Transform shifting clusters on repeated calls

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Hi @lmcinnes,
I am trying to use UMAP to show the increasing ability to seperate different classes over the training process of a neural network, similar to [https://medium.com/@rakesh.chada/understanding-neural-networks-by-embedding-hidden-representations-f256842ebf3a](url)

My idea was to select an epoch e, a layer l, as well as an input i.
I would then fit a UMAP instance on the high dimensional representations of i at layer l for that epoch e.
Afterwards, I want to use the learned transformation on other epochs for a fixed i and l.

However, the transformations for the same e, l and i seem to change after several runs on other e's. I don't mean some noisy variations, that I would expect to derive from the non-deterministic nature of the algorithm, but all the clusters are changing their shape and position within the embedding.

On the images you can see results of alternating transforms on epoch e=83 / 82 for fixed l and i.


Do you know, why the clusters are shifting so much and how I could possibly prevent this?
Thank you very much for your time.

Start by reproducing the alternating fit/transform sequence described for epochs 82 and 83 with a fixed layer and input, then inspect the UMAP fit and transform entry points for state changes across repeated calls. Done means repeated transforms of the same fitted representation remain stable, or the stateful behavior is documented with a reliable explanation.

Written by the indexing model from the issue text.