Python is the language of choice to train a TensorFlow model; however, after defining a computational graph in Python, there are no constraints regarding using it with another language to execute the learning operations defined.
Always keep in mind that we use Python to define a graph and this definition can be exported in a portable and language-agnostic representation (Protobuf)—this representation can then be used in any other language to create a concrete graph and using it within a session.
The TensorFlow Python API is complete and easy to use. Therefore, we can extend the previous example to measure the accuracy (defining the accuracy measurement operation in the graph) and use this metric to perform model selection.
Selecting the best model means storing the model parameters at the end of each epoch and moving the parameters...
