最近在知乎上看到一个不错的GAN的入门案例,于是稍微修改了一下后分享出来!
我们都知道GAN主要用来生成,相比于生成图片,我们这次选择更为简单的生成一个一维函数来大致了解GAN的流程及代码实现。
我们的原始数据为y = 2x^2 + 1,我们让GAN来生成与之接近的分布!
代码:
import torch import torch.nn as nn import matplotlib.pyplot as plt import numpy as np torch.manual_seed(1) np.random.seed(1) # 学习率 LR_G = 0.0001 LR_D = 0.0001 BATCH_SIZE = 64 N_IDEAS = 5 # 输入的噪声维度,可以自己设定(经过神经网络后会把维度调整) ART_COMPONETS = 15 # 噪声输入后的输出维度 PAINT_POINTS = np.stack([np.linspace(-1,1,ART_COMPONETS) for _ in range(BATCH_SIZE)],0) # 我们原始数据的x坐标,-1~1均匀分布 def artist_work(): a = np.ones((BATCH_SIZE,1)) * 2 paints = a * np.power(PAINT_POINTS,2) + (a-1) # y = 2x^2 + 1 paints = torch.from_numpy(paints).float() return paints # 网络结构 G = nn.Sequential( nn.Linear(N_IDEAS,128), nn.ReLU(), nn.Linear(128,ART_COMPONETS) ) D = nn.Sequential( nn.Linear(ART_COMPONETS,128), nn.ReLU(), nn.Linear(128,1), nn.Sigmoid() ) #优化器与损失函数 optimizer_G = torch.optim.Adam(G.parameters(),lr=LR_G) optimizer_D = torch.optim.Adam(D.parameters(),lr=LR_D) Criterion = torch.nn.BCELoss() # 开始训练 plt.ion() G_losses = [] #储存了损失方便自己画图可视化 D_losses = [] for step in range(10000): artist_painting = artist_work() G_idea = torch.randn(BATCH_SIZE,N_IDEAS) G_paintings = G(G_idea) pro_atrist0 = D(artist_painting) pro_atrist1 = D(G_paintings) G_loss = -1/torch.mean(torch.log(1.-pro_atrist1)) G_losses.append(G_loss.item()) D_loss = Criterion(pro_atrist0, torch.ones_like( pro_atrist0))+Criterion(pro_atrist1, torch.zeros_like(pro_atrist1)) D_losses.append(D_loss.item()) optimizer_G.zero_grad() G_loss.backward(retain_graph=True) #因为D的反向传播需要用到G,所以设置为True optimizer_D.zero_grad() D_loss.backward( ) optimizer_G.step() optimizer_D.step() if step % 200 == 0: # plotting 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='original data') plt.text(-.5, 2.3, 'D accuracy=%.2f (0.5 for D to converge)' % pro_atrist0.data.numpy().mean(), fontdict={'size': 13}) # plt.text(-.5, 2, 'G_loss= %.2f ' % G_loss.data.numpy(), fontdict={'size': 13}) plt.ylim((0, 3));plt.legend(loc='upper right', fontsize=10);plt.draw();plt.pause(0.1) print('训练结束') plt.ioff() plt.show()
结果:
可以看到在网络结构很简单的情况下还是可以取得一个不错的结果!
参考资料:
PyTorch搭建GAN网络 - 知乎 (zhihu.com)