MLlab09-2.py

#여러 개의 계층을 이용하여 XOR을 구현
import numpy as np
import tensorflow as tf

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]), 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 = - tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) * tf.log(1-hypothesis))
train = tf.train.GradientDescentOptimizer(learning_rate=0.1).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(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,W3,W4]))

    h, c, a = sess.run([hypothesis, predicted, accuracy],feed_dict={X:x_data, Y:y_data})
    print("\nHypothesis: ", h, "\nCorrect: ", c, "\nAccuracy: ",a)