train.py

Prepared_data=prepare_data(questions_for_token,answers_for_token)
encoder_input_data=Prepared_data[0]
decoder_input_data=Prepared_data[1]
decoder_output_data=Prepared_data[2]
maxlen_answers=Prepared_data[3]
nb_words=Prepared_data[4]
word_index=Prepared_data[5]
tokenizer=Prepared_data[6]
embedding_matrix=emb_mat(nb_words)
encoder_inputs = tf.keras.layers.Input(shape=( None , ))
encoder_embedding = tf.keras.layers.Embedding( nb_words+1, embed_size , mask_zero=True, weights=[embedding_matrix]) (encoder_inputs)
encoder_outputs , state_h , state_c = tf.keras.layers.LSTM( 200 , return_state=True )( encoder_embedding )
encoder_states = [ state_h , state_c ]

decoder_inputs = tf.keras.layers.Input(shape=( None ,  ))
decoder_embedding = tf.keras.layers.Embedding( nb_words+1, embed_size , mask_zero=True,weights=[embedding_matrix]) (decoder_inputs)
decoder_lstm = tf.keras.layers.LSTM( 200 , return_state=True , return_sequences=True )
decoder_outputs , _ , _ = decoder_lstm ( decoder_embedding , initial_state=encoder_states )

decoder_dense = tf.keras.layers.Dense( nb_words+1 , activation=tf.keras.activations.softmax ) 
output = decoder_dense ( decoder_outputs )

model = tf.keras.models.Model([encoder_inputs, decoder_inputs], output )


model.compile(optimizer=tf.keras.optimizers.RMSprop(), loss='categorical_crossentropy', metrics=['accuracy'])
model.fit([encoder_input_data , decoder_input_data], decoder_output_data, batch_size=10, epochs=20, callbacks=callbacks_list)