User Portlet
| Discussions |
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| This is definitely a bug. Thanks for reporting it! Will be fixed for 11.3. |
| [@Mike Sollami][at0]: There are two colorization nets available right now: NetModel["ColorNet Image Colorization Trained on Places Data (Raw Model)"] and NetModel["ColorNet Image Colorization Trained on ImageNet Competition Data... |
| Thanks Mike! I absolutely agree that we need some more Python examples. RNNs are annoying due to unrolling, so that needs some special care. Noted in our to-do list. |
| > Ideally I want to have these image files in folders If you are dealing with image files, then there is a nice solution: instead of using images, use the filename to the image instead (and try use JPG: this is the fastest out-of-core format). So:... |
| This is still not entirely clarified, as "RLNN" is still a non-standard acronym (as far as I know). Are you referring to policy networks in reinforcement learning? Perhaps send me a link to a paper or page which does what you want. |
| [@Marco Thiel][at0], [@David Proffer][at1], [@Arno Bosse][at2], [@Tobias Kramer][at3] : we have decided to resume GPU support for OSX. We are working on a paclet update that we are hoping to release soon. Apologies again for the inconvenience caused!... |
| Just to mention: `Drop` could also be used (maybe more similar to pop). Both `Take` and `Drop` work on `NetChain`, whilst `Take` works on `NetGraph` as well. This is mentioned in the `NetChain` and `NetGraph` docs. |
| For 11.0 and 11.1 there is only one way of defining a custom loss: build it out of existing net layers (any net that has a scalar output can be used for training). There are many more layers coming in 11.1 which will allow for a great deal more... |
| We build other libraries against CUDA 7.5, which would need to be rebuilt. So we would need to push a patch. |
| > After searching for a few hours, I can't find any examples of text-based Neural Networks. Image processing is great and all but NLP is key to what I am building. This is not an accident: v11.0 is not very good for text. This was reserved for... |