https://github.com/openai/supervised-reptile
Code for the paper "On First-Order Meta-Learning Algorithms"
https://github.com/openai/supervised-reptile
paper
Last synced: 5 months ago
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Code for the paper "On First-Order Meta-Learning Algorithms"
- Host: GitHub
- URL: https://github.com/openai/supervised-reptile
- Owner: openai
- License: mit
- Created: 2017-12-05T22:50:22.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2023-05-20T19:59:33.000Z (over 2 years ago)
- Last Synced: 2025-04-12T11:58:17.594Z (6 months ago)
- Topics: paper
- Language: JavaScript
- Homepage: https://arxiv.org/abs/1803.02999
- Size: 1.16 MB
- Stars: 1,007
- Watchers: 156
- Forks: 213
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
**Status:** Archive (code is provided as-is, no updates expected)
# supervised-reptile
[Reptile](https://arxiv.org/abs/1803.02999) training code for [Omniglot](https://github.com/brendenlake/omniglot) and [Mini-ImageNet](https://openreview.net/pdf?id=rJY0-Kcll).
Reptile is a meta-learning algorithm that finds a good initialization. It works by sampling a task, training on the sampled task, and then updating the initialization towards the new weights for the task.
# Getting the data
The [fetch_data.sh](fetch_data.sh) script creates a `data/` directory and downloads Omniglot and Mini-ImageNet into it. The data is on the order of 5GB, so the download takes 10-20 minutes on a reasonably fast internet connection.
```
$ ./fetch_data.sh
Fetching omniglot/images_background ...
Extracting omniglot/images_background ...
Fetching omniglot/images_evaluation ...
Extracting omniglot/images_evaluation ...
Fetching Mini-ImageNet train set ...
Fetching wnid: n01532829
Fetching wnid: n01558993
Fetching wnid: n01704323
Fetching wnid: n01749939
...
```
If you want to download Omniglot but not Mini-ImageNet, you can simply kill the script after it starts downloading Mini-ImageNet. The script automatically deletes partially-downloaded data when it is killed early.
# Reproducing training runs
You can train models with the `run_omniglot.py` and `run_miniimagenet.py` scripts. Hyper-parameters are specified as flags (see `--help` for a detailed list). Here are the commands used for the paper:
```shell
# transductive 1-shot 5-way Omniglot.
python -u run_omniglot.py --shots 1 --inner-batch 10 --inner-iters 5 --meta-step 1 --meta-batch 5 --meta-iters 100000 --eval-batch 5 --eval-iters 50 --learning-rate 0.001 --meta-step-final 0 --train-shots 10 --checkpoint ckpt_o15t --transductive
# transductive 1-shot 5-way Mini-ImageNet.
python -u run_miniimagenet.py --shots 1 --inner-batch 10 --inner-iters 8 --meta-step 1 --meta-batch 5 --meta-iters 100000 --eval-batch 5 --eval-iters 50 --learning-rate 0.001 --meta-step-final 0 --train-shots 15 --checkpoint ckpt_m15t --transductive
# 5-shot 5-way Mini-ImageNet.
python -u run_miniimagenet.py --inner-batch 10 --inner-iters 8 --meta-step 1 --meta-batch 5 --meta-iters 100000 --eval-batch 15 --eval-iters 50 --learning-rate 0.001 --meta-step-final 0 --train-shots 15 --checkpoint ckpt_m55
# 1-shot 5-way Mini-ImageNet.
python -u run_miniimagenet.py --shots 1 --inner-batch 10 --inner-iters 8 --meta-step 1 --meta-batch 5 --meta-iters 100000 --eval-batch 5 --eval-iters 50 --learning-rate 0.001 --meta-step-final 0 --train-shots 15 --checkpoint ckpt_m15
# 5-shot 5-way Omniglot.
python -u run_omniglot.py --train-shots 10 --inner-batch 10 --inner-iters 5 --learning-rate 0.001 --meta-step 1 --meta-step-final 0 --meta-batch 5 --meta-iters 100000 --eval-batch 5 --eval-iters 50 --checkpoint ckpt_o55
# 1-shot 5-way Omniglot.
python -u run_omniglot.py --shots 1 --inner-batch 10 --inner-iters 5 --meta-step 1 --meta-batch 5 --meta-iters 100000 --eval-batch 5 --eval-iters 50 --learning-rate 0.001 --meta-step-final 0 --train-shots 10 --checkpoint ckpt_o15
# 1-shot 20-way Omniglot.
python -u run_omniglot.py --shots 1 --classes 20 --inner-batch 20 --inner-iters 10 --meta-step 1 --meta-batch 5 --meta-iters 200000 --eval-batch 10 --eval-iters 50 --learning-rate 0.0005 --meta-step-final 0 --train-shots 10 --checkpoint ckpt_o120
# 5-shot 20-way Omniglot.
python -u run_omniglot.py --classes 20 --inner-batch 20 --inner-iters 10 --meta-step 1 --meta-batch 5 --meta-iters 200000 --eval-batch 10 --eval-iters 50 --learning-rate 0.0005 --meta-step-final 0 --train-shots 10 --checkpoint ckpt_o520
```
Training creates checkpoints. Currently, you cannot resume training from a checkpoint, but you can re-run evaluation from a checkpoint by passing `--pretrained`. You can use TensorBoard on the checkpoint directories to see approximate learning curves during training and testing.
To evaluate with transduction, pass the `--transductive` flag. In this implementation, transductive evaluation is faster than non-transductive evaluation since it makes better use of batches.
# Comparing different inner-loop gradient combinations
Here are the commands for comparing different gradient combinations. The `--foml` flag indicates that only the final gradient should be used.
```shell
# Shared hyper-parameters for all experiments.
shared="--sgd --seed 0 --inner-batch 25 --learning-rate 0.003 --meta-step-final 0 --meta-iters 40000 --eval-batch 25 --eval-iters 5 --eval-interval 1"
python run_omniglot.py --inner-iters 1 --train-shots 5 --meta-step 0.25 --checkpoint g1_ckpt $shared | tee g1.txt
python run_omniglot.py --inner-iters 2 --train-shots 10 --meta-step 0.25 --checkpoint g1_g2_ckpt $shared | tee g1_g2.txt
python run_omniglot.py --inner-iters 2 --train-shots 10 --meta-step 0.125 --checkpoint half_g1_g2_ckpt $shared | tee half_g1_g2.txt
python run_omniglot.py --foml --inner-iters 2 --train-shots 10 --meta-step 0.25 --checkpoint g2_ckpt $shared | tee g2.txt
python run_omniglot.py --inner-iters 3 --train-shots 15 --meta-step 0.25 --checkpoint g1_g2_g3_ckpt $shared | tee g1_g2_g3.txt
python run_omniglot.py --inner-iters 3 --train-shots 15 --meta-step 0.08325 --checkpoint third_g1_g2_g3_ckpt $shared | tee third_g1_g2_g3.txt
python run_omniglot.py --foml --inner-iters 3 --train-shots 15 --meta-step 0.25 --checkpoint g3_ckpt $shared | tee g3.txt
python run_omniglot.py --foml --inner-iters 4 --train-shots 20 --meta-step 0.25 --checkpoint g4_ckpt $shared | tee g4.txt
python run_omniglot.py --inner-iters 4 --train-shots 20 --meta-step 0.25 --checkpoint g1_g2_g3_g4_ckpt $shared | tee g1_g2_g3_g4.txt
python run_omniglot.py --inner-iters 4 --train-shots 20 --meta-step 0.0625 --checkpoint fourth_g1_g2_g3_g4_ckpt $shared | tee fourth_g1_g2_g3_g4.txt
```