fashionmnist训练函数 pytorch

以下是使用PyTorch进行FashionMNIST训练的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import FashionMNIST

# Define the neural network model
class FashionMNISTNet(nn.Module):
    def __init__(self):
        super(FashionMNISTNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=5, padding=2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=5, padding=2)
        self.fc1 = nn.Linear(64*7*7, 1024)
        self.fc2 = nn.Linear(1024, 10)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = nn.functional.max_pool2d(x, 2)
        x = nn.functional.relu(self.conv2(x))
        x = nn.functional.max_pool2d(x, 2)
        x = x.view(-1, 64*7*7)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Set device to GPU if available, otherwise use CPU
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Load FashionMNIST dataset
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
train_dataset = FashionMNIST(root='./data', train=True, transform=transform, download=True)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

# Initialize the neural network model and optimizer
model = FashionMNISTNet().to(device)
optimizer = optim.Adam(model.parameters())

# Train the model
criterion = nn.CrossEntropyLoss()
num_epochs = 10
for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()

        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 100 == 99:
            print('[Epoch %d, Batch %5d] Loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

print('Finished Training')