- Initialize your shallow neural network with the desired architecture.
- Define your function Y = f(X) that takes the network outputs X as input and produces the desired output Y. This function represents the relationship between the network outputs and the target outputs.
- Calculate the gradient of Y with respect to X.
- Use the calculated gradients to update the network weights using gradient descent or another suitable optimization algorithm.
I want to train a shallow neural network using known output gradients rather than input/output training pair data
2 views (last 30 days)
Show older comments
I have a shallow network defined by:
net = fitnet([64,112],'traingd');
The outputs of this network feed INTO a function Y = f(X) where X is the vector of net outputs X=net(I).
I calculate the gradient of Y w.r.t X and want to then train net based on these gradients rather than input/output data for the net.
0 Comments
Answers (1)
Gagan Agarwal
on 25 Oct 2023
Hi James,
I understand that you are trying to train a shallow neural network using the known output gradients.
To train a shallow neural network using known output gradients, you can follow these steps:
For additional information, please refer to the following documentation:
I hope this helps!
0 Comments
See Also
Categories
Find more on Deep Learning Toolbox in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!