Custom loss function by multipling the output network by the input

The network I created takes a vector in input and gives me in output a vector of coefficients of shorter length than the input. Do I have to calculate the loss by multiplying these coefficients by the input vector and calculate the mse between this multiplication and the memory vectors not passed to the network how can I do? I’m using trainNetwork. Thanks

Answer 1

[Himanshu]

Hello Lup,

I understand that you are trying to implement a custom loss function for your neural network in MATLAB, where the loss is calculated by multiplying the output of the network by the input vector and then computing the mean squared error between this result and some memory vectors that were not passed to the network.

To implement a custom loss function, you need to create a function that calculates the loss according to your specific requirements. This function takes the predictions from the network, the memory vectors, and the original input vector as arguments. Inside this function, perform the multiplication of the network's output coefficients with the input vector, and then compute the MSE between this product and the memory vectors.

You can pass the custom loss function as a function handle into the "lossFcn" parameter in the "trainNetwork" function.

Please refer to the below documentation to learn more about the loss function in the "trainNetwork" function.

Train deep learning neural network - MATLAB trainnet - MathWorks India

Please refer to the below documentation on how to define custom operations as MATLAB functions for Deep Learning Operations.

Define Custom Deep Learning Operations - MATLAB & Simulink - MathWorks India

I hope this helps.