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What is the parameter minimum performance gradient (trainParam.min_grad) of traingd?

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AntonyH on 25 Sep 2020
Commented: Mohamed Elsefy on 12 Nov 2020 at 0:45
I use the training function "traingd" to train a shallow neural network:
trainedNet = train(net,X,T)
For the training function "traingd": How is the parameter minimum performance gradient (net.trainParam.min_grad) defined?
As the gradient for the gradient descent is usually a vector, but net.trainParam.min_grad is a scalar value, I am confused.
Is it the change in the performace (loss) between 2 iterations, and if yes: Does it refer to the training, validation or testing errror?
Thanks in advance!
I use MATLAB 2013 and 2015 with the neural network toolbox.


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Accepted Answer

Rishabh Mishra
Rishabh Mishra on 28 Sep 2020
Edited: Rishabh Mishra on 28 Sep 2020
Based on your description of the issue, I would state a few points:
  1. I agree that gradient descent is vector quantity & points in the direction of maximum change of the cost function.
  2. The ‘net.trainParam.min_grad’ is a scalar(numeric) quantity. The parameter ‘min_grad’ denotes the minimum magnitude (which is scalar) of gradient descent (which is vector), for which the training of neural network terminates.
  3. When the magnitude of gradient descent becomes less than ‘min_grad’, the neural network model is said to be optimized (and hence, further training stops).
For better understanding, refer the following links:
Hope this helps.

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