name: "laya_ir"
input: "data"
input_dim: 1
input_dim: 1
input_dim: 114
input_dim: 114


layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}



layer {
  name: "relu1"
  type: "PReLU"
  bottom: "conv1"
  top: "conv1"
}

layer {
  name: "norm1"
  type: "BatchNorm"
  bottom: "conv1"
  top: "conv1"

}


#########################



layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "conv1"
  top: "conv1_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}



layer {
  name: "relu1_1"
  type: "PReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}

layer {
  name: "norm1_1"
  type: "BatchNorm"
  bottom: "conv1_1"
  top: "conv1_1"

}



#########################



layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1_1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}

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: 96
    pad: 0
    kernel_size: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu2_1"
  type: "PReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}


layer {
  name: "norm2_1"
  type: "BatchNorm"
  bottom: "conv2_1"
  top: "conv2_1"

}


layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    pad: 0
    kernel_size: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}


layer {
  name: "relu2_2"
  type: "PReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "norm2_2"
  type: "BatchNorm"
  bottom: "conv2_2"
  top: "conv2_2"

}



#########################



layer {
  name: "conv2_2_1"
  type: "Convolution"
  bottom: "conv2_2"
  top: "conv2_2_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}


layer {
  name: "relu2_2_1"
  type: "PReLU"
  bottom: "conv2_2_1"
  top: "conv2_2_1"
}

layer {
  name: "norm2_2_1"
  type: "BatchNorm"
  bottom: "conv2_2_1"
  top: "conv2_2_1"

}



#########################




layer {
  name: "pool2_2"
  type: "Pooling"
  bottom: "conv2_2_1"
  top: "pool2_2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}

layer {
  name: "conv2_3"
  type: "Convolution"
  bottom: "pool2_2"
  top: "conv2_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu2_3"
  type: "PReLU"
  bottom: "conv2_3"
  top: "conv2_3"
}

layer {
  name: "norm2_3"
  type: "BatchNorm"
  bottom: "conv2_3"
  top: "conv2_3"

}


layer {
  name: "conv2_4"
  type: "Convolution"
  bottom: "conv2_3"
  top: "conv2_4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu2_4"
  type: "PReLU"
  bottom: "conv2_4"
  top: "conv2_4"
}

layer {
  name: "norm2_4"
  type: "BatchNorm"
  bottom: "conv2_4"
  top: "conv2_4"

}

layer {
  name: "conv2_5"
  type: "Convolution"
  bottom: "conv2_4"
  top: "conv2_5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu2_5"
  type: "PReLU"
  bottom: "conv2_5"
  top: "conv2_5"
}

layer {
  name: "norm2_5"
  type: "BatchNorm"
  bottom: "conv2_5"
  top: "conv2_5"

}

layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2_5"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
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: 192
    pad: 0
    kernel_size: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu3_1"
  type: "PReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}

layer {
  name: "norm3_1"
  type: "BatchNorm"
  bottom: "conv3_1"
  top: "conv3_1"

}


layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu3_2"
  type: "PReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}

layer {
  name: "norm3_2"
  type: "BatchNorm"
  bottom: "conv3_2"
  top: "conv3_2"

}

layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "conv3_2"
  top: "conv4_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu4_1"
  type: "PReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}

layer {
  name: "norm4_1"
  type: "BatchNorm"
  bottom: "conv4_1"
  top: "conv4_1"

}


layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 0
    kernel_size: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu4_2"
  type: "PReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}

layer {
  name: "norm4_2"
  type: "BatchNorm"
  bottom: "conv4_2"
  top: "conv4_2"

}








layer {
  name: "conv4_2_1"
  type: "Convolution"
  bottom: "conv4_2"
  top: "conv4_2_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_2_1"
  type: "PReLU"
  bottom: "conv4_2_1"
  top: "conv4_2_1"
}

layer {
  name: "norm4_2_1"
  type: "BatchNorm"
  bottom: "conv4_2_1"
  top: "conv4_2_1"

}




layer {
  name: "pool4_2"
  type: "Pooling"
  bottom: "conv4_2_1"
  top: "pool4_2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}


layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "pool4_2"
  top: "conv4_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu4_3"
  type: "PReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}

layer {
  name: "norm4_3"
  type: "BatchNorm"
  bottom: "conv4_3"
  top: "conv4_3"

}

layer {
  name: "conv4_4"
  type: "Convolution"
  bottom: "conv4_3"
  top: "conv4_4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 0
    kernel_size: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu4_4"
  type: "PReLU"
  bottom: "conv4_4"
  top: "conv4_4"
}

layer {
  name: "norm4_4"
  type: "BatchNorm"
  bottom: "conv4_4"
  top: "conv4_4"

}

layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "conv4_4"
  top: "conv5_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 2
    stride: 1
	weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu5_1"
  type: "PReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}

layer {
  name: "norm5_1"
  type: "BatchNorm"
  bottom: "conv5_1"
  top: "conv5_1"

}

layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv5_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 0
    kernel_size: 2
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu5_2"
  type: "PReLU"
  bottom: "conv5_2"
  top: "conv5_2"
}

layer {
  name: "norm5_2"
  type: "BatchNorm"
  bottom: "conv5_2"
  top: "conv5_2"

}

layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "conv5_2"
  top: "conv5_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu5_3"
  type: "PReLU"
  bottom: "conv5_3"
  top: "conv5_3"
}

layer {
  name: "norm5_3"
  type: "BatchNorm"
  bottom: "conv5_3"
  top: "conv5_3"

}

layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5_3"
  top: "pool5"
  pooling_param {
    pool: AVE
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  #propagate_down: false
  inner_product_param {
    num_output: 512
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu6"
  type: "PReLU"
  bottom: "fc6"
  top: "fc6"
}

layer {
  name: "norm6"
  type: "BatchNorm"
  bottom: "fc6"
  top: "fc6"

}

layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.0 #0.6
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 256
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}



layer {
  name: "norm7"
  type: "BatchNorm"
  bottom: "fc7"
  top: "fc7"

}