keras2c

IMPORTANT: Keras2C has started updating to be compatible with newer versions of Python on July 9th, 2025. Changes may be breaking. To use the original/stable version, use the Release v1.0.2. https://github.com/PlasmaControl/keras2c/releases/tag/v1.0.2 with the command .. code-block:: bash

git clone git@github.com:PlasmaControl/keras2c.git --branch v1.0.2

keras2c is a library for deploying keras neural networks in C99, using only standard libraries. It is designed to be as simple as possible for real time applications.

Please cite this paper if you use this work in your research:

R. Conlin, K. Erickson, J. Abbate, and E. Kolemen, “Keras2c: A library for converting Keras neural networks to real-time compatible C,”
Engineering Applications of Artificial Intelligence, vol. 100, p. 104182, Apr. 2021, doi: 10.1016/j.engappai.2021.104182.

Quickstart

For windows, make sure that you have gcc installed. We recommend CYGWIN with make and gcc

After cloning the repo, install the necessary packages with pip install -r requirements.txt. Alternatively, create a conda environment using conda env create -f environment.yml.

keras2c can be used from the command line:

python -m keras2c [-h] [-m] [-t] model_path function_name

A library for converting the forward pass (inference) part of a keras model to
    a C function

positional arguments:
  model_path         File path to saved keras .h5 model file
  function_name      What to name the resulting C function

optional arguments:
  -h, --help         show this help message and exit
  -m, --malloc       Use dynamic memory for large arrays. Weights will be
                     saved to .csv files that will be loaded at runtime
  -t , --num_tests   Number of tests to generate. Default is 10

It can also be used with a python environment in the following manner:

from keras2c import k2c
k2c(model, function_name, malloc=False, num_tests=10, verbose=True)

For more information, see Installation and Usage

Supported Layers

• Core Layers: Dense, Activation, Dropout, Flatten, Input, Reshape, Permute, RepeatVector, ActivityRegularization, SpatialDropout1D, SpatialDropout2D, SpatialDropout3D
• Convolution Layers: Conv1D, Conv2D, Conv3D, Cropping1D, Cropping2D, Cropping3D, UpSampling1D, UpSampling2D, UpSampling3D, ZeroPadding1D, ZeroPadding2D, ZeroPadding3D
• Pooling Layers: MaxPooling1D, MaxPooling2D, AveragePooling1D, AveragePooling2D, GlobalMaxPooling1D, GlobalAveragePooling1D, GlobalMaxPooling2D, GlobalAveragePooling2D, GlobalMaxPooling3D,GlobalAveragePooling3D
• Recurrent Layers: SimpleRNN, GRU, LSTM, SimpleRNNCell, GRUCell, LSTMCell
• Embedding Layers: Embedding
• Merge Layers: Add, Subtract, Multiply, Average, Maximum, Minimum, Concatenate, Dot
• Advanced Activation Layers: LeakyReLU, PReLU, ELU, Softmax, ReLU
• Normalization Layers: BatchNormalization
• Noise Layers: GaussianNoise, GaussianDropout, AlphaDropout
• Layer Wrappers: TimeDistributed, Bidirectional

ToDo

• Core Layers: Lambda, Masking
• Convolution Layers: SeparableConv1D, SeparableConv2D, DepthwiseConv2D, Conv2DTranspose, Conv3DTranspose
• Pooling Layers: MaxPooling3D, AveragePooling3D
• Locally Connected Layers: LocallyConnected1D, LocallyConnected2D
• Recurrent Layers: ConvLSTM2D, ConvLSTM2DCell
• Merge Layers: Broadcasting merge between different sizes
• Misc: models made from submodels

Contribute

• Documentation: https://f0uriest.github.io/keras2c/
• Issue Tracker: https://github.com/f0uriest/keras2c/issues
• Source Code: https://github.com/f0uriest/keras2c/

License

The project is licensed under the LGPLv3 license.