# meta-learning-lstm

**Repository Path**: xbnpyk/meta-learning-lstm

## Basic Information

- **Project Name**: meta-learning-lstm
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-05-17
- **Last Updated**: 2025-03-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# meta-learning-lstm
This repo contains the code for the following paper:
https://openreview.net/pdf?id=rJY0-Kcll 

## Dependencies
The following libaries are necessary:
* [torch-autograd](https://github.com/twitter/torch-autograd)
* [torch-ipc](https://github.com/twitter/torch-ipc) (use version from commit 'c1b2984c4c2dae085005d385996f4c0660173b27')
* [torch-Dataset](https://github.com/twitter/torch-dataset)
* [moses](https://github.com/Yonaba/Moses)

## Training
Splits corresponding to meta-training, meta-validation, and meta-testing are 
placed in `data/miniImagenet/`. Download corresponding imagenet images and
place in folder called `images` and place folder in `data/miniImagenet/`.

To train a model:
```
th train/run-train.lua --task [1-shot or 5-shot task] --data config.imagenet --model [model name]
```

For example, to run matching-nets:
```
th train/run-train.lua --task config.5-shot-5-class --data config.imagenet --model config.baselines.train-matching-net
```

And, to run LSTM meta-learner for 5-shot task:
```
th train/run-train.lua --task config.5-shot-5-class --data config.imagenet --model config.lstm.train-imagenet-5shot
```

## Contact 
For questions about miniImagenet format, please contact Sachin Ravi at email given in the paper.