lab9-1: XOR을 위한 텐스플로우 딥넷트웍 https://www.youtube.com/watch?v=9i7FBbcZPMA&feature=youtu.be

lab9-1-1.py

import tensorflow as tf
import numpy as np

xy = np.loadtxt('train.txt', unpack=True)
x_data = xy[0:-1]
y_data = xy[-1]

X = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)

W = tf.Variable(tf.random_uniform([1, len(x_data)], -1.0, 1.0))

h = tf.matmul(W, X)
hypothesis = tf.div(1., 1.+tf.exp(-h))

cost = -tf.reduce_mean(Y*tf.log(hypothesis) + (1-Y)*tf.log(1-hypothesis))

a = tf.Variable(0.01)
optimizer = tf.train.GradientDescentOptimizer(a)
train = optimizer.minimize(cost)

init = tf.initialize_all_variables()

with tf.Session() as sess:
        sess.run(init)

        for step in xrange(1000):
                sess.run(train, feed_dict={X:x_data, Y:y_data})
                if step % 200 == 0:
                        print step, sess.run(cost, feed_dict={X:x_data, Y:y_data}), sess.run(W)


        correct_prediction = tf.equal(tf.floor(hypothesis+0.5), Y)
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
        print sess.run([hypothesis, tf.floor(hypothesis+0.5), correct_prediction, accuracy], feed_dict={X:x_data, Y:y_data})
        print "Accuracy:", accuracy.eval({X:x_data, Y:y_data})