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[D] Increasing sample size increases no of trainable parameters

Hi!

I was working with keras and tensorflow as backend on an NLP problem when I observed that increasing my training data size caused an increase in the number of trainable parameters even when batch size remained the same. From what I understand, trainable parameters are the weights which are learnt for each layer. If that is the case then it should not change irrespective of whether I increase or decrease my input data size.

So what is exactly happening here? The reason why this is important is because I perform normalization upon my data once it is fully loaded. This normalization would not work properly if I used a generator function.

submitted by /u/atif_hassan
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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.