生成对抗网络（GAN）代码+数据集

import torch import torch.nn.functional as nn import torch.autograd as autograd import torch.optim as optim import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import os from torch.autograd import Variable from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('../../MNIST_data', one_hot=True) mb_size = 64 Z_dim = 100 X_dim = mnist.train.images.shape[1] y_dim = mnist.train.labels.shape[1] h_dim = 128 c = 0 lr = 1e-3 def xavier_init(size): in_dim = size[0] xavier_stddev = 1. / np.sqrt(in_dim / 2.) return Variable(torch.randn(*size) * xavier_stddev, requires_grad=True) """ ==================== GENERATOR ======================== """ Wzh = xavier_init(size=[Z_dim, h_dim]) bzh = Variable(torch.zeros(h_dim), requires_grad=True) Whx = xavier_init(size=[h_dim, X_dim]) bhx = Variable(torch.zeros(X_dim), requires_grad=True) def G(z): h = nn.relu(z @ Wzh + bzh.repeat(z.size(0), 1)) X = nn.sigmoid(h @ Whx + bhx.repeat(h.size(0), 1)) return X """ ==================== DISCRIMINATOR ======================== """ Wxh = xavier_init(size=[X_dim, h_dim]) bxh = Variable(torch.zeros(h_dim), requires_grad=True) Why = xavier_init(size=[h_dim, 1]) bhy = Variable(torch.zeros(1), requires_grad=True) def D(X): h = nn.relu(X @ Wxh + bxh.repeat(X.size(0), 1)) y = nn.sigmoid(h @ Why + bhy.repeat(h.size(0), 1)) return y G_params = [Wzh, bzh, Whx, bhx] D_params = [Wxh, bxh, Why, bhy] params = G_params + D_params """ ===================== TRAINING ======================== """ def reset_grad(): for p in params: if p.grad is not None: data = p.grad.data p.grad = Variable(data.new().resize_as_(data).zero_()) G_solver = optim.Adam(G_params, lr=1e-3) D_solver = optim.Adam(D_params, lr=1e-3) ones_label = Variable(torch.ones(mb_size, 1)) zeros_label = Variable(torch.zeros(mb_size, 1)) for it in range(100000): # Sample data z = Variable(torch.randn(mb_size, Z_dim)) X, _ = mnist.train.next_batch(mb_size) X = Variable(torch.from_numpy(X)) # Dicriminator forward-loss-backward-update G_sample = G(z) D_real = D(X) D_fake = D(G_sample) D_loss_real = nn.binary_cross_entropy(D_real, ones_label) D_loss_fake = nn.binary_cross_entropy(D_fake, zeros_label) D_loss = D_loss_real + D_loss_fake D_loss.backward() D_solver.step() # Housekeeping - reset gradient reset_grad() # Generator forward-loss-backward-update z = Variable(torch.randn(mb_size, Z_dim)) G_sample = G(z) D_fake = D(G_sample) G_loss = nn.binary_cross_entropy(D_fake, ones_label) G_loss.backward() G_solver.step() # Housekeeping - reset gradient reset_grad() # Print and plot every now and then if it % 1000 == 0: print('Iter-{}; D_loss: {}; G_loss: {}'.format(it, D_loss.data.numpy(), G_loss.data.numpy())) samples = G(z).data.numpy()[:16] fig = plt.figure(figsize=(4, 4)) gs = gridspec.GridSpec(4, 4) gs.update(wspace=0.05, hspace=0.05) for i, sample in enumerate(samples): ax = plt.subplot(gs[i]) plt.axis('off') ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_aspect('equal') plt.imshow(sample.reshape(28, 28), cmap='Greys_r') if not os.path.exists('out/'): os.makedirs('out/') plt.savefig('out/{}.png'.format(str(c).zfill(3)), bbox_inches='tight') c += 1 plt.close(fig)