An implementation of Model-Agnostic Meta-Learning (MAML) in PyTorch with Torchmeta.
To avoid any conflict with your existing Python setup, it is suggested to work in a virtual environment with virtualenv
. To install virtualenv
:
pip install --upgrade virtualenv
Create a virtual environment, activate it and install the requirements in requirements.txt
.
virtualenv venv
source venv/bin/activate
pip install -r requirements.txt
- Python 3.5 or above
- PyTorch 1.2
- Torchvision 0.4
- Torchmeta 1.1
You can use train.py
to meta-train your model with MAML. For example, to run MAML on Omniglot 1-shot 5-way with default parameters from the original paper:
python train.py /path/to/data --dataset omniglot --num-ways 5 --num-shots 1 --use-cuda --step-size 0.4 --batch-size 32 --num-workers 8 --num-epochs 600 --output-folder /path/to/results
The meta-training script creates a configuration file you can use to meta-test your model. You can use test.py
to meta-test your model:
python test.py /path/to/results/config.json
The code available in this repository is mainly based on the paper
Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. International Conference on Machine Learning (ICML), 2017 [ArXiv]
If you want to cite this paper
@article{finn17maml,
author = {Chelsea Finn and Pieter Abbeel and Sergey Levine},
title = {Model-{A}gnostic {M}eta-{L}earning for {F}ast {A}daptation of {D}eep {N}etworks},
journal = {International Conference on Machine Learning (ICML)},
year = {2017},
url = {https://arxiv.org/abs/1703.03400}
}