pytorch mnist分類模型怎么構建

要使用PyTorch構建一個簡單的MNIST分類模型，你可以按照以下步驟進行：

1. 導入所需庫：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms

2. 定義模型結構：

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(28 * 28, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 10)
        self.dropout = nn.Dropout(0.5)

    def forward(self, x):
        x = x.view(-1, 28 * 28)
        x = torch.relu(self.fc1(x))
        x = self.dropout(x)
        x = torch.relu(self.fc2(x))
        x = self.dropout(x)
        x = self.fc3(x)
        return x

3. 下載并加載MNIST數據集：

transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])

trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)

4. 初始化模型、損失函數和優化器：

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Net().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

5. 訓練模型：

num_epochs = 10
for epoch in range(num_epochs):
    for i, data in enumerate(trainloader, 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()

    print(f"Epoch {epoch + 1}, Loss: {loss.item():.4f}")

6. 測試模型：

model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for data in testloader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f"Accuracy of the network on the test images: {100 * correct / total:.2f}%")

這個簡單的例子展示了如何使用PyTorch構建一個基本的MNIST分類模型。你可以根據需要調整模型結構、損失函數和優化器參數以獲得更好的性能。