03-1-xor-simple.py

# Lab 9 XOR
# based on https://github.com/hunkim/DeepLearningZeroToAll/blob/master/lab-09-1-xor.py

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

tf.set_random_seed(777)  # for reproducibility
learning_rate = 0.1


x_data = [[0, 0],
          [0, 1],
          [1, 0],
          [1, 1]]
y_data = [[0],
          [1],
          [1],
          [0]]
x_data = np.array(x_data, dtype=np.float32)
y_data = np.array(y_data, dtype=np.float32)

X = tf.placeholder(tf.float32, [None, 2])
Y = tf.placeholder(tf.float32, [None, 1])

W = tf.Variable(tf.random_normal([2, 1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')

# Hypothesis using sigmoid: tf.div(1., 1. + tf.exp(tf.matmul(X, W)))
hypothesis = tf.sigmoid(tf.matmul(X, W) + b)

# cost/loss function
cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
                       tf.log(1 - hypothesis))

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

# Accuracy computation
# True if hypothesis>0.5 else False
predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))


# Launch graph
with tf.Session() as sess:
    # Initialize TensorFlow variables
    sess.run(tf.global_variables_initializer())

    for step in range(10001):
        sess.run(train, feed_dict={X: x_data, Y: y_data})
        if step % 100 == 0:
            print(step, sess.run(cost, feed_dict={
                  X: x_data, Y: y_data}), sess.run(W))

    # Accuracy report
    h, c, a = sess.run([hypothesis, predicted, accuracy],
                       feed_dict={X: x_data, Y: y_data})
    print("\nHypothesis: ", h, "\nCorrect: ", c, "\nAccuracy: ", a)

    x_data2 = np.array(x_data)
    x1 = x_data2[:, [0] ]
    x2 = x_data2[:, [1] ]

    xval = range(0, 11)
    xval = [x / 10.0 for x in xval ]

    Wval = sess.run(W)

    print Wval
    print Wval[0], Wval[1]
    yval = xval * Wval[0] + Wval[1]

    plt.plot(x1, x2,"o")
    plt.plot(xval, yval, "k-")

    plt.show()