Damn, I have the [omniFilter_abs~] there too? I missed that. You can get
it here https://github.com/alexdrymonitis/filter_abstractions
The [command] object is a replacement of [shell]. If I'm not mistaken,
it doesn't work on Windows. You don't necessarily need it though, I use
it to load training data, but I do provide trained models, so you can
test them without needing to train them from scratch.
That's strange with [neuralnet~]. Do you have the respective binary?
On 9/17/24 10:22, João Pais wrote:
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!
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