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PyTorch实战：GAN生成对抗网络从零搭建完整代码

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Python客栈送红包、纸质书

我就废话不多说了，直接上代码吧！

         import torch
        
         import torch.nn as nn
        
         from torch.autograd import Variable
        
         import numpy as np
        
         import matplotlib.pyplot as plt
        
         torch.manual_seed(1)
        
         np.random.seed(1)
        
         BATCH_SIZE = 64
        
         LR_G = 0.0001
        
         LR_D = 0.0001
        
         N_IDEAS = 5
        
         ART_COMPONENTS = 15
        
         PAINT_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脚本之家。

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