# -*- coding: utf-8 -*-
"""
@author:XuMing（xuming624@qq.com)
@description:
"""
from keras import backend as K
from keras import initializers
from keras.engine.topology import Layer
class AttLayer(Layer):
def __init__(self, attention_dim):
self.init = initializers.get('normal')
self.supports_masking = True
self.attention_dim = attention_dim
super(AttLayer, self).__init__()
def build(self, input_shape):
assert len(input_shape) == 3
self.W = K.variable(self.init((input_shape[-1], self.attention_dim)))
self.b = K.variable(self.init((self.attention_dim,)))
self.u = K.variable(self.init((self.attention_dim, 1)))
self.trainable_weights = [self.W, self.b, self.u]
super(AttLayer, self).build(input_shape)
def compute_mask(self, inputs, mask=None):
return mask
def call(self, x, mask=None):
# size of x :[batch_size, sel_len, attention_dim]
# size of u :[batch_size, attention_dim]
# uit = tanh(xW+b)
uit = K.tanh(K.bias_add(K.dot(x, self.W), self.b))
ait = K.dot(uit, self.u)
ait = K.squeeze(ait, -1)
ait = K.exp(ait)
if mask is not None:
# Cast the mask to floatX to avoid float64 upcasting in theano
ait *= K.cast(mask, K.floatx())
ait /= K.cast(K.sum(ait, axis=1, keepdims=True) + K.epsilon(), K.floatx())
ait = K.expand_dims(ait)
weighted_input = x * ait
output = K.sum(weighted_input, axis=1)
return output
def compute_output_shape(self, input_shape):
return input_shape[0], input_shape[-1]
