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@ThisGuyIsNotAJumpingBear

The current method for creating x_neg shuffles the indices for labels and re-distributes them. However, this method lefts roughly 10% of labels unchanged, and hence 10% of x_neg pics are actually the 'good' samples.

I attempted to solve this problem by introducing a mask layer that contains random ints from 1 to 9, adding it to the original y data, and finding the remainder of 10. This ensures that the new distribution is i.i.d (as randint is i.i.d) and no more unchanged values after the shuffle.

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