I would like to introduce a new loss function.

I would like to introduce a loss function that is similar to the least squares method. Should I modify it to use Mean Squared Error (MSE) or should I adjust the gradient descent algorithm to incorporate this new loss function?

[Torsten]

You must tell us the context in which you want to make this change.

If you have a working general optimizer, you just need to change the objective function.

If you have a self-written specialized code, deeper changes might be necessary.

Answer 1

[Aravind]

Hi @usama,

In MATLAB, to incorporate custom loss functions into deep learning models, you need to define the loss function and integrate it within a custom training loop. Start by creating a function that calculates the loss according to your requirements, which can include using any MATLAB function, such as the Mean Square Error (MSE) function.

Next, set up your neural network and implement a custom training loop. For each epoch, shuffle the data and process it in mini-batches. During each iteration, perform a forward pass to generate predictions, evaluate the custom loss using the "dlfeval" function, compute the gradients, and update the model parameters with a specified learning rate. More information about the "dlfeval" function can be found here: https://www.mathworks.com/help/deeplearning/ref/dlfeval.html.

Here are some resources to help you get started with custom loss functions in MATLAB:

1. Defining custom training loops: https://www.mathworks.com/help/deeplearning/ug/define-custom-training-loops-loss-functions-and-networks.html
2. Defining custom loss functions: https://www.mathworks.com/help/deeplearning/ug/define-model-gradients-function-for-custom-training-loop.html
3. An example using custom training loops and model losses: https://www.mathworks.com/help/deeplearning/ug/train-generative-adversarial-network.html

I hope this helps with your query.

Answer 2

[Matt J]

If the context is deep learning, then you should be using trainnet, whcih allows you to specify the loss function without any other effort,

netTrained = trainnet(images,net,lossFcn,options)

though your loss function would need to adhere to guidelines discussed in the documentation.