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alquitable

Keras-core based tools to enhance Alquimodelia

https://github.com/alquimodelia/alquitable

Science Score: 44.0%

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
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    .zenodo.json file
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    Scientific vocabulary similarity
    Low similarity (10.1%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Keras-core based tools to enhance Alquimodelia

Basic Info
  • Host: GitHub
  • Owner: alquimodelia
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: main
  • Size: 412 KB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 2
  • Releases: 5
Created over 2 years ago · Last pushed about 1 year ago
Metadata Files
Readme Contributing License Code of conduct Citation Security

README.md

Alquitable

Alquitable is a Python package that provides a Keras-based set of tools to enhance Alquimodelia.

Python Keras

It provides the loss function and callbacks to apply to keras models

Usage

To use Alquitable, follow these steps:

bash pip install alquitable

Since Aquitable is based on keras-core you can choose which backend to use, otherwise it will default to tensorflow. To change backend change the KERAS-BACKEND enviromental variable. Follow this.

To get an arquiteture you only need to have a simple configuration and call the module:

```python

Previous code and imports

... from alquitable import losses, callbacks

Based on forecat StackedCNN

lossfuntion=losses.weightedloss callback = callbacks.StopOnNanLoss

StackedCNN.compile(loss=loss_funtion)

StackedCNN.fit( ... callbacks=callback )

```

Contribution

Contributions to Alquitable are welcome! If you find any issues or have suggestions for improvement, please feel free to contribute. Make sure to update tests as appropriate and follow the contribution guidelines.

License

Alquitable is licensed under the MIT License, which allows you to use, modify, and distribute the package according to the terms of the license. For more details, please refer to the LICENSE file.

Owner

  • Name: alquimodelia
  • Login: alquimodelia
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 0.0.3
message: "If you use this software, please cite it as below."
authors:
- family-names: "Santos"
  given-names: "João"
  orcid: "https://orcid.org/0009-0007-5995-8060"
title: "Alquimodelia: Alquitable"
version: 0.0.3
date-released: 2023-10-23
url: "https://github.com/alquimodelia/alquitable"
repository-code: "https://github.com/alquimodelia/alquitable"
keywords:
  - python
  - machine learning
  - modeling
  - keras
type: software
license: BSD-3-Clause
license-url: "https://github.com/alquimodelia/alquitable/blob/main/LICENSE"

GitHub Events

Total
  • Release event: 1
  • Push event: 2
  • Create event: 1
Last Year
  • Release event: 1
  • Push event: 2
  • Create event: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 2
  • Total pull requests: 2
  • Average time to close issues: N/A
  • Average time to close pull requests: 29 days
  • Total issue authors: 1
  • Total pull request authors: 1
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • JotaFan (2)
Pull Request Authors
  • JotaFan (2)
Top Labels
Issue Labels
enhancement (1) good first issue (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 60 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 4
  • Total maintainers: 1
pypi.org: alquitable

Keras-core based tools to enhance Alquimodelia

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 60 Last month
Rankings
Dependent packages count: 9.3%
Forks count: 29.9%
Average: 36.6%
Stargazers count: 38.9%
Dependent repos count: 68.2%
Maintainers (1)
JotaFan
Last synced: 6 months ago