1.配置caffe环境

[请参考此篇博客：http://blog.csdn.net/ws_20100/article/details/48850449]

本篇介绍如何在caffe环境下，实现"图像对图像"的卷积神经网络的训练。

2.文件结构

在配置好的caffe文件夹中，进入examples目录，创建CNN文件夹，并进入文件夹

$ cd caffe-master/examples/

$ mkdir CNN

$ cd CNN/

在CNN文件夹下面创建子文件夹

$ mkdir model snapshot TestPhotos TestLabels TrainPhotos TrainLabels

其中，

model用于以后存储卷积核矩阵和偏置向量；

snapshot用于存储训练中备份的caffe模型，每一段时间存储一次，防止断电等一些情况；

TrainPhotos、TrainLabels分别存储训练集输入和监督样本；

TestPhotos、TestLabels分别存储测试集输入和监督样本，不直接参与到训练中。

然后，将训练所用的输入样本和监督样本分别放入到TrainPhotos和TrainLabels中去。注意，样本文件名无所谓，但是排列次序必须一一对应。同样，将测试所用的输入样本和监督样本分别放入到TestPhotos和TestLabels中去。

3.产生训练和测试数据

1.)产生路径文件

在CNN文件夹下面(以下均是在此文件夹下)创建两个路径文件。

$ vim train.txt

输入内容：

examples/CNN/train.h5

:wq保存文档。

$ vim test.txt

输入内容：

examples/CNN/test.h5

:wq保存文档。

2.)产生训练数据

$ vim generate_train.m

输入内容：(把输入图像切成11*11的像素块，监督图像为3*3的像素块(由网络结构和卷积核大小决定)，步长为1个像素)

clear;close all;%% settings
folder_input = 'TrainPhotos';
folder_label = 'TrainLabels';
savepath = 'train.h5';
size_input = 11;
size_label = 3; % size_input - 12
stride = 1;%% initialization
data = zeros(size_input, size_input, 1, 1);
label = zeros(size_label, size_label, 1, 1);
padding = abs(size_input - size_label) / 2;
count = 0;%% read data
filepaths_input = dir(fullfile(folder_input,'*.jpg'));
filepaths_label = dir(fullfile(folder_label,'*.jpg'));if (length(filepaths_input)==length(filepaths_label))length = length(filepaths_input);
elseerror('The Number of Input is NOT equal to the Number of Label.');
end%% generate data
for i = 1 : lengthim_input = imread(fullfile(folder_input,filepaths_input(i).name));im_input = rgb2ycbcr(im_input);im_input = im2double(im_input(:, :, 1));im_label = imread(fullfile(folder_label,filepaths_label(i).name));im_label = im2double(im_label(:, :, 1));if size(im_input) == size(im_label)[hei,wid] = size(im_input);elseerror('The size of input and label are not equal.');endfor x = 1 : stride : hei-size_input+1for y = 1 :stride : wid-size_input+1subim_input = im_input(x : x+size_input-1, y : y+size_input-1);subim_label = im_label(x+padding : x+padding+size_label-1, y+padding : y+padding+size_label-1);count = count + 1;data(:, :, 1, count) = subim_input;label(:, :, 1, count) = subim_label;endendend%% randomized the data and label
order = randperm(count);
data = data(:, :, 1, order);
label = label(:, :, 1, order); %% writing to HDF5
chunksz = 128;
created_flag = false;
totalct = 0;for batchno = 1:floor(count/chunksz)last_read=(batchno-1)*chunksz;batchdata = data(:,:,1,last_read+1:last_read+chunksz); batchlabs = label(:,:,1,last_read+1:last_read+chunksz);startloc = struct('dat',[1,1,1,totalct+1], 'lab', [1,1,1,totalct+1]);curr_dat_sz = store2hdf5(savepath, batchdata, batchlabs, ~created_flag, startloc, chunksz); created_flag = true;totalct = curr_dat_sz(end);
end
h5disp(savepath);

终端下输入：

$ matlab -nodesktop -nosplash -logfile generate_train.log -r generate_train

产生训练数据train.h5。

3.)产生测试数据

$ vim generate_test.m

generate_test.m只需要将generate_test.m文件开头改为：

clear;close all;%% settings
folder_input = 'TestPhotos';
folder_label = 'TestLabels';
savepath = 'test.h5';
size_input = 11;
size_label = 3;
stride = 30;

将最后一段改成：

%% writing to HDF5
chunksz = 2;
created_flag = false;
totalct = 0;for batchno = 1:floor(count/chunksz)last_read=(batchno-1)*chunksz;batchdata = data(:,:,1,last_read+1:last_read+chunksz); batchlabs = label(:,:,1,last_read+1:last_read+chunksz);startloc = struct('dat',[1,1,1,totalct+1], 'lab', [1,1,1,totalct+1]);curr_dat_sz = store2hdf5(savepath, batchdata, batchlabs, ~created_flag, startloc, chunksz); created_flag = true;totalct = curr_dat_sz(end);
end
h5disp(savepath);

终端下输入：

$ matlab -nodesktop -nosplash -logfile generate_test.log -r generate_test

产生测试数据test.h5。仅仅用于判断训练到达什么地步。

4.建立训练文件

1.)建立solver文件

$ vim CNN_solver.prototxt

此为运行的配置文件，输入以下内容：

# The train/test net protocol buffer definition
net: "examples/CNN/CNN_net.prototxt"
test_iter: 556
# Carry out testing every 500 training iterations.
test_interval: 500
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.0001
momentum: 0.9
weight_decay: 0
# The learning rate policy
lr_policy: "fixed"
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: 15000000
# snapshot intermediate results
snapshot: 500
snapshot_prefix: "examples/CNN/snapshot/CNN"
# solver mode: CPU or GPU
solver_mode: GPU

:wq保存退出。

2.)建立net文件

$ vim CNN_net.prototxt

此为网络结构配置文件，可以配置网络层数，节点数，卷积核等参数。输入以下内容：

name: "CNN"
layer {name: "data"type: "HDF5Data"top: "data"top: "label"hdf5_data_param {source: "examples/CNN/train.txt"batch_size: 128}include: { phase: TRAIN }
}
layer {name: "data"type: "HDF5Data"top: "data"top: "label"hdf5_data_param {source: "examples/CNN/test.txt"batch_size: 2}include: { phase: TEST }
}layer {name: "conv1"type: "Convolution"bottom: "data"top: "conv1"param {lr_mult: 1}param {lr_mult: 0.1}convolution_param {num_output: 128kernel_size: 5stride: 1pad: 0weight_filler {type: "gaussian"std: 0.001}bias_filler {type: "constant"value: 0}}
}layer {name: "relu1"type: "ReLU"bottom: "conv1"top: "conv1"
}layer {name: "conv2"type: "Convolution"bottom: "conv1"top: "conv2"param {lr_mult: 1}param {lr_mult: 0.1}convolution_param {num_output: 64kernel_size: 3stride: 1pad: 0weight_filler {type: "gaussian"std: 0.001}bias_filler {type: "constant"value: 0}}
}layer {name: "relu2"type: "ReLU"bottom: "conv2"top: "conv2"
}layer {name: "conv3"type: "Convolution"bottom: "conv2"top: "conv3"param {lr_mult: 0.1}param {lr_mult: 0.1}convolution_param {num_output: 1kernel_size: 3stride: 1pad: 0weight_filler {type: "gaussian"std: 0.001}bias_filler {type: "constant"value: 0}}
}layer {name: "loss"type: "EuclideanLoss"bottom: "conv3"bottom: "label"top: "loss"
}

:wq保存退出。

5.CNN训练

$ vim train.sh

输入以下shell：

#!/bin/bash
cd ../../
./build/tools/caffe train --solver examples/CNN/CNN_solver.prototxt 2>&1 | tee examples/CNN/CNN.log

增加运行权限：

$ chmod +x train.sh

运行脚本文件：

$ ./train.sh

时间可能会运行几天，也可以提前退出(Ctrl+C)，因为在snapshot中有中间备份存储。

6.保存滤波器

1.)创建mat文件

$ cp CNN_net.prototxt CNN_mat.prototxt

将CNN_mat.prototxt文件开头两个layer段改为：

name: "CNN"
input: "data"
input_dim: 1
input_dim: 1
input_dim: 11
input_dim: 11input: "label"
input_dim: 1
input_dim: 1
input_dim: 3
input_dim: 3

:wq保存即可。

2.)创建M文件

$ vim saveFilters.m

输入以下内容：(第7行可以更改需要转换的caffemodel文件名)

caffe.reset_all();
clear; close all;
%% settings
%folder = 'examples/CNN/';
folder = './';
model = [folder 'CNN_mat.prototxt'];
weights = [folder 'snapshot/CNN_iter_550000.caffemodel'];
savepath = [folder 'model/x.mat'];
layers = 3;%% load model using mat_caffe
net = caffe.Net(model,weights,'test');%% reshap parameters
weights_conv = cell(layers,1);for idx = 1 : layersconv_filters = net.layers(['conv' num2str(idx)]).params(1).get_data();[~,fsize,channel,fnum] = size(conv_filters);if channel == 1weights = double(ones(fsize^2, fnum));elseweights = double(ones(channel, fsize^2, fnum));endfor i = 1 : channelfor j = 1 : fnumtemp = conv_filters(:,:,i,j);if channel == 1weights(:,j) = temp(:);elseweights(i,:,j) = temp(:);endendendweights_conv{idx} = weights;
end%% save parameters
weights_conv1 = weights_conv{1};
weights_conv2 = weights_conv{2};
weights_conv3 = weights_conv{3};
biases_conv1 = double(net.layers('conv1').params(2).get_data());
biases_conv2 = double(net.layers('conv2').params(2).get_data());
biases_conv3 = double(net.layers('conv3').params(2).get_data());save(savepath,'weights_conv1','biases_conv1','weights_conv2','biases_conv2','weights_conv3','biases_conv3');

:wq保存。

3.)运行M文件

$ matlab -nodesktop -nosplash -logfile saveFilters.log -r saveFilters

此时，在model中会生成x.mat文件。

7.CNN重构

已经知道了x.mat文件中，有三层卷积层的卷积核矩阵weights_conv*和偏置向量biases_conv*。

编写一个demo_net.m文件，使用这些参数构建卷积网络结构，对输入图像(矩阵)进行处理，即可得到结果。

不同应用有不同的源码，这里省略该文件源码。

本文可能叙述不全面，如有错误，欢迎指正！

Enjoy~~