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TCML-tensorflow

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Tensorflow implementation of Meta-Learning with Temporal Convolutions

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# TCML-tensorflow
A tensorflow implementation of [Meta-Learning with Temporal Convolutions](https://arxiv.org/abs/1707.03141)

Embedding for Omniglot dataset is only available now.

# Prerequisites
* Python 3.6+
* Tensorflow 1.3
* Image dataset for training/validation (Python dictionary-like object)

# Usage
```
python train.py --dataset omniglot --n 5 --k 1 --dilation 1 2 1 2 4 8 16 --lr 5e-4 --batch_size 64
```

It will print valiation loss and accuracy.
Checkpoints and summaries for other metrics are saved in `./runs/tcml_{input_dim}_{num_dense_filter}_{attention_value_dim}_{lr}`

# Results
Omniglot | 5-way, 1-shot | 5-way, 5-shot
------------ | ------------- | -------------
Accuracy | 95.12% | 95.01%

Since the paper didn't share exact hyperparameters, it is hard to make same results with the paper.
This code performs ~95% accuracy for 5-way environment with way smaller numbers of dilation stacks.

When I tried to use same dilation stack with paper(1 2 1 2 1 2 1 2 1 2 4 8 16), it is too deep, thus the loss converges at 1.60 with poor accuracy.

Still trying to find better hyperparameters to get higher performance.

# License
MIT

# Author
Donghwa Kim ([@storykim](https://github.com/storykim))

TCML-tensorflow

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