6 ms·

Show HN: A general-purpose encoder-decoder framework for Tensorflow

54 points by dennybritz 10y ago

aduffy 10y ago
There is already a seq2seq in the tree under contrib, is this one different from/replacing it? https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/seq2seq https://github.com/tensorflow/tensorflow/tree/master/tensorf...
- dennybritz 10y ago
  This is a good question and we should probably add this to the FAQ. tf.contrib.seq2seq is a low-level library that you can use to build seq2seq models; it is used internally by this project. The key difference here is that the google/seq2seq is an end-to-end pipeline that you can run with your own data and that comes with a lot of bells and whistles.
rotten 10y ago
To someone not active in the Tensorflow community, it is really not obvious what this is for. What are typical use cases? Why does the world need this?
- aduffy 10y ago
  Encoder-Decoder models are a very common technique in sequence-to-sequence models for deep learning. These models have had very big wins lately in NLP tasks such as translation, POS tagging, dialogue generation, etc. The Tensorflow documentation has an okay writeup about seq2seq models: https://www.tensorflow.org/tutorials/seq2seq https://www.tensorflow.org/tutorials/seq2seq The author of the library also has a small blurb about it on his blog: http://www.wildml.com/deep-learning-glossary/#seq2seq http://www.wildml.com/deep-learning-glossary/#seq2seq
braindead_in 10y ago
Are there any examples of seq2seq networks being used for tasks other than NLP? For example, can it be used for something like noise removal?
- dennybritz 10y ago
  Yes, these models can applied to a lot of non-NLP tasks. For example, I've seen seq2seq models applied to medical record prediction, program generation, etc. Noise removal seems like a good candidate.
- rahimnathwani 10y ago
  Noise removal? See this page (https://blog.keras.io/building-autoencoders-in-keras.html https://blog.keras.io/building-autoencoders-in-keras.html) and search for "Application to image denoising"

54 points by dennybritz 10y ago

There is already a seq2seq in the tree under contrib, is this one different from/replacing it? https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/seq2seq https://github.com/tensorflow/tensorflow/tree/master/tensorf...

dennybritz 10y ago
This is a good question and we should probably add this to the FAQ. tf.contrib.seq2seq is a low-level library that you can use to build seq2seq models; it is used internally by this project. The key difference here is that the google/seq2seq is an end-to-end pipeline that you can run with your own data and that comes with a lot of bells and whistles.

rotten 10y ago

To someone not active in the Tensorflow community, it is really not obvious what this is for. What are typical use cases? Why does the world need this?

aduffy 10y ago
Encoder-Decoder models are a very common technique in sequence-to-sequence models for deep learning. These models have had very big wins lately in NLP tasks such as translation, POS tagging, dialogue generation, etc. The Tensorflow documentation has an okay writeup about seq2seq models: https://www.tensorflow.org/tutorials/seq2seq https://www.tensorflow.org/tutorials/seq2seq The author of the library also has a small blurb about it on his blog: http://www.wildml.com/deep-learning-glossary/#seq2seq http://www.wildml.com/deep-learning-glossary/#seq2seq

braindead_in 10y ago

Are there any examples of seq2seq networks being used for tasks other than NLP? For example, can it be used for something like noise removal?

dennybritz 10y ago
Yes, these models can applied to a lot of non-NLP tasks. For example, I've seen seq2seq models applied to medical record prediction, program generation, etc. Noise removal seems like a good candidate.
rahimnathwani 10y ago
Noise removal? See this page (https://blog.keras.io/building-autoencoders-in-keras.html https://blog.keras.io/building-autoencoders-in-keras.html) and search for "Application to image denoising"