Prototxt文件升级和可视化

今天在阅读《Deep Learning of Binary Hash Codes for Fast Image Retrieval》论文提供的源码时，本想着将将作者设计的网络结构进行可视化，但是结果根本不对。通过和caffe官方提供的实例进行比对，发现原作者写的prototxt文件早已过时了。
原Prototxt部分内容如下：

name: "KevinNet_CIFAR10"layers {  layer {    name: "data"    type: "data"    source: "cifar10_train_leveldb"    meanfile: "../../data/ilsvrc12/imagenet_mean.binaryproto"    batchsize: 32    cropsize: 227    mirror: true    det_context_pad: 16    det_crop_mode: "warp"    det_fg_threshold: 0.5    det_bg_threshold: 0.5    det_fg_fraction: 0.25  }  top: "data"  top: "label"}layers {  layer {    name: "conv1"    type: "conv"    num_output: 96    kernelsize: 11    stride: 4    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0.    }    blobs_lr: 1.    blobs_lr: 2.    weight_decay: 1.    weight_decay: 0.  }  bottom: "data"  top: "conv1"}layers {  layer {    name: "relu1"    type: "relu"  }  bottom: "conv1"  top: "conv1"}layers {  layer {    name: "pool1"    type: "pool"    pool: MAX    kernelsize: 3    stride: 2  }  bottom: "conv1"  top: "pool1"}layers {  layer {    name: "norm1"    type: "lrn"    local_size: 5    alpha: 0.0001    beta: 0.75  }  bottom: "pool1"  top: "norm1"}...

可视化结果：

升级后的Prototxt：

name: "upgraded_KevinNet_CIFAR10"layer{    name:"data"    type:"Data"    top:"data"    top:"label"    window_data_param{        source:"cifar10_train_leveldb"        batch_size:32        mean_file:"../../data/ilsvrc12/imagenet_mean.binaryproto"        crop_size:227        mirror:true        context_pad:16        crop_mode:"wrap"        fg_threshold:0.5        bg_threshold:0.5        fg_fraction:0.25    }}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:96        kernel_size:11        stride:4        weight_filler{            type:"guassian"            std:0.01        }        bias_filler{            type:"constant"            value:0.        }    }}layer{    name:"relu1"    type:"ReLU"    bottom:"conv1"    top:"conv1"}layer{    name:"pool1"    type:"Pooling"    bottom:"conv1"    top:"pool1"    pooling_param{        pool:MAX        kernel_size:3        stride:2    }}layer{    name:"norm1"    type:"LRN"    bottom:"pool1"    top:"norm1"    lrn_param{        local_size:5        alpha:0.0001        beta:0.75    }}layer{    name:"conv2"    type:"Convolution"    bottom:"norm1"    top:"conv2"    param{        lr_mult:1        decay_mult:1.    }    param{        lr_mult:2        decay_mult:0.    }    convolution_param{        num_output:256        kernel_size:5        group:2        pad:2        weight_filler{            type:"guassian"            std:0.01        }        bias_filler{            type:"constant"            value:1.        }    }}layer{    name:"relu2"    type:"ReLU"    bottom:"conv2"    top:"conv2"}layer{    name:"pool2"    type:"Pooling"    bottom:"conv2"    top:"pool2"    pooling_param{        pool:MAX        kernel_size:3        stride:2    }}layer{    name:"norm2"    type:"LRN"    bottom:"pool2"    top:"norm2"    lrn_param{        local_size:5        alpha:0.0001        beta:0.75    }}layer{    name:"conv3"    type:"Convolution"    bottom:"norm2"    top:"conv3"    param{        lr_mult:1        decay_mult:1.    }    param{        lr_mult:2        decay_mult:0.    }    convolution_param{        num_output:384        kernel_size:3        pad:1        weight_filler{            type:"guassian"            std:0.01        }        bias_filler{            type:"constant"            value:0.        }    }}layer{    name:"relu3"    type:"ReLU"    bottom:"conv3"    top:"conv3"}layer{    name:"conv4"    type:"Convolution"    bottom:"conv3"    top:"conv4"    param{        lr_mult:1        decay_mult:1.    }    param{        lr_mult:2        decay_mult:0.    }    convolution_param{        num_output:384        kernel_size:3        pad:1        group:2        weight_filler{            type:"guassian"            std:0.01        }        bias_filler{            type:"constant"            value:0.        }    }}layer{    name:"relu4"    type:"ReLU"    bottom:"conv4"    top:"conv4"}layer{    name:"conv5"    type:"Convolution"    bottom:"conv4"    top:"conv5"    param{        lr_mult:1        decay_mult:1.    }    param{        lr_mult:2        decay_mult:0.    }    convolution_param{        num_output:256        kernel_size:3        pad:1        group:2        weight_filler{            type:"guassian"            std:0.01        }        bias_filler{            type:"constant"            value:0.        }    }}layer{    name:"relu5"    type:"ReLU"    bottom:"conv5"    top:"conv5"}layer{    name:"pool5"    type:"Pooling"    bottom:"conv5"    top:"pool5"    pooling_param{        kernel_size:3        pool:MAX        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.    }    inner_product_param{        num_output:4096        weight_filler {            type: "gaussian"            std: 0.005        }        bias_filler {            type: "constant"            value: 1.        }    }}layer{    name:"relu6"    type:"ReLU"    bottom:"fc6"    top:"fc6"}layer{    name:"drop6"    type:"Dropout"    bottom:"fc6"    top:"fc6"    dropout_param{        dropout_ratio:0.5    }}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:4096        weight_filler {            type: "gaussian"            std: 0.005        }        bias_filler {            type: "constant"            value: 1.        }    }}layer{    name:"relu7"    type:"ReLU"    bottom:"fc7"    top:"fc7"}layer{    name:"drop7"    type:"Dropout"    bottom:"fc7"    top:"fc7"    dropout_param{        dropout_ratio:0.5    }}layer{    name:"fc8_kevin"    type:"InnerProduct"    bottom:"fc7"    top:"fc8_kevin"    param{        lr_mult:1        decay_mult:1.    }    param{        lr_mult:2        decay_mult:0.    }    inner_product_param{        num_output:48        weight_filler {            type: "gaussian"            std: 0.005        }        bias_filler {            type: "constant"            value: 1.        }    }}layer{    name:"fc8_kevin_encode"    type:"Sigmoid"    bottom:"fc8_kevin"    top:"fc8_kevin_encode"}layer{    name:"fc8_pascal"    type:"InnerProduct"    bottom:"fc8_kevin_encode"    top:"fc8_pascal"    param{        lr_mult:10        decay_mult:1.    }    param{        lr_mult:20        decay_mult:0.    }    inner_product_param{        num_output:10        weight_filler {            type: "gaussian"            std: 0.01        }        bias_filler {            type: "constant"            value: 0.        }    }}layer{    name:"loss"    type:"SofmaxWithLoss"    bottom:"fc8_pascal"    bottom:"label"}

这里我是为了可视化网络结构，只是简单对照新版本升级了语法结构，并没有对后续的训练进行测试。
网络结构图如下：