https://github.com/unixpickle/anynet

Framework for artificial neural networks
https://github.com/unixpickle/anynet

deep-learning machine-learning

Last synced: about 1 year ago
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Framework for artificial neural networks

• Host: GitHub
• URL: https://github.com/unixpickle/anynet
• Owner: unixpickle
• Created: 2017-01-20T15:52:35.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2017-09-09T17:30:53.000Z (almost 9 years ago)
• Last Synced: 2025-04-06T20:23:59.070Z (about 1 year ago)
• Topics: deep-learning, machine-learning
• Language: Go
• Homepage:
• Size: 193 KB
• Stars: 34
• Watchers: 5
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# anynet [![GoDoc](https://godoc.org/github.com/unixpickle/anynet?status.svg)](https://godoc.org/github.com/unixpickle/anynet)

**anynet** is a [neural network](https://en.wikipedia.org/wiki/Artificial_neural_network) framework based on [anydiff](https://github.com/unixpickle/anydiff) and [anyvec](https://github.com/unixpickle/anyvec).

# Supported features

*anynet* ships with a ton of built-in features:

 * Feed-forward neural networks

   * Fully-connected layers

   * Convolution

   * Dropout

   * Max/Mean pooling

   * Batch normalization

   * Residual connections

   * Image scaling

   * Image padding

 * Recurrent neural networks

   * LSTM

   * Bidirectional RNNs

   * npRNN and IRNN (vanilla RNNs with ReLU activations)

 * Training setups

   * Vector-to-vector (standard feed-forward)

   * Sequence-to-sequence (standard RNN)

   * Sequence-to-vector

   * Connectionist Temporal Classification

 * Miscellaneous

   * Gumbel Softmax

Plenty of stuff is missing from the above list. Luckily, it's easy to write new APIs on top of *anynet*. Here is a non-exhaustive list of packages that work with *anynet*:

 * [unixpickle/anyrl](https://github.com/unixpickle/anyrl) - deep reinforcement learning

 * [unixpickle/lazyseq](https://github.com/unixpickle/lazyseq) - memory-efficient RNNs

 * [unixpickle/attention](https://github.com/unixpickle/attention) - attention mechanisms

 * [unixpickle/rwa](https://github.com/unixpickle/rwa) - a new attention-based RNN architecture

# TODO

Here are some minor things I'd like to get done at some point. None of these are very urgent, as *anynet* is already complete for the most part.

 * anyrnn

   * Tests comparing LSTM outputs to another implementation

   * GRU (gated recurrent units)

 * anysgd

   * Gradient clipping

   * Marshalling for RMSProp

   * Marshalling for Momentum