Fix example and loss normalization

[gbruno16]

Pull Request

Description

1) In the example of the readme file, each features has a size of 78, whereas the model expects a feature size of 102, since the default value for "aux_dim" is 24. I've changed this number, moreover the loss is computed against a new variable target of size 78, randomly generated. This addresses the issue #83.

2) Many standard deviations in the file "consts.py" look suspiciously small (10^-6). As a consequence, the uncommented NormalizedMSELoss results in NaNs or very large values (~10^22). In the original paper, they normalize the loss by dividing the outputs and targets by the standard deviation, while in our code (currently commented), we are dividing by the variance. I added a normalization flag to the loss and modified its normalization. Now the model can train with the normalized loss, although the loss remains substantially big (~10^11). This addresses the issue #29

How Has This Been Tested?

I added a new test for the normalized loss.

• [x] Yes

If your changes affect data processing, have you plotted any changes? i.e. have you done a quick sanity check?

• [x] Yes

Checklist:

• [x] My code follows OCF's coding style guidelines
• [x] I have performed a self-review of my own code
• [x] I have made corresponding changes to the documentation
• [x] I have added tests that prove my fix is effective or that my feature works
• [x] I have checked my code and corrected any misspellings

[jacobbieker]

Are you happy to have it merged in?

[gbruno16]

Yes, thank you!