生成一个基于pytorch深度学习框架，数据集采用fashion-mnist，模型使用Diffusion模型，添加数据可视化的，用Python语言写的完整代码

以下是一个基于PyTorch深度学习框架，使用Diffusion模型处理Fashion-MNIST数据集，并添加数据可视化的完整Python代码： ```python import torch import torch.nn as nn import torch.optim as optim from torchvision import datasets, transforms import matplotlib.pyplot as plt import numpy as np # 设置设备 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 超参数 batch_size = 128 epochs = 20 learning_rate = 1e-3 image_size = 28 channels = 1 n_steps = 1000 beta_start = 1e-4 beta_end = 0.02 # 数据预处理 transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)) ]) # 加载数据集 train_dataset = datasets.FashionMNIST(root='./data', train=True, transform=transform, download=True) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True) # 定义扩散模型 class DiffusionModel(nn.Module): def __init__(self): super(DiffusionModel, self).__init__() self.model = nn.Sequential( nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1), nn.ReLU(), nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1), nn.ReLU(), nn.Flatten(), nn.Linear(128 * 7 * 7, 1024), nn.ReLU(), nn.Linear(1024, 128 * 7 * 7), nn.ReLU(), nn.Unflatten(1, (128, 7, 7)), nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, output_padding=1), nn.ReLU(), nn.ConvTranspose2d(64, 64, kernel_size=3, stride=2, padding=1, output_padding=1), nn.ReLU(), nn.Conv2d(64, 1, kernel_size=3, stride=1, padding=1), nn.Tanh() ) def forward(self, x): return self.model(x) # 初始化模型、损失函数和优化器 model = DiffusionModel().to(device) criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) # 训练循环 for epoch in range(epochs): for i, (images, _) in enumerate(train_loader): images = images.to(device) # 生成时间步 t = torch.randint(0, n_steps, (batch_size,), device=device).long() # 计算beta betas = torch.linspace(beta_start, beta_end, n_steps).to(device) alpha = 1 - betas alpha_hat = torch.cumprod(alpha, dim=0) # 获取噪声 noise = torch.randn_like(images).to(device) # 计算noisy images sqrt_alpha_hat = torch.sqrt(alpha_hat[t].reshape(-1, 1, 1, 1)) sqrt_one_minus_alpha_hat = torch.sqrt(1 - alpha_hat[t].reshape(-1, 1, 1, 1)) noisy_images = sqrt_alpha_hat * images + sqrt_one_minus_alpha_hat * noise # 前向传播 outputs = model(noisy_images) loss = criterion(outputs, noise) # 反向传播和优化 optimizer.zero_grad() loss.backward() optimizer.step() print(f"Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}") # 可视化结果 with torch.no_grad(): # 生成随机噪声 sample = torch.randn(64, channels, image_size, image_size).to(device) # 反向扩散过程 for i in range(n_steps): betas_t = beta_end * torch.ones(batch_size, device=device) alpha_t = 1 - betas_t alpha_hat_t = torch.cumprod(alpha_t, dim=0)[-1] sqrt_recip_alpha_t = torch.sqrt(1 / alpha_t) sqrt_one_minus_alpha_hat_t = torch.sqrt(1 - alpha_hat_t) noise = torch.randn_like(sample).to(device) # 模型预测 model_input = sample model_output = model(model_input) # 更新样本 sample = sqrt_recip_alpha_t * (model_input - (1 - alpha_t) / torch.sqrt(1 - alpha_hat_t) * model_output) + torch.sqrt(betas_t) * noise # 显示生成的图片 grid = utils.make_grid(sample.cpu(), nrow=8) plt.figure(figsize=(8,8)) plt.imshow(grid.permute(1, 2, 0).squeeze(), cmap='gray') plt.axis('off') plt.show() ``` 这段代码实现了一个基本的Diffusion模型，用于处理Fashion-MNIST数据集，并包含了数据可视化功能。