Skip to content

Weight Normalization #414

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
richardassar wants to merge 2 commits into
base: master
Choose a base branch
from
Open

Weight Normalization #414

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
e3eb314
Adding weight-normalized versions of SeqGRU, SeqLSTM and SeqLSTMP
May 11, 2017
731fdaf
Updating README.md
May 11, 2017
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 5 additions & 2 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@ Modules that consider successive calls to `forward` as different time-steps in a
* [AbstractRecurrent](#rnn.AbstractRecurrent) : an abstract class inherited by Recurrent and LSTM;
* [Recurrent](#rnn.Recurrent) : a generalized recurrent neural network container;
* [LSTM](#rnn.LSTM) : a vanilla Long-Short Term Memory module;
* [FastLSTM](#rnn.FastLSTM) : a faster [LSTM](#rnn.LSTM) with optional support for batch normalization;
* [FastLSTM](#rnn.FastLSTM) : a faster [LSTM](#rnn.LSTM) with optional support for batch normalization;
* [GRU](#rnn.GRU) : Gated Recurrent Units module;
* [MuFuRu](#rnn.MuFuRu) : [Multi-function Recurrent Unit](https://arxiv.org/abs/1606.03002) module;
* [Recursor](#rnn.Recursor) : decorates a module to make it conform to the [AbstractRecurrent](#rnn.AbstractRecurrent) interface;
Expand All @@ -19,8 +19,11 @@ Modules that `forward` entire sequences through a decorated `AbstractRecurrent`
* [AbstractSequencer](#rnn.AbstractSequencer) : an abstract class inherited by Sequencer, Repeater, RecurrentAttention, etc.;
* [Sequencer](#rnn.Sequencer) : applies an encapsulated module to all elements in an input sequence (Tensor or Table);
* [SeqLSTM](#rnn.SeqLSTM) : a very fast version of `nn.Sequencer(nn.FastLSTM)` where the `input` and `output` are tensors;
* [SeqLSTMP](#rnn.SeqLSTMP) : `SeqLSTM` with a projection layer;
* [SeqLSTM_WN](#rnn.SeqLSTM) : a weight-normalized version of `nn.SeqLSTM`;
* [SeqLSTMP](#rnn.SeqLSTMP) : `SeqLSTM` with a projection layer;
* [SeqLSTMP_WN](#rnn.SeqLSTMP) : a weight-normalized version of `nn.SeqLSTMP`;
* [SeqGRU](#rnn.SeqGRU) : a very fast version of `nn.Sequencer(nn.GRU)` where the `input` and `output` are tensors;
* [SeqGRU_WN](#rnn.SeqGRU) : a weight-normalized version of `nn.SeqGRU`;
* [SeqBRNN](#rnn.SeqBRNN) : Bidirectional RNN based on SeqLSTM;
* [BiSequencer](#rnn.BiSequencer) : used for implementing Bidirectional RNNs and LSTMs;
* [BiSequencerLM](#rnn.BiSequencerLM) : used for implementing Bidirectional RNNs and LSTMs for language models;
Expand Down
Loading