Attention机制的本质思想理解

Posted 2022-12-13 vodka、

学习内容：

Attention机制的本质思想理解

有关空间注意力机制和通道注意力机制的内容可以参考一下文章《空间注意力机制和通道注意力机制详解》

注意力机制的原理从本质上可以怎么理解呢！？

如上图所示，可以将Source中的元素看成是一系列由<key，value>数据对构成的特殊结构。这时对于某个元素Query来说，可以计算每个key与Query之间的相似性，或者说是相关性。接着将相关性进行归一化，就可以得到每个Key对应Value的权重系数，最后用Value和权重系数进行加权求和，得到最终结果。可以预见的是，权重越大，该处的Value的重要性就越高。因此在长串序列压缩时就可以通过重要程度，对原始数据信息进行取舍。（以下贴一段学习代码）

#coding=utf-8
'''
Single model may achieve LB scores at around 0.043
Don't need to be an expert of feature engineering
All you need is a GPU!!!!!!!

The code is tested on Keras 2.0.0 using Theano backend, and Python 3.5

referrence Code:https://www.kaggle.com/lystdo/lstm-with-word2vec-embeddings
'''

########################################
## import packages
########################################
import os
import re
import csv
import codecs
import numpy as np
import pandas as pd

########################################
## set directories and parameters
########################################



from keras import backend as K
from keras.engine.topology import Layer
# from keras import initializations
from keras import initializers, regularizers, constraints

np.random.seed(2018)

class Attention(Layer):
    def __init__(self,
                 W_regularizer=None, b_regularizer=None,
                 W_constraint=None, b_constraint=None,
                 bias=True, **kwargs):
        """
        Keras Layer that implements an Attention mechanism for temporal data.
        Supports Masking.
        Follows the work of Raffel et al. [https://arxiv.org/abs/1512.08756]
        # Input shape
            3D tensor with shape: `(samples, steps, features)`.
        # Output shape
            2D tensor with shape: `(samples, features)`.
        :param kwargs:

        """
        self.supports_masking = True
        # self.init = initializations.get('glorot_uniform')
        self.init = initializers.get('glorot_uniform')

        self.W_regularizer = regularizers.get(W_regularizer)
        self.b_regularizer = regularizers.get(b_regularizer)

        self.W_constraint = constraints.get(W_constraint)
        self.b_constraint = constraints.get(b_constraint)

        self.bias = bias

        self.features_dim = 0
        super(Attention, self).__init__(**kwargs)

    def build(self, input_shape):
        self.step_dim = input_shape[1]
        assert len(input_shape) == 3 # batch ,timestep , num_features
        print(input_shape)
        self.W = self.add_weight((input_shape[-1],), #num_features
                                 initializer=self.init,
                                 name='_W'.format(self.name),
                                 regularizer=self.W_regularizer,
                                 constraint=self.W_constraint)
        self.features_dim = input_shape[-1]

        if self.bias:
            self.b = self.add_weight((input_shape[1],),#timesteps
                                     initializer='zero',
                                     name='_b'.format(self.name),
                                     regularizer=self.b_regularizer,
                                     constraint=self.b_constraint)
        else:
            self.b = None

        self.built = True

    def compute_mask(self, input, input_mask=None):
        # do not pass the mask to the next layers
        return None

    def call(self, x, mask=None):
        features_dim = self.features_dim
        step_dim = self.step_dim
        print(K.reshape(x, (-1, features_dim)))# n, d
        print(K.reshape(self.W, (features_dim, 1)))# w= dx1
        print(K.dot(K.reshape(x, (-1, features_dim)), K.reshape(self.W, (features_dim, 1))))#nx1

        eij = K.reshape(K.dot(K.reshape(x, (-1, features_dim)), K.reshape(self.W, (features_dim, 1))), (-1, step_dim))#batch,step
        print(eij)
        if self.bias:
            eij += self.b

        eij = K.tanh(eij)

        a = K.exp(eij)

        # apply mask after the exp. will be re-normalized next
        if mask is not None:
            # Cast the mask to floatX to avoid float64 upcasting in theano
            a *= K.cast(mask, K.floatx())

        a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())
        print(a)
        a = K.expand_dims(a)
        print("expand_dims:")
        print(a)
        print("x:")
        print(x)
        weighted_input = x * a
        print(weighted_input.shape)
        return K.sum(weighted_input, axis=1)

    def compute_output_shape(self, input_shape):
        # return input_shape[0], input_shape[-1]
        return input_shape[0], self.features_dim