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Topic recognition #485
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Topic recognition #485
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Created modules.py and partially implemented vector quantizer class.
Added encoder and decoder for keras layers
Basic trainer class. Train steps which backpropagate and check loss.
Implemented data loading and preprocessing.
Moved VQVAE train class from train to modules.
Implement pixel cnn. Basic functionality works.
Added class methods in modules.py allowing models to be saved in .h5 format. Model fully trains but performance metrics are yet to be separated from train.py.
Put many lines of code into functions and made it overall neater.
Wrote readme.
Changed epoch number.
This reverts commit 8ce3cab.
Good Practice (Design/Commenting, TF/Torch Usage)Adequate use and implementation (code seems to be there, but can't verify if working, no outputs or training loss) -5 Recognition ProblemSolves problem (no results provided, no convergence plotted, no generations) -3 Commit LogMeaningful commit messages, could be more descriptive -1 DocumentationReadMe minimal (no architecture, background etc.) -2 Pull RequestSuccessful Pull Request (Working Algorithm Delivered on Time in Correct Branch) |
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Tried reverting changes to XUE4645768 folder by reverting to previous commit then force pushing that to PR. It's appearing to delete other students' files though? This sort of thing wasn't covered in the required git courses so I'm having trouble considering I only had half the day to figure this out. |
This is my second pull request, in my first request I accidentally did a request from the incorrect branch.
The initial request can be found here: #465
I didn't realise I made this mistake until today, I hope that's forgivable, thanks. Commits are dated before the due date.
This is the PatternFlow project submission for COMP3710 by Thomas Barton (s4641500). This includes a VQVAE with Pixel CNN trained and tested on the OASIS dataset. The algorithm trains using png slices of OASIS brains to create a seed for outputting slices based on an input gathered from encoding brains. Testing on this input can show a brain that looks either more healthy or unhealthy, based on the training dataset used. It can be used to gauge the health of a brain by comparing outputs with the VQVAE.
EDIT: Please also note that two README files have been committed. The one under XUE4645768 was automatically added for some reason. It is not mine so please disregard it.