I set up a model in keras (in python 2.7) to predict the next stock price in a particular sequence.

The model I used is shown below (edited to fit this page):

`model = Sequential()
    model.add(Dense(5, input_shape=(1, 1)))
    model.add(LSTM(5, return_sequences=True))
    model.add(Dense(1))
    model.add(Activation("linear"))
    model.compile(loss="mse", optimizer="Nadam",
                  metrics=["mape"])
    predict = model.fit(X, Y, epochs=epochs, verbose=1, validation_split=0.2, callbacks=[checkpoint_maker], shuffle=True, batch_size=count / 10 * 8)`

However, when I ran the model, I found that **val_mean_absolute_percentage_error decreases while the mean_absolute_percentage_error increases**.

Here is the graph I managed to generate after 1000 epochs.
[![Graph][1]][1]

Notice that the blue line is going up while the orange line is going down.

I have no idea why. 


I've read on many sources that if the loss is decreasing and the val_loss is increasing it means that

> (the) model is over fitting, that (it) is just memorizing the training data - https://stats.stackexchange.com/a/260346

so does that mean that in my case the model is "under fitting"?

P.S. [Code and files][2]


  [1]: https://i.sstatic.net/ik1yF.png
  [2]: https://github.com/Ryan-Kan/Machine-Learning/tree/master/Stock%20Prediction