An elegant and efficient implementation for ICML2017 paper: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
- adopted from cbfin's official implementation with equivalent performance on mini-imagenet
- clean, tiny code style and very easy-to-follow from comments almost every lines
- faster and trivial improvements, eg. 0.335s per epoch comparing with 0.563s per epoch, saving up to 3.8 hours for total 60,000 training process
- Download mini-Imagenet from here and extract them as :
miniimagenet/
├── images
├── n0210891500001298.jpg
├── n0287152500001298.jpg
...
├── test.csv
├── val.csv
└── train.csv
└── proc_images.py
then replace the path by your actual path in data_generator.py:
metatrain_folder = config.get('metatrain_folder', '/hdd1/liangqu/datasets/miniimagenet/train')
if True:
metaval_folder = config.get('metaval_folder', '/hdd1/liangqu/datasets/miniimagenet/test')
else:
metaval_folder = config.get('metaval_folder', '/hdd1/liangqu/datasets/miniimagenet/val')- resize all raw images to 84x84 size by
python proc_images.py- train
python main.pySince tf.summary is time-consuming, I turn it off by default. uncomment the 2 lines to turn it on:
# write graph to tensorboard
# tb = tf.summary.FileWriter(os.path.join('logs', 'mini'), sess.graph)
...
# summ_op
# tb.add_summary(result[1], iteration)and then minitor training process by:
tensorboard --logdir logs- test
python main.py --testAs MAML need generate 200,000 train/eval episodes before training, which usually takes up to 6~8 minutes, I use an cache file filelist.pkl to dump all these episodes for the first time and then next time the program will load from the cached file. It only takes several seconds to load from cached files.
generating episodes: 100%|█████████████████████████████████████████| 200000/200000 [04:38<00:00, 717.27it/s]