Awesome
MAML-Pytorch
PyTorch implementation of the supervised learning experiments from the paper: Model-Agnostic Meta-Learning (MAML).
Version 1.0: Both
MiniImagenet
andOmniglot
Datasets are supported! Have Fun~
Version 2.0: Re-write meta learner and basic learner. Solved some serious bugs in version 1.0.
For Tensorflow Implementation, please visit official HERE and simplier version HERE.
For First-Order Approximation Implementation, Reptile namely, please visit HERE.
Platform
- python: 3.x
- Pytorch: 0.4+
MiniImagenet
Howto
For 5-way 1-shot exp., it allocates nearly 6GB GPU memory.
miniimagenet/
├── images
├── n0210891500001298.jpg
├── n0287152500001298.jpg
...
├── test.csv
├── val.csv
└── train.csv
- modify the
path
inminiimagenet_train.py
:
mini = MiniImagenet('miniimagenet/', mode='train', n_way=args.n_way, k_shot=args.k_spt,
k_query=args.k_qry,
batchsz=10000, resize=args.imgsz)
...
mini_test = MiniImagenet('miniimagenet/', mode='test', n_way=args.n_way, k_shot=args.k_spt,
k_query=args.k_qry,
batchsz=100, resize=args.imgsz)
to your actual data path.
- just run
python miniimagenet_train.py
and the running screenshot is as follows:
If your reproducation perf. is not so good, maybe you can enlarge your training epoch
to get longer training. And MAML is notorious for its hard training. Therefore, this implementation only provide you a basic start point to begin your research.
and the performance below is true and achieved on my machine.
Benchmark
Model | Fine Tune | 5-way Acc. | 20-way Acc. | ||
---|---|---|---|---|---|
1-shot | 5-shot | 1-shot | 5-shot | ||
Matching Nets | N | 43.56% | 55.31% | 17.31% | 22.69% |
Meta-LSTM | 43.44% | 60.60% | 16.70% | 26.06% | |
MAML | Y | 48.7% | 63.11% | 16.49% | 19.29% |
Ours | Y | 46.2% | 60.3% | - | - |
Ominiglot
Howto
run python omniglot_train.py
, the program will download omniglot
dataset automatically.
decrease the value of args.task_num
to fit your GPU memory capacity.
For 5-way 1-shot exp., it allocates nearly 3GB GPU memory.
Refer to this Rep.
@misc{MAML_Pytorch,
author = {Liangqu Long},
title = {MAML-Pytorch Implementation},
year = {2018},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/dragen1860/MAML-Pytorch}},
commit = {master}
}