Mathing Networks for One Shot Learning in TensorFlow 2.0

Repository provides implementation of Matching Networks for One Shot Learning paper (https://arxiv.org/abs/1606.04080) in Tensorflow 2.0. Model has been tested on Omniglot and miniImagenet datasets.

Dependencies and installation

• Project has been tested on Ubuntu 18.04 with Python 3.6.8 and TensorFflow 2.0.0-alpha0
• The dependencies are Pillow and tqdm libraries, which are included in setup requirements
• Training and evaluating require matchnet lib. Run python setup.py install to install it
• To download Omniglot dataset run bash data/download_omniglot.sh from repository's root
• miniImagenet was taken from excellent project (https://github.com/renmengye/few-shot-ssl-public) and placed into data/mini-imagenet folder

Repository Structure

The repository organized as follows. matchnet folder contains library with model and data-loading routines. data serves as a default directory for the datasets (change configs to specify different data path). scripts contains training and evaluation scripts. tests provides minimal tests for training. resulst folder contains description of training configurations and results as well as tranining log info.

Training and evaluating

Configuration of training and evaluation procedures is specified by .config files (specify data.datsaet_path if dataset has different path). Default config files for Omniglot and miniImagenet are omniglot.conf and miniimagenet respectively (omniglot set as a default choice of scripts' arguments). Scripts run_train.py and run_eval.py runs prodcures while setup_train.py and setup_eval.py contain basic logic for model launching. To run training procedure run the following commands from repository's root

• python scripts/train/run_train.py --config scripts/omniglot.conf for Omniglot
• python scripts/train/run_train.py --config scripts/miniimagenet.conf for miniImagent

To run evaluation procedure run the following commands from repository's root

• python scripts/eval/run_eval.py --config scripts/omniglot.conf for Omniglot
• python scripts/eval/run_eval.py --config scripts/miniimagenet.conf for miniImanet

Training procedure generates log file that can be found in results/logs directory after training will be finished. Name of the log file contains date and time and will be printed in stdout in the beginning.

Tests

To run basic tests run following command from root directory (for now tests required GPU support)

• python -m unittest tests/*

Results

Obtained results for Omniglot after 30 epochs with train (val part was not engaged yet)

Acknowledgements

• Thanks to Albert Berenguel Centeno (https://github.com/gitabcworld) for his PyTorch implementation which helped me to sort out tough parts of the training procedure.

References

[1] Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra Matching Networks for One Shot Learning (https://arxiv.org/abs/1606.04080)

[2] Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum The Omniglot Challenge: A 3-Year Progress Report (https://arxiv.org/abs/1902.03477)