pytorch GAN生成对抗网络

简书链接

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()