GAN.rar_989gan_gan63.com_www.348gan com_www.989gan_人工智能

clear; clc; % -----------加载数据 load('mnist_uint8', 'train_x'); train_x = double(reshape(train_x,60000,28,28))/255; train_x = permute(train_x,[1,3,2]); train_x = reshape(train_x,60000,784); % -----------------定义模型 generator = GAN_nnsetup([100,512,784]); discriminator = GAN_nnsetup([784,512,1]); % -----------开始训练 batch_size = 60; epoch = 1;%原来是100 images_num = 60000; batch_num = ceil(images_num / batch_size); learning_rate = 0.001; for e=1:epoch kk = randperm(images_num); for t=1:batch_num % 准备数据 images_real = train_x(kk((t - 1) * batch_size + 1:t * batch_size), :, :); noise = unifrnd(-1, 1, batch_size, 100); % 开始训练 % -----------更新generator，固定discriminator generator = GAN_nnff(generator, noise); images_fake = generator.layers{generator.layers_count}.a; discriminator = GAN_nnff(discriminator, images_fake); logits_fake = discriminator.layers{discriminator.layers_count}.z; discriminator = nnbp_d(discriminator, logits_fake, ones(batch_size, 1)); generator = nnbp_g(generator, discriminator); generator = GAN_nnapplygrade(generator, learning_rate); % -----------更新discriminator，固定generator generator = GAN_nnff(generator, noise); images_fake = generator.layers{generator.layers_count}.a; images = [images_fake;images_real]; discriminator = GAN_nnff(discriminator, images); logits = discriminator.layers{discriminator.layers_count}.z; labels = [zeros(batch_size,1);ones(batch_size,1)]; discriminator = nnbp_d(discriminator, logits, labels); discriminator = GAN_nnapplygrade(discriminator, learning_rate); % ----------------输出loss if t == batch_num c_loss = sigmoid_cross_entropy(logits(1:batch_size), ones(batch_size, 1)); d_loss = sigmoid_cross_entropy(logits, labels); fprintf('c_loss:"%f",d_loss:"%f"\n',c_loss, d_loss); end if t == batch_num path = ['C:\Users\Administrator\Documents\MATLAB\pictures/epoch_',int2str(e),'_t_',int2str(t),'.png']; save_images(images_fake, [4, 4], path); fprintf('save_sample:%s\n', path); end end end %%链接：https://www.jianshu.com/p/3f89e95f891d %%來源：简书 %%简书著作权归作者所有，任何形式的转载都请联系作者获得授权并注明出处。