Convert Keras model to C++

I found myself in a similar situation but needed to not only support forward passes of sequential Keras models in C++ but also of more complex models build with the functional API.

So I wrote a new library called frugally-deep. You can find it on GitHub and it is published under the MIT License: https://github.com/Dobiasd/frugally-deep

Additionally to supporting many common layer types it can keep up with (and sometimes even beat) the performance of TensorFlow on a single CPU. You can find up-to-date benchmark results for some common model in the repo.

By automatic testing frugally-deep guarantees that the output of a model used with it in C++ is exactly the same as if run with Keras in Python.

If your keras model is trained using tensorflow backend, you can save the keras model as a tensorflow model following this code: https://github.com/amir-abdi/keras_to_tensorflow

Here is a shorter version of the code:

from keras import backend as K
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import graph_io

weight_file_path = 'path to your keras model'
net_model = load_model(weight_file_path)
sess = K.get_session()

constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), 'name of the output tensor')
graph_io.write_graph(constant_graph, 'output_folder_path', 'output.pb', as_text=False)
print('saved the constant graph (ready for inference) at: ', osp.join('output_folder_path', 'output.pb'))

To answer my own question and have a solution - I wrote a plain c++ solution called keras2cpp (its code available on github).

In this solution you store network architecture (in json) and weights (in hdf5). Then you can dump a network to a plain text file with provided script. You can use obtained text file with network in pure c++ code. There are no dependencies on python libraries or hdf5. It should work for theano and tensorflow backend.