https://github.com/yuantinghsieh/tf_tcn

Tensorflow Temporal Convolutional Network
https://github.com/yuantinghsieh/tf_tcn

convolutional-neural-networks sequence-to-sequence tensorflow

Last synced: 3 months ago
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Tensorflow Temporal Convolutional Network

• Host: GitHub
• URL: https://github.com/yuantinghsieh/tf_tcn
• Owner: YuanTingHsieh
• Created: 2018-05-23T03:25:47.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2023-11-08T22:21:27.000Z (about 1 year ago)
• Last Synced: 2023-11-10T07:38:22.717Z (about 1 year ago)
• Topics: convolutional-neural-networks, sequence-to-sequence, tensorflow
• Language: Python
• Homepage:
• Size: 10.9 MB
• Stars: 82
• Watchers: 6
• Forks: 39
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# TF TCN

*Tensorflow Temporal Convolutional Network*

This is an implementation of [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://arxiv.org/abs/1803.01271) in TensorFlow.

I've verified that given same argument, my network has exactly same number of parameter as his model. It is able to reach the same loss/accuracy level in these problems, BUT sometimes it gets good result a little slower than the [original implementation in Torch](https://github.com/locuslab/TCN).

This repository mainly follows the structure of the original repo. And for illustration of different tasks, you could take a look at [keras TCN](https://github.com/philipperemy/keras-tcn). The author provides some nice figures there.

Some codes are modified from [original implementation](https://github.com/locuslab/TCN), [keras TCN](https://github.com/philipperemy/keras-tcn), and [openai](https://github.com/openai/weightnorm/tree/master/tensorflow).

## Domains and Datasets

This repository contains the benchmarks to the following tasks, with details explained in each sub-directory:

  - The Adding Problem with various T (we evaluated on T=200, 400, 600)

  - Copying Memory Task with various T (we evaluated on T=500, 1000, 2000)

  - Sequential MNIST digit classification

  - Permuted Sequential MNIST (based on Seq. MNIST, but more challenging)

  - PennTreebank [SMALL] word-level language modeling (LM)

    

## Run

In the root directory of this repo, type the following to run different experiments

```

python3 -m [module_name] [args]

python3 -m adding_problem.add_test [args]

python3 -m copymem.copymem_test [args]

python3 -m mnist_pixel.pmnist_test.py --epo 10

```

## References

[1] Bai, Shaojie, J. Zico Kolter, and Vladlen Koltun. "An empirical evaluation of generic convolutional and recurrent networks for sequence modeling." arXiv preprint arXiv:1803.01271 (2018).

[2] Salimans, Tim, and Diederik P. Kingma. "Weight normalization: A simple reparameterization to accelerate training of deep neural networks." Advances in Neural Information Processing Systems. 2016.