GoogLeNet

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GoogLeNet 系列

GoogLeNet v1

Going Deeper with Convolutions
参考：http://blog.csdn.net/sunbaigui/article/details/50807362
论文网络结构定义中，accuracy-1,accuracy-5,辅助loss层指定所占比例，定义:

layer {  name: "loss1/ave_pool"  type: "Pooling"  bottom: "inception_4a/output"  top: "loss1/ave_pool"  pooling_param {    pool: AVE    kernel_size: 5    stride: 3  }}layer {  name: "loss1/conv"  type: "Convolution"  bottom: "loss1/ave_pool"  top: "loss1/conv"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param {    num_output: 128    kernel_size: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0.2    }  }}layer {  name: "loss1/relu_conv"  type: "ReLU"  bottom: "loss1/conv"  top: "loss1/conv"}layer {  name: "loss1/fc"  type: "InnerProduct"  bottom: "loss1/conv"  top: "loss1/fc"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  inner_product_param {    num_output: 1024    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0.2    }  }}layer {  name: "loss1/relu_fc"  type: "ReLU"  bottom: "loss1/fc"  top: "loss1/fc"}layer {  name: "loss1/drop_fc"  type: "Dropout"  bottom: "loss1/fc"  top: "loss1/fc"  dropout_param {    dropout_ratio: 0.7 #loss2:0.7 loss3:0.6  }}layer {  name: "loss1/classifier"  type: "InnerProduct"  bottom: "loss1/fc"  top: "loss1/classifier"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  inner_product_param {    num_output: 1000    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "loss1/loss"  type: "SoftmaxWithLoss"  bottom: "loss1/classifier"  bottom: "label"  top: "loss1/loss1"  loss_weight: 0.3 # loss2:0.3  loss3:1}layer {  name: "loss1/top-1"  type: "Accuracy"  bottom: "loss1/classifier"  bottom: "label"  top: "loss1/top-1"  include {    phase: TEST  }}layer {  name: "loss1/top-5"  type: "Accuracy"  bottom: "loss1/classifier"  bottom: "label"  top: "loss1/top-5"  include {    phase: TEST  }  accuracy_param {    top_k: 5  }}

GoogLeNet v2

Rethinking the Inception Architecture for Computer Vision
参考：http://blog.csdn.net/shuzfan/article/details/50738394

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