movidius_ncs_prelu.py

import tensorflow as tf

def prelu(x, scope, decoder=False):
    '''
    Performs the parametric relu operation. This implementation is based on:
    https://stackoverflow.com/questions/39975676/how-to-implement-prelu-activation-in-tensorflow
    For the decoder portion, prelu becomes just a normal prelu
    INPUTS:
    - x(Tensor): a 4D Tensor that undergoes prelu
    - scope(str): the string to name your prelu operation's alpha variable.
    - decoder(bool): if True, prelu becomes a normal relu.
    OUTPUTS:
    - pos + neg / x (Tensor): gives prelu output only during training; otherwise, just return x.
    '''
    #If decoder, then perform relu and just return the output
    if decoder:
        return tf.nn.relu(x, name=scope)

    alpha= tf.expand_dims(tf.get_variable(scope + 'alpha', x.get_shape()[-1],
                       initializer=tf.constant_initializer(0.0),
                        dtype=tf.float32), axis=0)
    pos = tf.nn.relu(x)
    # neg = alpha * (x - abs(x)) * 0.5
    # neg = alpha* tf.cast(tf.greater(0.0, x), tf.float32) * x
    # movidius list unsupported: tf. abs, log, pow, greater, elemwise division (realdiv), reciprocal, gather_nd, 
    # squared = x*x
    # sqrted = squared*.25 + 1
    # for i in range(10):
    #     sqrted = 0.5*(sqrted + tf.div(squared,sqrted))
    # neg = alpha * (x-sqrted)*.5
    neg = alpha * tf.nn.relu(tf.constant(-1.0)*x)
    return pos + neg