#!/usr/bin/env python

# -*- coding: utf-8 -*-

# @File : 自实现一个线性回归.py

# @Author: 赵路仓

# @Date : 2020/4/12

# @Desc :

# @Contact : 398333404@qq.com

import os

import tensorflow as tf

def linear_regression():

"""

自实现一个线性回归

:return:

"""

# 命名空间

with tf.variable_scope("prepared_data"):

# 准备数据

x = tf.random_normal(shape=[100, 1], name="Feature")

y_true = tf.matmul(x, [[0.08]]) + 0.7

# x = tf.constant([[1.0], [2.0], [3.0]])

# y_true = tf.constant([[0.78], [0.86], [0.94]])

with tf.variable_scope("create_model"):

# 2.构造函数

# 定义模型变量参数

weights = tf.Variable(initial_value=tf.random_normal(shape=[1, 1], name="Weights"))

bias = tf.Variable(initial_value=tf.random_normal(shape=[1, 1], name="Bias"))

y_predit = tf.matmul(x, weights) + bias

with tf.variable_scope("loss_function"):

# 3.构造损失函数

error = tf.reduce_mean(tf.square(y_predit - y_true))

with tf.variable_scope("optimizer"):

# 4.优化损失

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(error)

# 收集变量

tf.summary.scalar("error", error)

tf.summary.histogram("weights", weights)

tf.summary.histogram("bias", bias)

# 合并变量

merged = tf.summary.merge_all()

# 创建saver对象

saver = tf.train.Saver()

# 显式的初始化变量

init = tf.global_variables_initializer()

# 开启会话

with tf.Session() as sess:

# 初始化变量

sess.run(init)

# 创建事件文件

file_writer = tf.summary.FileWriter("E:/tmp/linear", graph=sess.graph)

# print(x.eval())

# print(y_true.eval())

# 查看初始化变量模型参数之后的值

print("训练前模型参数为:权重%f,偏置%f" % (weights.eval(), bias.eval()))

# 开始训练

for i in range(1000):

sess.run(optimizer)

print("第%d次参数为：权重%f,偏置%f,损失%f" % (i + 1, weights.eval(), bias.eval(), error.eval()))

# 运行合并变量操作

summary = sess.run(merged)

# 将每次迭代后的变量写入事件

file_writer.add_summary(summary, i)

# 保存模型

if i == 999:

saver.save(sess, "./tmp/model/my_linear.ckpt")

# # 加载模型

# if os.path.exists("./tmp/model/checkpoint"):

# saver.restore(sess, "./tmp/model/my_linear.ckpt")

print("参数为：权重%f,偏置%f,损失%f" % (weights.eval(), bias.eval(), error.eval()))

pre = [[0.5]]

prediction = tf.matmul(pre, weights) + bias

sess.run(prediction)

print(prediction.eval())

return None

if __name__ == "__main__":

linear_regression()

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