#!/usr/bin/env python
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
def model(x, w1, w2, b1, b2):
a = tf.matmul(x, w1)
b = tf.add(a,b1)
c = tf.sigmoid(b)
hidden = tf.sigmoid(tf.add(tf.matmul(x, w1), b1))
out = tf.nn.softmax(tf.add(tf.matmul(hidden, w2), b2))
return out
x = tf.placeholder("float", [4, 4])
w1 = tf.Variable(tf.random_normal([4,2]), name = 'w1')
w2 = tf.Variable(tf.random_normal([2,4]), name = 'w2')
b1 = tf.Variable(tf.random_normal([2]), name = 'b1')
b2 = tf.Variable(tf.random_normal([4]), name = 'b2')
pred = model(x, w1, w2, b1, b2)
cost = tf.reduce_sum(tf.pow(tf.sub(pred, x), 2))
optimizer = tf.train.AdamOptimizer().minimize(cost)
with tf.Session() as sess:
init = tf.initialize_all_variables()
sess.run(init)
input_data = np.array([[0,0,0,1],[0,0,1,0],[0,1,0,0],[1,0,0,0]], float)
for i in xrange(10000):
sess.run(optimizer, feed_dict = {x: input_data})
res = sess.run(pred, feed_dict = {x: input_data})
index = np.argmax(res, 1)
for i in xrange(4):
tmp = np.zeros((4,))
tmp[index[i]] = 1.
print res[i]
print tmp
