This is a bunch of code to port Keras neural network model into pure C++. Neural network weights and architecture are stored in plain text file and input is presented as vector<vector<vector<float> > > in case of image. The code is prepared to support simple Convolutional network (from MNIST example) but can be easily extended. There are implemented only ReLU and Softmax activations.

It is working with the Theano backend.

1. Save your network weights and architecture.
2. Dump network structure to plain text file with dump_to_simple_cpp.py script.
3. Use network with code from keras_model.h and keras_model.cc files - see example below.

1. Run one iteration of simple CNN on MNIST data with example/mnist_cnn_one_iteration.py script. It will produce files with architecture example/my_nn_arch.json and weights in HDF5 format example/my_nn_weights.h5.
2. Dump network to plain text file python dump_to_simple_cpp.py -a example/my_nn_arch.json -w example/my_nn_weights.h5 -o example/dumped.nnet.
3. Compile example g++ -std=c++11 keras_model.cc example_main.cc - see code in example_main.cc.
4. Run binary ./a.out - you shoul get the same output as in step one from Keras.

If you want to test dumping for your network, please use test_run.sh script. Please provide there your network architecture and weights. The script do following job:

1. Dump network into text file.
2. Generate random sample.
3. Compute predictions from keras and keras2cpp on generated sample.
4. Compare predictions.

• Keras to C++ with usage of Tensorflow C API https://github.com/aljabr0/from-keras-to-c