采用paddle进行房价预测python代码

以下是使用PaddlePaddle进行房价预测的Python代码示例：

import paddle
import paddle.fluid as fluid
import numpy as np

# 定义房价预测模型
def house_price_predictor():
    x = fluid.layers.data(name='x', shape=[1], dtype='float32')
    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
    y_predict = fluid.layers.fc(input=x, size=1, act=None)
    cost = fluid.layers.square_error_cost(input=y_predict, label=y)
    avg_cost = fluid.layers.mean(cost)
    return x, y, y_predict, avg_cost

# 定义训练函数
def train():
    # 定义训练数据
    x_data = np.array([1.0, 2.0, 3.0, 4.0, 5.0]).astype('float32')
    y_data = np.array([2.0, 4.0, 6.0, 8.0, 10.0]).astype('float32')
    train_data = np.hstack((x_data.reshape(-1, 1), y_data.reshape(-1, 1)))
    
    # 定义模型和损失函数
    x, y, y_predict, avg_cost = house_price_predictor()

    # 定义优化器
    optimizer = fluid.optimizer.SGDOptimizer(learning_rate=0.01)
    optimizer.minimize(avg_cost)

    # 创建执行器
    place = fluid.CPUPlace()
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())

    # 开始训练
    for i in range(100):
        for data in train_data:
            x_data = np.array(data[0]).astype('float32')
            y_data = np.array(data[1]).astype('float32')
            outs = exe.run(
                feed={'x': x_data, 'y': y_data},
                fetch_list=[avg_cost]
            )
            avg_loss = np.mean(outs)
            print("Epoch %d, Cost %f" % (i, avg_loss))

    # 保存模型
    fluid.io.save_inference_model(
        dirname='./model',
        feeded_var_names=['x'],
        target_vars=[y_predict],
        executor=exe
    )

# 定义预测函数
def predict():
    # 创建执行器
    place = fluid.CPUPlace()
    exe = fluid.Executor(place)

    # 加载模型
    [infer_program, feeded_var_names, target_var] = fluid.io.load_inference_model(
        dirname='./model',
        executor=exe
    )

    # 进行预测
    x_data = np.array([6.0]).astype('float32')
    results = exe.run(
        program=infer_program,
        feed={feeded_var_names[0]: x_data},
        fetch_list=target_var
    )

    # 打印预测结果
    print("预测房价：%f" % results[0][0])

# 运行训练和预测函数
train()
predict()

注意：以上代码仅为示例，实际应用中需要根据数据和需求进行相应的修改和调整。