请在paddle上训练一个回归预测的CNN，输入是1205600*600的规格，输出也是同样规格，两层卷积，两层线性层，两层反卷积层，请输出完整代码

以下是使用PaddlePaddle训练回归预测CNN的完整代码：

import paddle.fluid as fluid

# 创建神经网络结构
def cnn_model(input):
    conv1 = fluid.layers.conv2d(input=input, num_filters=32, filter_size=3, stride=1, padding=1, act='relu')
    pool1 = fluid.layers.pool2d(input=conv1, pool_size=2, pool_stride=2, pool_type='max')
    conv2 = fluid.layers.conv2d(input=pool1, num_filters=64, filter_size=3, stride=1, padding=1, act='relu')
    pool2 = fluid.layers.pool2d(input=conv2, pool_size=2, pool_stride=2, pool_type='max')
    fc1 = fluid.layers.fc(input=pool2, size=1024, act='relu')
    fc2 = fluid.layers.fc(input=fc1, size=1205600, act=None)
    reshape = fluid.layers.reshape(fc2, [-1, 1205600, 1, 1])
    deconv1 = fluid.layers.conv2d_transpose(input=reshape, num_filters=64, filter_size=3, stride=1, padding=1, act='relu')
    deconv2 = fluid.layers.conv2d_transpose(input=deconv1, num_filters=32, filter_size=3, stride=1, padding=1, act='relu')
    deconv3 = fluid.layers.conv2d_transpose(input=deconv2, num_filters=1, filter_size=3, stride=1, padding=1, act=None)
    return deconv3

# 定义输入输出数据的形状
input_shape = [None, 1205600, 600, 1]
output_shape = [None, 1205600, 600, 1]

# 创建占位符
input = fluid.data(name='input', shape=input_shape, dtype='float32')
output = fluid.data(name='output', shape=output_shape, dtype='float32')

# 调用神经网络结构
predict = cnn_model(input)

# 定义损失函数
cost = fluid.layers.square_error_cost(input=predict, label=output)
avg_cost = fluid.layers.mean(cost)

# 定义优化方法
optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.001)
opts = optimizer.minimize(avg_cost)

# 创建执行器
place = fluid.CPUPlace() # 如果有GPU可用，把这行代码改成fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

# 开始训练
import numpy as np

train_reader = paddle.batch(paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=1000), batch_size=128)

for pass_id in range(10):
    for batch_id, data in enumerate(train_reader()):
        # 把输入和输出数据转化为符合框架要求的格式
        train_data = np.array(map(lambda x: x[0], data)).astype('float32').reshape(-1, 1205600, 600, 1)
        y = np.array(map(lambda x: x[1], data)).astype('float32').reshape(-1, 1205600, 600, 1)

        # 训练模型
        outs = exe.run(feed={'input': train_data, 'output': y},
                       fetch_list=[avg_cost])
        
        # 输出损失函数值
        if batch_id % 100 == 0:
            print("Pass %d, Batch %d, cost %f" % (pass_id, batch_id, outs[0][0]))

请注意，以上模型结构、超参数等只是示例代码，实际应用时需要根据具体问题进行选择和调整。

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