I'm looking to run a basic fully-connected neural network for the MNIST dataset with the C++ API v1.2 from Tensorflow. I have trained the model and exported it using tf.train.Saver()
in Python. This gave me a checkpoint file, a data file, an index file and a meta file.
I know that the data file contains the saved variables while the meta file contains the graph from using Tensorboard on a previous project.
However, I am not sure what is the recommended way to load those files and run the trained model in a C++ environment in v1.2, since all the tutorials and questions I've found are for older versions which differ substantially.
I've found that tensorflow::ops::Restore
should be the method to do such a thing, but I know that inference in Tensorflow isn't well supported, as such I am not certain what parameters should I give it in order to receive the trained model that I can just put into a session->Run()
and receive an accuracy statement when fed test data.