I’m learning the lesson 4 with Entity Embedding and there are something that I’m not understand. (I ran the notebook in order until
m.lr_find() to find the appropriate learning rate stop at 59%
When I’m fitting the model, the loss and RMSPE are too small and it was not decreasing but oscillating not in any order
For dropout parameter, I wonder why it is so small. emb_drop = 0.04 ; drops = [0.001,0.01]. It means that the model is very easy to overfitting because we drop almost nothing.
This is the first time I attack the structure learning. I am very appreciate if someone can help me to clarify these problem. Thank you so much
I post the same question at Wiki_lesson4 and get the answer. The reason why the exp_rmspe is so small comparing to the original notebook is because of setting val_idx= before learning. If someone try to run the notebook in order, should comment this line to get the proper results.
The notebook has several different ways of defining the validation indices. If you just run the notebook cells in order, you set val_idx= (what you do before training with the entire dataset) before forming your model. If you ran the model with a single validation index, you would be comparing your rmspe to a single value. See what your validation set is, and if it is a single value, try change it to the last two weeks of data via:
val_idx = np.flatnonzero(
(df.index<=datetime.datetime(2014,9,17)) & (df.index>=datetime.datetime(2014,8,1)))