LDMnet implementation in pytorch

This repo implements LDMNet presented in the following work:

LDMNet: Low Dimensional Manifold Regularized Neural Networks Wei Zhu, Qiang Qiu, Jiaji Huang, Robert Calderbank, Guillermo Sapiro, Ingrid Daubechies

This implementation uses:

• pytorch > 0.4.1 for network models
• skorch >= 0.4.0 as a high level model training API
• sacred > 0.7.4 for argument and experiment management

To run

To run use:

python main.py with mnist
python main.py with cifar10
python main.py with svhn

You can change parameters as follows:

python main.py with mnist train_size=1000 device=cuda dropout=0.5 alphaupdate.lambda_bar=0.01

Call python main.py print_config to see all parameters available.

Results, arguments, run info and network weights for each run will be stored in ldmnet_runs, under a directory corresponding to the run's id number.

To be implemented

• test command to load previous run and evaluate on test set