Convert Keras model to C++

Question

I am using Keras (with Theano) to train my CNN model. Does anyone has idea how can I use it in my C++ application? Does anyone tried something similar? I have idea to write some python code that will generate a c++ code with network functions - any suggestion on it?

I found a similar question here how to use Tensorflow Keras model in C++ but without answer.

Answer 1

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.

Answer 2

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.

Answer 3

I had a similar need--I wanted to embed Keras models in a C++ application--and decided to write my own library: Kerasify

Design goals of Kerasify:

• Compatibility with image processing Sequential networks generated by Keras using Theano backend. (Could work with Tensorflow if you switch around matrix col/row ordering).
• No external dependencies, standard library, C++11 features OK.
• Model stored on disk in binary format that can be quickly read.
• Model stored in memory in contiguous block for better cache performance.
• Doesn't throw exceptions, returns only bool on error.
• CPU only, no GPU

Example code, unit tests, etc. at the github link. It's not fully complete, it only supports the narrow subset of Keras functions I'm using, but it should be extensible with a little effort.

Answer 4

The solutions found here are quite good, but if your model has some different types of layers not supported by these libraries, I would recommend doing the following:

• Converting the Keras model to a tensorflow model.
• Freeze the model and use Tranform graph tool provided by tensorflow (you'll have to build it from source with bazel)
• Compile the C++ API tensorflow library to use it in your project.
• Use the C++ API tensorflow library and link the libraries to your project.

If you want to use a something differentcompiler than bazel (like g++ for example) you can follow this great tuturial:

http://tuatini.me/building-tensorflow-as-a-standalone-project/

Answer 5

The easiest way is probably to make a system call to a Python script that writes the predictions to a binary or HDF5 file, which can be read in from C++. You can also directly integrate Python into C++.

If you need to deploy and distribute this easily, you can look into self-contained Python installations like Anaconda, but your best bet may be to avoid Keras and use the C++ interface to Caffe or Tensorflow. I wouldn't recommend Tensorflow since using it from C++ isn't standard; see this discussion. Caffe is arguably the second most-popular deep learning library so you can't really go wrong.

Answer 6

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'))