iota2.learning.pytorch.lightweight_temporal_attention_encoder
Lightweight Temporal Attention Encoder module
Credits: https://github.com/VSainteuf/lightweight-temporal-attention-pytorch
MIT License
Copyright (c) 2020 VSainteuf (Vivien Sainte Fare Garnot)
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
The module is heavily inspired by the works of Vaswani et al. on self-attention and their pytorch implementation of the Transformer served as code base for the present script.
paper: https://arxiv.org/abs/1706.03762 code: github.com/jadore801120/attention-is-all-you-need-pytorch
Functions
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Sinusoid position encoding table positions: int or list of integer, if int range(positions) |
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Sinusoid position encoding table positions: int or list of integer, if int range(positions) |
Classes
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Multi-Head Attention module |
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Scaled Dot-Product Attention |