Re: [neuralnet] v0.3
João Pais <[email protected]>
| Newsgroups | gmane.comp.multimedia.puredata.general |
|---|---|
| Message-ID | <[email protected]> |
> This abstraction is in the examples/abstractions directory. I just > realised that I have my local osc_abs prepended to the abstraction > name. Just remove that and it should load. > Ah yes - but then the omniFilter_abs~ is also not present. Also the [command] object isn't loaded, from which library does it come? Strangely, on 06 and 07 examples, [neuralnet~ models/audio_autoencoder~ encoder] doesn't create, but [neuralnet models/audio_autoencoder~ encoder] does. (I'm on windows) > Well, using a neural network boils down to the training dataset that > you'll assemble. Get as many input/output combinations as you can. > Then you'll have to choose the right structure and activation and loss > functions, plus optimizer (although, usually the latter is an Adam). > > Your question is a bit vague, and explaining how to set up a neural > network in an email is not an easy task. Especially for me, since I'm > not an expert (even though I coded this library). > that's true. in this context, it would envolve getting a 1-(or 2-)dimensional data, and detecting a pattern over time (probably between 0.1 and 1.5 seconds). > Cheers > > On 9/15/24 23:37, João Pais wrote: >> >> Hi, is the patch osc_abs/fm_3 missing from the package? >> >> I'm looking for a way to make a model for leapmotion to recognize >> gestures (coming from combinations of xyz or velocity vectores for >> each finger, for example). Would you advise using this library for this? >> >> Best, >> >> JOao >> >> >>> [neuralnet] update! Version 0.3 has just been released! >>> >>> * New activation functions added >>> * Access to the internal structure of a trained network (e.g. the >>> latent space) >>> * Storing weights and biases during training for visualization >>> * Save models during training >>> * Signal-rate version of the object! >>> * Audio autoencoder example added! >>> >>> Binaries for Linux, Raspberry Pi 3,4,5, macOS, and Windows (thanks >>> Ben Wesch for macOS and Windows) are available through deken. >>> Souces are available on >>> GitHubhttps://github.com/alexdrymonitis/neuralnet >>> Thanks to Ben Wesch, Dan Wilcox, IOhannes m zmoelnig, Christof >>> Ressi, and others! >>> >>> Enjoy! >>> --- [email protected] - the Pure Data mailinglist https://lists.iem.at/hyperkitty/list/[email protected]/message/6GXHSG7F7QK4B4NVNOPGS7LUSTRCCRBL/ To unsubscribe send an email to [email protected] mailing list UNSUBSCRIBE and account-management -> https://lists.iem.at/