基于CNN卷积神经网络Matlab实现源码

function [kernel_c1,kernel_c2,weight_c2,weight_full_1,weight_full_2,weight_arr2num]=CNN_upweight(Error,train_data,state_c1,state_s1,state_c2,state_s2_temp1,state_s2_temp2,state_s2,... state_f1,state_f2,kernel_c1,kernel_c2,weight_c2,weight_full_1,weight_full_2,weight_arr2num,yita,state_c2_temp1) %%% 完成参数更新，权值和卷积核 %% 结点数目 layer_f2_num=size(state_f2,2); layer_f1_num=size(state_f1,2); layer_c2_num=size(state_c2,3); layer_c1_num=size(state_c1,3); [c2_row,c2_col,~]=size(state_c2); [c1_row,c1_col,~]=size(state_c1); [s2_row,s2_col,~]=size(state_s2_temp1); [kernel_row,kernel_col]=size(kernel_c1(:,:,1)); %% 保存网络权值 weight_full_2_temp=weight_full_2; weight_full_1_temp=weight_full_1; weight_arr2num_temp=weight_arr2num; kernel_c2_temp=kernel_c2; kernel_c1_temp=kernel_c1; weight_c2_temp=weight_c2; %% 更新weight_full_2 for n=1:layer_f2_num delta_weight_full_2_temp(:,n)=2*Error(1,n)*state_f1'; end size(delta_weight_full_2_temp); weight_full_2_temp=weight_full_2_temp-yita*delta_weight_full_2_temp; %% 更新weight_full_1 for n=1:layer_f2_num for m=1:layer_f1_num delta_weight_full_1_temp(:,m)=2*Error(1,n)*weight_full_2(m,n)*state_s2'; end weight_full_1_temp=weight_full_1_temp-yita*delta_weight_full_1_temp; end %% 更新weight_arr2num for m=1:layer_c2_num for n=1:layer_f2_num count_delta_weight_arr2num_temp=zeros(s2_row,s2_col); for k=1:layer_f1_num delta_weight_arr2num_temp(:,:,m)=2*Error(1,n)*weight_full_1(m,k)*weight_full_2(k,n)*exp(-state_s2_temp2(1,m))/(1+exp(-state_s2_temp2(1,m))).^2*state_s2_temp1(:,:,m); count_delta_weight_arr2num_temp=count_delta_weight_arr2num_temp+delta_weight_arr2num_temp(:,:,m); end weight_arr2num_temp(:,:,m)=weight_arr2num_temp(:,:,m)-yita*count_delta_weight_arr2num_temp; end end %% 更新kernel_c2 for m=1:layer_c2_num for n=1:layer_f2_num count_delta_state_s2_temp1=zeros(s2_row,s2_col); for k=1:layer_f1_num delta_state_s2_temp1(:,:,m)=2*Error(1,n)*weight_full_1(m,k)*weight_full_2(k,n)*exp(-state_s2_temp2(1,m))/(1+exp(-state_s2_temp2(1,m))).^2*weight_arr2num(:,:,m); count_delta_state_s2_temp1=count_delta_state_s2_temp1+delta_state_s2_temp1(:,:,m); end delta_state_c2=kron(count_delta_state_s2_temp1,ones(2,2)/4); count=zeros(kernel_row,kernel_col); for k_row=1:c2_row for k_col=1:c2_col count=count+state_c2_temp1(k_row:k_row+kernel_row-1,k_col:k_col+kernel_col-1)*delta_state_c2(k_row,k_col); end end kernel_c2_temp(:,:,m)=kernel_c2_temp(:,:,m)-yita*count; end end %% 更新 weight_c2 for n=1:layer_f2_num for m=1:layer_c2_num count_delta_state_s2_temp1=zeros(s2_row,s2_col); for kk=1:layer_f1_num delta_state_s2_temp1(:,:,m)=2*Error(1,n)*weight_full_1(m,kk)*weight_full_2(kk,n)*exp(-state_s2_temp2(1,m))/(1+exp(-state_s2_temp2(1,m))).^2*weight_arr2num(:,:,m); count_delta_state_s2_temp1=count_delta_state_s2_temp1+delta_state_s2_temp1(:,:,m); end delta_state_c2=kron(count_delta_state_s2_temp1,ones(2,2)/4); delta_state_c2_temp1(:,:,m)=conv2(delta_state_c2,kernel_c2(:,:,m),'full'); for k=1:layer_c1_num delta_weight_c2_temp(k,m)=sum(sum(delta_state_c2_temp1(:,:,m).*state_s1(:,:,k))); end end weight_c2_temp=weight_c2_temp-yita*delta_weight_c2_temp; end %% 更新 kernel_c1 for n=1:layer_f2_num for m=1:layer_c2_num count_delta_state_s2_temp1=zeros(s2_row,s2_col); for kk=1:layer_f1_num delta_state_s2_temp1(:,:,m)=2*Error(1,n)*weight_full_1(m,kk)*weight_full_2(kk,n)*exp(-state_s2_temp2(1,m))/(1+exp(-state_s2_temp2(1,m))).^2*weight_arr2num(:,:,m); count_delta_state_s2_temp1=count_delta_state_s2_temp1+delta_state_s2_temp1(:,:,m); end delta_state_c2=kron(count_delta_state_s2_temp1,ones(2,2)/4); delta_state_c2_temp1(:,:,m)=conv2(delta_state_c2,kernel_c2(:,:,m),'full'); end for k=1:layer_c1_num [x,y,~]=size(state_c2_temp1); count1=zeros(x,y); for m=1:layer_c2_num count_temp=weight_c2(k,m)*delta_state_c2_temp1(:,:,m); count1=count1+count_temp; end delta_state_s1(:,:,k)=count1; delta_state_c1(:,:,k)=kron(delta_state_s1(:,:,k),ones(2,2)/4); % count2=zeros(kernel_row,kernel_col); for k_row=1:c1_row for k_col=1:c1_col count=count+train_data(k_row:k_row+kernel_row-1,k_col:k_col+kernel_col-1)*delta_state_c1(k_row,k_col,k); end end kernel_c1_temp(:,:,k)=kernel_c1_temp(:,:,k)-yita*count; end end %% 权值更新 weight_c2=weight_c2_temp; kernel_c1=kernel_c1_temp; kernel_c2=kernel_c2_temp; weight_arr2num=weight_arr2num_temp; weight_full_2=weight_full_2_temp; weight_full_1=weight_full_1_temp; end