Home Verkennen Help
Inloggen
nschense
/
alzheimers_nn
1
0
Vork 0
Bestanden Issues 0 Pull-aanvragen 0 Wiki

Geen omschrijving

Boom: 72c64b23d9
Aftakkingen Labels
alzheimers_nn
ZIP TAR.GZ
nschense 72c64b23d9 Update 'README.md' 2 maanden geleden
.vscode a02334abbf More work done on overall stats - not sure why so many files changed 8 maanden geleden
utils a02334abbf More work done on overall stats - not sure why so many files changed 8 maanden geleden
.gitignore a02334abbf More work done on overall stats - not sure why so many files changed 8 maanden geleden
LP_ADNIMERGE.csv a02334abbf More work done on overall stats - not sure why so many files changed 8 maanden geleden
README.md 72c64b23d9 Update 'README.md' 2 maanden geleden
bayesian.py a02334abbf More work done on overall stats - not sure why so many files changed 8 maanden geleden
config.toml 300dc9dcf4 Update 'config.toml' in preparation for sharing 2 maanden geleden
ensemble_predict.py a02334abbf More work done on overall stats - not sure why so many files changed 8 maanden geleden
ruff.toml a02334abbf More work done on overall stats - not sure why so many files changed 8 maanden geleden
sensitivity_analysis.py a02334abbf More work done on overall stats - not sure why so many files changed 8 maanden geleden
threshold.py 1083b2301b Completed refactor to xarray - working on minimial calibration and sensitivity analysis 8 maanden geleden
threshold_refac.py 1083b2301b Completed refactor to xarray - working on minimial calibration and sensitivity analysis 8 maanden geleden
threshold_xarray.py 1083b2301b Completed refactor to xarray - working on minimial calibration and sensitivity analysis 8 maanden geleden
train_cnn.py a02334abbf More work done on overall stats - not sure why so many files changed 8 maanden geleden

README.md

Alzheimers Diagnosis Neural Net Project Rewrite

This code is the current version for the Alzheimers CNN uncertanity estimation project. The project consists of a set of main scripts (in the root folder) and some utilities. In order to use the project:

  1. Edit "config.toml" with the details of the ensemble that you would like to train (size, name, epochs etc). Make sure that the model name and ensemble name are the same if you'd like to run the ensemble analysis later.
  2. Run "train_cnn.py". This will train and save a new ensemble of CNN models using the name and congfiguration options given in the config file.
  3. Run "ensemble_predict.py". This will generate the predictions of the models on the test and validation datasets and save them to the model ensemble folder.
  4. Run "threshold_xarray.py". This run some analysis on the ensemble and generates a set of graphs and statistics.

"bayesian.py" is unfinished and does not currently work. The other two threshold files are old implementations. 'sensitivity_analysis.py' can be optionally used to generate some model number sensitivity data, but the implementation is buggy currently. Apologies for the messy code throughout!