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 : 398333404@qq.comimport osimport tensorflow as tfdef 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 Noneif __name__ == '__main__': linear_regression()

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