【图像生成技术】人工智能在医疗健康领域的应用实例：图像生成技术的革新实践

 import torch

    
 import torch.nn as nn
    
 import torchvision.transforms as transforms
    
 from torch.utils.data import DataLoader
    
 from torchvision.datasets import MNIST # 示例数据集，实际应用中需替换为医学图像数据集
    
  
    
 # 定义生成器
    
 class Generator(nn.Module):
    
     def __init__(...):
    
     ...
    
     def forward(...):
    
     ...
    
  
    
 # 定义判别器
    
 class Discriminator(nn.Module):
    
     def __init__(...):
    
     ...
    
     def forward(...):
    
     ...
    
  
    
 # 超参数设置
    
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
 lr = 0.0002
    
 batch_size = 64
    
 epochs = 200
    
  
    
 # 加载医学图像数据集 (此处使用MNIST作为示意，请替换为真实医学图像数据集)
    
 transform = transforms.Compose([...])
    
 dataset = MNIST(root='./data', train=True, download=True, transform=transform)
    
 dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
    
  
    
 # 初始化模型与优化器
    
 netG = Generator().to(device)
    
 netD = Discriminator().to(device)
    
 criterion = nn.BCELoss()
    
 optimizerD = torch.optim.Adam(netD.parameters(), lr=lr)
    
 optimizerG = torch.optim.Adam(netG.parameters(), lr=lr)
    
  
    
 # 训练循环
    
 for epoch in range(epochs):
    
     for i, data in enumerate(dataloader, 0):
    
     real_images = data[0].to(device)
    
     ...
    
     # 更新判别器
    
     ...
    
     # 更新生成器
    
     ...
    
     # 打印训练状态
    
     ...
    
  
    
 print("Training completed.")