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- #!/usr/bin/env python
- # -*- coding: utf-8 -*-
- __author__ = 'maxim'
- import tensorflow as tf
- import numpy as np
- def random_normal(shape):
- return (np.random.random(shape) - 0.5) * 2
- input_size = 2
- hidden_size = 16
- output_size = 1
- x = tf.placeholder(dtype=tf.float32, name="X")
- y = tf.placeholder(dtype=tf.float32, name="Y")
- W1 = tf.Variable(random_normal((input_size, hidden_size)), dtype=tf.float32, name="W1")
- W2 = tf.Variable(random_normal((hidden_size, output_size)), dtype=tf.float32, name="W2")
- b1 = tf.Variable(random_normal(hidden_size), dtype=tf.float32, name="b1")
- b2 = tf.Variable(random_normal(output_size), dtype=tf.float32, name="b2")
- l1 = tf.sigmoid(tf.add(tf.matmul(x, W1), b1), name="l1")
- result = tf.sigmoid(tf.add(tf.matmul(l1, W2), b2), name="l2") # Note: works much better without sigmoid
- r_squared = tf.square(result - y)
- loss = tf.reduce_mean(r_squared)
- optimizer = tf.train.GradientDescentOptimizer(0.1)
- train = optimizer.minimize(loss)
- train_x = np.array([[1, 0], [0, 1], [1, 1], [0, 0]]).reshape((4, 2))
- train_y = np.array([1, 1, 0, 0]).reshape((4, 1))
- with tf.Session() as sess:
- sess.run(tf.global_variables_initializer())
- for itr in range(10000):
- _, loss_val = sess.run([train, loss], {x: train_x, y: train_y})
- if itr % 100 == 0:
- prediction = sess.run(result, {x: [[1, 0]]})
- print('Epoch %d done. Loss=%.6f Prediction=%.6f' % (itr, loss_val, prediction))
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