Abstract
Archparse is a Python package that holds the purpose and capability of converting the contents of a text file to a functioning neural network based on the Tensorflow 2.X framework. This is able to be done with minimal written Python code and next to no knowledge of how to build models with Tensorflow directly. More specifically Archparse parses a text file with extension ".arch" which contains neural network architecture information that corresponds to either the Tensorflow 2.X API or custom code written with the Tensorflow 2.X API. Included in the initial version is the capacity to easily produce sequential autoencoders and sequential neural networks.
- Developers:
-
Vander Wal, Michael [1]
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Release Date:
- 2021-07-15
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
GNU Lesser General Public License v2.1
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 66986
- Site Accession Number:
- LLNL-CODE- 827607
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Vander Wal, Michael D.
Archparse.
Computer Software.
https://github.com/EnderWiggin14/ARCHPARSE.
USDOE National Nuclear Security Administration (NNSA).
15 Jul. 2021.
Web.
doi:10.11578/dc.20211115.2.
Vander Wal, Michael D.
(2021, July 15).
Archparse.
[Computer software].
https://github.com/EnderWiggin14/ARCHPARSE.
https://doi.org/10.11578/dc.20211115.2.
Vander Wal, Michael D.
"Archparse." Computer software.
July 15, 2021.
https://github.com/EnderWiggin14/ARCHPARSE.
https://doi.org/10.11578/dc.20211115.2.
@misc{
doecode_66986,
title = {Archparse},
author = {Vander Wal, Michael D.},
abstractNote = {Archparse is a Python package that holds the purpose and capability of converting the contents of a text file to a functioning neural network based on the Tensorflow 2.X framework. This is able to be done with minimal written Python code and next to no knowledge of how to build models with Tensorflow directly. More specifically Archparse parses a text file with extension ".arch" which contains neural network architecture information that corresponds to either the Tensorflow 2.X API or custom code written with the Tensorflow 2.X API. Included in the initial version is the capacity to easily produce sequential autoencoders and sequential neural networks.},
doi = {10.11578/dc.20211115.2},
url = {https://doi.org/10.11578/dc.20211115.2},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20211115.2}},
year = {2021},
month = {jul}
}