anynet is a neural network framework based on anydiff and anyvec.

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 - deep reinforcement learning
• unixpickle/lazyseq - memory-efficient RNNs
• unixpickle/attention - attention mechanisms
• unixpickle/rwa - a new attention-based RNN architecture

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