Can we customize the loss function of CNN in MATLAB 2018a

How can we build a custom loss function for our CNN in MATLAB 2018a?

Answer 1

[Snehal]

Hello,

I understand that you want to build a custom loss function in MATLAB R2018a.

In MATLAB R2018a, there is no direct support for defining custom loss functions due to the absence of features like automatic differentiation and custom layers. Implementing a custom loss function in this version requires manually coding the forward and backward passes, which involves a deep understanding of neural network mathematics and can be quite complex.

To manually implement a custom loss function in R2018a, the basic workflow that you can follow is:

1. Define the loss function mathematically
2. Modify the training loop for the forward pass
3. Manually calculate gradients for the backward pass
4. Update network weights using these gradients

However, for a more easy and efficient process, it is advisable to upgrade to MATLAB R2019b or later. These versions offer advanced deep learning capabilities, including the `dlnetwork` object, which facilitates the use of custom training loops, automatic differentiation, and custom loss functions.

You can refer to the following documentation for more details:

https://www.mathworks.com/help/deeplearning/ug/define-custom-training-loops-loss-functions-and-networks.html

Hope this helps.