CNN_python_卷积神经网络matlab代码_CNN

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% LeNet-5 %%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% 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 close all; clear; 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('real number %d network grid %d probility value %d \n',m,classify-1,p); end end