My second week had me actually submit my solution. It is a straight forward neural network so I didn’t expect anything great. With that said, they do give you 0.01 NMR (~$4) so it feels like you have some skin in the game. Once I get a model a bit higher (>0.02 Spearmen Correlation) I will put in more money. I added $100 that I can stake at some point.
Google Colab
It appears that if you add [“”] to the Colab settings you get 2x the memory. It is probably best that I didn’t find this sooner to make sure I cleaned up my code.
Large CSV Files
After looking around at some documentation I saw that converting each of the numeric fields into float16 saved me over 74% on memory. It still takes a while to get the CSV into my code but after that I can do quite a bit with the DataFrame pretty quickly.
Custom Loss/MEtric Method
I am still trying to find the TF version of the Spearman loss function. There is an implementation in PyTorch that I might be able to use.
