tensorflow学习笔记4

构造线性回归模型3

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

#随机生成1000个点，围绕在y=0.1x+0.3的直线周围
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])

#生成一些样本
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()

#生成一维的W矩阵，取值是[-1,1]之间的随机数
W = tf.Variable(tf.random.uniform([1],-1.0,1.0),name='W')
#生成一维的b矩阵，初始值是0
b = tf.Variable(tf.zeros([1]),name='b')
#预估值y
y = W * x_data + b

#以预估值y和实际值y_data之间的均方误差作为损失
loss = tf.reduce_mean(tf.square(y - y_data),name='loss')
#梯度下降优化参数
optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.5)
#训练的过程就是最小化loss
train = optimizer.minimize(loss,name='train')

sess = tf.compat.v1.Session()

init = tf.compat.v1.global_variables_initializer()
sess.run(init)

#执行20次训练
for step in range(20):
    sess.run(train)
    print("W=",sess.run(W),"b=",sess.run(b),"loss=",sess.run(loss))

#训练后的图像
plt.scatter(x_data,y_data,c='r')
plt.plot(x_data,x_data*sess.run(W)+sess.run(b))
plt.show()