argo

Argo is a library for deep learning algorithms based on tensorflow and sonnet.

Installation

Requirements (stable):

• tensorflow-datasets 1.2.0
• tensorflow-estimator 1.14.0
• tensorflow-gpu 1.14.0
• tensorflow-metadata 0.14.0
• tensorflow-probability 0.7.0
• sonnet 1.32
• torchfile
• seaborn
• matplotlib
• numpy

Or:

pip install -r requirements.txt

How to run the code:

To run the examples provided in the framework (or new ones) one can choose between three separate modes of running:

1. single: Runs a single instance of the configuration file

   python argo/runTraining.py configFile.conf single

2. pool: Runs a muliple experiments (if defined) from the configuration file

   python argo/runTraining.py configFile.conf pool

Submodules

VAE

python argo/runTraining.py examples/MNISTcontinuous.conf single

Helmholtz Machine

python argo/runTraining.py examples/ThreeByThree.conf single

Prediction

python argo/runTraining.py examples/GTSRB.conf single

How to run the code:

python3 argo/runTrainingVAE.py configFile.conf single/pool/stats

See ConfOptions.conf in examples/ for details regarding meaning of parameters and logging options.

License

MIT

Contributors

In alphabetical order.

Main contributors

• Luigi Malagò
• Csongor Varady
• Riccardo Volpi

Active contributors

• Alexandra Albu
• Cristian Alecsa
• Norbert Cristian Bereczki
• Robert Colt
• Delia Dumitru
• Alina Enescu
• Petru Hlihor
• Hector Javier Hortua
• Uddhipan Thakur

Former contributors

• Ria Arora
• Dimitri Marinelli
• Titus Nicolae
• Alexandra Peste
• Marginean Radu
• Septimia Sarbu

Acknowledgements

The library has been developed in the context of the DeepRiemann project, co-funded by the European Regional Development Fund and the Romanian Government through the Competitiveness Operational Programme 2014-2020, Action 1.1.4, project ID P_37_714, SMIS code 103321, contract no. 136/27.09.2016.