style_transfer

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name: "VGG_ILSVRC_16_layers"
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force_backward: true
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layer {
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  top: "data"
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  name: "input"
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  type: "Input"
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  input_param {
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    shape {
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      dim: 1
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      dim: 3
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      dim: 224
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      dim: 224
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    }
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  }
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}
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layer {
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  bottom: "data"
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  top: "conv1_1"
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  name: "conv1_1"
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  type: "Convolution"
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  convolution_param {
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    num_output: 64
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    pad: 1
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    kernel_size: 3
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  }
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}
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layer {
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  bottom: "conv1_1"
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  top: "conv1_1"
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  name: "relu1_1"
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  type: "ReLU"
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}
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layer {
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  bottom: "conv1_1"
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  top: "conv1_2"
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  name: "conv1_2"
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  type: "Convolution"
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  convolution_param {
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    num_output: 64
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    pad: 1
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    kernel_size: 3
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  }
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}
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layer {
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  bottom: "conv1_2"
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  top: "conv1_2"
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  name: "relu1_2"
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  type: "ReLU"
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}
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layer {
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  bottom: "conv1_2"
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  top: "pool1"
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  name: "pool1"
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  type: "Pooling"
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  pooling_param {
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    pool: MAX
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    kernel_size: 2
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    stride: 2
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  }
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}
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layer {
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  bottom: "pool1"
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  top: "conv2_1"
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  name: "conv2_1"
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  type: "Convolution"
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  convolution_param {
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    num_output: 128
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    pad: 1
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    kernel_size: 3
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  }
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}
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layer {
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  bottom: "conv2_1"
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  top: "conv2_1"
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  name: "relu2_1"
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  type: "ReLU"
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}
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layer {
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  bottom: "conv2_1"
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  top: "conv2_2"
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  name: "conv2_2"
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  type: "Convolution"
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  convolution_param {
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    num_output: 128
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    pad: 1
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    kernel_size: 3
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  }
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}
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layer {
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  bottom: "conv2_2"
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  top: "conv2_2"
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  name: "relu2_2"
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  type: "ReLU"
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}
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layer {
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  bottom: "conv2_2"
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  top: "pool2"
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  name: "pool2"
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  type: "Pooling"
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  pooling_param {
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    pool: MAX
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    kernel_size: 2
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    stride: 2
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  }
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}
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layer {
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  bottom: "pool2"
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  top: "conv3_1"
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  name: "conv3_1"
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  type: "Convolution"
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  convolution_param {
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    num_output: 256
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    pad: 1
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    kernel_size: 3
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  }
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}
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layer {
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  bottom: "conv3_1"
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  top: "conv3_1"
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  name: "relu3_1"
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  type: "ReLU"
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}
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layer {
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  bottom: "conv3_1"
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  top: "conv3_2"
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  name: "conv3_2"
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  type: "Convolution"
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  convolution_param {
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    num_output: 256
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    pad: 1
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    kernel_size: 3
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  }
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}
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layer {
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  bottom: "conv3_2"
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  top: "conv3_2"
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  name: "relu3_2"
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  type: "ReLU"
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}
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layer {
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  bottom: "conv3_2"
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  top: "conv3_3"
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  name: "conv3_3"
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  type: "Convolution"
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  convolution_param {
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    num_output: 256
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    pad: 1
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    kernel_size: 3
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  }
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}
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layer {
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  bottom: "conv3_3"
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  top: "conv3_3"
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  name: "relu3_3"
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  type: "ReLU"
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}
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layer {
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  bottom: "conv3_3"
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  top: "pool3"
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  name: "pool3"
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  type: "Pooling"
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  pooling_param {
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    pool: MAX
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    kernel_size: 2
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    stride: 2
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  }
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}
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layer {
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  bottom: "pool3"
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  top: "conv4_1"
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  name: "conv4_1"
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  type: "Convolution"
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  convolution_param {
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    num_output: 512
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    pad: 1
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    kernel_size: 3
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  }
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}
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layer {
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  bottom: "conv4_1"
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  top: "conv4_1"
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  name: "relu4_1"
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  type: "ReLU"
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}
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layer {
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  bottom: "conv4_1"
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  top: "conv4_2"
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  name: "conv4_2"
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  type: "Convolution"
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  convolution_param {
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    num_output: 512
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    pad: 1
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    kernel_size: 3
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  }
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}
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layer {
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  bottom: "conv4_2"
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  top: "conv4_2"
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  name: "relu4_2"
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  type: "ReLU"
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}
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layer {
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  bottom: "conv4_2"
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  top: "conv4_3"
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  name: "conv4_3"
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  type: "Convolution"
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  convolution_param {
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    num_output: 512
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    pad: 1
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    kernel_size: 3
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  }
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}
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layer {
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  bottom: "conv4_3"
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  top: "conv4_3"
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  name: "relu4_3"
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  type: "ReLU"
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}
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layer {
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  bottom: "conv4_3"
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  top: "pool4"
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  name: "pool4"
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  type: "Pooling"
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  pooling_param {
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    pool: MAX
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    kernel_size: 2
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    stride: 2
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  }
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}
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layer {
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  bottom: "pool4"
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  top: "conv5_1"
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  name: "conv5_1"
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  type: "Convolution"
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  convolution_param {
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    num_output: 512
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    pad: 1
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    kernel_size: 3
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  }
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}
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layer {
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  bottom: "conv5_1"
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  top: "conv5_1"
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  name: "relu5_1"
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  type: "ReLU"
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}
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layer {
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  bottom: "conv5_1"
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  top: "conv5_2"
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  name: "conv5_2"
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  type: "Convolution"
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  convolution_param {
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    num_output: 512
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    pad: 1
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    kernel_size: 3
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  }
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}
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layer {
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  bottom: "conv5_2"
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  top: "conv5_2"
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  name: "relu5_2"
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  type: "ReLU"
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}
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layer {
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  bottom: "conv5_2"
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  top: "conv5_3"
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  name: "conv5_3"
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  type: "Convolution"
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  convolution_param {
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    num_output: 512
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    pad: 1
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    kernel_size: 3
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  }
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}
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layer {
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  bottom: "conv5_3"
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  top: "conv5_3"
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  name: "relu5_3"
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  type: "ReLU"
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}
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layer {
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  bottom: "conv5_3"
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  top: "pool5"
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  name: "pool5"
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  type: "Pooling"
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  pooling_param {
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    pool: MAX
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    kernel_size: 2
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    stride: 2
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  }
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}
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