Library of multidimensional LSTM models and related code.
The 2D LSTM layers take tensors of the form (batch_size, height, width, depth), compatible with convolutional layers, as inputs. The library transposes and reshapes these tensors in a way that allows batches of images to be processed by LSTMs.
The library currently provides:
- a separable 2D LSTM layer
- a simple 2D convolutional layer that can be swapped out against 2D LSTM
- layers to reduce images to sequences and images to final state vectors
- layers for sequence classification, pixel-wise classification
There is 1D LSTM code in lstm1d.py. This code implements 1D LSTM versions
suitable as a basis for higher dimensional LSTMs. It is intended for constant
batch size and uses a different layout. Although the code is perfectly fine for
1D use, you may find other 1D LSTM implementations to be more convenient if you
are interested in sequence problems.
- PyramidLSTM
- support for 3D and 4D
- optional use of native fused LSTM op
- easy-to-use command line drivers and examples
- operators for patch-wise processing