# LTE - An alternative to LPT for people who don't care about "pitch"

**URL:** <https://discourse.flucoma.org/t/lte-an-alternative-to-lpt-for-people-who-dont-care-about-pitch/605>\
**Category:** Pre-Release Toolbox2 New Ideas\
**Created:** [July 22, 2020, 3:05pm UTC](https://discourse.flucoma.org/t/lte-an-alternative-to-lpt-for-people-who-dont-care-about-pitch/605 "2020-07-22T15:05:24Z")\
**Posts on this page:** 1\
**Showing post:** 12

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**Author:** ![weefuzzy](https://discourse.flucoma.org/user_avatar/discourse.flucoma.org/weefuzzy/32/362_2.png) [@weefuzzy](https://discourse.flucoma.org/u/weefuzzy)\
**Post date:** [March 7, 2021, 10:35pm UTC](https://discourse.flucoma.org/t/lte-an-alternative-to-lpt-for-people-who-dont-care-about-pitch/605/12 "2021-03-07T22:35:26Z")

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> [@rodrigo.constanzo](#):
>
> Not long after writing this I remembered how faffy setting up the autoencoder was, and how, as of yet, I’ve gotten anything converge at all.

It oughtn’t be that tricky to get something to converge (useful results is different). Always (always) start small, with low learning rates, and embrace tweaking the LR (to try and find a sweetish spot between not-converging and converging-but-too-slowly) as part of the model design. Be wary of making the network bigger, unless you’re sure you have enough training data to absorb the extra complexity (and can live with the extra computation).

> [@rodrigo.constanzo](#):
>
> Unless I’m mistaken, these two (PCA/MLP AE) are the only options for `transformpoint`-ing things in realtime?

I think UMAP’s had it since Alpha 07.

> [@rodrigo.constanzo](#):
>
> By this I meant, does MLP break if you set `@activation 2` and give me numbers that are \>1 or \<-1, does that fuck up the convergence.

No, it shouldn’t screw up convergence in and of itself: if the whole range of your feature is huge, then that might give odd results, but in general points outside -1:1 are fine (consider that the network is learning weights to apply to the inputs anyway, so these weights can just get smaller, within reason).

It doesn’t screw up the KD tree either. The tree doesn’t really care about the range of data in absolute terms, but the euclidean distance it uses implicitly assumes that features are comparably scaled (euclidean distance = sqrt of sum of squared distances of each feature for the two points being compared).

> [@rodrigo.constanzo](#):
>
> I guess my desire to come up with some somewhat robust/generalizable analysis/processes is that the faff for setting set up each is so significant that I wouldn’t want to have a bespoke corpus (and corresponding realtime)

I think some of this will partly be down to it being a new way of working (and there still being unsanded UX edges to the toolkit, natch): to a very large extent ‘programming’ with ML stuff _is_ about data monkeying as much / more than it’s about models. I’ve certainly seen the argument put forward that it constitutes a completely different programming paradigm.

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