https://github.com/istarjun/TapNet

Chainer Implementation of TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
https://github.com/istarjun/TapNet

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Chainer Implementation of TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning

• Host: GitHub
• URL: https://github.com/istarjun/TapNet
• Owner: istarjun
• Created: 2019-05-12T15:53:52.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-10-21T02:47:45.000Z (about 5 years ago)
• Last Synced: 2024-07-31T23:45:09.833Z (4 months ago)
• Language: Python
• Homepage:
• Size: 10.7 KB
• Stars: 56
• Watchers: 3
• Forks: 12
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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• awesome-few-shot-meta-learning - code - official (Chainer)

README

        
# TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning

Code for the ICML 2019 paper TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning

## Dependencies

* This code is tested on Ubuntu 16.04 with Python 3.6 and chainer 5.20

## Data  

### miniImageNet

#Download and unzip "mini-imagenet.tar.gz" from Google Drive link [[mini-ImageNet](https://drive.google.com/file/d/1DvYd7LMa0zvlqTM8oBdCWwQSxpZdf_D5/view?usp=sharing)]

#Place ``train.npz``, ``val.npz``, ``test.npz`` files in ``TapNet/miniImageNet_TapNet/data``

### tieredImageNet

#Download and unzip "tiered-imagenet.tar.gz" from Google Drive link [[tiered-ImageNet](https://drive.google.com/file/d/1zz7bAYus7EeoMokwUQlLc3OY_eoII8B7/view?usp=sharing)]

#Place images ``.npz`` and labels ``.pkl`` files in ``TapNet/tieredImageNet_TapNet/data``

## Running the code

```

#For miniImageNet experiment

cd /TapNet/miniImageNet_TapNet/scripts

python train_TapNet_miniImageNet.py --gpu {GPU device number}

                                    --n_shot {n_shot}

                                    --nb_class_train {number of classes in training}

                                    --nb_class_test {number of classes in test}

                                    --n_query_train {number of queries per class in training}

                                    --n_query_test {number of queries per class in test}

                                    --wd_rate {Weight decay rate}

                                    

                                    

#For tieredImageNet experiment

cd /TapNet/tieredImageNet_TapNet/scripts

python train_TapNet_tieredImageNet.py --gpu {GPU device number}

                                    --n_shot {n_shot}

                                    --nb_class_train {number of classes in training}

                                    --nb_class_test {number of classes in test}

                                    --n_query_train {number of queries per class in training}

                                    --n_query_test {number of queries per class in test}

                                    --wd_rate {Weight decay rate}

```