使用paddle框架写一段卷积神经网络代码

好的，以下是一个使用PaddlePaddle框架构建的卷积神经网络的代码：

import paddle

# 定义卷积神经网络
class CNN(paddle.nn.Layer):
    def __init__(self):
        super(CNN, self).__init__()
        # 定义卷积层和池化层
        self.conv1 = paddle.nn.Conv2D(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=2)
        self.pool1 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
        self.conv2 = paddle.nn.Conv2D(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=2)
        self.pool2 = paddle.nn.MaxPool2D(kernel_size=2, stride=2)
        # 定义全连接层
        self.fc1 = paddle.nn.Linear(in_features=32*7*7, out_features=128)
        self.fc2 = paddle.nn.Linear(in_features=128, out_features=10)

    def forward(self, x):
        x = self.conv1(x)
        x = paddle.nn.functional.relu(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = paddle.nn.functional.relu(x)
        x = self.pool2(x)
        x = paddle.flatten(x, start_axis=1, stop_axis=-1)
        x = self.fc1(x)
        x = paddle.nn.functional.relu(x)
        x = self.fc2(x)
        return x

# 定义训练过程
def train(model):
    # 定义数据集和数据加载器
    train_dataset = paddle.vision.datasets.MNIST(mode='train')
    val_dataset = paddle.vision.datasets.MNIST(mode='test')
    train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True)
    val_loader = paddle.io.DataLoader(val_dataset, batch_size=64, shuffle=False)

    # 定义优化器和损失函数
    optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
    criterion = paddle.nn.CrossEntropyLoss()

    # 训练模型
    epochs = 10
    for epoch in range(epochs):
        for i, (inputs, labels) in enumerate(train_loader):
            # 前向传播
            outputs = model(inputs)
            loss = criterion(outputs, labels)

            # 反向传播和更新参数
            optimizer.clear_grad()
            loss.backward()
            optimizer.step()

            # 打印训练信息
            if (i+1) % 100 == 0:
                print("Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}"
                      .format(epoch+1, epochs, i+1, len(train_loader), loss.numpy()[0]))

        # 在验证集上测试模型
        correct = 0
        total = 0
        for inputs, labels in val_loader:
            outputs = model(inputs)
            _, predicted = paddle.max(outputs, axis=1)
            total += labels.shape[0]
            correct += (predicted == labels).sum().numpy()[0]
        accuracy = 100 * correct / total
        print("Epoch [{}/{}], Val Accuracy: {:.2f}%".format(epoch+1, epochs, accuracy))

# 创建模型并进行训练
model = CNN()
train(model)

以上代码实现了一个简单的卷积神经网络，用于手写数字识别任务。您可以根据自己的需求进行修改和调整。