tensorflow_practice.py

# -*- coding: utf-8 -*-
"""
Created on Sun Sep  3 02:21:52 2017

@author: zhaoweiheng
"""

import numpy as np
import tensorflow as tf

tf.set_random_seed(0)

w = tf.Variable(tf.zeros([2, 1]))
b = tf.Variable(tf.zeros([1]))

x = tf.placeholder(tf.float32, shape=[None, 2])
t = tf.placeholder(tf.float32, shape=[None, 1])
y = tf.nn.sigmoid(tf.matmul(x, w) + b)


cross_entropy = - tf.reduce_sum(t * tf.log(y) + (1 - t) * tf.log(1 - y))
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)

correct_prediction = tf.equal(tf.to_float(tf.greater(y, 0.5)), t)

X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
Y = np.array([[0], [1], [1], [1]])

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

for epoch in range(200):
    sess.run(train_step, feed_dict={x: X, t: Y})

classified = correct_prediction.eval(session=sess, feed_dict={x: X, t: Y})
prob = y.eval(session=sess, feed_dict={x: X, t: Y})

print(sess.run(w))
print(sess.run(b))