lenet_BN_sgd.log

I0219 20:24:25.883813 101920 caffe.cpp:108] Use GPU with device ID 0
I0219 20:24:27.258891 101920 common.cpp:22] System entropy source not available, using fallback algorithm to generate seed instead.
I0219 20:24:27.258891 101920 caffe.cpp:116] Starting Optimization
I0219 20:24:27.258891 101920 solver.cpp:32] Initializing solver from parameters: 
test_iter: 100
test_interval: 500
base_lr: 0.01
display: 100
max_iter: 10000
lr_policy: "inv"
gamma: 0.0001
power: 0.75
momentum: 0.9
weight_decay: 0.0005
snapshot: 5000
snapshot_prefix: "lenet"
solver_mode: GPU
random_seed: 1234
net: "lenet_BN_train_valid.prototxt"
solver_type: SGD
delta: 1e-008
I0219 20:24:27.258891 101920 solver.cpp:77] Creating training net from net file: lenet_BN_train_valid.prototxt
E0219 20:24:27.259891 101920 upgrade_proto.cpp:603] Attempting to upgrade input file specified using deprecated transformation parameters: lenet_BN_train_valid.prototxt
I0219 20:24:27.259891 101920 upgrade_proto.cpp:606] Successfully upgraded file specified using deprecated data transformation parameters.
E0219 20:24:27.259891 101920 upgrade_proto.cpp:608] Note that future Caffe releases will only support transform_param messages for transformation fields.
I0219 20:24:27.259891 101920 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer mnist
I0219 20:24:27.259891 101920 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer prob
I0219 20:24:27.259891 101920 net.cpp:275] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0219 20:24:27.259891 101920 net.cpp:39] Initializing net from parameters: 
name: "LeNet"
layers {
  top: "data"
  top: "label"
  name: "mnist"
  type: DATA
  data_param {
    source: "mnist-train-leveldb"
    batch_size: 100
  }
  include {
    phase: TRAIN
  }
  transform_param {
    scale: 0.00390625
  }
}
layers {
  bottom: "data"
  top: "conv1"
  name: "conv1"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  bottom: "conv1"
  top: "conv1_bn"
  name: "conv1_bn"
  type: BN
  blobs_lr: 1
  blobs_lr: 1
  weight_decay: 0
  weight_decay: 0
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  bottom: "conv1_bn"
  top: "pool1"
  name: "pool1"
  type: POOLING
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layers {
  bottom: "pool1"
  top: "conv2"
  name: "conv2"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 50
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  bottom: "conv2"
  top: "conv2_bn"
  name: "conv2_bn"
  type: BN
  blobs_lr: 1
  blobs_lr: 1
  weight_decay: 0
  weight_decay: 0
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  bottom: "conv2_bn"
  top: "pool2"
  name: "pool2"
  type: POOLING
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layers {
  bottom: "pool2"
  top: "ip1"
  name: "ip1"
  type: INNER_PRODUCT
  blobs_lr: 1
  blobs_lr: 2
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  bottom: "ip1"
  top: "ip1"
  name: "relu1"
  type: RELU
}
layers {
  bottom: "ip1"
  top: "ip2"
  name: "ip2"
  type: INNER_PRODUCT
  blobs_lr: 1
  blobs_lr: 2
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  bottom: "ip2"
  bottom: "label"
  name: "loss"
  type: SOFTMAX_LOSS
  include {
    phase: TRAIN
  }
}
state {
  phase: TRAIN
}
I0219 20:24:27.259891 101920 net.cpp:67] Creating Layer mnist
I0219 20:24:27.259891 101920 net.cpp:356] mnist -> data
I0219 20:24:27.259891 101920 net.cpp:356] mnist -> label
I0219 20:24:27.259891 101920 net.cpp:96] Setting up mnist
I0219 20:24:27.259891 101920 data_layer.cpp:46] Opening leveldb mnist-train-leveldb
I0219 20:24:27.266892 101920 data_layer.cpp:130] output data size: 100,1,28,28
I0219 20:24:27.266892 101920 net.cpp:103] Top shape: 100 1 28 28 (78400)
I0219 20:24:27.266892 101920 net.cpp:103] Top shape: 100 1 1 1 (100)
I0219 20:24:27.266892 101920 net.cpp:67] Creating Layer conv1
I0219 20:24:27.266892 101920 net.cpp:394] conv1 <- data
I0219 20:24:27.266892 101920 net.cpp:356] conv1 -> conv1
I0219 20:24:27.266892 101920 net.cpp:96] Setting up conv1
I0219 20:24:27.529907 101920 net.cpp:103] Top shape: 100 20 24 24 (1152000)
I0219 20:24:27.529907 101920 net.cpp:67] Creating Layer conv1_bn
I0219 20:24:27.529907 101920 net.cpp:394] conv1_bn <- conv1
I0219 20:24:27.529907 101920 net.cpp:356] conv1_bn -> conv1_bn
I0219 20:24:27.529907 101920 net.cpp:96] Setting up conv1_bn
I0219 20:24:27.529907 101920 net.cpp:103] Top shape: 100 20 24 24 (1152000)
I0219 20:24:27.529907 101920 net.cpp:67] Creating Layer pool1
I0219 20:24:27.529907 101920 net.cpp:394] pool1 <- conv1_bn
I0219 20:24:27.529907 101920 net.cpp:356] pool1 -> pool1
I0219 20:24:27.529907 101920 net.cpp:96] Setting up pool1
I0219 20:24:27.529907 101920 net.cpp:103] Top shape: 100 20 12 12 (288000)
I0219 20:24:27.529907 101920 net.cpp:67] Creating Layer conv2
I0219 20:24:27.529907 101920 net.cpp:394] conv2 <- pool1
I0219 20:24:27.529907 101920 net.cpp:356] conv2 -> conv2
I0219 20:24:27.529907 101920 net.cpp:96] Setting up conv2
I0219 20:24:27.529907 101920 net.cpp:103] Top shape: 100 50 8 8 (320000)
I0219 20:24:27.530907 101920 net.cpp:67] Creating Layer conv2_bn
I0219 20:24:27.530907 101920 net.cpp:394] conv2_bn <- conv2
I0219 20:24:27.530907 101920 net.cpp:356] conv2_bn -> conv2_bn
I0219 20:24:27.530907 101920 net.cpp:96] Setting up conv2_bn
I0219 20:24:27.530907 101920 net.cpp:103] Top shape: 100 50 8 8 (320000)
I0219 20:24:27.530907 101920 net.cpp:67] Creating Layer pool2
I0219 20:24:27.530907 101920 net.cpp:394] pool2 <- conv2_bn
I0219 20:24:27.530907 101920 net.cpp:356] pool2 -> pool2
I0219 20:24:27.530907 101920 net.cpp:96] Setting up pool2
I0219 20:24:27.530907 101920 net.cpp:103] Top shape: 100 50 4 4 (80000)
I0219 20:24:27.530907 101920 net.cpp:67] Creating Layer ip1
I0219 20:24:27.530907 101920 net.cpp:394] ip1 <- pool2
I0219 20:24:27.530907 101920 net.cpp:356] ip1 -> ip1
I0219 20:24:27.530907 101920 net.cpp:96] Setting up ip1
I0219 20:24:27.533907 101920 net.cpp:103] Top shape: 100 500 1 1 (50000)
I0219 20:24:27.533907 101920 net.cpp:67] Creating Layer relu1
I0219 20:24:27.533907 101920 net.cpp:394] relu1 <- ip1
I0219 20:24:27.533907 101920 net.cpp:345] relu1 -> ip1 (in-place)
I0219 20:24:27.533907 101920 net.cpp:96] Setting up relu1
I0219 20:24:27.533907 101920 net.cpp:103] Top shape: 100 500 1 1 (50000)
I0219 20:24:27.533907 101920 net.cpp:67] Creating Layer ip2
I0219 20:24:27.534907 101920 net.cpp:394] ip2 <- ip1
I0219 20:24:27.534907 101920 net.cpp:356] ip2 -> ip2
I0219 20:24:27.534907 101920 net.cpp:96] Setting up ip2
I0219 20:24:27.534907 101920 net.cpp:103] Top shape: 100 10 1 1 (1000)
I0219 20:24:27.534907 101920 net.cpp:67] Creating Layer loss
I0219 20:24:27.534907 101920 net.cpp:394] loss <- ip2
I0219 20:24:27.534907 101920 net.cpp:394] loss <- label
I0219 20:24:27.534907 101920 net.cpp:356] loss -> (automatic)
I0219 20:24:27.534907 101920 net.cpp:96] Setting up loss
I0219 20:24:27.534907 101920 net.cpp:103] Top shape: 1 1 1 1 (1)
I0219 20:24:27.534907 101920 net.cpp:109]     with loss weight 1
I0219 20:24:27.534907 101920 net.cpp:170] loss needs backward computation.
I0219 20:24:27.534907 101920 net.cpp:170] ip2 needs backward computation.
I0219 20:24:27.534907 101920 net.cpp:170] relu1 needs backward computation.
I0219 20:24:27.534907 101920 net.cpp:170] ip1 needs backward computation.
I0219 20:24:27.534907 101920 net.cpp:170] pool2 needs backward computation.
I0219 20:24:27.534907 101920 net.cpp:170] conv2_bn needs backward computation.
I0219 20:24:27.534907 101920 net.cpp:170] conv2 needs backward computation.
I0219 20:24:27.534907 101920 net.cpp:170] pool1 needs backward computation.
I0219 20:24:27.534907 101920 net.cpp:170] conv1_bn needs backward computation.
I0219 20:24:27.534907 101920 net.cpp:170] conv1 needs backward computation.
I0219 20:24:27.534907 101920 net.cpp:172] mnist does not need backward computation.
I0219 20:24:27.534907 101920 net.cpp:467] Collecting Learning Rate and Weight Decay.
I0219 20:24:27.534907 101920 net.cpp:219] Network initialization done.
I0219 20:24:27.534907 101920 net.cpp:220] Memory required for data: 13966004
E0219 20:24:27.534907 101920 upgrade_proto.cpp:603] Attempting to upgrade input file specified using deprecated transformation parameters: lenet_BN_train_valid.prototxt
I0219 20:24:27.534907 101920 upgrade_proto.cpp:606] Successfully upgraded file specified using deprecated data transformation parameters.
E0219 20:24:27.534907 101920 upgrade_proto.cpp:608] Note that future Caffe releases will only support transform_param messages for transformation fields.
I0219 20:24:27.534907 101920 solver.cpp:161] Creating test net (#0) specified by net file: lenet_BN_train_valid.prototxt
I0219 20:24:27.535907 101920 net.cpp:275] The NetState phase (1) differed from the phase (0) specified by a rule in layer mnist
I0219 20:24:27.535907 101920 net.cpp:275] The NetState phase (1) differed from the phase (0) specified by a rule in layer loss
I0219 20:24:27.535907 101920 net.cpp:39] Initializing net from parameters: 
name: "LeNet"
layers {
  top: "data"
  top: "label"
  name: "mnist"
  type: DATA
  data_param {
    source: "mnist-test-leveldb"
    batch_size: 100
  }
  include {
    phase: TEST
  }
  transform_param {
    scale: 0.00390625
  }
}
layers {
  bottom: "data"
  top: "conv1"
  name: "conv1"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  bottom: "conv1"
  top: "conv1_bn"
  name: "conv1_bn"
  type: BN
  blobs_lr: 1
  blobs_lr: 1
  weight_decay: 0
  weight_decay: 0
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  bottom: "conv1_bn"
  top: "pool1"
  name: "pool1"
  type: POOLING
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layers {
  bottom: "pool1"
  top: "conv2"
  name: "conv2"
  type: CONVOLUTION
  blobs_lr: 1
  blobs_lr: 2
  convolution_param {
    num_output: 50
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  bottom: "conv2"
  top: "conv2_bn"
  name: "conv2_bn"
  type: BN
  blobs_lr: 1
  blobs_lr: 1
  weight_decay: 0
  weight_decay: 0
  bn_param {
    scale_filler {
      type: "constant"
      value: 1
    }
    shift_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  bottom: "conv2_bn"
  top: "pool2"
  name: "pool2"
  type: POOLING
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layers {
  bottom: "pool2"
  top: "ip1"
  name: "ip1"
  type: INNER_PRODUCT
  blobs_lr: 1
  blobs_lr: 2
  inner_product_param {
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  bottom: "ip1"
  top: "ip1"
  name: "relu1"
  type: RELU
}
layers {
  bottom: "ip1"
  top: "ip2"
  name: "ip2"
  type: INNER_PRODUCT
  blobs_lr: 1
  blobs_lr: 2
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layers {
  bottom: "ip2"
  top: "prob"
  name: "prob"
  type: SOFTMAX
  include {
    phase: TEST
  }
}
layers {
  bottom: "prob"
  bottom: "label"
  top: "accuracy"
  name: "accuracy"
  type: ACCURACY
  include {
    phase: TEST
  }
}
state {
  phase: TEST
}
I0219 20:24:27.535907 101920 net.cpp:67] Creating Layer mnist
I0219 20:24:27.571909 101920 net.cpp:356] mnist -> data
I0219 20:24:27.571909 101920 net.cpp:356] mnist -> label
I0219 20:24:27.571909 101920 net.cpp:96] Setting up mnist
I0219 20:24:27.571909 101920 data_layer.cpp:46] Opening leveldb mnist-test-leveldb
I0219 20:24:27.578910 101920 data_layer.cpp:130] output data size: 100,1,28,28
I0219 20:24:27.578910 101920 net.cpp:103] Top shape: 100 1 28 28 (78400)
I0219 20:24:27.578910 101920 net.cpp:103] Top shape: 100 1 1 1 (100)
I0219 20:24:27.578910 101920 net.cpp:67] Creating Layer conv1
I0219 20:24:27.578910 101920 net.cpp:394] conv1 <- data
I0219 20:24:27.578910 101920 net.cpp:356] conv1 -> conv1
I0219 20:24:27.578910 101920 net.cpp:96] Setting up conv1
I0219 20:24:27.578910 101920 net.cpp:103] Top shape: 100 20 24 24 (1152000)
I0219 20:24:27.578910 101920 net.cpp:67] Creating Layer conv1_bn
I0219 20:24:27.578910 101920 net.cpp:394] conv1_bn <- conv1
I0219 20:24:27.578910 101920 net.cpp:356] conv1_bn -> conv1_bn
I0219 20:24:27.578910 101920 net.cpp:96] Setting up conv1_bn
I0219 20:24:27.578910 101920 net.cpp:103] Top shape: 100 20 24 24 (1152000)
I0219 20:24:27.578910 101920 net.cpp:67] Creating Layer pool1
I0219 20:24:27.578910 101920 net.cpp:394] pool1 <- conv1_bn
I0219 20:24:27.578910 101920 net.cpp:356] pool1 -> pool1
I0219 20:24:27.578910 101920 net.cpp:96] Setting up pool1
I0219 20:24:27.578910 101920 net.cpp:103] Top shape: 100 20 12 12 (288000)
I0219 20:24:27.578910 101920 net.cpp:67] Creating Layer conv2
I0219 20:24:27.578910 101920 net.cpp:394] conv2 <- pool1
I0219 20:24:27.578910 101920 net.cpp:356] conv2 -> conv2
I0219 20:24:27.578910 101920 net.cpp:96] Setting up conv2
I0219 20:24:27.579910 101920 net.cpp:103] Top shape: 100 50 8 8 (320000)
I0219 20:24:27.579910 101920 net.cpp:67] Creating Layer conv2_bn
I0219 20:24:27.579910 101920 net.cpp:394] conv2_bn <- conv2
I0219 20:24:27.579910 101920 net.cpp:356] conv2_bn -> conv2_bn
I0219 20:24:27.579910 101920 net.cpp:96] Setting up conv2_bn
I0219 20:24:27.579910 101920 net.cpp:103] Top shape: 100 50 8 8 (320000)
I0219 20:24:27.579910 101920 net.cpp:67] Creating Layer pool2
I0219 20:24:27.579910 101920 net.cpp:394] pool2 <- conv2_bn
I0219 20:24:27.579910 101920 net.cpp:356] pool2 -> pool2
I0219 20:24:27.579910 101920 net.cpp:96] Setting up pool2
I0219 20:24:27.579910 101920 net.cpp:103] Top shape: 100 50 4 4 (80000)
I0219 20:24:27.579910 101920 net.cpp:67] Creating Layer ip1
I0219 20:24:27.579910 101920 net.cpp:394] ip1 <- pool2
I0219 20:24:27.579910 101920 net.cpp:356] ip1 -> ip1
I0219 20:24:27.579910 101920 net.cpp:96] Setting up ip1
I0219 20:24:27.582911 101920 net.cpp:103] Top shape: 100 500 1 1 (50000)
I0219 20:24:27.582911 101920 net.cpp:67] Creating Layer relu1
I0219 20:24:27.582911 101920 net.cpp:394] relu1 <- ip1
I0219 20:24:27.582911 101920 net.cpp:345] relu1 -> ip1 (in-place)
I0219 20:24:27.582911 101920 net.cpp:96] Setting up relu1
I0219 20:24:27.582911 101920 net.cpp:103] Top shape: 100 500 1 1 (50000)
I0219 20:24:27.582911 101920 net.cpp:67] Creating Layer ip2
I0219 20:24:27.582911 101920 net.cpp:394] ip2 <- ip1
I0219 20:24:27.582911 101920 net.cpp:356] ip2 -> ip2
I0219 20:24:27.582911 101920 net.cpp:96] Setting up ip2
I0219 20:24:27.582911 101920 net.cpp:103] Top shape: 100 10 1 1 (1000)
I0219 20:24:27.582911 101920 net.cpp:67] Creating Layer prob
I0219 20:24:27.582911 101920 net.cpp:394] prob <- ip2
I0219 20:24:27.582911 101920 net.cpp:356] prob -> prob
I0219 20:24:27.582911 101920 net.cpp:96] Setting up prob
I0219 20:24:27.582911 101920 net.cpp:103] Top shape: 100 10 1 1 (1000)
I0219 20:24:27.582911 101920 net.cpp:67] Creating Layer accuracy
I0219 20:24:27.582911 101920 net.cpp:394] accuracy <- prob
I0219 20:24:27.582911 101920 net.cpp:394] accuracy <- label
I0219 20:24:27.582911 101920 net.cpp:356] accuracy -> accuracy
I0219 20:24:27.582911 101920 net.cpp:96] Setting up accuracy
I0219 20:24:27.582911 101920 net.cpp:103] Top shape: 1 1 1 1 (1)
I0219 20:24:27.582911 101920 net.cpp:172] accuracy does not need backward computation.
I0219 20:24:27.582911 101920 net.cpp:172] prob does not need backward computation.
I0219 20:24:27.635913 101920 net.cpp:172] ip2 does not need backward computation.
I0219 20:24:27.635913 101920 net.cpp:172] relu1 does not need backward computation.
I0219 20:24:27.635913 101920 net.cpp:172] ip1 does not need backward computation.
I0219 20:24:27.635913 101920 net.cpp:172] pool2 does not need backward computation.
I0219 20:24:27.635913 101920 net.cpp:172] conv2_bn does not need backward computation.
I0219 20:24:27.635913 101920 net.cpp:172] conv2 does not need backward computation.
I0219 20:24:27.635913 101920 net.cpp:172] pool1 does not need backward computation.
I0219 20:24:27.635913 101920 net.cpp:172] conv1_bn does not need backward computation.
I0219 20:24:27.635913 101920 net.cpp:172] conv1 does not need backward computation.
I0219 20:24:27.635913 101920 net.cpp:172] mnist does not need backward computation.
I0219 20:24:27.635913 101920 net.cpp:208] This network produces output accuracy
I0219 20:24:27.635913 101920 net.cpp:467] Collecting Learning Rate and Weight Decay.
I0219 20:24:27.635913 101920 net.cpp:219] Network initialization done.
I0219 20:24:27.635913 101920 net.cpp:220] Memory required for data: 13970004
I0219 20:24:27.635913 101920 solver.cpp:51] Solver scaffolding done.
I0219 20:24:27.635913 101920 solver.cpp:170] Solving LeNet
I0219 20:24:27.635913 101920 solver.cpp:287] Iteration 0, Testing net (#0)
I0219 20:24:29.118999 101920 solver.cpp:338]     Test net output #0: accuracy = 0.0916
I0219 20:24:29.138000 101920 solver.cpp:231] Iteration 0, loss = 2.30219
I0219 20:24:29.138000 101920 solver.cpp:442] Iteration 0, lr = 0.01
I0219 20:24:32.218175 101920 solver.cpp:231] Iteration 100, loss = 0.323378
I0219 20:24:32.218175 101920 solver.cpp:442] Iteration 100, lr = 0.00992565
I0219 20:24:35.126341 101920 solver.cpp:231] Iteration 200, loss = 0.23759
I0219 20:24:35.126341 101920 solver.cpp:442] Iteration 200, lr = 0.00985258
I0219 20:24:38.196517 101920 solver.cpp:231] Iteration 300, loss = 0.149019
I0219 20:24:38.196517 101920 solver.cpp:442] Iteration 300, lr = 0.00978075
I0219 20:24:41.343698 101920 solver.cpp:231] Iteration 400, loss = 0.100363
I0219 20:24:41.343698 101920 solver.cpp:442] Iteration 400, lr = 0.00971013
I0219 20:24:44.105855 101920 solver.cpp:287] Iteration 500, Testing net (#0)
I0219 20:24:45.333925 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9754
I0219 20:24:45.338927 101920 solver.cpp:231] Iteration 500, loss = 0.0856673
I0219 20:24:45.338927 101920 solver.cpp:442] Iteration 500, lr = 0.00964069
I0219 20:24:48.222090 101920 solver.cpp:231] Iteration 600, loss = 0.137357
I0219 20:24:48.222090 101920 solver.cpp:442] Iteration 600, lr = 0.0095724
I0219 20:24:50.863242 101920 solver.cpp:231] Iteration 700, loss = 0.0835702
I0219 20:24:50.863242 101920 solver.cpp:442] Iteration 700, lr = 0.00950522
I0219 20:24:53.670403 101920 solver.cpp:231] Iteration 800, loss = 0.114954
I0219 20:24:53.670403 101920 solver.cpp:442] Iteration 800, lr = 0.00943913
I0219 20:24:56.469563 101920 solver.cpp:231] Iteration 900, loss = 0.0932496
I0219 20:24:56.470562 101920 solver.cpp:442] Iteration 900, lr = 0.00937411
I0219 20:24:59.068711 101920 solver.cpp:287] Iteration 1000, Testing net (#0)
I0219 20:25:00.195775 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9816
I0219 20:25:00.206776 101920 solver.cpp:231] Iteration 1000, loss = 0.0435796
I0219 20:25:00.206776 101920 solver.cpp:442] Iteration 1000, lr = 0.00931012
I0219 20:25:03.094941 101920 solver.cpp:231] Iteration 1100, loss = 0.0376325
I0219 20:25:03.094941 101920 solver.cpp:442] Iteration 1100, lr = 0.00924715
I0219 20:25:05.844099 101920 solver.cpp:231] Iteration 1200, loss = 0.0942016
I0219 20:25:05.844099 101920 solver.cpp:442] Iteration 1200, lr = 0.00918515
I0219 20:25:08.707262 101920 solver.cpp:231] Iteration 1300, loss = 0.0521515
I0219 20:25:08.707262 101920 solver.cpp:442] Iteration 1300, lr = 0.00912412
I0219 20:25:11.303411 101920 solver.cpp:231] Iteration 1400, loss = 0.0771083
I0219 20:25:11.303411 101920 solver.cpp:442] Iteration 1400, lr = 0.00906403
I0219 20:25:13.987565 101920 solver.cpp:287] Iteration 1500, Testing net (#0)
I0219 20:25:15.120630 101920 solver.cpp:338]     Test net output #0: accuracy = 0.985
I0219 20:25:15.197633 101920 solver.cpp:231] Iteration 1500, loss = 0.06446
I0219 20:25:15.197633 101920 solver.cpp:442] Iteration 1500, lr = 0.00900485
I0219 20:25:17.791782 101920 solver.cpp:231] Iteration 1600, loss = 0.031064
I0219 20:25:17.791782 101920 solver.cpp:442] Iteration 1600, lr = 0.00894657
I0219 20:25:20.486937 101920 solver.cpp:231] Iteration 1700, loss = 0.0331084
I0219 20:25:20.486937 101920 solver.cpp:442] Iteration 1700, lr = 0.00888916
I0219 20:25:23.185091 101920 solver.cpp:231] Iteration 1800, loss = 0.0699748
I0219 20:25:23.185091 101920 solver.cpp:442] Iteration 1800, lr = 0.0088326
I0219 20:25:25.974251 101920 solver.cpp:231] Iteration 1900, loss = 0.0318849
I0219 20:25:25.974251 101920 solver.cpp:442] Iteration 1900, lr = 0.00877687
I0219 20:25:28.543397 101920 solver.cpp:287] Iteration 2000, Testing net (#0)
I0219 20:25:29.640460 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9878
I0219 20:25:29.661461 101920 solver.cpp:231] Iteration 2000, loss = 0.0629812
I0219 20:25:29.661461 101920 solver.cpp:442] Iteration 2000, lr = 0.00872196
I0219 20:25:32.453620 101920 solver.cpp:231] Iteration 2100, loss = 0.0518312
I0219 20:25:32.453620 101920 solver.cpp:442] Iteration 2100, lr = 0.00866784
I0219 20:25:35.086771 101920 solver.cpp:231] Iteration 2200, loss = 0.0227572
I0219 20:25:35.086771 101920 solver.cpp:442] Iteration 2200, lr = 0.0086145
I0219 20:25:37.841929 101920 solver.cpp:231] Iteration 2300, loss = 0.0244286
I0219 20:25:37.841929 101920 solver.cpp:442] Iteration 2300, lr = 0.00856192
I0219 20:25:40.717093 101920 solver.cpp:231] Iteration 2400, loss = 0.0507805
I0219 20:25:40.717093 101920 solver.cpp:442] Iteration 2400, lr = 0.00851008
I0219 20:25:43.349244 101920 solver.cpp:287] Iteration 2500, Testing net (#0)
I0219 20:25:44.497309 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9859
I0219 20:25:44.510310 101920 solver.cpp:231] Iteration 2500, loss = 0.0219777
I0219 20:25:44.510310 101920 solver.cpp:442] Iteration 2500, lr = 0.00845897
I0219 20:25:47.213465 101920 solver.cpp:231] Iteration 2600, loss = 0.0499731
I0219 20:25:47.213465 101920 solver.cpp:442] Iteration 2600, lr = 0.00840857
I0219 20:25:49.963623 101920 solver.cpp:231] Iteration 2700, loss = 0.0391577
I0219 20:25:49.963623 101920 solver.cpp:442] Iteration 2700, lr = 0.00835886
I0219 20:25:52.668777 101920 solver.cpp:231] Iteration 2800, loss = 0.0185332
I0219 20:25:52.668777 101920 solver.cpp:442] Iteration 2800, lr = 0.00830984
I0219 20:25:55.485939 101920 solver.cpp:231] Iteration 2900, loss = 0.0167396
I0219 20:25:55.485939 101920 solver.cpp:442] Iteration 2900, lr = 0.00826148
I0219 20:25:58.029083 101920 solver.cpp:287] Iteration 3000, Testing net (#0)
I0219 20:25:59.158149 101920 solver.cpp:338]     Test net output #0: accuracy = 0.987
I0219 20:25:59.163148 101920 solver.cpp:231] Iteration 3000, loss = 0.0298753
I0219 20:25:59.163148 101920 solver.cpp:442] Iteration 3000, lr = 0.00821377
I0219 20:26:01.771297 101920 solver.cpp:231] Iteration 3100, loss = 0.0173053
I0219 20:26:01.771297 101920 solver.cpp:442] Iteration 3100, lr = 0.0081667
I0219 20:26:04.541456 101920 solver.cpp:231] Iteration 3200, loss = 0.0369976
I0219 20:26:04.541456 101920 solver.cpp:442] Iteration 3200, lr = 0.00812025
I0219 20:26:07.370617 101920 solver.cpp:231] Iteration 3300, loss = 0.0333898
I0219 20:26:07.371618 101920 solver.cpp:442] Iteration 3300, lr = 0.00807442
I0219 20:26:10.047771 101920 solver.cpp:231] Iteration 3400, loss = 0.015276
I0219 20:26:10.048771 101920 solver.cpp:442] Iteration 3400, lr = 0.00802918
I0219 20:26:12.734925 101920 solver.cpp:287] Iteration 3500, Testing net (#0)
I0219 20:26:13.852988 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9898
I0219 20:26:13.856988 101920 solver.cpp:231] Iteration 3500, loss = 0.00888279
I0219 20:26:13.856988 101920 solver.cpp:442] Iteration 3500, lr = 0.00798454
I0219 20:26:16.533143 101920 solver.cpp:231] Iteration 3600, loss = 0.0153337
I0219 20:26:16.533143 101920 solver.cpp:442] Iteration 3600, lr = 0.00794046
I0219 20:26:19.231297 101920 solver.cpp:231] Iteration 3700, loss = 0.0163696
I0219 20:26:19.231297 101920 solver.cpp:442] Iteration 3700, lr = 0.00789695
I0219 20:26:21.892448 101920 solver.cpp:231] Iteration 3800, loss = 0.0274265
I0219 20:26:21.892448 101920 solver.cpp:442] Iteration 3800, lr = 0.007854
I0219 20:26:24.654606 101920 solver.cpp:231] Iteration 3900, loss = 0.0280846
I0219 20:26:24.654606 101920 solver.cpp:442] Iteration 3900, lr = 0.00781158
I0219 20:26:27.394763 101920 solver.cpp:287] Iteration 4000, Testing net (#0)
I0219 20:26:28.608832 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9892
I0219 20:26:28.620833 101920 solver.cpp:231] Iteration 4000, loss = 0.0119719
I0219 20:26:28.620833 101920 solver.cpp:442] Iteration 4000, lr = 0.00776969
I0219 20:26:31.207981 101920 solver.cpp:231] Iteration 4100, loss = 0.00557243
I0219 20:26:31.207981 101920 solver.cpp:442] Iteration 4100, lr = 0.00772833
I0219 20:26:33.999141 101920 solver.cpp:231] Iteration 4200, loss = 0.00751942
I0219 20:26:33.999141 101920 solver.cpp:442] Iteration 4200, lr = 0.00768748
I0219 20:26:36.629292 101920 solver.cpp:231] Iteration 4300, loss = 0.0159704
I0219 20:26:36.629292 101920 solver.cpp:442] Iteration 4300, lr = 0.00764712
I0219 20:26:39.398449 101920 solver.cpp:231] Iteration 4400, loss = 0.0195673
I0219 20:26:39.398449 101920 solver.cpp:442] Iteration 4400, lr = 0.00760726
I0219 20:26:42.017599 101920 solver.cpp:287] Iteration 4500, Testing net (#0)
I0219 20:26:43.123662 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9899
I0219 20:26:43.137663 101920 solver.cpp:231] Iteration 4500, loss = 0.0226567
I0219 20:26:43.137663 101920 solver.cpp:442] Iteration 4500, lr = 0.00756788
I0219 20:26:45.888821 101920 solver.cpp:231] Iteration 4600, loss = 0.00898327
I0219 20:26:45.888821 101920 solver.cpp:442] Iteration 4600, lr = 0.00752897
I0219 20:26:48.785986 101920 solver.cpp:231] Iteration 4700, loss = 0.00437807
I0219 20:26:48.786986 101920 solver.cpp:442] Iteration 4700, lr = 0.00749052
I0219 20:26:51.433138 101920 solver.cpp:231] Iteration 4800, loss = 0.00555685
I0219 20:26:51.433138 101920 solver.cpp:442] Iteration 4800, lr = 0.00745253
I0219 20:26:54.230298 101920 solver.cpp:231] Iteration 4900, loss = 0.0127988
I0219 20:26:54.230298 101920 solver.cpp:442] Iteration 4900, lr = 0.00741498
I0219 20:26:56.887450 101920 solver.cpp:356] Snapshotting to lenet_iter_5000.caffemodel
I0219 20:26:56.895450 101920 solver.cpp:363] Snapshotting solver state to lenet_iter_5000.caffemodel.solverstate
I0219 20:26:56.899451 101920 solver.cpp:287] Iteration 5000, Testing net (#0)
I0219 20:26:58.003515 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9908
I0219 20:26:58.013514 101920 solver.cpp:231] Iteration 5000, loss = 0.015663
I0219 20:26:58.013514 101920 solver.cpp:442] Iteration 5000, lr = 0.00737788
I0219 20:27:00.788673 101920 solver.cpp:231] Iteration 5100, loss = 0.0169028
I0219 20:27:00.788673 101920 solver.cpp:442] Iteration 5100, lr = 0.0073412
I0219 20:27:03.455826 101920 solver.cpp:231] Iteration 5200, loss = 0.00675082
I0219 20:27:03.455826 101920 solver.cpp:442] Iteration 5200, lr = 0.00730495
I0219 20:27:06.232985 101920 solver.cpp:231] Iteration 5300, loss = 0.00372812
I0219 20:27:06.232985 101920 solver.cpp:442] Iteration 5300, lr = 0.00726911
I0219 20:27:08.875135 101920 solver.cpp:231] Iteration 5400, loss = 0.00410843
I0219 20:27:08.875135 101920 solver.cpp:442] Iteration 5400, lr = 0.00723368
I0219 20:27:11.657294 101920 solver.cpp:287] Iteration 5500, Testing net (#0)
I0219 20:27:12.787359 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9887
I0219 20:27:12.806360 101920 solver.cpp:231] Iteration 5500, loss = 0.0117428
I0219 20:27:12.807360 101920 solver.cpp:442] Iteration 5500, lr = 0.00719865
I0219 20:27:15.413509 101920 solver.cpp:231] Iteration 5600, loss = 0.0132238
I0219 20:27:15.413509 101920 solver.cpp:442] Iteration 5600, lr = 0.00716402
I0219 20:27:18.219671 101920 solver.cpp:231] Iteration 5700, loss = 0.0137579
I0219 20:27:18.219671 101920 solver.cpp:442] Iteration 5700, lr = 0.00712977
I0219 20:27:20.808818 101920 solver.cpp:231] Iteration 5800, loss = 0.00497267
I0219 20:27:20.808818 101920 solver.cpp:442] Iteration 5800, lr = 0.0070959
I0219 20:27:23.590977 101920 solver.cpp:231] Iteration 5900, loss = 0.00294243
I0219 20:27:23.590977 101920 solver.cpp:442] Iteration 5900, lr = 0.0070624
I0219 20:27:26.161124 101920 solver.cpp:287] Iteration 6000, Testing net (#0)
I0219 20:27:27.281188 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9897
I0219 20:27:27.287189 101920 solver.cpp:231] Iteration 6000, loss = 0.00346468
I0219 20:27:27.287189 101920 solver.cpp:442] Iteration 6000, lr = 0.00702927
I0219 20:27:30.089349 101920 solver.cpp:231] Iteration 6100, loss = 0.0104201
I0219 20:27:30.089349 101920 solver.cpp:442] Iteration 6100, lr = 0.0069965
I0219 20:27:32.643496 101920 solver.cpp:231] Iteration 6200, loss = 0.0109253
I0219 20:27:32.643496 101920 solver.cpp:442] Iteration 6200, lr = 0.00696408
I0219 20:27:35.436655 101920 solver.cpp:231] Iteration 6300, loss = 0.0113819
I0219 20:27:35.436655 101920 solver.cpp:442] Iteration 6300, lr = 0.00693201
I0219 20:27:38.090806 101920 solver.cpp:231] Iteration 6400, loss = 0.00391983
I0219 20:27:38.090806 101920 solver.cpp:442] Iteration 6400, lr = 0.00690029
I0219 20:27:40.816962 101920 solver.cpp:287] Iteration 6500, Testing net (#0)
I0219 20:27:41.913025 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9903
I0219 20:27:41.925026 101920 solver.cpp:231] Iteration 6500, loss = 0.0024418
I0219 20:27:41.925026 101920 solver.cpp:442] Iteration 6500, lr = 0.0068689
I0219 20:27:44.510174 101920 solver.cpp:231] Iteration 6600, loss = 0.00319171
I0219 20:27:44.510174 101920 solver.cpp:442] Iteration 6600, lr = 0.00683784
I0219 20:27:47.298333 101920 solver.cpp:231] Iteration 6700, loss = 0.00863161
I0219 20:27:47.298333 101920 solver.cpp:442] Iteration 6700, lr = 0.00680711
I0219 20:27:49.879482 101920 solver.cpp:231] Iteration 6800, loss = 0.00959494
I0219 20:27:49.879482 101920 solver.cpp:442] Iteration 6800, lr = 0.0067767
I0219 20:27:52.699642 101920 solver.cpp:231] Iteration 6900, loss = 0.0102416
I0219 20:27:52.699642 101920 solver.cpp:442] Iteration 6900, lr = 0.0067466
I0219 20:27:55.473801 101920 solver.cpp:287] Iteration 7000, Testing net (#0)
I0219 20:27:56.587864 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9914
I0219 20:27:56.663869 101920 solver.cpp:231] Iteration 7000, loss = 0.0030493
I0219 20:27:56.663869 101920 solver.cpp:442] Iteration 7000, lr = 0.00671681
I0219 20:27:59.254017 101920 solver.cpp:231] Iteration 7100, loss = 0.00213348
I0219 20:27:59.254017 101920 solver.cpp:442] Iteration 7100, lr = 0.00668733
I0219 20:28:02.059177 101920 solver.cpp:231] Iteration 7200, loss = 0.00286794
I0219 20:28:02.059177 101920 solver.cpp:442] Iteration 7200, lr = 0.00665815
I0219 20:28:04.666326 101920 solver.cpp:231] Iteration 7300, loss = 0.00784281
I0219 20:28:04.666326 101920 solver.cpp:442] Iteration 7300, lr = 0.00662927
I0219 20:28:07.445485 101920 solver.cpp:231] Iteration 7400, loss = 0.00841386
I0219 20:28:07.445485 101920 solver.cpp:442] Iteration 7400, lr = 0.00660067
I0219 20:28:09.988631 101920 solver.cpp:287] Iteration 7500, Testing net (#0)
I0219 20:28:11.104696 101920 solver.cpp:338]     Test net output #0: accuracy = 0.991
I0219 20:28:11.110695 101920 solver.cpp:231] Iteration 7500, loss = 0.00848323
I0219 20:28:11.110695 101920 solver.cpp:442] Iteration 7500, lr = 0.00657236
I0219 20:28:13.898854 101920 solver.cpp:231] Iteration 7600, loss = 0.00240813
I0219 20:28:13.898854 101920 solver.cpp:442] Iteration 7600, lr = 0.00654433
I0219 20:28:16.455001 101920 solver.cpp:231] Iteration 7700, loss = 0.00191419
I0219 20:28:16.455001 101920 solver.cpp:442] Iteration 7700, lr = 0.00651658
I0219 20:28:19.070150 101920 solver.cpp:231] Iteration 7800, loss = 0.00295593
I0219 20:28:19.070150 101920 solver.cpp:442] Iteration 7800, lr = 0.00648911
I0219 20:28:21.787307 101920 solver.cpp:231] Iteration 7900, loss = 0.00673551
I0219 20:28:21.787307 101920 solver.cpp:442] Iteration 7900, lr = 0.0064619
I0219 20:28:24.451458 101920 solver.cpp:287] Iteration 8000, Testing net (#0)
I0219 20:28:25.621526 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9913
I0219 20:28:25.665527 101920 solver.cpp:231] Iteration 8000, loss = 0.00720231
I0219 20:28:25.665527 101920 solver.cpp:442] Iteration 8000, lr = 0.00643496
I0219 20:28:28.343682 101920 solver.cpp:231] Iteration 8100, loss = 0.00756929
I0219 20:28:28.343682 101920 solver.cpp:442] Iteration 8100, lr = 0.00640827
I0219 20:28:31.128840 101920 solver.cpp:231] Iteration 8200, loss = 0.00201314
I0219 20:28:31.128840 101920 solver.cpp:442] Iteration 8200, lr = 0.00638185
I0219 20:28:33.700988 101920 solver.cpp:231] Iteration 8300, loss = 0.00172469
I0219 20:28:33.700988 101920 solver.cpp:442] Iteration 8300, lr = 0.00635568
I0219 20:28:36.505147 101920 solver.cpp:231] Iteration 8400, loss = 0.00279293
I0219 20:28:36.505147 101920 solver.cpp:442] Iteration 8400, lr = 0.00632975
I0219 20:28:39.076295 101920 solver.cpp:287] Iteration 8500, Testing net (#0)
I0219 20:28:40.196359 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9904
I0219 20:28:40.201359 101920 solver.cpp:231] Iteration 8500, loss = 0.00631739
I0219 20:28:40.201359 101920 solver.cpp:442] Iteration 8500, lr = 0.00630407
I0219 20:28:42.982518 101920 solver.cpp:231] Iteration 8600, loss = 0.00666775
I0219 20:28:42.982518 101920 solver.cpp:442] Iteration 8600, lr = 0.00627864
I0219 20:28:45.588667 101920 solver.cpp:231] Iteration 8700, loss = 0.00658094
I0219 20:28:45.588667 101920 solver.cpp:442] Iteration 8700, lr = 0.00625344
I0219 20:28:48.405828 101920 solver.cpp:231] Iteration 8800, loss = 0.00169822
I0219 20:28:48.406828 101920 solver.cpp:442] Iteration 8800, lr = 0.00622847
I0219 20:28:51.037979 101920 solver.cpp:231] Iteration 8900, loss = 0.00170645
I0219 20:28:51.037979 101920 solver.cpp:442] Iteration 8900, lr = 0.00620374
I0219 20:28:53.760134 101920 solver.cpp:287] Iteration 9000, Testing net (#0)
I0219 20:28:54.887199 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9902
I0219 20:28:54.894199 101920 solver.cpp:231] Iteration 9000, loss = 0.00284398
I0219 20:28:54.894199 101920 solver.cpp:442] Iteration 9000, lr = 0.00617924
I0219 20:28:57.467346 101920 solver.cpp:231] Iteration 9100, loss = 0.00578002
I0219 20:28:57.467346 101920 solver.cpp:442] Iteration 9100, lr = 0.00615496
I0219 20:29:00.250506 101920 solver.cpp:231] Iteration 9200, loss = 0.00624887
I0219 20:29:00.250506 101920 solver.cpp:442] Iteration 9200, lr = 0.0061309
I0219 20:29:02.771651 101920 solver.cpp:231] Iteration 9300, loss = 0.00631996
I0219 20:29:02.771651 101920 solver.cpp:442] Iteration 9300, lr = 0.00610706
I0219 20:29:05.428802 101920 solver.cpp:231] Iteration 9400, loss = 0.00152145
I0219 20:29:05.428802 101920 solver.cpp:442] Iteration 9400, lr = 0.00608343
I0219 20:29:08.158958 101920 solver.cpp:287] Iteration 9500, Testing net (#0)
I0219 20:29:09.268021 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9905
I0219 20:29:09.274022 101920 solver.cpp:231] Iteration 9500, loss = 0.00170842
I0219 20:29:09.274022 101920 solver.cpp:442] Iteration 9500, lr = 0.00606002
I0219 20:29:11.904172 101920 solver.cpp:231] Iteration 9600, loss = 0.0025672
I0219 20:29:11.904172 101920 solver.cpp:442] Iteration 9600, lr = 0.00603682
I0219 20:29:14.641330 101920 solver.cpp:231] Iteration 9700, loss = 0.0057964
I0219 20:29:14.641330 101920 solver.cpp:442] Iteration 9700, lr = 0.00601382
I0219 20:29:17.284481 101920 solver.cpp:231] Iteration 9800, loss = 0.00612196
I0219 20:29:17.284481 101920 solver.cpp:442] Iteration 9800, lr = 0.00599102
I0219 20:29:20.012636 101920 solver.cpp:231] Iteration 9900, loss = 0.00597476
I0219 20:29:20.012636 101920 solver.cpp:442] Iteration 9900, lr = 0.00596843
I0219 20:29:22.656787 101920 solver.cpp:356] Snapshotting to lenet_iter_10000.caffemodel
I0219 20:29:22.663789 101920 solver.cpp:363] Snapshotting solver state to lenet_iter_10000.caffemodel.solverstate
I0219 20:29:22.673789 101920 solver.cpp:268] Iteration 10000, loss = 0.00146509
I0219 20:29:22.673789 101920 solver.cpp:287] Iteration 10000, Testing net (#0)
I0219 20:29:23.761850 101920 solver.cpp:338]     Test net output #0: accuracy = 0.9909
I0219 20:29:23.761850 101920 solver.cpp:273] Optimization Done.
I0219 20:29:23.761850 101920 caffe.cpp:130] Optimization Done.