ops.py

import math
import numpy as np
import tensorflow as tf

from tensorflow.python.framework import ops



def linear(input_, output_size, scope=None, stddev=0.5, bias_start=0.0, with_w=False):
    shape = input_.get_shape().as_list()

    with tf.variable_scope(scope or "Linear"):
        matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,
                                 tf.random_normal_initializer(stddev=stddev))
        bias = tf.get_variable("bias", [output_size],
            initializer=tf.random_normal_initializer(stddev=stddev))
        if with_w:
            return tf.matmul(input_, matrix) + bias, matrix, bias
        else:
            return tf.matmul(input_, matrix) + bias

def squash(input,factor=0.01):
        return tf.select(input>0.5,(1-factor)+factor*input,input*factor)
        #return 1/(1+tf.exp(-30*(input-0.5)))
def kl_divergence(a,b):

    quo = tf.divide(a,b)
    log_q = tf.log(quo)

    return tf.reduce_sum(tf.multiply(a,log_q))

def js_divergence(a,b):
    m = tf.multiply(0.5,tf.add(a,b))
    klam = tf.multiply(0.5,kl_divergence(a,m))
    klbm = tf.multiply(0.5,kl_divergence(b,m))

    return tf.reduce_sum(tf.add(klam,klbm))