# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def real_func():
    return


def emperor():
    num_points = 1000
    vectors_set = []
    for i in range(num_points):
        x1 = np.random.normal(0.0, 0.55)
        y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03)
        vectors_set.append([x1, y1])

    x_data = [v[0] for v in vectors_set]
    y_data = [v[1] for v in vectors_set]

    # plt.scatter(x_data, y_data, c='r')
    # plt.show()

    W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W')
    b = tf.Variable(tf.zeros([1]), name='b')
    y = W * x_data + b

    loss = tf.reduce_mean(tf.square(y - y_data), name='loss')
    optimizer = tf.train.GradientDescentOptimizer(0.5)
    train = optimizer.minimize(loss, name='train')
    sess = tf.Session()
    init = tf.global_variables_initializer()
    sess.run(init)
    # print('W=', sess.run(W), 'b=', sess.run(b), 'loss=', sess.run(loss))



    for step in range(20):
        sess.run(train)
        print('W=', sess.run(W), 'b=', sess.run(b), 'loss=', sess.run(loss))
    writer = tf.summary.FileWriter(r'C:\Users\Administrator\Desktop\meatwice\meatwice\01newCognition\reinforcement_learning\new_test_tensorflow/tmp', sess.graph)



    plt.scatter(x_data, y_data, c='r')
    plt.plot(x_data, sess.run(W) * x_data + sess.run(b))
    plt.show()



if __name__ == "__main__":
    emperor()

运行结果：