For my master's thesis, I will have to do an inference with a pre-built / pre-trained (with TensorFlow) deep neural network model. I received it in two different formats (hdf5 / h5 and frozen graph = .pb). The inference shall be done on a cluster, so far we only have a GPU-version (with TensorRT and a uff Model) running. So my first job seems to be to do inference on one CPU before making a usage possible on the cluster.
We are using the model within computational fluid dynamics (CFD) simulations – that is also my academic background, and as you can therefore imagine I have only a little knowledge about deep learning. Anyway, it is not my job to change/train the model but just to use it for inference. Our CFD-Code is written in C++, which is the only programming language I am using on an advanced level (obviously it is no problem to use C, but I have no idea of python).
After going through many Google searches I recognized that I do not have a real idea how to start things off. I thought it would be possible to skip all the training and TensorFlow stuff. I know how neural networks work and how they calculate their output values from their input values. I also have the most important theoretical knowledge, but no programming knowledge in this field. Is it somehow possible to use the model they gave me (so either hdf5/h5 or frozen graph) and build an inference code using exclusively C or C++? I already found the C API and installed it within a docker container (where I also have Tensorflow), but I am really not sure what the next step is. What can I do with the C API? How would you write a C/C++-Code for inference with a DNN-model that is prepared to inference with it?