jmkim0309/maml-pytorch

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

MAML-Pytorch

PyTorch implementation of the supervised learning experiments from the paper: Model-Agnostic Meta-Learning (MAML): https://arxiv.org/abs/1703.03400

Both MiniImagenet and Omniglot Datasets are supported! Have Fun~

For Tensorflow Implementation, please visit HERE. For First-Order Implementation, Reptile namely, please visit HERE.

Platform

• python: 3.x
• Pytorch: 0.4+

MiniImagenet

Howto

1. download MiniImagenet dataset from here, splitting: train/val/test.csv from here.
2. extract it like:

miniimagenet/
├── images
	├── n0210891500001298.jpg  
	├── n0287152500001298.jpg 
	...
├── test.csv
├── val.csv
└── train.csv

3. modify the path in miniimagenet_train.py:

		# batchsz here means total episode number
		mini = MiniImagenet('/hdd1/liangqu/datasets/miniimagenet/', mode='train', n_way=n_way, k_shot=k_shot, k_query=k_query,
		                    batchsz=10000, resize=imgsz)
		...
		mini_test = MiniImagenet('/hdd1/liangqu/datasets/miniimagenet/', mode='test', n_way=n_way, k_shot=k_shot, k_query=k_query,
				                    batchsz=600, resize=imgsz)

to your actual data path.

4. just run python miniimagenet_main.py and running screenshot is as follows:

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	48.1%	62.2%	-	-

Ominiglot

Howto

run python omniglot_train.py, the program will download omniglot dataset automatically.

decrease the value of meta_batchsz to fit your GPU memory capacity.