I am trying to understand how to use the tf.keras.layers.Attention shown here:
I am trying to use it with encoder decoder seq2seq model. Below is my code:
encoder_inputs = Input(shape=(max_len_text,))
enc_emb = Embedding(x_voc_size, latent_dim,trainable=True)(encoder_inputs)
encoder_lstm=LSTM(latent_dim, return_state=True, return_sequences=True)
encoder_outputs, state_h, state_c= encoder_lstm(enc_emb)
decoder_inputs = Input(shape=(max_len_summary,))
dec_emb_layer = Embedding(y_voc_size, latent_dim,trainable=True)
dec_emb = dec_emb_layer(decoder_inputs)
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs,decoder_fwd_state, decoder_back_state = decoder_lstm(dec_emb,initial_state=[state_h, state_c])
My question is, how to use the given Attention layer in keras with this model? I am not able to understand their document.
If you are using RNN, I would not recommend using the above class.
While analysing tf.keras.layers.Attention Github code to better understand your conundrum, the first line I could come across was - "This class is suitable for Dense or CNN networks, and not for RNN networks"
I would recommend writing your own seq to seq model which can be done with less than a dozen lines of code. For e.g.: https://www.tensorflow.org/tutorials/text/nmt_with_attention
To write your own custom attention layer(based on whether you prefer Bahdanau, Luong, Raffel, Yang etc), perhaps this post outlining a basic essence may help: Custom Attention Layer using in Keras