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learning-class-invariant-features's Introduction

Neural Networks Regularization Through Invariant Features Learning.

This repository contains the code of the paper Neural Networks Regularization Through Learning Invariant Features 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.

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