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, 输出数据的规格和输入数据一样，锁定两个输入数据，做回归预测，请把训练过程也写出来

# 导入需要的库
import paddle
from paddle.nn import Conv2D, Linear, Conv2DTranspose, Sequential

# 定义CNN模型
class SimpleCNN(paddle.nn.Layer):
def __init__(self):
super(SimpleCNN, self).__init__()

# 定义两个卷积层
self.conv1 = Conv2D(in_channels=5, out_channels=10, kernel_size=(3, 3), padding=(1, 1))
self.conv2 = Conv2D(in_channels=10, out_channels=20, kernel_size=(3, 3), padding=(1, 1))

# 定义两个线性层
self.linear1 = Linear(in_features=20*600*600, out_features=1024)
self.linear2 = Linear(in_features=1024, out_features=20*600*600)

# 定义两个反卷积层
self.deconv1 = Conv2DTranspose(20, 10, kernel_size=(3,3), stride=2, padding=1, output_padding=1)
self.deconv2 = Conv2DTranspose(10, 5, kernel_size=(3, 3), stride=2, padding=1, output_padding=1)

def forward(self, x):
x = paddle.reshape(x, [-1, 5, 600, 600])

x = self.conv1(x)
x = paddle.nn.functional.relu(x)

x = self.conv2(x)
x = paddle.nn.functional.relu(x)

x = paddle.reshape(x, [-1, 20*600*600])
x = self.linear1(x)
x = paddle.nn.functional.relu(x)

x = self.linear2(x)
x = paddle.reshape(x, [-1, 20, 600, 600])

x = self.deconv1(x)
x = paddle.nn.functional.relu(x)

x = self.deconv2(x)
x = paddle.reshape(x, [-1, 5, 600, 600])

return x

# 定义训练函数
def train(model):

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

# 定义训练数据和标签
x_data = paddle.randn([120, 5, 600, 600], dtype='float32')
y_data = x_data.clone()

# 训练过程
for epoch in range(5):
y_pred = model(x_data)
loss = loss_fn(y_pred, y_data)
loss.backward()
opt.step()
opt.clear_grad()

print("Epoch %d, loss=%.4f" % (epoch+1, loss.numpy()))

# 实例化模型并训练
model = SimpleCNN()
train(model)