= Timeline of Progress =

== Tuesday, February 27th, 2024 ==
- Determined that lack of effectiveness for low values of epsilon for the FGSM attack is normal ([[https://pytorch.org/tutorials/beginner/fgsm_tutorial.html#accuracy-vs-epsilon|PyTorch Example Results]]). 
- Finished trainable, saveable MNIST model
- Working on manipulating the MNIST dataset for cross validation and filtering
- Looking into implementing [[https://www.cs.toronto.edu/~kriz/cifar.html|CIFAR-10]] due to the model architecture and the nature of the images being classified

== Thursday, February 29th, 2024 ==
- Created functionality for Kuwahara filtering of batches of 64 images at runtime
- Encountering crash in last batch

== Monday, March 4th, 2024 ==
- Last batch of epoch doesn't have 64 images, batch size now variable
- Encountered crash when testing at end of epoch
	- Fixed crash, testing required specifying batch size
- All 14 epochs train successfully on filtered data
- Added `--filter` option to enable filtering on training and test data
	- Encountered crash, `args` not passed to `test` function
	- `args` variable now passed to `test` function
	- Filtered and unfiltered models saved to different files
- Tested filtered model with FGSM attack
	- Got results inline with unfiltered model
	- Realized that I forgot to save the filtered model
	- Tested actually filtered model with FGSM attack
	- Got really good results inline with hypothesis