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[D] Is there a machine learning framework that allows you to manually change the layer weights after each update step?

I am looking to sparse training; that is, during each training step, only a very small fraction of the weights go through the forward and backwards pass. So, only a small fraction of weights need to be loaded into memory at once.

I was thinking of having the weights saved in sqlite or hdf5, and then having a keras layer which copies the relevant weights stored for the forward pass. And then after the update step, those weights are update into the disk.

I know there are ways to directly setting the weights outside training https://stackoverflow.com/questions/51354186/how-to-update-weights-manually-with-keras

But I am wondering if any framework allows manually updating variables, after they have been already updated in a training step.

submitted by /u/Research2Vec
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.