使用pytorch训练mnist数据集上的卷积神经网络

使用PyTorch训练MNIST数据集上的卷积神经网络可以按照以下步骤进行：

1. 导入必要的库和数据集

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

# 加载MNIST数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor(), download=True)

# 定义批次大小
batch_size = 64

# 创建数据加载器
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

2. 定义卷积神经网络模型

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU()
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 7 * 7, 128)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        out = self.conv1(x)
        out = self.relu1(out)
        out = self.conv2(out)
        out = self.relu2(out)
        out = self.pool(out)
        out = out.view(out.size(), -1)
        out = self.fc1(out)
        out = self.relu3(out)
        out = self.fc2(out)
        return out

# 创建模型实例
model = ConvNet()

3. 定义损失函数和优化器

# 定义损失函数
criterion = nn.CrossEntropyLoss()

# 定义优化器
optimizer = optim.Adam(model.parameters(), lr=.001)

4. 训练模型

# 定义训练函数
def train(model, train_loader, criterion, optimizer, num_epochs):
    for epoch in range(num_epochs):
        for i, (images, labels) in enumerate(train_loader):
            # 前向传播
            outputs = model(images)
            loss = criterion(outputs, labels)

            # 反向传播和优化
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            # 每100个批次打印一次训练信息
            if (i+1) % 100 == :
                print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

# 开始训练
num_epochs = 5
train(model, train_loader, criterion, optimizer, num_epochs)

5. 测试模型

# 定义测试函数
def test(model, test_loader):
    # 测试模式
    model.eval()

    # 定义变量
    correct = 
    total = 

    # 不计算梯度
    with torch.no_grad():
        for images, labels in test_loader:
            # 前向传播
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)

            # 统计正确率
            total += labels.size()
            correct += (predicted == labels).sum().item()

    # 输出测试结果
    print('Accuracy of the model on the 10000 test images: {:.2f} %'.format(100 * correct / total))

# 测试模型
test(model, test_loader)

以上就是使用PyTorch训练MNIST数据集上的卷积神经网络的完整代码。