tensorflow构造线性回归模型

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_point = 1000
vectors_set = []
for i in range(num_point):
    #生成正态分布的自变量
    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1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")

#经过计算得出预估值y

y = W*x_data + b

#以预估值y和真实值y_data的均方差作为loss_function

loss = tf.reduce_mean(tf.square(y - y_data),name="loss")
#采用梯度下降法来优化参数

optimizer = tf.train.GradientDescentOptimizer(0.5)

#训练的过程就是最小化这个误差值
train = optimizer.minimize(loss,name="train")

sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)

# 初始化的W和b是多少
print ("W =", sess.run(W), "b =", sess.run(b), "loss =", sess.run(loss))
# 执行20次训练
print("____________________1____________________-")
for step in range(20):
    sess.run(train)
    # 输出训练好的W和b
    print ("W =", sess.run(W), "b =", sess.run(b), "loss =", sess.run(loss))
writer = tf.train.SummaryWriter("./tmp", sess.graph)