GAN实战——生成手写字体

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
import torchvision
from torchvision import transforms
import timetime_start = time.time()# 生成器生成的数据在 [-1, 1]
transform = transforms.Compose([transforms.ToTensor(),  # 会做0-1归一化，也会channels, height, widthtransforms.Normalize((0.5,), (0.5,))
])train_ds = torchvision.datasets.MNIST('data', train=True, transform=transform)
dataLoader = torch.utils.data.DataLoader(train_ds, batch_size=64, shuffle=True)# 生成器网络定义
# 输入是长度为100的噪声（正态分布随机数）
# 输出为（1， 28， 28）的图片
class Generator(nn.Module):def __init__(self):super(Generator, self).__init__()self.main = nn.Sequential(nn.Linear(100, 256),nn.ReLU(),nn.Linear(256, 512),nn.ReLU(),nn.Linear(512, 28*28),nn.Tanh())def forward(self, x):img = self.main(x)img = img.view(-1, 28, 28, 1)return img# 判别器网络定义
class Discriminator(nn.Module):def __init__(self):super(Discriminator, self).__init__()self.main = nn.Sequential(nn.Linear(28*28, 512),nn.LeakyReLU(),nn.Linear(512, 256),nn.LeakyReLU(),nn.Linear(256, 1),nn.Sigmoid())def forward(self, x):x = x.view(-1, 28*28)x = self.main(x)return xdevice = 'cuda' if torch.cuda.is_available() else 'cpu'
print(device)
gen = Generator().to(device)
dis = Discriminator().to(device)
d_optimizer = torch.optim.Adam(dis.parameters(), lr=0.0001)
g_optimizer = torch.optim.Adam(gen.parameters(), lr=0.0001)# 损失函数
loss_fn = torch.nn.BCELoss()# 绘图函数
def gen_img_plot(model, test_input):prediction = np.squeeze(model(test_input).detach().cpu().numpy())fig = plt.figure(figsize=(4, 4))for i in range(16):plt.subplot(4, 4, i+1)plt.imshow((prediction[i] + 1)/2)plt.axis('off')plt.show()test_input = torch.randn(16, 100, device=device)# GAN训练
D_loss = []
G_loss = []# 训练循环
for epoch in range(20):d_epoch_loss = 0g_epoch_loss = 0count = len(dataLoader)  # 返回批次数for step, (img, _) in enumerate(dataLoader):img = img.to(device)size = img.size(0)random_noise = torch.randn(size, 100, device=device)# 判别器的损失与优化d_optimizer.zero_grad()real_output = dis(img)  # 对判别器输入真实图片， real_output是对真实图片的判断结果d_real_loss = loss_fn(real_output, torch.ones_like(real_output))  # 判别器在真实图像上的损失d_real_loss.backward()gen_img = gen(random_noise)fake_output = dis(gen_img.detach())  # 判别器输入生成的图片，fake_output对生成图片的预测d_fake_loss = loss_fn(fake_output, torch.zeros_like(fake_output))  # 判别器在生成图像上的损失d_fake_loss.backward()d_loss = d_real_loss + d_fake_lossd_optimizer.step()# 生成器的损失与优化g_optimizer.zero_grad()fake_output = dis(gen_img)g_loss = loss_fn(fake_output, torch.ones_like(fake_output))  # 生成器的损失g_loss.backward()g_optimizer.step()with torch.no_grad():d_epoch_loss += d_lossg_epoch_loss += g_losswith torch.no_grad():d_epoch_loss /= countg_epoch_loss /= countD_loss.append(d_epoch_loss)G_loss.append(g_epoch_loss)print("Epoch:", epoch)gen_img_plot(gen, test_input)time_end = time.time()
print("花费总时间为：", time_end - time_start)

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