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Deep learning architecture for computationally efficient activity recognition as described in the paper FilterNet: A many-to-many deep learning architecture for time series classification
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README.md

FilterNet: A many-to-many deep learning architecture for time series classification

This repository contains code to reproduce the results and figures in the paper: FilterNet: A many-to-many deep learning architecture for time series classification.

Setup

The easiest way to run this software is via the Anaconda Python distribution.

Running tests

In the root dir of this repo:

pytest tests

Reproducing Results

  1. Run the scripts in the scripts/ directory. These are very long-running scripts that reproduce each experimental condition many times. You might want to set, e.g., NUM_REPEATS=1 if you don't need this level of reproducibility.

  2. Run the notebooks to re-produce the figures. You might need to edit a few paths to specific models to match the filenames on your system, especially if you changed the NAME or NUM_REPEATS parameters.


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