validation accuracy for cnn showing different than in the plot
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in the plot it shows validation accuracy curve reached above 75% but the written validation accuraccy is just 66%! Is something wrong??
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Srivardhan Gadila
2021-12-29
0 个投票
When training finishes, the Results shows the finalized validation accuracy and the reason that training is finished. If the 'OutputNetwork' training option is set to 'last-iteration' (which is default), the finalized metrics correspond to the last training iteration. If the 'OutputNetwork' training option is set to 'best-validation-loss', the finalized metrics correspond to the iteration with the lowest validation loss. The iteration from which the final validation metrics are calculated is labeled Final in the plots. And from the plot, it is clear that the validation accuracy dropped after training on the final iteration of the data
Refer to the following pages for more information: Monitor Deep Learning Training Progress, trainingOptions & trainNetwork.
4 个评论
Srivardhan Gadila
2021-12-30
The said functionality has been introduced in R2021b version (Check the following release notes for more information) and you have to use the R2021b version in order to use this functionality. You have to add the name-value argument: 'OutputNetwork' with value 'best-validation-loss' in the trainingOptions (refer to https://www.mathworks.com/help/deeplearning/ref/trainingoptions.html#d123e136007) :
Training_Options = trainingOptions('sgdm', ...
'MiniBatchSize', Minibatch_Size, ...
'MaxEpochs', 2, ...
'InitialLearnRate', 1e-2, ...
'Shuffle', 'every-epoch', ...
'ValidationData', Resized_Validation_image, ...
'ValidationFrequency', Validation_Frequency, ...
'Verbose', false, ...
'Plots', 'training-progress', ...
'OutputNetwork', 'best-validation-loss');
The trainNetwork will return the network corresponding to the training iteration with the lowest validation loss.
new_user
2021-12-30
Srivardhan Gadila
2021-12-30
In that case, either you can reduce the value of "MiniBatchSize" and try it or train the network on cpu by setting the "ExecutionEnvironment" to "cpu". Both of these are input arguments of trainingOptions.
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