python 实现一个简单的线性回归案例

#!/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 : [e-mail]
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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