import tensorflow as tf
import numpy as np

tf.set_random_seed(777)
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])

W = tf.Variable(tf.random_normal([2,1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')

hyp = tf.sigmoid(tf.matmul(X, W) + b)
cost = -tf.reduce_mean(Y*tf.log(hyp)+(1-Y)*tf.log(1-hyp))
train = tf.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(cost)

predicted = tf.cast(hyp > 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(W))

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