Question answering system using SQuAD through Seq2Seq
I have my model which is the following:-def create_model(num_encoder_paragraph_tokens,max_encoder_paragraph_seq_length,
                 num_encoder_question_tokens,max_encoder_question_seq_length,num_decoder_tokens):

    hidden_units = 256
    embed_hidden_units = 100

    context_inputs = Input(shape=(None,), name='context_inputs')
    encoded_context = Embedding(input_dim=num_encoder_paragraph_tokens, output_dim=embed_hidden_units,
                                    input_length=max_encoder_paragraph_seq_length,
                                    name='context_embedding')(context_inputs)
    encoded_context = Dropout(0.3)(encoded_context)

    question_inputs = Input(shape=(None,), name='question_inputs')
    encoded_question = Embedding(input_dim=num_encoder_question_tokens, output_dim=embed_hidden_units,
                                     input_length=max_encoder_question_seq_length,
                                     name='question_embedding')(question_inputs)
    encoded_question = Dropout(0.3)(encoded_question)
    encoded_question = LSTM(units=embed_hidden_units, name='question_lstm')(encoded_question)
    encoded_question = RepeatVector(max_encoder_paragraph_seq_length)(encoded_question)

    merged = add([encoded_context, encoded_question])

    encoder_lstm = LSTM(units=hidden_units, return_state=True, name='encoder_lstm')
    encoder_outputs, encoder_state_h, encoder_state_c = encoder_lstm(merged)
    encoder_states = [encoder_state_h, encoder_state_c]

    decoder_inputs = Input(shape=(None, num_decoder_tokens), name='decoder_inputs')
    decoder_lstm = LSTM(units=hidden_units, return_state=True, return_sequences=True, name='decoder_lstm')
    decoder_outputs, decoder_state_h, decoder_state_c = decoder_lstm(decoder_inputs,
                                                                         initial_state=encoder_states)
    decoder_dense = Dense(units=num_decoder_tokens, activation='softmax', name='decoder_dense')
    decoder_outputs = decoder_dense(decoder_outputs)

    model = Model([context_inputs, question_inputs, decoder_inputs], decoder_outputs)
    model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

    encoder_model = Model([context_inputs, question_inputs], encoder_states)

    decoder_state_inputs = [Input(shape=(hidden_units,)), Input(shape=(hidden_units,))]
    decoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_state_inputs)
    decoder_states = [state_h, state_c]
    decoder_outputs = decoder_dense(decoder_outputs)
    decoder_model = Model([decoder_inputs] + decoder_state_inputs, [decoder_outputs] + decoder_states)

    return model, encoder_model, decoder_modelimage1381×959 48.7 KBWhen I train it?history = model.fit_generator(generator=train_gen, steps_per_epoch=train_num_batches,
                                           epochs=epochs,
                                           verbose=1, validation_data=test_gen, validation_steps=test_num_batches,
                                           callbacks=callbacks)image1422×617 38.9 KBimage1008×568 25.8 KBvalidation loss is not improving, so what is causing the problems?