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| I believe this is due to some overzealous standardization step in the automated processing pipeline (boolean vectors are converted to numerical vectors for processing). You can disable that using the "Minimal" feature extraction: data = {True,... |
| I am not sure I understand your problem correctly. If what you are asking is how to get the prediction for specific values it is just ``` result[{200, 210, 215, 235}] (* {82.839, 82.8391, 82.8392, 82.8393} *) ``` By the way, there are ways... |
| Just a note, `ResizeLayer` has been updated to work on arbitrary rank ResizeLayer[{1, 2, 3, 4, 5}][RandomReal[1, {3, 2, 2, 2, 2, 2}]] // Dimensions (* {3, 1, 2, 3, 4, 5} *) |
| The best importer right now is from ONNX so I would start with a Tensorflow -> ONNX converter like this https://github.com/onnx/tensorflow-onnx There is also a small tutorial by Microsoft ... |
| There are no secret advantages, just a misunderstanding. Indeed I am not sorting by `Last` but by `First` (see my post). I thought you where asking a general question about the sorting algorithms. |
| The low level PNG importer `ImageReadPNG` is automatically caching the result. If you use the internal function directly you must clean it up yourself with ``` Image`ImportExportDump`DeleteCachePNG[] ``` This should hopefully solve the memory... |
| It might be a bug, I cannot reproduce it in my version. You should contact the support team using https://www.wolfram.com/support/contact/email/?topic=feedback |
| Hi Michael, you can explore the internals of the `DimensionReducerFunction` to check the preprocessor—ideally you should be able to do ``` Information[_DimensionReducerFunction, "FeatureExtractor"] ``` but we have not hook it up there yet.... |
| Thanks for the wonderful post and for making me discover Townscaper! |
| Color conversion using external profiles is very slow. I would suggest to plot `fujiPearlProfile`, save it and then combine it with a separate plot for each image using `Show`. |