dynamic-auto-sizing
Science Score: 57.0%
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Low similarity (8.7%) to scientific vocabulary
Last synced: 6 months ago
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Repository
Basic Info
- Host: GitHub
- Owner: vcahlik
- License: mit
- Language: Jupyter Notebook
- Default Branch: master
- Size: 18.8 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Created over 5 years ago
· Last pushed over 1 year ago
Metadata Files
Readme
License
Citation
README.md
Dynamic Auto-Sizing

Implementation of the dynamic auto-sizing technique which we presented in the paper Adapting the Size of Artificial Neural Networks Using Dynamic Auto-Sizing. Dynamic auto-sizing allows artificial neural networks to automatically adapt their size to the problem domain. Besides the TensorFlow implementation of dynamic auto-sizing, this repository additionally contains the complete Jupyter notebooks with experiments.
Repository structure
- nets - Python package with the implementations
- notebooks - Jupyter notebooks with experiments
- standalone - Experiments with dynamic auto-sizing
- anytime - Experiments with dynamic auto-sizing as an underlying technique for anytime algorithms
- misc - Supporting code, e.g. for the rendering of figures
How to run
- Clone the repository,
cdinto it. - Install required packages by running
pip install -r requirements.txt(Python version 3.9 is recommended). - Open Jupyter Lab using
jupyter lab.
Citing
In case you find this repository helpful, feel free to cite our related publication Adapting the Size of Artificial Neural Networks Using Dynamic Auto-Sizing:
@inproceedings{10000471,
title = {Adapting the Size of Artificial Neural Networks Using Dynamic Auto-Sizing},
author = {Cahlik, Vojtech and Kordik, Pavel and Cepek, Miroslav},
year = 2022,
booktitle = {2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT)},
volume = {},
number = {},
pages = {592--596},
doi = {10.1109/CSIT56902.2022.10000471}
}
Owner
- Name: Vojtech Cahlik
- Login: vcahlik
- Kind: user
- Location: Prague
- Company: Faculty of Information Technologies, Czech Technical University in Prague
- Website: cahlik.net
- Repositories: 1
- Profile: https://github.com/vcahlik
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Cahlik"
given-names: "Vojtech"
orcid: "https://orcid.org/0000-0002-4222-8746"
- family-names: "Kordik"
given-names: "Pavel"
orcid: "https://orcid.org/0000-0003-1433-0089"
- family-names: "Cepek"
given-names: "Miroslav"
orcid: "https://orcid.org/0000-0002-3765-867X"
title: "Dynamic Auto-Sizing"
url: "https://github.com/vcahlik/dynamic-auto-sizing"
preferred-citation:
type: proceedings
authors:
- family-names: "Cahlik"
given-names: "Vojtech"
orcid: "https://orcid.org/0000-0002-4222-8746"
- family-names: "Kordik"
given-names: "Pavel"
orcid: "https://orcid.org/0000-0003-1433-0089"
- family-names: "Cepek"
given-names: "Miroslav"
orcid: "https://orcid.org/0000-0002-3765-867X"
doi: "10.1109/CSIT56902.2022.10000471"
url: "https://doi.org/10.1109/CSIT56902.2022.10000471"
collection-title: "2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT)"
start: 592
end: 596
title: "Adapting the Size of Artificial Neural Networks Using Dynamic Auto-Sizing"
year: 2022
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Dependencies
requirements.txt
pypi
- Babel ==2.10.3
- Jinja2 ==3.1.2
- Keras-Preprocessing ==1.1.2
- Markdown ==3.4.1
- MarkupSafe ==2.1.1
- Pillow ==9.2.0
- Pygments ==2.13.0
- Send2Trash ==1.8.0
- Werkzeug ==2.2.2
- absl-py ==1.3.0
- anyio ==3.6.2
- argon2-cffi ==21.3.0
- argon2-cffi-bindings ==21.2.0
- asttokens ==2.0.8
- astunparse ==1.6.3
- attrs ==22.1.0
- backcall ==0.2.0
- beautifulsoup4 ==4.11.1
- bleach ==5.0.1
- cachetools ==5.2.0
- certifi ==2022.9.24
- cffi ==1.15.1
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- cycler ==0.11.0
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- decorator ==5.1.1
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- entrypoints ==0.4
- executing ==1.1.1
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- gast ==0.4.0
- google-auth ==2.13.0
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- google-pasta ==0.2.0
- grpcio ==1.50.0
- h5py ==3.7.0
- idna ==3.4
- imageio ==2.22.2
- importlib-metadata ==5.0.0
- ipykernel ==6.16.1
- ipython ==8.5.0
- ipython-genutils ==0.2.0
- jedi ==0.18.1
- joblib ==1.2.0
- json5 ==0.9.10
- jsonschema ==4.16.0
- jupyter-server ==1.21.0
- jupyter_client ==7.4.3
- jupyter_core ==4.11.2
- jupyterlab ==3.5.0
- jupyterlab-pygments ==0.2.2
- jupyterlab_server ==2.16.1
- keras ==2.10.0
- kiwisolver ==1.4.4
- libclang ==14.0.6
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- mistune ==2.0.4
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- nbclient ==0.7.0
- nbconvert ==7.2.2
- nbformat ==5.7.0
- nest-asyncio ==1.5.6
- notebook ==6.5.1
- notebook_shim ==0.2.0
- numpy ==1.23.4
- oauthlib ==3.2.2
- opt-einsum ==3.3.0
- packaging ==21.3
- pandas ==1.5.1
- pandocfilters ==1.5.0
- parso ==0.8.3
- pexpect ==4.8.0
- pickleshare ==0.7.5
- pip ==22.2.2
- prometheus-client ==0.15.0
- prompt-toolkit ==3.0.31
- protobuf ==3.19.6
- psutil ==5.9.3
- ptyprocess ==0.7.0
- pure-eval ==0.2.2
- pyasn1 ==0.4.8
- pyasn1-modules ==0.2.8
- pycparser ==2.21
- pyparsing ==3.0.9
- pyrsistent ==0.18.1
- python-dateutil ==2.8.2
- pytz ==2022.5
- pyzmq ==24.0.1
- requests ==2.28.1
- requests-oauthlib ==1.3.1
- rsa ==4.9
- scikit-learn ==1.1.2
- scipy ==1.9.3
- setuptools ==63.4.1
- six ==1.16.0
- sniffio ==1.3.0
- soupsieve ==2.3.2.post1
- stack-data ==0.5.1
- tensorboard ==2.10.1
- tensorboard-data-server ==0.6.1
- tensorboard-plugin-wit ==1.8.1
- tensorflow ==2.10.0
- tensorflow-estimator ==2.10.0
- tensorflow-io-gcs-filesystem ==0.27.0
- termcolor ==2.0.1
- terminado ==0.16.0
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- tinycss2 ==1.2.1
- tomli ==2.0.1
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- urllib3 ==1.26.12
- wcwidth ==0.2.5
- webencodings ==0.5.1
- websocket-client ==1.4.1
- wheel ==0.37.1
- wrapt ==1.14.1
- zipp ==3.10.0