14-LinearRegression.py

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

#linear regression
number_of_points = 500
x_point = []
y_point = []
a = 0.22
b = 0.78
for i in range(number_of_points):
	x = np.random.normal(0.0,0.5)
	y = a*x+b+np.random.normal(0.0,0.1)
	x_point.append([x])
	y_point.append([y])
plt.plot(x_point,y_point, 'o', label = 'Input data')
plt.legend()
#plt.show()

#y = Ax+b
A = tf.Variable(tf.random_uniform([1],-1.0,1.0))
B = tf.Variable(tf.zeros([1]))
y = A * x_point + B

#mean square error
cost_function = tf.reduce_mean(tf.square(y - y_point))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(cost_function)
model = tf.initialize_all_variables()

with tf.Session() as session:
	session.run(model)
	for step in range(0,21):
		session.run(train)
		if(step % 5) == 0:
			plt.plot(x_point, y_point, 'o', label = 'step = {}'.format(step))
			plt.plot(x_point, session.run(A) * x_point + session.run(B))
			plt.legend()
			plt.show()