linearReg1.py

#from __future__ import print_function
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

#  Note, minimize() knows to modify W and b because Variable objects are trainable=True by default

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



# Initialize the variables (i.e. assign their default value)

init = tf.global_variables_initializer()



# Start training

with tf.Session() as sess:



    # Run the initializer

    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='Original data')

    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')

    plt.legend()

    plt.show()



    # Testing example, as requested (Issue #2)

    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()