linear_regression.py

'''
A linear regression learning algorithm example using TensorFlow library.
'''

from __future__ import print_function

import tensorflow as tf
import numpy as np 
import matplotlib.pyplot as plt
rng = np.random

#parameters

learning_rate = 0.01
training_epochs = 1000
display_step = 50

# Training data
train_X = np.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 = np.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
# minimize() know to modify W and b because variable objects are trainable = true by default

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

#Initializing the variables
init = tf.global_variables_initializer()

#lauch the 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 log 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) )
    
    #graphic display
    plt.plot(train_X, train_Y, 'ro', label = 'Original data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label = 'Fitted line')
    plt.legend()
    plt.show()
    
    #Testing example 
    test_X = np.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
    test_Y = np.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])
    
    print("Testing... (Mean square loss Comparison)")
    testing_cost = sess.run(tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 *             test_X.shape[0]),feed_dict={X: test_X, Y: test_Y})
    print("testing cost =", testing_cost)
    print("absolute mean square loss difference:", abs(training_cost - testing_cost))
    
    plt.plot(test_X, test_Y, 'bo', label = 'testing data')
    plt.plot(train_X, sess.run(W)* train_X + sess.run(b), label = 'fitted line')
    plt.legend()
    plt.show()