(Tensorflow) XOR Neural Network 만들기

이번 포스트는 Tensorflow로 XOR 기능을 수행하는 Neural Network를 만들 것입니다.

활성화 함수로는 Sigmoid를 사용하고 손실 함수로는 교차 엔트로피(Cross Entropy) 사용했습니다.

XOR Neural Network

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XOR Neural Network 그림

XOR Neural Network 코드

import tensorflow as tf
import numpy as np
 
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, 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/loss function
# 교차엔트로피
cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
                       tf.log(1 - hypothesis))
train = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(cost)
 
# hypothesis>0.5가 True=1, False=0
predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))
 
 
 
with tf.Session() as sess:
    # 초기화
    sess.run(tf.global_variables_initializer())
 
    #학습
    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
    hypo, c, acc = sess.run([hypothesis, predicted, accuracy],
                       feed_dict={X: x_data, Y: y_data})
    print("\nHypothesis: ", hypo, "\nCorrect: ", c, "\nAccuracy: ", acc)