[DL] XOR 학습시키기 1

04 Dec 2020

머신러닝

XOR 학습시키기(TF1.xx)

TF1.15를 사용한 DNN 모델을 만든다.

Library

사용한 Library를 나열한다.

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

from sklearn.metrics import classification_report

학습 진행

TF1.15로 학습을 진행해본다.

XOR DataSet

x_data = np.array([[0,0],[1,0],[0,1],[1,1]], dtype=np.float32)
t_data = np.array([[0],[1],[1],[0]], dtype=np.float32)

placeholder, weight, bias, hyperthesis, logit

X = tf.placeholder(shape=[None, x_data.shape[1]], dtype=tf.float32)
T = tf.placeholder(shape=[None,1], dtype=tf.float32)

W2 = tf.Variable(tf.random.normal([x_data.shape[1], 100]), name='weight2')
b2 = tf.Variable(tf.random.normal([1]), name='bias2')
layer2 = tf.sigmoid(tf.matmul(X,W2)+b2)

W3 = tf.Variable(tf.random.normal([100,6]), name='weight3')
b3 = tf.Variable(tf.random.normal([6]), name='bias3')
layer2 = tf.sigmoid(tf.matmul(layer2,W3)+b3)

W4 = tf.Variable(tf.random.normal([6,1]), name='weight4')
b4 = tf.Variable(tf.random.normal([1]), name='bias4')

logit = tf.matmul(layer2, W4) + b4
H = tf.sigmoid(logit)

loss, train, session

loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logit, labels=T))

train = tf.train.GradientDescentOptimizer(learning_rate=1e-1).minimize(loss)

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

학습 진행

for step in range(30000):
    _, loss_val = sess.run([train, loss], feed_dict={X:x_data, T:t_data})
    if step%3000==0:
	    print('loss : {}'.format(loss_val))

결과

accuracy = tf.cast(H>=0.5, dtype=tf.float32)
result = sess.run(accuracy, feed_dict={X:x_data})
print(classification_report(t_data,result))