- Use the run_dgc.py script for training, and validation and test deep generative classifiers.
python run_dgc.py <options>
python run_dgc.py -h
usage: run_dgc.py [-h] [--batch-size N] [--path PATH] [--epochs N] [--seed N] [--gpu N] [-e ENCODER] [-w N]
[--start-epoch N]
deep generative classifier for chestXray
optional arguments:
-h, --help show this help message and exit
--batch-size N input batch size for training (default: 16)
--path PATH data path
--epochs N number of epochs to train (default: 10)
--seed N random seed (default: 1)
--gpu N the GPU number (default auto schedule)
-e ENCODER, --encoder ENCODER
the encoder
-w N, --lossweight N weight of KL divergence (default: 0)
--start-epoch N manual epoch number (useful on restarts)
results will be saved to 'models' directory under path
- Use the run_baseline.py script for training, and validation and test deep generative classifiers.
python run_baseline.py <options>
python run_baseline.py -h
usage: run_baseline.py [-h] [--batch-size N] [--path PATH] [--epochs N] [--seed N] [-e ENCODER] [--gpu N]
[--start-epoch N]
baseline classifiers for chestXray
optional arguments:
-h, --help show this help message and exit
--batch-size N input batch size for training (default: 16)
--path PATH data path
--epochs N number of epochs to train (default: 10)
--seed N random seed (default: 1)
-e ENCODER, --encoder ENCODER
the encoder
--gpu N the GPU number (default auto schedule)
--start-epoch N manual epoch number (useful on restarts)
results will be saved to 'models/baselines' directory under path