ctest2.m

clc
clear
%% 构造真实训练样本 60000个样本 1*784维（28*28展开）
load mnist_uint8;

train_x = double(train_x(1:60000,:)) / 255;
label_x = double(train_y);

train_y = double(ones(size(train_x,1),1));

train_x = mapminmax(train_x, 0, 1);
train_x = [train_x,label_x];

%% 构造模拟训练样本 60000个样本 1*100维
test_x = normrnd(0,1,[60000,100]); % 0-255的整数

test_x = mapminmax(test_x, 0, 1);

test_y = double(zeros(size(test_x,1),1));
test_y_rel = double(ones(size(test_x,1),1));

%%
nn_G_t = nnsetup([110 1200 784]);
nn_G_t.activation_function = 'sigm';
nn_G_t.output = 'sigm';

nn_D = nnsetup([794 100 1]);
nn_D.weightPenaltyL2 = 1e-4;  %  L2 weight decay
nn.dropoutFraction = 0.5;   %  Dropout fraction 
nn.learningRate = 0.1;                %  Sigm require a lower learning rate
nn_D.activation_function = 'sigm';
nn_D.output = 'sigm';
% nn_D.weightPenaltyL2 = 1e-4;  %  L2 weight decay

nn_G = nnsetup([110 1200 784 100 1]);
nn_D.weightPenaltyL2 = 1e-4;  %  L2 weight decay
nn.dropoutFraction = 0.5;   %  Dropout fraction 
nn.learningRate = 0.1;                %  Sigm require a lower learning rate
nn_G.activation_function = 'sigm';
nn_G.output = 'sigm';
% nn_G.weightPenaltyL2 = 1e-4;  %  L2 weight decay

opts.numepochs =  1;        %  Number of full sweeps through data
opts.batchsize = 100;       %  Take a mean gradient step over this many samples

%%
num = 1000;
tic
for each = 1:2000
    disp(['each ======================',num2str(each)]);
    %---------------------------------
    for i = 1:length(nn_G_t.W)
        nn_G_t.W{i} = nn_G.W{i};
    end
    G_output = nn_G_out(nn_G_t, [test_x,label_x]);
    %-----------------------------------------
    index = randperm(60000);
    train_data_D = [train_x(index(1:num),:);[G_output(index(1:num),:),label_x(index(1:num),:)]];
    
    train_y_D = [train_y(index(1:num),:);test_y(index(1:num),:)];
   
    nn_D = nntrain(nn_D, train_data_D, train_y_D, opts);
 
    %------------------------------------------
    for i = 1:length(nn_D.W)
        if size(nn_G.W{length(nn_G.W)-i+1},2) == size(nn_D.W{length(nn_D.W)-i+1},2)
            nn_G.W{length(nn_G.W)-i+1} = nn_D.W{length(nn_D.W)-i+1};
        else
            nn_G.W{length(nn_G.W)-i+1} = nn_D.W{length(nn_D.W)-i+1}(:,1:end-10);
        end
    end
    nn_G.W{1}(1:200,101:end) = 0;
    nn_G.W{1}(201:end,2:100) = 0;
    
    nn_G = nntrain(nn_G, [test_x(index(1:num),:),label_x(index(1:num),:)], test_y_rel(index(1:num),:), opts);
 
    nn_G.W{1}(1:200,101:end) = 0;
    nn_G.W{1}(201:end,2:100) = 0;
    
end
toc
for i = 1:length(nn_G_t.W)
    nn_G_t.W{i} = nn_G.W{i};
end
fin_output = nn_G_out(nn_G_t, [test_x,label_x]);
%% show result
for i= 1:1000:60000
    a = fin_output(i,:);
    a = reshape(a,28,28);
    imshow(imresize(a,20)',[]);
    pause(0.1);
end