根据 train_test.prototxt文件生成 deploy.prototxt文件

本文参考博文

（1）介绍 *_train_test.prototxt文件与 *_deploy.prototxt文件的不同：http://blog.csdn.net/sunshine_in_moon/article/details/49472901    

（2）生成deploy文件的Python代码：http://www.cnblogs.com/denny402/p/5685818.html       

*_train_test.prototxt文件

这是训练与测试网络配置文件

*_deploy.prototxt文件
这是模型构造文件

在博文http://www.cnblogs.com/denny402/p/5685818.html     中给出了生成 deploy.prototxt文件的Python源代码，但是每个网络不同，修改起来比较麻烦，下面给出该博文中以mnist为例生成deploy文件的源代码，可根据自己网络的设置做出相应修改：（下方代码未测试）

# -*- coding: utf-8 -*-

from caffe import layers as L,params as P,to_proto
root='/home/xxx/'
deploy=root+'mnist/deploy.prototxt' #文件保存路径

def create_deploy():
#少了第一层，data层
conv1=L.Convolution(bottom='data', kernel_size=5, stride=1,num_output=20, pad=0,weight_filler=dict(type='xavier'))
pool1=L.Pooling(conv1, pool=P.Pooling.MAX, kernel_size=2, stride=2)
conv2=L.Convolution(pool1, kernel_size=5, stride=1,num_output=50, pad=0,weight_filler=dict(type='xavier'))
pool2=L.Pooling(conv2, pool=P.Pooling.MAX, kernel_size=2, stride=2)
fc3=L.InnerProduct(pool2, num_output=500,weight_filler=dict(type='xavier'))
relu3=L.ReLU(fc3, in_place=True)
fc4 = L.InnerProduct(relu3, num_output=10,weight_filler=dict(type='xavier'))
#最后没有accuracy层，但有一个Softmax层
prob=L.Softmax(fc4)
return to_proto(prob)
def write_deploy(): 
with open(deploy, 'w') as f:
f.write('name:"Lenet"
')
f.write('input:"data"
')
f.write('input_dim:1
')
f.write('input_dim:3
')
f.write('input_dim:28
')
f.write('input_dim:28
')
f.write(str(create_deploy()))
if __name__ == '__main__':
write_deploy()

　　

用代码生成deploy文件还是比较麻烦。我们在构建深度学习网络时，肯定会先定义好训练与测试网络的配置文件——*_train_test.prototxt文件，我们可以通过修改*_train_test.prototxt文件 来生成 deploy 文件。以cifar10为例先简单介绍一下两者的区别。

（1）deploy 文件中的数据层更为简单，即将*_train_test.prototxt文件中的输入训练数据lmdb与输入测试数据lmdb这两层删除，取而代之的是，

layer {
name: "data"
type: "Input"
top: "data"
input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
}

　　

注：shape: { dim: 1 dim: 3 dim: 32 dim: 32 }代表含义：

shape {
dim: 1 #num，对待识别样本进行数据增广的数量，可自行定义。一般会进行5次crop，之后分别flip。如果该值为10则表示一个样本会变成10个，之后输入到网络进行识别。如果不进行数据增广，可以设置成1
dim: 3 #通道数，表示RGB三个通道
dim: 32 #图像的长和宽，通过 *_train_test.prototxt文件中数据输入层的crop_size获取
dim: 32}

　　

（2）卷积层和全连接层中weight_filler{}与bias_filler{}两个参数不用再填写，因为这两个参数的值，由已经训练好的模型*.caffemodel文件提供。如下所示代码，将*_train_test.prototxt文件中的weight_filler、bias_filler全部删除。

layer {                              # weight_filler、bias_filler删除
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1   #权重w的学习率倍数
  }
  param {
    lr_mult: 2    #偏置b的学习率倍数
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
  }
}

　　

删除后变为

layer { 
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
}
}

　　

（3）输出层的变化  
     1）没有了test模块测试精度 ，将该层删除     
     2）输出层

1）*_deploy.prototxt文件的构造和*_train_test.prototxt文件的构造最为明显的不同点是，deploy文件没有test网络中的test模块，只有训练模块，即将*_train_test.prototxt中最后部分的test模块测试精度删除，即将如下代码删除。

layer { #删除该层
name: "accuracy"
type: "Accuracy"
bottom: "ip2"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}

　　

2） 输出层 

*_train_test.prototxt文件

layer{
name: "loss" #注意此处层名称与下面的不同
type: "SoftmaxWithLoss" #注意此处与下面的不同
bottom: "ip2"
bottom: "label" #注意标签项在下面没有了，因为下面的预测属于哪个标签，因此不能提供标签
top: "loss"
}

　　

*_deploy.prototxt文件

layer {
name: "prob"
type: "Softmax"
bottom: "ip2"
top: "prob"
}

　　

注意在两个文件中输出层的类型都发生了变化一个是SoftmaxWithLoss，另一个是Softmax。另外为了方便区分训练与应用输出，训练是输出时是loss，应用时是prob。

下面给出CIFAR10中的配置文件cifar10_quick_train_test.prototxt与其模型构造文件  cifar10_quick.prototxt 直观展示两者的区别。

cifar10_quick_train_test.prototxt文件代码

name: "CIFAR10_quick"
layer {               #该层去掉
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mean_file: "examples/cifar10/mean.binaryproto"
  }
  data_param {
    source: "examples/cifar10/cifar10_train_lmdb"
    batch_size: 100
    backend: LMDB
  }
}
layer {             #该层去掉
  name: "cifar"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mean_file: "examples/cifar10/mean.binaryproto"
  }
  data_param {
    source: "examples/cifar10/cifar10_test_lmdb"
    batch_size: 100
    backend: LMDB
  }
}
layer {                        #将下方的weight_filler、bias_filler全部删除
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.0001
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "pool1"
  top: "pool1"
}
layer {                         #weight_filler、bias_filler删除
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {                         #weight_filler、bias_filler删除
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: AVE
    kernel_size: 3
    stride: 2
  }
}
layer {                       #weight_filler、bias_filler删除
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 64
    weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {                              # weight_filler、bias_filler删除
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {                                  #将该层删除
  name: "accuracy"
  type: "Accuracy"
  bottom: "ip2"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {                                 #修改
  name: "loss"       #---loss  修改为  prob 
  type: "SoftmaxWithLoss"             # SoftmaxWithLoss 修改为 softmax
  bottom: "ip2"
  bottom: "label"          #去掉
  top: "loss"
}

　　

以下为cifar10_quick.prototxt

layer {               #将两个输入层修改为该层
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }     #注意shape中变量值的修改，CIFAR10中的 *_train_test.protxt文件中没有 crop_size
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1   #权重W的学习率倍数
}
  param {
    lr_mult: 2   #偏置b的学习率倍数
  }
  convolution_param {
    num_output: 32
    pad: 2   #加边为2
   kernel_size: 5
    stride: 1
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX    #Max Pooling
   kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "pool1"
  top: "pool1"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    stride: 1
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: AVE   #均值池化
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    stride: 1
  }
}
layer {
  name: "relu3"
  type: "ReLU"  #使用ReLU激励函数，这里需要注意的是，本层的bottom和top都是conv3>
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: AVE 
kernel_size: 3
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool3"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 64
  }
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 10
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "ip2"
  top: "prob"
}

　　

---------------------
作者：修炼打怪的小乌龟
来源：CSDN
原文：https://blog.csdn.net/u010417185/article/details/52137825
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