采用paddle进行房价预测python代码
时间: 2023-09-14 17:04:53 浏览: 100
房价预测Python代码
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以下是使用PaddlePaddle进行房价预测的Python代码示例:
```python
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。
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