pytorch训练两个数字的mnist数据集

可以使用以下代码来训练两个数字的mnist数据集：

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

# Define the two digits to train on
digit1 = 3
digit2 = 7

# Define the transformation to be applied to the data
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

# Load the MNIST dataset
train_dataset = datasets.MNIST('data', train=True, download=True, transform=transform)

# Filter the dataset to only include the two digits of interest
train_dataset = torch.utils.data.Subset(train_dataset, torch.where((train_dataset.targets == digit1) | (train_dataset.targets == digit2))[0])

# Define the model to be used
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.dropout = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 2)

    def forward(self, x):
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
        x = nn.functional.relu(nn.functional.max_pool2d(self.dropout(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

model = Net()

# Define the optimizer and loss function
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
criterion = nn.CrossEntropyLoss()

# Train the model
model.train()
for epoch in range(5):
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

请问您还有其他问题吗？