Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"
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Model-Agnostic Meta-Learning

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, see this codebase.


This code requires the following:

  • python 2.* or python 3.*
  • TensorFlow v1.0+


For the Omniglot and MiniImagenet data, see the usage instructions in data/omniglot_resized/ and data/miniImagenet/ respectively.


To run the code, see the usage instructions at the top of


To ask questions or report issues, please open an issue on the issues tracker.

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Deep Learning