利用程序写caffe协议：同名层

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利用程序写协议，最大的困难在于：同名层的书写：

# coding=gbkfrom caffe import layers as L, params as Pimport caffens = caffe.NetSpec()ns.Features,ns.Headposes,ns.Smiles = L.Data(name="data", ntop=3,                 include={'phase':caffe.TRAIN})ns.test_data = L.Data(name="data", ntop = 0, top=['Features','Headposes','Smiles'],                 include={'phase':caffe.TEST})print '{}'.format(ns.to_proto())

输出：

layer {  name: "data"  type: "Data"  top: "Features"  top: "Headposes"  top: "Smiles"  include {    phase: TRAIN  }}layer {  name: "data"  type: "Data"  top: "Features"  top: "Headposes"  top: "Smiles"  include {    phase: TEST  }}

黏在一起的书写：

n.relu1= L.ReLU(n.conv1,ntop = 0,top='conv1')n.norm1 = L.LRN(n.conv1,local_size=5,alpha=0.0001,beta=0.75)

结果：

这里写图片描述

code:

n.conv1 = L.Convolution(n.Features, kernel_size=11,stride=4, num_output=96,                             param=[dict(lr_mult=1,decay_mult=1), dict(lr_mult=2,decay_mult=0)],                            weight_filler=dict(type='gaussian',std= 0.01),                            bias_filler=dict(type='constant',value= 0))

output:

layer {  name: "conv1"  type: "Convolution"  bottom: "Features"  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: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0    }  }}

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