To add an attention layer to a Bi-LSTM, you will need to first define the attention layer itself and then incorporate it into the Bi-LSTM model.
Here's an example of how you can do this in Keras:
1. First, define the attention layer. This can be done using the Attention layer provided by the keras.layers module. For example:
from keras.layers import Attention
attention_layer = Attention(units=10)This creates an attention layer with 10 units. You can adjust the number of units according to your needs.
2. Next, incorporate the attention layer into the Bi-LSTM model. To do this, you will need to define the input and output of the attention layer. The input will be the output of the Bi-LSTM, and the output will be the attention-weighted representation of the input.
For example:
from keras.layers import LSTM, Input
inputs = Input(shape=(max_len,))
x = Embedding(input_dim=vocab_size, output_dim=embedding_dim)(inputs)
x = Bidirectional(LSTM(units=64, return_sequences=True))(x)
x = attention_layer(x)This defines an input layer, followed by an embedding layer and a Bi-LSTM layer. The output of the Bi-LSTM layer is then passed through the attention layer to generate the attention-weighted representation.
You can then add additional layers, such as a dense layer, to the model as needed.
outputs = Dense(units=1, activation='sigmoid')(x)
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])This creates a model with an attention layer incorporated into a Bi-LSTM. You can then train and evaluate the model as you would any other Keras model.
