Virtual Adversarial training (VAT) implemented with Theano

Python code for reproducing the results showed in the paper:"Distributional Smoothing with Virtual Adversarial Training" http://arxiv.org/abs/1507.00677

Required libraries

python 2.7, numpy 1.9, theano 0.7.0, docopt 0.6.2

Examples on synthetic dataset

Model's contours on synthetic datasets with different regularization methods (Fig.3,4 in our paper)

./vis_model_contours.sh

The coutour images will be saved in ./figure.

Examples on MNIST dataset

Download mnist.pkl

cd dataset
./download_mnist.sh

###VAT for supervised learning on MNIST dataset

python train_mnist_sup.py --cost_type=VAT_finite_diff --epsilon=2.1 --layer_sizes=784-1200-600-300-150-10 --save_filename=<filename>

###VAT for semi-supervised learning on MNIST dataset (with 100 labeled samples)

python train_mnist_semisup.py --cost_type=VAT_finite_diff --epsilon=0.3 --layer_sizes=784-1200-1200-10 --num_labeled_samples=100 --save_filename=<filename>

After finish training, the trained classifer will be saved with <filename> in ./trained_model.

You can obtain a test error of the trained classifier saved with <filename> by the following command:

python test_mnist.py --load_filename=<filename>

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If you find bug or problem, please report it!