This project was developed for Statoil/C-CORE Iceberg Classifier Challenge on Kaggle

There are three main files:

1. models.py contains the model classes.

2. train.py loads and prepares training and x-validation datasets and feeds them to an ensemble of models in batches. It uses k-folding cross validation to estimate the test error. It also saves model checkpoints, model description, and a log for viewing in Tensorboard. These are saved in a separate timestamped folder for each run to make it easier to keep track of experiments.

3. test.py loads and prepares test data. It then loads the model from the latest checkpoint created by train.py and iterates through the test data. Inspired by the work of Yarin Gal, I used drop-out during inference to create a confidence interval around each prediction. This confidence interval is treated as a measure of certainty of the model in its prediction, and it is used to adjust the predictions to achieve lower log-loss. Finally, it creates a CSV file ready to be submitted to Kaggle.