pytorch卷积minist代码

可以直接用PyTorch提供的MNIST数据集进行训练，下面是一个简单的卷积神经网络模型代码：

import torch
from torch import nn
from torchvision import datasets, transforms

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 7 * 7, 256)
        self.fc2 = nn.Linear(256, 10)
        self.dropout = nn.Dropout(p=0.5)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = self.pool(nn.functional.relu(self.conv3(x)))
        x = x.view(-1, 64 * 7 * 7)
        x = nn.functional.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

# 加载数据
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
train_set = datasets.MNIST('data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
test_set = datasets.MNIST('data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=True)

# 训练模型
model = Net().to('cuda')
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.NLLLoss()
for epoch in range(10):
    running_loss = 0
    for images, labels in train_loader:
        optimizer.zero_grad()
        images, labels = images.to('cuda'), labels.to('cuda')
        output = model(images)
        loss = criterion(output, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f'Epoch {epoch+1} Loss: {running_loss/len(train_loader)}')

# 测试模型
model.eval()
correct = 0
with torch.no_grad():
    for images, labels in test_loader:
        images, labels = images.to('cuda'), labels.to('cuda')
        output = model(images)
        _, predicted = torch.max(output.data, 1)
        correct += (predicted == labels).sum().item()
print(f'Test Accuracy: {correct/len(test_set)}')

在这个代码中，我们定义了一个名为“Net”的卷积神经网络模型，包含3个卷积层、3个最大池化层、2个全连接层和1个Dropout层，共计128万个参数。然后我们加载MNIST数据集，进行训练和测试。在训练过程中，我们使用Adam优化器和负对数似然损失函数，每个Epoch训练一遍。在测试过程中，我们关闭梯度计算，仅仅测试模型的预测精度。