As is tradition in this book, I will show you how the entire architecture for this model fits together here:
def build_models(lstm_units, num_encoder_tokens, num_decoder_tokens): # train model encoder_input = Input(shape=(None, num_encoder_tokens), name='encoder_input') encoder_outputs, state_h, state_c = LSTM(lstm_units, return_state=True, name="encoder_lstm")(encoder_input) encoder_states = [state_h, state_c] decoder_input = Input(shape=(None, num_decoder_tokens), name='decoder_input') decoder_lstm = LSTM(lstm_units, return_sequences=True, return_state=True, name="decoder_lstm") decoder_outputs, _, _ = decoder_lstm(decoder_input, initial_state=encoder_states) decoder_dense = Dense(num_decoder_tokens, activation ...
