xor_nn.py

# xor_nn.py

import tensorflow as tf

x_data = [[0,0],
          [0,1],
          [1,0],
          [1,1]]

y_data = [[0],
          [1],
          [1],
          [0]]


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

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)

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)

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

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
train = optimizer.minimize(cost)

sess = tf.Session()
sess.run(tf.global_variables_initializer())

# accuracy computation
predict = tf.cast(hypothesis > 0.5, dtype = tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predict,Y),
                                     dtype = tf.float32))
# start training
for step in range(10001):
    cost_val, W1_val,W2_val, b1_val,b2_val,_ = \
        sess.run([cost, W1,W2,b1,b2, train],
                 feed_dict={X:x_data, Y:y_data})
    if step % 20 == 0:
        print(step, cost_val, W1_val,W2_val, b1_val,b2_val)

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

# predict : test model

print(sess.run(predict, feed_dict = {X:x_data}))