Dice.py

import tensorflow as tf
#tf.enable_eager_execution()

def dice(_x, axis=-1, epsilon=0.000000001, name=''):
    with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
        alphas = tf.get_variable('alpha' + name, _x.get_shape()[-1],
                                 initializer=tf.constant_initializer(0.0),
                                 dtype=tf.float32)
        input_shape = list(_x.get_shape())

        reduction_axes = list(range(len(input_shape)))
        del reduction_axes[axis]
        broadcast_shape = [1] * len(input_shape)
        broadcast_shape[axis] = input_shape[axis]

    # case: train mode (uses stats of the current batch)
    mean = tf.reduce_mean(_x, axis=reduction_axes)
    brodcast_mean = tf.reshape(mean, broadcast_shape)
    std = tf.reduce_mean(tf.square(_x - brodcast_mean) + epsilon, axis=reduction_axes)
    std = tf.sqrt(std)
    brodcast_std = tf.reshape(std, broadcast_shape)
    x_normed = (_x - brodcast_mean) / (brodcast_std + epsilon)
    # x_normed = tf.layers.batch_normalization(_x, center=False, scale=False)
    x_p = tf.sigmoid(x_normed)

    return alphas * (1.0 - x_p) * _x + x_p * _x


# prelu
def parametric_relu(_x):
    alphas = tf.get_variable('alpha', _x.get_shape()[-1],
                             initializer=tf.constant_initializer(0.0),
                             dtype=tf.float32)
    pos = tf.nn.relu(_x)
    neg = alphas * (_x - abs(_x)) * 0.5

    return pos + neg