In [1]:

    

import numpy as np
import tensorflow as tf

xy = np.loadtxt('../data/xor_data.txt', unpack=True)

x_data = np.transpose( xy[0:-1] )
y_data = np.reshape( xy[-1], (4,1) )

X = tf.placeholder(tf.float32, name = 'X-input')
Y = tf.placeholder(tf.float32, name = 'Y-input')

# Deep network configuration.: Use more layers. 
W1 = tf.Variable(tf.random_uniform( [2,5], -1.0, 1.0),name = 'weight1')
W2 = tf.Variable(tf.random_uniform( [5,4], -1.0, 1.0),name = 'weight2')
W3 = tf.Variable(tf.random_uniform( [4,1], -1.0, 1.0),name = 'weight3')

b1 = tf.Variable(tf.zeros([5]), name="bias1")
b2 = tf.Variable(tf.zeros([4]), name="bias2")
b3 = tf.Variable(tf.zeros([1]), name="bias3")

# Add histogram
w1_hist = tf.summary.histogram("weights1", W1)
w2_hist = tf.summary.histogram("weights2", W2)
w3_hist = tf.summary.histogram("weights3", W3)

b1_hist = tf.summary.histogram("biases1", b1)
b2_hist = tf.summary.histogram("biases2", b2)
b3_hist = tf.summary.histogram("biases3", b3)

y_hist = tf.summary.histogram("y", Y)


# Hypotheses 
with tf.name_scope("layer2") as scope:
    L2 =  tf.sigmoid(tf.matmul(X,W1)+b1)

with tf.name_scope("layer3") as scope:
    L3 =  tf.sigmoid(tf.matmul(L2,W2)+b2)

with tf.name_scope("layer4") as scope:
    hypothesis = tf.sigmoid( tf.matmul(L3,W3) + b3)

# Cost function 
with tf.name_scope("cost") as scope:
    cost = -tf.reduce_mean( Y*tf.log(hypothesis)+(1-Y)* tf.log(1.-hypothesis) )
    cost_summ = tf.summary.scalar("cost", cost)
    
# Minimize cost.
a = tf.Variable(0.1)
with tf.name_scope("train") as scope:
    optimizer = tf.train.GradientDescentOptimizer(a)
    train = optimizer.minimize(cost)

# Initializa all variables.
init = tf.global_variables_initializer()


# Launch the graph
with tf.Session() as sess:

    # tensorboard merge
    merged = tf.summary.merge_all()
    writer = tf.summary.FileWriter("../logs/xor_logs", sess.graph)
    
    sess.run(init)

    # Run graph.
    for step in range(20001):
        sess.run(train, feed_dict={X:x_data, Y:y_data})
        if step % 2000 == 0:
            summary, _ = sess.run( [merged, train], feed_dict={X:x_data, Y:y_data})
            writer.add_summary(summary, step)
    
    # Test model
    correct_prediction = tf.equal( tf.floor(hypothesis+0.5), Y)
    accuracy = tf.reduce_mean(tf.cast( correct_prediction, "float" ) )
    
    # Check accuracy
    print( sess.run( [hypothesis, tf.floor(hypothesis+0.5), correct_prediction, accuracy], 
                   feed_dict={X:x_data, Y:y_data}) )
    print( "Accuracy:", accuracy.eval({X:x_data, Y:y_data}) )


    

    




[array([[  8.82743858e-04],
       [  9.98684108e-01],
       [  9.98744011e-01],
       [  2.38674600e-03]], dtype=float32), array([[ 0.],
       [ 1.],
       [ 1.],
       [ 0.]], dtype=float32), array([[ True],
       [ True],
       [ True],
       [ True]], dtype=bool), 1.0]
Accuracy: 1.0