生成pytorch卷积神经网络minist数据集代码

当然可以！以下是一个简单的 PyTorch 卷积神经网络处理 MNIST 数据集的代码：

import torch
from torch import nn, optim
from torchvision import datasets, transforms

# Define a transform to normalize the data
transform = transforms.Compose([transforms.ToTensor(),
                                transforms.Normalize((0.5,), (0.5,)),
                              ])

# Download and load the training data
trainset = datasets.MNIST('~/.pytorch/MNIST_data/', download=True, train=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

# Define the model
model = nn.Sequential(nn.Conv2d(1, 32, 3, padding=1),
                      nn.ReLU(),
                      nn.MaxPool2d(2, 2),
                      nn.Conv2d(32, 64, 3, padding=1),
                      nn.ReLU(),
                      nn.MaxPool2d(2, 2),
                      nn.Flatten(),
                      nn.Linear(7*7*64, 512),
                      nn.ReLU(),
                      nn.Linear(512, 10),
                      nn.LogSoftmax(dim=1))

# Define the loss function and optimizer
criterion = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.003)

# Train the model
epochs = 5
for e in range(epochs):
    running_loss = 0
    for images, labels in trainloader:
        # Clear the gradients
        optimizer.zero_grad()

        # Forward pass
        output = model(images)
        loss = criterion(output, labels)

        # Backward pass and optimization
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
    else:
        print(f"Training loss: {running_loss/len(trainloader)}")

希望这段代码可以帮到你！