Significance of cross-validation in tuning weights in CNN

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Venkat
Venkat 2018-4-30
评论： Venkat 2018-5-6
Hi ML experts,
In MATLAB, the dataset is divided into training and validation data using splitEachLabel. The training data is used to train the CNN model and tune its weights in such a way that the error is minimal.
Can anyone please tell me the whether the inbuilt validation that happens during the CNN training has any role towards fine-tuning the filter weights? Does it make any difference or is it just to see how well our model is generalized before we do the actual testing?

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Prajit T R
Prajit T R 2018-5-2
Hi Venkat
The validation set is separate from the training set, and hence they do not influence the filter-weights.
The validation data is used to test the accuracy of the model and help you decide whether you have to change the hyper-parameters or not. Basically, it is a method to determine the effectiveness of the actual model before the actual testing takes place.
This is a recommended practice, because in its absence, the model would be highly sensitive to the training data.
Hope this helps.
Cheers.
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Greg Heath
Greg Heath 2018-5-3
In particular, if the validation performance decreases for 6 (default but adjustable) continuous epochs, training will stop.
Hope this helps.
Greg
Venkat
Venkat 2018-5-6
Hi Greg,
So Validation also help in early stopping, correct?
Thanks

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