linear_regression_tensorflow

from __future__ import print_function 
import tensorflow as tf
import numpy 
import matplotlib.pyplot as plt
import 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(random.rand(), name = "weight")
b = tf.Variable(random.rand(), name = "bias")

#construct a linear model 
pred = tf.add(tf.mul(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)

#initializing the variables
init = tf.global_variables_initializer()

#launch 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 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("traning cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
     
#graphical 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 = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
test_Y = numpy.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})  # same function as cost above
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()     

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