PyTorch搭建卷积神经网络(CNN)实现手写数字识别测试

好的，让我们开始。首先，需要准备 MNIST 数据集。可以使用 PyTorch 内置的 torchvision.datasets 库来下载并加载数据集。

import torch
import torchvision
import torchvision.transforms as transforms

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5,), (0.5,))])

trainset = torchvision.datasets.MNIST(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.MNIST(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64,
                                         shuffle=False, num_workers=2)

接下来，我们可以定义我们的卷积神经网络模型。这里我们定义了两个卷积层和两个全连接层。

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output

net = Net()

接下来，我们可以定义损失函数和优化器。在这个例子中，我们使用交叉熵损失函数和随机梯度下降优化器。

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

现在，我们可以开始训练我们的模型。在每个 epoch 中，我们将遍历整个训练集，并使用随机梯度下降进行优化。此外，我们还将计算模型在测试集上的准确率。

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
        if i % 100 == 99:    # 每 100 个 mini-batches 输出一次损失
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

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

    print('Accuracy of the network on the 10000 test images: %d %%' % (
        100 * correct / total))

训练完成后，我们可以保存模型并测试它在新数据上的性能。

PATH = './cnn.pth'
torch.save(net.state_dict(), PATH)

net = Net()
net.load_state_dict(torch.load(PATH))

# 测试
dataiter = iter(testloader)
images, labels = dataiter.next()

outputs = net(images)

_, predicted = torch.max(outputs, 1)

print('Predicted: ', ' '.join('%5s' % predicted[j] for j in range(10)))

恭喜你，现在你已经成功地使用 PyTorch 搭建了一个卷积神经网络，并且能够对手写数字进行识别！