我就废话不多说了，直接上代码吧！

import torchimport torch.nn as nnfrom torch.autograd import Variableimport numpy as npimport matplotlib.pyplot as plttorch.manual_seed(1)np.random.seed(1)BATCH_SIZE = 64LR_G = 0.0001LR_D = 0.0001N_IDEAS = 5ART_COMPONENTS = 15PAINT_POINTS = np.vstack([np.linspace(-1,1,ART_COMPONENTS) for _ in range(BATCH_SIZE)])def artist_works(): a = np.random.uniform(1,2,size=BATCH_SIZE)[:,np.newaxis] paintings = a*np.power(PAINT_POINTS,2) + (a-1) paintings = torch.from_numpy(paintings).float() return Variable(paintings)G = nn.Sequential( nn.Linear(N_IDEAS,128), nn.ReLU(), nn.Linear(128,ART_COMPONENTS),)D = nn.Sequential( nn.Linear(ART_COMPONENTS,128), nn.ReLU(), nn.Linear(128,1), nn.Sigmoid(),)opt_D = torch.optim.Adam(D.parameters(),lr=LR_D)opt_G = torch.optim.Adam(G.parameters(),lr=LR_G)plt.ion()for step in range(10000): artist_paintings = artist_works() G_ideas = Variable(torch.randn(BATCH_SIZE,N_IDEAS)) G_paintings = G(G_ideas) prob_artist0 = D(artist_paintings) prob_artist1 = D(G_paintings) D_loss = - torch.mean(torch.log(prob_artist0) + torch.log(1-prob_artist1)) G_loss = torch.mean(torch.log(1 - prob_artist1)) opt_D.zero_grad() D_loss.backward(retain_variables=True) opt_D.step() opt_G.zero_grad() G_loss.backward() opt_G.step() if step % 50 == 0: plt.cla() plt.plot(PAINT_POINTS[0],G_paintings.data.numpy()[0],c='#4ad631',lw=3,label='Generated painting',) plt.plot(PAINT_POINTS[0],2 * np.power(PAINT_POINTS[0], 2) + 1,c='#74BCFF',lw=3,label='upper bound',) plt.plot(PAINT_POINTS[0],1 * np.power(PAINT_POINTS[0], 2) + 0,c='#FF9359',lw=3,label='lower bound',) plt.text(-.5,2.3,'D accuracy=%.2f (0.5 for D to converge)' % prob_artist0.data.numpy().mean(), fontdict={'size':15}) plt.text(-.5, 2, 'D score= %.2f (-1.38 for G to converge)' % -D_loss.data.numpy(), fontdict={'size': 15}) plt.ylim((0,3)) plt.legend(loc='upper right', fontsize=12) plt.draw() plt.pause(0.01)plt.ioff()plt.show()

以上这篇pytorch GAN生成对抗网络实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。