Can I feed a neural network with a "predefined" set of training images at every iteration ?

4 views (last 30 days)
Hi everyone,
I am working with a convolutional neural network (GoogLeNet) but instead of using classic "full" images, I am working with patches cropped out of the images. In other words, each class contains several images (which are actually subfolders), and each image contains several patches (the png files).
I wrote a simple function that reads n random patches (png files) belonging to m random images at every run, and was wondering how to implement it in the training process. I basically want to use those n randomly generated training png files (minibatch) at every iteration. Should this be done within the "trainNetwork" function?
Is there any question/example that deals with this topic?
Thank you very much.
Best regards
M J on 25 Sep 2020
Edited: M J on 25 Sep 2020
Yes. For example, assuming I have 100 iterations per epoch, I would like to repeatedly generate 100 random training subsets (based on my set of rules, as mentioned above), each of which would be fed to the network at every iteration.

Sign in to comment.

Accepted Answer

Srivardhan Gadila
Srivardhan Gadila on 28 Sep 2020
Based on the above information in question & comments I think using the custom training loop would be a good Idea. You can refer to Train Network Using Custom Training Loop & Deep Learning Custom Training Loops for more information.

More Answers (0)

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!