深度学习之自实现线性回归

import tensorflow as tf
import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'


def linear_regression():
    """
    自实现一个线性回归
    :return:
    """
    # (1)准备数据
    X = tf.compat.v1.random_normal(shape=[100,1])
    y_true = tf.matmul(X,[[0.8]])+0.7

    # (2)构造模型
    # 定义模型参数  用变量
    weights = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1,1]))
    bias = tf.Variable(initial_value=tf.compat.v1.random_normal(shape=[1,1]))
    y_predict = tf.matmul(X,weights)+bias
    # (3)构造损失函数
    error=tf.reduce_mean(tf.square(y_predict-y_true))

    # (4)优化损失
    optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate=0.01).minimize(error)


    # 显示地初始化变量
    init = tf.compat.v1.global_variables_initializer()

    # 开启会话
    with tf.compat.v1.Session() as sess:
        #初始化变量
        sess.run(init)

        #查看初始化模型参数之后的值
        print("训练前模型参数为：权重%f,偏置%f,损失为%f"%(weights.eval(),bias.eval(),error.eval()))

        #开始训练
        for i in range(1000):
            sess.run(optimizer)
            print("第%d次训练后模型参数为：权重%f,偏置%f,损失为%f" % (i+1,weights.eval(), bias.eval(), error.eval()))




    return None


if __name__ == "__main__":
    linear_regression()