Tensorflow-线性回归

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

num_points = 1000
vector_set = []
#构造训练数据
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)
	vector_set.append([x1,y1])

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

#plt.plot(x_data,y_data,'ro')
#plt.legend()
#plt.show()

w = tf.Variable(tf.random_uniform([1],-1.0,1.0))
b = tf.Variable(tf.zeros([1]))
y = w*x_data + b
#损失函数
loss = tf.reduce_mean(tf.square(y-y_data))
#梯度下降学习算法
train = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

with tf.Session() as sess:
	sess.run(tf.initialize_all_variables())
	#初始参数
	print(sess.run(w),sess.run(b))
	for i in range(10):
		sess.run(train)
		#print(i,sess.run(w)*x_data+sess.run(b))
		print(i,sess.run(loss))
		print(i,sess.run(w),sess.run(b))
		#可视化回归过程
		if i>=0:
			plt.plot(x_data,y_data,'ro')
			plt.plot(x_data,sess.run(w)*x_data + sess.run(b))
			plt.xlabel("x")
			plt.xlim(-2,2)
			plt.ylim(0.1,0.6)
			plt.ylabel('y')
			plt.legend()
			plt.show()