{"id":29199096,"url":"https://github.com/plasmacontrol/keras2c2","last_synced_at":"2025-07-02T09:32:06.146Z","repository":{"id":300260621,"uuid":"996632503","full_name":"PlasmaControl/keras2c2","owner":"PlasmaControl","description":"An update to Keras2c which supports Python 3.9, 3.10, 3.11, 3.12","archived":false,"fork":false,"pushed_at":"2025-06-20T17:28:47.000Z","size":6238,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-06-20T18:31:49.196Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"C","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"lgpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/PlasmaControl.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-06-05T08:25:11.000Z","updated_at":"2025-06-20T17:24:30.000Z","dependencies_parsed_at":"2025-06-20T18:43:39.188Z","dependency_job_id":null,"html_url":"https://github.com/PlasmaControl/keras2c2","commit_stats":null,"previous_names":["plasmacontrol/keras2c2"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/PlasmaControl/keras2c2","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PlasmaControl%2Fkeras2c2","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PlasmaControl%2Fkeras2c2/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PlasmaControl%2Fkeras2c2/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PlasmaControl%2Fkeras2c2/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/PlasmaControl","download_url":"https://codeload.github.com/PlasmaControl/keras2c2/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/PlasmaControl%2Fkeras2c2/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263111271,"owners_count":23415418,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-07-02T09:30:50.915Z","updated_at":"2025-07-02T09:32:06.137Z","avatar_url":"https://github.com/PlasmaControl.png","language":"C","funding_links":[],"categories":[],"sub_categories":[],"readme":"#######\nkeras2c2\n#######\n\n|Build-Status| |Codecov|\n\n|License| |DOI|\n\n\nkeras2c2 is an update to keras2c that supports Python 3.9 to 3.12, on Linux, MacOS, and Windows. It adds addtional features.\n\nkeras2c is a library for deploying keras neural networks in C99, using only standard libraries.\nIt is designed to be as simple as possible for real time applications.\n\nPlease cite `this paper \u003chttps://doi.org/10.1016/j.engappai.2021.104182\u003e`_ if you use this work in your research:\n\n.. code-block:: bibtex\n\n    R. Conlin, K. Erickson, J. Abbate, and E. Kolemen, “Keras2c: A library for converting Keras neural networks to real-time compatible C,” \n    Engineering Applications of Artificial Intelligence, vol. 100, p. 104182, Apr. 2021, doi: 10.1016/j.engappai.2021.104182.\n\nQuickstart\n**********\n\nFor windows, make sure that you have gcc installed. We recommend CYGWIN with make and gcc\n\nAfter cloning the repo, install the necessary packages with ``pip install -r requirements.txt``.\nAlternatively, create a conda environment using ``conda env create -f environment.yml``.\n\nkeras2c can be used from the command line:\n\n.. code-block:: bash\n\n    python -m keras2c [-h] [-m] [-t] model_path function_name\n\n    A library for converting the forward pass (inference) part of a keras model to\n        a C function\n\n    positional arguments:\n      model_path         File path to saved keras .h5 model file\n      function_name      What to name the resulting C function\n     \n    optional arguments:\n      -h, --help         show this help message and exit\n      -m, --malloc       Use dynamic memory for large arrays. Weights will be\n                         saved to .csv files that will be loaded at runtime\n      -t , --num_tests   Number of tests to generate. Default is 10\n\n\nIt can also be used with a python environment in the following manner:\n\n.. code-block:: python\n\n    from keras2c import k2c\n    k2c(model, function_name, malloc=False, num_tests=10, verbose=True)\n\nFor more information, see `Installation \u003chttps://f0uriest.github.io/keras2c/installation.html\u003e`_ and  `Usage \u003chttps://f0uriest.github.io/keras2c/usage.html\u003e`_\n\n\nSupported Layers\n****************\n- **Core Layers**: Dense, Activation, Dropout, Flatten, Input, Reshape, Permute, RepeatVector,  ActivityRegularization, SpatialDropout1D, SpatialDropout2D, SpatialDropout3D\n- **Convolution Layers**: Conv1D, Conv2D, Conv3D, Cropping1D, Cropping2D, Cropping3D, UpSampling1D, UpSampling2D, UpSampling3D, ZeroPadding1D, ZeroPadding2D, ZeroPadding3D\n- **Pooling Layers**: MaxPooling1D, MaxPooling2D, AveragePooling1D, AveragePooling2D, GlobalMaxPooling1D, GlobalAveragePooling1D, GlobalMaxPooling2D, GlobalAveragePooling2D, GlobalMaxPooling3D,GlobalAveragePooling3D\n- **Recurrent Layers**: SimpleRNN, GRU, LSTM, SimpleRNNCell, GRUCell, LSTMCell\n- **Embedding Layers**: Embedding\n- **Merge Layers**: Add, Subtract, Multiply, Average, Maximum, Minimum, Concatenate, Dot\n- **Advanced Activation Layers**: LeakyReLU, PReLU, ELU, Softmax, ReLU\n- **Normalization Layers**: BatchNormalization\n- **Noise Layers**: GaussianNoise, GaussianDropout, AlphaDropout\n- **Layer Wrappers**: TimeDistributed, Bidirectional\n  \nToDo\n****\n- **Core Layers**: Lambda, Masking\n- **Convolution Layers**: SeparableConv1D, SeparableConv2D, DepthwiseConv2D, Conv2DTranspose, Conv3DTranspose\n- **Pooling Layers**: MaxPooling3D, AveragePooling3D\n- **Locally Connected Layers**: LocallyConnected1D, LocallyConnected2D\n- **Recurrent Layers**: ConvLSTM2D, ConvLSTM2DCell\n- **Merge Layers**: Broadcasting merge between different sizes\n- **Misc**: models made from submodels\n\n\n\nContribute\n**********\n\n- Documentation: `\u003chttps://f0uriest.github.io/keras2c/\u003e`_\n- Issue Tracker: `\u003chttps://github.com/f0uriest/keras2c/issues\u003e`_\n- Source Code: `\u003chttps://github.com/f0uriest/keras2c/\u003e`_\n  \nLicense\n*******\n\nThe project is licensed under the LGPLv3 license.\n\n\n.. |Build-Status| image:: https://travis-ci.org/f0uriest/keras2c.svg?branch=master\n    :target: https://travis-ci.org/f0uriest/keras2c\n    :alt: Build Status\n.. |Codecov| image:: https://codecov.io/gh/f0uriest/keras2c/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/f0uriest/keras2c\n    :alt: Code Coverage\n.. |License| image:: https://img.shields.io/github/license/f0uriest/keras2c\n    :target: https://github.com/f0uriest/keras2c/blob/master/LICENSE\n    :alt: License: LGPLv3\n.. |DOI| image:: https://zenodo.org/badge/193152058.svg\n    :target: https://zenodo.org/badge/latestdoi/193152058\n    :alt: Please Cite Keras2c!\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fplasmacontrol%2Fkeras2c2","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fplasmacontrol%2Fkeras2c2","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fplasmacontrol%2Fkeras2c2/lists"}