In the data/ folder you will see compressed numpy zip files, this is observed data from particle interaction simulations. Check file my_submission.py before starting. You aim is to:
- Train three neural networks for the three datasets and submit
test_y.npzfor each dataset. In each case, create a validation split of 10% from the training set. - Write a trainer script / notebook for us to check your code. In case of script it should be callable from CLI like
$python train.py <something>.npz
You get hired if a single network can perform well on all three datasets.
We code in pytorch and prefer developers using that. You will have to mention the number of parameters of your model, for pytorch get code here. test_loss is the MSE between your submission and our golden. Scoring formula is given as follows:
Score = (MegaParams * TestLoss) ** -0.5
Eg #1: num_params = 151024; test_loss = 1.342 --> score = (0.151 * 1.342) ** -0.5 ~ 2.22
Eg #2: num_params = 3023432; test_loss = 0.442 --> score = (3.023 * 0.442) ** -0.5 ~ 0.865
Eg #2: num_params = 98923; test_loss = 0.902 --> score = (0.098 * 0.902) ** -0.5 ~ 3.36
Thus you can see that even if larger model gives the lowest test loss, it's score is the lowest. Whereas smallest model with mid-loss has the highest score.
Since your code will be run only once, aim for quick and dirty implementations of code over so called "correct" (if correct means writing unittests or classes for what should be a 99 LoC). Read more here and here.
For submitting use the file my_submission.py. It will write a .npz file. Once completed mail the results and files to ks2labs@gmail.com
If you have any doubts raise an issue in the repo.
Q: Do I need to perform feature engineering?
A: No. Both input and output is raw observation values and feature engineering can cause problems in general use.
Q: Do I need a GPU to train network?
A: No. The model trains just fine on a CPU machine (mine being 2020 MBP, 16GB RAM & 10th Gen i5).