GAN-MATLAB

GAN-MATLAB

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;

% 真实样本认为为标签 [1 0]； 生成样本为[0 1];

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

% normalize

train_x = mapminmax(train_x, 0, 1);

 

rand('state',0)

%% 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([100 784]);

nn_G_t.activation_function = 'sigm';

nn_G_t.output = 'sigm';

 

nn_D = nnsetup([784 100 1]);

nn_D.weightPenaltyL2 = 1e-4;  %  L2 weight decay

nn.dropoutFraction = 0.5;   %  Dropout fraction 

nn.learningRate = 0.01;                %  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([100 784 100 1]);

nn_G.weightPenaltyL2 = 1e-4;  %  L2 weight decay

nn.dropoutFraction = 0.5;   %  Dropout fraction 

nn.learningRate = 0.01;                %  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 =
1:1500

    %----------计算G的输出：假样本------------------- 

    for i = 1:length(nn_G_t.W)   %共享网络参数

       
nn_G_t.W{i} = nn_G.W{i};

    end

   
G_output = nn_G_out(nn_G_t, test_x);

    %-----------训练D------------------------------

   
index = randperm(60000);

   
train_data_D = [train_x(index(1:num),:);G_output(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);%训练D

    %-----------训练G-------------------------------

    for i = 1:length(nn_D.W)  %共享训练的D的网络参数

       
nn_G.W{length(nn_G.W)-i+1} = nn_D.W{length(nn_D.W)-i+1};

    end

    %训练G：此时假样本标签为1，认为是真样本

   
nn_G = nntrain(nn_G, test_x(index(1:num),:), test_y_rel(index(1:num),:),
opts);

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);

 

 

%函数nn_G_out为：
function output = nn_G_out(nn, x)
    nn.testing = 1;
    nn = nnff(nn, x, zeros(size(x,1), nn.size(end)));
    nn.testing = 0;
    output = nn.a{end};
end