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

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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


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M J on 25 Sep 2020
Thank you for your answer! My function generates only a small subset of the training images (n random png files). Should I then use a for loop to create a new training image datastore at each iteration?
Sindar on 25 Sep 2020
I'm sorry, I'm not sure - my familiarity with the problem is shallow. My first thought would be that you want a single training set for every iteration, so you should create the image datastore once and stick with it. (Perhaps increase n? or run multiple times to gather a larger training set)
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.

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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.

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