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