Hi Sahar,
I understand that you are facing a problem with how to calculate minibatch indices in the "trainNetwork" function and how to structure a custom training loop that would incorporate your custom loss function. The solutions to both of your questions are as follows:
Firstly, regarding your question about writing your own "dlgradient" function, it is not possible to write a custom "dlgradient" function. However, you can define a custom loss function that includes all the additional parameters and pass it to the "dlgradient" function to compute gradients. But there are some limitations. For instance, it does not support calculating higher-order derivatives when using "dlnetwork" objects that contain custom layers with a custom backward function. Depending on your loss function, you might use "dlfeval" in conjunction with "dlgradient."
Secondly, you can keep track of your minibatch array indices if you use a custom regression layer within a custom training loop, as it provides full control over the process. However, the "trainNetwork" function handles the batching internally, so it is not directly controllable by the user. You can access other parameters like the current minibatch training loss and validation frequency, but MATLAB hides the minibatch indices from the user. By using a custom training loop, you can calculate the minibatch indices manually.
For more information about the following topics refer to the following documentation:
- trainingOptions: https://www.mathworks.com/help/deeplearning/ref/trainingoptions.html?s_tid=doc_ta#d126e216218
- OutputFcn: https://www.mathworks.com/help/deeplearning/ug/customize-output-during-deep-learning-training.html
- "dlfeval" and "dlgradient": https://www.mathworks.com/help/deeplearning/ug/include-automatic-differentiation.html
I hope it helps.
