backgammon/tensorflow_impl_tests/normal_main.py

import tensorflow as tf
import numpy as np
import time

class Everything:

    def __init__(self):

        self.output_size = 1
        self.hidden_size = 40
        self.input_size = 30

        self.input = tf.placeholder('float', [1, self.input_size])

        xavier_init = tf.contrib.layers.xavier_initializer()


        W_1 = tf.get_variable("w_1", (self.input_size, self.hidden_size),
                              initializer=xavier_init)
        W_2 = tf.get_variable("w_2", (self.hidden_size, self.output_size),
                              initializer=xavier_init)

        b_1 = tf.get_variable("b_1", (self.hidden_size,),
                              initializer=tf.zeros_initializer)
        b_2 = tf.get_variable("b_2", (self.output_size,),
                              initializer=tf.zeros_initializer)

        value_after_input = tf.sigmoid(tf.matmul(self.input, W_1) + b_1, name='hidden_layer')

        self.value = tf.sigmoid(tf.matmul(value_after_input, W_2) + b_2, name='output_layer')

        apply_gradients = []


        trainable_vars = tf.trainable_variables()
        gradients = tf.gradients(self.value, trainable_vars)

        difference_in_values = tf.reshape(tf.subtract(0.9, self.value, name='difference_in_values'), [])

        with tf.variable_scope('apply_gradients'):
            for gradient, trainable_var in zip(gradients, trainable_vars):
                backprop_calc = 0.1 * difference_in_values * gradient
                grad_apply = trainable_var.assign_add(backprop_calc)
                apply_gradients.append(grad_apply)


        self.training_op = tf.group(*apply_gradients, name='training_op')



    def eval(self):
        input = np.array([0, 2, 0, 0, 0, 0, -5, 0, -3, 0, 0, 0, 5, -5, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, -2, 0, 0, 0, 1, 0])
        start = time.time()
        sess = tf.Session()
        sess.run(tf.global_variables_initializer())
        for i in range(20):
            val = sess.run(self.value, feed_dict={self.input: input.reshape(1,-1)})
        print(time.time() - start)
        print(val)
        sess.run(self.training_op, feed_dict={self.input: input.reshape(1,-1)})
        val = sess.run(self.value, feed_dict={self.input: input.reshape(1, -1)})
        print(val)

everything = Everything()
everything.eval()