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Neural Networks Regularization Through Class-wise Invariant Representation Learning.

This repository contains the code of the paper Neural Networks Regularization Through Class-wise Invariant Representation Learning. S.Belharbi, C.Chatelain, R.Hérault, S.Adam. 2017.ArXiv.

Please cite this paper if you use the code in this repository as part of a published research project.

Requirements:

  • Python (2.7).
  • Theano (0.9).
  • Numpy (1.13).
  • Keras (2.0).
  • Matplotlib (1.2)
  • Yaml (3.10).

To run this code, you need to uncompress the MNIST dataset:

$ unzip data/mnist.pkl.zip -d data/
$ unzip data/mnist_bin17.pkl.zip -d data/

To generate mnist-noise and mnist-img, please see the file mnist_manip.py.

The folder config_yaml contains yaml files to configure an experiment. For instance, this is the content of the yaml file to run an experiment using an mlp with 3 hidden layers:

corrupt_input_l: 0.0
debug_code: false
extreme_random: true
h_ind: [false, false, true, false]
h_w: 0.0
hint: true
max_epochs: 400
model: train3_new_dup
nbr_sup: 1000
norm_gh: false
norm_gsup: false
repet: 0
run: 0
start_corrupting: 0
start_hint: 110
use_batch_normalization: [false, false, false, false]
use_sparsity: false
use_sparsity_in_pred: false
use_unsupervised: false

To run this experiment on a GPU:

$ THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32  python train3_new_dup.py train3_new_dup_0_1000_3_0_0_0_0_0_False_False_False_False_False_110.yaml

To use Slurm, see the folder jobs.