paddle.fluid模块如何实现网络构建，请代码示例

以下是一个简单的神经网络构建的代码示例：

import paddle
import paddle.fluid as fluid

# 定义输入数据形状
image_shape = [3, 28, 28]

# 定义输入数据和标签的占位符
image = fluid.data(name='image', shape=image_shape, dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')

# 定义卷积神经网络
conv_pool_1 = fluid.nets.simple_img_conv_pool(
                input=image,
                filter_size=5,
                num_filters=20,
                pool_size=2,
                pool_stride=2,
                act='relu'
                )
conv_pool_2 = fluid.nets.simple_img_conv_pool(
                input=conv_pool_1,
                filter_size=5,
                num_filters=50,
                pool_size=2,
                pool_stride=2,
                act='relu'
                )
predict = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')

# 定义损失函数和优化器
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.001)
opts = optimizer.minimize(avg_cost)

# 定义一个执行器
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

# 训练和测试模型
for epoch in range(10):
    for batch_id, data in enumerate(train_reader()):
        image_data, label_data = data
        loss, = exe.run(fluid.default_main_program(),
                        feed={'image': image_data, 'label': label_data},
                        fetch_list=[avg_cost])
    print("Epoch %d, cost = %f" % (epoch, loss[0]))