pytorch mnist分类模型怎么构建 - 问答

要使用PyTorch构建一个简单的MNIST分类模型，你可以按照以下步骤进行：

导入所需库：

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

定义模型结构：

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

下载并加载MNIST数据集：

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)

初始化模型、损失函数和优化器：

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)

训练模型：

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}")

测试模型：

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}%")

这个简单的例子展示了如何使用PyTorch构建一个基本的MNIST分类模型。你可以根据需要调整模型结构、损失函数和优化器参数以获得更好的性能。

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