Ring-Constrained Structure Search for Tensor Network Decomposition (Demo)
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Paper
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Graph-Constrained Structure Search for Tensor Network Decomposition<br/><br/>



Requirements
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 * Python 3.7.3<br/>
 
 * Tensorflow 1.13.1
 
Usage
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First you need to start agents with

     CUDA_VISIBLE_DEVICES=0 python agent.py 0
     
The last 0 stands for the id of the agent. You can spawn multiply agents with each one using one gpu by modifying the visible device id. <br/>

Then start the main script by

    python TRGA.py data.npz 50 40 200

The argvs stands for the name of data, the numbers of individuals in one generation, the numbers of individuals after elimation, the alpha hyperparameter, correspondingly. Here we provide a demo of learning the low-dimensional representation of a TR format tensor. The details of the algorithm will be saved in a `.log` file providing following information
* TR structures searched in every generation.
* RSE^{2}, log 1/(CR), fitness score.


Acknowledgement
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 * The code is modified based on the [TNGA](https://github.com/minogame/icml2020-TNGA)