Hi,
Based on your description of the issue, I would state a few points:
- I agree that gradient descent is vector quantity & points in the direction of maximum change of the cost function.
- 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.
- 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.
