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# Lab 9 XOR
import tensorflow as tf
import numpy as np
tf.set_random_seed(777) # for reproducibility
learning_rate = 0.1
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])
W1 = tf.Variable(tf.random_normal([2, 10]), name='weight1')
b1 = tf.Variable(tf.random_normal([10]), name='bias1')
layer1 = tf.sigmoid(tf.matmul(X, W1) + b1)
W2 = tf.Variable(tf.random_normal([10, 10]), name='weight2')
b2 = tf.Variable(tf.random_normal([10]), name='bias2')
layer2 = tf.sigmoid(tf.matmul(layer1, W2) + b2)
W3 = tf.Variable(tf.random_normal([10, 10]), name='weight3')
b3 = tf.Variable(tf.random_normal([10]), name='bias3')
layer3 = tf.sigmoid(tf.matmul(layer2, W3) + b3)
W4 = tf.Variable(tf.random_normal([10, 1]), name='weight4')
b4 = tf.Variable(tf.random_normal([1]), name='bias4')
hypothesis = tf.sigmoid(tf.matmul(layer3, W4) + b4)
# 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([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: [[ 7.80511764e-04]
[ 9.99238133e-01]
[ 9.98379230e-01]
[ 1.55659032e-03]]
Correct: [[ 0.]
[ 1.]
[ 1.]
[ 0.]]
Accuracy: 1.0
'''