HCI (Human-Computer Interaction) 수업 과제
문제
2개 또는 그 이상을 사용하여 XOR 기능을 수행하는 뉴럴 네트워크를 제작
- tensorflow를 사용하여 구현
XOR (배타적 논리합)
▼ 진리표
X |
Y |
X XOR Y |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
0 |
1 |
1 |
1 |
0 |
XOR 퍼셉트론
코드
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | import tensorflow as tf import numpy as np x_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32) y_data = np.array([[0], [1], [1], [0]], dtype=np.float32) X = tf.placeholder(tf.float32) Y = tf.placeholder(tf.float32) W1 = tf.Variable(tf.random_normal([2,10])) b1 = tf.Variable(tf.random_normal([10])) layer = tf.sigmoid(tf.matmul(X, W1) + b1) W2 = tf.Variable(tf.random_normal([10, 1])) b2 = tf.Variable(tf.random_normal([1])) hypothesis = tf.sigmoid(tf.matmul(layer, W2) + b2) cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) * tf.log(1 - hypothesis)) learning_rate = tf.Variable(0.1) train = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) 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: sess.run(tf.global_variables_initializer()) for step in range(25001): sess.run(train, feed_dict={X: x_data, Y: y_data}) if step % 50 == 0: print(step, sess.run(cost, feed_dict={X: x_data, Y: y_data}), sess.run([W1, W2])) hypothesis, predicted, accuracy = sess.run([hypothesis, predicted, accuracy], feed_dict={X: x_data, Y: y_data}) print("\nHypothesis: \n", hypothesis, "\nPredicted: \n", predicted, "\nAccuracy: \n", accuracy) | cs |
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