keras2c

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 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, 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

Contribute

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

License