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OpenNNG is a toolkit that offers an easy interface to generative models.

Project description

OpenNNG (Work in progress...)

OpenNNG (Open Neural Network Generator) is a general purpose data generator toolkit that uses TensorFlow 2.0. Supported architectures:

Key features

OpenNNG focuses on modularity to support advanced modeling and training capabilities:

  • usage of predefined models
  • creation of custom architectures
  • domain adaptation


Clone repository

If you want to use OpenNNG as a command line interface where the processes of training, evaluating etc. are all automated, run the following commands:

git clone
pip install -r requirements.txt


If you want to use OpenNNG as an API and have more flexibility, install it via pip:

pip install opennng


OpenNNG requires:

  • Python >= 3.6
  • TensorFlow >= 2.0.0rc0
  • Pillow >=6.1

Data processing

Data must be saved in Numpy .npy files. In this example we will use the mnist dataset to generate new images. For this purpose, use the script with mnist as argument. This command will automatically download mnist (raw and processed) dataset in data/ directory.

python3 mnist


To train and generate new samples, a YAML configuration file must be provided. Here is an exhaustive list of all the configuration parameters.


To train, run with a YAML configuration file as parameter.

python3 <path_to_yaml_config_file>

This script will automatically generate 10 samples that shows how the training process evolves at evrey checkpoint. To disable this functionality, set generate_train_samples to False in YAML configuration file.

Model Samples
ConvVAESmall alt text alt text alt text alt text alt text alt text alt text alt text alt text alt text
ConvGANSmall alt text alt text alt text alt text alt text alt text alt text alt text alt text alt text


To generate a new sample, run with a YAML configuration file as parameter.

python3 <path_to_yaml_config_file>

Project details

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