How could we use Bahdanau attention in a stacked LSTM model? - keras

Question

I aim to use attention in a stacked LSTM model, but I don't know how to add AdditiveAttention mechanism of Keras between encoder and decoder layers. Let say, we have an input layer, an encoder, and a decoder, and a dense classification layer, and we aim our decoder to pay attention on all the hidden states of the encoder (h = [h1, ..., hT]) in deriving its outputs. Is there any high-level coding using the Keras whereby I can do? For example,
input_layer = Input(shape=(T, f))
x = input_layer
x = LSTM(num_neurons1, return_sequences=True)(x)
# Adding attention here, but I don't know how?
x = LSTM(num_neurons2)(x)
output_layer = Dense(1, 'sigmoid')(x)
model = Model(input_layer, output_layer)
...
I think this is wrong to use: x = AdditiveAttention(x, x). Am I right?

Answer 1

Maybe it is helpful for your issue ?
This is a classification model with LSTM and attention for classification on
character-level:
first create a custom layer for attention :
class attention(Layer):
def init(self,**kwargs):
super(attention,self).init(**kwargs)
def build(self,input_shape):
self.W=self.add_weight(name='attention_weight', shape=(input_shape[-1],1),
initializer='random_normal', trainable=True)
self.b=self.add_weight(name='attention_bias', shape=(input_shape[1],1),
initializer='zeros', trainable=True)
super(attention, self).build(input_shape)
def call(self,x):
# Alignment scores. Pass them through tanh function
e = K.tanh(K.dot(x,self.W)+self.b)
# Remove dimension of size 1
e = K.squeeze(e, axis=-1)
# Compute the weights
alpha = K.softmax(e)
# Reshape to tensorFlow format
alpha = K.expand_dims(alpha, axis=-1)
# Compute the context vector
context = x * alpha
context = K.sum(context, axis=1)
return context
LEN_CHA = 64 # number of characters
LEN_Input = 110 # depend on the longest sentence, padded with zero
def LSTM_model_attention(Labels=3):
model = Sequential()
model.add(Embedding(LEN_CHA, EMBEDDING_DIM, input_length=LEN_INPUT))
model.add(SpatialDropout1D(0.7))
model.add(Bidirectional(LSTM(256, return_sequences=True)))
model.add(attention())
model.add(Dense(Labels, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
return model
LSTM_attention = LSTM_model_attention()
LSTM_attention.summary()