This repository is made as supplementary material for a tutorial. The tutorial shows how to use Recurrent Neural Nets as generative models. Specifically, it shows how such a model can be used to sample images and classify them. For more information, see the file "tutorial.md" in the repository.

This repository contains pre-trained generative models. The "trained/minst_models" folder contains all 10 models for each of MNIST digits. Each pre-trained model was trained on MNIST training data for 12 epochs using "Adam" optimizer with a standard set of hyperparameters and a batch size of 32 examples. Cross-entropy was used as a loss function.

Here are a few examples of generated digits:

Clone the repository,

git clone https://github.com/X-rayLaser/generative-rnn.git

switch to the project's directory,

cd generative-rnn

create a virtual environment for Python and activate it,

which python3
/usr/bin/python3

virtualenv --python='/usr/bin/python3' venv
. venv/bin/activate

finally, install dependencies with pip

pip install -r requirements.txt

Generate images of a digit "8"

python generate_mnist.py --digit=8

Estimate classification accuracy on 500 MNIST test examples

python classification.py --num_images=500

Train a model on a digit "8" using 200 MNIST images for 100 epochs

python train_mnist.py --digit=8 --num_images=200 --epochs=100

Train all 10 models, one for each digit for 10 epochs on 1000 images

python train_mnist.py --all_digits=True --num_images=1000 --epochs=10

This software is licensed under MIT license (see LICENSE).

The software uses third party libraries that are distributed under their own terms (see LICENSE-3RD-PARTY).