使用Python实现深度学习模型：生成对抗网络（GAN）

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as np

# 定义数据预处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=(0.5,), std=(0.5,))  # 将图像归一化到[-1, 1]范围内
])

# 下载并加载训练数据
train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
train_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)

class Generator(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Generator, self).__init__()
        self.main = nn.Sequential(
            nn.Linear(input_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, hidden_size),
            nn.ReLU(),
            nn.Linear(hidden_size, output_size),
            nn.Tanh()
        )

    def forward(self, x):
        return self.main(x)


class Discriminator(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(Discriminator, self).__init__()
        self.main = nn.Sequential(
            nn.Linear(input_size, hidden_size),
            nn.LeakyReLU(0.2),
            nn.Linear(hidden_size, hidden_size),
            nn.LeakyReLU(0.2),
            nn.Linear(hidden_size, output_size),
            nn.Sigmoid()
        )

    def forward(self, x):
        return self.main(x)

# 定义模型参数
input_size = 100  # 噪声向量的维度
hidden_size = 256
image_size = 28 * 28  # MNIST图像的维度

# 创建生成器和判别器实例
G = Generator(input_size, hidden_size, image_size)
D = Discriminator(image_size, hidden_size, 1)

criterion = nn.BCELoss()
lr = 0.0002

# 创建生成器和判别器的优化器
optimizer_G = optim.Adam(G.parameters(), lr=lr)
optimizer_D = optim.Adam(D.parameters(), lr=lr)

num_epochs = 50

for epoch in range(num_epochs):
    for i, (images, _) in enumerate(train_loader):
        batch_size = images.size(0)
        images = images.view(batch_size, -1)

        # 创建标签
        real_labels = torch.ones(batch_size, 1)
        fake_labels = torch.zeros(batch_size, 1)

        # 训练判别器
        outputs = D(images)
        d_loss_real = criterion(outputs, real_labels)
        real_score = outputs

        z = torch.randn(batch_size, input_size)
        fake_images = G(z)
        outputs = D(fake_images.detach())
        d_loss_fake = criterion(outputs, fake_labels)
        fake_score = outputs

        d_loss = d_loss_real + d_loss_fake
        optimizer_D.zero_grad()
        d_loss.backward()
        optimizer_D.step()

        # 训练生成器
        z = torch.randn(batch_size, input_size)
        fake_images = G(z)
        outputs = D(fake_images)
        g_loss = criterion(outputs, real_labels)

        optimizer_G.zero_grad()
        g_loss.backward()
        optimizer_G.step()

    print(f'Epoch [{epoch+1}/{num_epochs}], d_loss: {d_loss.item():.4f}, g_loss: {g_loss.item():.4f}, '
          f'D(x): {real_score.mean().item():.4f}, D(G(z)): {fake_score.mean().item():.4f}')

# 生成一些新图像
z = torch.randn(64, input_size)
fake_images = G(z)
fake_images = fake_images.view(fake_images.size(0), 1, 28, 28)

# 可视化生成的图像
grid = torchvision.utils.make_grid(fake_images, nrow=8, normalize=True)
plt.imshow(grid.permute(1, 2, 0).detach().numpy())
plt.title('Generated Images')
plt.show()