Neural Networks with Keras Cookbook

How to do it...

With this, let's look at how we can build the encoder decoder architecture, along with the attention mechanism. The code file is available as Machine_translation.ipynb in GitHub.

1. Build the encoder, as shown in the following code:

encoder_inputs = Input(shape=(eng_max_length,))en_x= Embedding(num_encoder_tokens+1, embedding_size)(encoder_inputs)en_x = Dropout(0.1)(en_x)encoder = LSTM(256, return_sequences=True, unroll=True)(en_x)encoder_last = encoder[:,-1,:]

2. Build the decoder, as follows:

decoder_inputs = Input(shape=(fr_max_length,))dex= Embedding(num_decoder_tokens+1, embedding_size)decoder= dex(decoder_inputs)decoder = Dropout(0.1)(decoder)decoder = LSTM(256, return_sequences=True, unroll=True)(decoder, initial_state=[encoder_last, ...