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Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"
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cbfinn/maml
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This repo contains code accompaning the paper,Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (Finn et al., ICML 2017). It includes code for running the few-shot supervised learning domain experiments, including sinusoid regression, Omniglot classification, and MiniImagenet classification.
For the experiments in the RL domain, seethis codebase.
This code requires the following:
- python 2.* or python 3.*
- TensorFlow v1.0+
For the Omniglot and MiniImagenet data, see the usage instructions indata/omniglot_resized/resize_images.py anddata/miniImagenet/proc_images.py respectively.
To run the code, see the usage instructions at the top ofmain.py.
To ask questions or report issues, please open an issue on theissues tracker.