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# Lab 9 XOR

import tensorflow as tf

import numpy as np

tf.set_random_seed(777) # for reproducibility

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

y_data = np.array([[0], [1], [1], [0]], 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=0.1).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):

_, cost_val, w_val = sess.run(

[train, cost, W], feed_dict={X: x_data, Y: y_data}

)

if step % 100 == 0:

print(step, cost_val, w_val)

# 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)

'''

Hypothesis: [[ 0.5]

[ 0.5]

[ 0.5]

[ 0.5]]

Correct: [[ 0.]

[ 0.]

[ 0.]

[ 0.]]

Accuracy: 0.5

'''