机器学习框架PaddlePaddle的模板工程范例

# !/usr/bin/env python
# -*- coding:utf-8 -*-

import paddle.fluid as fluid
import numpy as np

# 步骤一 ：读取数据
np.random.seed(0)
x_data = np.random.randint(5, size=(10, 4))
y_data = []
for i in range(10):
        # 假设方程式为 y=4a+6b+7c+2d
        y = 4*x_data[i][0]+6*x_data[i][1]+7*x_data[i][2]+2*x_data[i][3]
        y_data.append([y])
# 转换数据成为numpy类型
x_data_numpy=np.array(x_data).astype(‘float32‘)
y_data_numpy = np.array(y_data).astype(‘float32‘)
# print(x_data, y_data, x_data_numpy, y_data_numpy)

# 步骤二：定义神经网络
x = fluid.layers.data(name=‘x‘, shape=[4], dtype=‘float32‘)
y = fluid.layers.data(name=‘y‘, shape=[1], dtype=‘float32‘)
y_predict = fluid.layers.fc(input=x, size=1, act=None)

#步骤三：定义损失函数
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)

#步骤四：定义优化方法
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.05)
sgd_optimizer.minimize(avg_cost)

#步骤五：参数初始化
place = fluid.CPUPlace()
exe = fluid.Executor(place=place)
exe.run(fluid.default_startup_program())

#步骤六：训练数据
for i in range(500):
    print(‘{:-^50}‘.format(i))
    outs = exe.run(program=fluid.default_main_program(),
            feed={‘x‘:x_data_numpy, ‘y‘:y_data_numpy},
            fetch_list=[y_predict.name, avg_cost.name])
    print(outs)