使用paddle将以下LeNet代码改为ResNet网络模型class LeNet(paddle.nn.Layer): def __init__(self): super(LeNet, self).__init__() # 创建卷积和池化层块，每个卷积层使用relu激活函数，后面跟着一个2x2的池化 self.conv1 = paddle.nn.Conv2D(3, 32, 3, 1, 1) self.relu1 = paddle.nn.ReLU() self.max_pool1 = paddle.nn.MaxPool2D(2, 2) self.conv2 = paddle.nn.Conv2D(32, 64, 3, 1, 1) self.relu2 = paddle.nn.ReLU() self.max_pool2 = paddle.nn.MaxPool2D(2, 2) self.avg_pool = AdaptiveAvgPool2D(1) self.linear= paddle.nn.Linear(64, 2) # 网络的前向计算过程 def forward(self, x): x = self.max_pool1(self.relu1(self.conv1(x))) x = self.max_pool2(self.relu2(self.conv2(x))) x = self.avg_pool(x) x = paddle.reshape(x, [x.shape[0],-1]) x = self.linear(x) return x paddle.Model(LeNet()).summary((-1,3,256,256))

以下是将LeNet代码改为ResNet网络模型的代码：

import paddle.nn.functional as F

class ResNet(paddle.nn.Layer):
    def __init__(self, num_classes=2):
        super(ResNet, self).__init__()

        # 第一个卷积层，输出通道数为64，卷积核大小为7x7，步长为2，padding为3
        self.conv1 = paddle.nn.Conv2D(3, 64, kernel_size=7, stride=2, padding=3, bias_attr=False)
        self.bn1 = paddle.nn.BatchNorm2D(64)
        self.relu = paddle.nn.ReLU()
        self.max_pool = paddle.nn.MaxPool2D(kernel_size=3, stride=2, padding=1)

        # ResNet的主体部分，包括4个残差块
        self.layer1 = self._make_layer(64, 3)
        self.layer2 = self._make_layer(128, 4, stride=2)
        self.layer3 = self._make_layer(256, 6, stride=2)
        self.layer4 = self._make_layer(512, 3, stride=2)

        # 全局平均池化层
        self.avg_pool = paddle.nn.AdaptiveAvgPool2D((1, 1))

        # 分类器
        self.fc = paddle.nn.Linear(512, num_classes)

    def _make_layer(self, channels, blocks, stride=1):
        layers = []
        # 下采样，对输入进行降维
        downsample = None
        if stride != 1 or self.in_channels != channels:
            downsample = paddle.nn.Sequential(
                paddle.nn.Conv2D(self.in_channels, channels, kernel_size=1, stride=stride, bias_attr=False),
                paddle.nn.BatchNorm2D(channels)
            )

        layers.append(ResidualBlock(self.in_channels, channels, stride, downsample))
        self.in_channels = channels

        for _ in range(1, blocks):
            layers.append(ResidualBlock(channels, channels))

        return paddle.nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.max_pool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avg_pool(x)
        x = paddle.flatten(x, 1)
        x = self.fc(x)

        return x


class ResidualBlock(paddle.nn.Layer):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResidualBlock, self).__init__()
        self.conv1 = paddle.nn.Conv2D(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias_attr=False)
        self.bn1 = paddle.nn.BatchNorm2D(out_channels)
        self.relu = paddle.nn.ReLU()
        self.conv2 = paddle.nn.Conv2D(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias_attr=False)
        self.bn2 = paddle.nn.BatchNorm2D(out_channels)
        self.downsample = downsample

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out

paddle.Model(ResNet()).summary((-1,3,256,256))

这里我们定义了一个ResNet网络模型，包括一个卷积层，一个最大池化层，4个残差块，一个全局平均池化层和一个分类器。其中，每个残差块包含两个卷积层和一个跳跃连接，卷积层的输出通道数和步长根据网络深度不同而不同。在模型前向传播过程中，我们先使用一次卷积和池化操作，然后将特征图输入到各个残差块中，最后进行全局平均池化和分类。