import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' #내 맥북에서 발생되는 에러를 없애기 위한 코드

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]])

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, 1]), name='weight2')
b2 = tf.Variable(tf.random_normal([1]), name='bias2')
hypothesis = tf.sigmoid(tf.matmul(layer1, W2) + b2)

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]))

    h, c, a = sess.run([hypothesis, predicted, accuracy], feed_dict={X: x_data, Y:y_data})
    print("\nH: ", h, "\n C:",c,"\n Acc:", a)
