model = Sequential()
    model.add(LSTM(
        256,
        input_shape=(network_input.shape[1], network_input.shape[2]),
        return_sequences=True
    ))
    model.add(Dropout(0.3))
    model.add(LSTM(512, return_sequences=True))
    model.add(Dropout(0.3))
    model.add(LSTM(512))
    model.add(Dense(256))
    model.add(Dropout(0.3))
    model.add(Dense(n_vocab))
    model.add(Activation('softmax'))
    model.summary()
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

    filepath = "weights-improvement-{epoch:02d}-{loss:.4f}-bigger.hdf5"
    checkpoint = ModelCheckpoint(
        filepath,
        monitor='loss',
        verbose=0,
        save_best_only=True,
        mode='min'
    )
    callbacks_list = [checkpoint]

    history = model.fit(network_input, network_output, validation_split=0.33,
                        epochs=600, batch_size=64, callbacks=callbacks_list)
    print(history.history.keys())

    # acc history
    plt.plot(history.history['acc'])
    plt.plot(history.history['val_acc'])
    plt.title('model accuracy')
    plt.ylabel('accuracy')
    plt.xlabel('epoch')
    plt.legend(['train', 'test'], loc='upper left')
    plt.savefig("acc_history.png")
    plt.close()

    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])
    plt.title('model loss')
    plt.ylabel('loss')
    plt.xlabel('epoch')
    plt.legend(['train', 'test'], loc='upper left')
    plt.savefig("history_loss.png")