rpatrik96

import pytorch_lightning as pl
import wandb
from os.path import dirname
import subprocess

from pytorch_lightning.loggers.wandb import WandbLogger
from pytorch_lightning.utilities.cli import LightningCLI


class WandBOfflineSync(pl.LightningModule):

rpatrik96

#include <boost/python.hpp>
using namespace boost::python;
/************************************************************/
/* C++ code which will be ported to Python with Boost.Python*/
/************************************************************/


/****************************/
/* Porting template classes */
/****************************/

rpatrik96

Weight Matrix Modification - a regularization approach

General description:

Author: Patrik Reizinger

The project contains the source files (without the datasets) which implement WMM (Weight Matrix Modification,) a weight matrix-based regularization technique for Deep Neural Networks. In the following the proposed methods are shortly introduced, including the evalutaion framework.

Weight shuffling

Weight shuffling is based on the assumption that locally the coefficients of a weight matrix are correlated. Based on this, I hypothesize that shuffling the weight within a rectangular window - which is under the beforementioned assumption a way of adding correlated noise to the weights - may help reduce overfitting.

rpatrik96

rpatrik96

rpatrik96