This folder includes code related to testing and evaluating the performance of neural networks. The individual files are described in the table below. The References column lists the publications you should cite if you use the scripts in your published research.
| File | Description | References |
|---|---|---|
| kfold_data_aug.py | A script for performing k-fold cross-validation using data augmentation. | 1, 2 |
| kfold_transfer_learning.py | A script for performing k-fold cross-validation using transfer learning. | 1, 2 |
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Chollet, F. (2015-) Keras: Deep learning library for Theano and Tensorflow. URL: https://github.com/fchollet/keras
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Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M. Prettenhofer, P., Weiss, R., Dubourg, V. Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011) scikit-learn: Machine learning in Python. Journal of Machine Learning Research (12), 2825–2830. URL: http://www.jmlr.org/papers/v12/pedregosa11a.html