Thanks both for replying!
Okay I’ll try to expand on my explanation a little more:
I’m conducting a regression task where I’m trying to return a value (k) between 0-1.
There are more k=1 values than k=0 as shown above in an exponential fashion.
I have weighted each image by 1-k in the hopes of seeing more of the rare images which have low k.
I’m concerned that: say you have 6 million images and you restrict num_samples to 25 thousand for example, that you will not get balanced batches where you see equal number of low k images as high k images. (This is what i meant by a balanced sample set, apologies for the lingo)…simply because the multinomial sampler might not explore the high k images enough as they’re down-weighted so much.