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"\n')
f.write('input:"data"\n')
f.write('input_dim:1\n')
f.write('input_dim:3\n')
f.write('input_dim:28\n')
f.write('input_dim:28\n')
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
版权声明：本文为博主原创文章，转载请附上博文链接！
