如何将注意力层添加到Bi-LSTM中

Question

我正在开发一个Bi-LSTM模型，并想为它添加一个注意力层。但我不知道如何添加它。

我目前的型号代码是

model = Sequential()
model.add(Embedding(max_words, 1152, input_length=max_len, weights=[embeddings]))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Bidirectional(LSTM(32)))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.summary()

模型摘要为

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_1 (Embedding)      (None, 1152, 1152)        278396928 
_________________________________________________________________
batch_normalization_1 (Batch (None, 1152, 1152)        4608      
_________________________________________________________________
activation_1 (Activation)    (None, 1152, 1152)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 1152, 1152)        0         
_________________________________________________________________
bidirectional_1 (Bidirection (None, 64)                303360    
_________________________________________________________________
batch_normalization_2 (Batch (None, 64)                256       
_________________________________________________________________
activation_2 (Activation)    (None, 64)                0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 65        
=================================================================
Total params: 278,705,217
Trainable params: 278,702,785
Non-trainable params: 2,432

Answer 1

这可能是一个具有自定义层的自定义解决方案，该自定义层计算位置/时间维度上的注意力

from tensorflow.keras.layers import Layer
from tensorflow.keras import backend as K
class Attention(Layer):

def __init__(self, return_sequences=True):
self.return_sequences = return_sequences
super(Attention,self).__init__()

def build(self, input_shape):

self.W=self.add_weight(name="att_weight", shape=(input_shape[-1],1),
initializer="normal")
self.b=self.add_weight(name="att_bias", shape=(input_shape[1],1),
initializer="zeros")

super(Attention,self).build(input_shape)

def call(self, x):

e = K.tanh(K.dot(x,self.W)+self.b)
a = K.softmax(e, axis=1)
output = x*a

if self.return_sequences:
return output

return K.sum(output, axis=1)

其被构建为接收3D张量并输出3D张量CCD_ 1或2D张量CCD_。低于伪示例

# dummy data creation
max_len = 100
max_words = 333
emb_dim = 126
n_sample = 5
X = np.random.randint(0,max_words, (n_sample,max_len))
Y = np.random.randint(0,2, n_sample)

带return_sequences=True

model = Sequential()
model.add(Embedding(max_words, emb_dim, input_length=max_len))
model.add(Bidirectional(LSTM(32, return_sequences=True)))
model.add(Attention(return_sequences=True)) # receive 3D and output 3D
model.add(LSTM(32))
model.add(Dense(1, activation='sigmoid'))
model.summary()
model.compile('adam', 'binary_crossentropy')
model.fit(X,Y, epochs=3)

带return_sequences=False

model = Sequential()
model.add(Embedding(max_words, emb_dim, input_length=max_len))
model.add(Bidirectional(LSTM(32, return_sequences=True)))
model.add(Attention(return_sequences=False)) # receive 3D and output 2D
model.add(Dense(1, activation='sigmoid'))
model.summary()
model.compile('adam', 'binary_crossentropy')
model.fit(X,Y, epochs=3)

你可以很容易地将其集成到你的网络中

这是正在运行的笔记本

Answer 2

如果有人在外部只使用Tensorflow而不使用keras，这就是方法。

import tensorflow as tf
class Attention(tf.keras.layers.Layer):
def __init__(self, return_sequences=True, name=None, **kwargs):
super(Attention, self).__init__(name=name)
self.return_sequences = return_sequences
super(Attention, self).__init__(**kwargs)
def build(self, input_shape):

self.W=self.add_weight(name="att_weight", shape=(input_shape[-1],1),
initializer="glorot_uniform", trainable=True)
self.b=self.add_weight(name="att_bias", shape=(input_shape[1],1),
initializer="glorot_uniform", trainable=True)

super(Attention, self).build(input_shape)
def call(self, x):

e = tf.keras.activations.tanh(tf.keras.backend.dot(x, self.W) + self.b)
a = tf.keras.activations.softmax(e, axis=1)
output = x * a

if self.return_sequences:
return a, output

return a, tf.keras.backend.sum(output, axis=1)
def get_config(self):
config = super().get_config().copy()
config.update({
'return_sequences': self.return_sequences 
})
return config