Sequence Padding and Packing for RNNs
Training Recurrent Neural Networks (RNNs) can be tricky when dealing with sequences of different lengths. Imagine we have a batch of 8 sequences where their lengths are: 6, 5, 4, 7, 2, 3, 8, and 7.
This is where padding comes and pad all sequences to the maximum length (8 in this case) with meaningless values. This creates an 8×8 matrix for computations, even though some sequences are shorter. This wastes processing power because we perform unnecessary calculations (64 computations instead of the actual 45 needed).
For this , packing plays an important role as It packs the sequences into a data structure that preserves their original lengths before padding. By doing so, the RNN model can process only the non-padded portions of each sequence, effectively reducing the computational overhead.
How to handle sequence padding and packing in PyTorch for RNNs?
There are many dataset that have sequences with variable lengths and recurrent neural networks (RNNs) require fixed-length inputs. To address this challenge, sequence padding and packing techniques are used, particularly in PyTorch, a popular deep learning framework. The article demonstrates how sequence padding ensures uniformity in sequence lengths by adding zeros to shorter sequences, while sequence packing compresses padded sequences for efficient processing in RNNs.
Table of Content
- Sequence Padding and Packing for RNNs
- Implementation of Sequence Padding and Sequence Packing
- Handling Sequence Padding and Packing in PyTorch for RNNs