28

GitHub - google-research/mixmatch

 4 years ago
source link: https://github.com/google-research/mixmatch
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.

README.md

MixMatch - A Holistic Approach to Semi-Supervised Learning

Code for the paper: "MixMatch - A Holistic Approach to Semi-Supervised Learning" by David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver and Colin Raffel.

This is not an officially supported Google product.

Setup

Important: ML_DATA is a shell environment variable that should point to the location where the datasets are installed. See the Install datasets section for more details.

Install dependencies

sudo apt install python3-dev python3-virtualenv python3-tk imagemagick
virtualenv -p python3 --system-site-packages env3
. env3/bin/activate
pip install -r requirements.txt

Install datasets

export ML_DATA="path to where you want the datasets saved"
# Download datasets
CUDA_VISIBLE_DEVICES= ./scripts/create_datasets.py
cp $ML_DATA/svhn-test.tfrecord $ML_DATA/svhn_noextra-test.tfrecord

# Create semi-supervised subsets
for seed in 1 2 3 4 5; do
    for size in 250 500 1000 2000 4000; do
        CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=$seed --size=$size $ML_DATA/SSL/svhn $ML_DATA/svhn-train.tfrecord $ML_DATA/svhn-extra.tfrecord &
        CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=$seed --size=$size $ML_DATA/SSL/svhn_noextra $ML_DATA/svhn-train.tfrecord &
        CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=$seed --size=$size $ML_DATA/SSL/cifar10 $ML_DATA/cifar10-train.tfrecord &
    done
    CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=$seed --size=10000 $ML_DATA/SSL/cifar100 $ML_DATA/cifar100-train.tfrecord &
    CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=$seed --size=1000 $ML_DATA/SSL/stl10 $ML_DATA/stl10-train.tfrecord $ML_DATA/stl10-unlabeled.tfrecord &
    wait
done
CUDA_VISIBLE_DEVICES= scripts/create_split.py --seed=1 --size=5000 $ML_DATA/SSL/stl10 $ML_DATA/stl10-train.tfrecord $ML_DATA/stl10-unlabeled.tfrecord

Install privacy datasets

CUDA_VISIBLE_DEVICES= ./privacy/scripts/create_datasets.py

for size in 27 38 77 156 355 671 867; do
CUDA_VISIBLE_DEVICES= ./privacy/scripts/create_split.py --size=$size $ML_DATA/SSL/svhn500 $ML_DATA/svhn500-train.tfrecord &
done; wait

for size in 96 185 353 719 1415 2631 3523; do
CUDA_VISIBLE_DEVICES= ./privacy/scripts/create_split.py --size=$size $ML_DATA/SSL/svhn300 $ML_DATA/svhn300-train.tfrecord &
done; wait

for size in 56 81 109 138 266 525 1059 2171 4029 5371; do
CUDA_VISIBLE_DEVICES= ./privacy/scripts/create_split.py --size=$size $ML_DATA/SSL/svhn200 $ML_DATA/svhn200-train.tfrecord &
done; wait

for size in 145 286 558 1082 2172 4078 5488; do
CUDA_VISIBLE_DEVICES= ./privacy/scripts/create_split.py --size=$size $ML_DATA/SSL/svhn200s150 $ML_DATA/svhn200s150-train.tfrecord &
done; wait

Running

For example, training a mixmatch with 32 filters on cifar10 shuffled with seed=3, 250 labeled samples and 5000 validation samples:

CUDA_VISIBLE_DEVICES=0 python mixmatch.py --filters=32 --dataset=cifar10.3@250-5000 --w_match=75 --beta=0.75

Available labelled sizes are 250, 500, 1000, 2000, 4000. For validation, available sizes are 1, 5000. Possible shuffling seeds are 1, 2, 3, 4, 5 and 0 for no shuffling (0 is not used in practiced since data requires to be shuffled for gradient descent to work properly).

Valid dataset names

for dataset in cifar10 svhn svhn_noextra; do
for seed in 1 2 3 4 5; do
for valid in 1 5000; do
for size in 250 500 1000 2000 4000; do
    echo "${dataset}.${seed}@${size}-${valid}"
done; done; done; done

for seed in 1 2 3 4 5; do
for valid in 1 5000; do
    echo "cifar100.${seed}@10000-${valid}"
done; done

for seed in 1 2 3 4 5; do
for valid in 1 5000; do
    echo "stl10.${seed}@1000-${valid}"
done; done
echo "stl10.1@5000-1"

Monitoring training progress

You can point tensorboard to the training folder (by default it is --train_dir=./experiments) to monitor the training process:

tensorboard.sh --port 6007 --logdir experiments/

Checkpoint accuracy

We compute the median accuracy of the last 20 checkpoints in the paper, this is done through this code:

# Following the previous example in which we trained cifar10.3@250-5000, extracting accuracy:
./scripts/extract_accuracy.py experiments/compare/cifar10.3@250-5000/MixMatch_archresnet_batch64_beta0.75_ema0.999_filters32_lr0.002_nclass10_repeat4_scales3_w_match75.0_wd0.02
# The command above will create a stats/accuracy.json file in the model folder.
# The format is JSON so you can either see its content as a text file or process it to your liking.

Reproducing tables from the paper

Check the contents of the runs/*.sh files, these will give you the commands (and the hyper-parameters) to reproduce the results from the paper.

Citing this work

@article{berthelot2019mixmatch,
  title={MixMatch: A Holistic Approach to Semi-Supervised Learning},
  author={Berthelot, David and Carlini, Nicholas and Goodfellow, Ian and Papernot, Nicolas and Oliver, Avital and Raffel, Colin},
  journal={arXiv preprint arXiv:1905.02249},
  year={2019}
}

report

Recommend

About Joyk


Aggregate valuable and interesting links.
Joyk means Joy of geeK