# -*- coding:utf-8 -*-
"""
Author:
Weichen Shen,wcshen1994@163.com
"""
import tensorflow as tf
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.initializers import Zeros, glorot_normal
from tensorflow.python.keras.layers import Layer
from tensorflow.python.keras.regularizers import l2
from .activation import activation_fun
class LocalActivationUnit(Layer):
"""The LocalActivationUnit used in DIN with which the representation of
user interests varies adaptively given different candidate items.
Input shape
- A list of two 3D tensor with shape: ``(batch_size, 1, embedding_size)`` and ``(batch_size, T, embedding_size)``
Output shape
- 3D tensor with shape: ``(batch_size, T, 1)``.
Arguments
- **hidden_size**:list of positive integer, the attention net layer number and units in each layer.
- **activation**: Activation function to use in attention net.
- **l2_reg**: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix of attention net.
- **keep_prob**: float between 0 and 1. Fraction of the units to keep of attention net.
- **use_bn**: bool. Whether use BatchNormalization before activation or not in attention net.
- **seed**: A Python integer to use as random seed.
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, hidden_size=(64, 32), activation='sigmoid', l2_reg=0, keep_prob=1, use_bn=False, seed=1024, **kwargs):
self.hidden_size = hidden_size
self.activation = activation
self.l2_reg = l2_reg
self.keep_prob = keep_prob
self.use_bn = use_bn
self.seed = seed
super(LocalActivationUnit, self).__init__(**kwargs)
self.supports_masking = True
def build(self, input_shape):
if not isinstance(input_shape, list) or len(input_shape) != 2:
raise ValueError('A `LocalActivationUnit` layer should be called '
'on a list of 2 inputs')
if len(input_shape[0]) != 3 or len(input_shape[1]) != 3:
raise ValueError("Unexpected inputs dimensions %d and %d, expect to be 3 dimensions" % (
len(input_shape[0]), len(input_shape[1])))
if input_shape[0][-1] != input_shape[1][-1] or input_shape[0][1] != 1:
raise ValueError('A `LocalActivationUnit` layer requires '
'inputs of a two inputs with shape (None,1,embedding_size) and (None,T,embedding_size)'
'Got different shapes: %s,%s' % (input_shape))
size = 4 * \
int(input_shape[0][-1]
) if len(self.hidden_size) == 0 else self.hidden_size[-1]
self.kernel = self.add_weight(shape=(size, 1),
initializer=glorot_normal(
seed=self.seed),
name="kernel")
self.bias = self.add_weight(
shape=(1,), initializer=Zeros(), name="bias")
super(LocalActivationUnit, self).build(
input_shape) # Be sure to call this somewhere!
def call(self, inputs, **kwargs):
query, keys = inputs
keys_len = keys.get_shape()[1]
queries = K.repeat_elements(query, keys_len, 1)
att_input = tf.concat(
[queries, keys, queries - keys, queries * keys], axis=-1)
att_out = MLP(self.hidden_size, self.activation, self.l2_reg,
self.keep_prob, self.use_bn, seed=self.seed)(att_input)
attention_score = tf.nn.bias_add(tf.tensordot(
att_out, self.kernel, axes=(-1, 0)), self.bias)
return attention_score
def compute_output_shape(self, input_shape):
return input_shape[1][:2] + (1,)
def compute_mask(self, inputs, mask):
return mask
def get_config(self,):
config = {'activation': self.activation, 'hidden_size': self.hidden_size,
'l2_reg': self.l2_reg, 'keep_prob': self.keep_prob, 'use_bn': self.use_bn, 'seed': self.seed}
base_config = super(LocalActivationUnit, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class MLP(Layer):
"""The Multi Layer Percetron
Input shape
- nD tensor with shape: ``(batch_size, ..., input_dim)``. The most common situation would be a 2D input with shape ``(batch_size, input_dim)``.
Output shape
- nD tensor with shape: ``(batch_size, ..., hidden_size[-1])``. For instance, for a 2D input with shape ``(batch_size, input_dim)``, the output would have shape ``(batch_size, hidden_size[-1])``.
Arguments
- **hidden_size**:list of positive integer, the layer number and units in each layer.
- **activation**: Activation function to use.
- **l2_reg**: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix.
- **keep_prob**: float between 0 and 1. Fraction of the units to keep.
- **use_bn**: bool. Whether use BatchNormalization before activation or not.
- **seed**: A Python integer to use as random seed.
"""
def __init__(self, hidden_size, activation='relu', l2_reg=0, keep_prob=1, use_bn=False, seed=1024, **kwargs):
self.hidden_size = hidden_size
self.activation = activation
self.keep_prob = keep_prob
self.seed = seed
self.l2_reg = l2_reg
self.use_bn = use_bn
super(MLP, self).__init__(**kwargs)
def build(self, input_shape):
input_size = input_shape[-1]
hidden_units = [int(input_size)] + list(self.hidden_size)
self.kernels = [self.add_weight(name='kernel' + str(i),
shape=(
hidden_units[i], hidden_units[i+1]),
initializer=glorot_normal(
seed=self.seed),
regularizer=l2(self.l2_reg),
trainable=True) for i in range(len(self.hidden_size))]
self.bias = [self.add_weight(name='bias' + str(i),
shape=(self.hidden_size[i],),
initializer=Zeros(),
trainable=True) for i in range(len(self.hidden_size))]
super(MLP, self).build(input_shape) # Be sure to call this somewhere!
def call(self, inputs, training=None, **kwargs):
deep_input = inputs
for i in range(len(self.hidden_size)):
fc = tf.nn.bias_add(tf.tensordot(
deep_input, self.kernels[i], axes=(-1, 0)), self.bias[i])
# fc = Dense(self.hidden_size[i], activation=None, \
# kernel_initializer=glorot_normal(seed=self.seed), \
# kernel_regularizer=l2(self.l2_reg))(deep_input)
if self.use_bn:
fc = tf.keras.layers.BatchNormalization()(fc)
fc = activation_fun(self.activation, fc)
#fc = tf.nn.dropout(fc, self.keep_prob)
fc = tf.keras.layers.Dropout(1 - self.keep_prob)(fc,)
deep_input = fc
return deep_input
def compute_output_shape(self, input_shape):
if len(self.hidden_size) > 0:
shape = input_shape[:-1] + (self.hidden_size[-1],)
else:
shape = input_shape
return tuple(shape)
def get_config(self,):
config = {'activation': self.activation, 'hidden_size': self.hidden_size,
'l2_reg': self.l2_reg, 'use_bn': self.use_bn, 'keep_prob': self.keep_prob, 'seed': self.seed}
base_config = super(MLP, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
class PredictionLayer(Layer):
"""
Arguments
- **activation**: Activation function to use.
- **use_bias**: bool.Whether add bias term or not.
"""
def __init__(self, activation='sigmoid', use_bias=True, **kwargs):
self.activation = activation
self.use_bias = use_bias
super(PredictionLayer, self).__init__(**kwargs)
def build(self, input_shape):
if self.use_bias:
self.global_bias = self.add_weight(
shape=(1,), initializer=Zeros(), name="global_bias")
# Be sure to call this somewhere!
super(PredictionLayer, self).build(input_shape)
def call(self, inputs, **kwargs):
x = inputs
if self.use_bias:
x = tf.nn.bias_add(x, self.global_bias, data_format='NHWC')
output = activation_fun(self.activation, x)
output = tf.reshape(output, (-1, 1))
return output
def compute_output_shape(self, input_shape):
return (None, 1)
def get_config(self,):
config = {'activation': self.activation, 'use_bias': self.use_bias}
base_config = super(PredictionLayer, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
