train_test_00001_00006_00000.prototxt.txt

name: "/tmp/scratch/train_test_00001_00006_00000.prototxt"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  data_param {
    prefetch: 10
    batch_size: 16
    source: "/lustre/atlas/proj-shared/hep109/xsy_work/data/prong/train-lmdb00001"
    backend: LMDB
  }
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  #transform_param {
  #mirror:  true
  #scale: 0.00390625
  #}
  data_param {
    prefetch: 10
    batch_size: 100
    source: "/lustre/atlas/proj-shared/hep109/xsy_work/data/prong/test-lmdb00001"
    backend: LMDB
  }
}
layer {
  name: "jitter"
  type: "DummyData"
  top: "jitter"
  include {
    phase: TRAIN
  }
  dummy_data_param {
    data_filler {
       type: "gaussian"
       mean: 1.0
       std:  0.01
      }
    shape {
       dim: 16
       dim: 2
       dim: 100
       dim: 80
      }
   }
}

layer {
  name: "jitter"
  type: "DummyData"
  top: "jitter"
  include {
    phase: TEST
  }
  dummy_data_param {
    data_filler {
       type: "constant"
       value:  1.0
      }   
    shape {
       dim: 100
       dim: 2
       dim: 100
       dim: 80
      }
   }
}
layer {
  name: "jitteredData"
  type: "Eltwise"
  bottom: "data"
  bottom: "jitter"
  top: "jitteredData"
  eltwise_param {
   operation:PROD
  }
}

layer {
  name: "slice"
  type: "Slice"
  bottom: "jitteredData"
  top: "data0_0"
  top: "data0_1"
  slice_param {
     slice_dim: 1
     slice_point: 1
 }
}
layer {
  name: "conv1_0"
  type: "Convolution"
  bottom: "data0_0"
  top: "conv1_0"
  param {
    lr_mult: 1
  decay_mult: 1
  }
  param {
    lr_mult: 2
  decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_h: 7
    kernel_w: 7
    stride_h: 2
    stride_w: 2
    pad_h: 3
    pad_w: 3
    weight_filler {
      type: "gaussian"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data0_1"
  top: "conv1_1"
  param {
    lr_mult: 1
  decay_mult: 1
  }
  param {
    lr_mult: 2
  decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_h: 7
    kernel_w: 7
    stride_h: 2
    stride_w: 2
    pad_h: 3
    pad_w: 3
    weight_filler {
      type: "gaussian"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer{
  name:"drop2_0"
  type: "Dropout"
  bottom: "conv1_0"
  top: "conv1_0"
  dropout_param {
    dropout_ratio: 0.680607
  }
}
layer{
  name:"drop2_1"
  type: "Dropout"
  bottom: "conv1_1"
  top: "conv1_1"
  dropout_param {
    dropout_ratio: 0.680607
  }
}
layer {
  name: "relu1_0"
  type: "ReLU"
  bottom: "conv1_0"
  top: "conv1_0"
}
layer {
  name: "relu1_1"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "pool2_0"
  type: "Pooling"
  bottom: "conv1_0"
  top: "pool2_0"
  pooling_param {
    pool: MAX
    kernel_h: 37
    kernel_w: 3
    stride_h: 5
    stride_w: 3
  }
}
layer {
  name: "pool2_1"
  type: "Pooling"
  bottom: "conv1_1"
  top: "pool2_1"
  pooling_param {
    pool: MAX
    kernel_h: 37
    kernel_w: 3
    stride_h: 5
    stride_w: 3
  }
}
layer {
  name: "sigmoid2_0"
  type: "Sigmoid"
  bottom: "pool2_0"
  top: "pool2_0"
}
layer {
  name: "sigmoid2_1"
  type: "Sigmoid"
  bottom: "pool2_1"
  top: "pool2_1"
}
layer {
  name: "conv3_0"
  type: "Convolution"
  bottom: "pool2_0"
  top: "conv3_0"
  param {
    lr_mult: 1
  decay_mult: 1
  }
  param {
    lr_mult: 2
  decay_mult: 0
  }
  convolution_param {
    num_output: 7
    kernel_h: 1
    kernel_w: 4
    stride_h: 1
    stride_w: 1
    pad_h: 0
    pad_w: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2_1"
  top: "conv3_1"
  param {
    lr_mult: 1
  decay_mult: 1
  }
  param {
    lr_mult: 2
  decay_mult: 0
  }
  convolution_param {
    num_output: 7
    kernel_h: 1
    kernel_w: 4
    stride_h: 1
    stride_w: 1
    pad_h: 0
    pad_w: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv4_0"
  type: "Convolution"
  bottom: "conv3_0"
  top: "conv4_0"
  param {
    lr_mult: 1
  decay_mult: 1
  }
  param {
    lr_mult: 2
  decay_mult: 0
  }
  convolution_param {
    num_output: 46
    kernel_h: 1
    kernel_w: 1
    stride_h: 1
    stride_w: 1
    pad_h: 0
    pad_w: 0
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv4_1"
  param {
    lr_mult: 1
  decay_mult: 1
  }
  param {
    lr_mult: 2
  decay_mult: 0
  }
  convolution_param {
    num_output: 46
    kernel_h: 1
    kernel_w: 1
    stride_h: 1
    stride_w: 1
    pad_h: 0
    pad_w: 0
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer{
  name:"drop5_0"
  type: "Dropout"
  bottom: "conv4_0"
  top: "conv4_0"
  dropout_param {
    dropout_ratio: 0.100000
  }
}
layer{
  name:"drop5_1"
  type: "Dropout"
  bottom: "conv4_1"
  top: "conv4_1"
  dropout_param {
    dropout_ratio: 0.100000
  }
}
layer {
  name: "conv5_0"
  type: "Convolution"
  bottom: "conv4_0"
  top: "conv5_0"
  param {
    lr_mult: 1
  decay_mult: 1
  }
  param {
    lr_mult: 2
  decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_h: 1
    kernel_w: 9
    stride_h: 1
    stride_w: 1
    pad_h: 0
    pad_w: 4
    weight_filler {
      type: "positive_unitball"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv5_1"
  param {
    lr_mult: 1
  decay_mult: 1
  }
  param {
    lr_mult: 2
  decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_h: 1
    kernel_w: 9
    stride_h: 1
    stride_w: 1
    pad_h: 0
    pad_w: 4
    weight_filler {
      type: "positive_unitball"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu5_0"
  type: "ReLU"
  bottom: "conv5_0"
  top: "conv5_0"
}
layer {
  name: "relu5_1"
  type: "ReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv6_0"
  type: "Convolution"
  bottom: "conv5_0"
  top: "conv6_0"
  param {
    lr_mult: 1
  decay_mult: 1
  }
  param {
    lr_mult: 2
  decay_mult: 0
  }
  convolution_param {
    num_output: 58
    kernel_h: 1
    kernel_w: 1
    stride_h: 1
    stride_w: 1
    pad_h: 0
    pad_w: 0
    weight_filler {
      type: "positive_unitball"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv6_1"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv6_1"
  param {
    lr_mult: 1
  decay_mult: 1
  }
  param {
    lr_mult: 2
  decay_mult: 0
  }
  convolution_param {
    num_output: 58
    kernel_h: 1
    kernel_w: 1
    stride_h: 1
    stride_w: 1
    pad_h: 0
    pad_w: 0
    weight_filler {
      type: "positive_unitball"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu6_0"
  type: "ReLU"
  bottom: "conv6_0"
  top: "conv6_0"
}
layer {
  name: "relu6_1"
  type: "ReLU"
  bottom: "conv6_1"
  top: "conv6_1"
}
layer {
  name:"norm7_0"
  type: "LRN"
  bottom: "conv6_0"
  top: "norm7_0"
  lrn_param {
    local_size: 1
    alpha: 0.000100
    beta: 0.750000
  }
}
layer {
  name:"norm7_1"
  type: "LRN"
  bottom: "conv6_1"
  top: "norm7_1"
  lrn_param {
    local_size: 1
    alpha: 0.000100
    beta: 0.750000
  }
}
layer {
  name: "inception_8_0/1x1"
  type: "Convolution"
  bottom: "norm7_0"
  top: "inception_8_0/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_8_0/relu_1x1"
  type: "ReLU"
  bottom: "inception_8_0/1x1"
  top: "inception_8_0/1x1"
}
layer {
  name: "inception_8_0/3x3_reduce"
  type: "Convolution"
  bottom: "norm7_0"
  top: "inception_8_0/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_8_0/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_8_0/3x3_reduce"
  top: "inception_8_0/3x3_reduce"
}
layer {
  name: "inception_8_0/3x3"
  type: "Convolution"
  bottom: "inception_8_0/3x3_reduce"
  top: "inception_8_0/3x3"
  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: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_8_0/relu_3x3"
  type: "ReLU"
  bottom: "inception_8_0/3x3"
  top: "inception_8_0/3x3"
}
layer {
  name: "inception_8_0/5x5_reduce"
  type: "Convolution"
  bottom: "norm7_0"
  top: "inception_8_0/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_8_0/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_8_0/5x5_reduce"
  top: "inception_8_0/5x5_reduce"
}
layer {
  name: "inception_8_0/5x5"
  type: "Convolution"
  bottom: "inception_8_0/5x5_reduce"
  top: "inception_8_0/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_8_0/relu_5x5"
  type: "ReLU"
  bottom: "inception_8_0/5x5"
  top: "inception_8_0/5x5"
}
layer {
  name: "inception_8_0/pool"
  type: "Pooling"
  bottom: "norm7_0"
  top: "inception_8_0/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_8_0/pool_proj"
  type: "Convolution"
  bottom: "inception_8_0/pool"
  top: "inception_8_0/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_8_0/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_8_0/pool_proj"
  top: "inception_8_0/pool_proj"
}
layer {
  name: "inception_8_0/output"
  type: "Concat"
  bottom: "inception_8_0/1x1"
  bottom: "inception_8_0/3x3"
  bottom: "inception_8_0/5x5"
  bottom: "inception_8_0/pool_proj"
  top: "inception8_0"
}
layer {
  name: "inception_8_1/1x1"
  type: "Convolution"
  bottom: "norm7_1"
  top: "inception_8_1/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_8_1/relu_1x1"
  type: "ReLU"
  bottom: "inception_8_1/1x1"
  top: "inception_8_1/1x1"
}
layer {
  name: "inception_8_1/3x3_reduce"
  type: "Convolution"
  bottom: "norm7_1"
  top: "inception_8_1/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_8_1/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_8_1/3x3_reduce"
  top: "inception_8_1/3x3_reduce"
}
layer {
  name: "inception_8_1/3x3"
  type: "Convolution"
  bottom: "inception_8_1/3x3_reduce"
  top: "inception_8_1/3x3"
  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: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_8_1/relu_3x3"
  type: "ReLU"
  bottom: "inception_8_1/3x3"
  top: "inception_8_1/3x3"
}
layer {
  name: "inception_8_1/5x5_reduce"
  type: "Convolution"
  bottom: "norm7_1"
  top: "inception_8_1/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_8_1/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_8_1/5x5_reduce"
  top: "inception_8_1/5x5_reduce"
}
layer {
  name: "inception_8_1/5x5"
  type: "Convolution"
  bottom: "inception_8_1/5x5_reduce"
  top: "inception_8_1/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_8_1/relu_5x5"
  type: "ReLU"
  bottom: "inception_8_1/5x5"
  top: "inception_8_1/5x5"
}
layer {
  name: "inception_8_1/pool"
  type: "Pooling"
  bottom: "norm7_1"
  top: "inception_8_1/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_8_1/pool_proj"
  type: "Convolution"
  bottom: "inception_8_1/pool"
  top: "inception_8_1/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_8_1/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_8_1/pool_proj"
  top: "inception_8_1/pool_proj"
}
layer {
  name: "inception_8_1/output"
  type: "Concat"
  bottom: "inception_8_1/1x1"
  bottom: "inception_8_1/3x3"
  bottom: "inception_8_1/5x5"
  bottom: "inception_8_1/pool_proj"
  top: "inception8_1"
}
layer {
  name: "pool9_0"
  type: "Pooling"
  bottom: "inception8_0"
  top: "pool9_0"
  pooling_param {
    pool: MAX
    kernel_h: 1
    kernel_w: 1
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "pool9_1"
  type: "Pooling"
  bottom: "inception8_1"
  top: "pool9_1"
  pooling_param {
    pool: MAX
    kernel_h: 1
    kernel_w: 1
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "concat9_0"
  type: "Concat"
  bottom: "pool9_0"
  bottom: "pool9_1"
  top: "concat9_0"
  concat_param {
    axis: 1
  }
}
layer {
  name: "inception_10_0/1x1"
  type: "Convolution"
  bottom: "concat9_0"
  top: "inception_10_0/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_10_0/relu_1x1"
  type: "ReLU"
  bottom: "inception_10_0/1x1"
  top: "inception_10_0/1x1"
}
layer {
  name: "inception_10_0/3x3_reduce"
  type: "Convolution"
  bottom: "concat9_0"
  top: "inception_10_0/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_10_0/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_10_0/3x3_reduce"
  top: "inception_10_0/3x3_reduce"
}
layer {
  name: "inception_10_0/3x3"
  type: "Convolution"
  bottom: "inception_10_0/3x3_reduce"
  top: "inception_10_0/3x3"
  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: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_10_0/relu_3x3"
  type: "ReLU"
  bottom: "inception_10_0/3x3"
  top: "inception_10_0/3x3"
}
layer {
  name: "inception_10_0/5x5_reduce"
  type: "Convolution"
  bottom: "concat9_0"
  top: "inception_10_0/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_10_0/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_10_0/5x5_reduce"
  top: "inception_10_0/5x5_reduce"
}
layer {
  name: "inception_10_0/5x5"
  type: "Convolution"
  bottom: "inception_10_0/5x5_reduce"
  top: "inception_10_0/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_10_0/relu_5x5"
  type: "ReLU"
  bottom: "inception_10_0/5x5"
  top: "inception_10_0/5x5"
}
layer {
  name: "inception_10_0/pool"
  type: "Pooling"
  bottom: "concat9_0"
  top: "inception_10_0/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_10_0/pool_proj"
  type: "Convolution"
  bottom: "inception_10_0/pool"
  top: "inception_10_0/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_10_0/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_10_0/pool_proj"
  top: "inception_10_0/pool_proj"
}
layer {
  name: "inception_10_0/output"
  type: "Concat"
  bottom: "inception_10_0/1x1"
  bottom: "inception_10_0/3x3"
  bottom: "inception_10_0/5x5"
  bottom: "inception_10_0/pool_proj"
  top: "inception10_0"
}
layer {
  name:"norm11_0"
  type: "LRN"
  bottom: "inception10_0"
  top: "norm11_0"
  lrn_param {
    local_size: 1
    alpha: 0.906179
    beta: 0.250000
    norm_region: WITHIN_CHANNEL
  }
}
layer {
  name: "finalip"
  type: "InnerProduct"
  bottom: "norm11_0"
  top: "ipFinal"
  inner_product_param {
    num_output: 8
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "ipFinal"
  bottom: "label"
  top: "accuracy"
  include {
	 phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ipFinal"
  bottom: "label"
  top: "loss"
}