卷积的研究和图像处理的研究 matlab

%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; 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; kernel_f1=kernel_f1_temp; weight_f1=weight_f1_temp; weight_output=weight_output_temp; end