tristandeleu/ pytorch-maml
Need help with pytorch-maml?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.
About the developer
145 Stars 
20 Forks 
MIT License 
92 Commits 
5 Opened issues 
Description
An Implementation of Model-Agnostic Meta-Learning in PyTorch with Torchmeta
Services available
Contributors list
Readme
Model-Agnostic Meta-Learning
An implementation of Model-Agnostic Meta-Learning (MAML) in PyTorch with Torchmeta.
Getting started
To avoid any conflict with your existing Python setup, it is suggested to work in a virtual environment with
virtualenv. To install
virtualenv:
bash pip install --upgrade virtualenvCreate a virtual environment, activate it and install the requirements in
requirements.txt.
bash virtualenv venv source venv/bin/activate pip install -r requirements.txt
Requirements
- Python 3.6 or above
- PyTorch 1.5
- Torchvision 0.6
- Torchmeta 1.4.6
Usage
train.pyto meta-train your model with MAML. For example, to run MAML on Omniglot 1-shot 5-way with default parameters from the original paper:
bash 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/resultsThe meta-training script creates a configuration file you can use to meta-test your model. You can use
test.pyto meta-test your model:
bash python test.py /path/to/results/config.json
References
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 = {http://arxiv.org/abs/1703.03400}
}