How to add attention layer to a Bi-LSTM

Question

I am developing a Bi-LSTM model and want to add a attention layer to it. But I am not getting how to add it.

My current code for the model is

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()

And the model summary is

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

Marcionism

Answer 1

This can be a possible custom solution with a custom layer that computes attention on the positional/temporal dimension

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)

it's build to receive 3D tensors and output 3D tensors (return_sequences=True) or 2D tensors (return_sequences=False). below a dummy example

# 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)

with 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)

with 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)

You can integrate it into your networks easily

here the running notebook

Piety

This yielded amazing results on my current problem, thanks a lot – Wesley

Answer 2

In case, someone is using only Tensorflow and not keras externally, this is the way to do it.

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

Pane

what if we dont have an embedding layer?? – Baptistery