name: "face_64"
input: "data"
input_shape {
  dim:1
  dim:3
  dim:64
  dim:64
}

layer {
    name: "ConvNdBackward1"
    type: "Convolution"
    bottom: "data"
    top: "ConvNdBackward1"
    convolution_param {
        num_output: 32
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward2_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward1"
    top: "BatchNormBackward2"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward2_scale"
    type: "Scale"
    bottom: "BatchNormBackward2"
    top: "BatchNormBackward2"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward3"
    type: "ReLU"
    bottom: "BatchNormBackward2"
    top: "BatchNormBackward2"
}
layer {
    name: "MaxPool2dBackward4"
    type: "Pooling"
    bottom: "BatchNormBackward2"
    top: "MaxPool2dBackward4"
    pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
        pad: 0
    }
}
layer {
    name: "ConvNdBackward5"
    type: "Convolution"
    bottom: "MaxPool2dBackward4"
    top: "ConvNdBackward5"
    convolution_param {
        num_output: 32
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward6_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward5"
    top: "BatchNormBackward6"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward6_scale"
    type: "Scale"
    bottom: "BatchNormBackward6"
    top: "BatchNormBackward6"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward7"
    type: "ReLU"
    bottom: "BatchNormBackward6"
    top: "BatchNormBackward6"
}
layer {
    name: "MaxPool2dBackward8"
    type: "Pooling"
    bottom: "BatchNormBackward6"
    top: "MaxPool2dBackward8"
    pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
        pad: 0
    }
}
layer {
    name: "ConvNdBackward9"
    type: "Convolution"
    bottom: "MaxPool2dBackward8"
    top: "ConvNdBackward9"
    convolution_param {
        num_output: 64
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward10_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward9"
    top: "BatchNormBackward10"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward10_scale"
    type: "Scale"
    bottom: "BatchNormBackward10"
    top: "BatchNormBackward10"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward11"
    type: "ReLU"
    bottom: "BatchNormBackward10"
    top: "BatchNormBackward10"
}
layer {
    name: "MaxPool2dBackward12"
    type: "Pooling"
    bottom: "BatchNormBackward10"
    top: "MaxPool2dBackward12"
    pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
        pad: 0
    }
}
layer {
    name: "ConvNdBackward13"
    type: "Convolution"
    bottom: "MaxPool2dBackward12"
    top: "ConvNdBackward13"
    convolution_param {
        num_output: 128
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward14_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward13"
    top: "BatchNormBackward14"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward14_scale"
    type: "Scale"
    bottom: "BatchNormBackward14"
    top: "BatchNormBackward14"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward15"
    type: "ReLU"
    bottom: "BatchNormBackward14"
    top: "BatchNormBackward14"
}
layer {
    name: "MaxPool2dBackward16"
    type: "Pooling"
    bottom: "BatchNormBackward14"
    top: "MaxPool2dBackward16"
    pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
        pad: 0
    }
}
layer {
    name: "ConvNdBackward17"
    type: "Convolution"
    bottom: "MaxPool2dBackward16"
    top: "ConvNdBackward17"
    convolution_param {
        num_output: 32
        group: 1
        pad_h: 1
        pad_w: 1
        kernel_h: 3
        kernel_w: 3
        stride: 1
        dilation: 1
    }
}
layer {
    name: "BatchNormBackward18_bn"
    type: "BatchNorm"
    bottom: "ConvNdBackward17"
    top: "BatchNormBackward18"
    batch_norm_param {
        use_global_stats: true
        eps: 1e-05
    }
}
layer {
    name: "BatchNormBackward18_scale"
    type: "Scale"
    bottom: "BatchNormBackward18"
    top: "BatchNormBackward18"
    scale_param {
        bias_term: true
    }
}
layer {
    name: "ThresholdBackward19"
    type: "ReLU"
    bottom: "BatchNormBackward18"
    top: "BatchNormBackward18"
}
layer {
    name: "AvgPool2dBackward20"
    type: "Pooling"
    bottom: "BatchNormBackward18"
    top: "AvgPool2dBackward20"
    pooling_param {
        pool: AVE
        kernel_size: 4
        stride: 4
        pad: 0
    }
}
layer {
    name: "ConvNdBackward21"
    type: "Convolution"
    bottom: "AvgPool2dBackward20"
    top: "ConvNdBackward21"
    convolution_param {
        num_output: 2
        group: 1
        pad_h: 0
        pad_w: 0
        kernel_h: 1
        kernel_w: 1
        stride: 1
        dilation: 1
        bias_term: false
    }
}
layer {
  name:"prob"
  type:"Softmax"
  bottom:"ConvNdBackward21"
  top:"prob"
}