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# Lab 9 XOR

import tensorflow as tf

import numpy as np

tf.set_random_seed(777) # for reproducibility

learning_rate = 0.01

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], name='x-input')

Y = tf.placeholder(tf.float32, [None, 1], name='y-input')

with tf.name_scope("layer1"):

W1 = tf.Variable(tf.random_normal([2, 2]), name='weight1')

b1 = tf.Variable(tf.random_normal([2]), name='bias1')

layer1 = tf.sigmoid(tf.matmul(X, W1) + b1)

w1_hist = tf.summary.histogram("weights1", W1)

b1_hist = tf.summary.histogram("biases1", b1)

layer1_hist = tf.summary.histogram("layer1", layer1)

with tf.name_scope("layer2"):

W2 = tf.Variable(tf.random_normal([2, 1]), name='weight2')

b2 = tf.Variable(tf.random_normal([1]), name='bias2')

hypothesis = tf.sigmoid(tf.matmul(layer1, W2) + b2)

w2_hist = tf.summary.histogram("weights2", W2)

b2_hist = tf.summary.histogram("biases2", b2)

hypothesis_hist = tf.summary.histogram("hypothesis", hypothesis)

# cost/loss function

with tf.name_scope("cost"):

cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *

tf.log(1 - hypothesis))

cost_summ = tf.summary.scalar("cost", cost)

with tf.name_scope("train"):

train = tf.train.AdamOptimizer(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))

accuracy_summ = tf.summary.scalar("accuracy", accuracy)

# Launch graph

with tf.Session() as sess:

# tensorboard --logdir=./logs/xor_logs

merged_summary = tf.summary.merge_all()

writer = tf.summary.FileWriter("./logs/xor_logs_r0_01")

writer.add_graph(sess.graph) # Show the graph

# Initialize TensorFlow variables

sess.run(tf.global_variables_initializer())

for step in range(10001):

summary, _ = sess.run([merged_summary, train], feed_dict={X: x_data, Y: y_data})

writer.add_summary(summary, global_step=step)

if step % 100 == 0:

print(step, sess.run(cost, feed_dict={

X: x_data, Y: y_data}), sess.run([W1, W2]))

# 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: [[ 6.13103184e-05]

[ 9.99936938e-01]

[ 9.99950767e-01]

[ 5.97514772e-05]]

Correct: [[ 0.]

[ 1.]

[ 1.]

[ 0.]]

Accuracy: 1.0

'''