To code the implementation, we'll start by defining the hyperparameters, then we will define the model, followed by building and executing the training loop. We conclude by checking to see if our model is overfitting and build an inference code that loads the latest checkpoints and then makes predictions on the basis of learned parameters.
Let's code the implementation!
Defining hyperparameters
We will define all of the required hyperparameters in the hy_param.py file and then import it as a module in our other codes. This makes it easy in deployment, and is good practice to make your code as modular as possible. Let's look into the hyperparameter configurations that we have in our hy_param.py file:
#!/usr/bin...
