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  • vgg16原始的protocol

    # Enter your network definition here.
    # Use Shift+Enter to update the visualization.name: "VGG_ILSVRC_16_layers"
    input: "data"
    input_dim: 16
    input_dim: 3
    input_dim: 224
    input_dim: 224
    layers {
      bottom: "data"
      top: "conv1_1"
      name: "conv1_1"
      type: CONVOLUTION
      convolution_param {
        num_output: 64
        pad: 1
        kernel_size: 3
      }
    }
    layers {
      bottom: "conv1_1"
      top: "conv1_1"
      name: "relu1_1"
      type: RELU
    }
    layers {
      bottom: "conv1_1"
      top: "conv1_2"
      name: "conv1_2"
      type: CONVOLUTION
      convolution_param {
        num_output: 64
        pad: 1
        kernel_size: 3
      }
    }
    layers {
      bottom: "conv1_2"
      top: "conv1_2"
      name: "relu1_2"
      type: RELU
    }
    layers {
      bottom: "conv1_2"
      top: "pool1"
      name: "pool1"
      type: POOLING
      pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
      }
    }
    layers {
      bottom: "pool1"
      top: "conv2_1"
      name: "conv2_1"
      type: CONVOLUTION
      convolution_param {
        num_output: 128
        pad: 1
        kernel_size: 3
      }
    }
    layers {
      bottom: "conv2_1"
      top: "conv2_1"
      name: "relu2_1"
      type: RELU
    }
    layers {
      bottom: "conv2_1"
      top: "conv2_2"
      name: "conv2_2"
      type: CONVOLUTION
      convolution_param {
        num_output: 128
        pad: 1
        kernel_size: 3
      }
    }
    layers {
      bottom: "conv2_2"
      top: "conv2_2"
      name: "relu2_2"
      type: RELU
    }
    layers {
      bottom: "conv2_2"
      top: "pool2"
      name: "pool2"
      type: POOLING
      pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
      }
    }
    layers {
      bottom: "pool2"
      top: "conv3_1"
      name: "conv3_1"
      type: CONVOLUTION
      convolution_param {
        num_output: 256
        pad: 1
        kernel_size: 3
      }
    }
    layers {
      bottom: "conv3_1"
      top: "conv3_1"
      name: "relu3_1"
      type: RELU
    }
    layers {
      bottom: "conv3_1"
      top: "conv3_2"
      name: "conv3_2"
      type: CONVOLUTION
      convolution_param {
        num_output: 256
        pad: 1
        kernel_size: 3
      }
    }
    layers {
      bottom: "conv3_2"
      top: "conv3_2"
      name: "relu3_2"
      type: RELU
    }
    layers {
      bottom: "conv3_2"
      top: "conv3_3"
      name: "conv3_3"
      type: CONVOLUTION
      convolution_param {
        num_output: 256
        pad: 1
        kernel_size: 3
      }
    }
    layers {
      bottom: "conv3_3"
      top: "conv3_3"
      name: "relu3_3"
      type: RELU
    }
    layers {
      bottom: "conv3_3"
      top: "pool3"
      name: "pool3"
      type: POOLING
      pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
      }
    }
    layers {
      bottom: "pool3"
      top: "conv4_1"
      name: "conv4_1"
      type: CONVOLUTION
      convolution_param {
        num_output: 512
        pad: 1
        kernel_size: 3
      }
    }
    layers {
      bottom: "conv4_1"
      top: "conv4_1"
      name: "relu4_1"
      type: RELU
    }
    layers {
      bottom: "conv4_1"
      top: "conv4_2"
      name: "conv4_2"
      type: CONVOLUTION
      convolution_param {
        num_output: 512
        pad: 1
        kernel_size: 3
      }
    }
    layers {
      bottom: "conv4_2"
      top: "conv4_2"
      name: "relu4_2"
      type: RELU
    }
    layers {
      bottom: "conv4_2"
      top: "conv4_3"
      name: "conv4_3"
      type: CONVOLUTION
      convolution_param {
        num_output: 512
        pad: 1
        kernel_size: 3
      }
    }
    layers {
      bottom: "conv4_3"
      top: "conv4_3"
      name: "relu4_3"
      type: RELU
    }
    layers {
      bottom: "conv4_3"
      top: "pool4"
      name: "pool4"
      type: POOLING
      pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
      }
    }
    layers {
      bottom: "pool4"
      top: "conv5_1"
      name: "conv5_1"
      type: CONVOLUTION
      convolution_param {
        num_output: 512
        pad: 1
        kernel_size: 3
      }
    }
    layers {
      bottom: "conv5_1"
      top: "conv5_1"
      name: "relu5_1"
      type: RELU
    }
    layers {
      bottom: "conv5_1"
      top: "conv5_2"
      name: "conv5_2"
      type: CONVOLUTION
      convolution_param {
        num_output: 512
        pad: 1
        kernel_size: 3
      }
    }
    layers {
      bottom: "conv5_2"
      top: "conv5_2"
      name: "relu5_2"
      type: RELU
    }
    layers {
      bottom: "conv5_2"
      top: "conv5_3"
      name: "conv5_3"
      type: CONVOLUTION
      convolution_param {
        num_output: 512
        pad: 1
        kernel_size: 3
      }
    }
    layers {
      bottom: "conv5_3"
      top: "conv5_3"
      name: "relu5_3"
      type: RELU
    }
    layers {
      bottom: "conv5_3"
      top: "pool5"
      name: "pool5"
      type: POOLING
      pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
      }
    }
    layers {
      bottom: "pool5"
      top: "fc6"
      name: "fc6"
      type: INNER_PRODUCT
      inner_product_param {
        num_output: 4096
      }
    }
    layers {
      bottom: "fc6"
      top: "fc6"
      name: "relu6"
      type: RELU
    }
    layers {
      bottom: "fc6"
      top: "fc6"
      name: "drop6"
      type: DROPOUT
      dropout_param {
        dropout_ratio: 0.5
      }
    }
    layers {
      bottom: "fc6"
      top: "fc7"
      name: "fc7"
      type: INNER_PRODUCT
      inner_product_param {
        num_output: 4096
      }
    }
    layers {
      bottom: "fc7"
      top: "fc7"
      name: "relu7"
      type: RELU
    }
    layers {
      bottom: "fc7"
      top: "fc7"
      name: "drop7"
      type: DROPOUT
      dropout_param {
        dropout_ratio: 0.5
      }
    }
    layers {
      bottom: "fc7"
      top: "fc8"
      name: "fc8"
      type: INNER_PRODUCT
      inner_product_param {
        num_output: 1000
      }
    }
    layers {
      bottom: "fc8"
      top: "prob"
      name: "prob"
      type: SOFTMAX
    }
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  • 原文地址:https://www.cnblogs.com/ymjyqsx/p/7582569.html
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