TensorFlow（3） 线性回归

生成数据

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

拟合数据

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)  # 梯度下降算法
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.train.SummaryWriter("./tmp",sess.graph)

绘制模型

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