The objective of the competition was to develop a deep learning model in order to predict the pressure of ventilators given some parameters and a control variable. The competition was organized by Princeton University and & Google Brain at the Kaggle platform.
Fig. Samples of u_in (input variable) & pressure for train and test data
- Models: Bi-LSTM (4 stacked layers)
- Scheduler: cosine annealing scheduler with warm restarts
The final submission consists of an ensemble of 2 models as described aboved at different checkpoints. These checkpoints were obtained before the restart of the scheduler every 50 epochs.
python train_lstm.py --folds 0 1 2 3 4 5 6 7 8 9 10 --name model1 --dropout 0.0
python train_lstm.py --folds 0 1 2 3 4 5 6 7 8 9 10 --name model1 --dropout 0.15Generate OOF predictions and test predictions for the 2 training models at different checkpoints
python predict.py --folds 0 1 2 3 4 5 6 7 8 9 10 --name model1
--dropout 0.0 --checkpoints None 400 450 500 550 600
python predict.py --folds 0 1 2 3 4 5 6 7 8 9 10 --name model2
--dropout 0.15 --checkpoints None 400 450 500 550 600For running the ensemble, change the parameter MODEL_NAMES and run ensemble.py
# Ensemble models:
# {'model1':[None,600,550],'model2':[None,600,550,400]}
python ensemble.pyOverall code structure is as follows:
| Model | Public LB | Private LB |
|---|---|---|
| Model1 best | 0.1346 | 0.1371 |
| Model1 checkpoint - 600 | 0.1348 | 0.1366 |
| Model1 checkpoint - 550 | 0.1355 | 0.1373 |
| Model2 best | 0.1352 | 0.1376 |
| Model1 checkpoint - 600 | 0.1351 | 0.1375 |
| Model1 checkpoint - 400 | 0.1378 | 0.1400 |
| ------ | ----------- | ----------- |
| Median Ensemble | 0.1312 | 0.1336 |