keras2c

Note: This is a fork of the original keras2c library. The licence remains the same as the original library. This fork adds additional features and installment instructions.

Install conda environment (for MacOS):

conda env create -f conda_keras2c.yml
conda develop /path/to/this/repo

Installation for linux:

cd /path/to/this/repo
conda create -n keras2c python=3.11.5
conda activate keras2c
python -m pip install -r requirements
conda develop .

Then you can run keras2c code from anywhere on your machine after activating conda environment keras2c.

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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.

Quickstart

After cloning the repo, install the necessary packages with pip install -r requirements.txt.

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 <https://f0uriest.github.io/keras2c/installation.html>_ and Usage <https://f0uriest.github.io/keras2c/usage.html>_

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, ThresholdedReLU, 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

License

The project is licensed under the LGPLv3 license.