请在paddle上写一个cnn网络，两个卷积层，两个线性层，两个反卷积层，输入数据的规格是120*5*600*600

非常感谢您的问题！以下是基于Paddle框架的CNN网络实现示例，该模型包括两个卷积层、两个线性层和两个反卷积层，输入数据的规格为120*5*600*600：

import paddle.nn.functional as F
import paddle.nn as nn

class CNN(nn.Layer):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2D(in_channels=5, out_channels=16, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2D(16)
        self.pool1 = nn.MaxPool2D(kernel_size=2, stride=2)
        
        self.conv2 = nn.Conv2D(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2D(32)
        self.pool2 = nn.MaxPool2D(kernel_size=2, stride=2)
        
        self.fc1 = nn.Linear(in_features=32*150*150, out_features=256)
        self.fc2 = nn.Linear(in_features=256, out_features=120*600*600)
        
        self.tconv1 = nn.Conv2DTranspose(in_channels=120, out_channels=64, kernel_size=3, stride=2, padding=1, output_padding=1)
        self.tconv2 = nn.Conv2DTranspose(in_channels=64, out_channels=5, kernel_size=3, stride=2, padding=1, output_padding=1)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(self.bn1(x))
        x = self.pool1(x)
        
        x = self.conv2(x)
        x = F.relu(self.bn2(x))
        x = self.pool2(x)
        
        x = x.reshape(x.shape[0], -1)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        x = x.reshape(x.shape[0], 120, 600, 600)
        
        x = F.relu(self.tconv1(x))
        x = self.tconv2(x)
        
        return x

希望这个实现应对你的问题！