0
$\begingroup$

I am currently fine tuning VGG16 network to do a binary classification task. I have to admit that the training and testing samples are relatively small (around ~60 for training and ~15 for testing). I have tried data augmentation techniques from keras modules (imagedatagenerator).

When I feed these data into the VGG16 network (~5 epochs), the network's training accuracy and validation accuracy both fluctuates as the figure below. Attached with figures showing the accuracies and losses.

enter image description here

enter image description here

May I know what does this phenomenon indicate? Many thanks!

$\endgroup$
0
$\begingroup$

You train your neural network with gradient descent, hence you will find only a LOCAL minimum for your choice of loss. Every time your run your neural network you typically initialize your net differently. Hence it is likely that you will end up in a different local minimum, which may be better or worse than the previous one...

This phenomenon just indicates that your optimization problem is not convex, hence has not a single minimum. You can try using a classifier that has a convex objective as e.g. Support Vector Machines.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.