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| You could use "survived" -> NetDecoder[{"Function", First}] To pluck out the real from the list generated by `LinearLayer[1]` But the easiest would be to not attach anything to the "survived" port and just use LinearLayer[{}] ... |
| Hi Dalila, I cannot reproduce the error you see with this simple example ``` data = ResourceData["Sample Data: Fisher's Irises"]; c = Classify[data -> "Species"] FeatureImpactPlot[c] ``` Can you share a sample of the data you are using? |
| Thank you, the results are indeed reasonable. |
| Thank you very much for your guidance. I have tested 3 images and obtained the results and errors. I would greatly appreciate it if you could kindly review them and provide your feedback. |
| Hello! I'll be presenting '[Reinforcement Learning applied to Feedback Control][1]' tomorrow on YouTube. Please join and ask questions or leave them below. [1]: https://community.wolfram.com/groups/-/m/t/3356797 |
| This algorithm was originally called Model Synthesis. I created for my PhD. I published this in 2007, nine years before Wave Function Collapse and they are the same algorithm. If you're going to call it Wave Function Collapse, you should at least... |
| It's a bit hidden but you can use the same spec as the nets and layers (e.g. NetChain). ``` NetEncoder[{"Function", Flatten[IntegerDigits[#, 2, 8]] &, "Varying"}] ``` Depending on your application, you can also avoid flattening in the encoder... |
| &[Wolfram Notebook][1] [1]: https://www.wolframcloud.com/obj/d1bbed68-2aed-4fd9-9297-d61e98fb99ee |