[深度学习]python深度学习 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 : 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()