%%% matlab实现LeNet-5
%%% 作者：xd.wp
%%% 时间：2016.10.22 14:29
%% 程序说明
% 1、池化（pooling）采用平均2*2
% 2、网络结点数说明：
% 输入层：28*28
% 第一层：24*24（卷积）*20
% tanh
% 第二层：12*12（pooling）*20
% 第三层：100(全连接)
% 第四层：10(softmax)
% 3、网络训练部分采用800个样本，检验部分采用100个样本
clear all;clc;
%% 网络初始化
layer_c1_num=20;
layer_s1_num=20;
layer_f1_num=100;
layer_output_num=10;
%权值调整步进
yita=0.01;
%bias初始化
bias_c1=(2*rand(1,20)-ones(1,20))/sqrt(20);
bias_f1=(2*rand(1,100)-ones(1,100))/sqrt(20);
%卷积核初始化
[kernel_c1,kernel_f1]=init_kernel(layer_c1_num,layer_f1_num);
%pooling核初始化
pooling_a=ones(2,2)/4;
%全连接层的权值
weight_f1=(2*rand(20,100)-ones(20,100))/sqrt(20);
weight_output=(2*rand(100,10)-ones(100,10))/sqrt(100);
disp('网络初始化完成......');
%% 开始网络训练
disp('开始网络训练......');
for iter=1:20
for n=1:20
for m=0:9
%读取样本
train_data=imread(strcat(num2str(m),'_',num2str(n),'.bmp'));
train_data=double(train_data);
% 去均值
% train_data=wipe_off_average(train_data);
%前向传递,进入卷积层1
for k=1:layer_c1_num
state_c1(:,:,k)=convolution(train_data,kernel_c1(:,:,k));
%进入激励函数
state_c1(:,:,k)=tanh(state_c1(:,:,k)+bias_c1(1,k));
%进入pooling1
state_s1(:,:,k)=pooling(state_c1(:,:,k),pooling_a);
end
%进入f1层
[state_f1_pre,state_f1_temp]=convolution_f1(state_s1,kernel_f1,weight_f1);
%进入激励函数
for nn=1:layer_f1_num
state_f1(1,nn)=tanh(state_f1_pre(:,:,nn)+bias_f1(1,nn));
end
%进入softmax层
for nn=1:layer_output_num
output(1,nn)=exp(state_f1*weight_output(:,nn))/sum(exp(state_f1*weight_output));
end
%% 误差计算部分
Error_cost=-output(1,m+1);
% if (Error_cost<-0.98)
% break;
% end
%% 参数调整部分
[kernel_c1,kernel_f1,weight_f1,weight_output,bias_c1,bias_f1]=CNN_upweight(yita,Error_cost,m,train_data,...
state_c1,state_s1,...
state_f1,state_f1_temp,...
output,...
kernel_c1,kernel_f1,weight_f1,weight_output,bias_c1,bias_f1);
end
end
end
disp('网络训练完成，开始检验......');
count=0;
for n=1:20
for m=0:9
%读取样本
train_data=imread(strcat(num2str(m),'_',num2str(n),'.bmp'));
train_data=double(train_data);
% 去均值
% train_data=wipe_off_average(train_data);
%前向传递,进入卷积层1
for k=1:layer_c1_num
state_c1(:,:,k)=convolution(train_data,kernel_c1(:,:,k));
%进入激励函数
state_c1(:,:,k)=tanh(state_c1(:,:,k)+bias_c1(1,k));
%进入pooling1
state_s1(:,:,k)=pooling(state_c1(:,:,k),pooling_a);
end
%进入f1层
[state_f1_pre,state_f1_temp]=convolution_f1(state_s1,kernel_f1,weight_f1);
%进入激励函数
for nn=1:layer_f1_num
state_f1(1,nn)=tanh(state_f1_pre(:,:,nn)+bias_f1(1,nn));
end
%进入softmax层
for nn=1:layer_output_num
output(1,nn)=exp(state_f1*weight_output(:,nn))/sum(exp(state_f1*weight_output));
end
[p,classify]=max(output);
if (classify==m+1)
count=count+1;
end
fprintf('真实数字为%d 网络标记为%d 概率值为%d \n',m,classify-1,p);
end
end
%init_kernel.m
function [kernel_c1,kernel_f1]=init_kernel(layer_c1_num,layer_f1_num)
%% 卷积核初始化
for n=1:layer_c1_num
kernel_c1(:,:,n)=(2*rand(5,5)-ones(5,5))/12;
end
for n=1:layer_f1_num
kernel_f1(:,:,n)=(2*rand(12,12)-ones(12,12));
end
end
%convolution.m
function [state]=convolution(data,kernel)
%实现卷积层操作
[data_row,data_col]=size(data);
[kernel_row,kernel_col]=size(kernel);
for m=1:data_col-kernel_col+1
for n=1:data_row-kernel_row+1
state(m,n)=sum(sum(data(m:m+kernel_row-1,n:n+kernel_col-1).*kernel));
end
end
end
%pooling.m
function state=pooling(data,pooling_a)
%% 实现取样层pooling操作
[data_row,data_col]=size(data);
[pooling_row,pooling_col]=size(pooling_a);
for m=1:data_col/pooling_col
for n=1:data_row/pooling_row
state(m,n)=sum(sum(data(2*m-1:2*m,2*n-1:2*n).*pooling_a));
end
end
end
%convolution_f1.m
function [state_f1,state_f1_temp]=convolution_f1(state_s1,kernel_f1,weight_f1)
%% 完成卷积层2操作
layer_f1_num=size(weight_f1,2);
layer_s1_num=size(weight_f1,1);
%%
for n=1:layer_f1_num
count=0;
for m=1:layer_s1_num
temp=state_s1(:,:,m)*weight_f1(m,n);
count=count+temp;
end
state_f1_temp(:,:,n)=count;
state_f1(:,:,n)=convolution(state_f1_temp(:,:,n),kernel_f1(:,:,n));
end
end
%CNN_upweight.m
function [kernel_c1,kernel_f1,weight_f1,weight_output,bias_c1,bias_f1]=CNN_upweight(yita,Error_cost,classify,train_data,state_c1,state_s1,state_f1,state_f1_temp,...
output,kernel_c1,kernel_f1,weight_f1,weight_output,bias_c1,bias_f1)
%%% 完成参数更新，权值和卷积核
%% 结点数目
layer_c1_num=size(state_c1,3);
layer_s1_num=size(state_s1,3);
layer_f1_num=size(state_f1,2);
layer_output_num=size(output,2);
[c1_row,c1_col,~]=size(state_c1);
[s1_row,s1_col,~]=size(state_s1);
[kernel_c1_row,kernel_c1_col]=size(kernel_c1(:,:,1));
[kernel_f1_row,kernel_f1_col]=size(kernel_f1(:,:,1));
%% 保存网络权值
kernel_c1_temp=kernel_c1;
kernel_f1_temp=kernel_f1;
weight_f1_temp=weight_f1;
weight_output_temp=weight_output;
%% Error计算
label=zeros(1,layer_output_num);
label(1,classify+1)=1;
delta_layer_output=output-label;
%% 更新weight_output
for n=1:layer_output_num
delta_weight_output_temp(:,n)=delta_layer_output(1,n)*state_f1';
end
weight_output_temp=weight_output_temp-yita*delta_weight_output_temp;
%% 更新bias_f1以及kernel_f1
for n=1:layer_f1_num
count=0;
for m=1:layer_output_num
count=count+delta_layer_output(1,m)*weight_output(n,m);
end
%bias_f1
delta_layer_f1(1,n)=count*(1-tanh(state_f1(1,n)).^2);
delta_bias_f1(1,n)=delta_layer_f1(1,n);
%kernel_f1
delta_kernel_f1_temp(:,:,n)=delta_layer_f1(1,n)*state_f1_temp(:,:,n);
end
bias_f1=bias_f1-yita*delta_bias_f1;
kernel_f1_temp=kernel_f1_temp-yita*delta_kernel_f1_temp;
%% 更新weight_f1
for n=1:layer_f1_num
delta_layer_f1_temp(:,:,n)=delta_layer_f1(1,n)*kernel_f1(:,:,n);
end
for n=1:layer_s1_num
for m=1:layer_f1_num
delta_weight_f1_temp(n,m)=sum(sum(delta_layer_f1_temp(:,:,m).*state_s1(:,:,n)));
end
end
weight_f1_temp=weight_f1_temp-yita*delta_weight_f1_temp;
%% 更新 bias_c1
for n=1:layer_s1_num
count=0;
for m=1:layer_f1_num
count=count+delta_layer_f1_temp(:,:,m)*weight_f1(n,m);
end
delta_layer_s1(:,:,n)=count;
delta_layer_c1(:,:,n)=kron(delta_layer_s1(:,:,n),ones(2,2)/4).*(1-tanh(state_c1(:,:,n)).^2);
delta_bias_c1(1,n)=sum(sum(delta_layer_c1(:,:,n)));
end
bias_c1=bias_c1-yita*delta_bias_c1;
%% 更新 kernel_c1
for n=1:layer_c1_num
delta_kernel_c1_temp(:,:,n)=rot90(conv2(train_data,rot90(delta_layer_c1(:,:,n),2),'valid'),2);
end
kernel_c1_temp=kernel_c1_temp-yita*delta_kernel_c1_temp;
%% 网络权值更新
kernel_c1=kernel_c1_temp;
kerne
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