linear_regression.py

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

rng = numpy.random

#Parameters 
learning_rate = 0.01
training_epochs = 1000 
display_step = 50


#Training data

train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])

n_samples = train_X.shape[0]
 
# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")

# Set model weights 
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")

# Construct a linear model
pred = tf.add(tf.multiply(X , W), b)

# Mean squared error
cost = tf.reduce_sum(tf.pow(pred -Y, 2))/ (2*n_samples)

#Gradient descent 
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

#Intializing the variables
init = tf.global_variables_initializer()

#Launch Graph
with tf.Session() as sess:
	sess.run(init)


	#Fit all training data
	for epoch in range(training_epochs):
		for (x, y) in zip(train_X, train_Y):
			sess.run(optimizer, feed_dict={X:x , Y:y})

		#Display logs per epoch step
		if (epoch + 1) % display_step == 0:
			c = sess.run(cost , feed_dict={X: train_X, Y: train_Y})
			print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
                "W=", sess.run(W), "b=", sess.run(b))

	print("Optimization finished!")
	training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
	print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')


	#Graphic Display
	plt.plot(train_X, train_Y, 'ro', label='Orignal data')
	plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label = "Fitted Line")
	plt.legend()
	plt.show()