Evolving Attention Model
This repository contains the implementation of the model proposed in Evolving Attention with Residual Convolutions.
EAResnet50 Model
The implementation is mainly adapted from the TF Official Models repo. Please specify your directory that contains the imagenet and the output directory in the train.sh, and then conduct the training by execute sh -x train.sh.
EA-DC-Transformer
Our requirements and dataset setting refers to the paper: George Zerveas et al. [A Transformer-based Framework for Multivariate Time Series Representation Learning] , in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '21), August 14-18, 2021. ArXiV version: (https://arxiv.org/abs/2010.02803)
Requirements
The recommended requirements can be installed by: pip install -r requirements.txt
Dataset
Download dataset files and place them in separate directories, one for regression and one for classification. Classification: (http://www.timeseriesclassification.com/Downloads/Archives/Multivariate2018_ts.zip) Regression: (https://zenodo.org/record/3902651#.YB5P0OpOm3s)
Train model command example
python src/main.py --task regression --data AppliancesEnergy --epochs 100 --model EACausalConv --gpu 0 --isdilated TRUE --alpha 0.5 --beta 0.3 --k 0.25 --v 0.25 --d_model 128