name: "GoogLeNet"
layers {
  name: "data"
  type: DATA
  top: "data"
  top: "label"
  data_param {
    source: "train_lmdb"
    backend: LMDB
    batch_size: 128 
  }
  transform_param {
    crop_size: 224
    mean_file: "places_mean.binaryproto"
    mirror: true
  }
  include: { phase: TRAIN }
}
layers {
  name: "data"
  type: DATA
  top: "data"
  top: "label"
  data_param {
    source: "test_lmdb"
    backend: LMDB
    batch_size: 32
  }
  transform_param {
    crop_size: 224
    mean_file: "places_mean.binaryproto"
    mirror: false
  }
  include: { phase: TEST }
}
layers {
  name: "conv1"
  type: CONVOLUTION
  bottom: "data"
  top: "conv1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 64
    kernel_size: 7
    stride: 2
    pad: 3
    weight_filler {
      type: "gaussian"
      std: 0.015
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu1"
  type: RELU
  bottom: "conv1"
  top: "conv1"
}
layers {
  name: "pool1"
  type: POOLING
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 0
  }
}
layers {
  name: "norm1"
  type: LRN
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layers {
  name: "reduction2"
  type: CONVOLUTION
  bottom: "norm1"
  top: "reduction2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    group: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_reduction2"
  type: RELU
  bottom: "reduction2"
  top: "reduction2"
}
layers {
  name: "conv2"
  type: CONVOLUTION
  bottom: "reduction2"
  top: "conv2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    group: 1
    weight_filler {
      type: "gaussian"
      std: 0.02
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu2"
  type: RELU
  bottom: "conv2"
  top: "conv2"
}
layers {
  name: "norm2"
  type: LRN
  bottom: "conv2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layers {
  name: "pool2"
  type: POOLING
  bottom: "norm2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 0
  }
}
# Inception module 1 ***************
layers {
  name: "icp1_reduction1"
  type: CONVOLUTION
  bottom: "pool2"
  top: "icp1_reduction1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 96
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp1_reduction1"
  type: RELU
  bottom: "icp1_reduction1"
  top: "icp1_reduction1"
}
layers {
  name: "icp1_reduction2"
  type: CONVOLUTION
  bottom: "pool2"
  top: "icp1_reduction2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 16
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp1_reduction2"
  type: RELU
  bottom: "icp1_reduction2"
  top: "icp1_reduction2"
}
layers {
  name: "icp1_pool"
  type: POOLING
  bottom: "pool2"
  top: "icp1_pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
# ***********
layers {
  name: "icp1_out0"
  type: CONVOLUTION
  bottom: "pool2"
  top: "icp1_out0"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp1_out0"
  type: RELU
  bottom: "icp1_out0"
  top: "icp1_out0"
}
layers {
  name: "icp1_out1"
  type: CONVOLUTION
  bottom: "icp1_reduction1"
  top: "icp1_out1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.04
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp1_out1"
  type: RELU
  bottom: "icp1_out1"
  top: "icp1_out1"
}
layers {
  name: "icp1_out2"
  type: CONVOLUTION
  bottom: "icp1_reduction2"
  top: "icp1_out2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "gaussian"
      std: 0.08
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp1_out2"
  type: RELU
  bottom: "icp1_out2"
  top: "icp1_out2"
}
layers {
  name: "icp1_out3"
  type: CONVOLUTION
  bottom: "icp1_pool"
  top: "icp1_out3"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 32
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp1_out3"
  type: RELU
  bottom: "icp1_out3"
  top: "icp1_out3"
}
# Concat them together
layers {
  name: "icp2_in"
  type: CONCAT
  bottom: "icp1_out0"
  bottom: "icp1_out1"
  bottom: "icp1_out2"
  bottom: "icp1_out3"
  top: "icp2_in"
}

# Inception module 2 ***************
layers {
  name: "icp2_reduction1"
  type: CONVOLUTION
  bottom: "icp2_in"
  top: "icp2_reduction1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp2_reduction1"
  type: RELU
  bottom: "icp2_reduction1"
  top: "icp2_reduction1"
}
layers {
  name: "icp2_reduction2"
  type: CONVOLUTION
  bottom: "icp2_in"
  top: "icp2_reduction2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 32
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp2_reduction2"
  type: RELU
  bottom: "icp2_reduction2"
  top: "icp2_reduction2"
}
layers {
  name: "icp2_pool"
  type: POOLING
  bottom: "icp2_in"
  top: "icp2_pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
# ***********
layers {
  name: "icp2_out0"
  type: CONVOLUTION
  bottom: "icp2_in"
  top: "icp2_out0"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp2_out0"
  type: RELU
  bottom: "icp2_out0"
  top: "icp2_out0"
}
layers {
  name: "icp2_out1"
  type: CONVOLUTION
  bottom: "icp2_reduction1"
  top: "icp2_out1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.04
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp2_out1"
  type: RELU
  bottom: "icp2_out1"
  top: "icp2_out1"
}
layers {
  name: "icp2_out2"
  type: CONVOLUTION
  bottom: "icp2_reduction2"
  top: "icp2_out2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 96
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "gaussian"
      std: 0.08
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp2_out2"
  type: RELU
  bottom: "icp2_out2"
  top: "icp2_out2"
}
layers {
  name: "icp2_out3"
  type: CONVOLUTION
  bottom: "icp2_pool"
  top: "icp2_out3"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp2_out3"
  type: RELU
  bottom: "icp2_out3"
  top: "icp2_out3"
}
# Concat them together
layers {
  name: "icp2_out"
  type: CONCAT
  bottom: "icp2_out0"
  bottom: "icp2_out1"
  bottom: "icp2_out2"
  bottom: "icp2_out3"
  top: "icp2_out"
}
layers {
  name: "icp3_in"
  type: POOLING
  bottom: "icp2_out"
  top: "icp3_in"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 0
  }
}

# Inception module 3 ***************
layers {
  name: "icp3_reduction1"
  type: CONVOLUTION
  bottom: "icp3_in"
  top: "icp3_reduction1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 96
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp3_reduction1"
  type: RELU
  bottom: "icp3_reduction1"
  top: "icp3_reduction1"
}
layers {
  name: "icp3_reduction2"
  type: CONVOLUTION
  bottom: "icp3_in"
  top: "icp3_reduction2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 16
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp3_reduction2"
  type: RELU
  bottom: "icp3_reduction2"
  top: "icp3_reduction2"
}
layers {
  name: "icp3_pool"
  type: POOLING
  bottom: "icp3_in"
  top: "icp3_pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
# ***********
layers {
  name: "icp3_out0"
  type: CONVOLUTION
  bottom: "icp3_in"
  top: "icp3_out0"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp3_out0"
  type: RELU
  bottom: "icp3_out0"
  top: "icp3_out0"
}
layers {
  name: "icp3_out1"
  type: CONVOLUTION
  bottom: "icp3_reduction1"
  top: "icp3_out1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 208
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.04
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp3_out1"
  type: RELU
  bottom: "icp3_out1"
  top: "icp3_out1"
}
layers {
  name: "icp3_out2"
  type: CONVOLUTION
  bottom: "icp3_reduction2"
  top: "icp3_out2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 48
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "gaussian"
      std: 0.08
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp3_out2"
  type: RELU
  bottom: "icp3_out2"
  top: "icp3_out2"
}
layers {
  name: "icp3_out3"
  type: CONVOLUTION
  bottom: "icp3_pool"
  top: "icp3_out3"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp3_out3"
  type: RELU
  bottom: "icp3_out3"
  top: "icp3_out3"
}
# Concat them together
layers {
  name: "icp3_out"
  type: CONCAT
  bottom: "icp3_out0"
  bottom: "icp3_out1"
  bottom: "icp3_out2"
  bottom: "icp3_out3"
  top: "icp3_out"
}

# first classification branch ************
layers {
  name: "cls1_pool"
  type: POOLING
  bottom: "icp3_out"
  top: "cls1_pool"
  pooling_param {
    pool: AVE
    kernel_size: 5
    stride: 3
    pad: 0
    # this padding is somewhat special
  }
}
layers {
  name: "cls1_reduction"
  type: CONVOLUTION
  bottom: "cls1_pool"
  top: "cls1_reduction"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_cls1_reduction"
  type: RELU
  bottom: "cls1_reduction"
  top: "cls1_reduction"
}
layers {
  name: "cls1_fc1"
  type: INNER_PRODUCT
  bottom: "cls1_reduction"
  top: "cls1_fc1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_cls1_fc1"
  type: RELU
  bottom: "cls1_fc1"
  top: "cls1_fc1"
}
layers {
  name: "cls1_drop"
  type: DROPOUT
  bottom: "cls1_fc1"
  top: "cls1_fc1"
  dropout_param {
    dropout_ratio: 0.7
  }
}
layers {
  name: "cls1_fc2"
  type: INNER_PRODUCT
  bottom: "cls1_fc1"
  top: "cls1_fc2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 205
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "loss1"
  type: SOFTMAX_LOSS
  bottom: "cls1_fc2"
  bottom: "label"
  top: "loss1"
  loss_weight: 1
}

# Inception module 4 ***************
layers {
  name: "icp4_reduction1"
  type: CONVOLUTION
  bottom: "icp3_out"
  top: "icp4_reduction1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 112
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp4_reduction1"
  type: RELU
  bottom: "icp4_reduction1"
  top: "icp4_reduction1"
}
layers {
  name: "icp4_reduction2"
  type: CONVOLUTION
  bottom: "icp3_out"
  top: "icp4_reduction2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 24
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp4_reduction2"
  type: RELU
  bottom: "icp4_reduction2"
  top: "icp4_reduction2"
}
layers {
  name: "icp4_pool"
  type: POOLING
  bottom: "icp3_out"
  top: "icp4_pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
# ***********
layers {
  name: "icp4_out0"
  type: CONVOLUTION
  bottom: "icp3_out"
  top: "icp4_out0"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 160
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp4_out0"
  type: RELU
  bottom: "icp4_out0"
  top: "icp4_out0"
}
layers {
  name: "icp4_out1"
  type: CONVOLUTION
  bottom: "icp4_reduction1"
  top: "icp4_out1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 224
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.04
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp4_out1"
  type: RELU
  bottom: "icp4_out1"
  top: "icp4_out1"
}
layers {
  name: "icp4_out2"
  type: CONVOLUTION
  bottom: "icp4_reduction2"
  top: "icp4_out2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "gaussian"
      std: 0.08
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp4_out2"
  type: RELU
  bottom: "icp4_out2"
  top: "icp4_out2"
}
layers {
  name: "icp4_out3"
  type: CONVOLUTION
  bottom: "icp4_pool"
  top: "icp4_out3"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp4_out3"
  type: RELU
  bottom: "icp4_out3"
  top: "icp4_out3"
}
# Concat them together
layers {
  name: "icp4_out"
  type: CONCAT
  bottom: "icp4_out0"
  bottom: "icp4_out1"
  bottom: "icp4_out2"
  bottom: "icp4_out3"
  top: "icp4_out"
}

# Inception module 5 ***************
layers {
  name: "icp5_reduction1"
  type: CONVOLUTION
  bottom: "icp4_out"
  top: "icp5_reduction1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp5_reduction1"
  type: RELU
  bottom: "icp5_reduction1"
  top: "icp5_reduction1"
}
layers {
  name: "icp5_reduction2"
  type: CONVOLUTION
  bottom: "icp4_out"
  top: "icp5_reduction2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 24
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp5_reduction2"
  type: RELU
  bottom: "icp5_reduction2"
  top: "icp5_reduction2"
}
layers {
  name: "icp5_pool"
  type: POOLING
  bottom: "icp4_out"
  top: "icp5_pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
# ***********
layers {
  name: "icp5_out0"
  type: CONVOLUTION
  bottom: "icp4_out"
  top: "icp5_out0"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp5_out0"
  type: RELU
  bottom: "icp5_out0"
  top: "icp5_out0"
}
layers {
  name: "icp5_out1"
  type: CONVOLUTION
  bottom: "icp5_reduction1"
  top: "icp5_out1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.04
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp5_out1"
  type: RELU
  bottom: "icp5_out1"
  top: "icp5_out1"
}
layers {
  name: "icp5_out2"
  type: CONVOLUTION
  bottom: "icp5_reduction2"
  top: "icp5_out2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "gaussian"
      std: 0.08
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp5_out2"
  type: RELU
  bottom: "icp5_out2"
  top: "icp5_out2"
}
layers {
  name: "icp5_out3"
  type: CONVOLUTION
  bottom: "icp5_pool"
  top: "icp5_out3"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp5_out3"
  type: RELU
  bottom: "icp5_out3"
  top: "icp5_out3"
}
# Concat them together
layers {
  name: "icp5_out"
  type: CONCAT
  bottom: "icp5_out0"
  bottom: "icp5_out1"
  bottom: "icp5_out2"
  bottom: "icp5_out3"
  top: "icp5_out"
}

# Inception module 6 ***************
layers {
  name: "icp6_reduction1"
  type: CONVOLUTION
  bottom: "icp5_out"
  top: "icp6_reduction1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 144
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp6_reduction1"
  type: RELU
  bottom: "icp6_reduction1"
  top: "icp6_reduction1"
}
layers {
  name: "icp6_reduction2"
  type: CONVOLUTION
  bottom: "icp5_out"
  top: "icp6_reduction2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 32
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp6_reduction2"
  type: RELU
  bottom: "icp6_reduction2"
  top: "icp6_reduction2"
}
layers {
  name: "icp6_pool"
  type: POOLING
  bottom: "icp5_out"
  top: "icp6_pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
# ***********
layers {
  name: "icp6_out0"
  type: CONVOLUTION
  bottom: "icp5_out"
  top: "icp6_out0"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 112
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp6_out0"
  type: RELU
  bottom: "icp6_out0"
  top: "icp6_out0"
}
layers {
  name: "icp6_out1"
  type: CONVOLUTION
  bottom: "icp6_reduction1"
  top: "icp6_out1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 288
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.04
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp6_out1"
  type: RELU
  bottom: "icp6_out1"
  top: "icp6_out1"
}
layers {
  name: "icp6_out2"
  type: CONVOLUTION
  bottom: "icp6_reduction2"
  top: "icp6_out2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "gaussian"
      std: 0.08
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp6_out2"
  type: RELU
  bottom: "icp6_out2"
  top: "icp6_out2"
}
layers {
  name: "icp6_out3"
  type: CONVOLUTION
  bottom: "icp6_pool"
  top: "icp6_out3"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp6_out3"
  type: RELU
  bottom: "icp6_out3"
  top: "icp6_out3"
}
# Concat them together
layers {
  name: "icp6_out"
  type: CONCAT
  bottom: "icp6_out0"
  bottom: "icp6_out1"
  bottom: "icp6_out2"
  bottom: "icp6_out3"
  top: "icp6_out"
}

# second classification branch ************
layers {
  name: "cls2_pool"
  type: POOLING
  bottom: "icp6_out"
  top: "cls2_pool"
  pooling_param {
    pool: AVE
    kernel_size: 5
    stride: 3
    pad: 0
    # this padding is somewhat special
  }
}
layers {
  name: "cls2_reduction"
  type: CONVOLUTION
  bottom: "cls2_pool"
  top: "cls2_reduction"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_cls2_reduction"
  type: RELU
  bottom: "cls2_reduction"
  top: "cls2_reduction"
}
layers {
  name: "cls2_fc1"
  type: INNER_PRODUCT
  bottom: "cls2_reduction"
  top: "cls2_fc1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_cls2_fc1"
  type: RELU
  bottom: "cls2_fc1"
  top: "cls2_fc1"
}
layers {
  name: "cls2_drop"
  type: DROPOUT
  bottom: "cls2_fc1"
  top: "cls2_fc1"
  dropout_param {
    dropout_ratio: 0.7
  }
}
layers {
  name: "cls2_fc2"
  type: INNER_PRODUCT
  bottom: "cls2_fc1"
  top: "cls2_fc2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 205
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "loss2"
  type: SOFTMAX_LOSS
  bottom: "cls2_fc2"
  bottom: "label"
  top: "loss2"
  loss_weight: 1
}

# Inception module 7 ***************
layers {
  name: "icp7_reduction1"
  type: CONVOLUTION
  bottom: "icp6_out"
  top: "icp7_reduction1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 160
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp7_reduction1"
  type: RELU
  bottom: "icp7_reduction1"
  top: "icp7_reduction1"
}
layers {
  name: "icp7_reduction2"
  type: CONVOLUTION
  bottom: "icp6_out"
  top: "icp7_reduction2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 32
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp7_reduction2"
  type: RELU
  bottom: "icp7_reduction2"
  top: "icp7_reduction2"
}
layers {
  name: "icp7_pool"
  type: POOLING
  bottom: "icp6_out"
  top: "icp7_pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
# ***********
layers {
  name: "icp7_out0"
  type: CONVOLUTION
  bottom: "icp6_out"
  top: "icp7_out0"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 256
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp7_out0"
  type: RELU
  bottom: "icp7_out0"
  top: "icp7_out0"
}
layers {
  name: "icp7_out1"
  type: CONVOLUTION
  bottom: "icp7_reduction1"
  top: "icp7_out1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 320
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.04
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp7_out1"
  type: RELU
  bottom: "icp7_out1"
  top: "icp7_out1"
}
layers {
  name: "icp7_out2"
  type: CONVOLUTION
  bottom: "icp7_reduction2"
  top: "icp7_out2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 128
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "gaussian"
      std: 0.08
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp7_out2"
  type: RELU
  bottom: "icp7_out2"
  top: "icp7_out2"
}
layers {
  name: "icp7_out3"
  type: CONVOLUTION
  bottom: "icp7_pool"
  top: "icp7_out3"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp7_out3"
  type: RELU
  bottom: "icp7_out3"
  top: "icp7_out3"
}
# Concat them together
layers {
  name: "icp7_out"
  type: CONCAT
  bottom: "icp7_out0"
  bottom: "icp7_out1"
  bottom: "icp7_out2"
  bottom: "icp7_out3"
  top: "icp7_out"
}
layers {
  name: "icp8_in"
  type: POOLING
  bottom: "icp7_out"
  top: "icp8_in"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 0
  }
}
# Inception module 8 ***************
layers {
  name: "icp8_reduction1"
  type: CONVOLUTION
  bottom: "icp8_in"
  top: "icp8_reduction1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 160
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp8_reduction1"
  type: RELU
  bottom: "icp8_reduction1"
  top: "icp8_reduction1"
}
layers {
  name: "icp8_reduction2"
  type: CONVOLUTION
  bottom: "icp8_in"
  top: "icp8_reduction2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 32
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp8_reduction2"
  type: RELU
  bottom: "icp8_reduction2"
  top: "icp8_reduction2"
}
layers {
  name: "icp8_pool"
  type: POOLING
  bottom: "icp8_in"
  top: "icp8_pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
# ***********
layers {
  name: "icp8_out0"
  type: CONVOLUTION
  bottom: "icp8_in"
  top: "icp8_out0"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 256
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp8_out0"
  type: RELU
  bottom: "icp8_out0"
  top: "icp8_out0"
}
layers {
  name: "icp8_out1"
  type: CONVOLUTION
  bottom: "icp8_reduction1"
  top: "icp8_out1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 320
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.04
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp8_out1"
  type: RELU
  bottom: "icp8_out1"
  top: "icp8_out1"
}
layers {
  name: "icp8_out2"
  type: CONVOLUTION
  bottom: "icp8_reduction2"
  top: "icp8_out2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 128
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "gaussian"
      std: 0.08
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp8_out2"
  type: RELU
  bottom: "icp8_out2"
  top: "icp8_out2"
}
layers {
  name: "icp8_out3"
  type: CONVOLUTION
  bottom: "icp8_pool"
  top: "icp8_out3"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp8_out3"
  type: RELU
  bottom: "icp8_out3"
  top: "icp8_out3"
}
# Concat them together
layers {
  name: "icp8_out"
  type: CONCAT
  bottom: "icp8_out0"
  bottom: "icp8_out1"
  bottom: "icp8_out2"
  bottom: "icp8_out3"
  top: "icp8_out"
}

# Inception module 9 ***************
layers {
  name: "icp9_reduction1"
  type: CONVOLUTION
  bottom: "icp8_out"
  top: "icp9_reduction1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp9_reduction1"
  type: RELU
  bottom: "icp9_reduction1"
  top: "icp9_reduction1"
}
layers {
  name: "icp9_reduction2"
  type: CONVOLUTION
  bottom: "icp8_out"
  top: "icp9_reduction2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 48
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp9_reduction2"
  type: RELU
  bottom: "icp9_reduction2"
  top: "icp9_reduction2"
}
layers {
  name: "icp9_pool"
  type: POOLING
  bottom: "icp8_out"
  top: "icp9_pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
# ***********
layers {
  name: "icp9_out0"
  type: CONVOLUTION
  bottom: "icp8_out"
  top: "icp9_out0"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 384
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp9_out0"
  type: RELU
  bottom: "icp9_out0"
  top: "icp9_out0"
}
layers {
  name: "icp9_out1"
  type: CONVOLUTION
  bottom: "icp9_reduction1"
  top: "icp9_out1"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.04
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp9_out1"
  type: RELU
  bottom: "icp9_out1"
  top: "icp9_out1"
}
layers {
  name: "icp9_out2"
  type: CONVOLUTION
  bottom: "icp9_reduction2"
  top: "icp9_out2"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 128
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "gaussian"
      std: 0.08
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp9_out2"
  type: RELU
  bottom: "icp9_out2"
  top: "icp9_out2"
}
layers {
  name: "icp9_out3"
  type: CONVOLUTION
  bottom: "icp9_pool"
  top: "icp9_out3"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "relu_icp9_out3"
  type: RELU
  bottom: "icp9_out3"
  top: "icp9_out3"
}
# Concat them together
layers {
  name: "icp9_out"
  type: CONCAT
  bottom: "icp9_out0"
  bottom: "icp9_out1"
  bottom: "icp9_out2"
  bottom: "icp9_out3"
  top: "icp9_out"
}

# third classification branch
layers {
  name: "cls3_pool"
  type: POOLING
  bottom: "icp9_out"
  top: "cls3_pool"
  pooling_param {
    pool: AVE
    kernel_size: 7
    stride: 1
    pad: 0
    # This padding is somewhat special
  }
}
layers {
  name: "cls3_drop"
  type: DROPOUT
  bottom: "cls3_pool"
  top: "cls3_pool"
  dropout_param {
    dropout_ratio: 0.4
  }
}
layers {
  name: "cls3_fc"
  type: INNER_PRODUCT
  bottom: "cls3_pool"
  top: "cls3_fc"
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
  inner_product_param {
    num_output: 205
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layers {
  name: "loss3"
  type: SOFTMAX_LOSS
  bottom: "cls3_fc"
  bottom: "label"
  top: "loss3"
  loss_weight: 1
}
layers {
  name: "accuracy1"
  type: ACCURACY
  bottom: "cls3_fc"
  bottom: "label"
  top: "accuracy1"
  include: { phase: TEST }
}
layers {
  name: "accuracy5"
  type: ACCURACY
  bottom: "cls3_fc"
  bottom: "label"
  top: "accuracy5"
  include: { phase: TEST }
  accuracy_param {
    top_k: 5
  }
}