linear-regression

#!/usr/bin/env python
# coding=utf-8

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

num_points=1000
vectors_set=[]

for idx 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.plot(x_data,y_data,'ro', label="Original data")
plt.legend()
plt.show()

#optimize linear regression with tensorflow
W=tf.Variable(tf.random_uniform([1],-1.0,1.0))
b=tf.Variable(tf.zeros([1]))

y=W*x_data+b

loss=tf.reduce_mean(tf.square(y-y_data))

optimizer=tf.train.GradientDescentOptimizer(0.5)
train=optimizer.minimize(loss)

init=tf.initialize_all_variables()

sess=tf.Session()
sess.run(init)
print 'params-before-training', sess.run(W), sess.run(b), '\n'

for step in xrange(20):
    sess.run(train)
    print 'loss', (step, sess.run(loss))
    print 'params', step, sess.run(W), sess.run(b), '\n'
    labelstr="training step="+('step+1')
    plt.plot(x_data,y_data,'ro', label=labelstr)
    plt.plot(x_data, sess.run(W)*x_data+sess.run(b))
    plt.legend()
    plt.show()

原始點

迭代1次

迭代20次