cpp:189] Iteration 28650, loss = 0.362536
I0224 22:45:09.905828 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.362535 (* 1 = 0.362535 loss)
I0224 22:45:09.905833 29812 solver.cpp:470] Iteration 28650, lr = 0.0007
I0224 22:45:29.298696 29812 solver.cpp:189] Iteration 28700, loss = 0.291691
I0224 22:45:29.298799 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.29169 (* 1 = 0.29169 loss)
I0224 22:45:29.298806 29812 solver.cpp:470] Iteration 28700, lr = 0.0007
I0224 22:45:48.683661 29812 solver.cpp:189] Iteration 28750, loss = 0.400635
I0224 22:45:48.683687 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.400635 (* 1 = 0.400635 loss)
I0224 22:45:48.683693 29812 solver.cpp:470] Iteration 28750, lr = 0.0007
I0224 22:46:08.070816 29812 solver.cpp:189] Iteration 28800, loss = 0.395627
I0224 22:46:08.070904 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.395627 (* 1 = 0.395627 loss)
I0224 22:46:08.070909 29812 solver.cpp:470] Iteration 28800, lr = 0.0007
I0224 22:46:27.457274 29812 solver.cpp:189] Iteration 28850, loss = 0.309079
I0224 22:46:27.457299 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.309079 (* 1 = 0.309079 loss)
I0224 22:46:27.457304 29812 solver.cpp:470] Iteration 28850, lr = 0.0007
I0224 22:46:46.853783 29812 solver.cpp:189] Iteration 28900, loss = 0.318913
I0224 22:46:46.853842 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.318912 (* 1 = 0.318912 loss)
I0224 22:46:46.853847 29812 solver.cpp:470] Iteration 28900, lr = 0.0007
I0224 22:47:06.242215 29812 solver.cpp:189] Iteration 28950, loss = 0.349191
I0224 22:47:06.242239 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.349191 (* 1 = 0.349191 loss)
I0224 22:47:06.242244 29812 solver.cpp:470] Iteration 28950, lr = 0.0007
I0224 22:47:25.383134 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_29000.caffemodel
I0224 22:47:25.508939 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_29000.solverstate
I0224 22:47:25.566617 29812 solver.cpp:266] Iteration 29000, Testing net (#0)
I0224 22:47:33.226507 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8632
I0224 22:47:33.226542 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.42105 (* 1 = 0.42105 loss)
I0224 22:47:33.514035 29812 solver.cpp:189] Iteration 29000, loss = 0.231332
I0224 22:47:33.514060 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.231332 (* 1 = 0.231332 loss)
I0224 22:47:33.514065 29812 solver.cpp:470] Iteration 29000, lr = 0.0007
I0224 22:47:52.897511 29812 solver.cpp:189] Iteration 29050, loss = 0.322117
I0224 22:47:52.897534 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.322117 (* 1 = 0.322117 loss)
I0224 22:47:52.897539 29812 solver.cpp:470] Iteration 29050, lr = 0.0007
I0224 22:48:12.294464 29812 solver.cpp:189] Iteration 29100, loss = 0.286115
I0224 22:48:12.294535 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.286115 (* 1 = 0.286115 loss)
I0224 22:48:12.294549 29812 solver.cpp:470] Iteration 29100, lr = 0.0007
I0224 22:48:31.692909 29812 solver.cpp:189] Iteration 29150, loss = 0.472977
I0224 22:48:31.692934 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.472977 (* 1 = 0.472977 loss)
I0224 22:48:31.692939 29812 solver.cpp:470] Iteration 29150, lr = 0.0007
I0224 22:48:51.088814 29812 solver.cpp:189] Iteration 29200, loss = 0.272688
I0224 22:48:51.088874 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.272688 (* 1 = 0.272688 loss)
I0224 22:48:51.088881 29812 solver.cpp:470] Iteration 29200, lr = 0.0007
I0224 22:49:10.488517 29812 solver.cpp:189] Iteration 29250, loss = 0.422207
I0224 22:49:10.488540 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.422207 (* 1 = 0.422207 loss)
I0224 22:49:10.488546 29812 solver.cpp:470] Iteration 29250, lr = 0.0007
I0224 22:49:29.891983 29812 solver.cpp:189] Iteration 29300, loss = 0.359882
I0224 22:49:29.892071 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.359882 (* 1 = 0.359882 loss)
I0224 22:49:29.892076 29812 solver.cpp:470] Iteration 29300, lr = 0.0007
I0224 22:49:49.283320 29812 solver.cpp:189] Iteration 29350, loss = 0.38945
I0224 22:49:49.283344 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.389449 (* 1 = 0.389449 loss)
I0224 22:49:49.283349 29812 solver.cpp:470] Iteration 29350, lr = 0.0007
I0224 22:50:08.683475 29812 solver.cpp:189] Iteration 29400, loss = 0.366351
I0224 22:50:08.683584 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.366351 (* 1 = 0.366351 loss)
I0224 22:50:08.683590 29812 solver.cpp:470] Iteration 29400, lr = 0.0007
I0224 22:50:28.077790 29812 solver.cpp:189] Iteration 29450, loss = 0.436501
I0224 22:50:28.077813 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.436501 (* 1 = 0.436501 loss)
I0224 22:50:28.077818 29812 solver.cpp:470] Iteration 29450, lr = 0.0007
I0224 22:50:47.479310 29812 solver.cpp:189] Iteration 29500, loss = 0.254231
I0224 22:50:47.479372 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.254231 (* 1 = 0.254231 loss)
I0224 22:50:47.479377 29812 solver.cpp:470] Iteration 29500, lr = 0.0007
I0224 22:51:06.869334 29812 solver.cpp:189] Iteration 29550, loss = 0.33341
I0224 22:51:06.869359 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.33341 (* 1 = 0.33341 loss)
I0224 22:51:06.869364 29812 solver.cpp:470] Iteration 29550, lr = 0.0007
I0224 22:51:26.264892 29812 solver.cpp:189] Iteration 29600, loss = 0.460138
I0224 22:51:26.264952 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.460138 (* 1 = 0.460138 loss)
I0224 22:51:26.264957 29812 solver.cpp:470] Iteration 29600, lr = 0.0007
I0224 22:51:45.656453 29812 solver.cpp:189] Iteration 29650, loss = 0.447913
I0224 22:51:45.656481 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.447913 (* 1 = 0.447913 loss)
I0224 22:51:45.656487 29812 solver.cpp:470] Iteration 29650, lr = 0.0007
I0224 22:52:05.053982 29812 solver.cpp:189] Iteration 29700, loss = 0.47708
I0224 22:52:05.054018 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.47708 (* 1 = 0.47708 loss)
I0224 22:52:05.054024 29812 solver.cpp:470] Iteration 29700, lr = 0.0007
I0224 22:52:24.442337 29812 solver.cpp:189] Iteration 29750, loss = 0.225274
I0224 22:52:24.442363 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.225273 (* 1 = 0.225273 loss)
I0224 22:52:24.442368 29812 solver.cpp:470] Iteration 29750, lr = 0.0007
I0224 22:52:43.828289 29812 solver.cpp:189] Iteration 29800, loss = 0.381005
I0224 22:52:43.828380 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.381005 (* 1 = 0.381005 loss)
I0224 22:52:43.828394 29812 solver.cpp:470] Iteration 29800, lr = 0.0007
I0224 22:53:03.227597 29812 solver.cpp:189] Iteration 29850, loss = 0.288478
I0224 22:53:03.227622 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.288478 (* 1 = 0.288478 loss)
I0224 22:53:03.227627 29812 solver.cpp:470] Iteration 29850, lr = 0.0007
I0224 22:53:22.620741 29812 solver.cpp:189] Iteration 29900, loss = 0.296908
I0224 22:53:22.620798 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.296908 (* 1 = 0.296908 loss)
I0224 22:53:22.620805 29812 solver.cpp:470] Iteration 29900, lr = 0.0007
I0224 22:53:42.014281 29812 solver.cpp:189] Iteration 29950, loss = 0.354859
I0224 22:53:42.014303 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.354859 (* 1 = 0.354859 loss)
I0224 22:53:42.014308 29812 solver.cpp:470] Iteration 29950, lr = 0.0007
I0224 22:54:01.160948 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_30000.caffemodel
I0224 22:54:01.285282 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_30000.solverstate
I0224 22:54:01.342589 29812 solver.cpp:266] Iteration 30000, Testing net (#0)
I0224 22:54:08.993722 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8674
I0224 22:54:08.993762 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.395904 (* 1 = 0.395904 loss)
I0224 22:54:09.280678 29812 solver.cpp:189] Iteration 30000, loss = 0.402496
I0224 22:54:09.280700 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.402495 (* 1 = 0.402495 loss)
I0224 22:54:09.280705 29812 solver.cpp:470] Iteration 30000, lr = 0.0007
I0224 22:54:28.675312 29812 solver.cpp:189] Iteration 30050, loss = 0.272732
I0224 22:54:28.675340 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.272731 (* 1 = 0.272731 loss)
I0224 22:54:28.675345 29812 solver.cpp:470] Iteration 30050, lr = 0.0007
I0224 22:54:48.058737 29812 solver.cpp:189] Iteration 30100, loss = 0.256681
I0224 22:54:48.058815 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.25668 (* 1 = 0.25668 loss)
I0224 22:54:48.058821 29812 solver.cpp:470] Iteration 30100, lr = 0.0007
I0224 22:55:07.462734 29812 solver.cpp:189] Iteration 30150, loss = 0.371258
I0224 22:55:07.462759 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.371258 (* 1 = 0.371258 loss)
I0224 22:55:07.462764 29812 solver.cpp:470] Iteration 30150, lr = 0.0007
I0224 22:55:26.851586 29812 solver.cpp:189] Iteration 30200, loss = 0.199172
I0224 22:55:26.851649 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.199172 (* 1 = 0.199172 loss)
I0224 22:55:26.851656 29812 solver.cpp:470] Iteration 30200, lr = 0.0007
I0224 22:55:46.250690 29812 solver.cpp:189] Iteration 30250, loss = 0.360352
I0224 22:55:46.250713 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.360352 (* 1 = 0.360352 loss)
I0224 22:55:46.250720 29812 solver.cpp:470] Iteration 30250, lr = 0.0007
I0224 22:56:05.647178 29812 solver.cpp:189] Iteration 30300, loss = 0.229248
I0224 22:56:05.647269 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.229248 (* 1 = 0.229248 loss)
I0224 22:56:05.647274 29812 solver.cpp:470] Iteration 30300, lr = 0.0007
I0224 22:56:25.032065 29812 solver.cpp:189] Iteration 30350, loss = 0.395376
I0224 22:56:25.032088 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.395376 (* 1 = 0.395376 loss)
I0224 22:56:25.032093 29812 solver.cpp:470] Iteration 30350, lr = 0.0007
I0224 22:56:44.424352 29812 solver.cpp:189] Iteration 30400, loss = 0.476951
I0224 22:56:44.424404 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.476951 (* 1 = 0.476951 loss)
I0224 22:56:44.424409 29812 solver.cpp:470] Iteration 30400, lr = 0.0007
I0224 22:57:03.829597 29812 solver.cpp:189] Iteration 30450, loss = 0.453474
I0224 22:57:03.829622 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.453474 (* 1 = 0.453474 loss)
I0224 22:57:03.829627 29812 solver.cpp:470] Iteration 30450, lr = 0.0007
I0224 22:57:23.221953 29812 solver.cpp:189] Iteration 30500, loss = 0.291582
I0224 22:57:23.222038 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.291582 (* 1 = 0.291582 loss)
I0224 22:57:23.222043 29812 solver.cpp:470] Iteration 30500, lr = 0.0007
I0224 22:57:42.619426 29812 solver.cpp:189] Iteration 30550, loss = 0.311324
I0224 22:57:42.619451 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.311324 (* 1 = 0.311324 loss)
I0224 22:57:42.619457 29812 solver.cpp:470] Iteration 30550, lr = 0.0007
I0224 22:58:02.005759 29812 solver.cpp:189] Iteration 30600, loss = 0.33668
I0224 22:58:02.005831 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.33668 (* 1 = 0.33668 loss)
I0224 22:58:02.005846 29812 solver.cpp:470] Iteration 30600, lr = 0.0007
I0224 22:58:21.392968 29812 solver.cpp:189] Iteration 30650, loss = 0.264833
I0224 22:58:21.392992 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.264833 (* 1 = 0.264833 loss)
I0224 22:58:21.392997 29812 solver.cpp:470] Iteration 30650, lr = 0.0007
I0224 22:58:40.788552 29812 solver.cpp:189] Iteration 30700, loss = 0.376979
I0224 22:58:40.788641 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.376979 (* 1 = 0.376979 loss)
I0224 22:58:40.788647 29812 solver.cpp:470] Iteration 30700, lr = 0.0007
I0224 22:59:00.181998 29812 solver.cpp:189] Iteration 30750, loss = 0.309233
I0224 22:59:00.182020 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.309233 (* 1 = 0.309233 loss)
I0224 22:59:00.182025 29812 solver.cpp:470] Iteration 30750, lr = 0.0007
I0224 22:59:19.579459 29812 solver.cpp:189] Iteration 30800, loss = 0.300113
I0224 22:59:19.579552 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.300113 (* 1 = 0.300113 loss)
I0224 22:59:19.579567 29812 solver.cpp:470] Iteration 30800, lr = 0.0007
I0224 22:59:38.975905 29812 solver.cpp:189] Iteration 30850, loss = 0.41641
I0224 22:59:38.975929 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.41641 (* 1 = 0.41641 loss)
I0224 22:59:38.975934 29812 solver.cpp:470] Iteration 30850, lr = 0.0007
I0224 22:59:58.368854 29812 solver.cpp:189] Iteration 30900, loss = 0.223776
I0224 22:59:58.368943 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.223776 (* 1 = 0.223776 loss)
I0224 22:59:58.368949 29812 solver.cpp:470] Iteration 30900, lr = 0.0007
I0224 23:00:17.774118 29812 solver.cpp:189] Iteration 30950, loss = 0.207226
I0224 23:00:17.774142 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.207226 (* 1 = 0.207226 loss)
I0224 23:00:17.774147 29812 solver.cpp:470] Iteration 30950, lr = 0.0007
I0224 23:00:36.930415 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_31000.caffemodel
I0224 23:00:37.050037 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_31000.solverstate
I0224 23:00:37.107784 29812 solver.cpp:266] Iteration 31000, Testing net (#0)
I0224 23:00:44.759737 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8638
I0224 23:00:44.759773 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.414999 (* 1 = 0.414999 loss)
I0224 23:00:45.046272 29812 solver.cpp:189] Iteration 31000, loss = 0.354237
I0224 23:00:45.046293 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.354237 (* 1 = 0.354237 loss)
I0224 23:00:45.046298 29812 solver.cpp:470] Iteration 31000, lr = 0.0007
I0224 23:01:04.428148 29812 solver.cpp:189] Iteration 31050, loss = 0.376901
I0224 23:01:04.428172 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.376901 (* 1 = 0.376901 loss)
I0224 23:01:04.428179 29812 solver.cpp:470] Iteration 31050, lr = 0.0007
I0224 23:01:23.803141 29812 solver.cpp:189] Iteration 31100, loss = 0.37879
I0224 23:01:23.803228 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.37879 (* 1 = 0.37879 loss)
I0224 23:01:23.803235 29812 solver.cpp:470] Iteration 31100, lr = 0.0007
I0224 23:01:43.198559 29812 solver.cpp:189] Iteration 31150, loss = 0.27725
I0224 23:01:43.198582 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.27725 (* 1 = 0.27725 loss)
I0224 23:01:43.198587 29812 solver.cpp:470] Iteration 31150, lr = 0.0007
I0224 23:02:02.589521 29812 solver.cpp:189] Iteration 31200, loss = 0.432223
I0224 23:02:02.589561 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.432223 (* 1 = 0.432223 loss)
I0224 23:02:02.589567 29812 solver.cpp:470] Iteration 31200, lr = 0.0007
I0224 23:02:21.957934 29812 solver.cpp:189] Iteration 31250, loss = 0.344186
I0224 23:02:21.957957 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.344186 (* 1 = 0.344186 loss)
I0224 23:02:21.957962 29812 solver.cpp:470] Iteration 31250, lr = 0.0007
I0224 23:02:41.340764 29812 solver.cpp:189] Iteration 31300, loss = 0.371688
I0224 23:02:41.340832 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.371688 (* 1 = 0.371688 loss)
I0224 23:02:41.340847 29812 solver.cpp:470] Iteration 31300, lr = 0.0007
I0224 23:03:00.722615 29812 solver.cpp:189] Iteration 31350, loss = 0.359246
I0224 23:03:00.722640 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.359246 (* 1 = 0.359246 loss)
I0224 23:03:00.722646 29812 solver.cpp:470] Iteration 31350, lr = 0.0007
I0224 23:03:20.107631 29812 solver.cpp:189] Iteration 31400, loss = 0.366838
I0224 23:03:20.107718 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.366838 (* 1 = 0.366838 loss)
I0224 23:03:20.107723 29812 solver.cpp:470] Iteration 31400, lr = 0.0007
I0224 23:03:39.494081 29812 solver.cpp:189] Iteration 31450, loss = 0.436912
I0224 23:03:39.494106 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.436911 (* 1 = 0.436911 loss)
I0224 23:03:39.494112 29812 solver.cpp:470] Iteration 31450, lr = 0.0007
I0224 23:03:58.888525 29812 solver.cpp:189] Iteration 31500, loss = 0.310941
I0224 23:03:58.888593 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.310941 (* 1 = 0.310941 loss)
I0224 23:03:58.888600 29812 solver.cpp:470] Iteration 31500, lr = 0.0007
I0224 23:04:18.279083 29812 solver.cpp:189] Iteration 31550, loss = 0.265422
I0224 23:04:18.279108 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.265422 (* 1 = 0.265422 loss)
I0224 23:04:18.279113 29812 solver.cpp:470] Iteration 31550, lr = 0.0007
I0224 23:04:37.670464 29812 solver.cpp:189] Iteration 31600, loss = 0.299357
I0224 23:04:37.670524 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.299356 (* 1 = 0.299356 loss)
I0224 23:04:37.670531 29812 solver.cpp:470] Iteration 31600, lr = 0.0007
I0224 23:04:57.058325 29812 solver.cpp:189] Iteration 31650, loss = 0.397534
I0224 23:04:57.058348 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.397534 (* 1 = 0.397534 loss)
I0224 23:04:57.058353 29812 solver.cpp:470] Iteration 31650, lr = 0.0007
I0224 23:05:16.445739 29812 solver.cpp:189] Iteration 31700, loss = 0.344157
I0224 23:05:16.445776 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.344157 (* 1 = 0.344157 loss)
I0224 23:05:16.445782 29812 solver.cpp:470] Iteration 31700, lr = 0.0007
I0224 23:05:35.827611 29812 solver.cpp:189] Iteration 31750, loss = 0.28037
I0224 23:05:35.827636 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.28037 (* 1 = 0.28037 loss)
I0224 23:05:35.827642 29812 solver.cpp:470] Iteration 31750, lr = 0.0007
I0224 23:05:55.218302 29812 solver.cpp:189] Iteration 31800, loss = 0.409821
I0224 23:05:55.218370 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.409821 (* 1 = 0.409821 loss)
I0224 23:05:55.218385 29812 solver.cpp:470] Iteration 31800, lr = 0.0007
I0224 23:06:14.607349 29812 solver.cpp:189] Iteration 31850, loss = 0.438249
I0224 23:06:14.607374 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.438249 (* 1 = 0.438249 loss)
I0224 23:06:14.607379 29812 solver.cpp:470] Iteration 31850, lr = 0.0007
I0224 23:06:33.992662 29812 solver.cpp:189] Iteration 31900, loss = 0.387042
I0224 23:06:33.992748 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.387042 (* 1 = 0.387042 loss)
I0224 23:06:33.992754 29812 solver.cpp:470] Iteration 31900, lr = 0.0007
I0224 23:06:53.371505 29812 solver.cpp:189] Iteration 31950, loss = 0.340515
I0224 23:06:53.371528 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.340515 (* 1 = 0.340515 loss)
I0224 23:06:53.371533 29812 solver.cpp:470] Iteration 31950, lr = 0.0007
I0224 23:07:12.516317 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_32000.caffemodel
I0224 23:07:12.641405 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_32000.solverstate
I0224 23:07:12.699244 29812 solver.cpp:266] Iteration 32000, Testing net (#0)
I0224 23:07:20.358713 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8714
I0224 23:07:20.358748 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.38661 (* 1 = 0.38661 loss)
I0224 23:07:20.646222 29812 solver.cpp:189] Iteration 32000, loss = 0.35128
I0224 23:07:20.646250 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.35128 (* 1 = 0.35128 loss)
I0224 23:07:20.646255 29812 solver.cpp:470] Iteration 32000, lr = 0.0007
I0224 23:07:40.033303 29812 solver.cpp:189] Iteration 32050, loss = 0.33318
I0224 23:07:40.033329 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.33318 (* 1 = 0.33318 loss)
I0224 23:07:40.033332 29812 solver.cpp:470] Iteration 32050, lr = 0.0007
I0224 23:07:59.414603 29812 solver.cpp:189] Iteration 32100, loss = 0.205913
I0224 23:07:59.414661 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.205913 (* 1 = 0.205913 loss)
I0224 23:07:59.414669 29812 solver.cpp:470] Iteration 32100, lr = 0.0007
I0224 23:08:18.814111 29812 solver.cpp:189] Iteration 32150, loss = 0.239554
I0224 23:08:18.814138 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.239554 (* 1 = 0.239554 loss)
I0224 23:08:18.814143 29812 solver.cpp:470] Iteration 32150, lr = 0.0007
I0224 23:08:38.201899 29812 solver.cpp:189] Iteration 32200, loss = 0.456316
I0224 23:08:38.201977 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.456316 (* 1 = 0.456316 loss)
I0224 23:08:38.201982 29812 solver.cpp:470] Iteration 32200, lr = 0.0007
I0224 23:08:57.595017 29812 solver.cpp:189] Iteration 32250, loss = 0.380369
I0224 23:08:57.595041 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.380369 (* 1 = 0.380369 loss)
I0224 23:08:57.595047 29812 solver.cpp:470] Iteration 32250, lr = 0.0007
I0224 23:09:16.984208 29812 solver.cpp:189] Iteration 32300, loss = 0.305174
I0224 23:09:16.984299 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.305174 (* 1 = 0.305174 loss)
I0224 23:09:16.984305 29812 solver.cpp:470] Iteration 32300, lr = 0.0007
I0224 23:09:36.371170 29812 solver.cpp:189] Iteration 32350, loss = 0.277242
I0224 23:09:36.371194 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.277242 (* 1 = 0.277242 loss)
I0224 23:09:36.371201 29812 solver.cpp:470] Iteration 32350, lr = 0.0007
I0224 23:09:55.759500 29812 solver.cpp:189] Iteration 32400, loss = 0.375779
I0224 23:09:55.759560 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.375779 (* 1 = 0.375779 loss)
I0224 23:09:55.759567 29812 solver.cpp:470] Iteration 32400, lr = 0.0007
I0224 23:10:15.153354 29812 solver.cpp:189] Iteration 32450, loss = 0.334539
I0224 23:10:15.153378 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.334538 (* 1 = 0.334538 loss)
I0224 23:10:15.153383 29812 solver.cpp:470] Iteration 32450, lr = 0.0007
I0224 23:10:34.538251 29812 solver.cpp:189] Iteration 32500, loss = 0.352361
I0224 23:10:34.538352 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.352361 (* 1 = 0.352361 loss)
I0224 23:10:34.538359 29812 solver.cpp:470] Iteration 32500, lr = 0.0007
I0224 23:10:53.918757 29812 solver.cpp:189] Iteration 32550, loss = 0.337056
I0224 23:10:53.918781 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.337056 (* 1 = 0.337056 loss)
I0224 23:10:53.918787 29812 solver.cpp:470] Iteration 32550, lr = 0.0007
I0224 23:11:13.303128 29812 solver.cpp:189] Iteration 32600, loss = 0.328427
I0224 23:11:13.303196 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.328427 (* 1 = 0.328427 loss)
I0224 23:11:13.303201 29812 solver.cpp:470] Iteration 32600, lr = 0.0007
I0224 23:11:32.689378 29812 solver.cpp:189] Iteration 32650, loss = 0.294357
I0224 23:11:32.689404 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.294357 (* 1 = 0.294357 loss)
I0224 23:11:32.689409 29812 solver.cpp:470] Iteration 32650, lr = 0.0007
I0224 23:11:52.075353 29812 solver.cpp:189] Iteration 32700, loss = 0.320471
I0224 23:11:52.075392 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.320471 (* 1 = 0.320471 loss)
I0224 23:11:52.075397 29812 solver.cpp:470] Iteration 32700, lr = 0.0007
I0224 23:12:11.465400 29812 solver.cpp:189] Iteration 32750, loss = 0.338624
I0224 23:12:11.465423 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.338624 (* 1 = 0.338624 loss)
I0224 23:12:11.465428 29812 solver.cpp:470] Iteration 32750, lr = 0.0007
I0224 23:12:30.862494 29812 solver.cpp:189] Iteration 32800, loss = 0.316962
I0224 23:12:30.862581 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.316962 (* 1 = 0.316962 loss)
I0224 23:12:30.862587 29812 solver.cpp:470] Iteration 32800, lr = 0.0007
I0224 23:12:50.251775 29812 solver.cpp:189] Iteration 32850, loss = 0.388067
I0224 23:12:50.251801 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.388067 (* 1 = 0.388067 loss)
I0224 23:12:50.251806 29812 solver.cpp:470] Iteration 32850, lr = 0.0007
I0224 23:13:09.642941 29812 solver.cpp:189] Iteration 32900, loss = 0.301935
I0224 23:13:09.643029 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.301934 (* 1 = 0.301934 loss)
I0224 23:13:09.643035 29812 solver.cpp:470] Iteration 32900, lr = 0.0007
I0224 23:13:29.018755 29812 solver.cpp:189] Iteration 32950, loss = 0.318891
I0224 23:13:29.018779 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.318891 (* 1 = 0.318891 loss)
I0224 23:13:29.018784 29812 solver.cpp:470] Iteration 32950, lr = 0.0007
I0224 23:13:48.167841 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_33000.caffemodel
I0224 23:13:48.286684 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_33000.solverstate
I0224 23:13:48.344580 29812 solver.cpp:266] Iteration 33000, Testing net (#0)
I0224 23:13:55.982446 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8739
I0224 23:13:55.982482 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.393454 (* 1 = 0.393454 loss)
I0224 23:13:56.268837 29812 solver.cpp:189] Iteration 33000, loss = 0.288131
I0224 23:13:56.268859 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.288131 (* 1 = 0.288131 loss)
I0224 23:13:56.268865 29812 solver.cpp:470] Iteration 33000, lr = 0.0007
I0224 23:14:15.671159 29812 solver.cpp:189] Iteration 33050, loss = 0.382099
I0224 23:14:15.671185 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.382099 (* 1 = 0.382099 loss)
I0224 23:14:15.671190 29812 solver.cpp:470] Iteration 33050, lr = 0.0007
I0224 23:14:35.065402 29812 solver.cpp:189] Iteration 33100, loss = 0.372205
I0224 23:14:35.065475 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.372205 (* 1 = 0.372205 loss)
I0224 23:14:35.065490 29812 solver.cpp:470] Iteration 33100, lr = 0.0007
I0224 23:14:54.456310 29812 solver.cpp:189] Iteration 33150, loss = 0.281076
I0224 23:14:54.456333 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.281076 (* 1 = 0.281076 loss)
I0224 23:14:54.456339 29812 solver.cpp:470] Iteration 33150, lr = 0.0007
I0224 23:15:13.848187 29812 solver.cpp:189] Iteration 33200, loss = 0.32419
I0224 23:15:13.848286 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.32419 (* 1 = 0.32419 loss)
I0224 23:15:13.848292 29812 solver.cpp:470] Iteration 33200, lr = 0.0007
I0224 23:15:33.245632 29812 solver.cpp:189] Iteration 33250, loss = 0.229297
I0224 23:15:33.245656 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.229297 (* 1 = 0.229297 loss)
I0224 23:15:33.245661 29812 solver.cpp:470] Iteration 33250, lr = 0.0007
I0224 23:15:52.644397 29812 solver.cpp:189] Iteration 33300, loss = 0.233392
I0224 23:15:52.644479 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.233391 (* 1 = 0.233391 loss)
I0224 23:15:52.644485 29812 solver.cpp:470] Iteration 33300, lr = 0.0007
I0224 23:16:12.041097 29812 solver.cpp:189] Iteration 33350, loss = 0.315009
I0224 23:16:12.041121 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.315009 (* 1 = 0.315009 loss)
I0224 23:16:12.041126 29812 solver.cpp:470] Iteration 33350, lr = 0.0007
I0224 23:16:31.433670 29812 solver.cpp:189] Iteration 33400, loss = 0.236869
I0224 23:16:31.433730 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.236868 (* 1 = 0.236868 loss)
I0224 23:16:31.433737 29812 solver.cpp:470] Iteration 33400, lr = 0.0007
I0224 23:16:50.834331 29812 solver.cpp:189] Iteration 33450, loss = 0.273319
I0224 23:16:50.834357 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.273319 (* 1 = 0.273319 loss)
I0224 23:16:50.834362 29812 solver.cpp:470] Iteration 33450, lr = 0.0007
I0224 23:17:10.242760 29812 solver.cpp:189] Iteration 33500, loss = 0.322064
I0224 23:17:10.242800 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.322064 (* 1 = 0.322064 loss)
I0224 23:17:10.242806 29812 solver.cpp:470] Iteration 33500, lr = 0.0007
I0224 23:17:29.635151 29812 solver.cpp:189] Iteration 33550, loss = 0.236045
I0224 23:17:29.635175 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.236045 (* 1 = 0.236045 loss)
I0224 23:17:29.635180 29812 solver.cpp:470] Iteration 33550, lr = 0.0007
I0224 23:17:49.020884 29812 solver.cpp:189] Iteration 33600, loss = 0.356496
I0224 23:17:49.020970 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.356496 (* 1 = 0.356496 loss)
I0224 23:17:49.020977 29812 solver.cpp:470] Iteration 33600, lr = 0.0007
I0224 23:18:08.420140 29812 solver.cpp:189] Iteration 33650, loss = 0.287707
I0224 23:18:08.420163 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.287707 (* 1 = 0.287707 loss)
I0224 23:18:08.420168 29812 solver.cpp:470] Iteration 33650, lr = 0.0007
I0224 23:18:27.812207 29812 solver.cpp:189] Iteration 33700, loss = 0.252382
I0224 23:18:27.812319 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.252382 (* 1 = 0.252382 loss)
I0224 23:18:27.812332 29812 solver.cpp:470] Iteration 33700, lr = 0.0007
I0224 23:18:47.211563 29812 solver.cpp:189] Iteration 33750, loss = 0.28887
I0224 23:18:47.211586 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.28887 (* 1 = 0.28887 loss)
I0224 23:18:47.211591 29812 solver.cpp:470] Iteration 33750, lr = 0.0007
I0224 23:19:06.602998 29812 solver.cpp:189] Iteration 33800, loss = 0.37297
I0224 23:19:06.603066 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.37297 (* 1 = 0.37297 loss)
I0224 23:19:06.603081 29812 solver.cpp:470] Iteration 33800, lr = 0.0007
I0224 23:19:26.004124 29812 solver.cpp:189] Iteration 33850, loss = 0.261281
I0224 23:19:26.004148 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.261281 (* 1 = 0.261281 loss)
I0224 23:19:26.004153 29812 solver.cpp:470] Iteration 33850, lr = 0.0007
I0224 23:19:45.398820 29812 solver.cpp:189] Iteration 33900, loss = 0.128746
I0224 23:19:45.398888 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.128746 (* 1 = 0.128746 loss)
I0224 23:19:45.398903 29812 solver.cpp:470] Iteration 33900, lr = 0.0007
I0224 23:20:04.801012 29812 solver.cpp:189] Iteration 33950, loss = 0.327802
I0224 23:20:04.801038 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.327802 (* 1 = 0.327802 loss)
I0224 23:20:04.801043 29812 solver.cpp:470] Iteration 33950, lr = 0.0007
I0224 23:20:23.948801 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_34000.caffemodel
I0224 23:20:24.072275 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_34000.solverstate
I0224 23:20:24.129891 29812 solver.cpp:266] Iteration 34000, Testing net (#0)
I0224 23:20:31.796072 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8742
I0224 23:20:31.796110 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.391325 (* 1 = 0.391325 loss)
I0224 23:20:32.083559 29812 solver.cpp:189] Iteration 34000, loss = 0.467023
I0224 23:20:32.083580 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.467023 (* 1 = 0.467023 loss)
I0224 23:20:32.083585 29812 solver.cpp:470] Iteration 34000, lr = 0.0007
I0224 23:20:51.476938 29812 solver.cpp:189] Iteration 34050, loss = 0.351703
I0224 23:20:51.476961 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.351703 (* 1 = 0.351703 loss)
I0224 23:20:51.476966 29812 solver.cpp:470] Iteration 34050, lr = 0.0007
I0224 23:21:10.874145 29812 solver.cpp:189] Iteration 34100, loss = 0.321862
I0224 23:21:10.874204 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.321862 (* 1 = 0.321862 loss)
I0224 23:21:10.874210 29812 solver.cpp:470] Iteration 34100, lr = 0.0007
I0224 23:21:30.270228 29812 solver.cpp:189] Iteration 34150, loss = 0.319705
I0224 23:21:30.270252 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.319705 (* 1 = 0.319705 loss)
I0224 23:21:30.270257 29812 solver.cpp:470] Iteration 34150, lr = 0.0007
I0224 23:21:49.665776 29812 solver.cpp:189] Iteration 34200, loss = 0.314655
I0224 23:21:49.665851 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.314655 (* 1 = 0.314655 loss)
I0224 23:21:49.665865 29812 solver.cpp:470] Iteration 34200, lr = 0.0007
I0224 23:22:09.052842 29812 solver.cpp:189] Iteration 34250, loss = 0.254154
I0224 23:22:09.052866 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.254154 (* 1 = 0.254154 loss)
I0224 23:22:09.052871 29812 solver.cpp:470] Iteration 34250, lr = 0.0007
I0224 23:22:28.446449 29812 solver.cpp:189] Iteration 34300, loss = 0.177554
I0224 23:22:28.446509 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.177554 (* 1 = 0.177554 loss)
I0224 23:22:28.446516 29812 solver.cpp:470] Iteration 34300, lr = 0.0007
I0224 23:22:47.837088 29812 solver.cpp:189] Iteration 34350, loss = 0.559688
I0224 23:22:47.837112 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.559689 (* 1 = 0.559689 loss)
I0224 23:22:47.837118 29812 solver.cpp:470] Iteration 34350, lr = 0.0007
I0224 23:23:07.238368 29812 solver.cpp:189] Iteration 34400, loss = 0.262949
I0224 23:23:07.238454 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.262949 (* 1 = 0.262949 loss)
I0224 23:23:07.238461 29812 solver.cpp:470] Iteration 34400, lr = 0.0007
I0224 23:23:26.621028 29812 solver.cpp:189] Iteration 34450, loss = 0.237831
I0224 23:23:26.621052 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.237831 (* 1 = 0.237831 loss)
I0224 23:23:26.621057 29812 solver.cpp:470] Iteration 34450, lr = 0.0007
I0224 23:23:46.026564 29812 solver.cpp:189] Iteration 34500, loss = 0.353969
I0224 23:23:46.026635 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.353969 (* 1 = 0.353969 loss)
I0224 23:23:46.026649 29812 solver.cpp:470] Iteration 34500, lr = 0.0007
I0224 23:24:05.417368 29812 solver.cpp:189] Iteration 34550, loss = 0.244861
I0224 23:24:05.417392 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.244861 (* 1 = 0.244861 loss)
I0224 23:24:05.417397 29812 solver.cpp:470] Iteration 34550, lr = 0.0007
I0224 23:24:24.808727 29812 solver.cpp:189] Iteration 34600, loss = 0.242363
I0224 23:24:24.808802 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.242363 (* 1 = 0.242363 loss)
I0224 23:24:24.808817 29812 solver.cpp:470] Iteration 34600, lr = 0.0007
I0224 23:24:44.196032 29812 solver.cpp:189] Iteration 34650, loss = 0.167576
I0224 23:24:44.196055 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.167577 (* 1 = 0.167577 loss)
I0224 23:24:44.196059 29812 solver.cpp:470] Iteration 34650, lr = 0.0007
I0224 23:25:03.587362 29812 solver.cpp:189] Iteration 34700, loss = 0.217598
I0224 23:25:03.587422 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.217598 (* 1 = 0.217598 loss)
I0224 23:25:03.587429 29812 solver.cpp:470] Iteration 34700, lr = 0.0007
I0224 23:25:22.976474 29812 solver.cpp:189] Iteration 34750, loss = 0.274095
I0224 23:25:22.976497 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.274095 (* 1 = 0.274095 loss)
I0224 23:25:22.976503 29812 solver.cpp:470] Iteration 34750, lr = 0.0007
I0224 23:25:42.369807 29812 solver.cpp:189] Iteration 34800, loss = 0.339248
I0224 23:25:42.369848 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.339248 (* 1 = 0.339248 loss)
I0224 23:25:42.369853 29812 solver.cpp:470] Iteration 34800, lr = 0.0007
I0224 23:26:01.765336 29812 solver.cpp:189] Iteration 34850, loss = 0.34915
I0224 23:26:01.765362 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.34915 (* 1 = 0.34915 loss)
I0224 23:26:01.765367 29812 solver.cpp:470] Iteration 34850, lr = 0.0007
I0224 23:26:21.161447 29812 solver.cpp:189] Iteration 34900, loss = 0.230991
I0224 23:26:21.161514 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.230992 (* 1 = 0.230992 loss)
I0224 23:26:21.161530 29812 solver.cpp:470] Iteration 34900, lr = 0.0007
I0224 23:26:40.565610 29812 solver.cpp:189] Iteration 34950, loss = 0.178882
I0224 23:26:40.565632 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.178882 (* 1 = 0.178882 loss)
I0224 23:26:40.565637 29812 solver.cpp:470] Iteration 34950, lr = 0.0007
I0224 23:26:59.707906 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_35000.caffemodel
I0224 23:26:59.828898 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_35000.solverstate
I0224 23:26:59.886426 29812 solver.cpp:266] Iteration 35000, Testing net (#0)
I0224 23:27:07.533838 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8772
I0224 23:27:07.533872 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.374381 (* 1 = 0.374381 loss)
I0224 23:27:07.821715 29812 solver.cpp:189] Iteration 35000, loss = 0.396062
I0224 23:27:07.821739 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.396062 (* 1 = 0.396062 loss)
I0224 23:27:07.821744 29812 solver.cpp:470] Iteration 35000, lr = 0.0007
I0224 23:27:27.209872 29812 solver.cpp:189] Iteration 35050, loss = 0.294848
I0224 23:27:27.209895 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.294848 (* 1 = 0.294848 loss)
I0224 23:27:27.209899 29812 solver.cpp:470] Iteration 35050, lr = 0.0007
I0224 23:27:46.597265 29812 solver.cpp:189] Iteration 35100, loss = 0.353865
I0224 23:27:46.597326 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.353866 (* 1 = 0.353866 loss)
I0224 23:27:46.597332 29812 solver.cpp:470] Iteration 35100, lr = 0.0007
I0224 23:28:05.987114 29812 solver.cpp:189] Iteration 35150, loss = 0.469084
I0224 23:28:05.987138 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.469084 (* 1 = 0.469084 loss)
I0224 23:28:05.987143 29812 solver.cpp:470] Iteration 35150, lr = 0.0007
I0224 23:28:25.375068 29812 solver.cpp:189] Iteration 35200, loss = 0.233392
I0224 23:28:25.375113 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.233392 (* 1 = 0.233392 loss)
I0224 23:28:25.375118 29812 solver.cpp:470] Iteration 35200, lr = 0.0007
I0224 23:28:44.756698 29812 solver.cpp:189] Iteration 35250, loss = 0.192872
I0224 23:28:44.756722 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.192872 (* 1 = 0.192872 loss)
I0224 23:28:44.756728 29812 solver.cpp:470] Iteration 35250, lr = 0.0007
I0224 23:29:04.138232 29812 solver.cpp:189] Iteration 35300, loss = 0.369299
I0224 23:29:04.138290 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.369299 (* 1 = 0.369299 loss)
I0224 23:29:04.138295 29812 solver.cpp:470] Iteration 35300, lr = 0.0007
I0224 23:29:23.517484 29812 solver.cpp:189] Iteration 35350, loss = 0.402817
I0224 23:29:23.517508 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.402817 (* 1 = 0.402817 loss)
I0224 23:29:23.517513 29812 solver.cpp:470] Iteration 35350, lr = 0.0007
I0224 23:29:42.905649 29812 solver.cpp:189] Iteration 35400, loss = 0.226326
I0224 23:29:42.905710 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.226326 (* 1 = 0.226326 loss)
I0224 23:29:42.905716 29812 solver.cpp:470] Iteration 35400, lr = 0.0007
I0224 23:30:02.293810 29812 solver.cpp:189] Iteration 35450, loss = 0.267114
I0224 23:30:02.293833 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.267114 (* 1 = 0.267114 loss)
I0224 23:30:02.293838 29812 solver.cpp:470] Iteration 35450, lr = 0.0007
I0224 23:30:21.679342 29812 solver.cpp:189] Iteration 35500, loss = 0.50259
I0224 23:30:21.679416 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.50259 (* 1 = 0.50259 loss)
I0224 23:30:21.679431 29812 solver.cpp:470] Iteration 35500, lr = 0.0007
I0224 23:30:41.077967 29812 solver.cpp:189] Iteration 35550, loss = 0.138174
I0224 23:30:41.077991 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.138175 (* 1 = 0.138175 loss)
I0224 23:30:41.077997 29812 solver.cpp:470] Iteration 35550, lr = 0.0007
I0224 23:31:00.455097 29812 solver.cpp:189] Iteration 35600, loss = 0.289813
I0224 23:31:00.455168 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.289813 (* 1 = 0.289813 loss)
I0224 23:31:00.455183 29812 solver.cpp:470] Iteration 35600, lr = 0.0007
I0224 23:31:19.844509 29812 solver.cpp:189] Iteration 35650, loss = 0.232835
I0224 23:31:19.844534 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.232835 (* 1 = 0.232835 loss)
I0224 23:31:19.844539 29812 solver.cpp:470] Iteration 35650, lr = 0.0007
I0224 23:31:39.233068 29812 solver.cpp:189] Iteration 35700, loss = 0.263199
I0224 23:31:39.233129 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.263199 (* 1 = 0.263199 loss)
I0224 23:31:39.233134 29812 solver.cpp:470] Iteration 35700, lr = 0.0007
I0224 23:31:58.619272 29812 solver.cpp:189] Iteration 35750, loss = 0.337092
I0224 23:31:58.619297 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.337093 (* 1 = 0.337093 loss)
I0224 23:31:58.619302 29812 solver.cpp:470] Iteration 35750, lr = 0.0007
I0224 23:32:18.016973 29812 solver.cpp:189] Iteration 35800, loss = 0.307653
I0224 23:32:18.017081 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.307653 (* 1 = 0.307653 loss)
I0224 23:32:18.017087 29812 solver.cpp:470] Iteration 35800, lr = 0.0007
I0224 23:32:37.399166 29812 solver.cpp:189] Iteration 35850, loss = 0.22137
I0224 23:32:37.399189 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.22137 (* 1 = 0.22137 loss)
I0224 23:32:37.399195 29812 solver.cpp:470] Iteration 35850, lr = 0.0007
I0224 23:32:56.780882 29812 solver.cpp:189] Iteration 35900, loss = 0.21929
I0224 23:32:56.780975 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.21929 (* 1 = 0.21929 loss)
I0224 23:32:56.780982 29812 solver.cpp:470] Iteration 35900, lr = 0.0007
I0224 23:33:16.171031 29812 solver.cpp:189] Iteration 35950, loss = 0.31936
I0224 23:33:16.171056 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.31936 (* 1 = 0.31936 loss)
I0224 23:33:16.171061 29812 solver.cpp:470] Iteration 35950, lr = 0.0007
I0224 23:33:35.305894 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_36000.caffemodel
I0224 23:33:35.431723 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_36000.solverstate
I0224 23:33:35.490774 29812 solver.cpp:266] Iteration 36000, Testing net (#0)
I0224 23:33:43.138191 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8785
I0224 23:33:43.138226 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.376506 (* 1 = 0.376506 loss)
I0224 23:33:43.425071 29812 solver.cpp:189] Iteration 36000, loss = 0.306763
I0224 23:33:43.425093 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.306763 (* 1 = 0.306763 loss)
I0224 23:33:43.425099 29812 solver.cpp:470] Iteration 36000, lr = 0.0007
I0224 23:34:02.806170 29812 solver.cpp:189] Iteration 36050, loss = 0.228126
I0224 23:34:02.806195 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.228126 (* 1 = 0.228126 loss)
I0224 23:34:02.806200 29812 solver.cpp:470] Iteration 36050, lr = 0.0007
I0224 23:34:22.194355 29812 solver.cpp:189] Iteration 36100, loss = 0.185383
I0224 23:34:22.194439 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.185383 (* 1 = 0.185383 loss)
I0224 23:34:22.194444 29812 solver.cpp:470] Iteration 36100, lr = 0.0007
I0224 23:34:41.570677 29812 solver.cpp:189] Iteration 36150, loss = 0.251715
I0224 23:34:41.570700 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.251715 (* 1 = 0.251715 loss)
I0224 23:34:41.570706 29812 solver.cpp:470] Iteration 36150, lr = 0.0007
I0224 23:35:00.955433 29812 solver.cpp:189] Iteration 36200, loss = 0.193081
I0224 23:35:00.955468 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.193081 (* 1 = 0.193081 loss)
I0224 23:35:00.955474 29812 solver.cpp:470] Iteration 36200, lr = 0.0007
I0224 23:35:20.339391 29812 solver.cpp:189] Iteration 36250, loss = 0.247178
I0224 23:35:20.339414 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.247178 (* 1 = 0.247178 loss)
I0224 23:35:20.339419 29812 solver.cpp:470] Iteration 36250, lr = 0.0007
I0224 23:35:39.723798 29812 solver.cpp:189] Iteration 36300, loss = 0.185321
I0224 23:35:39.723858 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.185322 (* 1 = 0.185322 loss)
I0224 23:35:39.723865 29812 solver.cpp:470] Iteration 36300, lr = 0.0007
I0224 23:35:59.115123 29812 solver.cpp:189] Iteration 36350, loss = 0.373051
I0224 23:35:59.115147 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.373052 (* 1 = 0.373052 loss)
I0224 23:35:59.115154 29812 solver.cpp:470] Iteration 36350, lr = 0.0007
I0224 23:36:18.505779 29812 solver.cpp:189] Iteration 36400, loss = 0.337681
I0224 23:36:18.505839 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.337682 (* 1 = 0.337682 loss)
I0224 23:36:18.505846 29812 solver.cpp:470] Iteration 36400, lr = 0.0007
I0224 23:36:37.897442 29812 solver.cpp:189] Iteration 36450, loss = 0.326621
I0224 23:36:37.897467 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.326621 (* 1 = 0.326621 loss)
I0224 23:36:37.897474 29812 solver.cpp:470] Iteration 36450, lr = 0.0007
I0224 23:36:57.280706 29812 solver.cpp:189] Iteration 36500, loss = 0.261622
I0224 23:36:57.280779 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.261623 (* 1 = 0.261623 loss)
I0224 23:36:57.280786 29812 solver.cpp:470] Iteration 36500, lr = 0.0007
I0224 23:37:16.667826 29812 solver.cpp:189] Iteration 36550, loss = 0.290585
I0224 23:37:16.667855 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.290585 (* 1 = 0.290585 loss)
I0224 23:37:16.667861 29812 solver.cpp:470] Iteration 36550, lr = 0.0007
I0224 23:37:36.058888 29812 solver.cpp:189] Iteration 36600, loss = 0.219132
I0224 23:37:36.058948 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.219132 (* 1 = 0.219132 loss)
I0224 23:37:36.058954 29812 solver.cpp:470] Iteration 36600, lr = 0.0007
I0224 23:37:55.448766 29812 solver.cpp:189] Iteration 36650, loss = 0.409248
I0224 23:37:55.448791 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.409248 (* 1 = 0.409248 loss)
I0224 23:37:55.448796 29812 solver.cpp:470] Iteration 36650, lr = 0.0007
I0224 23:38:14.838157 29812 solver.cpp:189] Iteration 36700, loss = 0.322031
I0224 23:38:14.838228 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.322031 (* 1 = 0.322031 loss)
I0224 23:38:14.838243 29812 solver.cpp:470] Iteration 36700, lr = 0.0007
I0224 23:38:34.222591 29812 solver.cpp:189] Iteration 36750, loss = 0.354935
I0224 23:38:34.222615 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.354936 (* 1 = 0.354936 loss)
I0224 23:38:34.222620 29812 solver.cpp:470] Iteration 36750, lr = 0.0007
I0224 23:38:53.610887 29812 solver.cpp:189] Iteration 36800, loss = 0.314287
I0224 23:38:53.610975 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.314287 (* 1 = 0.314287 loss)
I0224 23:38:53.610980 29812 solver.cpp:470] Iteration 36800, lr = 0.0007
I0224 23:39:13.006939 29812 solver.cpp:189] Iteration 36850, loss = 0.313983
I0224 23:39:13.006963 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.313984 (* 1 = 0.313984 loss)
I0224 23:39:13.006969 29812 solver.cpp:470] Iteration 36850, lr = 0.0007
I0224 23:39:32.394989 29812 solver.cpp:189] Iteration 36900, loss = 0.199484
I0224 23:39:32.395050 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.199484 (* 1 = 0.199484 loss)
I0224 23:39:32.395056 29812 solver.cpp:470] Iteration 36900, lr = 0.0007
I0224 23:39:51.779803 29812 solver.cpp:189] Iteration 36950, loss = 0.311818
I0224 23:39:51.779827 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.311819 (* 1 = 0.311819 loss)
I0224 23:39:51.779834 29812 solver.cpp:470] Iteration 36950, lr = 0.0007
I0224 23:40:10.918687 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_37000.caffemodel
I0224 23:40:11.042330 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_37000.solverstate
I0224 23:40:11.099918 29812 solver.cpp:266] Iteration 37000, Testing net (#0)
I0224 23:40:18.759867 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8708
I0224 23:40:18.759903 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.406072 (* 1 = 0.406072 loss)
I0224 23:40:19.047332 29812 solver.cpp:189] Iteration 37000, loss = 0.200993
I0224 23:40:19.047354 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.200993 (* 1 = 0.200993 loss)
I0224 23:40:19.047359 29812 solver.cpp:470] Iteration 37000, lr = 0.0007
I0224 23:40:38.442361 29812 solver.cpp:189] Iteration 37050, loss = 0.251165
I0224 23:40:38.442384 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.251165 (* 1 = 0.251165 loss)
I0224 23:40:38.442389 29812 solver.cpp:470] Iteration 37050, lr = 0.0007
I0224 23:40:57.842564 29812 solver.cpp:189] Iteration 37100, loss = 0.219896
I0224 23:40:57.842655 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.219897 (* 1 = 0.219897 loss)
I0224 23:40:57.842670 29812 solver.cpp:470] Iteration 37100, lr = 0.0007
I0224 23:41:17.241776 29812 solver.cpp:189] Iteration 37150, loss = 0.409608
I0224 23:41:17.241801 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.409608 (* 1 = 0.409608 loss)
I0224 23:41:17.241806 29812 solver.cpp:470] Iteration 37150, lr = 0.0007
I0224 23:41:36.626601 29812 solver.cpp:189] Iteration 37200, loss = 0.225925
I0224 23:41:36.626669 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.225925 (* 1 = 0.225925 loss)
I0224 23:41:36.626675 29812 solver.cpp:470] Iteration 37200, lr = 0.0007
I0224 23:41:56.023196 29812 solver.cpp:189] Iteration 37250, loss = 0.234029
I0224 23:41:56.023221 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.234029 (* 1 = 0.234029 loss)
I0224 23:41:56.023226 29812 solver.cpp:470] Iteration 37250, lr = 0.0007
I0224 23:42:15.418342 29812 solver.cpp:189] Iteration 37300, loss = 0.347651
I0224 23:42:15.418386 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.347651 (* 1 = 0.347651 loss)
I0224 23:42:15.418391 29812 solver.cpp:470] Iteration 37300, lr = 0.0007
I0224 23:42:34.824878 29812 solver.cpp:189] Iteration 37350, loss = 0.254002
I0224 23:42:34.824903 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.254002 (* 1 = 0.254002 loss)
I0224 23:42:34.824908 29812 solver.cpp:470] Iteration 37350, lr = 0.0007
I0224 23:42:54.224820 29812 solver.cpp:189] Iteration 37400, loss = 0.303771
I0224 23:42:54.224905 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.303771 (* 1 = 0.303771 loss)
I0224 23:42:54.224910 29812 solver.cpp:470] Iteration 37400, lr = 0.0007
I0224 23:43:13.618044 29812 solver.cpp:189] Iteration 37450, loss = 0.317851
I0224 23:43:13.618067 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.317851 (* 1 = 0.317851 loss)
I0224 23:43:13.618073 29812 solver.cpp:470] Iteration 37450, lr = 0.0007
I0224 23:43:33.013280 29812 solver.cpp:189] Iteration 37500, loss = 0.242727
I0224 23:43:33.013339 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.242727 (* 1 = 0.242727 loss)
I0224 23:43:33.013345 29812 solver.cpp:470] Iteration 37500, lr = 0.0007
I0224 23:43:52.407407 29812 solver.cpp:189] Iteration 37550, loss = 0.183459
I0224 23:43:52.407430 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.183459 (* 1 = 0.183459 loss)
I0224 23:43:52.407436 29812 solver.cpp:470] Iteration 37550, lr = 0.0007
I0224 23:44:11.806550 29812 solver.cpp:189] Iteration 37600, loss = 0.37168
I0224 23:44:11.806653 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.371681 (* 1 = 0.371681 loss)
I0224 23:44:11.806658 29812 solver.cpp:470] Iteration 37600, lr = 0.0007
I0224 23:44:31.198107 29812 solver.cpp:189] Iteration 37650, loss = 0.272731
I0224 23:44:31.198130 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.272731 (* 1 = 0.272731 loss)
I0224 23:44:31.198135 29812 solver.cpp:470] Iteration 37650, lr = 0.0007
I0224 23:44:50.595191 29812 solver.cpp:189] Iteration 37700, loss = 0.427067
I0224 23:44:50.595247 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.427068 (* 1 = 0.427068 loss)
I0224 23:44:50.595253 29812 solver.cpp:470] Iteration 37700, lr = 0.0007
I0224 23:45:09.998474 29812 solver.cpp:189] Iteration 37750, loss = 0.199125
I0224 23:45:09.998498 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.199125 (* 1 = 0.199125 loss)
I0224 23:45:09.998503 29812 solver.cpp:470] Iteration 37750, lr = 0.0007
I0224 23:45:29.383518 29812 solver.cpp:189] Iteration 37800, loss = 0.268583
I0224 23:45:29.383591 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.268583 (* 1 = 0.268583 loss)
I0224 23:45:29.383605 29812 solver.cpp:470] Iteration 37800, lr = 0.0007
I0224 23:45:48.782191 29812 solver.cpp:189] Iteration 37850, loss = 0.303583
I0224 23:45:48.782217 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.303583 (* 1 = 0.303583 loss)
I0224 23:45:48.782223 29812 solver.cpp:470] Iteration 37850, lr = 0.0007
I0224 23:46:08.178491 29812 solver.cpp:189] Iteration 37900, loss = 0.292978
I0224 23:46:08.178560 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.292978 (* 1 = 0.292978 loss)
I0224 23:46:08.178575 29812 solver.cpp:470] Iteration 37900, lr = 0.0007
I0224 23:46:27.566526 29812 solver.cpp:189] Iteration 37950, loss = 0.24188
I0224 23:46:27.566550 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.241881 (* 1 = 0.241881 loss)
I0224 23:46:27.566555 29812 solver.cpp:470] Iteration 37950, lr = 0.0007
I0224 23:46:46.715601 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_38000.caffemodel
I0224 23:46:46.833083 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_38000.solverstate
I0224 23:46:46.890362 29812 solver.cpp:266] Iteration 38000, Testing net (#0)
I0224 23:46:54.530277 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8809
I0224 23:46:54.530311 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.379488 (* 1 = 0.379488 loss)
I0224 23:46:54.817673 29812 solver.cpp:189] Iteration 38000, loss = 0.206895
I0224 23:46:54.817709 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.206895 (* 1 = 0.206895 loss)
I0224 23:46:54.817716 29812 solver.cpp:470] Iteration 38000, lr = 0.0007
I0224 23:47:14.212298 29812 solver.cpp:189] Iteration 38050, loss = 0.240746
I0224 23:47:14.212321 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.240746 (* 1 = 0.240746 loss)
I0224 23:47:14.212327 29812 solver.cpp:470] Iteration 38050, lr = 0.0007
I0224 23:47:33.603257 29812 solver.cpp:189] Iteration 38100, loss = 0.194407
I0224 23:47:33.603338 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.194407 (* 1 = 0.194407 loss)
I0224 23:47:33.603353 29812 solver.cpp:470] Iteration 38100, lr = 0.0007
I0224 23:47:53.003140 29812 solver.cpp:189] Iteration 38150, loss = 0.260055
I0224 23:47:53.003165 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.260055 (* 1 = 0.260055 loss)
I0224 23:47:53.003170 29812 solver.cpp:470] Iteration 38150, lr = 0.0007
I0224 23:48:12.408308 29812 solver.cpp:189] Iteration 38200, loss = 0.326519
I0224 23:48:12.408350 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.32652 (* 1 = 0.32652 loss)
I0224 23:48:12.408356 29812 solver.cpp:470] Iteration 38200, lr = 0.0007
I0224 23:48:31.809744 29812 solver.cpp:189] Iteration 38250, loss = 0.310315
I0224 23:48:31.809769 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.310316 (* 1 = 0.310316 loss)
I0224 23:48:31.809774 29812 solver.cpp:470] Iteration 38250, lr = 0.0007
I0224 23:48:51.201285 29812 solver.cpp:189] Iteration 38300, loss = 0.285436
I0224 23:48:51.201371 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.285436 (* 1 = 0.285436 loss)
I0224 23:48:51.201377 29812 solver.cpp:470] Iteration 38300, lr = 0.0007
I0224 23:49:10.600087 29812 solver.cpp:189] Iteration 38350, loss = 0.274076
I0224 23:49:10.600111 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.274076 (* 1 = 0.274076 loss)
I0224 23:49:10.600116 29812 solver.cpp:470] Iteration 38350, lr = 0.0007
I0224 23:49:29.996692 29812 solver.cpp:189] Iteration 38400, loss = 0.404774
I0224 23:49:29.996779 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.404775 (* 1 = 0.404775 loss)
I0224 23:49:29.996785 29812 solver.cpp:470] Iteration 38400, lr = 0.0007
I0224 23:49:49.378794 29812 solver.cpp:189] Iteration 38450, loss = 0.38033
I0224 23:49:49.378818 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.38033 (* 1 = 0.38033 loss)
I0224 23:49:49.378824 29812 solver.cpp:470] Iteration 38450, lr = 0.0007
I0224 23:50:08.772032 29812 solver.cpp:189] Iteration 38500, loss = 0.387664
I0224 23:50:08.772091 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.387664 (* 1 = 0.387664 loss)
I0224 23:50:08.772097 29812 solver.cpp:470] Iteration 38500, lr = 0.0007
I0224 23:50:28.169217 29812 solver.cpp:189] Iteration 38550, loss = 0.219664
I0224 23:50:28.169241 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.219664 (* 1 = 0.219664 loss)
I0224 23:50:28.169246 29812 solver.cpp:470] Iteration 38550, lr = 0.0007
I0224 23:50:47.563962 29812 solver.cpp:189] Iteration 38600, loss = 0.122681
I0224 23:50:47.564023 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.122681 (* 1 = 0.122681 loss)
I0224 23:50:47.564029 29812 solver.cpp:470] Iteration 38600, lr = 0.0007
I0224 23:51:06.949427 29812 solver.cpp:189] Iteration 38650, loss = 0.296588
I0224 23:51:06.949451 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.296588 (* 1 = 0.296588 loss)
I0224 23:51:06.949456 29812 solver.cpp:470] Iteration 38650, lr = 0.0007
I0224 23:51:26.346753 29812 solver.cpp:189] Iteration 38700, loss = 0.314188
I0224 23:51:26.346817 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.314188 (* 1 = 0.314188 loss)
I0224 23:51:26.346823 29812 solver.cpp:470] Iteration 38700, lr = 0.0007
I0224 23:51:45.733749 29812 solver.cpp:189] Iteration 38750, loss = 0.296709
I0224 23:51:45.733775 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.296709 (* 1 = 0.296709 loss)
I0224 23:51:45.733780 29812 solver.cpp:470] Iteration 38750, lr = 0.0007
I0224 23:52:05.117512 29812 solver.cpp:189] Iteration 38800, loss = 0.322056
I0224 23:52:05.117554 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.322056 (* 1 = 0.322056 loss)
I0224 23:52:05.117560 29812 solver.cpp:470] Iteration 38800, lr = 0.0007
I0224 23:52:24.511071 29812 solver.cpp:189] Iteration 38850, loss = 0.378693
I0224 23:52:24.511096 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.378693 (* 1 = 0.378693 loss)
I0224 23:52:24.511100 29812 solver.cpp:470] Iteration 38850, lr = 0.0007
I0224 23:52:43.907709 29812 solver.cpp:189] Iteration 38900, loss = 0.265599
I0224 23:52:43.907769 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.2656 (* 1 = 0.2656 loss)
I0224 23:52:43.907775 29812 solver.cpp:470] Iteration 38900, lr = 0.0007
I0224 23:53:03.297272 29812 solver.cpp:189] Iteration 38950, loss = 0.309941
I0224 23:53:03.297297 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.309941 (* 1 = 0.309941 loss)
I0224 23:53:03.297302 29812 solver.cpp:470] Iteration 38950, lr = 0.0007
I0224 23:53:22.447244 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_39000.caffemodel
I0224 23:53:22.550612 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_39000.solverstate
I0224 23:53:22.608081 29812 solver.cpp:266] Iteration 39000, Testing net (#0)
I0224 23:53:30.264215 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8848
I0224 23:53:30.264251 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.363095 (* 1 = 0.363095 loss)
I0224 23:53:30.551411 29812 solver.cpp:189] Iteration 39000, loss = 0.18213
I0224 23:53:30.551434 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.18213 (* 1 = 0.18213 loss)
I0224 23:53:30.551440 29812 solver.cpp:470] Iteration 39000, lr = 0.0007
I0224 23:53:49.929848 29812 solver.cpp:189] Iteration 39050, loss = 0.229745
I0224 23:53:49.929872 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.229745 (* 1 = 0.229745 loss)
I0224 23:53:49.929877 29812 solver.cpp:470] Iteration 39050, lr = 0.0007
I0224 23:54:09.310525 29812 solver.cpp:189] Iteration 39100, loss = 0.317953
I0224 23:54:09.310611 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.317953 (* 1 = 0.317953 loss)
I0224 23:54:09.310616 29812 solver.cpp:470] Iteration 39100, lr = 0.0007
I0224 23:54:28.703821 29812 solver.cpp:189] Iteration 39150, loss = 0.185556
I0224 23:54:28.703846 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.185556 (* 1 = 0.185556 loss)
I0224 23:54:28.703851 29812 solver.cpp:470] Iteration 39150, lr = 0.0007
I0224 23:54:48.087605 29812 solver.cpp:189] Iteration 39200, loss = 0.313772
I0224 23:54:48.087647 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.313772 (* 1 = 0.313772 loss)
I0224 23:54:48.087653 29812 solver.cpp:470] Iteration 39200, lr = 0.0007
I0224 23:55:07.483561 29812 solver.cpp:189] Iteration 39250, loss = 0.328649
I0224 23:55:07.483585 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.328649 (* 1 = 0.328649 loss)
I0224 23:55:07.483590 29812 solver.cpp:470] Iteration 39250, lr = 0.0007
I0224 23:55:26.875610 29812 solver.cpp:189] Iteration 39300, loss = 0.406582
I0224 23:55:26.875674 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.406582 (* 1 = 0.406582 loss)
I0224 23:55:26.875680 29812 solver.cpp:470] Iteration 39300, lr = 0.0007
I0224 23:55:46.258831 29812 solver.cpp:189] Iteration 39350, loss = 0.407843
I0224 23:55:46.258855 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.407843 (* 1 = 0.407843 loss)
I0224 23:55:46.258862 29812 solver.cpp:470] Iteration 39350, lr = 0.0007
I0224 23:56:05.651725 29812 solver.cpp:189] Iteration 39400, loss = 0.30088
I0224 23:56:05.651823 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.30088 (* 1 = 0.30088 loss)
I0224 23:56:05.651837 29812 solver.cpp:470] Iteration 39400, lr = 0.0007
I0224 23:56:25.035459 29812 solver.cpp:189] Iteration 39450, loss = 0.275478
I0224 23:56:25.035482 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.275478 (* 1 = 0.275478 loss)
I0224 23:56:25.035488 29812 solver.cpp:470] Iteration 39450, lr = 0.0007
I0224 23:56:44.430886 29812 solver.cpp:189] Iteration 39500, loss = 0.304643
I0224 23:56:44.430925 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.304643 (* 1 = 0.304643 loss)
I0224 23:56:44.430932 29812 solver.cpp:470] Iteration 39500, lr = 0.0007
I0224 23:57:03.817114 29812 solver.cpp:189] Iteration 39550, loss = 0.225914
I0224 23:57:03.817139 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.225914 (* 1 = 0.225914 loss)
I0224 23:57:03.817145 29812 solver.cpp:470] Iteration 39550, lr = 0.0007
I0224 23:57:23.208726 29812 solver.cpp:189] Iteration 39600, loss = 0.313901
I0224 23:57:23.208786 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.313901 (* 1 = 0.313901 loss)
I0224 23:57:23.208791 29812 solver.cpp:470] Iteration 39600, lr = 0.0007
I0224 23:57:42.590538 29812 solver.cpp:189] Iteration 39650, loss = 0.313605
I0224 23:57:42.590562 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.313605 (* 1 = 0.313605 loss)
I0224 23:57:42.590567 29812 solver.cpp:470] Iteration 39650, lr = 0.0007
I0224 23:58:01.977596 29812 solver.cpp:189] Iteration 39700, loss = 0.305868
I0224 23:58:01.977655 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.305868 (* 1 = 0.305868 loss)
I0224 23:58:01.977661 29812 solver.cpp:470] Iteration 39700, lr = 0.0007
I0224 23:58:21.359637 29812 solver.cpp:189] Iteration 39750, loss = 0.213296
I0224 23:58:21.359664 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.213296 (* 1 = 0.213296 loss)
I0224 23:58:21.359669 29812 solver.cpp:470] Iteration 39750, lr = 0.0007
I0224 23:58:40.748939 29812 solver.cpp:189] Iteration 39800, loss = 0.217694
I0224 23:58:40.749012 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.217694 (* 1 = 0.217694 loss)
I0224 23:58:40.749027 29812 solver.cpp:470] Iteration 39800, lr = 0.0007
I0224 23:59:00.131786 29812 solver.cpp:189] Iteration 39850, loss = 0.287909
I0224 23:59:00.131810 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.287909 (* 1 = 0.287909 loss)
I0224 23:59:00.131815 29812 solver.cpp:470] Iteration 39850, lr = 0.0007
I0224 23:59:19.516888 29812 solver.cpp:189] Iteration 39900, loss = 0.267571
I0224 23:59:19.516958 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.267572 (* 1 = 0.267572 loss)
I0224 23:59:19.516973 29812 solver.cpp:470] Iteration 39900, lr = 0.0007
I0224 23:59:38.900702 29812 solver.cpp:189] Iteration 39950, loss = 0.136432
I0224 23:59:38.900728 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.136432 (* 1 = 0.136432 loss)
I0224 23:59:38.900733 29812 solver.cpp:470] Iteration 39950, lr = 0.0007
I0224 23:59:58.039335 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_40000.caffemodel
I0224 23:59:58.160774 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_40000.solverstate
I0224 23:59:58.218528 29812 solver.cpp:266] Iteration 40000, Testing net (#0)
I0225 00:00:05.860815 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8892
I0225 00:00:05.860851 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.344288 (* 1 = 0.344288 loss)
I0225 00:00:06.147936 29812 solver.cpp:189] Iteration 40000, loss = 0.200952
I0225 00:00:06.147960 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.200952 (* 1 = 0.200952 loss)
I0225 00:00:06.147966 29812 solver.cpp:470] Iteration 40000, lr = 0.00049
I0225 00:00:25.532529 29812 solver.cpp:189] Iteration 40050, loss = 0.140542
I0225 00:00:25.532552 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.140542 (* 1 = 0.140542 loss)
I0225 00:00:25.532558 29812 solver.cpp:470] Iteration 40050, lr = 0.00049
I0225 00:00:44.919021 29812 solver.cpp:189] Iteration 40100, loss = 0.264053
I0225 00:00:44.919132 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.264053 (* 1 = 0.264053 loss)
I0225 00:00:44.919138 29812 solver.cpp:470] Iteration 40100, lr = 0.00049
I0225 00:01:04.305982 29812 solver.cpp:189] Iteration 40150, loss = 0.128375
I0225 00:01:04.306006 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.128375 (* 1 = 0.128375 loss)
I0225 00:01:04.306011 29812 solver.cpp:470] Iteration 40150, lr = 0.00049
I0225 00:01:23.688114 29812 solver.cpp:189] Iteration 40200, loss = 0.135055
I0225 00:01:23.688206 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.135055 (* 1 = 0.135055 loss)
I0225 00:01:23.688221 29812 solver.cpp:470] Iteration 40200, lr = 0.00049
I0225 00:01:43.065870 29812 solver.cpp:189] Iteration 40250, loss = 0.279253
I0225 00:01:43.065894 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.279253 (* 1 = 0.279253 loss)
I0225 00:01:43.065899 29812 solver.cpp:470] Iteration 40250, lr = 0.00049
I0225 00:02:02.452611 29812 solver.cpp:189] Iteration 40300, loss = 0.234823
I0225 00:02:02.452651 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.234824 (* 1 = 0.234824 loss)
I0225 00:02:02.452657 29812 solver.cpp:470] Iteration 40300, lr = 0.00049
I0225 00:02:21.840646 29812 solver.cpp:189] Iteration 40350, loss = 0.230097
I0225 00:02:21.840672 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.230097 (* 1 = 0.230097 loss)
I0225 00:02:21.840677 29812 solver.cpp:470] Iteration 40350, lr = 0.00049
I0225 00:02:41.232870 29812 solver.cpp:189] Iteration 40400, loss = 0.316516
I0225 00:02:41.232910 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.316516 (* 1 = 0.316516 loss)
I0225 00:02:41.232916 29812 solver.cpp:470] Iteration 40400, lr = 0.00049
I0225 00:03:00.623507 29812 solver.cpp:189] Iteration 40450, loss = 0.162077
I0225 00:03:00.623531 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.162077 (* 1 = 0.162077 loss)
I0225 00:03:00.623538 29812 solver.cpp:470] Iteration 40450, lr = 0.00049
I0225 00:03:20.006814 29812 solver.cpp:189] Iteration 40500, loss = 0.211204
I0225 00:03:20.006886 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.211204 (* 1 = 0.211204 loss)
I0225 00:03:20.006901 29812 solver.cpp:470] Iteration 40500, lr = 0.00049
I0225 00:03:39.393051 29812 solver.cpp:189] Iteration 40550, loss = 0.182122
I0225 00:03:39.393075 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.182122 (* 1 = 0.182122 loss)
I0225 00:03:39.393081 29812 solver.cpp:470] Iteration 40550, lr = 0.00049
I0225 00:03:58.783444 29812 solver.cpp:189] Iteration 40600, loss = 0.338108
I0225 00:03:58.783536 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.338108 (* 1 = 0.338108 loss)
I0225 00:03:58.783550 29812 solver.cpp:470] Iteration 40600, lr = 0.00049
I0225 00:04:18.167625 29812 solver.cpp:189] Iteration 40650, loss = 0.172015
I0225 00:04:18.167649 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.172016 (* 1 = 0.172016 loss)
I0225 00:04:18.167654 29812 solver.cpp:470] Iteration 40650, lr = 0.00049
I0225 00:04:37.564075 29812 solver.cpp:189] Iteration 40700, loss = 0.242085
I0225 00:04:37.564116 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.242085 (* 1 = 0.242085 loss)
I0225 00:04:37.564121 29812 solver.cpp:470] Iteration 40700, lr = 0.00049
I0225 00:04:56.949550 29812 solver.cpp:189] Iteration 40750, loss = 0.200694
I0225 00:04:56.949574 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.200694 (* 1 = 0.200694 loss)
I0225 00:04:56.949580 29812 solver.cpp:470] Iteration 40750, lr = 0.00049
I0225 00:05:16.332599 29812 solver.cpp:189] Iteration 40800, loss = 0.209066
I0225 00:05:16.332706 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.209066 (* 1 = 0.209066 loss)
I0225 00:05:16.332712 29812 solver.cpp:470] Iteration 40800, lr = 0.00049
I0225 00:05:35.717833 29812 solver.cpp:189] Iteration 40850, loss = 0.195154
I0225 00:05:35.717859 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.195154 (* 1 = 0.195154 loss)
I0225 00:05:35.717864 29812 solver.cpp:470] Iteration 40850, lr = 0.00049
I0225 00:05:55.102264 29812 solver.cpp:189] Iteration 40900, loss = 0.249237
I0225 00:05:55.102306 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.249237 (* 1 = 0.249237 loss)
I0225 00:05:55.102313 29812 solver.cpp:470] Iteration 40900, lr = 0.00049
I0225 00:06:14.494304 29812 solver.cpp:189] Iteration 40950, loss = 0.22297
I0225 00:06:14.494338 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.22297 (* 1 = 0.22297 loss)
I0225 00:06:14.494344 29812 solver.cpp:470] Iteration 40950, lr = 0.00049
I0225 00:06:33.644870 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_41000.caffemodel
I0225 00:06:33.746582 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_41000.solverstate
I0225 00:06:33.804157 29812 solver.cpp:266] Iteration 41000, Testing net (#0)
I0225 00:06:41.459871 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8923
I0225 00:06:41.459905 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.340619 (* 1 = 0.340619 loss)
I0225 00:06:41.745625 29812 solver.cpp:189] Iteration 41000, loss = 0.170117
I0225 00:06:41.745648 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.170118 (* 1 = 0.170118 loss)
I0225 00:06:41.745653 29812 solver.cpp:470] Iteration 41000, lr = 0.00049
I0225 00:07:01.148186 29812 solver.cpp:189] Iteration 41050, loss = 0.378901
I0225 00:07:01.148211 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.378901 (* 1 = 0.378901 loss)
I0225 00:07:01.148216 29812 solver.cpp:470] Iteration 41050, lr = 0.00049
I0225 00:07:20.533195 29812 solver.cpp:189] Iteration 41100, loss = 0.113572
I0225 00:07:20.533265 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.113572 (* 1 = 0.113572 loss)
I0225 00:07:20.533280 29812 solver.cpp:470] Iteration 41100, lr = 0.00049
I0225 00:07:39.923786 29812 solver.cpp:189] Iteration 41150, loss = 0.138815
I0225 00:07:39.923810 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.138815 (* 1 = 0.138815 loss)
I0225 00:07:39.923816 29812 solver.cpp:470] Iteration 41150, lr = 0.00049
I0225 00:07:59.314467 29812 solver.cpp:189] Iteration 41200, loss = 0.162321
I0225 00:07:59.314546 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.162321 (* 1 = 0.162321 loss)
I0225 00:07:59.314561 29812 solver.cpp:470] Iteration 41200, lr = 0.00049
I0225 00:08:18.716117 29812 solver.cpp:189] Iteration 41250, loss = 0.140158
I0225 00:08:18.716143 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.140159 (* 1 = 0.140159 loss)
I0225 00:08:18.716150 29812 solver.cpp:470] Iteration 41250, lr = 0.00049
I0225 00:08:38.116894 29812 solver.cpp:189] Iteration 41300, loss = 0.252207
I0225 00:08:38.116976 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.252207 (* 1 = 0.252207 loss)
I0225 00:08:38.116991 29812 solver.cpp:470] Iteration 41300, lr = 0.00049
I0225 00:08:57.504262 29812 solver.cpp:189] Iteration 41350, loss = 0.205473
I0225 00:08:57.504287 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.205473 (* 1 = 0.205473 loss)
I0225 00:08:57.504292 29812 solver.cpp:470] Iteration 41350, lr = 0.00049
I0225 00:09:16.897857 29812 solver.cpp:189] Iteration 41400, loss = 0.178141
I0225 00:09:16.897928 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.178141 (* 1 = 0.178141 loss)
I0225 00:09:16.897943 29812 solver.cpp:470] Iteration 41400, lr = 0.00049
I0225 00:09:36.293486 29812 solver.cpp:189] Iteration 41450, loss = 0.229198
I0225 00:09:36.293512 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.229198 (* 1 = 0.229198 loss)
I0225 00:09:36.293519 29812 solver.cpp:470] Iteration 41450, lr = 0.00049
I0225 00:09:55.686316 29812 solver.cpp:189] Iteration 41500, loss = 0.224187
I0225 00:09:55.686404 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.224187 (* 1 = 0.224187 loss)
I0225 00:09:55.686410 29812 solver.cpp:470] Iteration 41500, lr = 0.00049
I0225 00:10:15.080045 29812 solver.cpp:189] Iteration 41550, loss = 0.21034
I0225 00:10:15.080070 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.21034 (* 1 = 0.21034 loss)
I0225 00:10:15.080075 29812 solver.cpp:470] Iteration 41550, lr = 0.00049
I0225 00:10:34.472419 29812 solver.cpp:189] Iteration 41600, loss = 0.123427
I0225 00:10:34.472488 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.123428 (* 1 = 0.123428 loss)
I0225 00:10:34.472502 29812 solver.cpp:470] Iteration 41600, lr = 0.00049
I0225 00:10:53.863662 29812 solver.cpp:189] Iteration 41650, loss = 0.226369
I0225 00:10:53.863687 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.226369 (* 1 = 0.226369 loss)
I0225 00:10:53.863692 29812 solver.cpp:470] Iteration 41650, lr = 0.00049
I0225 00:11:13.263113 29812 solver.cpp:189] Iteration 41700, loss = 0.314452
I0225 00:11:13.263181 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.314452 (* 1 = 0.314452 loss)
I0225 00:11:13.263187 29812 solver.cpp:470] Iteration 41700, lr = 0.00049
I0225 00:11:32.664415 29812 solver.cpp:189] Iteration 41750, loss = 0.160714
I0225 00:11:32.664440 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.160714 (* 1 = 0.160714 loss)
I0225 00:11:32.664446 29812 solver.cpp:470] Iteration 41750, lr = 0.00049
I0225 00:11:52.058430 29812 solver.cpp:189] Iteration 41800, loss = 0.113712
I0225 00:11:52.058490 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.113712 (* 1 = 0.113712 loss)
I0225 00:11:52.058497 29812 solver.cpp:470] Iteration 41800, lr = 0.00049
I0225 00:12:11.451464 29812 solver.cpp:189] Iteration 41850, loss = 0.26003
I0225 00:12:11.451490 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.26003 (* 1 = 0.26003 loss)
I0225 00:12:11.451496 29812 solver.cpp:470] Iteration 41850, lr = 0.00049
I0225 00:12:30.845464 29812 solver.cpp:189] Iteration 41900, loss = 0.180959
I0225 00:12:30.845554 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.18096 (* 1 = 0.18096 loss)
I0225 00:12:30.845561 29812 solver.cpp:470] Iteration 41900, lr = 0.00049
I0225 00:12:50.249330 29812 solver.cpp:189] Iteration 41950, loss = 0.159208
I0225 00:12:50.249353 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.159208 (* 1 = 0.159208 loss)
I0225 00:12:50.249359 29812 solver.cpp:470] Iteration 41950, lr = 0.00049
I0225 00:13:09.399580 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_42000.caffemodel
I0225 00:13:09.515149 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_42000.solverstate
I0225 00:13:09.572793 29812 solver.cpp:266] Iteration 42000, Testing net (#0)
I0225 00:13:17.233568 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8857
I0225 00:13:17.233603 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.362665 (* 1 = 0.362665 loss)
I0225 00:13:17.520165 29812 solver.cpp:189] Iteration 42000, loss = 0.194749
I0225 00:13:17.520191 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.194749 (* 1 = 0.194749 loss)
I0225 00:13:17.520196 29812 solver.cpp:470] Iteration 42000, lr = 0.00049
I0225 00:13:36.912433 29812 solver.cpp:189] Iteration 42050, loss = 0.315975
I0225 00:13:36.912456 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.315975 (* 1 = 0.315975 loss)
I0225 00:13:36.912461 29812 solver.cpp:470] Iteration 42050, lr = 0.00049
I0225 00:13:56.313279 29812 solver.cpp:189] Iteration 42100, loss = 0.199711
I0225 00:13:56.313349 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.199711 (* 1 = 0.199711 loss)
I0225 00:13:56.313364 29812 solver.cpp:470] Iteration 42100, lr = 0.00049
I0225 00:14:15.707499 29812 solver.cpp:189] Iteration 42150, loss = 0.231337
I0225 00:14:15.707525 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.231337 (* 1 = 0.231337 loss)
I0225 00:14:15.707530 29812 solver.cpp:470] Iteration 42150, lr = 0.00049
I0225 00:14:35.106672 29812 solver.cpp:189] Iteration 42200, loss = 0.107624
I0225 00:14:35.106746 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.107624 (* 1 = 0.107624 loss)
I0225 00:14:35.106752 29812 solver.cpp:470] Iteration 42200, lr = 0.00049
I0225 00:14:54.494988 29812 solver.cpp:189] Iteration 42250, loss = 0.176427
I0225 00:14:54.495012 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.176427 (* 1 = 0.176427 loss)
I0225 00:14:54.495018 29812 solver.cpp:470] Iteration 42250, lr = 0.00049
I0225 00:15:13.890693 29812 solver.cpp:189] Iteration 42300, loss = 0.220507
I0225 00:15:13.890753 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.220507 (* 1 = 0.220507 loss)
I0225 00:15:13.890758 29812 solver.cpp:470] Iteration 42300, lr = 0.00049
I0225 00:15:33.286144 29812 solver.cpp:189] Iteration 42350, loss = 0.138544
I0225 00:15:33.286166 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.138544 (* 1 = 0.138544 loss)
I0225 00:15:33.286171 29812 solver.cpp:470] Iteration 42350, lr = 0.00049
I0225 00:15:52.672698 29812 solver.cpp:189] Iteration 42400, loss = 0.272971
I0225 00:15:52.672771 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.272971 (* 1 = 0.272971 loss)
I0225 00:15:52.672787 29812 solver.cpp:470] Iteration 42400, lr = 0.00049
I0225 00:16:12.062104 29812 solver.cpp:189] Iteration 42450, loss = 0.176605
I0225 00:16:12.062129 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.176605 (* 1 = 0.176605 loss)
I0225 00:16:12.062134 29812 solver.cpp:470] Iteration 42450, lr = 0.00049
I0225 00:16:31.447614 29812 solver.cpp:189] Iteration 42500, loss = 0.178307
I0225 00:16:31.447654 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.178307 (* 1 = 0.178307 loss)
I0225 00:16:31.447660 29812 solver.cpp:470] Iteration 42500, lr = 0.00049
I0225 00:16:50.846344 29812 solver.cpp:189] Iteration 42550, loss = 0.270039
I0225 00:16:50.846370 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.270039 (* 1 = 0.270039 loss)
I0225 00:16:50.846375 29812 solver.cpp:470] Iteration 42550, lr = 0.00049
I0225 00:17:10.251552 29812 solver.cpp:189] Iteration 42600, loss = 0.387955
I0225 00:17:10.251610 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.387955 (* 1 = 0.387955 loss)
I0225 00:17:10.251616 29812 solver.cpp:470] Iteration 42600, lr = 0.00049
I0225 00:17:29.644968 29812 solver.cpp:189] Iteration 42650, loss = 0.164965
I0225 00:17:29.644992 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.164965 (* 1 = 0.164965 loss)
I0225 00:17:29.644999 29812 solver.cpp:470] Iteration 42650, lr = 0.00049
I0225 00:17:49.044811 29812 solver.cpp:189] Iteration 42700, loss = 0.220298
I0225 00:17:49.044852 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.220298 (* 1 = 0.220298 loss)
I0225 00:17:49.044857 29812 solver.cpp:470] Iteration 42700, lr = 0.00049
I0225 00:18:08.436113 29812 solver.cpp:189] Iteration 42750, loss = 0.268856
I0225 00:18:08.436137 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.268856 (* 1 = 0.268856 loss)
I0225 00:18:08.436143 29812 solver.cpp:470] Iteration 42750, lr = 0.00049
I0225 00:18:27.818177 29812 solver.cpp:189] Iteration 42800, loss = 0.183758
I0225 00:18:27.818249 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.183758 (* 1 = 0.183758 loss)
I0225 00:18:27.818264 29812 solver.cpp:470] Iteration 42800, lr = 0.00049
I0225 00:18:47.215381 29812 solver.cpp:189] Iteration 42850, loss = 0.169263
I0225 00:18:47.215405 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.169263 (* 1 = 0.169263 loss)
I0225 00:18:47.215411 29812 solver.cpp:470] Iteration 42850, lr = 0.00049
I0225 00:19:06.621315 29812 solver.cpp:189] Iteration 42900, loss = 0.121935
I0225 00:19:06.621377 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.121935 (* 1 = 0.121935 loss)
I0225 00:19:06.621383 29812 solver.cpp:470] Iteration 42900, lr = 0.00049
I0225 00:19:26.015611 29812 solver.cpp:189] Iteration 42950, loss = 0.317579
I0225 00:19:26.015635 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.317579 (* 1 = 0.317579 loss)
I0225 00:19:26.015640 29812 solver.cpp:470] Iteration 42950, lr = 0.00049
I0225 00:19:45.161098 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_43000.caffemodel
I0225 00:19:45.264823 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_43000.solverstate
I0225 00:19:45.322121 29812 solver.cpp:266] Iteration 43000, Testing net (#0)
I0225 00:19:52.990496 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8914
I0225 00:19:52.990533 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.357081 (* 1 = 0.357081 loss)
I0225 00:19:53.277919 29812 solver.cpp:189] Iteration 43000, loss = 0.185837
I0225 00:19:53.277942 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.185837 (* 1 = 0.185837 loss)
I0225 00:19:53.277947 29812 solver.cpp:470] Iteration 43000, lr = 0.00049
I0225 00:20:12.667994 29812 solver.cpp:189] Iteration 43050, loss = 0.207931
I0225 00:20:12.668020 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.207932 (* 1 = 0.207932 loss)
I0225 00:20:12.668025 29812 solver.cpp:470] Iteration 43050, lr = 0.00049
I0225 00:20:32.050029 29812 solver.cpp:189] Iteration 43100, loss = 0.268841
I0225 00:20:32.050070 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.268841 (* 1 = 0.268841 loss)
I0225 00:20:32.050075 29812 solver.cpp:470] Iteration 43100, lr = 0.00049
I0225 00:20:51.431035 29812 solver.cpp:189] Iteration 43150, loss = 0.137381
I0225 00:20:51.431058 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.137381 (* 1 = 0.137381 loss)
I0225 00:20:51.431063 29812 solver.cpp:470] Iteration 43150, lr = 0.00049
I0225 00:21:10.818214 29812 solver.cpp:189] Iteration 43200, loss = 0.16738
I0225 00:21:10.818289 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.16738 (* 1 = 0.16738 loss)
I0225 00:21:10.818303 29812 solver.cpp:470] Iteration 43200, lr = 0.00049
I0225 00:21:30.188591 29812 solver.cpp:189] Iteration 43250, loss = 0.33628
I0225 00:21:30.188614 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.336281 (* 1 = 0.336281 loss)
I0225 00:21:30.188619 29812 solver.cpp:470] Iteration 43250, lr = 0.00049
I0225 00:21:49.574705 29812 solver.cpp:189] Iteration 43300, loss = 0.182047
I0225 00:21:49.574744 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.182047 (* 1 = 0.182047 loss)
I0225 00:21:49.574751 29812 solver.cpp:470] Iteration 43300, lr = 0.00049
I0225 00:22:08.960222 29812 solver.cpp:189] Iteration 43350, loss = 0.139895
I0225 00:22:08.960245 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.139896 (* 1 = 0.139896 loss)
I0225 00:22:08.960250 29812 solver.cpp:470] Iteration 43350, lr = 0.00049
I0225 00:22:28.336236 29812 solver.cpp:189] Iteration 43400, loss = 0.141714
I0225 00:22:28.336276 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.141715 (* 1 = 0.141715 loss)
I0225 00:22:28.336282 29812 solver.cpp:470] Iteration 43400, lr = 0.00049
I0225 00:22:47.716235 29812 solver.cpp:189] Iteration 43450, loss = 0.216725
I0225 00:22:47.716260 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.216725 (* 1 = 0.216725 loss)
I0225 00:22:47.716265 29812 solver.cpp:470] Iteration 43450, lr = 0.00049
I0225 00:23:07.102421 29812 solver.cpp:189] Iteration 43500, loss = 0.151901
I0225 00:23:07.102490 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.151901 (* 1 = 0.151901 loss)
I0225 00:23:07.102496 29812 solver.cpp:470] Iteration 43500, lr = 0.00049
I0225 00:23:26.493358 29812 solver.cpp:189] Iteration 43550, loss = 0.157594
I0225 00:23:26.493383 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.157594 (* 1 = 0.157594 loss)
I0225 00:23:26.493388 29812 solver.cpp:470] Iteration 43550, lr = 0.00049
I0225 00:23:45.878058 29812 solver.cpp:189] Iteration 43600, loss = 0.162686
I0225 00:23:45.878168 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.162686 (* 1 = 0.162686 loss)
I0225 00:23:45.878175 29812 solver.cpp:470] Iteration 43600, lr = 0.00049
I0225 00:24:05.263195 29812 solver.cpp:189] Iteration 43650, loss = 0.119747
I0225 00:24:05.263219 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.119747 (* 1 = 0.119747 loss)
I0225 00:24:05.263224 29812 solver.cpp:470] Iteration 43650, lr = 0.00049
I0225 00:24:24.660480 29812 solver.cpp:189] Iteration 43700, loss = 0.149451
I0225 00:24:24.660574 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.149451 (* 1 = 0.149451 loss)
I0225 00:24:24.660589 29812 solver.cpp:470] Iteration 43700, lr = 0.00049
I0225 00:24:44.047787 29812 solver.cpp:189] Iteration 43750, loss = 0.168484
I0225 00:24:44.047811 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.168484 (* 1 = 0.168484 loss)
I0225 00:24:44.047816 29812 solver.cpp:470] Iteration 43750, lr = 0.00049
I0225 00:25:03.429721 29812 solver.cpp:189] Iteration 43800, loss = 0.15615
I0225 00:25:03.429780 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.15615 (* 1 = 0.15615 loss)
I0225 00:25:03.429785 29812 solver.cpp:470] Iteration 43800, lr = 0.00049
I0225 00:25:22.815652 29812 solver.cpp:189] Iteration 43850, loss = 0.179814
I0225 00:25:22.815678 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.179814 (* 1 = 0.179814 loss)
I0225 00:25:22.815685 29812 solver.cpp:470] Iteration 43850, lr = 0.00049
I0225 00:25:42.197829 29812 solver.cpp:189] Iteration 43900, loss = 0.21915
I0225 00:25:42.197868 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.219151 (* 1 = 0.219151 loss)
I0225 00:25:42.197875 29812 solver.cpp:470] Iteration 43900, lr = 0.00049
I0225 00:26:01.582543 29812 solver.cpp:189] Iteration 43950, loss = 0.122255
I0225 00:26:01.582566 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.122255 (* 1 = 0.122255 loss)
I0225 00:26:01.582572 29812 solver.cpp:470] Iteration 43950, lr = 0.00049
I0225 00:26:20.714786 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_44000.caffemodel
I0225 00:26:20.831871 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_44000.solverstate
I0225 00:26:20.890130 29812 solver.cpp:266] Iteration 44000, Testing net (#0)
I0225 00:26:28.534834 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8885
I0225 00:26:28.534870 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.352336 (* 1 = 0.352336 loss)
I0225 00:26:28.820199 29812 solver.cpp:189] Iteration 44000, loss = 0.185614
I0225 00:26:28.820220 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.185614 (* 1 = 0.185614 loss)
I0225 00:26:28.820226 29812 solver.cpp:470] Iteration 44000, lr = 0.00049
I0225 00:26:48.207409 29812 solver.cpp:189] Iteration 44050, loss = 0.19734
I0225 00:26:48.207433 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.19734 (* 1 = 0.19734 loss)
I0225 00:26:48.207439 29812 solver.cpp:470] Iteration 44050, lr = 0.00049
I0225 00:27:07.601872 29812 solver.cpp:189] Iteration 44100, loss = 0.161112
I0225 00:27:07.601933 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.161112 (* 1 = 0.161112 loss)
I0225 00:27:07.601939 29812 solver.cpp:470] Iteration 44100, lr = 0.00049
I0225 00:27:26.988940 29812 solver.cpp:189] Iteration 44150, loss = 0.333717
I0225 00:27:26.988965 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.333717 (* 1 = 0.333717 loss)
I0225 00:27:26.988971 29812 solver.cpp:470] Iteration 44150, lr = 0.00049
I0225 00:27:46.381533 29812 solver.cpp:189] Iteration 44200, loss = 0.106159
I0225 00:27:46.381575 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.106159 (* 1 = 0.106159 loss)
I0225 00:27:46.381582 29812 solver.cpp:470] Iteration 44200, lr = 0.00049
I0225 00:28:05.763908 29812 solver.cpp:189] Iteration 44250, loss = 0.20279
I0225 00:28:05.763933 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.20279 (* 1 = 0.20279 loss)
I0225 00:28:05.763939 29812 solver.cpp:470] Iteration 44250, lr = 0.00049
I0225 00:28:25.150833 29812 solver.cpp:189] Iteration 44300, loss = 0.299284
I0225 00:28:25.150919 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.299284 (* 1 = 0.299284 loss)
I0225 00:28:25.150926 29812 solver.cpp:470] Iteration 44300, lr = 0.00049
I0225 00:28:44.543694 29812 solver.cpp:189] Iteration 44350, loss = 0.198652
I0225 00:28:44.543717 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.198652 (* 1 = 0.198652 loss)
I0225 00:28:44.543722 29812 solver.cpp:470] Iteration 44350, lr = 0.00049
I0225 00:29:03.939388 29812 solver.cpp:189] Iteration 44400, loss = 0.13362
I0225 00:29:03.939460 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.13362 (* 1 = 0.13362 loss)
I0225 00:29:03.939474 29812 solver.cpp:470] Iteration 44400, lr = 0.00049
I0225 00:29:23.330317 29812 solver.cpp:189] Iteration 44450, loss = 0.191599
I0225 00:29:23.330343 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.191599 (* 1 = 0.191599 loss)
I0225 00:29:23.330348 29812 solver.cpp:470] Iteration 44450, lr = 0.00049
I0225 00:29:42.719579 29812 solver.cpp:189] Iteration 44500, loss = 0.14776
I0225 00:29:42.719667 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.147761 (* 1 = 0.147761 loss)
I0225 00:29:42.719674 29812 solver.cpp:470] Iteration 44500, lr = 0.00049
I0225 00:30:02.103240 29812 solver.cpp:189] Iteration 44550, loss = 0.110787
I0225 00:30:02.103263 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.110787 (* 1 = 0.110787 loss)
I0225 00:30:02.103268 29812 solver.cpp:470] Iteration 44550, lr = 0.00049
I0225 00:30:21.493027 29812 solver.cpp:189] Iteration 44600, loss = 0.126337
I0225 00:30:21.493099 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.126337 (* 1 = 0.126337 loss)
I0225 00:30:21.493114 29812 solver.cpp:470] Iteration 44600, lr = 0.00049
I0225 00:30:40.872308 29812 solver.cpp:189] Iteration 44650, loss = 0.140327
I0225 00:30:40.872330 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.140327 (* 1 = 0.140327 loss)
I0225 00:30:40.872336 29812 solver.cpp:470] Iteration 44650, lr = 0.00049
I0225 00:31:00.259768 29812 solver.cpp:189] Iteration 44700, loss = 0.290103
I0225 00:31:00.259809 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.290103 (* 1 = 0.290103 loss)
I0225 00:31:00.259815 29812 solver.cpp:470] Iteration 44700, lr = 0.00049
I0225 00:31:19.640734 29812 solver.cpp:189] Iteration 44750, loss = 0.220671
I0225 00:31:19.640758 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.220671 (* 1 = 0.220671 loss)
I0225 00:31:19.640763 29812 solver.cpp:470] Iteration 44750, lr = 0.00049
I0225 00:31:39.030050 29812 solver.cpp:189] Iteration 44800, loss = 0.138938
I0225 00:31:39.030138 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.138938 (* 1 = 0.138938 loss)
I0225 00:31:39.030153 29812 solver.cpp:470] Iteration 44800, lr = 0.00049
I0225 00:31:58.412259 29812 solver.cpp:189] Iteration 44850, loss = 0.12917
I0225 00:31:58.412284 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.12917 (* 1 = 0.12917 loss)
I0225 00:31:58.412291 29812 solver.cpp:470] Iteration 44850, lr = 0.00049
I0225 00:32:17.799795 29812 solver.cpp:189] Iteration 44900, loss = 0.198082
I0225 00:32:17.799886 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.198082 (* 1 = 0.198082 loss)
I0225 00:32:17.799901 29812 solver.cpp:470] Iteration 44900, lr = 0.00049
I0225 00:32:37.177801 29812 solver.cpp:189] Iteration 44950, loss = 0.174932
I0225 00:32:37.177824 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.174932 (* 1 = 0.174932 loss)
I0225 00:32:37.177830 29812 solver.cpp:470] Iteration 44950, lr = 0.00049
I0225 00:32:56.321184 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_45000.caffemodel
I0225 00:32:56.445897 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_45000.solverstate
I0225 00:32:56.504021 29812 solver.cpp:266] Iteration 45000, Testing net (#0)
I0225 00:33:04.161309 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8869
I0225 00:33:04.161347 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.36374 (* 1 = 0.36374 loss)
I0225 00:33:04.448459 29812 solver.cpp:189] Iteration 45000, loss = 0.20558
I0225 00:33:04.448483 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.20558 (* 1 = 0.20558 loss)
I0225 00:33:04.448488 29812 solver.cpp:470] Iteration 45000, lr = 0.00049
I0225 00:33:23.846918 29812 solver.cpp:189] Iteration 45050, loss = 0.202525
I0225 00:33:23.846945 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.202525 (* 1 = 0.202525 loss)
I0225 00:33:23.846950 29812 solver.cpp:470] Iteration 45050, lr = 0.00049
I0225 00:33:43.234513 29812 solver.cpp:189] Iteration 45100, loss = 0.260818
I0225 00:33:43.234606 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.260819 (* 1 = 0.260819 loss)
I0225 00:33:43.234622 29812 solver.cpp:470] Iteration 45100, lr = 0.00049
I0225 00:34:02.621383 29812 solver.cpp:189] Iteration 45150, loss = 0.188921
I0225 00:34:02.621408 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.188921 (* 1 = 0.188921 loss)
I0225 00:34:02.621413 29812 solver.cpp:470] Iteration 45150, lr = 0.00049
I0225 00:34:22.024924 29812 solver.cpp:189] Iteration 45200, loss = 0.192153
I0225 00:34:22.025013 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.192153 (* 1 = 0.192153 loss)
I0225 00:34:22.025019 29812 solver.cpp:470] Iteration 45200, lr = 0.00049
I0225 00:34:41.422875 29812 solver.cpp:189] Iteration 45250, loss = 0.192959
I0225 00:34:41.422899 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.192959 (* 1 = 0.192959 loss)
I0225 00:34:41.422904 29812 solver.cpp:470] Iteration 45250, lr = 0.00049
I0225 00:35:00.815907 29812 solver.cpp:189] Iteration 45300, loss = 0.180234
I0225 00:35:00.815946 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.180234 (* 1 = 0.180234 loss)
I0225 00:35:00.815953 29812 solver.cpp:470] Iteration 45300, lr = 0.00049
I0225 00:35:20.211948 29812 solver.cpp:189] Iteration 45350, loss = 0.195262
I0225 00:35:20.211973 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.195262 (* 1 = 0.195262 loss)
I0225 00:35:20.211979 29812 solver.cpp:470] Iteration 45350, lr = 0.00049
I0225 00:35:39.608757 29812 solver.cpp:189] Iteration 45400, loss = 0.242697
I0225 00:35:39.608844 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.242697 (* 1 = 0.242697 loss)
I0225 00:35:39.608850 29812 solver.cpp:470] Iteration 45400, lr = 0.00049
I0225 00:35:59.001065 29812 solver.cpp:189] Iteration 45450, loss = 0.143446
I0225 00:35:59.001087 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.143446 (* 1 = 0.143446 loss)
I0225 00:35:59.001093 29812 solver.cpp:470] Iteration 45450, lr = 0.00049
I0225 00:36:18.398762 29812 solver.cpp:189] Iteration 45500, loss = 0.286819
I0225 00:36:18.398849 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.286819 (* 1 = 0.286819 loss)
I0225 00:36:18.398855 29812 solver.cpp:470] Iteration 45500, lr = 0.00049
I0225 00:36:37.797417 29812 solver.cpp:189] Iteration 45550, loss = 0.214292
I0225 00:36:37.797441 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.214292 (* 1 = 0.214292 loss)
I0225 00:36:37.797447 29812 solver.cpp:470] Iteration 45550, lr = 0.00049
I0225 00:36:57.197597 29812 solver.cpp:189] Iteration 45600, loss = 0.118025
I0225 00:36:57.197635 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.118025 (* 1 = 0.118025 loss)
I0225 00:36:57.197641 29812 solver.cpp:470] Iteration 45600, lr = 0.00049
I0225 00:37:16.591351 29812 solver.cpp:189] Iteration 45650, loss = 0.205848
I0225 00:37:16.591374 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.205848 (* 1 = 0.205848 loss)
I0225 00:37:16.591380 29812 solver.cpp:470] Iteration 45650, lr = 0.00049
I0225 00:37:35.974056 29812 solver.cpp:189] Iteration 45700, loss = 0.182947
I0225 00:37:35.974097 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.182947 (* 1 = 0.182947 loss)
I0225 00:37:35.974102 29812 solver.cpp:470] Iteration 45700, lr = 0.00049
I0225 00:37:55.369015 29812 solver.cpp:189] Iteration 45750, loss = 0.18335
I0225 00:37:55.369040 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.18335 (* 1 = 0.18335 loss)
I0225 00:37:55.369045 29812 solver.cpp:470] Iteration 45750, lr = 0.00049
I0225 00:38:14.765832 29812 solver.cpp:189] Iteration 45800, loss = 0.243732
I0225 00:38:14.765916 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.243732 (* 1 = 0.243732 loss)
I0225 00:38:14.765923 29812 solver.cpp:470] Iteration 45800, lr = 0.00049
I0225 00:38:34.152729 29812 solver.cpp:189] Iteration 45850, loss = 0.115929
I0225 00:38:34.152753 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.115929 (* 1 = 0.115929 loss)
I0225 00:38:34.152758 29812 solver.cpp:470] Iteration 45850, lr = 0.00049
I0225 00:38:53.542908 29812 solver.cpp:189] Iteration 45900, loss = 0.252769
I0225 00:38:53.542944 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.252769 (* 1 = 0.252769 loss)
I0225 00:38:53.542950 29812 solver.cpp:470] Iteration 45900, lr = 0.00049
I0225 00:39:12.941618 29812 solver.cpp:189] Iteration 45950, loss = 0.147875
I0225 00:39:12.941642 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.147875 (* 1 = 0.147875 loss)
I0225 00:39:12.941648 29812 solver.cpp:470] Iteration 45950, lr = 0.00049
I0225 00:39:32.094745 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_46000.caffemodel
I0225 00:39:32.217236 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_46000.solverstate
I0225 00:39:32.275317 29812 solver.cpp:266] Iteration 46000, Testing net (#0)
I0225 00:39:39.911994 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8869
I0225 00:39:39.912029 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.381425 (* 1 = 0.381425 loss)
I0225 00:39:40.198792 29812 solver.cpp:189] Iteration 46000, loss = 0.246203
I0225 00:39:40.198812 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.246204 (* 1 = 0.246204 loss)
I0225 00:39:40.198818 29812 solver.cpp:470] Iteration 46000, lr = 0.00049
I0225 00:39:59.595088 29812 solver.cpp:189] Iteration 46050, loss = 0.326468
I0225 00:39:59.595113 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.326468 (* 1 = 0.326468 loss)
I0225 00:39:59.595118 29812 solver.cpp:470] Iteration 46050, lr = 0.00049
I0225 00:40:18.989462 29812 solver.cpp:189] Iteration 46100, loss = 0.16423
I0225 00:40:18.989547 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.16423 (* 1 = 0.16423 loss)
I0225 00:40:18.989553 29812 solver.cpp:470] Iteration 46100, lr = 0.00049
I0225 00:40:38.383260 29812 solver.cpp:189] Iteration 46150, loss = 0.198483
I0225 00:40:38.383283 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.198483 (* 1 = 0.198483 loss)
I0225 00:40:38.383298 29812 solver.cpp:470] Iteration 46150, lr = 0.00049
I0225 00:40:57.784245 29812 solver.cpp:189] Iteration 46200, loss = 0.162338
I0225 00:40:57.784330 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.162338 (* 1 = 0.162338 loss)
I0225 00:40:57.784337 29812 solver.cpp:470] Iteration 46200, lr = 0.00049
I0225 00:41:17.174747 29812 solver.cpp:189] Iteration 46250, loss = 0.224878
I0225 00:41:17.174769 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.224878 (* 1 = 0.224878 loss)
I0225 00:41:17.174775 29812 solver.cpp:470] Iteration 46250, lr = 0.00049
I0225 00:41:36.564163 29812 solver.cpp:189] Iteration 46300, loss = 0.172869
I0225 00:41:36.564267 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.172869 (* 1 = 0.172869 loss)
I0225 00:41:36.564285 29812 solver.cpp:470] Iteration 46300, lr = 0.00049
I0225 00:41:55.960688 29812 solver.cpp:189] Iteration 46350, loss = 0.233431
I0225 00:41:55.960712 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.233432 (* 1 = 0.233432 loss)
I0225 00:41:55.960717 29812 solver.cpp:470] Iteration 46350, lr = 0.00049
I0225 00:42:15.346905 29812 solver.cpp:189] Iteration 46400, loss = 0.152522
I0225 00:42:15.347002 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.152523 (* 1 = 0.152523 loss)
I0225 00:42:15.347018 29812 solver.cpp:470] Iteration 46400, lr = 0.00049
I0225 00:42:34.742141 29812 solver.cpp:189] Iteration 46450, loss = 0.312667
I0225 00:42:34.742166 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.312668 (* 1 = 0.312668 loss)
I0225 00:42:34.742172 29812 solver.cpp:470] Iteration 46450, lr = 0.00049
I0225 00:42:54.137096 29812 solver.cpp:189] Iteration 46500, loss = 0.19559
I0225 00:42:54.137161 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.195591 (* 1 = 0.195591 loss)
I0225 00:42:54.137166 29812 solver.cpp:470] Iteration 46500, lr = 0.00049
I0225 00:43:13.529657 29812 solver.cpp:189] Iteration 46550, loss = 0.269595
I0225 00:43:13.529682 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.269595 (* 1 = 0.269595 loss)
I0225 00:43:13.529687 29812 solver.cpp:470] Iteration 46550, lr = 0.00049
I0225 00:43:32.912478 29812 solver.cpp:189] Iteration 46600, loss = 0.282741
I0225 00:43:32.912550 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.282742 (* 1 = 0.282742 loss)
I0225 00:43:32.912565 29812 solver.cpp:470] Iteration 46600, lr = 0.00049
I0225 00:43:52.306901 29812 solver.cpp:189] Iteration 46650, loss = 0.154743
I0225 00:43:52.306924 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.154743 (* 1 = 0.154743 loss)
I0225 00:43:52.306931 29812 solver.cpp:470] Iteration 46650, lr = 0.00049
I0225 00:44:11.701150 29812 solver.cpp:189] Iteration 46700, loss = 0.162273
I0225 00:44:11.701241 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.162273 (* 1 = 0.162273 loss)
I0225 00:44:11.701247 29812 solver.cpp:470] Iteration 46700, lr = 0.00049
I0225 00:44:31.093389 29812 solver.cpp:189] Iteration 46750, loss = 0.138626
I0225 00:44:31.093412 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.138626 (* 1 = 0.138626 loss)
I0225 00:44:31.093417 29812 solver.cpp:470] Iteration 46750, lr = 0.00049
I0225 00:44:50.490505 29812 solver.cpp:189] Iteration 46800, loss = 0.137211
I0225 00:44:50.490577 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.137211 (* 1 = 0.137211 loss)
I0225 00:44:50.490592 29812 solver.cpp:470] Iteration 46800, lr = 0.00049
I0225 00:45:09.887161 29812 solver.cpp:189] Iteration 46850, loss = 0.169934
I0225 00:45:09.887187 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.169934 (* 1 = 0.169934 loss)
I0225 00:45:09.887192 29812 solver.cpp:470] Iteration 46850, lr = 0.00049
I0225 00:45:29.279747 29812 solver.cpp:189] Iteration 46900, loss = 0.176483
I0225 00:45:29.279786 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.176483 (* 1 = 0.176483 loss)
I0225 00:45:29.279791 29812 solver.cpp:470] Iteration 46900, lr = 0.00049
I0225 00:45:48.675050 29812 solver.cpp:189] Iteration 46950, loss = 0.276905
I0225 00:45:48.675073 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.276905 (* 1 = 0.276905 loss)
I0225 00:45:48.675079 29812 solver.cpp:470] Iteration 46950, lr = 0.00049
I0225 00:46:07.831043 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_47000.caffemodel
I0225 00:46:07.932544 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_47000.solverstate
I0225 00:46:07.989771 29812 solver.cpp:266] Iteration 47000, Testing net (#0)
I0225 00:46:15.638928 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8921
I0225 00:46:15.638968 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.356338 (* 1 = 0.356338 loss)
I0225 00:46:15.926453 29812 solver.cpp:189] Iteration 47000, loss = 0.138782
I0225 00:46:15.926477 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.138782 (* 1 = 0.138782 loss)
I0225 00:46:15.926483 29812 solver.cpp:470] Iteration 47000, lr = 0.00049
I0225 00:46:35.308290 29812 solver.cpp:189] Iteration 47050, loss = 0.187185
I0225 00:46:35.308313 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.187185 (* 1 = 0.187185 loss)
I0225 00:46:35.308320 29812 solver.cpp:470] Iteration 47050, lr = 0.00049
I0225 00:46:54.692881 29812 solver.cpp:189] Iteration 47100, loss = 0.127558
I0225 00:46:54.692998 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.127558 (* 1 = 0.127558 loss)
I0225 00:46:54.693004 29812 solver.cpp:470] Iteration 47100, lr = 0.00049
I0225 00:47:14.089884 29812 solver.cpp:189] Iteration 47150, loss = 0.284294
I0225 00:47:14.089907 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.284294 (* 1 = 0.284294 loss)
I0225 00:47:14.089912 29812 solver.cpp:470] Iteration 47150, lr = 0.00049
I0225 00:47:33.485719 29812 solver.cpp:189] Iteration 47200, loss = 0.141435
I0225 00:47:33.485785 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.141436 (* 1 = 0.141436 loss)
I0225 00:47:33.485791 29812 solver.cpp:470] Iteration 47200, lr = 0.00049
I0225 00:47:52.867164 29812 solver.cpp:189] Iteration 47250, loss = 0.0985024
I0225 00:47:52.867189 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0985027 (* 1 = 0.0985027 loss)
I0225 00:47:52.867195 29812 solver.cpp:470] Iteration 47250, lr = 0.00049
I0225 00:48:12.246403 29812 solver.cpp:189] Iteration 47300, loss = 0.127902
I0225 00:48:12.246465 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.127902 (* 1 = 0.127902 loss)
I0225 00:48:12.246471 29812 solver.cpp:470] Iteration 47300, lr = 0.00049
I0225 00:48:31.633493 29812 solver.cpp:189] Iteration 47350, loss = 0.206777
I0225 00:48:31.633517 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.206777 (* 1 = 0.206777 loss)
I0225 00:48:31.633523 29812 solver.cpp:470] Iteration 47350, lr = 0.00049
I0225 00:48:51.019817 29812 solver.cpp:189] Iteration 47400, loss = 0.159068
I0225 00:48:51.019876 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.159069 (* 1 = 0.159069 loss)
I0225 00:48:51.019881 29812 solver.cpp:470] Iteration 47400, lr = 0.00049
I0225 00:49:10.414741 29812 solver.cpp:189] Iteration 47450, loss = 0.202813
I0225 00:49:10.414764 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.202813 (* 1 = 0.202813 loss)
I0225 00:49:10.414769 29812 solver.cpp:470] Iteration 47450, lr = 0.00049
I0225 00:49:29.799878 29812 solver.cpp:189] Iteration 47500, loss = 0.0976954
I0225 00:49:29.799937 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0976957 (* 1 = 0.0976957 loss)
I0225 00:49:29.799943 29812 solver.cpp:470] Iteration 47500, lr = 0.00049
I0225 00:49:49.192941 29812 solver.cpp:189] Iteration 47550, loss = 0.239451
I0225 00:49:49.192965 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.239451 (* 1 = 0.239451 loss)
I0225 00:49:49.192970 29812 solver.cpp:470] Iteration 47550, lr = 0.00049
I0225 00:50:08.577597 29812 solver.cpp:189] Iteration 47600, loss = 0.190139
I0225 00:50:08.577668 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.190139 (* 1 = 0.190139 loss)
I0225 00:50:08.577683 29812 solver.cpp:470] Iteration 47600, lr = 0.00049
I0225 00:50:27.954736 29812 solver.cpp:189] Iteration 47650, loss = 0.166888
I0225 00:50:27.954759 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.166888 (* 1 = 0.166888 loss)
I0225 00:50:27.954766 29812 solver.cpp:470] Iteration 47650, lr = 0.00049
I0225 00:50:47.337730 29812 solver.cpp:189] Iteration 47700, loss = 0.249788
I0225 00:50:47.337780 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.249788 (* 1 = 0.249788 loss)
I0225 00:50:47.337786 29812 solver.cpp:470] Iteration 47700, lr = 0.00049
I0225 00:51:06.726145 29812 solver.cpp:189] Iteration 47750, loss = 0.128856
I0225 00:51:06.726171 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.128857 (* 1 = 0.128857 loss)
I0225 00:51:06.726177 29812 solver.cpp:470] Iteration 47750, lr = 0.00049
I0225 00:51:26.122220 29812 solver.cpp:189] Iteration 47800, loss = 0.197667
I0225 00:51:26.122278 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.197668 (* 1 = 0.197668 loss)
I0225 00:51:26.122284 29812 solver.cpp:470] Iteration 47800, lr = 0.00049
I0225 00:51:45.498939 29812 solver.cpp:189] Iteration 47850, loss = 0.188723
I0225 00:51:45.498965 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.188723 (* 1 = 0.188723 loss)
I0225 00:51:45.498970 29812 solver.cpp:470] Iteration 47850, lr = 0.00049
I0225 00:52:04.880404 29812 solver.cpp:189] Iteration 47900, loss = 0.105926
I0225 00:52:04.880512 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105926 (* 1 = 0.105926 loss)
I0225 00:52:04.880519 29812 solver.cpp:470] Iteration 47900, lr = 0.00049
I0225 00:52:24.269804 29812 solver.cpp:189] Iteration 47950, loss = 0.153092
I0225 00:52:24.269827 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.153092 (* 1 = 0.153092 loss)
I0225 00:52:24.269831 29812 solver.cpp:470] Iteration 47950, lr = 0.00049
I0225 00:52:43.417363 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_48000.caffemodel
I0225 00:52:43.528869 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_48000.solverstate
I0225 00:52:43.586705 29812 solver.cpp:266] Iteration 48000, Testing net (#0)
I0225 00:52:51.235487 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8889
I0225 00:52:51.235525 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.352841 (* 1 = 0.352841 loss)
I0225 00:52:51.523360 29812 solver.cpp:189] Iteration 48000, loss = 0.202407
I0225 00:52:51.523382 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.202407 (* 1 = 0.202407 loss)
I0225 00:52:51.523388 29812 solver.cpp:470] Iteration 48000, lr = 0.00049
I0225 00:53:10.913902 29812 solver.cpp:189] Iteration 48050, loss = 0.200994
I0225 00:53:10.913926 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.200994 (* 1 = 0.200994 loss)
I0225 00:53:10.913931 29812 solver.cpp:470] Iteration 48050, lr = 0.00049
I0225 00:53:30.293593 29812 solver.cpp:189] Iteration 48100, loss = 0.144583
I0225 00:53:30.293653 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.144584 (* 1 = 0.144584 loss)
I0225 00:53:30.293658 29812 solver.cpp:470] Iteration 48100, lr = 0.00049
I0225 00:53:49.679288 29812 solver.cpp:189] Iteration 48150, loss = 0.195892
I0225 00:53:49.679312 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.195892 (* 1 = 0.195892 loss)
I0225 00:53:49.679317 29812 solver.cpp:470] Iteration 48150, lr = 0.00049
I0225 00:54:09.065773 29812 solver.cpp:189] Iteration 48200, loss = 0.145024
I0225 00:54:09.065845 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.145024 (* 1 = 0.145024 loss)
I0225 00:54:09.065858 29812 solver.cpp:470] Iteration 48200, lr = 0.00049
I0225 00:54:28.455554 29812 solver.cpp:189] Iteration 48250, loss = 0.225018
I0225 00:54:28.455579 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.225018 (* 1 = 0.225018 loss)
I0225 00:54:28.455584 29812 solver.cpp:470] Iteration 48250, lr = 0.00049
I0225 00:54:47.846248 29812 solver.cpp:189] Iteration 48300, loss = 0.160265
I0225 00:54:47.846307 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.160265 (* 1 = 0.160265 loss)
I0225 00:54:47.846320 29812 solver.cpp:470] Iteration 48300, lr = 0.00049
I0225 00:55:07.226339 29812 solver.cpp:189] Iteration 48350, loss = 0.101935
I0225 00:55:07.226363 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.101935 (* 1 = 0.101935 loss)
I0225 00:55:07.226368 29812 solver.cpp:470] Iteration 48350, lr = 0.00049
I0225 00:55:26.618333 29812 solver.cpp:189] Iteration 48400, loss = 0.248798
I0225 00:55:26.618374 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.248798 (* 1 = 0.248798 loss)
I0225 00:55:26.618381 29812 solver.cpp:470] Iteration 48400, lr = 0.00049
I0225 00:55:45.998273 29812 solver.cpp:189] Iteration 48450, loss = 0.272817
I0225 00:55:45.998298 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.272817 (* 1 = 0.272817 loss)
I0225 00:55:45.998303 29812 solver.cpp:470] Iteration 48450, lr = 0.00049
I0225 00:56:05.397068 29812 solver.cpp:189] Iteration 48500, loss = 0.206281
I0225 00:56:05.397107 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.206282 (* 1 = 0.206282 loss)
I0225 00:56:05.397114 29812 solver.cpp:470] Iteration 48500, lr = 0.00049
I0225 00:56:24.777669 29812 solver.cpp:189] Iteration 48550, loss = 0.297506
I0225 00:56:24.777693 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.297506 (* 1 = 0.297506 loss)
I0225 00:56:24.777699 29812 solver.cpp:470] Iteration 48550, lr = 0.00049
I0225 00:56:44.163708 29812 solver.cpp:189] Iteration 48600, loss = 0.120619
I0225 00:56:44.163789 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.120619 (* 1 = 0.120619 loss)
I0225 00:56:44.163794 29812 solver.cpp:470] Iteration 48600, lr = 0.00049
I0225 00:57:03.556782 29812 solver.cpp:189] Iteration 48650, loss = 0.18739
I0225 00:57:03.556807 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.18739 (* 1 = 0.18739 loss)
I0225 00:57:03.556812 29812 solver.cpp:470] Iteration 48650, lr = 0.00049
I0225 00:57:22.935088 29812 solver.cpp:189] Iteration 48700, loss = 0.106769
I0225 00:57:22.935163 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.106769 (* 1 = 0.106769 loss)
I0225 00:57:22.935178 29812 solver.cpp:470] Iteration 48700, lr = 0.00049
I0225 00:57:42.321127 29812 solver.cpp:189] Iteration 48750, loss = 0.217402
I0225 00:57:42.321151 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.217402 (* 1 = 0.217402 loss)
I0225 00:57:42.321156 29812 solver.cpp:470] Iteration 48750, lr = 0.00049
I0225 00:58:01.710644 29812 solver.cpp:189] Iteration 48800, loss = 0.107964
I0225 00:58:01.710734 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.107964 (* 1 = 0.107964 loss)
I0225 00:58:01.710741 29812 solver.cpp:470] Iteration 48800, lr = 0.00049
I0225 00:58:21.089828 29812 solver.cpp:189] Iteration 48850, loss = 0.156306
I0225 00:58:21.089853 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.156306 (* 1 = 0.156306 loss)
I0225 00:58:21.089857 29812 solver.cpp:470] Iteration 48850, lr = 0.00049
I0225 00:58:40.465934 29812 solver.cpp:189] Iteration 48900, loss = 0.227205
I0225 00:58:40.465994 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.227205 (* 1 = 0.227205 loss)
I0225 00:58:40.466001 29812 solver.cpp:470] Iteration 48900, lr = 0.00049
I0225 00:58:59.849509 29812 solver.cpp:189] Iteration 48950, loss = 0.121165
I0225 00:58:59.849534 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.121166 (* 1 = 0.121166 loss)
I0225 00:58:59.849539 29812 solver.cpp:470] Iteration 48950, lr = 0.00049
I0225 00:59:18.995848 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_49000.caffemodel
I0225 00:59:19.106606 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_49000.solverstate
I0225 00:59:19.165395 29812 solver.cpp:266] Iteration 49000, Testing net (#0)
I0225 00:59:26.816712 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8875
I0225 00:59:26.816750 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.367466 (* 1 = 0.367466 loss)
I0225 00:59:27.102751 29812 solver.cpp:189] Iteration 49000, loss = 0.138173
I0225 00:59:27.102772 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.138173 (* 1 = 0.138173 loss)
I0225 00:59:27.102777 29812 solver.cpp:470] Iteration 49000, lr = 0.00049
I0225 00:59:46.499119 29812 solver.cpp:189] Iteration 49050, loss = 0.156226
I0225 00:59:46.499141 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.156226 (* 1 = 0.156226 loss)
I0225 00:59:46.499147 29812 solver.cpp:470] Iteration 49050, lr = 0.00049
I0225 01:00:05.895809 29812 solver.cpp:189] Iteration 49100, loss = 0.265896
I0225 01:00:05.895896 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.265897 (* 1 = 0.265897 loss)
I0225 01:00:05.895902 29812 solver.cpp:470] Iteration 49100, lr = 0.00049
I0225 01:00:25.290002 29812 solver.cpp:189] Iteration 49150, loss = 0.39861
I0225 01:00:25.290025 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.39861 (* 1 = 0.39861 loss)
I0225 01:00:25.290030 29812 solver.cpp:470] Iteration 49150, lr = 0.00049
I0225 01:00:44.683950 29812 solver.cpp:189] Iteration 49200, loss = 0.110105
I0225 01:00:44.684021 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.110106 (* 1 = 0.110106 loss)
I0225 01:00:44.684036 29812 solver.cpp:470] Iteration 49200, lr = 0.00049
I0225 01:01:04.085808 29812 solver.cpp:189] Iteration 49250, loss = 0.237144
I0225 01:01:04.085830 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.237144 (* 1 = 0.237144 loss)
I0225 01:01:04.085835 29812 solver.cpp:470] Iteration 49250, lr = 0.00049
I0225 01:01:23.480190 29812 solver.cpp:189] Iteration 49300, loss = 0.301167
I0225 01:01:23.480252 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.301167 (* 1 = 0.301167 loss)
I0225 01:01:23.480257 29812 solver.cpp:470] Iteration 49300, lr = 0.00049
I0225 01:01:42.874789 29812 solver.cpp:189] Iteration 49350, loss = 0.166276
I0225 01:01:42.874814 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.166276 (* 1 = 0.166276 loss)
I0225 01:01:42.874819 29812 solver.cpp:470] Iteration 49350, lr = 0.00049
I0225 01:02:02.265094 29812 solver.cpp:189] Iteration 49400, loss = 0.15077
I0225 01:02:02.265156 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.15077 (* 1 = 0.15077 loss)
I0225 01:02:02.265161 29812 solver.cpp:470] Iteration 49400, lr = 0.00049
I0225 01:02:21.663023 29812 solver.cpp:189] Iteration 49450, loss = 0.100901
I0225 01:02:21.663048 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.100901 (* 1 = 0.100901 loss)
I0225 01:02:21.663053 29812 solver.cpp:470] Iteration 49450, lr = 0.00049
I0225 01:02:41.060921 29812 solver.cpp:189] Iteration 49500, loss = 0.21607
I0225 01:02:41.060992 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.21607 (* 1 = 0.21607 loss)
I0225 01:02:41.061005 29812 solver.cpp:470] Iteration 49500, lr = 0.00049
I0225 01:03:00.456655 29812 solver.cpp:189] Iteration 49550, loss = 0.220467
I0225 01:03:00.456679 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.220468 (* 1 = 0.220468 loss)
I0225 01:03:00.456684 29812 solver.cpp:470] Iteration 49550, lr = 0.00049
I0225 01:03:19.851323 29812 solver.cpp:189] Iteration 49600, loss = 0.160652
I0225 01:03:19.851407 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.160652 (* 1 = 0.160652 loss)
I0225 01:03:19.851413 29812 solver.cpp:470] Iteration 49600, lr = 0.00049
I0225 01:03:39.238495 29812 solver.cpp:189] Iteration 49650, loss = 0.134027
I0225 01:03:39.238518 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.134027 (* 1 = 0.134027 loss)
I0225 01:03:39.238523 29812 solver.cpp:470] Iteration 49650, lr = 0.00049
I0225 01:03:58.618052 29812 solver.cpp:189] Iteration 49700, loss = 0.146336
I0225 01:03:58.618093 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.146336 (* 1 = 0.146336 loss)
I0225 01:03:58.618098 29812 solver.cpp:470] Iteration 49700, lr = 0.00049
I0225 01:04:18.013367 29812 solver.cpp:189] Iteration 49750, loss = 0.137936
I0225 01:04:18.013391 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.137936 (* 1 = 0.137936 loss)
I0225 01:04:18.013396 29812 solver.cpp:470] Iteration 49750, lr = 0.00049
I0225 01:04:37.403233 29812 solver.cpp:189] Iteration 49800, loss = 0.26167
I0225 01:04:37.403311 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.26167 (* 1 = 0.26167 loss)
I0225 01:04:37.403326 29812 solver.cpp:470] Iteration 49800, lr = 0.00049
I0225 01:04:56.795907 29812 solver.cpp:189] Iteration 49850, loss = 0.109705
I0225 01:04:56.795933 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.109705 (* 1 = 0.109705 loss)
I0225 01:04:56.795938 29812 solver.cpp:470] Iteration 49850, lr = 0.00049
I0225 01:05:16.191359 29812 solver.cpp:189] Iteration 49900, loss = 0.166486
I0225 01:05:16.191434 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.166487 (* 1 = 0.166487 loss)
I0225 01:05:16.191439 29812 solver.cpp:470] Iteration 49900, lr = 0.00049
I0225 01:05:35.583282 29812 solver.cpp:189] Iteration 49950, loss = 0.0994475
I0225 01:05:35.583307 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0994477 (* 1 = 0.0994477 loss)
I0225 01:05:35.583312 29812 solver.cpp:470] Iteration 49950, lr = 0.00049
I0225 01:05:54.725062 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_50000.caffemodel
I0225 01:05:54.827500 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_50000.solverstate
I0225 01:05:54.885076 29812 solver.cpp:266] Iteration 50000, Testing net (#0)
I0225 01:06:02.529114 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.89
I0225 01:06:02.529148 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.361259 (* 1 = 0.361259 loss)
I0225 01:06:02.815315 29812 solver.cpp:189] Iteration 50000, loss = 0.26115
I0225 01:06:02.815337 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.26115 (* 1 = 0.26115 loss)
I0225 01:06:02.815343 29812 solver.cpp:470] Iteration 50000, lr = 0.00049
I0225 01:06:22.199910 29812 solver.cpp:189] Iteration 50050, loss = 0.179858
I0225 01:06:22.199935 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.179858 (* 1 = 0.179858 loss)
I0225 01:06:22.199939 29812 solver.cpp:470] Iteration 50050, lr = 0.00049
I0225 01:06:41.588583 29812 solver.cpp:189] Iteration 50100, loss = 0.175244
I0225 01:06:41.588636 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.175244 (* 1 = 0.175244 loss)
I0225 01:06:41.588659 29812 solver.cpp:470] Iteration 50100, lr = 0.00049
I0225 01:07:00.989727 29812 solver.cpp:189] Iteration 50150, loss = 0.181177
I0225 01:07:00.989750 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.181177 (* 1 = 0.181177 loss)
I0225 01:07:00.989756 29812 solver.cpp:470] Iteration 50150, lr = 0.00049
I0225 01:07:20.386945 29812 solver.cpp:189] Iteration 50200, loss = 0.232369
I0225 01:07:20.387006 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.232369 (* 1 = 0.232369 loss)
I0225 01:07:20.387011 29812 solver.cpp:470] Iteration 50200, lr = 0.00049
I0225 01:07:39.777897 29812 solver.cpp:189] Iteration 50250, loss = 0.223005
I0225 01:07:39.777922 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.223006 (* 1 = 0.223006 loss)
I0225 01:07:39.777928 29812 solver.cpp:470] Iteration 50250, lr = 0.00049
I0225 01:07:59.167042 29812 solver.cpp:189] Iteration 50300, loss = 0.154371
I0225 01:07:59.167098 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.154371 (* 1 = 0.154371 loss)
I0225 01:07:59.167104 29812 solver.cpp:470] Iteration 50300, lr = 0.00049
I0225 01:08:18.562268 29812 solver.cpp:189] Iteration 50350, loss = 0.255533
I0225 01:08:18.562291 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.255533 (* 1 = 0.255533 loss)
I0225 01:08:18.562296 29812 solver.cpp:470] Iteration 50350, lr = 0.00049
I0225 01:08:37.951812 29812 solver.cpp:189] Iteration 50400, loss = 0.219131
I0225 01:08:37.951882 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.219131 (* 1 = 0.219131 loss)
I0225 01:08:37.951897 29812 solver.cpp:470] Iteration 50400, lr = 0.00049
I0225 01:08:57.343617 29812 solver.cpp:189] Iteration 50450, loss = 0.140266
I0225 01:08:57.343641 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.140266 (* 1 = 0.140266 loss)
I0225 01:08:57.343646 29812 solver.cpp:470] Iteration 50450, lr = 0.00049
I0225 01:09:16.734643 29812 solver.cpp:189] Iteration 50500, loss = 0.123184
I0225 01:09:16.734683 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.123184 (* 1 = 0.123184 loss)
I0225 01:09:16.734689 29812 solver.cpp:470] Iteration 50500, lr = 0.00049
I0225 01:09:36.130386 29812 solver.cpp:189] Iteration 50550, loss = 0.24184
I0225 01:09:36.130409 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.24184 (* 1 = 0.24184 loss)
I0225 01:09:36.130414 29812 solver.cpp:470] Iteration 50550, lr = 0.00049
I0225 01:09:55.534832 29812 solver.cpp:189] Iteration 50600, loss = 0.199236
I0225 01:09:55.534873 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.199236 (* 1 = 0.199236 loss)
I0225 01:09:55.534878 29812 solver.cpp:470] Iteration 50600, lr = 0.00049
I0225 01:10:14.924216 29812 solver.cpp:189] Iteration 50650, loss = 0.295838
I0225 01:10:14.924240 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.295838 (* 1 = 0.295838 loss)
I0225 01:10:14.924247 29812 solver.cpp:470] Iteration 50650, lr = 0.00049
I0225 01:10:34.320698 29812 solver.cpp:189] Iteration 50700, loss = 0.181664
I0225 01:10:34.320780 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.181664 (* 1 = 0.181664 loss)
I0225 01:10:34.320785 29812 solver.cpp:470] Iteration 50700, lr = 0.00049
I0225 01:10:53.715991 29812 solver.cpp:189] Iteration 50750, loss = 0.276255
I0225 01:10:53.716017 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.276255 (* 1 = 0.276255 loss)
I0225 01:10:53.716022 29812 solver.cpp:470] Iteration 50750, lr = 0.00049
I0225 01:11:13.112128 29812 solver.cpp:189] Iteration 50800, loss = 0.123758
I0225 01:11:13.112197 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.123758 (* 1 = 0.123758 loss)
I0225 01:11:13.112205 29812 solver.cpp:470] Iteration 50800, lr = 0.00049
I0225 01:11:32.511416 29812 solver.cpp:189] Iteration 50850, loss = 0.250235
I0225 01:11:32.511441 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.250236 (* 1 = 0.250236 loss)
I0225 01:11:32.511447 29812 solver.cpp:470] Iteration 50850, lr = 0.00049
I0225 01:11:51.897102 29812 solver.cpp:189] Iteration 50900, loss = 0.190233
I0225 01:11:51.897172 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.190233 (* 1 = 0.190233 loss)
I0225 01:11:51.897178 29812 solver.cpp:470] Iteration 50900, lr = 0.00049
I0225 01:12:11.283900 29812 solver.cpp:189] Iteration 50950, loss = 0.169961
I0225 01:12:11.283923 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.169961 (* 1 = 0.169961 loss)
I0225 01:12:11.283929 29812 solver.cpp:470] Iteration 50950, lr = 0.00049
I0225 01:12:30.425413 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_51000.caffemodel
I0225 01:12:30.546980 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_51000.solverstate
I0225 01:12:30.604890 29812 solver.cpp:266] Iteration 51000, Testing net (#0)
I0225 01:12:38.247551 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8958
I0225 01:12:38.247588 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.349562 (* 1 = 0.349562 loss)
I0225 01:12:38.536108 29812 solver.cpp:189] Iteration 51000, loss = 0.0881547
I0225 01:12:38.536133 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.088155 (* 1 = 0.088155 loss)
I0225 01:12:38.536137 29812 solver.cpp:470] Iteration 51000, lr = 0.00049
I0225 01:12:57.916868 29812 solver.cpp:189] Iteration 51050, loss = 0.119565
I0225 01:12:57.916893 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.119565 (* 1 = 0.119565 loss)
I0225 01:12:57.916898 29812 solver.cpp:470] Iteration 51050, lr = 0.00049
I0225 01:13:17.300968 29812 solver.cpp:189] Iteration 51100, loss = 0.158681
I0225 01:13:17.301040 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.158681 (* 1 = 0.158681 loss)
I0225 01:13:17.301045 29812 solver.cpp:470] Iteration 51100, lr = 0.00049
I0225 01:13:36.689067 29812 solver.cpp:189] Iteration 51150, loss = 0.244085
I0225 01:13:36.689095 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.244085 (* 1 = 0.244085 loss)
I0225 01:13:36.689100 29812 solver.cpp:470] Iteration 51150, lr = 0.00049
I0225 01:13:56.068052 29812 solver.cpp:189] Iteration 51200, loss = 0.153366
I0225 01:13:56.068125 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.153366 (* 1 = 0.153366 loss)
I0225 01:13:56.068140 29812 solver.cpp:470] Iteration 51200, lr = 0.00049
I0225 01:14:15.453253 29812 solver.cpp:189] Iteration 51250, loss = 0.151748
I0225 01:14:15.453275 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.151748 (* 1 = 0.151748 loss)
I0225 01:14:15.453280 29812 solver.cpp:470] Iteration 51250, lr = 0.00049
I0225 01:14:34.852572 29812 solver.cpp:189] Iteration 51300, loss = 0.122324
I0225 01:14:34.852632 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.122324 (* 1 = 0.122324 loss)
I0225 01:14:34.852638 29812 solver.cpp:470] Iteration 51300, lr = 0.00049
I0225 01:14:54.234639 29812 solver.cpp:189] Iteration 51350, loss = 0.143323
I0225 01:14:54.234661 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.143323 (* 1 = 0.143323 loss)
I0225 01:14:54.234666 29812 solver.cpp:470] Iteration 51350, lr = 0.00049
I0225 01:15:13.624927 29812 solver.cpp:189] Iteration 51400, loss = 0.156975
I0225 01:15:13.624991 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.156975 (* 1 = 0.156975 loss)
I0225 01:15:13.624997 29812 solver.cpp:470] Iteration 51400, lr = 0.00049
I0225 01:15:33.007931 29812 solver.cpp:189] Iteration 51450, loss = 0.18105
I0225 01:15:33.007954 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.18105 (* 1 = 0.18105 loss)
I0225 01:15:33.007961 29812 solver.cpp:470] Iteration 51450, lr = 0.00049
I0225 01:15:52.388944 29812 solver.cpp:189] Iteration 51500, loss = 0.174978
I0225 01:15:52.388986 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.174978 (* 1 = 0.174978 loss)
I0225 01:15:52.388991 29812 solver.cpp:470] Iteration 51500, lr = 0.00049
I0225 01:16:11.779412 29812 solver.cpp:189] Iteration 51550, loss = 0.272713
I0225 01:16:11.779438 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.272713 (* 1 = 0.272713 loss)
I0225 01:16:11.779443 29812 solver.cpp:470] Iteration 51550, lr = 0.00049
I0225 01:16:31.169970 29812 solver.cpp:189] Iteration 51600, loss = 0.192065
I0225 01:16:31.170039 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.192065 (* 1 = 0.192065 loss)
I0225 01:16:31.170054 29812 solver.cpp:470] Iteration 51600, lr = 0.00049
I0225 01:16:50.547642 29812 solver.cpp:189] Iteration 51650, loss = 0.247848
I0225 01:16:50.547667 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.247848 (* 1 = 0.247848 loss)
I0225 01:16:50.547672 29812 solver.cpp:470] Iteration 51650, lr = 0.00049
I0225 01:17:09.932884 29812 solver.cpp:189] Iteration 51700, loss = 0.079352
I0225 01:17:09.932955 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0793521 (* 1 = 0.0793521 loss)
I0225 01:17:09.932970 29812 solver.cpp:470] Iteration 51700, lr = 0.00049
I0225 01:17:29.318503 29812 solver.cpp:189] Iteration 51750, loss = 0.201333
I0225 01:17:29.318526 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.201333 (* 1 = 0.201333 loss)
I0225 01:17:29.318531 29812 solver.cpp:470] Iteration 51750, lr = 0.00049
I0225 01:17:48.706176 29812 solver.cpp:189] Iteration 51800, loss = 0.212344
I0225 01:17:48.706269 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.212345 (* 1 = 0.212345 loss)
I0225 01:17:48.706284 29812 solver.cpp:470] Iteration 51800, lr = 0.00049
I0225 01:18:08.095504 29812 solver.cpp:189] Iteration 51850, loss = 0.156873
I0225 01:18:08.095526 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.156873 (* 1 = 0.156873 loss)
I0225 01:18:08.095531 29812 solver.cpp:470] Iteration 51850, lr = 0.00049
I0225 01:18:27.482398 29812 solver.cpp:189] Iteration 51900, loss = 0.254696
I0225 01:18:27.482456 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.254696 (* 1 = 0.254696 loss)
I0225 01:18:27.482461 29812 solver.cpp:470] Iteration 51900, lr = 0.00049
I0225 01:18:46.865430 29812 solver.cpp:189] Iteration 51950, loss = 0.179027
I0225 01:18:46.865454 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.179027 (* 1 = 0.179027 loss)
I0225 01:18:46.865460 29812 solver.cpp:470] Iteration 51950, lr = 0.00049
I0225 01:19:05.994818 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_52000.caffemodel
I0225 01:19:06.099282 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_52000.solverstate
I0225 01:19:06.156889 29812 solver.cpp:266] Iteration 52000, Testing net (#0)
I0225 01:19:13.807813 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8918
I0225 01:19:13.807848 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.363033 (* 1 = 0.363033 loss)
I0225 01:19:14.094988 29812 solver.cpp:189] Iteration 52000, loss = 0.102935
I0225 01:19:14.095010 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.102935 (* 1 = 0.102935 loss)
I0225 01:19:14.095016 29812 solver.cpp:470] Iteration 52000, lr = 0.00049
I0225 01:19:33.484230 29812 solver.cpp:189] Iteration 52050, loss = 0.173853
I0225 01:19:33.484252 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.173853 (* 1 = 0.173853 loss)
I0225 01:19:33.484257 29812 solver.cpp:470] Iteration 52050, lr = 0.00049
I0225 01:19:52.872172 29812 solver.cpp:189] Iteration 52100, loss = 0.1122
I0225 01:19:52.872257 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.1122 (* 1 = 0.1122 loss)
I0225 01:19:52.872263 29812 solver.cpp:470] Iteration 52100, lr = 0.00049
I0225 01:20:12.261641 29812 solver.cpp:189] Iteration 52150, loss = 0.253038
I0225 01:20:12.261665 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.253038 (* 1 = 0.253038 loss)
I0225 01:20:12.261669 29812 solver.cpp:470] Iteration 52150, lr = 0.00049
I0225 01:20:31.644608 29812 solver.cpp:189] Iteration 52200, loss = 0.131337
I0225 01:20:31.644675 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.131337 (* 1 = 0.131337 loss)
I0225 01:20:31.644680 29812 solver.cpp:470] Iteration 52200, lr = 0.00049
I0225 01:20:51.022876 29812 solver.cpp:189] Iteration 52250, loss = 0.168188
I0225 01:20:51.022897 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.168188 (* 1 = 0.168188 loss)
I0225 01:20:51.022903 29812 solver.cpp:470] Iteration 52250, lr = 0.00049
I0225 01:21:10.409409 29812 solver.cpp:189] Iteration 52300, loss = 0.165298
I0225 01:21:10.409448 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.165298 (* 1 = 0.165298 loss)
I0225 01:21:10.409453 29812 solver.cpp:470] Iteration 52300, lr = 0.00049
I0225 01:21:29.794620 29812 solver.cpp:189] Iteration 52350, loss = 0.100452
I0225 01:21:29.794646 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.100452 (* 1 = 0.100452 loss)
I0225 01:21:29.794651 29812 solver.cpp:470] Iteration 52350, lr = 0.00049
I0225 01:21:49.173871 29812 solver.cpp:189] Iteration 52400, loss = 0.11127
I0225 01:21:49.173960 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.11127 (* 1 = 0.11127 loss)
I0225 01:21:49.173974 29812 solver.cpp:470] Iteration 52400, lr = 0.00049
I0225 01:22:08.558574 29812 solver.cpp:189] Iteration 52450, loss = 0.202296
I0225 01:22:08.558598 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.202296 (* 1 = 0.202296 loss)
I0225 01:22:08.558604 29812 solver.cpp:470] Iteration 52450, lr = 0.00049
I0225 01:22:27.941320 29812 solver.cpp:189] Iteration 52500, loss = 0.14981
I0225 01:22:27.941359 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.149811 (* 1 = 0.149811 loss)
I0225 01:22:27.941365 29812 solver.cpp:470] Iteration 52500, lr = 0.00049
I0225 01:22:47.325806 29812 solver.cpp:189] Iteration 52550, loss = 0.289862
I0225 01:22:47.325830 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.289862 (* 1 = 0.289862 loss)
I0225 01:22:47.325834 29812 solver.cpp:470] Iteration 52550, lr = 0.00049
I0225 01:23:06.715309 29812 solver.cpp:189] Iteration 52600, loss = 0.17801
I0225 01:23:06.715399 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.17801 (* 1 = 0.17801 loss)
I0225 01:23:06.715404 29812 solver.cpp:470] Iteration 52600, lr = 0.00049
I0225 01:23:26.105592 29812 solver.cpp:189] Iteration 52650, loss = 0.236206
I0225 01:23:26.105615 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.236206 (* 1 = 0.236206 loss)
I0225 01:23:26.105620 29812 solver.cpp:470] Iteration 52650, lr = 0.00049
I0225 01:23:45.493649 29812 solver.cpp:189] Iteration 52700, loss = 0.186607
I0225 01:23:45.493687 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.186607 (* 1 = 0.186607 loss)
I0225 01:23:45.493692 29812 solver.cpp:470] Iteration 52700, lr = 0.00049
I0225 01:24:04.881618 29812 solver.cpp:189] Iteration 52750, loss = 0.123397
I0225 01:24:04.881642 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.123397 (* 1 = 0.123397 loss)
I0225 01:24:04.881647 29812 solver.cpp:470] Iteration 52750, lr = 0.00049
I0225 01:24:24.258813 29812 solver.cpp:189] Iteration 52800, loss = 0.117005
I0225 01:24:24.258872 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.117006 (* 1 = 0.117006 loss)
I0225 01:24:24.258877 29812 solver.cpp:470] Iteration 52800, lr = 0.00049
I0225 01:24:43.651388 29812 solver.cpp:189] Iteration 52850, loss = 0.182395
I0225 01:24:43.651414 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.182395 (* 1 = 0.182395 loss)
I0225 01:24:43.651419 29812 solver.cpp:470] Iteration 52850, lr = 0.00049
I0225 01:25:03.040295 29812 solver.cpp:189] Iteration 52900, loss = 0.203261
I0225 01:25:03.040372 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.203261 (* 1 = 0.203261 loss)
I0225 01:25:03.040387 29812 solver.cpp:470] Iteration 52900, lr = 0.00049
I0225 01:25:22.436174 29812 solver.cpp:189] Iteration 52950, loss = 0.145363
I0225 01:25:22.436203 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.145363 (* 1 = 0.145363 loss)
I0225 01:25:22.436208 29812 solver.cpp:470] Iteration 52950, lr = 0.00049
I0225 01:25:41.582499 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_53000.caffemodel
I0225 01:25:41.706305 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_53000.solverstate
I0225 01:25:41.763839 29812 solver.cpp:266] Iteration 53000, Testing net (#0)
I0225 01:25:49.410851 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8914
I0225 01:25:49.410888 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.373871 (* 1 = 0.373871 loss)
I0225 01:25:49.699141 29812 solver.cpp:189] Iteration 53000, loss = 0.174047
I0225 01:25:49.699167 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.174047 (* 1 = 0.174047 loss)
I0225 01:25:49.699172 29812 solver.cpp:470] Iteration 53000, lr = 0.00049
I0225 01:26:09.096477 29812 solver.cpp:189] Iteration 53050, loss = 0.2473
I0225 01:26:09.096503 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.2473 (* 1 = 0.2473 loss)
I0225 01:26:09.096509 29812 solver.cpp:470] Iteration 53050, lr = 0.00049
I0225 01:26:28.482596 29812 solver.cpp:189] Iteration 53100, loss = 0.122721
I0225 01:26:28.482667 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.122721 (* 1 = 0.122721 loss)
I0225 01:26:28.482672 29812 solver.cpp:470] Iteration 53100, lr = 0.00049
I0225 01:26:47.876138 29812 solver.cpp:189] Iteration 53150, loss = 0.339025
I0225 01:26:47.876163 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.339026 (* 1 = 0.339026 loss)
I0225 01:26:47.876168 29812 solver.cpp:470] Iteration 53150, lr = 0.00049
I0225 01:27:07.279296 29812 solver.cpp:189] Iteration 53200, loss = 0.214964
I0225 01:27:07.279336 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.214964 (* 1 = 0.214964 loss)
I0225 01:27:07.279341 29812 solver.cpp:470] Iteration 53200, lr = 0.00049
I0225 01:27:26.676523 29812 solver.cpp:189] Iteration 53250, loss = 0.154929
I0225 01:27:26.676548 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.154929 (* 1 = 0.154929 loss)
I0225 01:27:26.676553 29812 solver.cpp:470] Iteration 53250, lr = 0.00049
I0225 01:27:46.075773 29812 solver.cpp:189] Iteration 53300, loss = 0.0983403
I0225 01:27:46.075814 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0983406 (* 1 = 0.0983406 loss)
I0225 01:27:46.075819 29812 solver.cpp:470] Iteration 53300, lr = 0.00049
I0225 01:28:05.464068 29812 solver.cpp:189] Iteration 53350, loss = 0.123746
I0225 01:28:05.464092 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.123746 (* 1 = 0.123746 loss)
I0225 01:28:05.464097 29812 solver.cpp:470] Iteration 53350, lr = 0.00049
I0225 01:28:24.854413 29812 solver.cpp:189] Iteration 53400, loss = 0.149532
I0225 01:28:24.854470 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.149532 (* 1 = 0.149532 loss)
I0225 01:28:24.854476 29812 solver.cpp:470] Iteration 53400, lr = 0.00049
I0225 01:28:44.252666 29812 solver.cpp:189] Iteration 53450, loss = 0.169425
I0225 01:28:44.252689 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.169426 (* 1 = 0.169426 loss)
I0225 01:28:44.252693 29812 solver.cpp:470] Iteration 53450, lr = 0.00049
I0225 01:29:03.660784 29812 solver.cpp:189] Iteration 53500, loss = 0.152207
I0225 01:29:03.660888 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.152207 (* 1 = 0.152207 loss)
I0225 01:29:03.660894 29812 solver.cpp:470] Iteration 53500, lr = 0.00049
I0225 01:29:23.056361 29812 solver.cpp:189] Iteration 53550, loss = 0.183838
I0225 01:29:23.056385 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.183838 (* 1 = 0.183838 loss)
I0225 01:29:23.056391 29812 solver.cpp:470] Iteration 53550, lr = 0.00049
I0225 01:29:42.440021 29812 solver.cpp:189] Iteration 53600, loss = 0.147542
I0225 01:29:42.440094 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.147542 (* 1 = 0.147542 loss)
I0225 01:29:42.440109 29812 solver.cpp:470] Iteration 53600, lr = 0.00049
I0225 01:30:01.841691 29812 solver.cpp:189] Iteration 53650, loss = 0.169457
I0225 01:30:01.841714 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.169458 (* 1 = 0.169458 loss)
I0225 01:30:01.841720 29812 solver.cpp:470] Iteration 53650, lr = 0.00049
I0225 01:30:21.233896 29812 solver.cpp:189] Iteration 53700, loss = 0.169754
I0225 01:30:21.233935 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.169754 (* 1 = 0.169754 loss)
I0225 01:30:21.233940 29812 solver.cpp:470] Iteration 53700, lr = 0.00049
I0225 01:30:40.630244 29812 solver.cpp:189] Iteration 53750, loss = 0.121634
I0225 01:30:40.630269 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.121634 (* 1 = 0.121634 loss)
I0225 01:30:40.630273 29812 solver.cpp:470] Iteration 53750, lr = 0.00049
I0225 01:31:00.027793 29812 solver.cpp:189] Iteration 53800, loss = 0.120759
I0225 01:31:00.027833 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.120759 (* 1 = 0.120759 loss)
I0225 01:31:00.027839 29812 solver.cpp:470] Iteration 53800, lr = 0.00049
I0225 01:31:19.418882 29812 solver.cpp:189] Iteration 53850, loss = 0.149353
I0225 01:31:19.418905 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.149353 (* 1 = 0.149353 loss)
I0225 01:31:19.418910 29812 solver.cpp:470] Iteration 53850, lr = 0.00049
I0225 01:31:38.813560 29812 solver.cpp:189] Iteration 53900, loss = 0.162534
I0225 01:31:38.813612 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.162535 (* 1 = 0.162535 loss)
I0225 01:31:38.813618 29812 solver.cpp:470] Iteration 53900, lr = 0.00049
I0225 01:31:58.210877 29812 solver.cpp:189] Iteration 53950, loss = 0.120721
I0225 01:31:58.210899 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.120721 (* 1 = 0.120721 loss)
I0225 01:31:58.210904 29812 solver.cpp:470] Iteration 53950, lr = 0.00049
I0225 01:32:17.360491 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_54000.caffemodel
I0225 01:32:17.471463 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_54000.solverstate
I0225 01:32:17.528805 29812 solver.cpp:266] Iteration 54000, Testing net (#0)
I0225 01:32:25.176885 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8936
I0225 01:32:25.176921 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.366242 (* 1 = 0.366242 loss)
I0225 01:32:25.464727 29812 solver.cpp:189] Iteration 54000, loss = 0.156509
I0225 01:32:25.464750 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.156509 (* 1 = 0.156509 loss)
I0225 01:32:25.464756 29812 solver.cpp:470] Iteration 54000, lr = 0.00049
I0225 01:32:44.859530 29812 solver.cpp:189] Iteration 54050, loss = 0.155326
I0225 01:32:44.859555 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.155326 (* 1 = 0.155326 loss)
I0225 01:32:44.859560 29812 solver.cpp:470] Iteration 54050, lr = 0.00049
I0225 01:33:04.259815 29812 solver.cpp:189] Iteration 54100, loss = 0.114334
I0225 01:33:04.259874 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.114334 (* 1 = 0.114334 loss)
I0225 01:33:04.259881 29812 solver.cpp:470] Iteration 54100, lr = 0.00049
I0225 01:33:23.653429 29812 solver.cpp:189] Iteration 54150, loss = 0.0854481
I0225 01:33:23.653453 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0854484 (* 1 = 0.0854484 loss)
I0225 01:33:23.653460 29812 solver.cpp:470] Iteration 54150, lr = 0.00049
I0225 01:33:43.048173 29812 solver.cpp:189] Iteration 54200, loss = 0.148184
I0225 01:33:43.048238 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.148184 (* 1 = 0.148184 loss)
I0225 01:33:43.048244 29812 solver.cpp:470] Iteration 54200, lr = 0.00049
I0225 01:34:02.445138 29812 solver.cpp:189] Iteration 54250, loss = 0.145823
I0225 01:34:02.445161 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.145824 (* 1 = 0.145824 loss)
I0225 01:34:02.445166 29812 solver.cpp:470] Iteration 54250, lr = 0.00049
I0225 01:34:21.839360 29812 solver.cpp:189] Iteration 54300, loss = 0.18753
I0225 01:34:21.839450 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.18753 (* 1 = 0.18753 loss)
I0225 01:34:21.839457 29812 solver.cpp:470] Iteration 54300, lr = 0.00049
I0225 01:34:41.234896 29812 solver.cpp:189] Iteration 54350, loss = 0.108218
I0225 01:34:41.234922 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.108218 (* 1 = 0.108218 loss)
I0225 01:34:41.234928 29812 solver.cpp:470] Iteration 54350, lr = 0.00049
I0225 01:35:00.631487 29812 solver.cpp:189] Iteration 54400, loss = 0.148605
I0225 01:35:00.631557 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.148605 (* 1 = 0.148605 loss)
I0225 01:35:00.631572 29812 solver.cpp:470] Iteration 54400, lr = 0.00049
I0225 01:35:20.025128 29812 solver.cpp:189] Iteration 54450, loss = 0.126359
I0225 01:35:20.025151 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.12636 (* 1 = 0.12636 loss)
I0225 01:35:20.025156 29812 solver.cpp:470] Iteration 54450, lr = 0.00049
I0225 01:35:39.413604 29812 solver.cpp:189] Iteration 54500, loss = 0.158405
I0225 01:35:39.413688 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.158405 (* 1 = 0.158405 loss)
I0225 01:35:39.413693 29812 solver.cpp:470] Iteration 54500, lr = 0.00049
I0225 01:35:58.809506 29812 solver.cpp:189] Iteration 54550, loss = 0.291628
I0225 01:35:58.809530 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.291628 (* 1 = 0.291628 loss)
I0225 01:35:58.809536 29812 solver.cpp:470] Iteration 54550, lr = 0.00049
I0225 01:36:18.201980 29812 solver.cpp:189] Iteration 54600, loss = 0.19927
I0225 01:36:18.202049 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.19927 (* 1 = 0.19927 loss)
I0225 01:36:18.202054 29812 solver.cpp:470] Iteration 54600, lr = 0.00049
I0225 01:36:37.602965 29812 solver.cpp:189] Iteration 54650, loss = 0.0878103
I0225 01:36:37.602990 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0878106 (* 1 = 0.0878106 loss)
I0225 01:36:37.602995 29812 solver.cpp:470] Iteration 54650, lr = 0.00049
I0225 01:36:57.003520 29812 solver.cpp:189] Iteration 54700, loss = 0.143633
I0225 01:36:57.003562 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.143634 (* 1 = 0.143634 loss)
I0225 01:36:57.003568 29812 solver.cpp:470] Iteration 54700, lr = 0.00049
I0225 01:37:16.394711 29812 solver.cpp:189] Iteration 54750, loss = 0.144026
I0225 01:37:16.394733 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.144027 (* 1 = 0.144027 loss)
I0225 01:37:16.394738 29812 solver.cpp:470] Iteration 54750, lr = 0.00049
I0225 01:37:35.786774 29812 solver.cpp:189] Iteration 54800, loss = 0.148506
I0225 01:37:35.786869 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.148506 (* 1 = 0.148506 loss)
I0225 01:37:35.786883 29812 solver.cpp:470] Iteration 54800, lr = 0.00049
I0225 01:37:55.185524 29812 solver.cpp:189] Iteration 54850, loss = 0.106686
I0225 01:37:55.185546 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.106686 (* 1 = 0.106686 loss)
I0225 01:37:55.185552 29812 solver.cpp:470] Iteration 54850, lr = 0.00049
I0225 01:38:14.582108 29812 solver.cpp:189] Iteration 54900, loss = 0.109982
I0225 01:38:14.582168 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.109983 (* 1 = 0.109983 loss)
I0225 01:38:14.582173 29812 solver.cpp:470] Iteration 54900, lr = 0.00049
I0225 01:38:33.969323 29812 solver.cpp:189] Iteration 54950, loss = 0.148958
I0225 01:38:33.969347 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.148959 (* 1 = 0.148959 loss)
I0225 01:38:33.969353 29812 solver.cpp:470] Iteration 54950, lr = 0.00049
I0225 01:38:53.120688 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_55000.caffemodel
I0225 01:38:53.237480 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_55000.solverstate
I0225 01:38:53.294596 29812 solver.cpp:266] Iteration 55000, Testing net (#0)
I0225 01:39:00.949067 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8895
I0225 01:39:00.949103 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.366871 (* 1 = 0.366871 loss)
I0225 01:39:01.235543 29812 solver.cpp:189] Iteration 55000, loss = 0.216663
I0225 01:39:01.235563 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.216663 (* 1 = 0.216663 loss)
I0225 01:39:01.235569 29812 solver.cpp:470] Iteration 55000, lr = 0.00049
I0225 01:39:20.620687 29812 solver.cpp:189] Iteration 55050, loss = 0.167181
I0225 01:39:20.620710 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.167181 (* 1 = 0.167181 loss)
I0225 01:39:20.620715 29812 solver.cpp:470] Iteration 55050, lr = 0.00049
I0225 01:39:40.006923 29812 solver.cpp:189] Iteration 55100, loss = 0.132428
I0225 01:39:40.006989 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.132428 (* 1 = 0.132428 loss)
I0225 01:39:40.006996 29812 solver.cpp:470] Iteration 55100, lr = 0.00049
I0225 01:39:59.395220 29812 solver.cpp:189] Iteration 55150, loss = 0.130231
I0225 01:39:59.395243 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.130232 (* 1 = 0.130232 loss)
I0225 01:39:59.395248 29812 solver.cpp:470] Iteration 55150, lr = 0.00049
I0225 01:40:18.789244 29812 solver.cpp:189] Iteration 55200, loss = 0.129286
I0225 01:40:18.789316 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.129287 (* 1 = 0.129287 loss)
I0225 01:40:18.789331 29812 solver.cpp:470] Iteration 55200, lr = 0.00049
I0225 01:40:38.173727 29812 solver.cpp:189] Iteration 55250, loss = 0.211487
I0225 01:40:38.173750 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.211487 (* 1 = 0.211487 loss)
I0225 01:40:38.173755 29812 solver.cpp:470] Iteration 55250, lr = 0.00049
I0225 01:40:57.558949 29812 solver.cpp:189] Iteration 55300, loss = 0.124741
I0225 01:40:57.558993 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.124741 (* 1 = 0.124741 loss)
I0225 01:40:57.559000 29812 solver.cpp:470] Iteration 55300, lr = 0.00049
I0225 01:41:16.946493 29812 solver.cpp:189] Iteration 55350, loss = 0.128287
I0225 01:41:16.946518 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.128287 (* 1 = 0.128287 loss)
I0225 01:41:16.946523 29812 solver.cpp:470] Iteration 55350, lr = 0.00049
I0225 01:41:36.321632 29812 solver.cpp:189] Iteration 55400, loss = 0.227365
I0225 01:41:36.321702 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.227365 (* 1 = 0.227365 loss)
I0225 01:41:36.321717 29812 solver.cpp:470] Iteration 55400, lr = 0.00049
I0225 01:41:55.709801 29812 solver.cpp:189] Iteration 55450, loss = 0.142485
I0225 01:41:55.709828 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.142485 (* 1 = 0.142485 loss)
I0225 01:41:55.709835 29812 solver.cpp:470] Iteration 55450, lr = 0.00049
I0225 01:42:15.098618 29812 solver.cpp:189] Iteration 55500, loss = 0.177959
I0225 01:42:15.098686 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.177959 (* 1 = 0.177959 loss)
I0225 01:42:15.098701 29812 solver.cpp:470] Iteration 55500, lr = 0.00049
I0225 01:42:34.478284 29812 solver.cpp:189] Iteration 55550, loss = 0.278832
I0225 01:42:34.478307 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.278832 (* 1 = 0.278832 loss)
I0225 01:42:34.478312 29812 solver.cpp:470] Iteration 55550, lr = 0.00049
I0225 01:42:53.864517 29812 solver.cpp:189] Iteration 55600, loss = 0.171474
I0225 01:42:53.864559 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.171474 (* 1 = 0.171474 loss)
I0225 01:42:53.864564 29812 solver.cpp:470] Iteration 55600, lr = 0.00049
I0225 01:43:13.257213 29812 solver.cpp:189] Iteration 55650, loss = 0.148381
I0225 01:43:13.257238 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.148381 (* 1 = 0.148381 loss)
I0225 01:43:13.257244 29812 solver.cpp:470] Iteration 55650, lr = 0.00049
I0225 01:43:32.642120 29812 solver.cpp:189] Iteration 55700, loss = 0.198107
I0225 01:43:32.642205 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.198107 (* 1 = 0.198107 loss)
I0225 01:43:32.642211 29812 solver.cpp:470] Iteration 55700, lr = 0.00049
I0225 01:43:52.029105 29812 solver.cpp:189] Iteration 55750, loss = 0.105071
I0225 01:43:52.029129 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105072 (* 1 = 0.105072 loss)
I0225 01:43:52.029135 29812 solver.cpp:470] Iteration 55750, lr = 0.00049
I0225 01:44:11.419438 29812 solver.cpp:189] Iteration 55800, loss = 0.206009
I0225 01:44:11.419512 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.20601 (* 1 = 0.20601 loss)
I0225 01:44:11.419527 29812 solver.cpp:470] Iteration 55800, lr = 0.00049
I0225 01:44:30.800257 29812 solver.cpp:189] Iteration 55850, loss = 0.135315
I0225 01:44:30.800283 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.135315 (* 1 = 0.135315 loss)
I0225 01:44:30.800289 29812 solver.cpp:470] Iteration 55850, lr = 0.00049
I0225 01:44:50.189718 29812 solver.cpp:189] Iteration 55900, loss = 0.108967
I0225 01:44:50.189776 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.108967 (* 1 = 0.108967 loss)
I0225 01:44:50.189782 29812 solver.cpp:470] Iteration 55900, lr = 0.00049
I0225 01:45:09.568984 29812 solver.cpp:189] Iteration 55950, loss = 0.247718
I0225 01:45:09.569007 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.247719 (* 1 = 0.247719 loss)
I0225 01:45:09.569011 29812 solver.cpp:470] Iteration 55950, lr = 0.00049
I0225 01:45:28.715237 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_56000.caffemodel
I0225 01:45:28.830901 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_56000.solverstate
I0225 01:45:28.888353 29812 solver.cpp:266] Iteration 56000, Testing net (#0)
I0225 01:45:36.552237 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8931
I0225 01:45:36.552265 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.37428 (* 1 = 0.37428 loss)
I0225 01:45:36.839517 29812 solver.cpp:189] Iteration 56000, loss = 0.185158
I0225 01:45:36.839542 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.185158 (* 1 = 0.185158 loss)
I0225 01:45:36.839548 29812 solver.cpp:470] Iteration 56000, lr = 0.00049
I0225 01:45:56.220259 29812 solver.cpp:189] Iteration 56050, loss = 0.166454
I0225 01:45:56.220283 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.166454 (* 1 = 0.166454 loss)
I0225 01:45:56.220288 29812 solver.cpp:470] Iteration 56050, lr = 0.00049
I0225 01:46:15.620018 29812 solver.cpp:189] Iteration 56100, loss = 0.341817
I0225 01:46:15.620090 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.341818 (* 1 = 0.341818 loss)
I0225 01:46:15.620095 29812 solver.cpp:470] Iteration 56100, lr = 0.00049
I0225 01:46:35.007350 29812 solver.cpp:189] Iteration 56150, loss = 0.0788148
I0225 01:46:35.007375 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0788151 (* 1 = 0.0788151 loss)
I0225 01:46:35.007380 29812 solver.cpp:470] Iteration 56150, lr = 0.00049
I0225 01:46:54.399237 29812 solver.cpp:189] Iteration 56200, loss = 0.135846
I0225 01:46:54.399277 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.135846 (* 1 = 0.135846 loss)
I0225 01:46:54.399282 29812 solver.cpp:470] Iteration 56200, lr = 0.00049
I0225 01:47:13.788240 29812 solver.cpp:189] Iteration 56250, loss = 0.150327
I0225 01:47:13.788266 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.150327 (* 1 = 0.150327 loss)
I0225 01:47:13.788272 29812 solver.cpp:470] Iteration 56250, lr = 0.00049
I0225 01:47:33.171244 29812 solver.cpp:189] Iteration 56300, loss = 0.147492
I0225 01:47:33.171286 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.147492 (* 1 = 0.147492 loss)
I0225 01:47:33.171293 29812 solver.cpp:470] Iteration 56300, lr = 0.00049
I0225 01:47:52.552057 29812 solver.cpp:189] Iteration 56350, loss = 0.185108
I0225 01:47:52.552080 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.185109 (* 1 = 0.185109 loss)
I0225 01:47:52.552085 29812 solver.cpp:470] Iteration 56350, lr = 0.00049
I0225 01:48:11.940860 29812 solver.cpp:189] Iteration 56400, loss = 0.151792
I0225 01:48:11.940923 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.151793 (* 1 = 0.151793 loss)
I0225 01:48:11.940930 29812 solver.cpp:470] Iteration 56400, lr = 0.00049
I0225 01:48:31.330358 29812 solver.cpp:189] Iteration 56450, loss = 0.0968956
I0225 01:48:31.330381 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0968959 (* 1 = 0.0968959 loss)
I0225 01:48:31.330386 29812 solver.cpp:470] Iteration 56450, lr = 0.00049
I0225 01:48:50.719391 29812 solver.cpp:189] Iteration 56500, loss = 0.159735
I0225 01:48:50.719482 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.159735 (* 1 = 0.159735 loss)
I0225 01:48:50.719488 29812 solver.cpp:470] Iteration 56500, lr = 0.00049
I0225 01:49:10.114434 29812 solver.cpp:189] Iteration 56550, loss = 0.128479
I0225 01:49:10.114459 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.128479 (* 1 = 0.128479 loss)
I0225 01:49:10.114464 29812 solver.cpp:470] Iteration 56550, lr = 0.00049
I0225 01:49:29.494513 29812 solver.cpp:189] Iteration 56600, loss = 0.208632
I0225 01:49:29.494554 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.208632 (* 1 = 0.208632 loss)
I0225 01:49:29.494560 29812 solver.cpp:470] Iteration 56600, lr = 0.00049
I0225 01:49:48.876056 29812 solver.cpp:189] Iteration 56650, loss = 0.0924012
I0225 01:49:48.876080 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0924015 (* 1 = 0.0924015 loss)
I0225 01:49:48.876086 29812 solver.cpp:470] Iteration 56650, lr = 0.00049
I0225 01:50:08.266083 29812 solver.cpp:189] Iteration 56700, loss = 0.172686
I0225 01:50:08.266141 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.172686 (* 1 = 0.172686 loss)
I0225 01:50:08.266146 29812 solver.cpp:470] Iteration 56700, lr = 0.00049
I0225 01:50:27.658277 29812 solver.cpp:189] Iteration 56750, loss = 0.183369
I0225 01:50:27.658303 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.183369 (* 1 = 0.183369 loss)
I0225 01:50:27.658308 29812 solver.cpp:470] Iteration 56750, lr = 0.00049
I0225 01:50:47.047164 29812 solver.cpp:189] Iteration 56800, loss = 0.0882597
I0225 01:50:47.047250 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0882601 (* 1 = 0.0882601 loss)
I0225 01:50:47.047256 29812 solver.cpp:470] Iteration 56800, lr = 0.00049
I0225 01:51:06.438580 29812 solver.cpp:189] Iteration 56850, loss = 0.101385
I0225 01:51:06.438603 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.101385 (* 1 = 0.101385 loss)
I0225 01:51:06.438608 29812 solver.cpp:470] Iteration 56850, lr = 0.00049
I0225 01:51:25.831533 29812 solver.cpp:189] Iteration 56900, loss = 0.087132
I0225 01:51:25.831609 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0871322 (* 1 = 0.0871322 loss)
I0225 01:51:25.831625 29812 solver.cpp:470] Iteration 56900, lr = 0.00049
I0225 01:51:45.221410 29812 solver.cpp:189] Iteration 56950, loss = 0.163098
I0225 01:51:45.221432 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.163098 (* 1 = 0.163098 loss)
I0225 01:51:45.221438 29812 solver.cpp:470] Iteration 56950, lr = 0.00049
I0225 01:52:04.372809 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_57000.caffemodel
I0225 01:52:04.481129 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_57000.solverstate
I0225 01:52:04.539170 29812 solver.cpp:266] Iteration 57000, Testing net (#0)
I0225 01:52:12.189666 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8923
I0225 01:52:12.189702 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.35437 (* 1 = 0.35437 loss)
I0225 01:52:12.476099 29812 solver.cpp:189] Iteration 57000, loss = 0.11508
I0225 01:52:12.476120 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.11508 (* 1 = 0.11508 loss)
I0225 01:52:12.476126 29812 solver.cpp:470] Iteration 57000, lr = 0.00049
I0225 01:52:31.872915 29812 solver.cpp:189] Iteration 57050, loss = 0.13957
I0225 01:52:31.872938 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.139571 (* 1 = 0.139571 loss)
I0225 01:52:31.872944 29812 solver.cpp:470] Iteration 57050, lr = 0.00049
I0225 01:52:51.269681 29812 solver.cpp:189] Iteration 57100, loss = 0.0929057
I0225 01:52:51.269744 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.092906 (* 1 = 0.092906 loss)
I0225 01:52:51.269750 29812 solver.cpp:470] Iteration 57100, lr = 0.00049
I0225 01:53:10.664757 29812 solver.cpp:189] Iteration 57150, loss = 0.248075
I0225 01:53:10.664783 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.248076 (* 1 = 0.248076 loss)
I0225 01:53:10.664788 29812 solver.cpp:470] Iteration 57150, lr = 0.00049
I0225 01:53:30.068161 29812 solver.cpp:189] Iteration 57200, loss = 0.0894089
I0225 01:53:30.068244 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0894092 (* 1 = 0.0894092 loss)
I0225 01:53:30.068250 29812 solver.cpp:470] Iteration 57200, lr = 0.00049
I0225 01:53:49.464920 29812 solver.cpp:189] Iteration 57250, loss = 0.197827
I0225 01:53:49.464943 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.197827 (* 1 = 0.197827 loss)
I0225 01:53:49.464948 29812 solver.cpp:470] Iteration 57250, lr = 0.00049
I0225 01:54:08.865703 29812 solver.cpp:189] Iteration 57300, loss = 0.122139
I0225 01:54:08.865754 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.12214 (* 1 = 0.12214 loss)
I0225 01:54:08.865761 29812 solver.cpp:470] Iteration 57300, lr = 0.00049
I0225 01:54:28.260006 29812 solver.cpp:189] Iteration 57350, loss = 0.209578
I0225 01:54:28.260030 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.209579 (* 1 = 0.209579 loss)
I0225 01:54:28.260035 29812 solver.cpp:470] Iteration 57350, lr = 0.00049
I0225 01:54:47.654784 29812 solver.cpp:189] Iteration 57400, loss = 0.0745112
I0225 01:54:47.654876 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0745115 (* 1 = 0.0745115 loss)
I0225 01:54:47.654883 29812 solver.cpp:470] Iteration 57400, lr = 0.00049
I0225 01:55:07.046845 29812 solver.cpp:189] Iteration 57450, loss = 0.100063
I0225 01:55:07.046871 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.100063 (* 1 = 0.100063 loss)
I0225 01:55:07.046876 29812 solver.cpp:470] Iteration 57450, lr = 0.00049
I0225 01:55:26.441184 29812 solver.cpp:189] Iteration 57500, loss = 0.116505
I0225 01:55:26.441277 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.116505 (* 1 = 0.116505 loss)
I0225 01:55:26.441293 29812 solver.cpp:470] Iteration 57500, lr = 0.00049
I0225 01:55:45.839586 29812 solver.cpp:189] Iteration 57550, loss = 0.202019
I0225 01:55:45.839612 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.202019 (* 1 = 0.202019 loss)
I0225 01:55:45.839617 29812 solver.cpp:470] Iteration 57550, lr = 0.00049
I0225 01:56:05.242588 29812 solver.cpp:189] Iteration 57600, loss = 0.131888
I0225 01:56:05.242656 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.131888 (* 1 = 0.131888 loss)
I0225 01:56:05.242671 29812 solver.cpp:470] Iteration 57600, lr = 0.00049
I0225 01:56:24.635984 29812 solver.cpp:189] Iteration 57650, loss = 0.145763
I0225 01:56:24.636008 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.145764 (* 1 = 0.145764 loss)
I0225 01:56:24.636013 29812 solver.cpp:470] Iteration 57650, lr = 0.00049
I0225 01:56:44.024113 29812 solver.cpp:189] Iteration 57700, loss = 0.16274
I0225 01:56:44.024173 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.16274 (* 1 = 0.16274 loss)
I0225 01:56:44.024194 29812 solver.cpp:470] Iteration 57700, lr = 0.00049
I0225 01:57:03.418119 29812 solver.cpp:189] Iteration 57750, loss = 0.153996
I0225 01:57:03.418143 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.153996 (* 1 = 0.153996 loss)
I0225 01:57:03.418148 29812 solver.cpp:470] Iteration 57750, lr = 0.00049
I0225 01:57:22.815477 29812 solver.cpp:189] Iteration 57800, loss = 0.259689
I0225 01:57:22.815574 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.259689 (* 1 = 0.259689 loss)
I0225 01:57:22.815590 29812 solver.cpp:470] Iteration 57800, lr = 0.00049
I0225 01:57:42.207334 29812 solver.cpp:189] Iteration 57850, loss = 0.169744
I0225 01:57:42.207356 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.169744 (* 1 = 0.169744 loss)
I0225 01:57:42.207361 29812 solver.cpp:470] Iteration 57850, lr = 0.00049
I0225 01:58:01.607633 29812 solver.cpp:189] Iteration 57900, loss = 0.091
I0225 01:58:01.607679 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0910003 (* 1 = 0.0910003 loss)
I0225 01:58:01.607686 29812 solver.cpp:470] Iteration 57900, lr = 0.00049
I0225 01:58:20.993475 29812 solver.cpp:189] Iteration 57950, loss = 0.189771
I0225 01:58:20.993500 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.189771 (* 1 = 0.189771 loss)
I0225 01:58:20.993506 29812 solver.cpp:470] Iteration 57950, lr = 0.00049
I0225 01:58:40.136147 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_58000.caffemodel
I0225 01:58:40.256484 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_58000.solverstate
I0225 01:58:40.313856 29812 solver.cpp:266] Iteration 58000, Testing net (#0)
I0225 01:58:47.961966 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8904
I0225 01:58:47.962002 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.373973 (* 1 = 0.373973 loss)
I0225 01:58:48.249965 29812 solver.cpp:189] Iteration 58000, loss = 0.142873
I0225 01:58:48.249985 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.142874 (* 1 = 0.142874 loss)
I0225 01:58:48.249991 29812 solver.cpp:470] Iteration 58000, lr = 0.00049
I0225 01:59:07.640336 29812 solver.cpp:189] Iteration 58050, loss = 0.233573
I0225 01:59:07.640372 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.233574 (* 1 = 0.233574 loss)
I0225 01:59:07.640377 29812 solver.cpp:470] Iteration 58050, lr = 0.00049
I0225 01:59:27.036677 29812 solver.cpp:189] Iteration 58100, loss = 0.261834
I0225 01:59:27.036767 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.261834 (* 1 = 0.261834 loss)
I0225 01:59:27.036773 29812 solver.cpp:470] Iteration 58100, lr = 0.00049
I0225 01:59:46.435353 29812 solver.cpp:189] Iteration 58150, loss = 0.198117
I0225 01:59:46.435376 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.198117 (* 1 = 0.198117 loss)
I0225 01:59:46.435381 29812 solver.cpp:470] Iteration 58150, lr = 0.00049
I0225 02:00:05.831423 29812 solver.cpp:189] Iteration 58200, loss = 0.198685
I0225 02:00:05.831514 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.198686 (* 1 = 0.198686 loss)
I0225 02:00:05.831529 29812 solver.cpp:470] Iteration 58200, lr = 0.00049
I0225 02:00:25.233810 29812 solver.cpp:189] Iteration 58250, loss = 0.224078
I0225 02:00:25.233834 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.224079 (* 1 = 0.224079 loss)
I0225 02:00:25.233839 29812 solver.cpp:470] Iteration 58250, lr = 0.00049
I0225 02:00:44.620093 29812 solver.cpp:189] Iteration 58300, loss = 0.218727
I0225 02:00:44.620156 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.218727 (* 1 = 0.218727 loss)
I0225 02:00:44.620160 29812 solver.cpp:470] Iteration 58300, lr = 0.00049
I0225 02:01:04.011500 29812 solver.cpp:189] Iteration 58350, loss = 0.276404
I0225 02:01:04.011525 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.276405 (* 1 = 0.276405 loss)
I0225 02:01:04.011530 29812 solver.cpp:470] Iteration 58350, lr = 0.00049
I0225 02:01:23.401799 29812 solver.cpp:189] Iteration 58400, loss = 0.107504
I0225 02:01:23.401839 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.107504 (* 1 = 0.107504 loss)
I0225 02:01:23.401844 29812 solver.cpp:470] Iteration 58400, lr = 0.00049
I0225 02:01:42.786572 29812 solver.cpp:189] Iteration 58450, loss = 0.145415
I0225 02:01:42.786597 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.145415 (* 1 = 0.145415 loss)
I0225 02:01:42.786603 29812 solver.cpp:470] Iteration 58450, lr = 0.00049
I0225 02:02:02.181677 29812 solver.cpp:189] Iteration 58500, loss = 0.195153
I0225 02:02:02.181742 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.195153 (* 1 = 0.195153 loss)
I0225 02:02:02.181748 29812 solver.cpp:470] Iteration 58500, lr = 0.00049
I0225 02:02:21.575239 29812 solver.cpp:189] Iteration 58550, loss = 0.224213
I0225 02:02:21.575263 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.224214 (* 1 = 0.224214 loss)
I0225 02:02:21.575268 29812 solver.cpp:470] Iteration 58550, lr = 0.00049
I0225 02:02:40.959244 29812 solver.cpp:189] Iteration 58600, loss = 0.0767452
I0225 02:02:40.959316 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0767455 (* 1 = 0.0767455 loss)
I0225 02:02:40.959331 29812 solver.cpp:470] Iteration 58600, lr = 0.00049
I0225 02:03:00.350957 29812 solver.cpp:189] Iteration 58650, loss = 0.13907
I0225 02:03:00.350983 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.13907 (* 1 = 0.13907 loss)
I0225 02:03:00.350989 29812 solver.cpp:470] Iteration 58650, lr = 0.00049
I0225 02:03:19.741159 29812 solver.cpp:189] Iteration 58700, loss = 0.150937
I0225 02:03:19.741200 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.150937 (* 1 = 0.150937 loss)
I0225 02:03:19.741205 29812 solver.cpp:470] Iteration 58700, lr = 0.00049
I0225 02:03:39.129528 29812 solver.cpp:189] Iteration 58750, loss = 0.1284
I0225 02:03:39.129552 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.1284 (* 1 = 0.1284 loss)
I0225 02:03:39.129559 29812 solver.cpp:470] Iteration 58750, lr = 0.00049
I0225 02:03:58.520072 29812 solver.cpp:189] Iteration 58800, loss = 0.140208
I0225 02:03:58.520144 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.140208 (* 1 = 0.140208 loss)
I0225 02:03:58.520159 29812 solver.cpp:470] Iteration 58800, lr = 0.00049
I0225 02:04:17.913920 29812 solver.cpp:189] Iteration 58850, loss = 0.168317
I0225 02:04:17.913946 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.168317 (* 1 = 0.168317 loss)
I0225 02:04:17.913952 29812 solver.cpp:470] Iteration 58850, lr = 0.00049
I0225 02:04:37.306354 29812 solver.cpp:189] Iteration 58900, loss = 0.0823449
I0225 02:04:37.306392 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0823452 (* 1 = 0.0823452 loss)
I0225 02:04:37.306398 29812 solver.cpp:470] Iteration 58900, lr = 0.00049
I0225 02:04:56.692198 29812 solver.cpp:189] Iteration 58950, loss = 0.146165
I0225 02:04:56.692224 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.146165 (* 1 = 0.146165 loss)
I0225 02:04:56.692229 29812 solver.cpp:470] Iteration 58950, lr = 0.00049
I0225 02:05:15.841951 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_59000.caffemodel
I0225 02:05:15.946010 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_59000.solverstate
I0225 02:05:16.003726 29812 solver.cpp:266] Iteration 59000, Testing net (#0)
I0225 02:05:23.656977 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8951
I0225 02:05:23.657016 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.363418 (* 1 = 0.363418 loss)
I0225 02:05:23.945364 29812 solver.cpp:189] Iteration 59000, loss = 0.259399
I0225 02:05:23.945389 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.259399 (* 1 = 0.259399 loss)
I0225 02:05:23.945394 29812 solver.cpp:470] Iteration 59000, lr = 0.00049
I0225 02:05:43.326012 29812 solver.cpp:189] Iteration 59050, loss = 0.145918
I0225 02:05:43.326035 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.145918 (* 1 = 0.145918 loss)
I0225 02:05:43.326040 29812 solver.cpp:470] Iteration 59050, lr = 0.00049
I0225 02:06:02.708243 29812 solver.cpp:189] Iteration 59100, loss = 0.169891
I0225 02:06:02.708317 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.169891 (* 1 = 0.169891 loss)
I0225 02:06:02.708330 29812 solver.cpp:470] Iteration 59100, lr = 0.00049
I0225 02:06:22.093190 29812 solver.cpp:189] Iteration 59150, loss = 0.153708
I0225 02:06:22.093216 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.153708 (* 1 = 0.153708 loss)
I0225 02:06:22.093221 29812 solver.cpp:470] Iteration 59150, lr = 0.00049
I0225 02:06:41.475066 29812 solver.cpp:189] Iteration 59200, loss = 0.150071
I0225 02:06:41.475148 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.150071 (* 1 = 0.150071 loss)
I0225 02:06:41.475155 29812 solver.cpp:470] Iteration 59200, lr = 0.00049
I0225 02:07:00.858021 29812 solver.cpp:189] Iteration 59250, loss = 0.0830772
I0225 02:07:00.858045 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0830775 (* 1 = 0.0830775 loss)
I0225 02:07:00.858052 29812 solver.cpp:470] Iteration 59250, lr = 0.00049
I0225 02:07:20.229821 29812 solver.cpp:189] Iteration 59300, loss = 0.131471
I0225 02:07:20.229866 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.131471 (* 1 = 0.131471 loss)
I0225 02:07:20.229871 29812 solver.cpp:470] Iteration 59300, lr = 0.00049
I0225 02:07:39.620784 29812 solver.cpp:189] Iteration 59350, loss = 0.181693
I0225 02:07:39.620808 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.181694 (* 1 = 0.181694 loss)
I0225 02:07:39.620812 29812 solver.cpp:470] Iteration 59350, lr = 0.00049
I0225 02:07:59.012554 29812 solver.cpp:189] Iteration 59400, loss = 0.116575
I0225 02:07:59.012626 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.116575 (* 1 = 0.116575 loss)
I0225 02:07:59.012641 29812 solver.cpp:470] Iteration 59400, lr = 0.00049
I0225 02:08:18.400727 29812 solver.cpp:189] Iteration 59450, loss = 0.2108
I0225 02:08:18.400750 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.210801 (* 1 = 0.210801 loss)
I0225 02:08:18.400754 29812 solver.cpp:470] Iteration 59450, lr = 0.00049
I0225 02:08:37.792233 29812 solver.cpp:189] Iteration 59500, loss = 0.181145
I0225 02:08:37.792326 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.181145 (* 1 = 0.181145 loss)
I0225 02:08:37.792332 29812 solver.cpp:470] Iteration 59500, lr = 0.00049
I0225 02:08:57.179308 29812 solver.cpp:189] Iteration 59550, loss = 0.105907
I0225 02:08:57.179332 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105908 (* 1 = 0.105908 loss)
I0225 02:08:57.179337 29812 solver.cpp:470] Iteration 59550, lr = 0.00049
I0225 02:09:16.561151 29812 solver.cpp:189] Iteration 59600, loss = 0.0590462
I0225 02:09:16.561213 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0590464 (* 1 = 0.0590464 loss)
I0225 02:09:16.561218 29812 solver.cpp:470] Iteration 59600, lr = 0.00049
I0225 02:09:35.940407 29812 solver.cpp:189] Iteration 59650, loss = 0.23922
I0225 02:09:35.940429 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.23922 (* 1 = 0.23922 loss)
I0225 02:09:35.940435 29812 solver.cpp:470] Iteration 59650, lr = 0.00049
I0225 02:09:55.329707 29812 solver.cpp:189] Iteration 59700, loss = 0.105258
I0225 02:09:55.329779 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105258 (* 1 = 0.105258 loss)
I0225 02:09:55.329793 29812 solver.cpp:470] Iteration 59700, lr = 0.00049
I0225 02:10:14.712111 29812 solver.cpp:189] Iteration 59750, loss = 0.148302
I0225 02:10:14.712136 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.148302 (* 1 = 0.148302 loss)
I0225 02:10:14.712142 29812 solver.cpp:470] Iteration 59750, lr = 0.00049
I0225 02:10:34.084925 29812 solver.cpp:189] Iteration 59800, loss = 0.185634
I0225 02:10:34.084987 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.185635 (* 1 = 0.185635 loss)
I0225 02:10:34.084993 29812 solver.cpp:470] Iteration 59800, lr = 0.00049
I0225 02:10:53.469696 29812 solver.cpp:189] Iteration 59850, loss = 0.166012
I0225 02:10:53.469719 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.166012 (* 1 = 0.166012 loss)
I0225 02:10:53.469724 29812 solver.cpp:470] Iteration 59850, lr = 0.00049
I0225 02:11:12.862114 29812 solver.cpp:189] Iteration 59900, loss = 0.121202
I0225 02:11:12.862176 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.121202 (* 1 = 0.121202 loss)
I0225 02:11:12.862184 29812 solver.cpp:470] Iteration 59900, lr = 0.00049
I0225 02:11:32.250357 29812 solver.cpp:189] Iteration 59950, loss = 0.110645
I0225 02:11:32.250381 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.110645 (* 1 = 0.110645 loss)
I0225 02:11:32.250387 29812 solver.cpp:470] Iteration 59950, lr = 0.00049
I0225 02:11:51.376878 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_60000.caffemodel
I0225 02:11:51.479001 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_60000.solverstate
I0225 02:11:51.536407 29812 solver.cpp:266] Iteration 60000, Testing net (#0)
I0225 02:11:59.191336 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8867
I0225 02:11:59.191373 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.402339 (* 1 = 0.402339 loss)
I0225 02:11:59.477756 29812 solver.cpp:189] Iteration 60000, loss = 0.169451
I0225 02:11:59.477777 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.169451 (* 1 = 0.169451 loss)
I0225 02:11:59.477787 29812 solver.cpp:470] Iteration 60000, lr = 0.000343
I0225 02:12:18.876328 29812 solver.cpp:189] Iteration 60050, loss = 0.161856
I0225 02:12:18.876353 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.161856 (* 1 = 0.161856 loss)
I0225 02:12:18.876359 29812 solver.cpp:470] Iteration 60050, lr = 0.000343
I0225 02:12:38.277420 29812 solver.cpp:189] Iteration 60100, loss = 0.0441246
I0225 02:12:38.277492 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0441249 (* 1 = 0.0441249 loss)
I0225 02:12:38.277506 29812 solver.cpp:470] Iteration 60100, lr = 0.000343
I0225 02:12:57.665384 29812 solver.cpp:189] Iteration 60150, loss = 0.212162
I0225 02:12:57.665412 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.212163 (* 1 = 0.212163 loss)
I0225 02:12:57.665418 29812 solver.cpp:470] Iteration 60150, lr = 0.000343
I0225 02:13:17.063729 29812 solver.cpp:189] Iteration 60200, loss = 0.0335091
I0225 02:13:17.063769 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0335094 (* 1 = 0.0335094 loss)
I0225 02:13:17.063776 29812 solver.cpp:470] Iteration 60200, lr = 0.000343
I0225 02:13:36.459024 29812 solver.cpp:189] Iteration 60250, loss = 0.0743607
I0225 02:13:36.459048 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.074361 (* 1 = 0.074361 loss)
I0225 02:13:36.459054 29812 solver.cpp:470] Iteration 60250, lr = 0.000343
I0225 02:13:55.847587 29812 solver.cpp:189] Iteration 60300, loss = 0.0709992
I0225 02:13:55.847650 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0709995 (* 1 = 0.0709995 loss)
I0225 02:13:55.847656 29812 solver.cpp:470] Iteration 60300, lr = 0.000343
I0225 02:14:15.236537 29812 solver.cpp:189] Iteration 60350, loss = 0.117399
I0225 02:14:15.236562 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.117399 (* 1 = 0.117399 loss)
I0225 02:14:15.236568 29812 solver.cpp:470] Iteration 60350, lr = 0.000343
I0225 02:14:34.626881 29812 solver.cpp:189] Iteration 60400, loss = 0.0756683
I0225 02:14:34.626932 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0756686 (* 1 = 0.0756686 loss)
I0225 02:14:34.626938 29812 solver.cpp:470] Iteration 60400, lr = 0.000343
I0225 02:14:54.023563 29812 solver.cpp:189] Iteration 60450, loss = 0.0987867
I0225 02:14:54.023588 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.098787 (* 1 = 0.098787 loss)
I0225 02:14:54.023596 29812 solver.cpp:470] Iteration 60450, lr = 0.000343
I0225 02:15:13.421648 29812 solver.cpp:189] Iteration 60500, loss = 0.0503962
I0225 02:15:13.421718 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0503966 (* 1 = 0.0503966 loss)
I0225 02:15:13.421733 29812 solver.cpp:470] Iteration 60500, lr = 0.000343
I0225 02:15:32.811342 29812 solver.cpp:189] Iteration 60550, loss = 0.0571039
I0225 02:15:32.811367 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0571043 (* 1 = 0.0571043 loss)
I0225 02:15:32.811372 29812 solver.cpp:470] Iteration 60550, lr = 0.000343
I0225 02:15:52.204576 29812 solver.cpp:189] Iteration 60600, loss = 0.0607436
I0225 02:15:52.204638 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.060744 (* 1 = 0.060744 loss)
I0225 02:15:52.204644 29812 solver.cpp:470] Iteration 60600, lr = 0.000343
I0225 02:16:11.595829 29812 solver.cpp:189] Iteration 60650, loss = 0.120702
I0225 02:16:11.595852 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.120702 (* 1 = 0.120702 loss)
I0225 02:16:11.595857 29812 solver.cpp:470] Iteration 60650, lr = 0.000343
I0225 02:16:30.980922 29812 solver.cpp:189] Iteration 60700, loss = 0.0810018
I0225 02:16:30.980991 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0810022 (* 1 = 0.0810022 loss)
I0225 02:16:30.981006 29812 solver.cpp:470] Iteration 60700, lr = 0.000343
I0225 02:16:50.364841 29812 solver.cpp:189] Iteration 60750, loss = 0.0546413
I0225 02:16:50.364864 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0546418 (* 1 = 0.0546418 loss)
I0225 02:16:50.364871 29812 solver.cpp:470] Iteration 60750, lr = 0.000343
I0225 02:17:09.759220 29812 solver.cpp:189] Iteration 60800, loss = 0.0698264
I0225 02:17:09.759313 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0698268 (* 1 = 0.0698268 loss)
I0225 02:17:09.759330 29812 solver.cpp:470] Iteration 60800, lr = 0.000343
I0225 02:17:29.148190 29812 solver.cpp:189] Iteration 60850, loss = 0.0785755
I0225 02:17:29.148216 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0785759 (* 1 = 0.0785759 loss)
I0225 02:17:29.148221 29812 solver.cpp:470] Iteration 60850, lr = 0.000343
I0225 02:17:48.534313 29812 solver.cpp:189] Iteration 60900, loss = 0.119345
I0225 02:17:48.534353 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.119345 (* 1 = 0.119345 loss)
I0225 02:17:48.534358 29812 solver.cpp:470] Iteration 60900, lr = 0.000343
I0225 02:18:07.926460 29812 solver.cpp:189] Iteration 60950, loss = 0.164389
I0225 02:18:07.926484 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.16439 (* 1 = 0.16439 loss)
I0225 02:18:07.926491 29812 solver.cpp:470] Iteration 60950, lr = 0.000343
I0225 02:18:27.078519 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_61000.caffemodel
I0225 02:18:27.200296 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_61000.solverstate
I0225 02:18:27.258612 29812 solver.cpp:266] Iteration 61000, Testing net (#0)
I0225 02:18:34.911257 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9052
I0225 02:18:34.911294 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.32822 (* 1 = 0.32822 loss)
I0225 02:18:35.199362 29812 solver.cpp:189] Iteration 61000, loss = 0.0949442
I0225 02:18:35.199388 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0949447 (* 1 = 0.0949447 loss)
I0225 02:18:35.199393 29812 solver.cpp:470] Iteration 61000, lr = 0.000343
I0225 02:18:54.600261 29812 solver.cpp:189] Iteration 61050, loss = 0.120431
I0225 02:18:54.600286 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.120432 (* 1 = 0.120432 loss)
I0225 02:18:54.600291 29812 solver.cpp:470] Iteration 61050, lr = 0.000343
I0225 02:19:13.997067 29812 solver.cpp:189] Iteration 61100, loss = 0.0769133
I0225 02:19:13.997155 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0769138 (* 1 = 0.0769138 loss)
I0225 02:19:13.997162 29812 solver.cpp:470] Iteration 61100, lr = 0.000343
I0225 02:19:33.400503 29812 solver.cpp:189] Iteration 61150, loss = 0.0953538
I0225 02:19:33.400527 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0953542 (* 1 = 0.0953542 loss)
I0225 02:19:33.400534 29812 solver.cpp:470] Iteration 61150, lr = 0.000343
I0225 02:19:52.800243 29812 solver.cpp:189] Iteration 61200, loss = 0.10577
I0225 02:19:52.800330 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105771 (* 1 = 0.105771 loss)
I0225 02:19:52.800338 29812 solver.cpp:470] Iteration 61200, lr = 0.000343
I0225 02:20:12.201014 29812 solver.cpp:189] Iteration 61250, loss = 0.0586186
I0225 02:20:12.201038 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0586191 (* 1 = 0.0586191 loss)
I0225 02:20:12.201045 29812 solver.cpp:470] Iteration 61250, lr = 0.000343
I0225 02:20:31.603976 29812 solver.cpp:189] Iteration 61300, loss = 0.195679
I0225 02:20:31.604038 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.19568 (* 1 = 0.19568 loss)
I0225 02:20:31.604043 29812 solver.cpp:470] Iteration 61300, lr = 0.000343
I0225 02:20:50.995192 29812 solver.cpp:189] Iteration 61350, loss = 0.185092
I0225 02:20:50.995215 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.185092 (* 1 = 0.185092 loss)
I0225 02:20:50.995221 29812 solver.cpp:470] Iteration 61350, lr = 0.000343
I0225 02:21:10.392348 29812 solver.cpp:189] Iteration 61400, loss = 0.144442
I0225 02:21:10.392437 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.144442 (* 1 = 0.144442 loss)
I0225 02:21:10.392453 29812 solver.cpp:470] Iteration 61400, lr = 0.000343
I0225 02:21:29.784772 29812 solver.cpp:189] Iteration 61450, loss = 0.20901
I0225 02:21:29.784800 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.209011 (* 1 = 0.209011 loss)
I0225 02:21:29.784806 29812 solver.cpp:470] Iteration 61450, lr = 0.000343
I0225 02:21:49.187402 29812 solver.cpp:189] Iteration 61500, loss = 0.115521
I0225 02:21:49.187494 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.115522 (* 1 = 0.115522 loss)
I0225 02:21:49.187500 29812 solver.cpp:470] Iteration 61500, lr = 0.000343
I0225 02:22:08.583395 29812 solver.cpp:189] Iteration 61550, loss = 0.0623959
I0225 02:22:08.583418 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0623964 (* 1 = 0.0623964 loss)
I0225 02:22:08.583425 29812 solver.cpp:470] Iteration 61550, lr = 0.000343
I0225 02:22:27.971475 29812 solver.cpp:189] Iteration 61600, loss = 0.055763
I0225 02:22:27.971547 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0557635 (* 1 = 0.0557635 loss)
I0225 02:22:27.971564 29812 solver.cpp:470] Iteration 61600, lr = 0.000343
I0225 02:22:47.361400 29812 solver.cpp:189] Iteration 61650, loss = 0.172814
I0225 02:22:47.361424 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.172815 (* 1 = 0.172815 loss)
I0225 02:22:47.361430 29812 solver.cpp:470] Iteration 61650, lr = 0.000343
I0225 02:23:06.764797 29812 solver.cpp:189] Iteration 61700, loss = 0.123883
I0225 02:23:06.764839 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.123884 (* 1 = 0.123884 loss)
I0225 02:23:06.764845 29812 solver.cpp:470] Iteration 61700, lr = 0.000343
I0225 02:23:26.158931 29812 solver.cpp:189] Iteration 61750, loss = 0.188164
I0225 02:23:26.158956 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.188165 (* 1 = 0.188165 loss)
I0225 02:23:26.158962 29812 solver.cpp:470] Iteration 61750, lr = 0.000343
I0225 02:23:45.554754 29812 solver.cpp:189] Iteration 61800, loss = 0.0618452
I0225 02:23:45.554826 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0618457 (* 1 = 0.0618457 loss)
I0225 02:23:45.554841 29812 solver.cpp:470] Iteration 61800, lr = 0.000343
I0225 02:24:04.949323 29812 solver.cpp:189] Iteration 61850, loss = 0.151407
I0225 02:24:04.949347 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.151408 (* 1 = 0.151408 loss)
I0225 02:24:04.949352 29812 solver.cpp:470] Iteration 61850, lr = 0.000343
I0225 02:24:24.340389 29812 solver.cpp:189] Iteration 61900, loss = 0.0774079
I0225 02:24:24.340461 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0774084 (* 1 = 0.0774084 loss)
I0225 02:24:24.340476 29812 solver.cpp:470] Iteration 61900, lr = 0.000343
I0225 02:24:43.734061 29812 solver.cpp:189] Iteration 61950, loss = 0.108784
I0225 02:24:43.734086 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.108785 (* 1 = 0.108785 loss)
I0225 02:24:43.734091 29812 solver.cpp:470] Iteration 61950, lr = 0.000343
I0225 02:25:02.885694 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_62000.caffemodel
I0225 02:25:03.007959 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_62000.solverstate
I0225 02:25:03.065726 29812 solver.cpp:266] Iteration 62000, Testing net (#0)
I0225 02:25:10.718175 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8906
I0225 02:25:10.718216 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.396455 (* 1 = 0.396455 loss)
I0225 02:25:11.005661 29812 solver.cpp:189] Iteration 62000, loss = 0.155524
I0225 02:25:11.005681 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.155524 (* 1 = 0.155524 loss)
I0225 02:25:11.005687 29812 solver.cpp:470] Iteration 62000, lr = 0.000343
I0225 02:25:30.391705 29812 solver.cpp:189] Iteration 62050, loss = 0.120732
I0225 02:25:30.391728 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.120733 (* 1 = 0.120733 loss)
I0225 02:25:30.391736 29812 solver.cpp:470] Iteration 62050, lr = 0.000343
I0225 02:25:49.789013 29812 solver.cpp:189] Iteration 62100, loss = 0.160627
I0225 02:25:49.789126 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.160627 (* 1 = 0.160627 loss)
I0225 02:25:49.789134 29812 solver.cpp:470] Iteration 62100, lr = 0.000343
I0225 02:26:09.184329 29812 solver.cpp:189] Iteration 62150, loss = 0.117292
I0225 02:26:09.184355 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.117292 (* 1 = 0.117292 loss)
I0225 02:26:09.184360 29812 solver.cpp:470] Iteration 62150, lr = 0.000343
I0225 02:26:28.570868 29812 solver.cpp:189] Iteration 62200, loss = 0.0937728
I0225 02:26:28.570943 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0937733 (* 1 = 0.0937733 loss)
I0225 02:26:28.570958 29812 solver.cpp:470] Iteration 62200, lr = 0.000343
I0225 02:26:47.961455 29812 solver.cpp:189] Iteration 62250, loss = 0.118382
I0225 02:26:47.961479 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.118383 (* 1 = 0.118383 loss)
I0225 02:26:47.961486 29812 solver.cpp:470] Iteration 62250, lr = 0.000343
I0225 02:27:07.349961 29812 solver.cpp:189] Iteration 62300, loss = 0.197622
I0225 02:27:07.350033 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.197623 (* 1 = 0.197623 loss)
I0225 02:27:07.350049 29812 solver.cpp:470] Iteration 62300, lr = 0.000343
I0225 02:27:26.739783 29812 solver.cpp:189] Iteration 62350, loss = 0.141245
I0225 02:27:26.739807 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.141246 (* 1 = 0.141246 loss)
I0225 02:27:26.739814 29812 solver.cpp:470] Iteration 62350, lr = 0.000343
I0225 02:27:46.125478 29812 solver.cpp:189] Iteration 62400, loss = 0.114363
I0225 02:27:46.125546 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.114363 (* 1 = 0.114363 loss)
I0225 02:27:46.125562 29812 solver.cpp:470] Iteration 62400, lr = 0.000343
I0225 02:28:05.518662 29812 solver.cpp:189] Iteration 62450, loss = 0.160982
I0225 02:28:05.518703 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.160983 (* 1 = 0.160983 loss)
I0225 02:28:05.518710 29812 solver.cpp:470] Iteration 62450, lr = 0.000343
I0225 02:28:24.907160 29812 solver.cpp:189] Iteration 62500, loss = 0.143166
I0225 02:28:24.907201 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.143167 (* 1 = 0.143167 loss)
I0225 02:28:24.907207 29812 solver.cpp:470] Iteration 62500, lr = 0.000343
I0225 02:28:44.293133 29812 solver.cpp:189] Iteration 62550, loss = 0.0893693
I0225 02:28:44.293156 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0893698 (* 1 = 0.0893698 loss)
I0225 02:28:44.293162 29812 solver.cpp:470] Iteration 62550, lr = 0.000343
I0225 02:29:03.688555 29812 solver.cpp:189] Iteration 62600, loss = 0.112881
I0225 02:29:03.688633 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.112882 (* 1 = 0.112882 loss)
I0225 02:29:03.688655 29812 solver.cpp:470] Iteration 62600, lr = 0.000343
I0225 02:29:23.068706 29812 solver.cpp:189] Iteration 62650, loss = 0.186184
I0225 02:29:23.068730 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.186184 (* 1 = 0.186184 loss)
I0225 02:29:23.068737 29812 solver.cpp:470] Iteration 62650, lr = 0.000343
I0225 02:29:42.470373 29812 solver.cpp:189] Iteration 62700, loss = 0.187293
I0225 02:29:42.470412 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.187294 (* 1 = 0.187294 loss)
I0225 02:29:42.470417 29812 solver.cpp:470] Iteration 62700, lr = 0.000343
I0225 02:30:01.871603 29812 solver.cpp:189] Iteration 62750, loss = 0.0830237
I0225 02:30:01.871628 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0830241 (* 1 = 0.0830241 loss)
I0225 02:30:01.871634 29812 solver.cpp:470] Iteration 62750, lr = 0.000343
I0225 02:30:21.266851 29812 solver.cpp:189] Iteration 62800, loss = 0.0729032
I0225 02:30:21.266933 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0729036 (* 1 = 0.0729036 loss)
I0225 02:30:21.266949 29812 solver.cpp:470] Iteration 62800, lr = 0.000343
I0225 02:30:40.648030 29812 solver.cpp:189] Iteration 62850, loss = 0.136393
I0225 02:30:40.648056 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.136394 (* 1 = 0.136394 loss)
I0225 02:30:40.648061 29812 solver.cpp:470] Iteration 62850, lr = 0.000343
I0225 02:31:00.041028 29812 solver.cpp:189] Iteration 62900, loss = 0.131802
I0225 02:31:00.041070 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.131802 (* 1 = 0.131802 loss)
I0225 02:31:00.041076 29812 solver.cpp:470] Iteration 62900, lr = 0.000343
I0225 02:31:19.444424 29812 solver.cpp:189] Iteration 62950, loss = 0.0606443
I0225 02:31:19.444447 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0606447 (* 1 = 0.0606447 loss)
I0225 02:31:19.444453 29812 solver.cpp:470] Iteration 62950, lr = 0.000343
I0225 02:31:38.586971 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_63000.caffemodel
I0225 02:31:38.712486 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_63000.solverstate
I0225 02:31:38.770664 29812 solver.cpp:266] Iteration 63000, Testing net (#0)
I0225 02:31:46.417392 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8965
I0225 02:31:46.417428 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.369562 (* 1 = 0.369562 loss)
I0225 02:31:46.705147 29812 solver.cpp:189] Iteration 63000, loss = 0.227437
I0225 02:31:46.705171 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.227438 (* 1 = 0.227438 loss)
I0225 02:31:46.705178 29812 solver.cpp:470] Iteration 63000, lr = 0.000343
I0225 02:32:06.089345 29812 solver.cpp:189] Iteration 63050, loss = 0.0434328
I0225 02:32:06.089370 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0434332 (* 1 = 0.0434332 loss)
I0225 02:32:06.089376 29812 solver.cpp:470] Iteration 63050, lr = 0.000343
I0225 02:32:25.479451 29812 solver.cpp:189] Iteration 63100, loss = 0.124934
I0225 02:32:25.479522 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.124934 (* 1 = 0.124934 loss)
I0225 02:32:25.479538 29812 solver.cpp:470] Iteration 63100, lr = 0.000343
I0225 02:32:44.867956 29812 solver.cpp:189] Iteration 63150, loss = 0.115044
I0225 02:32:44.867981 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.115045 (* 1 = 0.115045 loss)
I0225 02:32:44.867987 29812 solver.cpp:470] Iteration 63150, lr = 0.000343
I0225 02:33:04.251602 29812 solver.cpp:189] Iteration 63200, loss = 0.154433
I0225 02:33:04.251643 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.154433 (* 1 = 0.154433 loss)
I0225 02:33:04.251651 29812 solver.cpp:470] Iteration 63200, lr = 0.000343
I0225 02:33:23.635059 29812 solver.cpp:189] Iteration 63250, loss = 0.159044
I0225 02:33:23.635083 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.159045 (* 1 = 0.159045 loss)
I0225 02:33:23.635088 29812 solver.cpp:470] Iteration 63250, lr = 0.000343
I0225 02:33:43.028120 29812 solver.cpp:189] Iteration 63300, loss = 0.177791
I0225 02:33:43.028195 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.177791 (* 1 = 0.177791 loss)
I0225 02:33:43.028211 29812 solver.cpp:470] Iteration 63300, lr = 0.000343
I0225 02:34:02.412194 29812 solver.cpp:189] Iteration 63350, loss = 0.0923439
I0225 02:34:02.412217 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0923443 (* 1 = 0.0923443 loss)
I0225 02:34:02.412224 29812 solver.cpp:470] Iteration 63350, lr = 0.000343
I0225 02:34:21.790993 29812 solver.cpp:189] Iteration 63400, loss = 0.208888
I0225 02:34:21.791108 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.208889 (* 1 = 0.208889 loss)
I0225 02:34:21.791115 29812 solver.cpp:470] Iteration 63400, lr = 0.000343
I0225 02:34:41.172582 29812 solver.cpp:189] Iteration 63450, loss = 0.176974
I0225 02:34:41.172606 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.176974 (* 1 = 0.176974 loss)
I0225 02:34:41.172612 29812 solver.cpp:470] Iteration 63450, lr = 0.000343
I0225 02:35:00.563843 29812 solver.cpp:189] Iteration 63500, loss = 0.147622
I0225 02:35:00.563916 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.147622 (* 1 = 0.147622 loss)
I0225 02:35:00.563932 29812 solver.cpp:470] Iteration 63500, lr = 0.000343
I0225 02:35:19.956941 29812 solver.cpp:189] Iteration 63550, loss = 0.0969383
I0225 02:35:19.956965 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0969387 (* 1 = 0.0969387 loss)
I0225 02:35:19.956971 29812 solver.cpp:470] Iteration 63550, lr = 0.000343
I0225 02:35:39.346545 29812 solver.cpp:189] Iteration 63600, loss = 0.166729
I0225 02:35:39.346606 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.16673 (* 1 = 0.16673 loss)
I0225 02:35:39.346611 29812 solver.cpp:470] Iteration 63600, lr = 0.000343
I0225 02:35:58.733194 29812 solver.cpp:189] Iteration 63650, loss = 0.128032
I0225 02:35:58.733219 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.128032 (* 1 = 0.128032 loss)
I0225 02:35:58.733224 29812 solver.cpp:470] Iteration 63650, lr = 0.000343
I0225 02:36:18.115867 29812 solver.cpp:189] Iteration 63700, loss = 0.0864802
I0225 02:36:18.115957 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0864806 (* 1 = 0.0864806 loss)
I0225 02:36:18.115963 29812 solver.cpp:470] Iteration 63700, lr = 0.000343
I0225 02:36:37.500404 29812 solver.cpp:189] Iteration 63750, loss = 0.086596
I0225 02:36:37.500429 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0865964 (* 1 = 0.0865964 loss)
I0225 02:36:37.500435 29812 solver.cpp:470] Iteration 63750, lr = 0.000343
I0225 02:36:56.880533 29812 solver.cpp:189] Iteration 63800, loss = 0.0978712
I0225 02:36:56.880604 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0978716 (* 1 = 0.0978716 loss)
I0225 02:36:56.880620 29812 solver.cpp:470] Iteration 63800, lr = 0.000343
I0225 02:37:16.265992 29812 solver.cpp:189] Iteration 63850, loss = 0.0869376
I0225 02:37:16.266017 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.086938 (* 1 = 0.086938 loss)
I0225 02:37:16.266023 29812 solver.cpp:470] Iteration 63850, lr = 0.000343
I0225 02:37:35.650818 29812 solver.cpp:189] Iteration 63900, loss = 0.107556
I0225 02:37:35.650863 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.107556 (* 1 = 0.107556 loss)
I0225 02:37:35.650871 29812 solver.cpp:470] Iteration 63900, lr = 0.000343
I0225 02:37:55.032667 29812 solver.cpp:189] Iteration 63950, loss = 0.0609444
I0225 02:37:55.032691 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0609448 (* 1 = 0.0609448 loss)
I0225 02:37:55.032696 29812 solver.cpp:470] Iteration 63950, lr = 0.000343
I0225 02:38:14.169495 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_64000.caffemodel
I0225 02:38:14.271904 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_64000.solverstate
I0225 02:38:14.329535 29812 solver.cpp:266] Iteration 64000, Testing net (#0)
I0225 02:38:21.983592 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8978
I0225 02:38:21.983628 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.363408 (* 1 = 0.363408 loss)
I0225 02:38:22.270201 29812 solver.cpp:189] Iteration 64000, loss = 0.0732212
I0225 02:38:22.270226 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0732216 (* 1 = 0.0732216 loss)
I0225 02:38:22.270231 29812 solver.cpp:470] Iteration 64000, lr = 0.000343
I0225 02:38:41.663466 29812 solver.cpp:189] Iteration 64050, loss = 0.159747
I0225 02:38:41.663492 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.159747 (* 1 = 0.159747 loss)
I0225 02:38:41.663498 29812 solver.cpp:470] Iteration 64050, lr = 0.000343
I0225 02:39:01.048916 29812 solver.cpp:189] Iteration 64100, loss = 0.0391391
I0225 02:39:01.049039 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0391395 (* 1 = 0.0391395 loss)
I0225 02:39:01.049046 29812 solver.cpp:470] Iteration 64100, lr = 0.000343
I0225 02:39:20.441040 29812 solver.cpp:189] Iteration 64150, loss = 0.0873583
I0225 02:39:20.441064 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0873588 (* 1 = 0.0873588 loss)
I0225 02:39:20.441071 29812 solver.cpp:470] Iteration 64150, lr = 0.000343
I0225 02:39:39.831830 29812 solver.cpp:189] Iteration 64200, loss = 0.0468705
I0225 02:39:39.831897 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.046871 (* 1 = 0.046871 loss)
I0225 02:39:39.831903 29812 solver.cpp:470] Iteration 64200, lr = 0.000343
I0225 02:39:59.223513 29812 solver.cpp:189] Iteration 64250, loss = 0.102956
I0225 02:39:59.223539 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.102957 (* 1 = 0.102957 loss)
I0225 02:39:59.223546 29812 solver.cpp:470] Iteration 64250, lr = 0.000343
I0225 02:40:18.619913 29812 solver.cpp:189] Iteration 64300, loss = 0.0685713
I0225 02:40:18.619983 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0685718 (* 1 = 0.0685718 loss)
I0225 02:40:18.619998 29812 solver.cpp:470] Iteration 64300, lr = 0.000343
I0225 02:40:38.011078 29812 solver.cpp:189] Iteration 64350, loss = 0.109188
I0225 02:40:38.011102 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.109188 (* 1 = 0.109188 loss)
I0225 02:40:38.011107 29812 solver.cpp:470] Iteration 64350, lr = 0.000343
I0225 02:40:57.400336 29812 solver.cpp:189] Iteration 64400, loss = 0.060786
I0225 02:40:57.400418 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0607864 (* 1 = 0.0607864 loss)
I0225 02:40:57.400426 29812 solver.cpp:470] Iteration 64400, lr = 0.000343
I0225 02:41:16.781857 29812 solver.cpp:189] Iteration 64450, loss = 0.0747681
I0225 02:41:16.781883 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0747686 (* 1 = 0.0747686 loss)
I0225 02:41:16.781889 29812 solver.cpp:470] Iteration 64450, lr = 0.000343
I0225 02:41:36.179908 29812 solver.cpp:189] Iteration 64500, loss = 0.105188
I0225 02:41:36.179980 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105189 (* 1 = 0.105189 loss)
I0225 02:41:36.179994 29812 solver.cpp:470] Iteration 64500, lr = 0.000343
I0225 02:41:55.572499 29812 solver.cpp:189] Iteration 64550, loss = 0.128697
I0225 02:41:55.572523 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.128697 (* 1 = 0.128697 loss)
I0225 02:41:55.572530 29812 solver.cpp:470] Iteration 64550, lr = 0.000343
I0225 02:42:14.963485 29812 solver.cpp:189] Iteration 64600, loss = 0.0643903
I0225 02:42:14.963577 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0643908 (* 1 = 0.0643908 loss)
I0225 02:42:14.963582 29812 solver.cpp:470] Iteration 64600, lr = 0.000343
I0225 02:42:34.349217 29812 solver.cpp:189] Iteration 64650, loss = 0.118539
I0225 02:42:34.349241 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.11854 (* 1 = 0.11854 loss)
I0225 02:42:34.349247 29812 solver.cpp:470] Iteration 64650, lr = 0.000343
I0225 02:42:53.747432 29812 solver.cpp:189] Iteration 64700, loss = 0.0408596
I0225 02:42:53.747500 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0408601 (* 1 = 0.0408601 loss)
I0225 02:42:53.747515 29812 solver.cpp:470] Iteration 64700, lr = 0.000343
I0225 02:43:13.142371 29812 solver.cpp:189] Iteration 64750, loss = 0.0737067
I0225 02:43:13.142395 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0737071 (* 1 = 0.0737071 loss)
I0225 02:43:13.142401 29812 solver.cpp:470] Iteration 64750, lr = 0.000343
I0225 02:43:32.532114 29812 solver.cpp:189] Iteration 64800, loss = 0.0989486
I0225 02:43:32.532174 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.098949 (* 1 = 0.098949 loss)
I0225 02:43:32.532184 29812 solver.cpp:470] Iteration 64800, lr = 0.000343
I0225 02:43:51.912837 29812 solver.cpp:189] Iteration 64850, loss = 0.112821
I0225 02:43:51.912861 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.112822 (* 1 = 0.112822 loss)
I0225 02:43:51.912868 29812 solver.cpp:470] Iteration 64850, lr = 0.000343
I0225 02:44:11.311066 29812 solver.cpp:189] Iteration 64900, loss = 0.0789135
I0225 02:44:11.311159 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0789139 (* 1 = 0.0789139 loss)
I0225 02:44:11.311175 29812 solver.cpp:470] Iteration 64900, lr = 0.000343
I0225 02:44:30.706084 29812 solver.cpp:189] Iteration 64950, loss = 0.231992
I0225 02:44:30.706111 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.231992 (* 1 = 0.231992 loss)
I0225 02:44:30.706117 29812 solver.cpp:470] Iteration 64950, lr = 0.000343
I0225 02:44:49.852169 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_65000.caffemodel
I0225 02:44:49.954359 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_65000.solverstate
I0225 02:44:50.012075 29812 solver.cpp:266] Iteration 65000, Testing net (#0)
I0225 02:44:57.668694 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.903
I0225 02:44:57.668735 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.3725 (* 1 = 0.3725 loss)
I0225 02:44:57.954375 29812 solver.cpp:189] Iteration 65000, loss = 0.0531246
I0225 02:44:57.954399 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.053125 (* 1 = 0.053125 loss)
I0225 02:44:57.954406 29812 solver.cpp:470] Iteration 65000, lr = 0.000343
I0225 02:45:17.349910 29812 solver.cpp:189] Iteration 65050, loss = 0.251734
I0225 02:45:17.349934 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.251735 (* 1 = 0.251735 loss)
I0225 02:45:17.349939 29812 solver.cpp:470] Iteration 65050, lr = 0.000343
I0225 02:45:36.751245 29812 solver.cpp:189] Iteration 65100, loss = 0.103616
I0225 02:45:36.751318 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.103616 (* 1 = 0.103616 loss)
I0225 02:45:36.751334 29812 solver.cpp:470] Iteration 65100, lr = 0.000343
I0225 02:45:56.157320 29812 solver.cpp:189] Iteration 65150, loss = 0.155701
I0225 02:45:56.157344 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.155701 (* 1 = 0.155701 loss)
I0225 02:45:56.157351 29812 solver.cpp:470] Iteration 65150, lr = 0.000343
I0225 02:46:15.559778 29812 solver.cpp:189] Iteration 65200, loss = 0.0339996
I0225 02:46:15.559847 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.034 (* 1 = 0.034 loss)
I0225 02:46:15.559864 29812 solver.cpp:470] Iteration 65200, lr = 0.000343
I0225 02:46:34.955102 29812 solver.cpp:189] Iteration 65250, loss = 0.06542
I0225 02:46:34.955128 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0654204 (* 1 = 0.0654204 loss)
I0225 02:46:34.955134 29812 solver.cpp:470] Iteration 65250, lr = 0.000343
I0225 02:46:54.358525 29812 solver.cpp:189] Iteration 65300, loss = 0.140497
I0225 02:46:54.358597 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.140498 (* 1 = 0.140498 loss)
I0225 02:46:54.358613 29812 solver.cpp:470] Iteration 65300, lr = 0.000343
I0225 02:47:13.756080 29812 solver.cpp:189] Iteration 65350, loss = 0.156232
I0225 02:47:13.756108 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.156232 (* 1 = 0.156232 loss)
I0225 02:47:13.756114 29812 solver.cpp:470] Iteration 65350, lr = 0.000343
I0225 02:47:33.145069 29812 solver.cpp:189] Iteration 65400, loss = 0.102105
I0225 02:47:33.145129 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.102105 (* 1 = 0.102105 loss)
I0225 02:47:33.145135 29812 solver.cpp:470] Iteration 65400, lr = 0.000343
I0225 02:47:52.545215 29812 solver.cpp:189] Iteration 65450, loss = 0.0526606
I0225 02:47:52.545239 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.052661 (* 1 = 0.052661 loss)
I0225 02:47:52.545245 29812 solver.cpp:470] Iteration 65450, lr = 0.000343
I0225 02:48:11.937849 29812 solver.cpp:189] Iteration 65500, loss = 0.0946793
I0225 02:48:11.937921 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0946797 (* 1 = 0.0946797 loss)
I0225 02:48:11.937935 29812 solver.cpp:470] Iteration 65500, lr = 0.000343
I0225 02:48:31.338492 29812 solver.cpp:189] Iteration 65550, loss = 0.119445
I0225 02:48:31.338516 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.119446 (* 1 = 0.119446 loss)
I0225 02:48:31.338522 29812 solver.cpp:470] Iteration 65550, lr = 0.000343
I0225 02:48:50.730733 29812 solver.cpp:189] Iteration 65600, loss = 0.105585
I0225 02:48:50.730825 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105586 (* 1 = 0.105586 loss)
I0225 02:48:50.730841 29812 solver.cpp:470] Iteration 65600, lr = 0.000343
I0225 02:49:10.132201 29812 solver.cpp:189] Iteration 65650, loss = 0.0843569
I0225 02:49:10.132226 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0843573 (* 1 = 0.0843573 loss)
I0225 02:49:10.132232 29812 solver.cpp:470] Iteration 65650, lr = 0.000343
I0225 02:49:29.519001 29812 solver.cpp:189] Iteration 65700, loss = 0.0691395
I0225 02:49:29.519042 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0691399 (* 1 = 0.0691399 loss)
I0225 02:49:29.519049 29812 solver.cpp:470] Iteration 65700, lr = 0.000343
I0225 02:49:48.905407 29812 solver.cpp:189] Iteration 65750, loss = 0.0638025
I0225 02:49:48.905433 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0638029 (* 1 = 0.0638029 loss)
I0225 02:49:48.905439 29812 solver.cpp:470] Iteration 65750, lr = 0.000343
I0225 02:50:08.303056 29812 solver.cpp:189] Iteration 65800, loss = 0.13936
I0225 02:50:08.303136 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.13936 (* 1 = 0.13936 loss)
I0225 02:50:08.303151 29812 solver.cpp:470] Iteration 65800, lr = 0.000343
I0225 02:50:27.697818 29812 solver.cpp:189] Iteration 65850, loss = 0.0823216
I0225 02:50:27.697844 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.082322 (* 1 = 0.082322 loss)
I0225 02:50:27.697850 29812 solver.cpp:470] Iteration 65850, lr = 0.000343
I0225 02:50:47.086421 29812 solver.cpp:189] Iteration 65900, loss = 0.122876
I0225 02:50:47.086508 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.122877 (* 1 = 0.122877 loss)
I0225 02:50:47.086513 29812 solver.cpp:470] Iteration 65900, lr = 0.000343
I0225 02:51:06.486572 29812 solver.cpp:189] Iteration 65950, loss = 0.0813952
I0225 02:51:06.486595 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0813956 (* 1 = 0.0813956 loss)
I0225 02:51:06.486601 29812 solver.cpp:470] Iteration 65950, lr = 0.000343
I0225 02:51:25.630744 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_66000.caffemodel
I0225 02:51:25.757983 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_66000.solverstate
I0225 02:51:25.817348 29812 solver.cpp:266] Iteration 66000, Testing net (#0)
I0225 02:51:33.458730 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8964
I0225 02:51:33.458767 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.379204 (* 1 = 0.379204 loss)
I0225 02:51:33.746503 29812 solver.cpp:189] Iteration 66000, loss = 0.0878516
I0225 02:51:33.746528 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.087852 (* 1 = 0.087852 loss)
I0225 02:51:33.746534 29812 solver.cpp:470] Iteration 66000, lr = 0.000343
I0225 02:51:53.135469 29812 solver.cpp:189] Iteration 66050, loss = 0.0823145
I0225 02:51:53.135493 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0823149 (* 1 = 0.0823149 loss)
I0225 02:51:53.135499 29812 solver.cpp:470] Iteration 66050, lr = 0.000343
I0225 02:52:12.534364 29812 solver.cpp:189] Iteration 66100, loss = 0.110394
I0225 02:52:12.534405 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.110394 (* 1 = 0.110394 loss)
I0225 02:52:12.534411 29812 solver.cpp:470] Iteration 66100, lr = 0.000343
I0225 02:52:31.924518 29812 solver.cpp:189] Iteration 66150, loss = 0.135033
I0225 02:52:31.924541 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.135033 (* 1 = 0.135033 loss)
I0225 02:52:31.924547 29812 solver.cpp:470] Iteration 66150, lr = 0.000343
I0225 02:52:51.314070 29812 solver.cpp:189] Iteration 66200, loss = 0.163605
I0225 02:52:51.314153 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.163606 (* 1 = 0.163606 loss)
I0225 02:52:51.314160 29812 solver.cpp:470] Iteration 66200, lr = 0.000343
I0225 02:53:10.713227 29812 solver.cpp:189] Iteration 66250, loss = 0.0721721
I0225 02:53:10.713253 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0721724 (* 1 = 0.0721724 loss)
I0225 02:53:10.713259 29812 solver.cpp:470] Iteration 66250, lr = 0.000343
I0225 02:53:30.096053 29812 solver.cpp:189] Iteration 66300, loss = 0.0517318
I0225 02:53:30.096124 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0517321 (* 1 = 0.0517321 loss)
I0225 02:53:30.096140 29812 solver.cpp:470] Iteration 66300, lr = 0.000343
I0225 02:53:49.491631 29812 solver.cpp:189] Iteration 66350, loss = 0.159356
I0225 02:53:49.491655 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.159356 (* 1 = 0.159356 loss)
I0225 02:53:49.491662 29812 solver.cpp:470] Iteration 66350, lr = 0.000343
I0225 02:54:08.890619 29812 solver.cpp:189] Iteration 66400, loss = 0.26267
I0225 02:54:08.890688 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.262671 (* 1 = 0.262671 loss)
I0225 02:54:08.890704 29812 solver.cpp:470] Iteration 66400, lr = 0.000343
I0225 02:54:28.271373 29812 solver.cpp:189] Iteration 66450, loss = 0.133583
I0225 02:54:28.271397 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.133584 (* 1 = 0.133584 loss)
I0225 02:54:28.271402 29812 solver.cpp:470] Iteration 66450, lr = 0.000343
I0225 02:54:47.657140 29812 solver.cpp:189] Iteration 66500, loss = 0.144857
I0225 02:54:47.657234 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.144858 (* 1 = 0.144858 loss)
I0225 02:54:47.657241 29812 solver.cpp:470] Iteration 66500, lr = 0.000343
I0225 02:55:07.041038 29812 solver.cpp:189] Iteration 66550, loss = 0.0713891
I0225 02:55:07.041062 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0713894 (* 1 = 0.0713894 loss)
I0225 02:55:07.041067 29812 solver.cpp:470] Iteration 66550, lr = 0.000343
I0225 02:55:26.428627 29812 solver.cpp:189] Iteration 66600, loss = 0.250975
I0225 02:55:26.428688 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.250975 (* 1 = 0.250975 loss)
I0225 02:55:26.428694 29812 solver.cpp:470] Iteration 66600, lr = 0.000343
I0225 02:55:45.823487 29812 solver.cpp:189] Iteration 66650, loss = 0.078605
I0225 02:55:45.823513 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0786053 (* 1 = 0.0786053 loss)
I0225 02:55:45.823519 29812 solver.cpp:470] Iteration 66650, lr = 0.000343
I0225 02:56:05.222429 29812 solver.cpp:189] Iteration 66700, loss = 0.0603773
I0225 02:56:05.222498 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0603776 (* 1 = 0.0603776 loss)
I0225 02:56:05.222513 29812 solver.cpp:470] Iteration 66700, lr = 0.000343
I0225 02:56:24.610765 29812 solver.cpp:189] Iteration 66750, loss = 0.0819665
I0225 02:56:24.610790 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0819668 (* 1 = 0.0819668 loss)
I0225 02:56:24.610795 29812 solver.cpp:470] Iteration 66750, lr = 0.000343
I0225 02:56:44.004420 29812 solver.cpp:189] Iteration 66800, loss = 0.100641
I0225 02:56:44.004473 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.100641 (* 1 = 0.100641 loss)
I0225 02:56:44.004480 29812 solver.cpp:470] Iteration 66800, lr = 0.000343
I0225 02:57:03.391341 29812 solver.cpp:189] Iteration 66850, loss = 0.111315
I0225 02:57:03.391366 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.111315 (* 1 = 0.111315 loss)
I0225 02:57:03.391372 29812 solver.cpp:470] Iteration 66850, lr = 0.000343
I0225 02:57:22.785989 29812 solver.cpp:189] Iteration 66900, loss = 0.112779
I0225 02:57:22.786082 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.11278 (* 1 = 0.11278 loss)
I0225 02:57:22.786087 29812 solver.cpp:470] Iteration 66900, lr = 0.000343
I0225 02:57:42.176403 29812 solver.cpp:189] Iteration 66950, loss = 0.0534125
I0225 02:57:42.176437 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0534128 (* 1 = 0.0534128 loss)
I0225 02:57:42.176457 29812 solver.cpp:470] Iteration 66950, lr = 0.000343
I0225 02:58:01.320869 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_67000.caffemodel
I0225 02:58:01.443954 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_67000.solverstate
I0225 02:58:01.502022 29812 solver.cpp:266] Iteration 67000, Testing net (#0)
I0225 02:58:09.152515 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.901
I0225 02:58:09.152541 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.360515 (* 1 = 0.360515 loss)
I0225 02:58:09.439431 29812 solver.cpp:189] Iteration 67000, loss = 0.176523
I0225 02:58:09.439455 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.176524 (* 1 = 0.176524 loss)
I0225 02:58:09.439461 29812 solver.cpp:470] Iteration 67000, lr = 0.000343
I0225 02:58:28.824949 29812 solver.cpp:189] Iteration 67050, loss = 0.079475
I0225 02:58:28.824973 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0794753 (* 1 = 0.0794753 loss)
I0225 02:58:28.824980 29812 solver.cpp:470] Iteration 67050, lr = 0.000343
I0225 02:58:48.203773 29812 solver.cpp:189] Iteration 67100, loss = 0.0990445
I0225 02:58:48.203814 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0990448 (* 1 = 0.0990448 loss)
I0225 02:58:48.203820 29812 solver.cpp:470] Iteration 67100, lr = 0.000343
I0225 02:59:07.583746 29812 solver.cpp:189] Iteration 67150, loss = 0.0957349
I0225 02:59:07.583771 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0957352 (* 1 = 0.0957352 loss)
I0225 02:59:07.583777 29812 solver.cpp:470] Iteration 67150, lr = 0.000343
I0225 02:59:26.963377 29812 solver.cpp:189] Iteration 67200, loss = 0.123855
I0225 02:59:26.963417 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.123855 (* 1 = 0.123855 loss)
I0225 02:59:26.963423 29812 solver.cpp:470] Iteration 67200, lr = 0.000343
I0225 02:59:46.344765 29812 solver.cpp:189] Iteration 67250, loss = 0.19878
I0225 02:59:46.344790 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.198781 (* 1 = 0.198781 loss)
I0225 02:59:46.344796 29812 solver.cpp:470] Iteration 67250, lr = 0.000343
I0225 03:00:05.733053 29812 solver.cpp:189] Iteration 67300, loss = 0.169153
I0225 03:00:05.733149 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.169153 (* 1 = 0.169153 loss)
I0225 03:00:05.733155 29812 solver.cpp:470] Iteration 67300, lr = 0.000343
I0225 03:00:25.120558 29812 solver.cpp:189] Iteration 67350, loss = 0.138647
I0225 03:00:25.120579 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.138647 (* 1 = 0.138647 loss)
I0225 03:00:25.120584 29812 solver.cpp:470] Iteration 67350, lr = 0.000343
I0225 03:00:44.509760 29812 solver.cpp:189] Iteration 67400, loss = 0.0867166
I0225 03:00:44.509800 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0867169 (* 1 = 0.0867169 loss)
I0225 03:00:44.509806 29812 solver.cpp:470] Iteration 67400, lr = 0.000343
I0225 03:01:03.896067 29812 solver.cpp:189] Iteration 67450, loss = 0.0732985
I0225 03:01:03.896091 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0732988 (* 1 = 0.0732988 loss)
I0225 03:01:03.896097 29812 solver.cpp:470] Iteration 67450, lr = 0.000343
I0225 03:01:23.286555 29812 solver.cpp:189] Iteration 67500, loss = 0.177827
I0225 03:01:23.286592 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.177828 (* 1 = 0.177828 loss)
I0225 03:01:23.286598 29812 solver.cpp:470] Iteration 67500, lr = 0.000343
I0225 03:01:42.669729 29812 solver.cpp:189] Iteration 67550, loss = 0.0681647
I0225 03:01:42.669755 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.068165 (* 1 = 0.068165 loss)
I0225 03:01:42.669762 29812 solver.cpp:470] Iteration 67550, lr = 0.000343
I0225 03:02:02.054023 29812 solver.cpp:189] Iteration 67600, loss = 0.113581
I0225 03:02:02.054093 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.113581 (* 1 = 0.113581 loss)
I0225 03:02:02.054107 29812 solver.cpp:470] Iteration 67600, lr = 0.000343
I0225 03:02:21.440783 29812 solver.cpp:189] Iteration 67650, loss = 0.0527079
I0225 03:02:21.440807 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0527082 (* 1 = 0.0527082 loss)
I0225 03:02:21.440812 29812 solver.cpp:470] Iteration 67650, lr = 0.000343
I0225 03:02:40.824169 29812 solver.cpp:189] Iteration 67700, loss = 0.211057
I0225 03:02:40.824265 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.211057 (* 1 = 0.211057 loss)
I0225 03:02:40.824280 29812 solver.cpp:470] Iteration 67700, lr = 0.000343
I0225 03:03:00.199957 29812 solver.cpp:189] Iteration 67750, loss = 0.0616592
I0225 03:03:00.199983 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0616595 (* 1 = 0.0616595 loss)
I0225 03:03:00.199990 29812 solver.cpp:470] Iteration 67750, lr = 0.000343
I0225 03:03:19.584636 29812 solver.cpp:189] Iteration 67800, loss = 0.051085
I0225 03:03:19.584700 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0510853 (* 1 = 0.0510853 loss)
I0225 03:03:19.584707 29812 solver.cpp:470] Iteration 67800, lr = 0.000343
I0225 03:03:38.967811 29812 solver.cpp:189] Iteration 67850, loss = 0.118434
I0225 03:03:38.967835 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.118434 (* 1 = 0.118434 loss)
I0225 03:03:38.967841 29812 solver.cpp:470] Iteration 67850, lr = 0.000343
I0225 03:03:58.352386 29812 solver.cpp:189] Iteration 67900, loss = 0.135259
I0225 03:03:58.352427 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.135259 (* 1 = 0.135259 loss)
I0225 03:03:58.352432 29812 solver.cpp:470] Iteration 67900, lr = 0.000343
I0225 03:04:17.734648 29812 solver.cpp:189] Iteration 67950, loss = 0.206642
I0225 03:04:17.734674 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.206642 (* 1 = 0.206642 loss)
I0225 03:04:17.734679 29812 solver.cpp:470] Iteration 67950, lr = 0.000343
I0225 03:04:36.873756 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_68000.caffemodel
I0225 03:04:36.997117 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_68000.solverstate
I0225 03:04:37.055145 29812 solver.cpp:266] Iteration 68000, Testing net (#0)
I0225 03:04:44.709211 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9005
I0225 03:04:44.709254 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.36614 (* 1 = 0.36614 loss)
I0225 03:04:44.996495 29812 solver.cpp:189] Iteration 68000, loss = 0.100655
I0225 03:04:44.996513 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.100656 (* 1 = 0.100656 loss)
I0225 03:04:44.996520 29812 solver.cpp:470] Iteration 68000, lr = 0.000343
I0225 03:05:04.389009 29812 solver.cpp:189] Iteration 68050, loss = 0.0767424
I0225 03:05:04.389034 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0767427 (* 1 = 0.0767427 loss)
I0225 03:05:04.389040 29812 solver.cpp:470] Iteration 68050, lr = 0.000343
I0225 03:05:23.785254 29812 solver.cpp:189] Iteration 68100, loss = 0.0851796
I0225 03:05:23.785348 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0851799 (* 1 = 0.0851799 loss)
I0225 03:05:23.785365 29812 solver.cpp:470] Iteration 68100, lr = 0.000343
I0225 03:05:43.185225 29812 solver.cpp:189] Iteration 68150, loss = 0.0725284
I0225 03:05:43.185250 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0725287 (* 1 = 0.0725287 loss)
I0225 03:05:43.185256 29812 solver.cpp:470] Iteration 68150, lr = 0.000343
I0225 03:06:02.579952 29812 solver.cpp:189] Iteration 68200, loss = 0.225202
I0225 03:06:02.580013 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.225202 (* 1 = 0.225202 loss)
I0225 03:06:02.580018 29812 solver.cpp:470] Iteration 68200, lr = 0.000343
I0225 03:06:21.976032 29812 solver.cpp:189] Iteration 68250, loss = 0.116837
I0225 03:06:21.976058 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.116837 (* 1 = 0.116837 loss)
I0225 03:06:21.976063 29812 solver.cpp:470] Iteration 68250, lr = 0.000343
I0225 03:06:41.368839 29812 solver.cpp:189] Iteration 68300, loss = 0.107496
I0225 03:06:41.368877 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.107496 (* 1 = 0.107496 loss)
I0225 03:06:41.368883 29812 solver.cpp:470] Iteration 68300, lr = 0.000343
I0225 03:07:00.767907 29812 solver.cpp:189] Iteration 68350, loss = 0.190789
I0225 03:07:00.767932 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.190789 (* 1 = 0.190789 loss)
I0225 03:07:00.767937 29812 solver.cpp:470] Iteration 68350, lr = 0.000343
I0225 03:07:20.165349 29812 solver.cpp:189] Iteration 68400, loss = 0.119342
I0225 03:07:20.165436 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.119343 (* 1 = 0.119343 loss)
I0225 03:07:20.165451 29812 solver.cpp:470] Iteration 68400, lr = 0.000343
I0225 03:07:39.552134 29812 solver.cpp:189] Iteration 68450, loss = 0.118644
I0225 03:07:39.552158 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.118644 (* 1 = 0.118644 loss)
I0225 03:07:39.552165 29812 solver.cpp:470] Iteration 68450, lr = 0.000343
I0225 03:07:58.937597 29812 solver.cpp:189] Iteration 68500, loss = 0.0442709
I0225 03:07:58.937671 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0442712 (* 1 = 0.0442712 loss)
I0225 03:07:58.937687 29812 solver.cpp:470] Iteration 68500, lr = 0.000343
I0225 03:08:18.339138 29812 solver.cpp:189] Iteration 68550, loss = 0.0807707
I0225 03:08:18.339164 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.080771 (* 1 = 0.080771 loss)
I0225 03:08:18.339169 29812 solver.cpp:470] Iteration 68550, lr = 0.000343
I0225 03:08:37.724742 29812 solver.cpp:189] Iteration 68600, loss = 0.0968771
I0225 03:08:37.724833 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0968774 (* 1 = 0.0968774 loss)
I0225 03:08:37.724848 29812 solver.cpp:470] Iteration 68600, lr = 0.000343
I0225 03:08:57.109716 29812 solver.cpp:189] Iteration 68650, loss = 0.134215
I0225 03:08:57.109740 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.134215 (* 1 = 0.134215 loss)
I0225 03:08:57.109745 29812 solver.cpp:470] Iteration 68650, lr = 0.000343
I0225 03:09:16.509819 29812 solver.cpp:189] Iteration 68700, loss = 0.050458
I0225 03:09:16.509892 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0504583 (* 1 = 0.0504583 loss)
I0225 03:09:16.509907 29812 solver.cpp:470] Iteration 68700, lr = 0.000343
I0225 03:09:35.897258 29812 solver.cpp:189] Iteration 68750, loss = 0.0415901
I0225 03:09:35.897284 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0415904 (* 1 = 0.0415904 loss)
I0225 03:09:35.897289 29812 solver.cpp:470] Iteration 68750, lr = 0.000343
I0225 03:09:55.287289 29812 solver.cpp:189] Iteration 68800, loss = 0.08383
I0225 03:09:55.287330 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0838302 (* 1 = 0.0838302 loss)
I0225 03:09:55.287336 29812 solver.cpp:470] Iteration 68800, lr = 0.000343
I0225 03:10:14.674877 29812 solver.cpp:189] Iteration 68850, loss = 0.17406
I0225 03:10:14.674906 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.174061 (* 1 = 0.174061 loss)
I0225 03:10:14.674911 29812 solver.cpp:470] Iteration 68850, lr = 0.000343
I0225 03:10:34.072409 29812 solver.cpp:189] Iteration 68900, loss = 0.0863968
I0225 03:10:34.072448 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.086397 (* 1 = 0.086397 loss)
I0225 03:10:34.072453 29812 solver.cpp:470] Iteration 68900, lr = 0.000343
I0225 03:10:53.459884 29812 solver.cpp:189] Iteration 68950, loss = 0.164026
I0225 03:10:53.459909 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.164026 (* 1 = 0.164026 loss)
I0225 03:10:53.459915 29812 solver.cpp:470] Iteration 68950, lr = 0.000343
I0225 03:11:12.603514 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_69000.caffemodel
I0225 03:11:12.723609 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_69000.solverstate
I0225 03:11:12.781049 29812 solver.cpp:266] Iteration 69000, Testing net (#0)
I0225 03:11:20.431485 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9032
I0225 03:11:20.431521 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.377374 (* 1 = 0.377374 loss)
I0225 03:11:20.718883 29812 solver.cpp:189] Iteration 69000, loss = 0.15283
I0225 03:11:20.718906 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.15283 (* 1 = 0.15283 loss)
I0225 03:11:20.718914 29812 solver.cpp:470] Iteration 69000, lr = 0.000343
I0225 03:11:40.109830 29812 solver.cpp:189] Iteration 69050, loss = 0.140699
I0225 03:11:40.109854 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.1407 (* 1 = 0.1407 loss)
I0225 03:11:40.109859 29812 solver.cpp:470] Iteration 69050, lr = 0.000343
I0225 03:11:59.510303 29812 solver.cpp:189] Iteration 69100, loss = 0.041835
I0225 03:11:59.510367 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0418353 (* 1 = 0.0418353 loss)
I0225 03:11:59.510375 29812 solver.cpp:470] Iteration 69100, lr = 0.000343
I0225 03:12:18.907637 29812 solver.cpp:189] Iteration 69150, loss = 0.108317
I0225 03:12:18.907662 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.108318 (* 1 = 0.108318 loss)
I0225 03:12:18.907668 29812 solver.cpp:470] Iteration 69150, lr = 0.000343
I0225 03:12:38.304273 29812 solver.cpp:189] Iteration 69200, loss = 0.183277
I0225 03:12:38.304335 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.183277 (* 1 = 0.183277 loss)
I0225 03:12:38.304342 29812 solver.cpp:470] Iteration 69200, lr = 0.000343
I0225 03:12:57.704295 29812 solver.cpp:189] Iteration 69250, loss = 0.0780434
I0225 03:12:57.704323 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0780438 (* 1 = 0.0780438 loss)
I0225 03:12:57.704327 29812 solver.cpp:470] Iteration 69250, lr = 0.000343
I0225 03:13:17.093144 29812 solver.cpp:189] Iteration 69300, loss = 0.0518001
I0225 03:13:17.093233 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0518005 (* 1 = 0.0518005 loss)
I0225 03:13:17.093240 29812 solver.cpp:470] Iteration 69300, lr = 0.000343
I0225 03:13:36.485620 29812 solver.cpp:189] Iteration 69350, loss = 0.106436
I0225 03:13:36.485653 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.106436 (* 1 = 0.106436 loss)
I0225 03:13:36.485659 29812 solver.cpp:470] Iteration 69350, lr = 0.000343
I0225 03:13:55.873370 29812 solver.cpp:189] Iteration 69400, loss = 0.0934291
I0225 03:13:55.873479 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0934295 (* 1 = 0.0934295 loss)
I0225 03:13:55.873495 29812 solver.cpp:470] Iteration 69400, lr = 0.000343
I0225 03:14:15.276612 29812 solver.cpp:189] Iteration 69450, loss = 0.0986263
I0225 03:14:15.276635 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0986267 (* 1 = 0.0986267 loss)
I0225 03:14:15.276640 29812 solver.cpp:470] Iteration 69450, lr = 0.000343
I0225 03:14:34.679975 29812 solver.cpp:189] Iteration 69500, loss = 0.160347
I0225 03:14:34.680066 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.160348 (* 1 = 0.160348 loss)
I0225 03:14:34.680073 29812 solver.cpp:470] Iteration 69500, lr = 0.000343
I0225 03:14:54.075758 29812 solver.cpp:189] Iteration 69550, loss = 0.0914854
I0225 03:14:54.075783 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0914858 (* 1 = 0.0914858 loss)
I0225 03:14:54.075788 29812 solver.cpp:470] Iteration 69550, lr = 0.000343
I0225 03:15:13.471262 29812 solver.cpp:189] Iteration 69600, loss = 0.128656
I0225 03:15:13.471302 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.128657 (* 1 = 0.128657 loss)
I0225 03:15:13.471308 29812 solver.cpp:470] Iteration 69600, lr = 0.000343
I0225 03:15:32.868549 29812 solver.cpp:189] Iteration 69650, loss = 0.133037
I0225 03:15:32.868573 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.133038 (* 1 = 0.133038 loss)
I0225 03:15:32.868579 29812 solver.cpp:470] Iteration 69650, lr = 0.000343
I0225 03:15:52.267632 29812 solver.cpp:189] Iteration 69700, loss = 0.0527343
I0225 03:15:52.267673 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0527347 (* 1 = 0.0527347 loss)
I0225 03:15:52.267678 29812 solver.cpp:470] Iteration 69700, lr = 0.000343
I0225 03:16:11.666753 29812 solver.cpp:189] Iteration 69750, loss = 0.0734571
I0225 03:16:11.666779 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0734575 (* 1 = 0.0734575 loss)
I0225 03:16:11.666785 29812 solver.cpp:470] Iteration 69750, lr = 0.000343
I0225 03:16:31.059872 29812 solver.cpp:189] Iteration 69800, loss = 0.0863961
I0225 03:16:31.059983 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0863965 (* 1 = 0.0863965 loss)
I0225 03:16:31.060000 29812 solver.cpp:470] Iteration 69800, lr = 0.000343
I0225 03:16:50.450369 29812 solver.cpp:189] Iteration 69850, loss = 0.0804907
I0225 03:16:50.450394 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0804911 (* 1 = 0.0804911 loss)
I0225 03:16:50.450400 29812 solver.cpp:470] Iteration 69850, lr = 0.000343
I0225 03:17:09.852777 29812 solver.cpp:189] Iteration 69900, loss = 0.0288043
I0225 03:17:09.852841 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0288046 (* 1 = 0.0288046 loss)
I0225 03:17:09.852849 29812 solver.cpp:470] Iteration 69900, lr = 0.000343
I0225 03:17:29.249198 29812 solver.cpp:189] Iteration 69950, loss = 0.0453616
I0225 03:17:29.249222 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.045362 (* 1 = 0.045362 loss)
I0225 03:17:29.249228 29812 solver.cpp:470] Iteration 69950, lr = 0.000343
I0225 03:17:48.391618 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_70000.caffemodel
I0225 03:17:48.497403 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_70000.solverstate
I0225 03:17:48.557430 29812 solver.cpp:266] Iteration 70000, Testing net (#0)
I0225 03:17:56.203435 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.895
I0225 03:17:56.203472 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.401272 (* 1 = 0.401272 loss)
I0225 03:17:56.490665 29812 solver.cpp:189] Iteration 70000, loss = 0.128152
I0225 03:17:56.490689 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.128152 (* 1 = 0.128152 loss)
I0225 03:17:56.490694 29812 solver.cpp:470] Iteration 70000, lr = 0.000343
I0225 03:18:15.882854 29812 solver.cpp:189] Iteration 70050, loss = 0.113075
I0225 03:18:15.882880 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.113076 (* 1 = 0.113076 loss)
I0225 03:18:15.882886 29812 solver.cpp:470] Iteration 70050, lr = 0.000343
I0225 03:18:35.267184 29812 solver.cpp:189] Iteration 70100, loss = 0.130404
I0225 03:18:35.267242 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.130404 (* 1 = 0.130404 loss)
I0225 03:18:35.267249 29812 solver.cpp:470] Iteration 70100, lr = 0.000343
I0225 03:18:54.655046 29812 solver.cpp:189] Iteration 70150, loss = 0.104923
I0225 03:18:54.655073 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.104923 (* 1 = 0.104923 loss)
I0225 03:18:54.655079 29812 solver.cpp:470] Iteration 70150, lr = 0.000343
I0225 03:19:14.051262 29812 solver.cpp:189] Iteration 70200, loss = 0.0618465
I0225 03:19:14.051301 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0618469 (* 1 = 0.0618469 loss)
I0225 03:19:14.051307 29812 solver.cpp:470] Iteration 70200, lr = 0.000343
I0225 03:19:33.442410 29812 solver.cpp:189] Iteration 70250, loss = 0.0959299
I0225 03:19:33.442435 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0959302 (* 1 = 0.0959302 loss)
I0225 03:19:33.442440 29812 solver.cpp:470] Iteration 70250, lr = 0.000343
I0225 03:19:52.833046 29812 solver.cpp:189] Iteration 70300, loss = 0.122243
I0225 03:19:52.833120 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.122244 (* 1 = 0.122244 loss)
I0225 03:19:52.833135 29812 solver.cpp:470] Iteration 70300, lr = 0.000343
I0225 03:20:12.221562 29812 solver.cpp:189] Iteration 70350, loss = 0.105508
I0225 03:20:12.221587 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105509 (* 1 = 0.105509 loss)
I0225 03:20:12.221592 29812 solver.cpp:470] Iteration 70350, lr = 0.000343
I0225 03:20:31.614179 29812 solver.cpp:189] Iteration 70400, loss = 0.0466848
I0225 03:20:31.614249 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0466852 (* 1 = 0.0466852 loss)
I0225 03:20:31.614265 29812 solver.cpp:470] Iteration 70400, lr = 0.000343
I0225 03:20:51.004127 29812 solver.cpp:189] Iteration 70450, loss = 0.155176
I0225 03:20:51.004151 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.155176 (* 1 = 0.155176 loss)
I0225 03:20:51.004158 29812 solver.cpp:470] Iteration 70450, lr = 0.000343
I0225 03:21:10.403785 29812 solver.cpp:189] Iteration 70500, loss = 0.0904538
I0225 03:21:10.403868 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0904541 (* 1 = 0.0904541 loss)
I0225 03:21:10.403875 29812 solver.cpp:470] Iteration 70500, lr = 0.000343
I0225 03:21:29.796875 29812 solver.cpp:189] Iteration 70550, loss = 0.0735072
I0225 03:21:29.796901 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0735075 (* 1 = 0.0735075 loss)
I0225 03:21:29.796907 29812 solver.cpp:470] Iteration 70550, lr = 0.000343
I0225 03:21:49.186666 29812 solver.cpp:189] Iteration 70600, loss = 0.0628104
I0225 03:21:49.186739 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0628108 (* 1 = 0.0628108 loss)
I0225 03:21:49.186754 29812 solver.cpp:470] Iteration 70600, lr = 0.000343
I0225 03:22:08.570504 29812 solver.cpp:189] Iteration 70650, loss = 0.103793
I0225 03:22:08.570528 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.103793 (* 1 = 0.103793 loss)
I0225 03:22:08.570534 29812 solver.cpp:470] Iteration 70650, lr = 0.000343
I0225 03:22:27.954366 29812 solver.cpp:189] Iteration 70700, loss = 0.0543607
I0225 03:22:27.954406 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0543611 (* 1 = 0.0543611 loss)
I0225 03:22:27.954412 29812 solver.cpp:470] Iteration 70700, lr = 0.000343
I0225 03:22:47.350930 29812 solver.cpp:189] Iteration 70750, loss = 0.079137
I0225 03:22:47.350955 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0791373 (* 1 = 0.0791373 loss)
I0225 03:22:47.350960 29812 solver.cpp:470] Iteration 70750, lr = 0.000343
I0225 03:23:06.747866 29812 solver.cpp:189] Iteration 70800, loss = 0.078682
I0225 03:23:06.747938 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0786823 (* 1 = 0.0786823 loss)
I0225 03:23:06.747954 29812 solver.cpp:470] Iteration 70800, lr = 0.000343
I0225 03:23:26.132458 29812 solver.cpp:189] Iteration 70850, loss = 0.0478611
I0225 03:23:26.132483 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0478614 (* 1 = 0.0478614 loss)
I0225 03:23:26.132488 29812 solver.cpp:470] Iteration 70850, lr = 0.000343
I0225 03:23:45.528545 29812 solver.cpp:189] Iteration 70900, loss = 0.0767881
I0225 03:23:45.528584 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0767885 (* 1 = 0.0767885 loss)
I0225 03:23:45.528590 29812 solver.cpp:470] Iteration 70900, lr = 0.000343
I0225 03:24:04.924772 29812 solver.cpp:189] Iteration 70950, loss = 0.12643
I0225 03:24:04.924795 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.12643 (* 1 = 0.12643 loss)
I0225 03:24:04.924801 29812 solver.cpp:470] Iteration 70950, lr = 0.000343
I0225 03:24:24.056419 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_71000.caffemodel
I0225 03:24:24.190454 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_71000.solverstate
I0225 03:24:24.250591 29812 solver.cpp:266] Iteration 71000, Testing net (#0)
I0225 03:24:31.902824 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.899
I0225 03:24:31.902863 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.373446 (* 1 = 0.373446 loss)
I0225 03:24:32.189239 29812 solver.cpp:189] Iteration 71000, loss = 0.115467
I0225 03:24:32.189260 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.115467 (* 1 = 0.115467 loss)
I0225 03:24:32.189266 29812 solver.cpp:470] Iteration 71000, lr = 0.000343
I0225 03:24:51.580957 29812 solver.cpp:189] Iteration 71050, loss = 0.143113
I0225 03:24:51.580983 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.143113 (* 1 = 0.143113 loss)
I0225 03:24:51.580989 29812 solver.cpp:470] Iteration 71050, lr = 0.000343
I0225 03:25:10.970183 29812 solver.cpp:189] Iteration 71100, loss = 0.120905
I0225 03:25:10.970257 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.120905 (* 1 = 0.120905 loss)
I0225 03:25:10.970273 29812 solver.cpp:470] Iteration 71100, lr = 0.000343
I0225 03:25:30.351929 29812 solver.cpp:189] Iteration 71150, loss = 0.144414
I0225 03:25:30.351954 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.144414 (* 1 = 0.144414 loss)
I0225 03:25:30.351960 29812 solver.cpp:470] Iteration 71150, lr = 0.000343
I0225 03:25:49.730216 29812 solver.cpp:189] Iteration 71200, loss = 0.126367
I0225 03:25:49.730311 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.126367 (* 1 = 0.126367 loss)
I0225 03:25:49.730327 29812 solver.cpp:470] Iteration 71200, lr = 0.000343
I0225 03:26:09.109946 29812 solver.cpp:189] Iteration 71250, loss = 0.157029
I0225 03:26:09.109969 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.15703 (* 1 = 0.15703 loss)
I0225 03:26:09.109974 29812 solver.cpp:470] Iteration 71250, lr = 0.000343
I0225 03:26:28.493152 29812 solver.cpp:189] Iteration 71300, loss = 0.0504814
I0225 03:26:28.493197 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0504818 (* 1 = 0.0504818 loss)
I0225 03:26:28.493203 29812 solver.cpp:470] Iteration 71300, lr = 0.000343
I0225 03:26:47.883900 29812 solver.cpp:189] Iteration 71350, loss = 0.0788758
I0225 03:26:47.883924 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0788762 (* 1 = 0.0788762 loss)
I0225 03:26:47.883930 29812 solver.cpp:470] Iteration 71350, lr = 0.000343
I0225 03:27:07.272567 29812 solver.cpp:189] Iteration 71400, loss = 0.0915027
I0225 03:27:07.272637 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0915031 (* 1 = 0.0915031 loss)
I0225 03:27:07.272652 29812 solver.cpp:470] Iteration 71400, lr = 0.000343
I0225 03:27:26.653723 29812 solver.cpp:189] Iteration 71450, loss = 0.0744293
I0225 03:27:26.653748 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0744297 (* 1 = 0.0744297 loss)
I0225 03:27:26.653754 29812 solver.cpp:470] Iteration 71450, lr = 0.000343
I0225 03:27:46.029584 29812 solver.cpp:189] Iteration 71500, loss = 0.141968
I0225 03:27:46.029655 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.141968 (* 1 = 0.141968 loss)
I0225 03:27:46.029670 29812 solver.cpp:470] Iteration 71500, lr = 0.000343
I0225 03:28:05.404939 29812 solver.cpp:189] Iteration 71550, loss = 0.0545036
I0225 03:28:05.404963 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.054504 (* 1 = 0.054504 loss)
I0225 03:28:05.404968 29812 solver.cpp:470] Iteration 71550, lr = 0.000343
I0225 03:28:24.782552 29812 solver.cpp:189] Iteration 71600, loss = 0.0588138
I0225 03:28:24.782642 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0588142 (* 1 = 0.0588142 loss)
I0225 03:28:24.782649 29812 solver.cpp:470] Iteration 71600, lr = 0.000343
I0225 03:28:44.163931 29812 solver.cpp:189] Iteration 71650, loss = 0.0539171
I0225 03:28:44.163955 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0539175 (* 1 = 0.0539175 loss)
I0225 03:28:44.163960 29812 solver.cpp:470] Iteration 71650, lr = 0.000343
I0225 03:29:03.556527 29812 solver.cpp:189] Iteration 71700, loss = 0.155558
I0225 03:29:03.556598 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.155558 (* 1 = 0.155558 loss)
I0225 03:29:03.556614 29812 solver.cpp:470] Iteration 71700, lr = 0.000343
I0225 03:29:22.940824 29812 solver.cpp:189] Iteration 71750, loss = 0.162795
I0225 03:29:22.940847 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.162795 (* 1 = 0.162795 loss)
I0225 03:29:22.940853 29812 solver.cpp:470] Iteration 71750, lr = 0.000343
I0225 03:29:42.317646 29812 solver.cpp:189] Iteration 71800, loss = 0.13345
I0225 03:29:42.317705 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.13345 (* 1 = 0.13345 loss)
I0225 03:29:42.317711 29812 solver.cpp:470] Iteration 71800, lr = 0.000343
I0225 03:30:01.699075 29812 solver.cpp:189] Iteration 71850, loss = 0.0785624
I0225 03:30:01.699101 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0785627 (* 1 = 0.0785627 loss)
I0225 03:30:01.699107 29812 solver.cpp:470] Iteration 71850, lr = 0.000343
I0225 03:30:21.080317 29812 solver.cpp:189] Iteration 71900, loss = 0.0757854
I0225 03:30:21.080401 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0757858 (* 1 = 0.0757858 loss)
I0225 03:30:21.080417 29812 solver.cpp:470] Iteration 71900, lr = 0.000343
I0225 03:30:40.463727 29812 solver.cpp:189] Iteration 71950, loss = 0.0861667
I0225 03:30:40.463752 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.086167 (* 1 = 0.086167 loss)
I0225 03:30:40.463757 29812 solver.cpp:470] Iteration 71950, lr = 0.000343
I0225 03:30:59.609405 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_72000.caffemodel
I0225 03:30:59.734009 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_72000.solverstate
I0225 03:30:59.793064 29812 solver.cpp:266] Iteration 72000, Testing net (#0)
I0225 03:31:07.451541 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9051
I0225 03:31:07.451578 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.368 (* 1 = 0.368 loss)
I0225 03:31:07.739001 29812 solver.cpp:189] Iteration 72000, loss = 0.0738069
I0225 03:31:07.739027 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0738072 (* 1 = 0.0738072 loss)
I0225 03:31:07.739033 29812 solver.cpp:470] Iteration 72000, lr = 0.000343
I0225 03:31:27.128161 29812 solver.cpp:189] Iteration 72050, loss = 0.163333
I0225 03:31:27.128190 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.163333 (* 1 = 0.163333 loss)
I0225 03:31:27.128196 29812 solver.cpp:470] Iteration 72050, lr = 0.000343
I0225 03:31:46.515385 29812 solver.cpp:189] Iteration 72100, loss = 0.10122
I0225 03:31:46.515476 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.101221 (* 1 = 0.101221 loss)
I0225 03:31:46.515483 29812 solver.cpp:470] Iteration 72100, lr = 0.000343
I0225 03:32:05.900780 29812 solver.cpp:189] Iteration 72150, loss = 0.0538131
I0225 03:32:05.900805 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0538134 (* 1 = 0.0538134 loss)
I0225 03:32:05.900812 29812 solver.cpp:470] Iteration 72150, lr = 0.000343
I0225 03:32:25.292872 29812 solver.cpp:189] Iteration 72200, loss = 0.129339
I0225 03:32:25.292913 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.12934 (* 1 = 0.12934 loss)
I0225 03:32:25.292919 29812 solver.cpp:470] Iteration 72200, lr = 0.000343
I0225 03:32:44.691202 29812 solver.cpp:189] Iteration 72250, loss = 0.104201
I0225 03:32:44.691229 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.104201 (* 1 = 0.104201 loss)
I0225 03:32:44.691236 29812 solver.cpp:470] Iteration 72250, lr = 0.000343
I0225 03:33:04.083454 29812 solver.cpp:189] Iteration 72300, loss = 0.0666474
I0225 03:33:04.083545 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0666477 (* 1 = 0.0666477 loss)
I0225 03:33:04.083551 29812 solver.cpp:470] Iteration 72300, lr = 0.000343
I0225 03:33:23.477229 29812 solver.cpp:189] Iteration 72350, loss = 0.070025
I0225 03:33:23.477253 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0700253 (* 1 = 0.0700253 loss)
I0225 03:33:23.477259 29812 solver.cpp:470] Iteration 72350, lr = 0.000343
I0225 03:33:42.871760 29812 solver.cpp:189] Iteration 72400, loss = 0.120078
I0225 03:33:42.871801 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.120078 (* 1 = 0.120078 loss)
I0225 03:33:42.871809 29812 solver.cpp:470] Iteration 72400, lr = 0.000343
I0225 03:34:02.262853 29812 solver.cpp:189] Iteration 72450, loss = 0.0804842
I0225 03:34:02.262877 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0804845 (* 1 = 0.0804845 loss)
I0225 03:34:02.262882 29812 solver.cpp:470] Iteration 72450, lr = 0.000343
I0225 03:34:21.658375 29812 solver.cpp:189] Iteration 72500, loss = 0.108447
I0225 03:34:21.658474 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.108447 (* 1 = 0.108447 loss)
I0225 03:34:21.658480 29812 solver.cpp:470] Iteration 72500, lr = 0.000343
I0225 03:34:41.052603 29812 solver.cpp:189] Iteration 72550, loss = 0.0846912
I0225 03:34:41.052628 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0846915 (* 1 = 0.0846915 loss)
I0225 03:34:41.052633 29812 solver.cpp:470] Iteration 72550, lr = 0.000343
I0225 03:35:00.456138 29812 solver.cpp:189] Iteration 72600, loss = 0.105267
I0225 03:35:00.456203 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105268 (* 1 = 0.105268 loss)
I0225 03:35:00.456210 29812 solver.cpp:470] Iteration 72600, lr = 0.000343
I0225 03:35:19.840291 29812 solver.cpp:189] Iteration 72650, loss = 0.0980992
I0225 03:35:19.840318 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0980995 (* 1 = 0.0980995 loss)
I0225 03:35:19.840324 29812 solver.cpp:470] Iteration 72650, lr = 0.000343
I0225 03:35:39.229508 29812 solver.cpp:189] Iteration 72700, loss = 0.0305147
I0225 03:35:39.229584 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0305151 (* 1 = 0.0305151 loss)
I0225 03:35:39.229599 29812 solver.cpp:470] Iteration 72700, lr = 0.000343
I0225 03:35:58.612522 29812 solver.cpp:189] Iteration 72750, loss = 0.105978
I0225 03:35:58.612560 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105979 (* 1 = 0.105979 loss)
I0225 03:35:58.612566 29812 solver.cpp:470] Iteration 72750, lr = 0.000343
I0225 03:36:18.003043 29812 solver.cpp:189] Iteration 72800, loss = 0.0948047
I0225 03:36:18.003134 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.094805 (* 1 = 0.094805 loss)
I0225 03:36:18.003139 29812 solver.cpp:470] Iteration 72800, lr = 0.000343
I0225 03:36:37.402997 29812 solver.cpp:189] Iteration 72850, loss = 0.0199309
I0225 03:36:37.403020 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0199313 (* 1 = 0.0199313 loss)
I0225 03:36:37.403025 29812 solver.cpp:470] Iteration 72850, lr = 0.000343
I0225 03:36:56.793529 29812 solver.cpp:189] Iteration 72900, loss = 0.0729982
I0225 03:36:56.793620 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0729986 (* 1 = 0.0729986 loss)
I0225 03:36:56.793627 29812 solver.cpp:470] Iteration 72900, lr = 0.000343
I0225 03:37:16.180454 29812 solver.cpp:189] Iteration 72950, loss = 0.0905089
I0225 03:37:16.180479 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0905092 (* 1 = 0.0905092 loss)
I0225 03:37:16.180483 29812 solver.cpp:470] Iteration 72950, lr = 0.000343
I0225 03:37:35.324790 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_73000.caffemodel
I0225 03:37:35.450213 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_73000.solverstate
I0225 03:37:35.508118 29812 solver.cpp:266] Iteration 73000, Testing net (#0)
I0225 03:37:43.160573 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9022
I0225 03:37:43.160609 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.368001 (* 1 = 0.368001 loss)
I0225 03:37:43.446419 29812 solver.cpp:189] Iteration 73000, loss = 0.111376
I0225 03:37:43.446442 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.111376 (* 1 = 0.111376 loss)
I0225 03:37:43.446449 29812 solver.cpp:470] Iteration 73000, lr = 0.000343
I0225 03:38:02.849756 29812 solver.cpp:189] Iteration 73050, loss = 0.0495466
I0225 03:38:02.849779 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0495469 (* 1 = 0.0495469 loss)
I0225 03:38:02.849786 29812 solver.cpp:470] Iteration 73050, lr = 0.000343
I0225 03:38:22.237414 29812 solver.cpp:189] Iteration 73100, loss = 0.0963304
I0225 03:38:22.237455 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0963307 (* 1 = 0.0963307 loss)
I0225 03:38:22.237462 29812 solver.cpp:470] Iteration 73100, lr = 0.000343
I0225 03:38:41.633761 29812 solver.cpp:189] Iteration 73150, loss = 0.139016
I0225 03:38:41.633785 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.139016 (* 1 = 0.139016 loss)
I0225 03:38:41.633791 29812 solver.cpp:470] Iteration 73150, lr = 0.000343
I0225 03:39:01.034416 29812 solver.cpp:189] Iteration 73200, loss = 0.0612111
I0225 03:39:01.034488 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0612115 (* 1 = 0.0612115 loss)
I0225 03:39:01.034504 29812 solver.cpp:470] Iteration 73200, lr = 0.000343
I0225 03:39:20.424983 29812 solver.cpp:189] Iteration 73250, loss = 0.137901
I0225 03:39:20.425007 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.137901 (* 1 = 0.137901 loss)
I0225 03:39:20.425012 29812 solver.cpp:470] Iteration 73250, lr = 0.000343
I0225 03:39:39.827036 29812 solver.cpp:189] Iteration 73300, loss = 0.123651
I0225 03:39:39.827129 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.123652 (* 1 = 0.123652 loss)
I0225 03:39:39.827146 29812 solver.cpp:470] Iteration 73300, lr = 0.000343
I0225 03:39:59.223866 29812 solver.cpp:189] Iteration 73350, loss = 0.0792683
I0225 03:39:59.223891 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0792686 (* 1 = 0.0792686 loss)
I0225 03:39:59.223896 29812 solver.cpp:470] Iteration 73350, lr = 0.000343
I0225 03:40:18.623113 29812 solver.cpp:189] Iteration 73400, loss = 0.105112
I0225 03:40:18.623186 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105112 (* 1 = 0.105112 loss)
I0225 03:40:18.623203 29812 solver.cpp:470] Iteration 73400, lr = 0.000343
I0225 03:40:38.014749 29812 solver.cpp:189] Iteration 73450, loss = 0.0779942
I0225 03:40:38.014772 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0779945 (* 1 = 0.0779945 loss)
I0225 03:40:38.014780 29812 solver.cpp:470] Iteration 73450, lr = 0.000343
I0225 03:40:57.400025 29812 solver.cpp:189] Iteration 73500, loss = 0.0716621
I0225 03:40:57.400095 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0716624 (* 1 = 0.0716624 loss)
I0225 03:40:57.400110 29812 solver.cpp:470] Iteration 73500, lr = 0.000343
I0225 03:41:16.802305 29812 solver.cpp:189] Iteration 73550, loss = 0.0661991
I0225 03:41:16.802330 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0661994 (* 1 = 0.0661994 loss)
I0225 03:41:16.802336 29812 solver.cpp:470] Iteration 73550, lr = 0.000343
I0225 03:41:36.194133 29812 solver.cpp:189] Iteration 73600, loss = 0.0706804
I0225 03:41:36.194195 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0706807 (* 1 = 0.0706807 loss)
I0225 03:41:36.194200 29812 solver.cpp:470] Iteration 73600, lr = 0.000343
I0225 03:41:55.597924 29812 solver.cpp:189] Iteration 73650, loss = 0.0959367
I0225 03:41:55.597947 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.095937 (* 1 = 0.095937 loss)
I0225 03:41:55.597954 29812 solver.cpp:470] Iteration 73650, lr = 0.000343
I0225 03:42:14.983645 29812 solver.cpp:189] Iteration 73700, loss = 0.0737158
I0225 03:42:14.983716 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0737161 (* 1 = 0.0737161 loss)
I0225 03:42:14.983731 29812 solver.cpp:470] Iteration 73700, lr = 0.000343
I0225 03:42:34.376101 29812 solver.cpp:189] Iteration 73750, loss = 0.0334279
I0225 03:42:34.376126 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0334282 (* 1 = 0.0334282 loss)
I0225 03:42:34.376132 29812 solver.cpp:470] Iteration 73750, lr = 0.000343
I0225 03:42:53.777089 29812 solver.cpp:189] Iteration 73800, loss = 0.0539833
I0225 03:42:53.777159 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0539836 (* 1 = 0.0539836 loss)
I0225 03:42:53.777174 29812 solver.cpp:470] Iteration 73800, lr = 0.000343
I0225 03:43:13.171216 29812 solver.cpp:189] Iteration 73850, loss = 0.0540976
I0225 03:43:13.171246 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0540979 (* 1 = 0.0540979 loss)
I0225 03:43:13.171252 29812 solver.cpp:470] Iteration 73850, lr = 0.000343
I0225 03:43:32.573065 29812 solver.cpp:189] Iteration 73900, loss = 0.026692
I0225 03:43:32.573153 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0266923 (* 1 = 0.0266923 loss)
I0225 03:43:32.573158 29812 solver.cpp:470] Iteration 73900, lr = 0.000343
I0225 03:43:51.958845 29812 solver.cpp:189] Iteration 73950, loss = 0.0760032
I0225 03:43:51.958869 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0760035 (* 1 = 0.0760035 loss)
I0225 03:43:51.958876 29812 solver.cpp:470] Iteration 73950, lr = 0.000343
I0225 03:44:11.100630 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_74000.caffemodel
I0225 03:44:11.227084 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_74000.solverstate
I0225 03:44:11.286139 29812 solver.cpp:266] Iteration 74000, Testing net (#0)
I0225 03:44:18.935583 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9017
I0225 03:44:18.935619 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.38655 (* 1 = 0.38655 loss)
I0225 03:44:19.223326 29812 solver.cpp:189] Iteration 74000, loss = 0.0828867
I0225 03:44:19.223347 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.082887 (* 1 = 0.082887 loss)
I0225 03:44:19.223353 29812 solver.cpp:470] Iteration 74000, lr = 0.000343
I0225 03:44:38.611764 29812 solver.cpp:189] Iteration 74050, loss = 0.0781859
I0225 03:44:38.611788 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0781862 (* 1 = 0.0781862 loss)
I0225 03:44:38.611794 29812 solver.cpp:470] Iteration 74050, lr = 0.000343
I0225 03:44:57.996788 29812 solver.cpp:189] Iteration 74100, loss = 0.0628535
I0225 03:44:57.996855 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0628538 (* 1 = 0.0628538 loss)
I0225 03:44:57.996861 29812 solver.cpp:470] Iteration 74100, lr = 0.000343
I0225 03:45:17.396416 29812 solver.cpp:189] Iteration 74150, loss = 0.0417813
I0225 03:45:17.396440 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0417816 (* 1 = 0.0417816 loss)
I0225 03:45:17.396445 29812 solver.cpp:470] Iteration 74150, lr = 0.000343
I0225 03:45:36.787907 29812 solver.cpp:189] Iteration 74200, loss = 0.0873467
I0225 03:45:36.787961 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.087347 (* 1 = 0.087347 loss)
I0225 03:45:36.787967 29812 solver.cpp:470] Iteration 74200, lr = 0.000343
I0225 03:45:56.177626 29812 solver.cpp:189] Iteration 74250, loss = 0.0969224
I0225 03:45:56.177662 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0969228 (* 1 = 0.0969228 loss)
I0225 03:45:56.177678 29812 solver.cpp:470] Iteration 74250, lr = 0.000343
I0225 03:46:15.568966 29812 solver.cpp:189] Iteration 74300, loss = 0.0691361
I0225 03:46:15.569037 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0691365 (* 1 = 0.0691365 loss)
I0225 03:46:15.569052 29812 solver.cpp:470] Iteration 74300, lr = 0.000343
I0225 03:46:34.962154 29812 solver.cpp:189] Iteration 74350, loss = 0.0943968
I0225 03:46:34.962178 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0943972 (* 1 = 0.0943972 loss)
I0225 03:46:34.962184 29812 solver.cpp:470] Iteration 74350, lr = 0.000343
I0225 03:46:54.357398 29812 solver.cpp:189] Iteration 74400, loss = 0.168273
I0225 03:46:54.357470 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.168274 (* 1 = 0.168274 loss)
I0225 03:46:54.357486 29812 solver.cpp:470] Iteration 74400, lr = 0.000343
I0225 03:47:13.754174 29812 solver.cpp:189] Iteration 74450, loss = 0.0773493
I0225 03:47:13.754200 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0773497 (* 1 = 0.0773497 loss)
I0225 03:47:13.754205 29812 solver.cpp:470] Iteration 74450, lr = 0.000343
I0225 03:47:33.140488 29812 solver.cpp:189] Iteration 74500, loss = 0.147888
I0225 03:47:33.140581 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.147888 (* 1 = 0.147888 loss)
I0225 03:47:33.140597 29812 solver.cpp:470] Iteration 74500, lr = 0.000343
I0225 03:47:52.537230 29812 solver.cpp:189] Iteration 74550, loss = 0.0531058
I0225 03:47:52.537252 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0531062 (* 1 = 0.0531062 loss)
I0225 03:47:52.537258 29812 solver.cpp:470] Iteration 74550, lr = 0.000343
I0225 03:48:11.929625 29812 solver.cpp:189] Iteration 74600, loss = 0.212355
I0225 03:48:11.929687 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.212355 (* 1 = 0.212355 loss)
I0225 03:48:11.929693 29812 solver.cpp:470] Iteration 74600, lr = 0.000343
I0225 03:48:31.321650 29812 solver.cpp:189] Iteration 74650, loss = 0.0821882
I0225 03:48:31.321672 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0821887 (* 1 = 0.0821887 loss)
I0225 03:48:31.321677 29812 solver.cpp:470] Iteration 74650, lr = 0.000343
I0225 03:48:50.717102 29812 solver.cpp:189] Iteration 74700, loss = 0.0331039
I0225 03:48:50.717195 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0331044 (* 1 = 0.0331044 loss)
I0225 03:48:50.717211 29812 solver.cpp:470] Iteration 74700, lr = 0.000343
I0225 03:49:10.109920 29812 solver.cpp:189] Iteration 74750, loss = 0.0275262
I0225 03:49:10.109943 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0275267 (* 1 = 0.0275267 loss)
I0225 03:49:10.109949 29812 solver.cpp:470] Iteration 74750, lr = 0.000343
I0225 03:49:29.494985 29812 solver.cpp:189] Iteration 74800, loss = 0.11194
I0225 03:49:29.495059 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.111941 (* 1 = 0.111941 loss)
I0225 03:49:29.495074 29812 solver.cpp:470] Iteration 74800, lr = 0.000343
I0225 03:49:48.885139 29812 solver.cpp:189] Iteration 74850, loss = 0.0701884
I0225 03:49:48.885164 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0701889 (* 1 = 0.0701889 loss)
I0225 03:49:48.885169 29812 solver.cpp:470] Iteration 74850, lr = 0.000343
I0225 03:50:08.293920 29812 solver.cpp:189] Iteration 74900, loss = 0.0998225
I0225 03:50:08.293989 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0998229 (* 1 = 0.0998229 loss)
I0225 03:50:08.294004 29812 solver.cpp:470] Iteration 74900, lr = 0.000343
I0225 03:50:27.681192 29812 solver.cpp:189] Iteration 74950, loss = 0.0786719
I0225 03:50:27.681220 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0786723 (* 1 = 0.0786723 loss)
I0225 03:50:27.681226 29812 solver.cpp:470] Iteration 74950, lr = 0.000343
I0225 03:50:46.825304 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_75000.caffemodel
I0225 03:50:46.950497 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_75000.solverstate
I0225 03:50:47.009644 29812 solver.cpp:266] Iteration 75000, Testing net (#0)
I0225 03:50:54.671895 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9044
I0225 03:50:54.671936 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.363658 (* 1 = 0.363658 loss)
I0225 03:50:54.959429 29812 solver.cpp:189] Iteration 75000, loss = 0.136369
I0225 03:50:54.959451 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.13637 (* 1 = 0.13637 loss)
I0225 03:50:54.959457 29812 solver.cpp:470] Iteration 75000, lr = 0.000343
I0225 03:51:14.351279 29812 solver.cpp:189] Iteration 75050, loss = 0.0517081
I0225 03:51:14.351304 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0517085 (* 1 = 0.0517085 loss)
I0225 03:51:14.351308 29812 solver.cpp:470] Iteration 75050, lr = 0.000343
I0225 03:51:33.733433 29812 solver.cpp:189] Iteration 75100, loss = 0.132119
I0225 03:51:33.733517 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.13212 (* 1 = 0.13212 loss)
I0225 03:51:33.733525 29812 solver.cpp:470] Iteration 75100, lr = 0.000343
I0225 03:51:53.113746 29812 solver.cpp:189] Iteration 75150, loss = 0.056215
I0225 03:51:53.113770 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0562154 (* 1 = 0.0562154 loss)
I0225 03:51:53.113776 29812 solver.cpp:470] Iteration 75150, lr = 0.000343
I0225 03:52:12.505183 29812 solver.cpp:189] Iteration 75200, loss = 0.125325
I0225 03:52:12.505241 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.125325 (* 1 = 0.125325 loss)
I0225 03:52:12.505247 29812 solver.cpp:470] Iteration 75200, lr = 0.000343
I0225 03:52:31.888720 29812 solver.cpp:189] Iteration 75250, loss = 0.13195
I0225 03:52:31.888744 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.13195 (* 1 = 0.13195 loss)
I0225 03:52:31.888751 29812 solver.cpp:470] Iteration 75250, lr = 0.000343
I0225 03:52:51.263526 29812 solver.cpp:189] Iteration 75300, loss = 0.0609376
I0225 03:52:51.263566 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.060938 (* 1 = 0.060938 loss)
I0225 03:52:51.263571 29812 solver.cpp:470] Iteration 75300, lr = 0.000343
I0225 03:53:10.648013 29812 solver.cpp:189] Iteration 75350, loss = 0.12522
I0225 03:53:10.648049 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.125221 (* 1 = 0.125221 loss)
I0225 03:53:10.648056 29812 solver.cpp:470] Iteration 75350, lr = 0.000343
I0225 03:53:30.034593 29812 solver.cpp:189] Iteration 75400, loss = 0.113725
I0225 03:53:30.034703 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.113726 (* 1 = 0.113726 loss)
I0225 03:53:30.034710 29812 solver.cpp:470] Iteration 75400, lr = 0.000343
I0225 03:53:49.411484 29812 solver.cpp:189] Iteration 75450, loss = 0.0892285
I0225 03:53:49.411509 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0892288 (* 1 = 0.0892288 loss)
I0225 03:53:49.411514 29812 solver.cpp:470] Iteration 75450, lr = 0.000343
I0225 03:54:08.800698 29812 solver.cpp:189] Iteration 75500, loss = 0.178738
I0225 03:54:08.800804 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.178739 (* 1 = 0.178739 loss)
I0225 03:54:08.800811 29812 solver.cpp:470] Iteration 75500, lr = 0.000343
I0225 03:54:28.181838 29812 solver.cpp:189] Iteration 75550, loss = 0.140914
I0225 03:54:28.181861 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.140914 (* 1 = 0.140914 loss)
I0225 03:54:28.181867 29812 solver.cpp:470] Iteration 75550, lr = 0.000343
I0225 03:54:47.558945 29812 solver.cpp:189] Iteration 75600, loss = 0.11688
I0225 03:54:47.559017 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.116881 (* 1 = 0.116881 loss)
I0225 03:54:47.559033 29812 solver.cpp:470] Iteration 75600, lr = 0.000343
I0225 03:55:06.936578 29812 solver.cpp:189] Iteration 75650, loss = 0.074268
I0225 03:55:06.936604 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0742683 (* 1 = 0.0742683 loss)
I0225 03:55:06.936609 29812 solver.cpp:470] Iteration 75650, lr = 0.000343
I0225 03:55:26.316998 29812 solver.cpp:189] Iteration 75700, loss = 0.0792385
I0225 03:55:26.317070 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0792388 (* 1 = 0.0792388 loss)
I0225 03:55:26.317077 29812 solver.cpp:470] Iteration 75700, lr = 0.000343
I0225 03:55:45.698958 29812 solver.cpp:189] Iteration 75750, loss = 0.0938328
I0225 03:55:45.698993 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0938331 (* 1 = 0.0938331 loss)
I0225 03:55:45.698999 29812 solver.cpp:470] Iteration 75750, lr = 0.000343
I0225 03:56:05.081234 29812 solver.cpp:189] Iteration 75800, loss = 0.0493694
I0225 03:56:05.081305 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0493697 (* 1 = 0.0493697 loss)
I0225 03:56:05.081320 29812 solver.cpp:470] Iteration 75800, lr = 0.000343
I0225 03:56:24.467316 29812 solver.cpp:189] Iteration 75850, loss = 0.162819
I0225 03:56:24.467340 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.162819 (* 1 = 0.162819 loss)
I0225 03:56:24.467346 29812 solver.cpp:470] Iteration 75850, lr = 0.000343
I0225 03:56:43.846745 29812 solver.cpp:189] Iteration 75900, loss = 0.0899127
I0225 03:56:43.846834 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.089913 (* 1 = 0.089913 loss)
I0225 03:56:43.846842 29812 solver.cpp:470] Iteration 75900, lr = 0.000343
I0225 03:57:03.226065 29812 solver.cpp:189] Iteration 75950, loss = 0.0974735
I0225 03:57:03.226089 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0974739 (* 1 = 0.0974739 loss)
I0225 03:57:03.226094 29812 solver.cpp:470] Iteration 75950, lr = 0.000343
I0225 03:57:22.367560 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_76000.caffemodel
I0225 03:57:22.471690 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_76000.solverstate
I0225 03:57:22.530946 29812 solver.cpp:266] Iteration 76000, Testing net (#0)
I0225 03:57:30.187453 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8982
I0225 03:57:30.187490 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.391547 (* 1 = 0.391547 loss)
I0225 03:57:30.474771 29812 solver.cpp:189] Iteration 76000, loss = 0.157134
I0225 03:57:30.474793 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.157135 (* 1 = 0.157135 loss)
I0225 03:57:30.474799 29812 solver.cpp:470] Iteration 76000, lr = 0.000343
I0225 03:57:49.871268 29812 solver.cpp:189] Iteration 76050, loss = 0.0607161
I0225 03:57:49.871294 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0607164 (* 1 = 0.0607164 loss)
I0225 03:57:49.871299 29812 solver.cpp:470] Iteration 76050, lr = 0.000343
I0225 03:58:09.267590 29812 solver.cpp:189] Iteration 76100, loss = 0.0683552
I0225 03:58:09.267671 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0683555 (* 1 = 0.0683555 loss)
I0225 03:58:09.267678 29812 solver.cpp:470] Iteration 76100, lr = 0.000343
I0225 03:58:28.663732 29812 solver.cpp:189] Iteration 76150, loss = 0.117826
I0225 03:58:28.663755 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.117826 (* 1 = 0.117826 loss)
I0225 03:58:28.663761 29812 solver.cpp:470] Iteration 76150, lr = 0.000343
I0225 03:58:48.058703 29812 solver.cpp:189] Iteration 76200, loss = 0.0679068
I0225 03:58:48.058751 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0679072 (* 1 = 0.0679072 loss)
I0225 03:58:48.058758 29812 solver.cpp:470] Iteration 76200, lr = 0.000343
I0225 03:59:07.461906 29812 solver.cpp:189] Iteration 76250, loss = 0.169294
I0225 03:59:07.461930 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.169294 (* 1 = 0.169294 loss)
I0225 03:59:07.461935 29812 solver.cpp:470] Iteration 76250, lr = 0.000343
I0225 03:59:26.853910 29812 solver.cpp:189] Iteration 76300, loss = 0.0660276
I0225 03:59:26.853961 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0660279 (* 1 = 0.0660279 loss)
I0225 03:59:26.853967 29812 solver.cpp:470] Iteration 76300, lr = 0.000343
I0225 03:59:46.249194 29812 solver.cpp:189] Iteration 76350, loss = 0.125129
I0225 03:59:46.249219 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.125129 (* 1 = 0.125129 loss)
I0225 03:59:46.249225 29812 solver.cpp:470] Iteration 76350, lr = 0.000343
I0225 04:00:05.642884 29812 solver.cpp:189] Iteration 76400, loss = 0.060212
I0225 04:00:05.642959 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0602124 (* 1 = 0.0602124 loss)
I0225 04:00:05.642966 29812 solver.cpp:470] Iteration 76400, lr = 0.000343
I0225 04:00:25.039232 29812 solver.cpp:189] Iteration 76450, loss = 0.0993475
I0225 04:00:25.039255 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0993479 (* 1 = 0.0993479 loss)
I0225 04:00:25.039260 29812 solver.cpp:470] Iteration 76450, lr = 0.000343
I0225 04:00:44.431639 29812 solver.cpp:189] Iteration 76500, loss = 0.118507
I0225 04:00:44.431679 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.118507 (* 1 = 0.118507 loss)
I0225 04:00:44.431686 29812 solver.cpp:470] Iteration 76500, lr = 0.000343
I0225 04:01:03.828368 29812 solver.cpp:189] Iteration 76550, loss = 0.070705
I0225 04:01:03.828392 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0707054 (* 1 = 0.0707054 loss)
I0225 04:01:03.828398 29812 solver.cpp:470] Iteration 76550, lr = 0.000343
I0225 04:01:23.216091 29812 solver.cpp:189] Iteration 76600, loss = 0.12912
I0225 04:01:23.216161 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.12912 (* 1 = 0.12912 loss)
I0225 04:01:23.216181 29812 solver.cpp:470] Iteration 76600, lr = 0.000343
I0225 04:01:42.611351 29812 solver.cpp:189] Iteration 76650, loss = 0.167872
I0225 04:01:42.611374 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.167873 (* 1 = 0.167873 loss)
I0225 04:01:42.611379 29812 solver.cpp:470] Iteration 76650, lr = 0.000343
I0225 04:02:02.014602 29812 solver.cpp:189] Iteration 76700, loss = 0.0517782
I0225 04:02:02.014674 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0517786 (* 1 = 0.0517786 loss)
I0225 04:02:02.014689 29812 solver.cpp:470] Iteration 76700, lr = 0.000343
I0225 04:02:21.405732 29812 solver.cpp:189] Iteration 76750, loss = 0.101874
I0225 04:02:21.405756 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.101874 (* 1 = 0.101874 loss)
I0225 04:02:21.405762 29812 solver.cpp:470] Iteration 76750, lr = 0.000343
I0225 04:02:40.796519 29812 solver.cpp:189] Iteration 76800, loss = 0.0471335
I0225 04:02:40.796629 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0471338 (* 1 = 0.0471338 loss)
I0225 04:02:40.796636 29812 solver.cpp:470] Iteration 76800, lr = 0.000343
I0225 04:03:00.189198 29812 solver.cpp:189] Iteration 76850, loss = 0.0386572
I0225 04:03:00.189234 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0386575 (* 1 = 0.0386575 loss)
I0225 04:03:00.189239 29812 solver.cpp:470] Iteration 76850, lr = 0.000343
I0225 04:03:19.580359 29812 solver.cpp:189] Iteration 76900, loss = 0.127278
I0225 04:03:19.580435 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.127278 (* 1 = 0.127278 loss)
I0225 04:03:19.580449 29812 solver.cpp:470] Iteration 76900, lr = 0.000343
I0225 04:03:38.970458 29812 solver.cpp:189] Iteration 76950, loss = 0.0269252
I0225 04:03:38.970484 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0269255 (* 1 = 0.0269255 loss)
I0225 04:03:38.970490 29812 solver.cpp:470] Iteration 76950, lr = 0.000343
I0225 04:03:58.121516 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_77000.caffemodel
I0225 04:03:58.247556 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_77000.solverstate
I0225 04:03:58.308001 29812 solver.cpp:266] Iteration 77000, Testing net (#0)
I0225 04:04:05.967993 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9013
I0225 04:04:05.968030 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.364458 (* 1 = 0.364458 loss)
I0225 04:04:06.254226 29812 solver.cpp:189] Iteration 77000, loss = 0.0502914
I0225 04:04:06.254250 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0502917 (* 1 = 0.0502917 loss)
I0225 04:04:06.254256 29812 solver.cpp:470] Iteration 77000, lr = 0.000343
I0225 04:04:25.654382 29812 solver.cpp:189] Iteration 77050, loss = 0.0283059
I0225 04:04:25.654410 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0283062 (* 1 = 0.0283062 loss)
I0225 04:04:25.654417 29812 solver.cpp:470] Iteration 77050, lr = 0.000343
I0225 04:04:45.051523 29812 solver.cpp:189] Iteration 77100, loss = 0.0900033
I0225 04:04:45.051596 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0900036 (* 1 = 0.0900036 loss)
I0225 04:04:45.051611 29812 solver.cpp:470] Iteration 77100, lr = 0.000343
I0225 04:05:04.451324 29812 solver.cpp:189] Iteration 77150, loss = 0.15491
I0225 04:05:04.451349 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.15491 (* 1 = 0.15491 loss)
I0225 04:05:04.451354 29812 solver.cpp:470] Iteration 77150, lr = 0.000343
I0225 04:05:23.847868 29812 solver.cpp:189] Iteration 77200, loss = 0.0518273
I0225 04:05:23.847935 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0518277 (* 1 = 0.0518277 loss)
I0225 04:05:23.847942 29812 solver.cpp:470] Iteration 77200, lr = 0.000343
I0225 04:05:43.235173 29812 solver.cpp:189] Iteration 77250, loss = 0.081593
I0225 04:05:43.235198 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0815933 (* 1 = 0.0815933 loss)
I0225 04:05:43.235204 29812 solver.cpp:470] Iteration 77250, lr = 0.000343
I0225 04:06:02.627252 29812 solver.cpp:189] Iteration 77300, loss = 0.0435932
I0225 04:06:02.627323 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0435936 (* 1 = 0.0435936 loss)
I0225 04:06:02.627338 29812 solver.cpp:470] Iteration 77300, lr = 0.000343
I0225 04:06:22.018978 29812 solver.cpp:189] Iteration 77350, loss = 0.0848976
I0225 04:06:22.019002 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.084898 (* 1 = 0.084898 loss)
I0225 04:06:22.019008 29812 solver.cpp:470] Iteration 77350, lr = 0.000343
I0225 04:06:41.415853 29812 solver.cpp:189] Iteration 77400, loss = 0.0845113
I0225 04:06:41.415913 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0845117 (* 1 = 0.0845117 loss)
I0225 04:06:41.415920 29812 solver.cpp:470] Iteration 77400, lr = 0.000343
I0225 04:07:00.816640 29812 solver.cpp:189] Iteration 77450, loss = 0.110688
I0225 04:07:00.816666 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.110688 (* 1 = 0.110688 loss)
I0225 04:07:00.816673 29812 solver.cpp:470] Iteration 77450, lr = 0.000343
I0225 04:07:20.214834 29812 solver.cpp:189] Iteration 77500, loss = 0.0680263
I0225 04:07:20.214926 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0680267 (* 1 = 0.0680267 loss)
I0225 04:07:20.214933 29812 solver.cpp:470] Iteration 77500, lr = 0.000343
I0225 04:07:39.602651 29812 solver.cpp:189] Iteration 77550, loss = 0.0249183
I0225 04:07:39.602675 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0249187 (* 1 = 0.0249187 loss)
I0225 04:07:39.602681 29812 solver.cpp:470] Iteration 77550, lr = 0.000343
I0225 04:07:58.992319 29812 solver.cpp:189] Iteration 77600, loss = 0.114525
I0225 04:07:58.992398 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.114525 (* 1 = 0.114525 loss)
I0225 04:07:58.992413 29812 solver.cpp:470] Iteration 77600, lr = 0.000343
I0225 04:08:18.385730 29812 solver.cpp:189] Iteration 77650, loss = 0.0696865
I0225 04:08:18.385753 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0696868 (* 1 = 0.0696868 loss)
I0225 04:08:18.385759 29812 solver.cpp:470] Iteration 77650, lr = 0.000343
I0225 04:08:37.777691 29812 solver.cpp:189] Iteration 77700, loss = 0.107634
I0225 04:08:37.777770 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.107634 (* 1 = 0.107634 loss)
I0225 04:08:37.777776 29812 solver.cpp:470] Iteration 77700, lr = 0.000343
I0225 04:08:57.171433 29812 solver.cpp:189] Iteration 77750, loss = 0.201147
I0225 04:08:57.171457 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.201148 (* 1 = 0.201148 loss)
I0225 04:08:57.171463 29812 solver.cpp:470] Iteration 77750, lr = 0.000343
I0225 04:09:16.573551 29812 solver.cpp:189] Iteration 77800, loss = 0.110553
I0225 04:09:16.573601 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.110553 (* 1 = 0.110553 loss)
I0225 04:09:16.573607 29812 solver.cpp:470] Iteration 77800, lr = 0.000343
I0225 04:09:35.967916 29812 solver.cpp:189] Iteration 77850, loss = 0.129342
I0225 04:09:35.967941 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.129342 (* 1 = 0.129342 loss)
I0225 04:09:35.967947 29812 solver.cpp:470] Iteration 77850, lr = 0.000343
I0225 04:09:55.362671 29812 solver.cpp:189] Iteration 77900, loss = 0.10715
I0225 04:09:55.362740 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.10715 (* 1 = 0.10715 loss)
I0225 04:09:55.362756 29812 solver.cpp:470] Iteration 77900, lr = 0.000343
I0225 04:10:14.754796 29812 solver.cpp:189] Iteration 77950, loss = 0.224339
I0225 04:10:14.754823 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.224339 (* 1 = 0.224339 loss)
I0225 04:10:14.754828 29812 solver.cpp:470] Iteration 77950, lr = 0.000343
I0225 04:10:33.906206 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_78000.caffemodel
I0225 04:10:34.031584 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_78000.solverstate
I0225 04:10:34.089751 29812 solver.cpp:266] Iteration 78000, Testing net (#0)
I0225 04:10:41.743386 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9025
I0225 04:10:41.743422 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.369967 (* 1 = 0.369967 loss)
I0225 04:10:42.032591 29812 solver.cpp:189] Iteration 78000, loss = 0.0576984
I0225 04:10:42.032615 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0576987 (* 1 = 0.0576987 loss)
I0225 04:10:42.032623 29812 solver.cpp:470] Iteration 78000, lr = 0.000343
I0225 04:11:01.423856 29812 solver.cpp:189] Iteration 78050, loss = 0.0351342
I0225 04:11:01.423879 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0351346 (* 1 = 0.0351346 loss)
I0225 04:11:01.423885 29812 solver.cpp:470] Iteration 78050, lr = 0.000343
I0225 04:11:20.811998 29812 solver.cpp:189] Iteration 78100, loss = 0.0742135
I0225 04:11:20.812093 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0742138 (* 1 = 0.0742138 loss)
I0225 04:11:20.812099 29812 solver.cpp:470] Iteration 78100, lr = 0.000343
I0225 04:11:40.209882 29812 solver.cpp:189] Iteration 78150, loss = 0.0508089
I0225 04:11:40.209908 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0508092 (* 1 = 0.0508092 loss)
I0225 04:11:40.209913 29812 solver.cpp:470] Iteration 78150, lr = 0.000343
I0225 04:11:59.597998 29812 solver.cpp:189] Iteration 78200, loss = 0.0562032
I0225 04:11:59.598091 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0562036 (* 1 = 0.0562036 loss)
I0225 04:11:59.598108 29812 solver.cpp:470] Iteration 78200, lr = 0.000343
I0225 04:12:18.995648 29812 solver.cpp:189] Iteration 78250, loss = 0.0447626
I0225 04:12:18.995676 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0447629 (* 1 = 0.0447629 loss)
I0225 04:12:18.995682 29812 solver.cpp:470] Iteration 78250, lr = 0.000343
I0225 04:12:38.377604 29812 solver.cpp:189] Iteration 78300, loss = 0.127958
I0225 04:12:38.377648 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.127958 (* 1 = 0.127958 loss)
I0225 04:12:38.377655 29812 solver.cpp:470] Iteration 78300, lr = 0.000343
I0225 04:12:57.779463 29812 solver.cpp:189] Iteration 78350, loss = 0.235261
I0225 04:12:57.779489 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.235261 (* 1 = 0.235261 loss)
I0225 04:12:57.779495 29812 solver.cpp:470] Iteration 78350, lr = 0.000343
I0225 04:13:17.168285 29812 solver.cpp:189] Iteration 78400, loss = 0.0758426
I0225 04:13:17.168346 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0758429 (* 1 = 0.0758429 loss)
I0225 04:13:17.168352 29812 solver.cpp:470] Iteration 78400, lr = 0.000343
I0225 04:13:36.556453 29812 solver.cpp:189] Iteration 78450, loss = 0.0446139
I0225 04:13:36.556478 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0446142 (* 1 = 0.0446142 loss)
I0225 04:13:36.556483 29812 solver.cpp:470] Iteration 78450, lr = 0.000343
I0225 04:13:55.945924 29812 solver.cpp:189] Iteration 78500, loss = 0.0707447
I0225 04:13:55.946014 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.070745 (* 1 = 0.070745 loss)
I0225 04:13:55.946022 29812 solver.cpp:470] Iteration 78500, lr = 0.000343
I0225 04:14:15.346467 29812 solver.cpp:189] Iteration 78550, loss = 0.0434961
I0225 04:14:15.346492 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0434964 (* 1 = 0.0434964 loss)
I0225 04:14:15.346496 29812 solver.cpp:470] Iteration 78550, lr = 0.000343
I0225 04:14:34.738018 29812 solver.cpp:189] Iteration 78600, loss = 0.101351
I0225 04:14:34.738076 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.101352 (* 1 = 0.101352 loss)
I0225 04:14:34.738083 29812 solver.cpp:470] Iteration 78600, lr = 0.000343
I0225 04:14:54.118127 29812 solver.cpp:189] Iteration 78650, loss = 0.0952501
I0225 04:14:54.118151 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0952504 (* 1 = 0.0952504 loss)
I0225 04:14:54.118157 29812 solver.cpp:470] Iteration 78650, lr = 0.000343
I0225 04:15:13.515379 29812 solver.cpp:189] Iteration 78700, loss = 0.09182
I0225 04:15:13.515419 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0918203 (* 1 = 0.0918203 loss)
I0225 04:15:13.515425 29812 solver.cpp:470] Iteration 78700, lr = 0.000343
I0225 04:15:32.899950 29812 solver.cpp:189] Iteration 78750, loss = 0.0745009
I0225 04:15:32.899976 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0745011 (* 1 = 0.0745011 loss)
I0225 04:15:32.899981 29812 solver.cpp:470] Iteration 78750, lr = 0.000343
I0225 04:15:52.295321 29812 solver.cpp:189] Iteration 78800, loss = 0.0431249
I0225 04:15:52.295410 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0431251 (* 1 = 0.0431251 loss)
I0225 04:15:52.295418 29812 solver.cpp:470] Iteration 78800, lr = 0.000343
I0225 04:16:11.675576 29812 solver.cpp:189] Iteration 78850, loss = 0.0741113
I0225 04:16:11.675604 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0741116 (* 1 = 0.0741116 loss)
I0225 04:16:11.675611 29812 solver.cpp:470] Iteration 78850, lr = 0.000343
I0225 04:16:31.069713 29812 solver.cpp:189] Iteration 78900, loss = 0.0188969
I0225 04:16:31.069802 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0188972 (* 1 = 0.0188972 loss)
I0225 04:16:31.069808 29812 solver.cpp:470] Iteration 78900, lr = 0.000343
I0225 04:16:50.462218 29812 solver.cpp:189] Iteration 78950, loss = 0.0656609
I0225 04:16:50.462241 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0656612 (* 1 = 0.0656612 loss)
I0225 04:16:50.462247 29812 solver.cpp:470] Iteration 78950, lr = 0.000343
I0225 04:17:09.611461 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_79000.caffemodel
I0225 04:17:09.740856 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_79000.solverstate
I0225 04:17:09.799151 29812 solver.cpp:266] Iteration 79000, Testing net (#0)
I0225 04:17:17.442762 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9061
I0225 04:17:17.442798 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.380741 (* 1 = 0.380741 loss)
I0225 04:17:17.729888 29812 solver.cpp:189] Iteration 79000, loss = 0.0313445
I0225 04:17:17.729915 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0313448 (* 1 = 0.0313448 loss)
I0225 04:17:17.729921 29812 solver.cpp:470] Iteration 79000, lr = 0.000343
I0225 04:17:37.112126 29812 solver.cpp:189] Iteration 79050, loss = 0.0539091
I0225 04:17:37.112153 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0539094 (* 1 = 0.0539094 loss)
I0225 04:17:37.112159 29812 solver.cpp:470] Iteration 79050, lr = 0.000343
I0225 04:17:56.494398 29812 solver.cpp:189] Iteration 79100, loss = 0.0631495
I0225 04:17:56.494493 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0631498 (* 1 = 0.0631498 loss)
I0225 04:17:56.494499 29812 solver.cpp:470] Iteration 79100, lr = 0.000343
I0225 04:18:15.880930 29812 solver.cpp:189] Iteration 79150, loss = 0.0554839
I0225 04:18:15.880955 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0554842 (* 1 = 0.0554842 loss)
I0225 04:18:15.880961 29812 solver.cpp:470] Iteration 79150, lr = 0.000343
I0225 04:18:35.259752 29812 solver.cpp:189] Iteration 79200, loss = 0.0678012
I0225 04:18:35.259822 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0678015 (* 1 = 0.0678015 loss)
I0225 04:18:35.259837 29812 solver.cpp:470] Iteration 79200, lr = 0.000343
I0225 04:18:54.633728 29812 solver.cpp:189] Iteration 79250, loss = 0.199732
I0225 04:18:54.633752 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.199732 (* 1 = 0.199732 loss)
I0225 04:18:54.633757 29812 solver.cpp:470] Iteration 79250, lr = 0.000343
I0225 04:19:14.021359 29812 solver.cpp:189] Iteration 79300, loss = 0.0964965
I0225 04:19:14.021456 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0964969 (* 1 = 0.0964969 loss)
I0225 04:19:14.021463 29812 solver.cpp:470] Iteration 79300, lr = 0.000343
I0225 04:19:33.397707 29812 solver.cpp:189] Iteration 79350, loss = 0.0777859
I0225 04:19:33.397732 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0777862 (* 1 = 0.0777862 loss)
I0225 04:19:33.397737 29812 solver.cpp:470] Iteration 79350, lr = 0.000343
I0225 04:19:52.785918 29812 solver.cpp:189] Iteration 79400, loss = 0.0713928
I0225 04:19:52.785962 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0713931 (* 1 = 0.0713931 loss)
I0225 04:19:52.785969 29812 solver.cpp:470] Iteration 79400, lr = 0.000343
I0225 04:20:12.168234 29812 solver.cpp:189] Iteration 79450, loss = 0.0491029
I0225 04:20:12.168258 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0491032 (* 1 = 0.0491032 loss)
I0225 04:20:12.168264 29812 solver.cpp:470] Iteration 79450, lr = 0.000343
I0225 04:20:31.550247 29812 solver.cpp:189] Iteration 79500, loss = 0.142515
I0225 04:20:31.550307 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.142515 (* 1 = 0.142515 loss)
I0225 04:20:31.550313 29812 solver.cpp:470] Iteration 79500, lr = 0.000343
I0225 04:20:50.935096 29812 solver.cpp:189] Iteration 79550, loss = 0.110186
I0225 04:20:50.935120 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.110187 (* 1 = 0.110187 loss)
I0225 04:20:50.935127 29812 solver.cpp:470] Iteration 79550, lr = 0.000343
I0225 04:21:10.315073 29812 solver.cpp:189] Iteration 79600, loss = 0.0866271
I0225 04:21:10.315184 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0866274 (* 1 = 0.0866274 loss)
I0225 04:21:10.315191 29812 solver.cpp:470] Iteration 79600, lr = 0.000343
I0225 04:21:29.698509 29812 solver.cpp:189] Iteration 79650, loss = 0.118348
I0225 04:21:29.698537 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.118348 (* 1 = 0.118348 loss)
I0225 04:21:29.698544 29812 solver.cpp:470] Iteration 79650, lr = 0.000343
I0225 04:21:49.084251 29812 solver.cpp:189] Iteration 79700, loss = 0.0417134
I0225 04:21:49.084316 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0417136 (* 1 = 0.0417136 loss)
I0225 04:21:49.084321 29812 solver.cpp:470] Iteration 79700, lr = 0.000343
I0225 04:22:08.470480 29812 solver.cpp:189] Iteration 79750, loss = 0.0767886
I0225 04:22:08.470505 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0767889 (* 1 = 0.0767889 loss)
I0225 04:22:08.470510 29812 solver.cpp:470] Iteration 79750, lr = 0.000343
I0225 04:22:27.849258 29812 solver.cpp:189] Iteration 79800, loss = 0.0527533
I0225 04:22:27.849330 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0527536 (* 1 = 0.0527536 loss)
I0225 04:22:27.849346 29812 solver.cpp:470] Iteration 79800, lr = 0.000343
I0225 04:22:47.235451 29812 solver.cpp:189] Iteration 79850, loss = 0.1098
I0225 04:22:47.235474 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.1098 (* 1 = 0.1098 loss)
I0225 04:22:47.235481 29812 solver.cpp:470] Iteration 79850, lr = 0.000343
I0225 04:23:06.620959 29812 solver.cpp:189] Iteration 79900, loss = 0.0842893
I0225 04:23:06.620998 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0842895 (* 1 = 0.0842895 loss)
I0225 04:23:06.621004 29812 solver.cpp:470] Iteration 79900, lr = 0.000343
I0225 04:23:25.995628 29812 solver.cpp:189] Iteration 79950, loss = 0.113659
I0225 04:23:25.995652 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.113659 (* 1 = 0.113659 loss)
I0225 04:23:25.995658 29812 solver.cpp:470] Iteration 79950, lr = 0.000343
I0225 04:23:45.127584 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_80000.caffemodel
I0225 04:23:45.248834 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_80000.solverstate
I0225 04:23:45.307461 29812 solver.cpp:266] Iteration 80000, Testing net (#0)
I0225 04:23:52.950053 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.8997
I0225 04:23:52.950088 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.380848 (* 1 = 0.380848 loss)
I0225 04:23:53.238559 29812 solver.cpp:189] Iteration 80000, loss = 0.108724
I0225 04:23:53.238584 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.108724 (* 1 = 0.108724 loss)
I0225 04:23:53.238595 29812 solver.cpp:470] Iteration 80000, lr = 0.0002401
I0225 04:24:12.633688 29812 solver.cpp:189] Iteration 80050, loss = 0.0342837
I0225 04:24:12.633713 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.034284 (* 1 = 0.034284 loss)
I0225 04:24:12.633719 29812 solver.cpp:470] Iteration 80050, lr = 0.0002401
I0225 04:24:32.030643 29812 solver.cpp:189] Iteration 80100, loss = 0.134945
I0225 04:24:32.030709 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.134945 (* 1 = 0.134945 loss)
I0225 04:24:32.030724 29812 solver.cpp:470] Iteration 80100, lr = 0.0002401
I0225 04:24:51.421967 29812 solver.cpp:189] Iteration 80150, loss = 0.0158001
I0225 04:24:51.421991 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0158003 (* 1 = 0.0158003 loss)
I0225 04:24:51.421998 29812 solver.cpp:470] Iteration 80150, lr = 0.0002401
I0225 04:25:10.820826 29812 solver.cpp:189] Iteration 80200, loss = 0.0697226
I0225 04:25:10.820899 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0697228 (* 1 = 0.0697228 loss)
I0225 04:25:10.820914 29812 solver.cpp:470] Iteration 80200, lr = 0.0002401
I0225 04:25:30.219703 29812 solver.cpp:189] Iteration 80250, loss = 0.0432333
I0225 04:25:30.219728 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0432335 (* 1 = 0.0432335 loss)
I0225 04:25:30.219734 29812 solver.cpp:470] Iteration 80250, lr = 0.0002401
I0225 04:25:49.613709 29812 solver.cpp:189] Iteration 80300, loss = 0.0820463
I0225 04:25:49.613801 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0820465 (* 1 = 0.0820465 loss)
I0225 04:25:49.613816 29812 solver.cpp:470] Iteration 80300, lr = 0.0002401
I0225 04:26:09.012825 29812 solver.cpp:189] Iteration 80350, loss = 0.10059
I0225 04:26:09.012848 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.100591 (* 1 = 0.100591 loss)
I0225 04:26:09.012855 29812 solver.cpp:470] Iteration 80350, lr = 0.0002401
I0225 04:26:28.399116 29812 solver.cpp:189] Iteration 80400, loss = 0.020416
I0225 04:26:28.399207 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0204162 (* 1 = 0.0204162 loss)
I0225 04:26:28.399214 29812 solver.cpp:470] Iteration 80400, lr = 0.0002401
I0225 04:26:47.795545 29812 solver.cpp:189] Iteration 80450, loss = 0.0588072
I0225 04:26:47.795572 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0588074 (* 1 = 0.0588074 loss)
I0225 04:26:47.795578 29812 solver.cpp:470] Iteration 80450, lr = 0.0002401
I0225 04:27:07.187749 29812 solver.cpp:189] Iteration 80500, loss = 0.0344639
I0225 04:27:07.187820 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0344642 (* 1 = 0.0344642 loss)
I0225 04:27:07.187835 29812 solver.cpp:470] Iteration 80500, lr = 0.0002401
I0225 04:27:26.574679 29812 solver.cpp:189] Iteration 80550, loss = 0.0684156
I0225 04:27:26.574703 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0684158 (* 1 = 0.0684158 loss)
I0225 04:27:26.574709 29812 solver.cpp:470] Iteration 80550, lr = 0.0002401
I0225 04:27:45.952699 29812 solver.cpp:189] Iteration 80600, loss = 0.0843139
I0225 04:27:45.952739 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0843142 (* 1 = 0.0843142 loss)
I0225 04:27:45.952745 29812 solver.cpp:470] Iteration 80600, lr = 0.0002401
I0225 04:28:05.342185 29812 solver.cpp:189] Iteration 80650, loss = 0.0999211
I0225 04:28:05.342208 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0999214 (* 1 = 0.0999214 loss)
I0225 04:28:05.342214 29812 solver.cpp:470] Iteration 80650, lr = 0.0002401
I0225 04:28:24.729184 29812 solver.cpp:189] Iteration 80700, loss = 0.0446483
I0225 04:28:24.729275 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0446485 (* 1 = 0.0446485 loss)
I0225 04:28:24.729281 29812 solver.cpp:470] Iteration 80700, lr = 0.0002401
I0225 04:28:44.117532 29812 solver.cpp:189] Iteration 80750, loss = 0.0732532
I0225 04:28:44.117557 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0732534 (* 1 = 0.0732534 loss)
I0225 04:28:44.117563 29812 solver.cpp:470] Iteration 80750, lr = 0.0002401
I0225 04:29:03.514740 29812 solver.cpp:189] Iteration 80800, loss = 0.0754575
I0225 04:29:03.514780 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0754578 (* 1 = 0.0754578 loss)
I0225 04:29:03.514786 29812 solver.cpp:470] Iteration 80800, lr = 0.0002401
I0225 04:29:22.903893 29812 solver.cpp:189] Iteration 80850, loss = 0.0808126
I0225 04:29:22.903918 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0808128 (* 1 = 0.0808128 loss)
I0225 04:29:22.903924 29812 solver.cpp:470] Iteration 80850, lr = 0.0002401
I0225 04:29:42.300978 29812 solver.cpp:189] Iteration 80900, loss = 0.0377008
I0225 04:29:42.301048 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0377011 (* 1 = 0.0377011 loss)
I0225 04:29:42.301064 29812 solver.cpp:470] Iteration 80900, lr = 0.0002401
I0225 04:30:01.698341 29812 solver.cpp:189] Iteration 80950, loss = 0.170617
I0225 04:30:01.698366 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.170617 (* 1 = 0.170617 loss)
I0225 04:30:01.698372 29812 solver.cpp:470] Iteration 80950, lr = 0.0002401
I0225 04:30:20.835186 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_81000.caffemodel
I0225 04:30:20.962874 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_81000.solverstate
I0225 04:30:21.021333 29812 solver.cpp:266] Iteration 81000, Testing net (#0)
I0225 04:30:28.674937 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9066
I0225 04:30:28.674978 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.380326 (* 1 = 0.380326 loss)
I0225 04:30:28.961719 29812 solver.cpp:189] Iteration 81000, loss = 0.0315016
I0225 04:30:28.961740 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0315018 (* 1 = 0.0315018 loss)
I0225 04:30:28.961745 29812 solver.cpp:470] Iteration 81000, lr = 0.0002401
I0225 04:30:48.358208 29812 solver.cpp:189] Iteration 81050, loss = 0.130512
I0225 04:30:48.358232 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.130512 (* 1 = 0.130512 loss)
I0225 04:30:48.358238 29812 solver.cpp:470] Iteration 81050, lr = 0.0002401
I0225 04:31:07.754514 29812 solver.cpp:189] Iteration 81100, loss = 0.117553
I0225 04:31:07.754628 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.117553 (* 1 = 0.117553 loss)
I0225 04:31:07.754636 29812 solver.cpp:470] Iteration 81100, lr = 0.0002401
I0225 04:31:27.151254 29812 solver.cpp:189] Iteration 81150, loss = 0.0841375
I0225 04:31:27.151279 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0841376 (* 1 = 0.0841376 loss)
I0225 04:31:27.151285 29812 solver.cpp:470] Iteration 81150, lr = 0.0002401
I0225 04:31:46.538368 29812 solver.cpp:189] Iteration 81200, loss = 0.0990882
I0225 04:31:46.538410 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0990883 (* 1 = 0.0990883 loss)
I0225 04:31:46.538416 29812 solver.cpp:470] Iteration 81200, lr = 0.0002401
I0225 04:32:05.933547 29812 solver.cpp:189] Iteration 81250, loss = 0.0669391
I0225 04:32:05.933573 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0669392 (* 1 = 0.0669392 loss)
I0225 04:32:05.933578 29812 solver.cpp:470] Iteration 81250, lr = 0.0002401
I0225 04:32:25.335424 29812 solver.cpp:189] Iteration 81300, loss = 0.0481052
I0225 04:32:25.335485 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0481053 (* 1 = 0.0481053 loss)
I0225 04:32:25.335491 29812 solver.cpp:470] Iteration 81300, lr = 0.0002401
I0225 04:32:44.733067 29812 solver.cpp:189] Iteration 81350, loss = 0.0662234
I0225 04:32:44.733094 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0662235 (* 1 = 0.0662235 loss)
I0225 04:32:44.733100 29812 solver.cpp:470] Iteration 81350, lr = 0.0002401
I0225 04:33:04.122136 29812 solver.cpp:189] Iteration 81400, loss = 0.117713
I0225 04:33:04.122202 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.117713 (* 1 = 0.117713 loss)
I0225 04:33:04.122217 29812 solver.cpp:470] Iteration 81400, lr = 0.0002401
I0225 04:33:23.524847 29812 solver.cpp:189] Iteration 81450, loss = 0.0571862
I0225 04:33:23.524871 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0571864 (* 1 = 0.0571864 loss)
I0225 04:33:23.524878 29812 solver.cpp:470] Iteration 81450, lr = 0.0002401
I0225 04:33:42.923542 29812 solver.cpp:189] Iteration 81500, loss = 0.0620001
I0225 04:33:42.923595 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0620003 (* 1 = 0.0620003 loss)
I0225 04:33:42.923601 29812 solver.cpp:470] Iteration 81500, lr = 0.0002401
I0225 04:34:02.328361 29812 solver.cpp:189] Iteration 81550, loss = 0.0956923
I0225 04:34:02.328384 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0956925 (* 1 = 0.0956925 loss)
I0225 04:34:02.328390 29812 solver.cpp:470] Iteration 81550, lr = 0.0002401
I0225 04:34:21.721807 29812 solver.cpp:189] Iteration 81600, loss = 0.0243157
I0225 04:34:21.721880 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0243158 (* 1 = 0.0243158 loss)
I0225 04:34:21.721895 29812 solver.cpp:470] Iteration 81600, lr = 0.0002401
I0225 04:34:41.116855 29812 solver.cpp:189] Iteration 81650, loss = 0.105182
I0225 04:34:41.116883 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105182 (* 1 = 0.105182 loss)
I0225 04:34:41.116888 29812 solver.cpp:470] Iteration 81650, lr = 0.0002401
I0225 04:35:00.518198 29812 solver.cpp:189] Iteration 81700, loss = 0.0739285
I0225 04:35:00.518280 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0739286 (* 1 = 0.0739286 loss)
I0225 04:35:00.518286 29812 solver.cpp:470] Iteration 81700, lr = 0.0002401
I0225 04:35:19.913640 29812 solver.cpp:189] Iteration 81750, loss = 0.0778288
I0225 04:35:19.913663 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.077829 (* 1 = 0.077829 loss)
I0225 04:35:19.913669 29812 solver.cpp:470] Iteration 81750, lr = 0.0002401
I0225 04:35:39.303761 29812 solver.cpp:189] Iteration 81800, loss = 0.0420663
I0225 04:35:39.303834 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0420665 (* 1 = 0.0420665 loss)
I0225 04:35:39.303849 29812 solver.cpp:470] Iteration 81800, lr = 0.0002401
I0225 04:35:58.691109 29812 solver.cpp:189] Iteration 81850, loss = 0.0597122
I0225 04:35:58.691133 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0597124 (* 1 = 0.0597124 loss)
I0225 04:35:58.691138 29812 solver.cpp:470] Iteration 81850, lr = 0.0002401
I0225 04:36:18.095763 29812 solver.cpp:189] Iteration 81900, loss = 0.0734429
I0225 04:36:18.095834 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.073443 (* 1 = 0.073443 loss)
I0225 04:36:18.095849 29812 solver.cpp:470] Iteration 81900, lr = 0.0002401
I0225 04:36:37.497771 29812 solver.cpp:189] Iteration 81950, loss = 0.0273056
I0225 04:36:37.497795 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0273057 (* 1 = 0.0273057 loss)
I0225 04:36:37.497802 29812 solver.cpp:470] Iteration 81950, lr = 0.0002401
I0225 04:36:56.648792 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_82000.caffemodel
I0225 04:36:56.769704 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_82000.solverstate
I0225 04:36:56.827543 29812 solver.cpp:266] Iteration 82000, Testing net (#0)
I0225 04:37:04.478163 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9086
I0225 04:37:04.478199 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.364248 (* 1 = 0.364248 loss)
I0225 04:37:04.764724 29812 solver.cpp:189] Iteration 82000, loss = 0.0615878
I0225 04:37:04.764766 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0615879 (* 1 = 0.0615879 loss)
I0225 04:37:04.764773 29812 solver.cpp:470] Iteration 82000, lr = 0.0002401
I0225 04:37:24.153856 29812 solver.cpp:189] Iteration 82050, loss = 0.0462122
I0225 04:37:24.153882 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0462123 (* 1 = 0.0462123 loss)
I0225 04:37:24.153888 29812 solver.cpp:470] Iteration 82050, lr = 0.0002401
I0225 04:37:43.548301 29812 solver.cpp:189] Iteration 82100, loss = 0.025688
I0225 04:37:43.548391 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0256881 (* 1 = 0.0256881 loss)
I0225 04:37:43.548398 29812 solver.cpp:470] Iteration 82100, lr = 0.0002401
I0225 04:38:02.942330 29812 solver.cpp:189] Iteration 82150, loss = 0.0862654
I0225 04:38:02.942354 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0862655 (* 1 = 0.0862655 loss)
I0225 04:38:02.942360 29812 solver.cpp:470] Iteration 82150, lr = 0.0002401
I0225 04:38:22.327864 29812 solver.cpp:189] Iteration 82200, loss = 0.066064
I0225 04:38:22.327918 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0660641 (* 1 = 0.0660641 loss)
I0225 04:38:22.327924 29812 solver.cpp:470] Iteration 82200, lr = 0.0002401
I0225 04:38:41.712376 29812 solver.cpp:189] Iteration 82250, loss = 0.0212661
I0225 04:38:41.712404 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0212662 (* 1 = 0.0212662 loss)
I0225 04:38:41.712410 29812 solver.cpp:470] Iteration 82250, lr = 0.0002401
I0225 04:39:01.113026 29812 solver.cpp:189] Iteration 82300, loss = 0.0849021
I0225 04:39:01.113132 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0849023 (* 1 = 0.0849023 loss)
I0225 04:39:01.113147 29812 solver.cpp:470] Iteration 82300, lr = 0.0002401
I0225 04:39:20.500447 29812 solver.cpp:189] Iteration 82350, loss = 0.0212902
I0225 04:39:20.500471 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0212904 (* 1 = 0.0212904 loss)
I0225 04:39:20.500478 29812 solver.cpp:470] Iteration 82350, lr = 0.0002401
I0225 04:39:39.888411 29812 solver.cpp:189] Iteration 82400, loss = 0.091222
I0225 04:39:39.888504 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0912221 (* 1 = 0.0912221 loss)
I0225 04:39:39.888520 29812 solver.cpp:470] Iteration 82400, lr = 0.0002401
I0225 04:39:59.285801 29812 solver.cpp:189] Iteration 82450, loss = 0.0597139
I0225 04:39:59.285826 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0597141 (* 1 = 0.0597141 loss)
I0225 04:39:59.285832 29812 solver.cpp:470] Iteration 82450, lr = 0.0002401
I0225 04:40:18.674976 29812 solver.cpp:189] Iteration 82500, loss = 0.124818
I0225 04:40:18.675050 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.124818 (* 1 = 0.124818 loss)
I0225 04:40:18.675065 29812 solver.cpp:470] Iteration 82500, lr = 0.0002401
I0225 04:40:38.062175 29812 solver.cpp:189] Iteration 82550, loss = 0.190023
I0225 04:40:38.062199 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.190024 (* 1 = 0.190024 loss)
I0225 04:40:38.062206 29812 solver.cpp:470] Iteration 82550, lr = 0.0002401
I0225 04:40:57.448235 29812 solver.cpp:189] Iteration 82600, loss = 0.0938618
I0225 04:40:57.448379 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0938619 (* 1 = 0.0938619 loss)
I0225 04:40:57.448395 29812 solver.cpp:470] Iteration 82600, lr = 0.0002401
I0225 04:41:16.842360 29812 solver.cpp:189] Iteration 82650, loss = 0.0359802
I0225 04:41:16.842386 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0359803 (* 1 = 0.0359803 loss)
I0225 04:41:16.842391 29812 solver.cpp:470] Iteration 82650, lr = 0.0002401
I0225 04:41:36.234349 29812 solver.cpp:189] Iteration 82700, loss = 0.120502
I0225 04:41:36.234473 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.120503 (* 1 = 0.120503 loss)
I0225 04:41:36.234483 29812 solver.cpp:470] Iteration 82700, lr = 0.0002401
I0225 04:41:55.620585 29812 solver.cpp:189] Iteration 82750, loss = 0.0836356
I0225 04:41:55.620610 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0836358 (* 1 = 0.0836358 loss)
I0225 04:41:55.620616 29812 solver.cpp:470] Iteration 82750, lr = 0.0002401
I0225 04:42:15.012995 29812 solver.cpp:189] Iteration 82800, loss = 0.0651822
I0225 04:42:15.013057 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0651824 (* 1 = 0.0651824 loss)
I0225 04:42:15.013072 29812 solver.cpp:470] Iteration 82800, lr = 0.0002401
I0225 04:42:34.406047 29812 solver.cpp:189] Iteration 82850, loss = 0.0809941
I0225 04:42:34.406071 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0809943 (* 1 = 0.0809943 loss)
I0225 04:42:34.406076 29812 solver.cpp:470] Iteration 82850, lr = 0.0002401
I0225 04:42:53.799945 29812 solver.cpp:189] Iteration 82900, loss = 0.125183
I0225 04:42:53.800015 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.125183 (* 1 = 0.125183 loss)
I0225 04:42:53.800030 29812 solver.cpp:470] Iteration 82900, lr = 0.0002401
I0225 04:43:13.186602 29812 solver.cpp:189] Iteration 82950, loss = 0.0391566
I0225 04:43:13.186637 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0391568 (* 1 = 0.0391568 loss)
I0225 04:43:13.186645 29812 solver.cpp:470] Iteration 82950, lr = 0.0002401
I0225 04:43:32.338259 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_83000.caffemodel
I0225 04:43:32.458211 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_83000.solverstate
I0225 04:43:32.515705 29812 solver.cpp:266] Iteration 83000, Testing net (#0)
I0225 04:43:40.175811 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9023
I0225 04:43:40.175848 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.392763 (* 1 = 0.392763 loss)
I0225 04:43:40.463085 29812 solver.cpp:189] Iteration 83000, loss = 0.0713538
I0225 04:43:40.463110 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.071354 (* 1 = 0.071354 loss)
I0225 04:43:40.463116 29812 solver.cpp:470] Iteration 83000, lr = 0.0002401
I0225 04:43:59.843299 29812 solver.cpp:189] Iteration 83050, loss = 0.0814634
I0225 04:43:59.843325 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0814636 (* 1 = 0.0814636 loss)
I0225 04:43:59.843333 29812 solver.cpp:470] Iteration 83050, lr = 0.0002401
I0225 04:44:19.227339 29812 solver.cpp:189] Iteration 83100, loss = 0.104392
I0225 04:44:19.227433 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.104392 (* 1 = 0.104392 loss)
I0225 04:44:19.227449 29812 solver.cpp:470] Iteration 83100, lr = 0.0002401
I0225 04:44:38.605810 29812 solver.cpp:189] Iteration 83150, loss = 0.0914028
I0225 04:44:38.605835 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.091403 (* 1 = 0.091403 loss)
I0225 04:44:38.605841 29812 solver.cpp:470] Iteration 83150, lr = 0.0002401
I0225 04:44:57.985013 29812 solver.cpp:189] Iteration 83200, loss = 0.120045
I0225 04:44:57.985080 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.120045 (* 1 = 0.120045 loss)
I0225 04:44:57.985085 29812 solver.cpp:470] Iteration 83200, lr = 0.0002401
I0225 04:45:17.369184 29812 solver.cpp:189] Iteration 83250, loss = 0.0574634
I0225 04:45:17.369207 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0574636 (* 1 = 0.0574636 loss)
I0225 04:45:17.369213 29812 solver.cpp:470] Iteration 83250, lr = 0.0002401
I0225 04:45:36.752089 29812 solver.cpp:189] Iteration 83300, loss = 0.0537654
I0225 04:45:36.752161 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0537656 (* 1 = 0.0537656 loss)
I0225 04:45:36.752182 29812 solver.cpp:470] Iteration 83300, lr = 0.0002401
I0225 04:45:56.131440 29812 solver.cpp:189] Iteration 83350, loss = 0.0588884
I0225 04:45:56.131464 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0588886 (* 1 = 0.0588886 loss)
I0225 04:45:56.131470 29812 solver.cpp:470] Iteration 83350, lr = 0.0002401
I0225 04:46:15.518672 29812 solver.cpp:189] Iteration 83400, loss = 0.0317361
I0225 04:46:15.518754 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0317363 (* 1 = 0.0317363 loss)
I0225 04:46:15.518769 29812 solver.cpp:470] Iteration 83400, lr = 0.0002401
I0225 04:46:34.901497 29812 solver.cpp:189] Iteration 83450, loss = 0.0388311
I0225 04:46:34.901521 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0388313 (* 1 = 0.0388313 loss)
I0225 04:46:34.901527 29812 solver.cpp:470] Iteration 83450, lr = 0.0002401
I0225 04:46:54.287763 29812 solver.cpp:189] Iteration 83500, loss = 0.0700114
I0225 04:46:54.287803 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0700116 (* 1 = 0.0700116 loss)
I0225 04:46:54.287811 29812 solver.cpp:470] Iteration 83500, lr = 0.0002401
I0225 04:47:13.673460 29812 solver.cpp:189] Iteration 83550, loss = 0.0279182
I0225 04:47:13.673486 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0279184 (* 1 = 0.0279184 loss)
I0225 04:47:13.673492 29812 solver.cpp:470] Iteration 83550, lr = 0.0002401
I0225 04:47:33.049654 29812 solver.cpp:189] Iteration 83600, loss = 0.0338904
I0225 04:47:33.049722 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0338906 (* 1 = 0.0338906 loss)
I0225 04:47:33.049738 29812 solver.cpp:470] Iteration 83600, lr = 0.0002401
I0225 04:47:52.436812 29812 solver.cpp:189] Iteration 83650, loss = 0.0574345
I0225 04:47:52.436836 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0574347 (* 1 = 0.0574347 loss)
I0225 04:47:52.436842 29812 solver.cpp:470] Iteration 83650, lr = 0.0002401
I0225 04:48:11.818498 29812 solver.cpp:189] Iteration 83700, loss = 0.049332
I0225 04:48:11.818583 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0493322 (* 1 = 0.0493322 loss)
I0225 04:48:11.818590 29812 solver.cpp:470] Iteration 83700, lr = 0.0002401
I0225 04:48:31.207212 29812 solver.cpp:189] Iteration 83750, loss = 0.0369924
I0225 04:48:31.207237 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0369925 (* 1 = 0.0369925 loss)
I0225 04:48:31.207242 29812 solver.cpp:470] Iteration 83750, lr = 0.0002401
I0225 04:48:50.585270 29812 solver.cpp:189] Iteration 83800, loss = 0.0877116
I0225 04:48:50.585331 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0877118 (* 1 = 0.0877118 loss)
I0225 04:48:50.585338 29812 solver.cpp:470] Iteration 83800, lr = 0.0002401
I0225 04:49:09.961835 29812 solver.cpp:189] Iteration 83850, loss = 0.0674764
I0225 04:49:09.961859 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0674766 (* 1 = 0.0674766 loss)
I0225 04:49:09.961865 29812 solver.cpp:470] Iteration 83850, lr = 0.0002401
I0225 04:49:29.354013 29812 solver.cpp:189] Iteration 83900, loss = 0.100623
I0225 04:49:29.354107 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.100623 (* 1 = 0.100623 loss)
I0225 04:49:29.354115 29812 solver.cpp:470] Iteration 83900, lr = 0.0002401
I0225 04:49:48.743451 29812 solver.cpp:189] Iteration 83950, loss = 0.140366
I0225 04:49:48.743479 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.140366 (* 1 = 0.140366 loss)
I0225 04:49:48.743484 29812 solver.cpp:470] Iteration 83950, lr = 0.0002401
I0225 04:50:07.891407 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_84000.caffemodel
I0225 04:50:08.010319 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_84000.solverstate
I0225 04:50:08.067893 29812 solver.cpp:266] Iteration 84000, Testing net (#0)
I0225 04:50:15.717126 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9069
I0225 04:50:15.717161 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.385159 (* 1 = 0.385159 loss)
I0225 04:50:16.004048 29812 solver.cpp:189] Iteration 84000, loss = 0.0370834
I0225 04:50:16.004070 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0370835 (* 1 = 0.0370835 loss)
I0225 04:50:16.004076 29812 solver.cpp:470] Iteration 84000, lr = 0.0002401
I0225 04:50:35.395671 29812 solver.cpp:189] Iteration 84050, loss = 0.0137722
I0225 04:50:35.395695 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0137723 (* 1 = 0.0137723 loss)
I0225 04:50:35.395701 29812 solver.cpp:470] Iteration 84050, lr = 0.0002401
I0225 04:50:54.789165 29812 solver.cpp:189] Iteration 84100, loss = 0.0373278
I0225 04:50:54.789232 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0373279 (* 1 = 0.0373279 loss)
I0225 04:50:54.789247 29812 solver.cpp:470] Iteration 84100, lr = 0.0002401
I0225 04:51:14.186316 29812 solver.cpp:189] Iteration 84150, loss = 0.102087
I0225 04:51:14.186339 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.102087 (* 1 = 0.102087 loss)
I0225 04:51:14.186346 29812 solver.cpp:470] Iteration 84150, lr = 0.0002401
I0225 04:51:33.583149 29812 solver.cpp:189] Iteration 84200, loss = 0.0574966
I0225 04:51:33.583209 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0574967 (* 1 = 0.0574967 loss)
I0225 04:51:33.583215 29812 solver.cpp:470] Iteration 84200, lr = 0.0002401
I0225 04:51:52.984380 29812 solver.cpp:189] Iteration 84250, loss = 0.0222714
I0225 04:51:52.984405 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0222715 (* 1 = 0.0222715 loss)
I0225 04:51:52.984411 29812 solver.cpp:470] Iteration 84250, lr = 0.0002401
I0225 04:52:12.371816 29812 solver.cpp:189] Iteration 84300, loss = 0.128291
I0225 04:52:12.371901 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.128291 (* 1 = 0.128291 loss)
I0225 04:52:12.371907 29812 solver.cpp:470] Iteration 84300, lr = 0.0002401
I0225 04:52:31.765940 29812 solver.cpp:189] Iteration 84350, loss = 0.0765487
I0225 04:52:31.765964 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0765488 (* 1 = 0.0765488 loss)
I0225 04:52:31.765970 29812 solver.cpp:470] Iteration 84350, lr = 0.0002401
I0225 04:52:51.150504 29812 solver.cpp:189] Iteration 84400, loss = 0.0734255
I0225 04:52:51.150590 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0734256 (* 1 = 0.0734256 loss)
I0225 04:52:51.150596 29812 solver.cpp:470] Iteration 84400, lr = 0.0002401
I0225 04:53:10.540772 29812 solver.cpp:189] Iteration 84450, loss = 0.146427
I0225 04:53:10.540797 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.146427 (* 1 = 0.146427 loss)
I0225 04:53:10.540803 29812 solver.cpp:470] Iteration 84450, lr = 0.0002401
I0225 04:53:29.931332 29812 solver.cpp:189] Iteration 84500, loss = 0.0357417
I0225 04:53:29.931391 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0357418 (* 1 = 0.0357418 loss)
I0225 04:53:29.931398 29812 solver.cpp:470] Iteration 84500, lr = 0.0002401
I0225 04:53:49.327539 29812 solver.cpp:189] Iteration 84550, loss = 0.135384
I0225 04:53:49.327564 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.135384 (* 1 = 0.135384 loss)
I0225 04:53:49.327569 29812 solver.cpp:470] Iteration 84550, lr = 0.0002401
I0225 04:54:08.720959 29812 solver.cpp:189] Iteration 84600, loss = 0.0197924
I0225 04:54:08.721050 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0197925 (* 1 = 0.0197925 loss)
I0225 04:54:08.721065 29812 solver.cpp:470] Iteration 84600, lr = 0.0002401
I0225 04:54:28.109247 29812 solver.cpp:189] Iteration 84650, loss = 0.0923288
I0225 04:54:28.109272 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0923289 (* 1 = 0.0923289 loss)
I0225 04:54:28.109277 29812 solver.cpp:470] Iteration 84650, lr = 0.0002401
I0225 04:54:47.510881 29812 solver.cpp:189] Iteration 84700, loss = 0.0714475
I0225 04:54:47.510951 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0714476 (* 1 = 0.0714476 loss)
I0225 04:54:47.510967 29812 solver.cpp:470] Iteration 84700, lr = 0.0002401
I0225 04:55:06.901510 29812 solver.cpp:189] Iteration 84750, loss = 0.05039
I0225 04:55:06.901535 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0503901 (* 1 = 0.0503901 loss)
I0225 04:55:06.901541 29812 solver.cpp:470] Iteration 84750, lr = 0.0002401
I0225 04:55:26.282227 29812 solver.cpp:189] Iteration 84800, loss = 0.0443093
I0225 04:55:26.282305 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0443094 (* 1 = 0.0443094 loss)
I0225 04:55:26.282320 29812 solver.cpp:470] Iteration 84800, lr = 0.0002401
I0225 04:55:45.672349 29812 solver.cpp:189] Iteration 84850, loss = 0.0246017
I0225 04:55:45.672375 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0246018 (* 1 = 0.0246018 loss)
I0225 04:55:45.672381 29812 solver.cpp:470] Iteration 84850, lr = 0.0002401
I0225 04:56:05.070138 29812 solver.cpp:189] Iteration 84900, loss = 0.0544281
I0225 04:56:05.070205 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0544282 (* 1 = 0.0544282 loss)
I0225 04:56:05.070220 29812 solver.cpp:470] Iteration 84900, lr = 0.0002401
I0225 04:56:24.455608 29812 solver.cpp:189] Iteration 84950, loss = 0.0800302
I0225 04:56:24.455632 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0800303 (* 1 = 0.0800303 loss)
I0225 04:56:24.455638 29812 solver.cpp:470] Iteration 84950, lr = 0.0002401
I0225 04:56:43.599522 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_85000.caffemodel
I0225 04:56:43.705396 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_85000.solverstate
I0225 04:56:43.763736 29812 solver.cpp:266] Iteration 85000, Testing net (#0)
I0225 04:56:51.413820 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.908
I0225 04:56:51.413856 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.382638 (* 1 = 0.382638 loss)
I0225 04:56:51.700656 29812 solver.cpp:189] Iteration 85000, loss = 0.0385001
I0225 04:56:51.700680 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0385002 (* 1 = 0.0385002 loss)
I0225 04:56:51.700686 29812 solver.cpp:470] Iteration 85000, lr = 0.0002401
I0225 04:57:11.098129 29812 solver.cpp:189] Iteration 85050, loss = 0.106699
I0225 04:57:11.098153 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.106699 (* 1 = 0.106699 loss)
I0225 04:57:11.098158 29812 solver.cpp:470] Iteration 85050, lr = 0.0002401
I0225 04:57:30.494281 29812 solver.cpp:189] Iteration 85100, loss = 0.0280645
I0225 04:57:30.494343 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0280646 (* 1 = 0.0280646 loss)
I0225 04:57:30.494350 29812 solver.cpp:470] Iteration 85100, lr = 0.0002401
I0225 04:57:49.885861 29812 solver.cpp:189] Iteration 85150, loss = 0.124378
I0225 04:57:49.885886 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.124378 (* 1 = 0.124378 loss)
I0225 04:57:49.885892 29812 solver.cpp:470] Iteration 85150, lr = 0.0002401
I0225 04:58:09.285498 29812 solver.cpp:189] Iteration 85200, loss = 0.0545473
I0225 04:58:09.285608 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0545474 (* 1 = 0.0545474 loss)
I0225 04:58:09.285622 29812 solver.cpp:470] Iteration 85200, lr = 0.0002401
I0225 04:58:28.677861 29812 solver.cpp:189] Iteration 85250, loss = 0.0261932
I0225 04:58:28.677886 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0261933 (* 1 = 0.0261933 loss)
I0225 04:58:28.677891 29812 solver.cpp:470] Iteration 85250, lr = 0.0002401
I0225 04:58:48.070694 29812 solver.cpp:189] Iteration 85300, loss = 0.0260109
I0225 04:58:48.070770 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0260111 (* 1 = 0.0260111 loss)
I0225 04:58:48.070785 29812 solver.cpp:470] Iteration 85300, lr = 0.0002401
I0225 04:59:07.458317 29812 solver.cpp:189] Iteration 85350, loss = 0.0883449
I0225 04:59:07.458343 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0883451 (* 1 = 0.0883451 loss)
I0225 04:59:07.458348 29812 solver.cpp:470] Iteration 85350, lr = 0.0002401
I0225 04:59:26.848181 29812 solver.cpp:189] Iteration 85400, loss = 0.0316787
I0225 04:59:26.848238 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0316788 (* 1 = 0.0316788 loss)
I0225 04:59:26.848245 29812 solver.cpp:470] Iteration 85400, lr = 0.0002401
I0225 04:59:46.248273 29812 solver.cpp:189] Iteration 85450, loss = 0.0507846
I0225 04:59:46.248296 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0507848 (* 1 = 0.0507848 loss)
I0225 04:59:46.248301 29812 solver.cpp:470] Iteration 85450, lr = 0.0002401
I0225 05:00:05.646705 29812 solver.cpp:189] Iteration 85500, loss = 0.0276613
I0225 05:00:05.646795 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0276614 (* 1 = 0.0276614 loss)
I0225 05:00:05.646802 29812 solver.cpp:470] Iteration 85500, lr = 0.0002401
I0225 05:00:25.040772 29812 solver.cpp:189] Iteration 85550, loss = 0.0692163
I0225 05:00:25.040796 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0692164 (* 1 = 0.0692164 loss)
I0225 05:00:25.040802 29812 solver.cpp:470] Iteration 85550, lr = 0.0002401
I0225 05:00:44.439987 29812 solver.cpp:189] Iteration 85600, loss = 0.0660386
I0225 05:00:44.440048 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0660388 (* 1 = 0.0660388 loss)
I0225 05:00:44.440054 29812 solver.cpp:470] Iteration 85600, lr = 0.0002401
I0225 05:01:03.832713 29812 solver.cpp:189] Iteration 85650, loss = 0.0596385
I0225 05:01:03.832738 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0596387 (* 1 = 0.0596387 loss)
I0225 05:01:03.832744 29812 solver.cpp:470] Iteration 85650, lr = 0.0002401
I0225 05:01:23.246300 29812 solver.cpp:189] Iteration 85700, loss = 0.0637747
I0225 05:01:23.246338 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0637748 (* 1 = 0.0637748 loss)
I0225 05:01:23.246345 29812 solver.cpp:470] Iteration 85700, lr = 0.0002401
I0225 05:01:42.633671 29812 solver.cpp:189] Iteration 85750, loss = 0.151642
I0225 05:01:42.633694 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.151642 (* 1 = 0.151642 loss)
I0225 05:01:42.633700 29812 solver.cpp:470] Iteration 85750, lr = 0.0002401
I0225 05:02:02.029575 29812 solver.cpp:189] Iteration 85800, loss = 0.0260678
I0225 05:02:02.029615 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0260679 (* 1 = 0.0260679 loss)
I0225 05:02:02.029620 29812 solver.cpp:470] Iteration 85800, lr = 0.0002401
I0225 05:02:21.426988 29812 solver.cpp:189] Iteration 85850, loss = 0.0726369
I0225 05:02:21.427013 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.072637 (* 1 = 0.072637 loss)
I0225 05:02:21.427018 29812 solver.cpp:470] Iteration 85850, lr = 0.0002401
I0225 05:02:40.825206 29812 solver.cpp:189] Iteration 85900, loss = 0.0464984
I0225 05:02:40.825266 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0464985 (* 1 = 0.0464985 loss)
I0225 05:02:40.825273 29812 solver.cpp:470] Iteration 85900, lr = 0.0002401
I0225 05:03:00.226068 29812 solver.cpp:189] Iteration 85950, loss = 0.0607904
I0225 05:03:00.226094 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0607905 (* 1 = 0.0607905 loss)
I0225 05:03:00.226099 29812 solver.cpp:470] Iteration 85950, lr = 0.0002401
I0225 05:03:19.377194 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_86000.caffemodel
I0225 05:03:19.482805 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_86000.solverstate
I0225 05:03:19.541527 29812 solver.cpp:266] Iteration 86000, Testing net (#0)
I0225 05:03:27.197079 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9017
I0225 05:03:27.197115 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.402184 (* 1 = 0.402184 loss)
I0225 05:03:27.484556 29812 solver.cpp:189] Iteration 86000, loss = 0.0983531
I0225 05:03:27.484580 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0983532 (* 1 = 0.0983532 loss)
I0225 05:03:27.484586 29812 solver.cpp:470] Iteration 86000, lr = 0.0002401
I0225 05:03:46.877387 29812 solver.cpp:189] Iteration 86050, loss = 0.0335019
I0225 05:03:46.877411 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.033502 (* 1 = 0.033502 loss)
I0225 05:03:46.877418 29812 solver.cpp:470] Iteration 86050, lr = 0.0002401
I0225 05:04:06.260104 29812 solver.cpp:189] Iteration 86100, loss = 0.0642024
I0225 05:04:06.260164 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0642025 (* 1 = 0.0642025 loss)
I0225 05:04:06.260170 29812 solver.cpp:470] Iteration 86100, lr = 0.0002401
I0225 05:04:25.649729 29812 solver.cpp:189] Iteration 86150, loss = 0.0746369
I0225 05:04:25.649755 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.074637 (* 1 = 0.074637 loss)
I0225 05:04:25.649761 29812 solver.cpp:470] Iteration 86150, lr = 0.0002401
I0225 05:04:45.044070 29812 solver.cpp:189] Iteration 86200, loss = 0.0396662
I0225 05:04:45.044124 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0396663 (* 1 = 0.0396663 loss)
I0225 05:04:45.044131 29812 solver.cpp:470] Iteration 86200, lr = 0.0002401
I0225 05:05:04.448145 29812 solver.cpp:189] Iteration 86250, loss = 0.0118314
I0225 05:05:04.448170 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0118314 (* 1 = 0.0118314 loss)
I0225 05:05:04.448181 29812 solver.cpp:470] Iteration 86250, lr = 0.0002401
I0225 05:05:23.829885 29812 solver.cpp:189] Iteration 86300, loss = 0.0147594
I0225 05:05:23.829978 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0147595 (* 1 = 0.0147595 loss)
I0225 05:05:23.829985 29812 solver.cpp:470] Iteration 86300, lr = 0.0002401
I0225 05:05:43.215430 29812 solver.cpp:189] Iteration 86350, loss = 0.0103254
I0225 05:05:43.215454 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0103255 (* 1 = 0.0103255 loss)
I0225 05:05:43.215459 29812 solver.cpp:470] Iteration 86350, lr = 0.0002401
I0225 05:06:02.608122 29812 solver.cpp:189] Iteration 86400, loss = 0.0233377
I0225 05:06:02.608196 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0233378 (* 1 = 0.0233378 loss)
I0225 05:06:02.608211 29812 solver.cpp:470] Iteration 86400, lr = 0.0002401
I0225 05:06:21.997493 29812 solver.cpp:189] Iteration 86450, loss = 0.0298733
I0225 05:06:21.997516 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0298734 (* 1 = 0.0298734 loss)
I0225 05:06:21.997524 29812 solver.cpp:470] Iteration 86450, lr = 0.0002401
I0225 05:06:41.384145 29812 solver.cpp:189] Iteration 86500, loss = 0.101846
I0225 05:06:41.384207 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.101846 (* 1 = 0.101846 loss)
I0225 05:06:41.384214 29812 solver.cpp:470] Iteration 86500, lr = 0.0002401
I0225 05:07:00.780448 29812 solver.cpp:189] Iteration 86550, loss = 0.146419
I0225 05:07:00.780472 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.146419 (* 1 = 0.146419 loss)
I0225 05:07:00.780478 29812 solver.cpp:470] Iteration 86550, lr = 0.0002401
I0225 05:07:20.166693 29812 solver.cpp:189] Iteration 86600, loss = 0.0244844
I0225 05:07:20.166784 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0244845 (* 1 = 0.0244845 loss)
I0225 05:07:20.166797 29812 solver.cpp:470] Iteration 86600, lr = 0.0002401
I0225 05:07:39.558781 29812 solver.cpp:189] Iteration 86650, loss = 0.0293525
I0225 05:07:39.558806 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0293526 (* 1 = 0.0293526 loss)
I0225 05:07:39.558812 29812 solver.cpp:470] Iteration 86650, lr = 0.0002401
I0225 05:07:58.957783 29812 solver.cpp:189] Iteration 86700, loss = 0.0417514
I0225 05:07:58.957846 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0417515 (* 1 = 0.0417515 loss)
I0225 05:07:58.957854 29812 solver.cpp:470] Iteration 86700, lr = 0.0002401
I0225 05:08:18.350755 29812 solver.cpp:189] Iteration 86750, loss = 0.137869
I0225 05:08:18.350780 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.137869 (* 1 = 0.137869 loss)
I0225 05:08:18.350785 29812 solver.cpp:470] Iteration 86750, lr = 0.0002401
I0225 05:08:37.751751 29812 solver.cpp:189] Iteration 86800, loss = 0.0642721
I0225 05:08:37.751822 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0642722 (* 1 = 0.0642722 loss)
I0225 05:08:37.751838 29812 solver.cpp:470] Iteration 86800, lr = 0.0002401
I0225 05:08:57.141372 29812 solver.cpp:189] Iteration 86850, loss = 0.175876
I0225 05:08:57.141398 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.175876 (* 1 = 0.175876 loss)
I0225 05:08:57.141404 29812 solver.cpp:470] Iteration 86850, lr = 0.0002401
I0225 05:09:16.526587 29812 solver.cpp:189] Iteration 86900, loss = 0.0223974
I0225 05:09:16.526646 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0223975 (* 1 = 0.0223975 loss)
I0225 05:09:16.526653 29812 solver.cpp:470] Iteration 86900, lr = 0.0002401
I0225 05:09:35.906960 29812 solver.cpp:189] Iteration 86950, loss = 0.162187
I0225 05:09:35.906983 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.162187 (* 1 = 0.162187 loss)
I0225 05:09:35.906990 29812 solver.cpp:470] Iteration 86950, lr = 0.0002401
I0225 05:09:55.051170 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_87000.caffemodel
I0225 05:09:55.172821 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_87000.solverstate
I0225 05:09:55.232244 29812 solver.cpp:266] Iteration 87000, Testing net (#0)
I0225 05:10:02.873334 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9026
I0225 05:10:02.873371 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.390904 (* 1 = 0.390904 loss)
I0225 05:10:03.160204 29812 solver.cpp:189] Iteration 87000, loss = 0.0462127
I0225 05:10:03.160224 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0462128 (* 1 = 0.0462128 loss)
I0225 05:10:03.160231 29812 solver.cpp:470] Iteration 87000, lr = 0.0002401
I0225 05:10:22.546665 29812 solver.cpp:189] Iteration 87050, loss = 0.0387823
I0225 05:10:22.546689 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0387824 (* 1 = 0.0387824 loss)
I0225 05:10:22.546694 29812 solver.cpp:470] Iteration 87050, lr = 0.0002401
I0225 05:10:41.930780 29812 solver.cpp:189] Iteration 87100, loss = 0.021505
I0225 05:10:41.930852 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0215051 (* 1 = 0.0215051 loss)
I0225 05:10:41.930868 29812 solver.cpp:470] Iteration 87100, lr = 0.0002401
I0225 05:11:01.312108 29812 solver.cpp:189] Iteration 87150, loss = 0.0467844
I0225 05:11:01.312130 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0467845 (* 1 = 0.0467845 loss)
I0225 05:11:01.312135 29812 solver.cpp:470] Iteration 87150, lr = 0.0002401
I0225 05:11:20.691079 29812 solver.cpp:189] Iteration 87200, loss = 0.134625
I0225 05:11:20.691144 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.134625 (* 1 = 0.134625 loss)
I0225 05:11:20.691151 29812 solver.cpp:470] Iteration 87200, lr = 0.0002401
I0225 05:11:40.075316 29812 solver.cpp:189] Iteration 87250, loss = 0.042241
I0225 05:11:40.075340 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0422411 (* 1 = 0.0422411 loss)
I0225 05:11:40.075346 29812 solver.cpp:470] Iteration 87250, lr = 0.0002401
I0225 05:11:59.456332 29812 solver.cpp:189] Iteration 87300, loss = 0.0434757
I0225 05:11:59.456394 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0434758 (* 1 = 0.0434758 loss)
I0225 05:11:59.456401 29812 solver.cpp:470] Iteration 87300, lr = 0.0002401
I0225 05:12:18.849247 29812 solver.cpp:189] Iteration 87350, loss = 0.0643194
I0225 05:12:18.849272 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0643194 (* 1 = 0.0643194 loss)
I0225 05:12:18.849278 29812 solver.cpp:470] Iteration 87350, lr = 0.0002401
I0225 05:12:38.232163 29812 solver.cpp:189] Iteration 87400, loss = 0.128553
I0225 05:12:38.232254 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.128553 (* 1 = 0.128553 loss)
I0225 05:12:38.232259 29812 solver.cpp:470] Iteration 87400, lr = 0.0002401
I0225 05:12:57.615739 29812 solver.cpp:189] Iteration 87450, loss = 0.0213613
I0225 05:12:57.615763 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0213613 (* 1 = 0.0213613 loss)
I0225 05:12:57.615769 29812 solver.cpp:470] Iteration 87450, lr = 0.0002401
I0225 05:13:16.995602 29812 solver.cpp:189] Iteration 87500, loss = 0.0354945
I0225 05:13:16.995641 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0354946 (* 1 = 0.0354946 loss)
I0225 05:13:16.995648 29812 solver.cpp:470] Iteration 87500, lr = 0.0002401
I0225 05:13:36.373704 29812 solver.cpp:189] Iteration 87550, loss = 0.113823
I0225 05:13:36.373728 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.113823 (* 1 = 0.113823 loss)
I0225 05:13:36.373733 29812 solver.cpp:470] Iteration 87550, lr = 0.0002401
I0225 05:13:55.746243 29812 solver.cpp:189] Iteration 87600, loss = 0.0471886
I0225 05:13:55.746315 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0471886 (* 1 = 0.0471886 loss)
I0225 05:13:55.746330 29812 solver.cpp:470] Iteration 87600, lr = 0.0002401
I0225 05:14:15.136147 29812 solver.cpp:189] Iteration 87650, loss = 0.0201207
I0225 05:14:15.136168 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0201208 (* 1 = 0.0201208 loss)
I0225 05:14:15.136174 29812 solver.cpp:470] Iteration 87650, lr = 0.0002401
I0225 05:14:34.530725 29812 solver.cpp:189] Iteration 87700, loss = 0.0293527
I0225 05:14:34.530794 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0293528 (* 1 = 0.0293528 loss)
I0225 05:14:34.530809 29812 solver.cpp:470] Iteration 87700, lr = 0.0002401
I0225 05:14:53.915521 29812 solver.cpp:189] Iteration 87750, loss = 0.105835
I0225 05:14:53.915546 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105835 (* 1 = 0.105835 loss)
I0225 05:14:53.915552 29812 solver.cpp:470] Iteration 87750, lr = 0.0002401
I0225 05:15:13.304358 29812 solver.cpp:189] Iteration 87800, loss = 0.0323683
I0225 05:15:13.304426 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0323683 (* 1 = 0.0323683 loss)
I0225 05:15:13.304440 29812 solver.cpp:470] Iteration 87800, lr = 0.0002401
I0225 05:15:32.688213 29812 solver.cpp:189] Iteration 87850, loss = 0.0155382
I0225 05:15:32.688238 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0155383 (* 1 = 0.0155383 loss)
I0225 05:15:32.688244 29812 solver.cpp:470] Iteration 87850, lr = 0.0002401
I0225 05:15:52.079138 29812 solver.cpp:189] Iteration 87900, loss = 0.067432
I0225 05:15:52.079176 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.067432 (* 1 = 0.067432 loss)
I0225 05:15:52.079182 29812 solver.cpp:470] Iteration 87900, lr = 0.0002401
I0225 05:16:11.458446 29812 solver.cpp:189] Iteration 87950, loss = 0.0676774
I0225 05:16:11.458470 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0676774 (* 1 = 0.0676774 loss)
I0225 05:16:11.458477 29812 solver.cpp:470] Iteration 87950, lr = 0.0002401
I0225 05:16:30.596366 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_88000.caffemodel
I0225 05:16:30.717537 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_88000.solverstate
I0225 05:16:30.776831 29812 solver.cpp:266] Iteration 88000, Testing net (#0)
I0225 05:16:38.434123 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9086
I0225 05:16:38.434160 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.378356 (* 1 = 0.378356 loss)
I0225 05:16:38.721230 29812 solver.cpp:189] Iteration 88000, loss = 0.0326959
I0225 05:16:38.721266 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.032696 (* 1 = 0.032696 loss)
I0225 05:16:38.721272 29812 solver.cpp:470] Iteration 88000, lr = 0.0002401
I0225 05:16:58.118037 29812 solver.cpp:189] Iteration 88050, loss = 0.0810702
I0225 05:16:58.118060 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0810702 (* 1 = 0.0810702 loss)
I0225 05:16:58.118067 29812 solver.cpp:470] Iteration 88050, lr = 0.0002401
I0225 05:17:17.505251 29812 solver.cpp:189] Iteration 88100, loss = 0.0581097
I0225 05:17:17.505358 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0581097 (* 1 = 0.0581097 loss)
I0225 05:17:17.505367 29812 solver.cpp:470] Iteration 88100, lr = 0.0002401
I0225 05:17:36.894634 29812 solver.cpp:189] Iteration 88150, loss = 0.102778
I0225 05:17:36.894659 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.102778 (* 1 = 0.102778 loss)
I0225 05:17:36.894665 29812 solver.cpp:470] Iteration 88150, lr = 0.0002401
I0225 05:17:56.275854 29812 solver.cpp:189] Iteration 88200, loss = 0.0499716
I0225 05:17:56.275895 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0499716 (* 1 = 0.0499716 loss)
I0225 05:17:56.275902 29812 solver.cpp:470] Iteration 88200, lr = 0.0002401
I0225 05:18:15.665735 29812 solver.cpp:189] Iteration 88250, loss = 0.0449459
I0225 05:18:15.665761 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.044946 (* 1 = 0.044946 loss)
I0225 05:18:15.665767 29812 solver.cpp:470] Iteration 88250, lr = 0.0002401
I0225 05:18:35.062429 29812 solver.cpp:189] Iteration 88300, loss = 0.0391582
I0225 05:18:35.062496 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0391583 (* 1 = 0.0391583 loss)
I0225 05:18:35.062511 29812 solver.cpp:470] Iteration 88300, lr = 0.0002401
I0225 05:18:54.448364 29812 solver.cpp:189] Iteration 88350, loss = 0.059798
I0225 05:18:54.448397 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0597981 (* 1 = 0.0597981 loss)
I0225 05:18:54.448402 29812 solver.cpp:470] Iteration 88350, lr = 0.0002401
I0225 05:19:13.838501 29812 solver.cpp:189] Iteration 88400, loss = 0.0884798
I0225 05:19:13.838589 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0884799 (* 1 = 0.0884799 loss)
I0225 05:19:13.838595 29812 solver.cpp:470] Iteration 88400, lr = 0.0002401
I0225 05:19:33.218488 29812 solver.cpp:189] Iteration 88450, loss = 0.0372572
I0225 05:19:33.218513 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0372572 (* 1 = 0.0372572 loss)
I0225 05:19:33.218518 29812 solver.cpp:470] Iteration 88450, lr = 0.0002401
I0225 05:19:52.619019 29812 solver.cpp:189] Iteration 88500, loss = 0.0233634
I0225 05:19:52.619060 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0233635 (* 1 = 0.0233635 loss)
I0225 05:19:52.619066 29812 solver.cpp:470] Iteration 88500, lr = 0.0002401
I0225 05:20:12.014199 29812 solver.cpp:189] Iteration 88550, loss = 0.049005
I0225 05:20:12.014225 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.049005 (* 1 = 0.049005 loss)
I0225 05:20:12.014230 29812 solver.cpp:470] Iteration 88550, lr = 0.0002401
I0225 05:20:31.407214 29812 solver.cpp:189] Iteration 88600, loss = 0.037589
I0225 05:20:31.407254 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0375891 (* 1 = 0.0375891 loss)
I0225 05:20:31.407259 29812 solver.cpp:470] Iteration 88600, lr = 0.0002401
I0225 05:20:50.797214 29812 solver.cpp:189] Iteration 88650, loss = 0.0344545
I0225 05:20:50.797238 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0344546 (* 1 = 0.0344546 loss)
I0225 05:20:50.797245 29812 solver.cpp:470] Iteration 88650, lr = 0.0002401
I0225 05:21:10.187261 29812 solver.cpp:189] Iteration 88700, loss = 0.0517666
I0225 05:21:10.187324 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0517666 (* 1 = 0.0517666 loss)
I0225 05:21:10.187330 29812 solver.cpp:470] Iteration 88700, lr = 0.0002401
I0225 05:21:29.569739 29812 solver.cpp:189] Iteration 88750, loss = 0.0289961
I0225 05:21:29.569763 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0289962 (* 1 = 0.0289962 loss)
I0225 05:21:29.569769 29812 solver.cpp:470] Iteration 88750, lr = 0.0002401
I0225 05:21:48.946717 29812 solver.cpp:189] Iteration 88800, loss = 0.103668
I0225 05:21:48.946787 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.103668 (* 1 = 0.103668 loss)
I0225 05:21:48.946804 29812 solver.cpp:470] Iteration 88800, lr = 0.0002401
I0225 05:22:08.338639 29812 solver.cpp:189] Iteration 88850, loss = 0.0864677
I0225 05:22:08.338663 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0864678 (* 1 = 0.0864678 loss)
I0225 05:22:08.338670 29812 solver.cpp:470] Iteration 88850, lr = 0.0002401
I0225 05:22:27.723119 29812 solver.cpp:189] Iteration 88900, loss = 0.0191604
I0225 05:22:27.723194 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0191605 (* 1 = 0.0191605 loss)
I0225 05:22:27.723211 29812 solver.cpp:470] Iteration 88900, lr = 0.0002401
I0225 05:22:47.109381 29812 solver.cpp:189] Iteration 88950, loss = 0.0743772
I0225 05:22:47.109405 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0743773 (* 1 = 0.0743773 loss)
I0225 05:22:47.109411 29812 solver.cpp:470] Iteration 88950, lr = 0.0002401
I0225 05:23:06.255669 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_89000.caffemodel
I0225 05:23:06.381605 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_89000.solverstate
I0225 05:23:06.440091 29812 solver.cpp:266] Iteration 89000, Testing net (#0)
I0225 05:23:14.090183 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9056
I0225 05:23:14.090219 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.385383 (* 1 = 0.385383 loss)
I0225 05:23:14.377951 29812 solver.cpp:189] Iteration 89000, loss = 0.0497852
I0225 05:23:14.377972 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0497853 (* 1 = 0.0497853 loss)
I0225 05:23:14.377979 29812 solver.cpp:470] Iteration 89000, lr = 0.0002401
I0225 05:23:33.767732 29812 solver.cpp:189] Iteration 89050, loss = 0.0749628
I0225 05:23:33.767757 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0749629 (* 1 = 0.0749629 loss)
I0225 05:23:33.767763 29812 solver.cpp:470] Iteration 89050, lr = 0.0002401
I0225 05:23:53.163650 29812 solver.cpp:189] Iteration 89100, loss = 0.0500415
I0225 05:23:53.163704 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0500416 (* 1 = 0.0500416 loss)
I0225 05:23:53.163710 29812 solver.cpp:470] Iteration 89100, lr = 0.0002401
I0225 05:24:12.558935 29812 solver.cpp:189] Iteration 89150, loss = 0.0554505
I0225 05:24:12.558960 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0554507 (* 1 = 0.0554507 loss)
I0225 05:24:12.558966 29812 solver.cpp:470] Iteration 89150, lr = 0.0002401
I0225 05:24:31.956614 29812 solver.cpp:189] Iteration 89200, loss = 0.0494555
I0225 05:24:31.956648 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0494556 (* 1 = 0.0494556 loss)
I0225 05:24:31.956655 29812 solver.cpp:470] Iteration 89200, lr = 0.0002401
I0225 05:24:51.350940 29812 solver.cpp:189] Iteration 89250, loss = 0.102465
I0225 05:24:51.350965 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.102465 (* 1 = 0.102465 loss)
I0225 05:24:51.350968 29812 solver.cpp:470] Iteration 89250, lr = 0.0002401
I0225 05:25:10.749640 29812 solver.cpp:189] Iteration 89300, loss = 0.0670348
I0225 05:25:10.749683 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0670349 (* 1 = 0.0670349 loss)
I0225 05:25:10.749691 29812 solver.cpp:470] Iteration 89300, lr = 0.0002401
I0225 05:25:30.147807 29812 solver.cpp:189] Iteration 89350, loss = 0.0234772
I0225 05:25:30.147831 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0234774 (* 1 = 0.0234774 loss)
I0225 05:25:30.147836 29812 solver.cpp:470] Iteration 89350, lr = 0.0002401
I0225 05:25:49.538424 29812 solver.cpp:189] Iteration 89400, loss = 0.0779525
I0225 05:25:49.538516 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0779526 (* 1 = 0.0779526 loss)
I0225 05:25:49.538532 29812 solver.cpp:470] Iteration 89400, lr = 0.0002401
I0225 05:26:08.925894 29812 solver.cpp:189] Iteration 89450, loss = 0.0443372
I0225 05:26:08.925916 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0443374 (* 1 = 0.0443374 loss)
I0225 05:26:08.925923 29812 solver.cpp:470] Iteration 89450, lr = 0.0002401
I0225 05:26:28.316525 29812 solver.cpp:189] Iteration 89500, loss = 0.101441
I0225 05:26:28.316586 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.101441 (* 1 = 0.101441 loss)
I0225 05:26:28.316592 29812 solver.cpp:470] Iteration 89500, lr = 0.0002401
I0225 05:26:47.715625 29812 solver.cpp:189] Iteration 89550, loss = 0.0244026
I0225 05:26:47.715651 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0244028 (* 1 = 0.0244028 loss)
I0225 05:26:47.715657 29812 solver.cpp:470] Iteration 89550, lr = 0.0002401
I0225 05:27:07.115304 29812 solver.cpp:189] Iteration 89600, loss = 0.010114
I0225 05:27:07.115375 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0101142 (* 1 = 0.0101142 loss)
I0225 05:27:07.115389 29812 solver.cpp:470] Iteration 89600, lr = 0.0002401
I0225 05:27:26.509520 29812 solver.cpp:189] Iteration 89650, loss = 0.0360041
I0225 05:27:26.509543 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0360043 (* 1 = 0.0360043 loss)
I0225 05:27:26.509548 29812 solver.cpp:470] Iteration 89650, lr = 0.0002401
I0225 05:27:45.910007 29812 solver.cpp:189] Iteration 89700, loss = 0.0310302
I0225 05:27:45.910048 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0310303 (* 1 = 0.0310303 loss)
I0225 05:27:45.910053 29812 solver.cpp:470] Iteration 89700, lr = 0.0002401
I0225 05:28:05.309279 29812 solver.cpp:189] Iteration 89750, loss = 0.0999331
I0225 05:28:05.309301 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0999333 (* 1 = 0.0999333 loss)
I0225 05:28:05.309306 29812 solver.cpp:470] Iteration 89750, lr = 0.0002401
I0225 05:28:24.708147 29812 solver.cpp:189] Iteration 89800, loss = 0.0472935
I0225 05:28:24.708221 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0472937 (* 1 = 0.0472937 loss)
I0225 05:28:24.708236 29812 solver.cpp:470] Iteration 89800, lr = 0.0002401
I0225 05:28:44.100946 29812 solver.cpp:189] Iteration 89850, loss = 0.0599202
I0225 05:28:44.100971 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0599203 (* 1 = 0.0599203 loss)
I0225 05:28:44.100977 29812 solver.cpp:470] Iteration 89850, lr = 0.0002401
I0225 05:29:03.492666 29812 solver.cpp:189] Iteration 89900, loss = 0.0193556
I0225 05:29:03.492724 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0193558 (* 1 = 0.0193558 loss)
I0225 05:29:03.492730 29812 solver.cpp:470] Iteration 89900, lr = 0.0002401
I0225 05:29:22.894438 29812 solver.cpp:189] Iteration 89950, loss = 0.0440778
I0225 05:29:22.894462 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.044078 (* 1 = 0.044078 loss)
I0225 05:29:22.894469 29812 solver.cpp:470] Iteration 89950, lr = 0.0002401
I0225 05:29:42.040904 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_90000.caffemodel
I0225 05:29:42.162437 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_90000.solverstate
I0225 05:29:42.220788 29812 solver.cpp:266] Iteration 90000, Testing net (#0)
I0225 05:29:49.878119 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9055
I0225 05:29:49.878154 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.401968 (* 1 = 0.401968 loss)
I0225 05:29:50.164121 29812 solver.cpp:189] Iteration 90000, loss = 0.0328823
I0225 05:29:50.164144 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0328825 (* 1 = 0.0328825 loss)
I0225 05:29:50.164150 29812 solver.cpp:470] Iteration 90000, lr = 0.0002401
I0225 05:30:09.552937 29812 solver.cpp:189] Iteration 90050, loss = 0.0618345
I0225 05:30:09.552961 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0618347 (* 1 = 0.0618347 loss)
I0225 05:30:09.552966 29812 solver.cpp:470] Iteration 90050, lr = 0.0002401
I0225 05:30:28.954253 29812 solver.cpp:189] Iteration 90100, loss = 0.11836
I0225 05:30:28.954314 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.11836 (* 1 = 0.11836 loss)
I0225 05:30:28.954323 29812 solver.cpp:470] Iteration 90100, lr = 0.0002401
I0225 05:30:48.343281 29812 solver.cpp:189] Iteration 90150, loss = 0.0908655
I0225 05:30:48.343307 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0908656 (* 1 = 0.0908656 loss)
I0225 05:30:48.343312 29812 solver.cpp:470] Iteration 90150, lr = 0.0002401
I0225 05:31:07.734841 29812 solver.cpp:189] Iteration 90200, loss = 0.0924222
I0225 05:31:07.734933 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0924224 (* 1 = 0.0924224 loss)
I0225 05:31:07.734941 29812 solver.cpp:470] Iteration 90200, lr = 0.0002401
I0225 05:31:27.122946 29812 solver.cpp:189] Iteration 90250, loss = 0.0806867
I0225 05:31:27.122968 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0806869 (* 1 = 0.0806869 loss)
I0225 05:31:27.122974 29812 solver.cpp:470] Iteration 90250, lr = 0.0002401
I0225 05:31:46.517514 29812 solver.cpp:189] Iteration 90300, loss = 0.130634
I0225 05:31:46.517585 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.130634 (* 1 = 0.130634 loss)
I0225 05:31:46.517601 29812 solver.cpp:470] Iteration 90300, lr = 0.0002401
I0225 05:32:05.913439 29812 solver.cpp:189] Iteration 90350, loss = 0.0473083
I0225 05:32:05.913461 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0473084 (* 1 = 0.0473084 loss)
I0225 05:32:05.913468 29812 solver.cpp:470] Iteration 90350, lr = 0.0002401
I0225 05:32:25.305110 29812 solver.cpp:189] Iteration 90400, loss = 0.0896924
I0225 05:32:25.305151 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0896926 (* 1 = 0.0896926 loss)
I0225 05:32:25.305156 29812 solver.cpp:470] Iteration 90400, lr = 0.0002401
I0225 05:32:44.691588 29812 solver.cpp:189] Iteration 90450, loss = 0.0400876
I0225 05:32:44.691617 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0400877 (* 1 = 0.0400877 loss)
I0225 05:32:44.691623 29812 solver.cpp:470] Iteration 90450, lr = 0.0002401
I0225 05:33:04.088024 29812 solver.cpp:189] Iteration 90500, loss = 0.0835244
I0225 05:33:04.088064 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0835246 (* 1 = 0.0835246 loss)
I0225 05:33:04.088070 29812 solver.cpp:470] Iteration 90500, lr = 0.0002401
I0225 05:33:23.470969 29812 solver.cpp:189] Iteration 90550, loss = 0.0688863
I0225 05:33:23.470993 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0688864 (* 1 = 0.0688864 loss)
I0225 05:33:23.470998 29812 solver.cpp:470] Iteration 90550, lr = 0.0002401
I0225 05:33:42.865166 29812 solver.cpp:189] Iteration 90600, loss = 0.12658
I0225 05:33:42.865206 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.12658 (* 1 = 0.12658 loss)
I0225 05:33:42.865212 29812 solver.cpp:470] Iteration 90600, lr = 0.0002401
I0225 05:34:02.262140 29812 solver.cpp:189] Iteration 90650, loss = 0.0477171
I0225 05:34:02.262164 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0477173 (* 1 = 0.0477173 loss)
I0225 05:34:02.262169 29812 solver.cpp:470] Iteration 90650, lr = 0.0002401
I0225 05:34:21.651352 29812 solver.cpp:189] Iteration 90700, loss = 0.126084
I0225 05:34:21.651406 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.126085 (* 1 = 0.126085 loss)
I0225 05:34:21.651412 29812 solver.cpp:470] Iteration 90700, lr = 0.0002401
I0225 05:34:41.037302 29812 solver.cpp:189] Iteration 90750, loss = 0.0569562
I0225 05:34:41.037327 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0569564 (* 1 = 0.0569564 loss)
I0225 05:34:41.037333 29812 solver.cpp:470] Iteration 90750, lr = 0.0002401
I0225 05:35:00.429591 29812 solver.cpp:189] Iteration 90800, loss = 0.0174327
I0225 05:35:00.429685 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0174328 (* 1 = 0.0174328 loss)
I0225 05:35:00.429702 29812 solver.cpp:470] Iteration 90800, lr = 0.0002401
I0225 05:35:19.819594 29812 solver.cpp:189] Iteration 90850, loss = 0.0785984
I0225 05:35:19.819618 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0785985 (* 1 = 0.0785985 loss)
I0225 05:35:19.819623 29812 solver.cpp:470] Iteration 90850, lr = 0.0002401
I0225 05:35:39.211946 29812 solver.cpp:189] Iteration 90900, loss = 0.0229073
I0225 05:35:39.212021 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0229074 (* 1 = 0.0229074 loss)
I0225 05:35:39.212028 29812 solver.cpp:470] Iteration 90900, lr = 0.0002401
I0225 05:35:58.614233 29812 solver.cpp:189] Iteration 90950, loss = 0.084397
I0225 05:35:58.614259 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0843971 (* 1 = 0.0843971 loss)
I0225 05:35:58.614265 29812 solver.cpp:470] Iteration 90950, lr = 0.0002401
I0225 05:36:17.759140 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_91000.caffemodel
I0225 05:36:17.883096 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_91000.solverstate
I0225 05:36:17.941546 29812 solver.cpp:266] Iteration 91000, Testing net (#0)
I0225 05:36:25.594633 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9084
I0225 05:36:25.594669 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.386219 (* 1 = 0.386219 loss)
I0225 05:36:25.882598 29812 solver.cpp:189] Iteration 91000, loss = 0.0722672
I0225 05:36:25.882622 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0722674 (* 1 = 0.0722674 loss)
I0225 05:36:25.882628 29812 solver.cpp:470] Iteration 91000, lr = 0.0002401
I0225 05:36:45.274535 29812 solver.cpp:189] Iteration 91050, loss = 0.0719765
I0225 05:36:45.274559 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0719768 (* 1 = 0.0719768 loss)
I0225 05:36:45.274566 29812 solver.cpp:470] Iteration 91050, lr = 0.0002401
I0225 05:37:04.672245 29812 solver.cpp:189] Iteration 91100, loss = 0.0377198
I0225 05:37:04.672330 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.03772 (* 1 = 0.03772 loss)
I0225 05:37:04.672348 29812 solver.cpp:470] Iteration 91100, lr = 0.0002401
I0225 05:37:24.069504 29812 solver.cpp:189] Iteration 91150, loss = 0.102016
I0225 05:37:24.069531 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.102016 (* 1 = 0.102016 loss)
I0225 05:37:24.069537 29812 solver.cpp:470] Iteration 91150, lr = 0.0002401
I0225 05:37:43.451530 29812 solver.cpp:189] Iteration 91200, loss = 0.0814167
I0225 05:37:43.451592 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0814169 (* 1 = 0.0814169 loss)
I0225 05:37:43.451598 29812 solver.cpp:470] Iteration 91200, lr = 0.0002401
I0225 05:38:02.844022 29812 solver.cpp:189] Iteration 91250, loss = 0.0736218
I0225 05:38:02.844048 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.073622 (* 1 = 0.073622 loss)
I0225 05:38:02.844053 29812 solver.cpp:470] Iteration 91250, lr = 0.0002401
I0225 05:38:22.246151 29812 solver.cpp:189] Iteration 91300, loss = 0.0849121
I0225 05:38:22.246219 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0849122 (* 1 = 0.0849122 loss)
I0225 05:38:22.246234 29812 solver.cpp:470] Iteration 91300, lr = 0.0002401
I0225 05:38:41.639948 29812 solver.cpp:189] Iteration 91350, loss = 0.0442569
I0225 05:38:41.639984 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0442571 (* 1 = 0.0442571 loss)
I0225 05:38:41.639991 29812 solver.cpp:470] Iteration 91350, lr = 0.0002401
I0225 05:39:01.033028 29812 solver.cpp:189] Iteration 91400, loss = 0.0448859
I0225 05:39:01.033081 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.044886 (* 1 = 0.044886 loss)
I0225 05:39:01.033087 29812 solver.cpp:470] Iteration 91400, lr = 0.0002401
I0225 05:39:20.430196 29812 solver.cpp:189] Iteration 91450, loss = 0.0739665
I0225 05:39:20.430220 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0739667 (* 1 = 0.0739667 loss)
I0225 05:39:20.430227 29812 solver.cpp:470] Iteration 91450, lr = 0.0002401
I0225 05:39:39.824362 29812 solver.cpp:189] Iteration 91500, loss = 0.0239762
I0225 05:39:39.824422 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0239765 (* 1 = 0.0239765 loss)
I0225 05:39:39.824429 29812 solver.cpp:470] Iteration 91500, lr = 0.0002401
I0225 05:39:59.220306 29812 solver.cpp:189] Iteration 91550, loss = 0.105701
I0225 05:39:59.220330 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.105701 (* 1 = 0.105701 loss)
I0225 05:39:59.220335 29812 solver.cpp:470] Iteration 91550, lr = 0.0002401
I0225 05:40:18.615173 29812 solver.cpp:189] Iteration 91600, loss = 0.0397891
I0225 05:40:18.615247 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0397893 (* 1 = 0.0397893 loss)
I0225 05:40:18.615262 29812 solver.cpp:470] Iteration 91600, lr = 0.0002401
I0225 05:40:38.009268 29812 solver.cpp:189] Iteration 91650, loss = 0.0758776
I0225 05:40:38.009292 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0758778 (* 1 = 0.0758778 loss)
I0225 05:40:38.009299 29812 solver.cpp:470] Iteration 91650, lr = 0.0002401
I0225 05:40:57.402856 29812 solver.cpp:189] Iteration 91700, loss = 0.023257
I0225 05:40:57.402916 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0232572 (* 1 = 0.0232572 loss)
I0225 05:40:57.402921 29812 solver.cpp:470] Iteration 91700, lr = 0.0002401
I0225 05:41:16.799751 29812 solver.cpp:189] Iteration 91750, loss = 0.0239197
I0225 05:41:16.799777 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0239199 (* 1 = 0.0239199 loss)
I0225 05:41:16.799783 29812 solver.cpp:470] Iteration 91750, lr = 0.0002401
I0225 05:41:36.196151 29812 solver.cpp:189] Iteration 91800, loss = 0.0365754
I0225 05:41:36.196193 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0365756 (* 1 = 0.0365756 loss)
I0225 05:41:36.196200 29812 solver.cpp:470] Iteration 91800, lr = 0.0002401
I0225 05:41:55.599748 29812 solver.cpp:189] Iteration 91850, loss = 0.0456328
I0225 05:41:55.599772 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.045633 (* 1 = 0.045633 loss)
I0225 05:41:55.599777 29812 solver.cpp:470] Iteration 91850, lr = 0.0002401
I0225 05:42:14.991474 29812 solver.cpp:189] Iteration 91900, loss = 0.0580303
I0225 05:42:14.991583 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0580305 (* 1 = 0.0580305 loss)
I0225 05:42:14.991600 29812 solver.cpp:470] Iteration 91900, lr = 0.0002401
I0225 05:42:34.391222 29812 solver.cpp:189] Iteration 91950, loss = 0.0594188
I0225 05:42:34.391247 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.059419 (* 1 = 0.059419 loss)
I0225 05:42:34.391250 29812 solver.cpp:470] Iteration 91950, lr = 0.0002401
I0225 05:42:53.539088 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_92000.caffemodel
I0225 05:42:53.661914 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_92000.solverstate
I0225 05:42:53.719951 29812 solver.cpp:266] Iteration 92000, Testing net (#0)
I0225 05:43:01.374963 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9064
I0225 05:43:01.375000 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.380683 (* 1 = 0.380683 loss)
I0225 05:43:01.661856 29812 solver.cpp:189] Iteration 92000, loss = 0.0534275
I0225 05:43:01.661882 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0534277 (* 1 = 0.0534277 loss)
I0225 05:43:01.661888 29812 solver.cpp:470] Iteration 92000, lr = 0.0002401
I0225 05:43:21.052206 29812 solver.cpp:189] Iteration 92050, loss = 0.120592
I0225 05:43:21.052229 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.120592 (* 1 = 0.120592 loss)
I0225 05:43:21.052237 29812 solver.cpp:470] Iteration 92050, lr = 0.0002401
I0225 05:43:40.445250 29812 solver.cpp:189] Iteration 92100, loss = 0.0367699
I0225 05:43:40.445309 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0367701 (* 1 = 0.0367701 loss)
I0225 05:43:40.445317 29812 solver.cpp:470] Iteration 92100, lr = 0.0002401
I0225 05:43:59.832298 29812 solver.cpp:189] Iteration 92150, loss = 0.0563738
I0225 05:43:59.832322 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.056374 (* 1 = 0.056374 loss)
I0225 05:43:59.832329 29812 solver.cpp:470] Iteration 92150, lr = 0.0002401
I0225 05:44:19.229737 29812 solver.cpp:189] Iteration 92200, loss = 0.0809375
I0225 05:44:19.229820 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0809377 (* 1 = 0.0809377 loss)
I0225 05:44:19.229825 29812 solver.cpp:470] Iteration 92200, lr = 0.0002401
I0225 05:44:38.615551 29812 solver.cpp:189] Iteration 92250, loss = 0.0191315
I0225 05:44:38.615576 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0191317 (* 1 = 0.0191317 loss)
I0225 05:44:38.615581 29812 solver.cpp:470] Iteration 92250, lr = 0.0002401
I0225 05:44:58.002622 29812 solver.cpp:189] Iteration 92300, loss = 0.0743442
I0225 05:44:58.002666 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0743444 (* 1 = 0.0743444 loss)
I0225 05:44:58.002673 29812 solver.cpp:470] Iteration 92300, lr = 0.0002401
I0225 05:45:17.396687 29812 solver.cpp:189] Iteration 92350, loss = 0.0403689
I0225 05:45:17.396711 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0403691 (* 1 = 0.0403691 loss)
I0225 05:45:17.396716 29812 solver.cpp:470] Iteration 92350, lr = 0.0002401
I0225 05:45:36.778106 29812 solver.cpp:189] Iteration 92400, loss = 0.104235
I0225 05:45:36.778167 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.104235 (* 1 = 0.104235 loss)
I0225 05:45:36.778173 29812 solver.cpp:470] Iteration 92400, lr = 0.0002401
I0225 05:45:56.163611 29812 solver.cpp:189] Iteration 92450, loss = 0.101426
I0225 05:45:56.163635 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.101426 (* 1 = 0.101426 loss)
I0225 05:45:56.163641 29812 solver.cpp:470] Iteration 92450, lr = 0.0002401
I0225 05:46:15.550688 29812 solver.cpp:189] Iteration 92500, loss = 0.117221
I0225 05:46:15.550726 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.117221 (* 1 = 0.117221 loss)
I0225 05:46:15.550732 29812 solver.cpp:470] Iteration 92500, lr = 0.0002401
I0225 05:46:34.930202 29812 solver.cpp:189] Iteration 92550, loss = 0.0526519
I0225 05:46:34.930227 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.052652 (* 1 = 0.052652 loss)
I0225 05:46:34.930233 29812 solver.cpp:470] Iteration 92550, lr = 0.0002401
I0225 05:46:54.318900 29812 solver.cpp:189] Iteration 92600, loss = 0.0124515
I0225 05:46:54.318971 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0124517 (* 1 = 0.0124517 loss)
I0225 05:46:54.318985 29812 solver.cpp:470] Iteration 92600, lr = 0.0002401
I0225 05:47:13.713817 29812 solver.cpp:189] Iteration 92650, loss = 0.0369125
I0225 05:47:13.713852 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0369128 (* 1 = 0.0369128 loss)
I0225 05:47:13.713860 29812 solver.cpp:470] Iteration 92650, lr = 0.0002401
I0225 05:47:33.101822 29812 solver.cpp:189] Iteration 92700, loss = 0.0774948
I0225 05:47:33.101881 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.077495 (* 1 = 0.077495 loss)
I0225 05:47:33.101888 29812 solver.cpp:470] Iteration 92700, lr = 0.0002401
I0225 05:47:52.481374 29812 solver.cpp:189] Iteration 92750, loss = 0.0684723
I0225 05:47:52.481396 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0684725 (* 1 = 0.0684725 loss)
I0225 05:47:52.481401 29812 solver.cpp:470] Iteration 92750, lr = 0.0002401
I0225 05:48:11.875907 29812 solver.cpp:189] Iteration 92800, loss = 0.0823297
I0225 05:48:11.875995 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0823299 (* 1 = 0.0823299 loss)
I0225 05:48:11.876001 29812 solver.cpp:470] Iteration 92800, lr = 0.0002401
I0225 05:48:31.266186 29812 solver.cpp:189] Iteration 92850, loss = 0.0726935
I0225 05:48:31.266209 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0726937 (* 1 = 0.0726937 loss)
I0225 05:48:31.266214 29812 solver.cpp:470] Iteration 92850, lr = 0.0002401
I0225 05:48:50.650667 29812 solver.cpp:189] Iteration 92900, loss = 0.0940191
I0225 05:48:50.650754 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0940193 (* 1 = 0.0940193 loss)
I0225 05:48:50.650763 29812 solver.cpp:470] Iteration 92900, lr = 0.0002401
I0225 05:49:10.037732 29812 solver.cpp:189] Iteration 92950, loss = 0.0736599
I0225 05:49:10.037756 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0736601 (* 1 = 0.0736601 loss)
I0225 05:49:10.037762 29812 solver.cpp:470] Iteration 92950, lr = 0.0002401
I0225 05:49:29.176870 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_93000.caffemodel
I0225 05:49:29.299779 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_93000.solverstate
I0225 05:49:29.358747 29812 solver.cpp:266] Iteration 93000, Testing net (#0)
I0225 05:49:37.007973 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.895
I0225 05:49:37.008008 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.43743 (* 1 = 0.43743 loss)
I0225 05:49:37.296046 29812 solver.cpp:189] Iteration 93000, loss = 0.0401166
I0225 05:49:37.296068 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0401168 (* 1 = 0.0401168 loss)
I0225 05:49:37.296074 29812 solver.cpp:470] Iteration 93000, lr = 0.0002401
I0225 05:49:56.685067 29812 solver.cpp:189] Iteration 93050, loss = 0.0391912
I0225 05:49:56.685094 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0391914 (* 1 = 0.0391914 loss)
I0225 05:49:56.685101 29812 solver.cpp:470] Iteration 93050, lr = 0.0002401
I0225 05:50:16.084965 29812 solver.cpp:189] Iteration 93100, loss = 0.0419143
I0225 05:50:16.085031 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0419145 (* 1 = 0.0419145 loss)
I0225 05:50:16.085037 29812 solver.cpp:470] Iteration 93100, lr = 0.0002401
I0225 05:50:35.483270 29812 solver.cpp:189] Iteration 93150, loss = 0.0839189
I0225 05:50:35.483294 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0839192 (* 1 = 0.0839192 loss)
I0225 05:50:35.483299 29812 solver.cpp:470] Iteration 93150, lr = 0.0002401
I0225 05:50:54.875876 29812 solver.cpp:189] Iteration 93200, loss = 0.0174832
I0225 05:50:54.875947 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0174834 (* 1 = 0.0174834 loss)
I0225 05:50:54.875963 29812 solver.cpp:470] Iteration 93200, lr = 0.0002401
I0225 05:51:14.276072 29812 solver.cpp:189] Iteration 93250, loss = 0.0149907
I0225 05:51:14.276096 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0149909 (* 1 = 0.0149909 loss)
I0225 05:51:14.276101 29812 solver.cpp:470] Iteration 93250, lr = 0.0002401
I0225 05:51:33.674638 29812 solver.cpp:189] Iteration 93300, loss = 0.0508529
I0225 05:51:33.674711 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0508531 (* 1 = 0.0508531 loss)
I0225 05:51:33.674727 29812 solver.cpp:470] Iteration 93300, lr = 0.0002401
I0225 05:51:53.067497 29812 solver.cpp:189] Iteration 93350, loss = 0.0537238
I0225 05:51:53.067525 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.053724 (* 1 = 0.053724 loss)
I0225 05:51:53.067531 29812 solver.cpp:470] Iteration 93350, lr = 0.0002401
I0225 05:52:12.465503 29812 solver.cpp:189] Iteration 93400, loss = 0.0267441
I0225 05:52:12.465562 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0267443 (* 1 = 0.0267443 loss)
I0225 05:52:12.465569 29812 solver.cpp:470] Iteration 93400, lr = 0.0002401
I0225 05:52:31.857563 29812 solver.cpp:189] Iteration 93450, loss = 0.00892183
I0225 05:52:31.857588 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.00892199 (* 1 = 0.00892199 loss)
I0225 05:52:31.857594 29812 solver.cpp:470] Iteration 93450, lr = 0.0002401
I0225 05:52:51.261468 29812 solver.cpp:189] Iteration 93500, loss = 0.0118297
I0225 05:52:51.261559 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0118298 (* 1 = 0.0118298 loss)
I0225 05:52:51.261564 29812 solver.cpp:470] Iteration 93500, lr = 0.0002401
I0225 05:53:10.658546 29812 solver.cpp:189] Iteration 93550, loss = 0.0615675
I0225 05:53:10.658572 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0615677 (* 1 = 0.0615677 loss)
I0225 05:53:10.658578 29812 solver.cpp:470] Iteration 93550, lr = 0.0002401
I0225 05:53:30.054263 29812 solver.cpp:189] Iteration 93600, loss = 0.0724856
I0225 05:53:30.054347 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0724858 (* 1 = 0.0724858 loss)
I0225 05:53:30.054354 29812 solver.cpp:470] Iteration 93600, lr = 0.0002401
I0225 05:53:49.456920 29812 solver.cpp:189] Iteration 93650, loss = 0.0361246
I0225 05:53:49.456944 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0361247 (* 1 = 0.0361247 loss)
I0225 05:53:49.456950 29812 solver.cpp:470] Iteration 93650, lr = 0.0002401
I0225 05:54:08.857590 29812 solver.cpp:189] Iteration 93700, loss = 0.0758044
I0225 05:54:08.857666 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0758045 (* 1 = 0.0758045 loss)
I0225 05:54:08.857681 29812 solver.cpp:470] Iteration 93700, lr = 0.0002401
I0225 05:54:28.246034 29812 solver.cpp:189] Iteration 93750, loss = 0.0632839
I0225 05:54:28.246059 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.063284 (* 1 = 0.063284 loss)
I0225 05:54:28.246064 29812 solver.cpp:470] Iteration 93750, lr = 0.0002401
I0225 05:54:47.641894 29812 solver.cpp:189] Iteration 93800, loss = 0.0331344
I0225 05:54:47.641989 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0331346 (* 1 = 0.0331346 loss)
I0225 05:54:47.642004 29812 solver.cpp:470] Iteration 93800, lr = 0.0002401
I0225 05:55:07.052228 29812 solver.cpp:189] Iteration 93850, loss = 0.0254583
I0225 05:55:07.052252 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0254585 (* 1 = 0.0254585 loss)
I0225 05:55:07.052258 29812 solver.cpp:470] Iteration 93850, lr = 0.0002401
I0225 05:55:26.444286 29812 solver.cpp:189] Iteration 93900, loss = 0.0948406
I0225 05:55:26.444327 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0948408 (* 1 = 0.0948408 loss)
I0225 05:55:26.444334 29812 solver.cpp:470] Iteration 93900, lr = 0.0002401
I0225 05:55:45.833096 29812 solver.cpp:189] Iteration 93950, loss = 0.139869
I0225 05:55:45.833122 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.139869 (* 1 = 0.139869 loss)
I0225 05:55:45.833128 29812 solver.cpp:470] Iteration 93950, lr = 0.0002401
I0225 05:56:04.984531 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_94000.caffemodel
I0225 05:56:05.106920 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_94000.solverstate
I0225 05:56:05.165606 29812 solver.cpp:266] Iteration 94000, Testing net (#0)
I0225 05:56:12.824818 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.912
I0225 05:56:12.824859 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.362121 (* 1 = 0.362121 loss)
I0225 05:56:13.111250 29812 solver.cpp:189] Iteration 94000, loss = 0.0500451
I0225 05:56:13.111274 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0500453 (* 1 = 0.0500453 loss)
I0225 05:56:13.111280 29812 solver.cpp:470] Iteration 94000, lr = 0.0002401
I0225 05:56:32.498960 29812 solver.cpp:189] Iteration 94050, loss = 0.0348346
I0225 05:56:32.498983 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0348348 (* 1 = 0.0348348 loss)
I0225 05:56:32.498988 29812 solver.cpp:470] Iteration 94050, lr = 0.0002401
I0225 05:56:51.896185 29812 solver.cpp:189] Iteration 94100, loss = 0.0654704
I0225 05:56:51.896227 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0654705 (* 1 = 0.0654705 loss)
I0225 05:56:51.896234 29812 solver.cpp:470] Iteration 94100, lr = 0.0002401
I0225 05:57:11.288873 29812 solver.cpp:189] Iteration 94150, loss = 0.0856186
I0225 05:57:11.288897 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0856187 (* 1 = 0.0856187 loss)
I0225 05:57:11.288902 29812 solver.cpp:470] Iteration 94150, lr = 0.0002401
I0225 05:57:30.674034 29812 solver.cpp:189] Iteration 94200, loss = 0.0735652
I0225 05:57:30.674127 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0735653 (* 1 = 0.0735653 loss)
I0225 05:57:30.674144 29812 solver.cpp:470] Iteration 94200, lr = 0.0002401
I0225 05:57:50.067905 29812 solver.cpp:189] Iteration 94250, loss = 0.0149205
I0225 05:57:50.067930 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0149207 (* 1 = 0.0149207 loss)
I0225 05:57:50.067936 29812 solver.cpp:470] Iteration 94250, lr = 0.0002401
I0225 05:58:09.462630 29812 solver.cpp:189] Iteration 94300, loss = 0.0980686
I0225 05:58:09.462687 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0980687 (* 1 = 0.0980687 loss)
I0225 05:58:09.462692 29812 solver.cpp:470] Iteration 94300, lr = 0.0002401
I0225 05:58:28.855461 29812 solver.cpp:189] Iteration 94350, loss = 0.0634307
I0225 05:58:28.855486 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0634309 (* 1 = 0.0634309 loss)
I0225 05:58:28.855492 29812 solver.cpp:470] Iteration 94350, lr = 0.0002401
I0225 05:58:48.237022 29812 solver.cpp:189] Iteration 94400, loss = 0.0590831
I0225 05:58:48.237067 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0590833 (* 1 = 0.0590833 loss)
I0225 05:58:48.237074 29812 solver.cpp:470] Iteration 94400, lr = 0.0002401
I0225 05:59:07.627037 29812 solver.cpp:189] Iteration 94450, loss = 0.025362
I0225 05:59:07.627061 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0253622 (* 1 = 0.0253622 loss)
I0225 05:59:07.627066 29812 solver.cpp:470] Iteration 94450, lr = 0.0002401
I0225 05:59:27.025326 29812 solver.cpp:189] Iteration 94500, loss = 0.0428344
I0225 05:59:27.025365 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0428345 (* 1 = 0.0428345 loss)
I0225 05:59:27.025372 29812 solver.cpp:470] Iteration 94500, lr = 0.0002401
I0225 05:59:46.405815 29812 solver.cpp:189] Iteration 94550, loss = 0.0341664
I0225 05:59:46.405839 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0341665 (* 1 = 0.0341665 loss)
I0225 05:59:46.405844 29812 solver.cpp:470] Iteration 94550, lr = 0.0002401
I0225 06:00:05.796079 29812 solver.cpp:189] Iteration 94600, loss = 0.0562806
I0225 06:00:05.796144 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0562808 (* 1 = 0.0562808 loss)
I0225 06:00:05.796150 29812 solver.cpp:470] Iteration 94600, lr = 0.0002401
I0225 06:00:25.187077 29812 solver.cpp:189] Iteration 94650, loss = 0.126331
I0225 06:00:25.187100 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.126331 (* 1 = 0.126331 loss)
I0225 06:00:25.187106 29812 solver.cpp:470] Iteration 94650, lr = 0.0002401
I0225 06:00:44.574898 29812 solver.cpp:189] Iteration 94700, loss = 0.0681454
I0225 06:00:44.574934 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0681456 (* 1 = 0.0681456 loss)
I0225 06:00:44.574939 29812 solver.cpp:470] Iteration 94700, lr = 0.0002401
I0225 06:01:03.971606 29812 solver.cpp:189] Iteration 94750, loss = 0.075844
I0225 06:01:03.971631 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0758441 (* 1 = 0.0758441 loss)
I0225 06:01:03.971637 29812 solver.cpp:470] Iteration 94750, lr = 0.0002401
I0225 06:01:23.355007 29812 solver.cpp:189] Iteration 94800, loss = 0.0551543
I0225 06:01:23.355078 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0551545 (* 1 = 0.0551545 loss)
I0225 06:01:23.355093 29812 solver.cpp:470] Iteration 94800, lr = 0.0002401
I0225 06:01:42.745962 29812 solver.cpp:189] Iteration 94850, loss = 0.0704756
I0225 06:01:42.745990 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0704758 (* 1 = 0.0704758 loss)
I0225 06:01:42.745995 29812 solver.cpp:470] Iteration 94850, lr = 0.0002401
I0225 06:02:02.138672 29812 solver.cpp:189] Iteration 94900, loss = 0.0509528
I0225 06:02:02.138743 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.050953 (* 1 = 0.050953 loss)
I0225 06:02:02.138758 29812 solver.cpp:470] Iteration 94900, lr = 0.0002401
I0225 06:02:21.521165 29812 solver.cpp:189] Iteration 94950, loss = 0.0258337
I0225 06:02:21.521188 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0258339 (* 1 = 0.0258339 loss)
I0225 06:02:21.521193 29812 solver.cpp:470] Iteration 94950, lr = 0.0002401
I0225 06:02:40.666091 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_95000.caffemodel
I0225 06:02:40.788082 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_95000.solverstate
I0225 06:02:40.847441 29812 solver.cpp:266] Iteration 95000, Testing net (#0)
I0225 06:02:48.497287 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9078
I0225 06:02:48.497323 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.38782 (* 1 = 0.38782 loss)
I0225 06:02:48.783953 29812 solver.cpp:189] Iteration 95000, loss = 0.0507105
I0225 06:02:48.783978 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0507107 (* 1 = 0.0507107 loss)
I0225 06:02:48.783985 29812 solver.cpp:470] Iteration 95000, lr = 0.0002401
I0225 06:03:08.180744 29812 solver.cpp:189] Iteration 95050, loss = 0.022711
I0225 06:03:08.180768 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0227112 (* 1 = 0.0227112 loss)
I0225 06:03:08.180774 29812 solver.cpp:470] Iteration 95050, lr = 0.0002401
I0225 06:03:27.570199 29812 solver.cpp:189] Iteration 95100, loss = 0.0473396
I0225 06:03:27.570243 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0473398 (* 1 = 0.0473398 loss)
I0225 06:03:27.570250 29812 solver.cpp:470] Iteration 95100, lr = 0.0002401
I0225 06:03:46.957787 29812 solver.cpp:189] Iteration 95150, loss = 0.0387433
I0225 06:03:46.957810 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0387435 (* 1 = 0.0387435 loss)
I0225 06:03:46.957816 29812 solver.cpp:470] Iteration 95150, lr = 0.0002401
I0225 06:04:06.348805 29812 solver.cpp:189] Iteration 95200, loss = 0.0630873
I0225 06:04:06.348845 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0630875 (* 1 = 0.0630875 loss)
I0225 06:04:06.348851 29812 solver.cpp:470] Iteration 95200, lr = 0.0002401
I0225 06:04:25.740725 29812 solver.cpp:189] Iteration 95250, loss = 0.0468495
I0225 06:04:25.740749 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0468497 (* 1 = 0.0468497 loss)
I0225 06:04:25.740756 29812 solver.cpp:470] Iteration 95250, lr = 0.0002401
I0225 06:04:45.139822 29812 solver.cpp:189] Iteration 95300, loss = 0.0238917
I0225 06:04:45.139861 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0238919 (* 1 = 0.0238919 loss)
I0225 06:04:45.139868 29812 solver.cpp:470] Iteration 95300, lr = 0.0002401
I0225 06:05:04.538936 29812 solver.cpp:189] Iteration 95350, loss = 0.059136
I0225 06:05:04.538960 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0591362 (* 1 = 0.0591362 loss)
I0225 06:05:04.538965 29812 solver.cpp:470] Iteration 95350, lr = 0.0002401
I0225 06:05:23.934269 29812 solver.cpp:189] Iteration 95400, loss = 0.115172
I0225 06:05:23.934340 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.115173 (* 1 = 0.115173 loss)
I0225 06:05:23.934355 29812 solver.cpp:470] Iteration 95400, lr = 0.0002401
I0225 06:05:43.319864 29812 solver.cpp:189] Iteration 95450, loss = 0.0460693
I0225 06:05:43.319888 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0460695 (* 1 = 0.0460695 loss)
I0225 06:05:43.319893 29812 solver.cpp:470] Iteration 95450, lr = 0.0002401
I0225 06:06:02.730006 29812 solver.cpp:189] Iteration 95500, loss = 0.0480589
I0225 06:06:02.730079 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0480591 (* 1 = 0.0480591 loss)
I0225 06:06:02.730095 29812 solver.cpp:470] Iteration 95500, lr = 0.0002401
I0225 06:06:22.124213 29812 solver.cpp:189] Iteration 95550, loss = 0.0439216
I0225 06:06:22.124238 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0439218 (* 1 = 0.0439218 loss)
I0225 06:06:22.124243 29812 solver.cpp:470] Iteration 95550, lr = 0.0002401
I0225 06:06:41.521168 29812 solver.cpp:189] Iteration 95600, loss = 0.0392881
I0225 06:06:41.521208 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0392884 (* 1 = 0.0392884 loss)
I0225 06:06:41.521214 29812 solver.cpp:470] Iteration 95600, lr = 0.0002401
I0225 06:07:00.918498 29812 solver.cpp:189] Iteration 95650, loss = 0.0394702
I0225 06:07:00.918524 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0394704 (* 1 = 0.0394704 loss)
I0225 06:07:00.918529 29812 solver.cpp:470] Iteration 95650, lr = 0.0002401
I0225 06:07:20.311291 29812 solver.cpp:189] Iteration 95700, loss = 0.048751
I0225 06:07:20.311352 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0487512 (* 1 = 0.0487512 loss)
I0225 06:07:20.311358 29812 solver.cpp:470] Iteration 95700, lr = 0.0002401
I0225 06:07:39.707259 29812 solver.cpp:189] Iteration 95750, loss = 0.126676
I0225 06:07:39.707284 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.126676 (* 1 = 0.126676 loss)
I0225 06:07:39.707290 29812 solver.cpp:470] Iteration 95750, lr = 0.0002401
I0225 06:07:59.099968 29812 solver.cpp:189] Iteration 95800, loss = 0.0788663
I0225 06:07:59.100044 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0788665 (* 1 = 0.0788665 loss)
I0225 06:07:59.100059 29812 solver.cpp:470] Iteration 95800, lr = 0.0002401
I0225 06:08:18.507772 29812 solver.cpp:189] Iteration 95850, loss = 0.0418014
I0225 06:08:18.507796 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0418016 (* 1 = 0.0418016 loss)
I0225 06:08:18.507802 29812 solver.cpp:470] Iteration 95850, lr = 0.0002401
I0225 06:08:37.904944 29812 solver.cpp:189] Iteration 95900, loss = 0.085587
I0225 06:08:37.905017 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0855872 (* 1 = 0.0855872 loss)
I0225 06:08:37.905033 29812 solver.cpp:470] Iteration 95900, lr = 0.0002401
I0225 06:08:57.299690 29812 solver.cpp:189] Iteration 95950, loss = 0.0160637
I0225 06:08:57.299713 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.016064 (* 1 = 0.016064 loss)
I0225 06:08:57.299718 29812 solver.cpp:470] Iteration 95950, lr = 0.0002401
I0225 06:09:16.454051 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_96000.caffemodel
I0225 06:09:16.576866 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_96000.solverstate
I0225 06:09:16.635808 29812 solver.cpp:266] Iteration 96000, Testing net (#0)
I0225 06:09:24.286406 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9029
I0225 06:09:24.286443 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.404113 (* 1 = 0.404113 loss)
I0225 06:09:24.573920 29812 solver.cpp:189] Iteration 96000, loss = 0.0491542
I0225 06:09:24.573945 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0491544 (* 1 = 0.0491544 loss)
I0225 06:09:24.573951 29812 solver.cpp:470] Iteration 96000, lr = 0.0002401
I0225 06:09:43.958521 29812 solver.cpp:189] Iteration 96050, loss = 0.0183695
I0225 06:09:43.958546 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0183697 (* 1 = 0.0183697 loss)
I0225 06:09:43.958552 29812 solver.cpp:470] Iteration 96050, lr = 0.0002401
I0225 06:10:03.358238 29812 solver.cpp:189] Iteration 96100, loss = 0.0459541
I0225 06:10:03.358278 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0459544 (* 1 = 0.0459544 loss)
I0225 06:10:03.358284 29812 solver.cpp:470] Iteration 96100, lr = 0.0002401
I0225 06:10:22.741746 29812 solver.cpp:189] Iteration 96150, loss = 0.0677448
I0225 06:10:22.741772 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.067745 (* 1 = 0.067745 loss)
I0225 06:10:22.741778 29812 solver.cpp:470] Iteration 96150, lr = 0.0002401
I0225 06:10:42.121227 29812 solver.cpp:189] Iteration 96200, loss = 0.0864775
I0225 06:10:42.121299 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0864777 (* 1 = 0.0864777 loss)
I0225 06:10:42.121315 29812 solver.cpp:470] Iteration 96200, lr = 0.0002401
I0225 06:11:01.516201 29812 solver.cpp:189] Iteration 96250, loss = 0.049078
I0225 06:11:01.516224 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0490782 (* 1 = 0.0490782 loss)
I0225 06:11:01.516229 29812 solver.cpp:470] Iteration 96250, lr = 0.0002401
I0225 06:11:20.912722 29812 solver.cpp:189] Iteration 96300, loss = 0.0686346
I0225 06:11:20.912770 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0686348 (* 1 = 0.0686348 loss)
I0225 06:11:20.912777 29812 solver.cpp:470] Iteration 96300, lr = 0.0002401
I0225 06:11:40.294050 29812 solver.cpp:189] Iteration 96350, loss = 0.0484802
I0225 06:11:40.294073 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0484805 (* 1 = 0.0484805 loss)
I0225 06:11:40.294080 29812 solver.cpp:470] Iteration 96350, lr = 0.0002401
I0225 06:11:59.671890 29812 solver.cpp:189] Iteration 96400, loss = 0.040204
I0225 06:11:59.672003 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0402042 (* 1 = 0.0402042 loss)
I0225 06:11:59.672010 29812 solver.cpp:470] Iteration 96400, lr = 0.0002401
I0225 06:12:19.059293 29812 solver.cpp:189] Iteration 96450, loss = 0.0811732
I0225 06:12:19.059319 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0811734 (* 1 = 0.0811734 loss)
I0225 06:12:19.059324 29812 solver.cpp:470] Iteration 96450, lr = 0.0002401
I0225 06:12:38.455175 29812 solver.cpp:189] Iteration 96500, loss = 0.0581506
I0225 06:12:38.455219 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0581507 (* 1 = 0.0581507 loss)
I0225 06:12:38.455224 29812 solver.cpp:470] Iteration 96500, lr = 0.0002401
I0225 06:12:57.845796 29812 solver.cpp:189] Iteration 96550, loss = 0.0133673
I0225 06:12:57.845823 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0133674 (* 1 = 0.0133674 loss)
I0225 06:12:57.845829 29812 solver.cpp:470] Iteration 96550, lr = 0.0002401
I0225 06:13:17.242408 29812 solver.cpp:189] Iteration 96600, loss = 0.0339902
I0225 06:13:17.242480 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0339904 (* 1 = 0.0339904 loss)
I0225 06:13:17.242494 29812 solver.cpp:470] Iteration 96600, lr = 0.0002401
I0225 06:13:36.629052 29812 solver.cpp:189] Iteration 96650, loss = 0.0200601
I0225 06:13:36.629076 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0200602 (* 1 = 0.0200602 loss)
I0225 06:13:36.629082 29812 solver.cpp:470] Iteration 96650, lr = 0.0002401
I0225 06:13:56.017262 29812 solver.cpp:189] Iteration 96700, loss = 0.0297906
I0225 06:13:56.017333 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0297908 (* 1 = 0.0297908 loss)
I0225 06:13:56.017340 29812 solver.cpp:470] Iteration 96700, lr = 0.0002401
I0225 06:14:15.414630 29812 solver.cpp:189] Iteration 96750, loss = 0.130011
I0225 06:14:15.414654 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.130011 (* 1 = 0.130011 loss)
I0225 06:14:15.414659 29812 solver.cpp:470] Iteration 96750, lr = 0.0002401
I0225 06:14:34.794795 29812 solver.cpp:189] Iteration 96800, loss = 0.0517701
I0225 06:14:34.794885 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0517702 (* 1 = 0.0517702 loss)
I0225 06:14:34.794891 29812 solver.cpp:470] Iteration 96800, lr = 0.0002401
I0225 06:14:54.187139 29812 solver.cpp:189] Iteration 96850, loss = 0.0270116
I0225 06:14:54.187163 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0270118 (* 1 = 0.0270118 loss)
I0225 06:14:54.187170 29812 solver.cpp:470] Iteration 96850, lr = 0.0002401
I0225 06:15:13.574847 29812 solver.cpp:189] Iteration 96900, loss = 0.0559471
I0225 06:15:13.574887 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0559473 (* 1 = 0.0559473 loss)
I0225 06:15:13.574892 29812 solver.cpp:470] Iteration 96900, lr = 0.0002401
I0225 06:15:32.964684 29812 solver.cpp:189] Iteration 96950, loss = 0.0516047
I0225 06:15:32.964709 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0516049 (* 1 = 0.0516049 loss)
I0225 06:15:32.964715 29812 solver.cpp:470] Iteration 96950, lr = 0.0002401
I0225 06:15:52.115918 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_97000.caffemodel
I0225 06:15:52.246729 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_97000.solverstate
I0225 06:15:52.307358 29812 solver.cpp:266] Iteration 97000, Testing net (#0)
I0225 06:15:59.959005 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9084
I0225 06:15:59.959045 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.380958 (* 1 = 0.380958 loss)
I0225 06:16:00.248148 29812 solver.cpp:189] Iteration 97000, loss = 0.0585113
I0225 06:16:00.248173 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0585115 (* 1 = 0.0585115 loss)
I0225 06:16:00.248185 29812 solver.cpp:470] Iteration 97000, lr = 0.0002401
I0225 06:16:19.645465 29812 solver.cpp:189] Iteration 97050, loss = 0.0788244
I0225 06:16:19.645491 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0788246 (* 1 = 0.0788246 loss)
I0225 06:16:19.645498 29812 solver.cpp:470] Iteration 97050, lr = 0.0002401
I0225 06:16:39.046180 29812 solver.cpp:189] Iteration 97100, loss = 0.102611
I0225 06:16:39.046243 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.102611 (* 1 = 0.102611 loss)
I0225 06:16:39.046250 29812 solver.cpp:470] Iteration 97100, lr = 0.0002401
I0225 06:16:58.435454 29812 solver.cpp:189] Iteration 97150, loss = 0.0731798
I0225 06:16:58.435478 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.07318 (* 1 = 0.07318 loss)
I0225 06:16:58.435484 29812 solver.cpp:470] Iteration 97150, lr = 0.0002401
I0225 06:17:17.833106 29812 solver.cpp:189] Iteration 97200, loss = 0.0728088
I0225 06:17:17.833202 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.072809 (* 1 = 0.072809 loss)
I0225 06:17:17.833209 29812 solver.cpp:470] Iteration 97200, lr = 0.0002401
I0225 06:17:37.232190 29812 solver.cpp:189] Iteration 97250, loss = 0.0408663
I0225 06:17:37.232214 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0408665 (* 1 = 0.0408665 loss)
I0225 06:17:37.232221 29812 solver.cpp:470] Iteration 97250, lr = 0.0002401
I0225 06:17:56.629063 29812 solver.cpp:189] Iteration 97300, loss = 0.0958267
I0225 06:17:56.629123 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.095827 (* 1 = 0.095827 loss)
I0225 06:17:56.629130 29812 solver.cpp:470] Iteration 97300, lr = 0.0002401
I0225 06:18:16.026105 29812 solver.cpp:189] Iteration 97350, loss = 0.07436
I0225 06:18:16.026130 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0743602 (* 1 = 0.0743602 loss)
I0225 06:18:16.026136 29812 solver.cpp:470] Iteration 97350, lr = 0.0002401
I0225 06:18:35.420557 29812 solver.cpp:189] Iteration 97400, loss = 0.0498201
I0225 06:18:35.420614 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0498203 (* 1 = 0.0498203 loss)
I0225 06:18:35.420621 29812 solver.cpp:470] Iteration 97400, lr = 0.0002401
I0225 06:18:54.825777 29812 solver.cpp:189] Iteration 97450, loss = 0.0220699
I0225 06:18:54.825800 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0220702 (* 1 = 0.0220702 loss)
I0225 06:18:54.825806 29812 solver.cpp:470] Iteration 97450, lr = 0.0002401
I0225 06:19:14.223567 29812 solver.cpp:189] Iteration 97500, loss = 0.084963
I0225 06:19:14.223606 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0849632 (* 1 = 0.0849632 loss)
I0225 06:19:14.223613 29812 solver.cpp:470] Iteration 97500, lr = 0.0002401
I0225 06:19:33.624336 29812 solver.cpp:189] Iteration 97550, loss = 0.0287197
I0225 06:19:33.624361 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0287199 (* 1 = 0.0287199 loss)
I0225 06:19:33.624366 29812 solver.cpp:470] Iteration 97550, lr = 0.0002401
I0225 06:19:53.021581 29812 solver.cpp:189] Iteration 97600, loss = 0.036521
I0225 06:19:53.021641 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0365212 (* 1 = 0.0365212 loss)
I0225 06:19:53.021648 29812 solver.cpp:470] Iteration 97600, lr = 0.0002401
I0225 06:20:12.416512 29812 solver.cpp:189] Iteration 97650, loss = 0.0788587
I0225 06:20:12.416537 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.078859 (* 1 = 0.078859 loss)
I0225 06:20:12.416542 29812 solver.cpp:470] Iteration 97650, lr = 0.0002401
I0225 06:20:31.804991 29812 solver.cpp:189] Iteration 97700, loss = 0.0626895
I0225 06:20:31.805083 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0626897 (* 1 = 0.0626897 loss)
I0225 06:20:31.805088 29812 solver.cpp:470] Iteration 97700, lr = 0.0002401
I0225 06:20:51.203503 29812 solver.cpp:189] Iteration 97750, loss = 0.020425
I0225 06:20:51.203526 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0204252 (* 1 = 0.0204252 loss)
I0225 06:20:51.203532 29812 solver.cpp:470] Iteration 97750, lr = 0.0002401
I0225 06:21:10.586838 29812 solver.cpp:189] Iteration 97800, loss = 0.0332779
I0225 06:21:10.586899 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0332781 (* 1 = 0.0332781 loss)
I0225 06:21:10.586905 29812 solver.cpp:470] Iteration 97800, lr = 0.0002401
I0225 06:21:29.988646 29812 solver.cpp:189] Iteration 97850, loss = 0.033341
I0225 06:21:29.988670 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0333412 (* 1 = 0.0333412 loss)
I0225 06:21:29.988677 29812 solver.cpp:470] Iteration 97850, lr = 0.0002401
I0225 06:21:49.381319 29812 solver.cpp:189] Iteration 97900, loss = 0.0638361
I0225 06:21:49.381362 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0638363 (* 1 = 0.0638363 loss)
I0225 06:21:49.381368 29812 solver.cpp:470] Iteration 97900, lr = 0.0002401
I0225 06:22:08.770874 29812 solver.cpp:189] Iteration 97950, loss = 0.170694
I0225 06:22:08.770900 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.170694 (* 1 = 0.170694 loss)
I0225 06:22:08.770905 29812 solver.cpp:470] Iteration 97950, lr = 0.0002401
I0225 06:22:27.924713 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_98000.caffemodel
I0225 06:22:28.052166 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_98000.solverstate
I0225 06:22:28.110863 29812 solver.cpp:266] Iteration 98000, Testing net (#0)
I0225 06:22:35.765090 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.904
I0225 06:22:35.765127 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.399353 (* 1 = 0.399353 loss)
I0225 06:22:36.053263 29812 solver.cpp:189] Iteration 98000, loss = 0.0756482
I0225 06:22:36.053287 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0756484 (* 1 = 0.0756484 loss)
I0225 06:22:36.053294 29812 solver.cpp:470] Iteration 98000, lr = 0.0002401
I0225 06:22:55.432817 29812 solver.cpp:189] Iteration 98050, loss = 0.046804
I0225 06:22:55.432840 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0468042 (* 1 = 0.0468042 loss)
I0225 06:22:55.432845 29812 solver.cpp:470] Iteration 98050, lr = 0.0002401
I0225 06:23:14.823575 29812 solver.cpp:189] Iteration 98100, loss = 0.0670195
I0225 06:23:14.823669 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0670197 (* 1 = 0.0670197 loss)
I0225 06:23:14.823686 29812 solver.cpp:470] Iteration 98100, lr = 0.0002401
I0225 06:23:34.217309 29812 solver.cpp:189] Iteration 98150, loss = 0.0690499
I0225 06:23:34.217334 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0690501 (* 1 = 0.0690501 loss)
I0225 06:23:34.217340 29812 solver.cpp:470] Iteration 98150, lr = 0.0002401
I0225 06:23:53.615183 29812 solver.cpp:189] Iteration 98200, loss = 0.071047
I0225 06:23:53.615272 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0710472 (* 1 = 0.0710472 loss)
I0225 06:23:53.615278 29812 solver.cpp:470] Iteration 98200, lr = 0.0002401
I0225 06:24:13.018052 29812 solver.cpp:189] Iteration 98250, loss = 0.104169
I0225 06:24:13.018077 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.104169 (* 1 = 0.104169 loss)
I0225 06:24:13.018084 29812 solver.cpp:470] Iteration 98250, lr = 0.0002401
I0225 06:24:32.408056 29812 solver.cpp:189] Iteration 98300, loss = 0.146145
I0225 06:24:32.408114 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.146145 (* 1 = 0.146145 loss)
I0225 06:24:32.408121 29812 solver.cpp:470] Iteration 98300, lr = 0.0002401
I0225 06:24:51.787425 29812 solver.cpp:189] Iteration 98350, loss = 0.0366443
I0225 06:24:51.787451 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0366445 (* 1 = 0.0366445 loss)
I0225 06:24:51.787457 29812 solver.cpp:470] Iteration 98350, lr = 0.0002401
I0225 06:25:11.171516 29812 solver.cpp:189] Iteration 98400, loss = 0.0560881
I0225 06:25:11.171588 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0560883 (* 1 = 0.0560883 loss)
I0225 06:25:11.171603 29812 solver.cpp:470] Iteration 98400, lr = 0.0002401
I0225 06:25:30.550523 29812 solver.cpp:189] Iteration 98450, loss = 0.151529
I0225 06:25:30.550546 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.151529 (* 1 = 0.151529 loss)
I0225 06:25:30.550552 29812 solver.cpp:470] Iteration 98450, lr = 0.0002401
I0225 06:25:49.937353 29812 solver.cpp:189] Iteration 98500, loss = 0.0530161
I0225 06:25:49.937432 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0530163 (* 1 = 0.0530163 loss)
I0225 06:25:49.937438 29812 solver.cpp:470] Iteration 98500, lr = 0.0002401
I0225 06:26:09.338774 29812 solver.cpp:189] Iteration 98550, loss = 0.0142767
I0225 06:26:09.338798 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0142769 (* 1 = 0.0142769 loss)
I0225 06:26:09.338804 29812 solver.cpp:470] Iteration 98550, lr = 0.0002401
I0225 06:26:28.727172 29812 solver.cpp:189] Iteration 98600, loss = 0.0688956
I0225 06:26:28.727236 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0688959 (* 1 = 0.0688959 loss)
I0225 06:26:28.727242 29812 solver.cpp:470] Iteration 98600, lr = 0.0002401
I0225 06:26:48.125329 29812 solver.cpp:189] Iteration 98650, loss = 0.0736421
I0225 06:26:48.125354 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0736423 (* 1 = 0.0736423 loss)
I0225 06:26:48.125360 29812 solver.cpp:470] Iteration 98650, lr = 0.0002401
I0225 06:27:07.525722 29812 solver.cpp:189] Iteration 98700, loss = 0.0469782
I0225 06:27:07.525794 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0469784 (* 1 = 0.0469784 loss)
I0225 06:27:07.525807 29812 solver.cpp:470] Iteration 98700, lr = 0.0002401
I0225 06:27:26.913280 29812 solver.cpp:189] Iteration 98750, loss = 0.062001
I0225 06:27:26.913305 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0620012 (* 1 = 0.0620012 loss)
I0225 06:27:26.913311 29812 solver.cpp:470] Iteration 98750, lr = 0.0002401
I0225 06:27:46.311331 29812 solver.cpp:189] Iteration 98800, loss = 0.0916164
I0225 06:27:46.311404 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0916167 (* 1 = 0.0916167 loss)
I0225 06:27:46.311419 29812 solver.cpp:470] Iteration 98800, lr = 0.0002401
I0225 06:28:05.702682 29812 solver.cpp:189] Iteration 98850, loss = 0.0692785
I0225 06:28:05.702710 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0692787 (* 1 = 0.0692787 loss)
I0225 06:28:05.702716 29812 solver.cpp:470] Iteration 98850, lr = 0.0002401
I0225 06:28:25.086159 29812 solver.cpp:189] Iteration 98900, loss = 0.118475
I0225 06:28:25.086238 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.118475 (* 1 = 0.118475 loss)
I0225 06:28:25.086246 29812 solver.cpp:470] Iteration 98900, lr = 0.0002401
I0225 06:28:44.475570 29812 solver.cpp:189] Iteration 98950, loss = 0.0581266
I0225 06:28:44.475594 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0581269 (* 1 = 0.0581269 loss)
I0225 06:28:44.475600 29812 solver.cpp:470] Iteration 98950, lr = 0.0002401
I0225 06:29:03.625438 29812 solver.cpp:334] Snapshotting to examples/cifar10_vgg/vgg16-msr_bn_iter_99000.caffemodel
I0225 06:29:03.751238 29812 solver.cpp:342] Snapshotting solver state to examples/cifar10_vgg/vgg16-msr_bn_iter_99000.solverstate
I0225 06:29:03.809355 29812 solver.cpp:266] Iteration 99000, Testing net (#0)
I0225 06:29:11.453202 29812 solver.cpp:315]     Test net output #0: fc7/acc = 0.9038
I0225 06:29:11.453236 29812 solver.cpp:315]     Test net output #1: fc7/loss3 = 0.400096 (* 1 = 0.400096 loss)
I0225 06:29:11.740612 29812 solver.cpp:189] Iteration 99000, loss = 0.0778707
I0225 06:29:11.740638 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.077871 (* 1 = 0.077871 loss)
I0225 06:29:11.740643 29812 solver.cpp:470] Iteration 99000, lr = 0.0002401
I0225 06:29:31.138856 29812 solver.cpp:189] Iteration 99050, loss = 0.0395351
I0225 06:29:31.138880 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0395354 (* 1 = 0.0395354 loss)
I0225 06:29:31.138887 29812 solver.cpp:470] Iteration 99050, lr = 0.0002401
I0225 06:29:50.530791 29812 solver.cpp:189] Iteration 99100, loss = 0.110129
I0225 06:29:50.530881 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.110129 (* 1 = 0.110129 loss)
I0225 06:29:50.530896 29812 solver.cpp:470] Iteration 99100, lr = 0.0002401
I0225 06:30:09.925431 29812 solver.cpp:189] Iteration 99150, loss = 0.0673916
I0225 06:30:09.925457 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0673919 (* 1 = 0.0673919 loss)
I0225 06:30:09.925462 29812 solver.cpp:470] Iteration 99150, lr = 0.0002401
I0225 06:30:29.312469 29812 solver.cpp:189] Iteration 99200, loss = 0.0202304
I0225 06:30:29.312530 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0202306 (* 1 = 0.0202306 loss)
I0225 06:30:29.312537 29812 solver.cpp:470] Iteration 99200, lr = 0.0002401
I0225 06:30:48.712376 29812 solver.cpp:189] Iteration 99250, loss = 0.0370934
I0225 06:30:48.712404 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0370936 (* 1 = 0.0370936 loss)
I0225 06:30:48.712410 29812 solver.cpp:470] Iteration 99250, lr = 0.0002401
I0225 06:31:08.101364 29812 solver.cpp:189] Iteration 99300, loss = 0.0612712
I0225 06:31:08.101405 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0612715 (* 1 = 0.0612715 loss)
I0225 06:31:08.101413 29812 solver.cpp:470] Iteration 99300, lr = 0.0002401
I0225 06:31:27.487718 29812 solver.cpp:189] Iteration 99350, loss = 0.0482222
I0225 06:31:27.487741 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0482224 (* 1 = 0.0482224 loss)
I0225 06:31:27.487746 29812 solver.cpp:470] Iteration 99350, lr = 0.0002401
I0225 06:31:46.872380 29812 solver.cpp:189] Iteration 99400, loss = 0.0142079
I0225 06:31:46.872450 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0142081 (* 1 = 0.0142081 loss)
I0225 06:31:46.872465 29812 solver.cpp:470] Iteration 99400, lr = 0.0002401
I0225 06:32:06.255065 29812 solver.cpp:189] Iteration 99450, loss = 0.029291
I0225 06:32:06.255090 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0292912 (* 1 = 0.0292912 loss)
I0225 06:32:06.255096 29812 solver.cpp:470] Iteration 99450, lr = 0.0002401
I0225 06:32:25.639245 29812 solver.cpp:189] Iteration 99500, loss = 0.131952
I0225 06:32:25.639338 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.131952 (* 1 = 0.131952 loss)
I0225 06:32:25.639353 29812 solver.cpp:470] Iteration 99500, lr = 0.0002401
I0225 06:32:45.022873 29812 solver.cpp:189] Iteration 99550, loss = 0.0177218
I0225 06:32:45.022897 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.017722 (* 1 = 0.017722 loss)
I0225 06:32:45.022902 29812 solver.cpp:470] Iteration 99550, lr = 0.0002401
I0225 06:33:04.411432 29812 solver.cpp:189] Iteration 99600, loss = 0.0620451
I0225 06:33:04.411473 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0620453 (* 1 = 0.0620453 loss)
I0225 06:33:04.411479 29812 solver.cpp:470] Iteration 99600, lr = 0.0002401
I0225 06:33:23.789950 29812 solver.cpp:189] Iteration 99650, loss = 0.0714484
I0225 06:33:23.789978 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0714486 (* 1 = 0.0714486 loss)
I0225 06:33:23.789983 29812 solver.cpp:470] Iteration 99650, lr = 0.0002401
I0225 06:33:43.180939 29812 solver.cpp:189] Iteration 99700, loss = 0.0184283
I0225 06:33:43.180981 29812 solver.cpp:204]     Train net output #0: fc7/loss3 = 0.0184285 (* 1 = 0.0184285 loss)
I0225 06:33:43.180987 298

cifar_vgg16_no_bn_baselr=0.0003.log

solver-vgg16-msr.prototxt

solver-vgg16-msr_bn.prototxt

vgg16-msr.prototxt

vgg16-msr_bn.prototxt

Hi,

Nice job!
A quick question, in your prototxt (e.g., vgg16-msr_bn.prototxt), there is

transform_param {
mirror: true
vertical_mirror: true
random_90_deg_rot: true
mean_file: "examples/cifar10/mean.binaryproto"
}

which I cannot find in any Caffe branch. Which version did you use for this experiment? Any pointer to this?

Thanks!