# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen,wcshen1994@163.com
"""
import tensorflow as tf
from tensorflow.python.keras.initializers import Zeros
from tensorflow.python.keras.layers import Layer
class Dice(Layer):
"""The Data Adaptive Activation Function in DIN,which can be viewed as a generalization of PReLu and can adaptively adjust the rectified point according to distribution of input data.
Input shape
- Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model.
Output shape
- Same shape as the input.
Arguments
- **axis** : Integer, the axis that should be used to compute data distribution (typically the features axis).
- **epsilon** : Small float added to variance to avoid dividing by zero.
References
- [Zhou G, Zhu X, Song C, et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1059-1068.](https://arxiv.org/pdf/1706.06978.pdf)
"""
def __init__(self, axis=-1, epsilon=1e-9, **kwargs):
self.axis = axis
self.epsilon = epsilon
super(Dice, self).__init__(**kwargs)
def build(self, input_shape):
self.bn = tf.keras.layers.BatchNormalization(
axis=self.axis, epsilon=self.epsilon, center=False, scale=False)
self.alphas = self.add_weight(shape=(input_shape[-1],), initializer=Zeros(
), dtype=tf.float32, name=self.name+'dice_alpha') # name='alpha_'+self.name
super(Dice, self).build(input_shape) # Be sure to call this somewhere!
def call(self, inputs, **kwargs):
inputs_normed = self.bn(inputs)
# tf.layers.batch_normalization(
# inputs, axis=self.axis, epsilon=self.epsilon, center=False, scale=False)
x_p = tf.sigmoid(inputs_normed)
return self.alphas * (1.0 - x_p) * inputs + x_p * inputs
def get_config(self,):
config = {'axis': self.axis, 'epsilon': self.epsilon}
base_config = super(Dice, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
def activation_fun(activation, fc):
if isinstance(activation, str):
fc = tf.keras.layers.Activation(activation)(fc)
elif issubclass(activation, Layer):
fc = activation()(fc)
else:
raise ValueError(
"Invalid activation,found %s.You should use a str or a Activation Layer Class." % (activation))
return fc
