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| NetTrain[ unet, images, "Output" -> masks |>, LossFunction -> {"Output" -> CrossEntropyLossLayer["Binary"]} ] |
| Hello Luca, A1: yes, it's the same A2: yes again Characters encoder: http://reference.wolfram.com/language/ref/netencoder/Characters.html Words (tokens) encoder: http://reference.wolfram.com/language/ref/netencoder/Tokens.html If you... |
| https://mathematica.stackexchange.com/a/154800 |
| Use `Classify` to convert your net into a `ClassifierFunction`. class = (Classify@myClassifier)[myDataItem, IndeterminateThreshold -> 0.8] Example: net = NetChain[ {10, 2, SoftmaxLayer[]}, "Input" -> 5, "Output" ->... |
| [@narendra ][at0] Replace x with # ([Slot][1]) which represent a pure function. ElementwiseLayer[#*LogisticSigmoid[#] &] [at0]: http://community.wolfram.com/web/narendra [1]: http://reference.wolfram.com/language/ref/Slot.html |
| ClearAll[DownTo]; DownTo/:Take[x_,DownTo[y_Integer]]:=Take[x,-Min[Length@x,Abs[y]]*Sign[y]] DownTo/:Drop[x_,DownTo[y_Integer]]:=Drop[x,-Min[Length@x,Abs[y]]*Sign[y]] Take[Range[10],DownTo[5]] {6,7,8,9,10} ... |
| Simply type `mathematica` in the Terminal. |
| bankroll = 100; wager = 10; payoff = 1; odds = 1/38; Print[{0, bankroll}] Do[ bankroll -= wager; bankroll += wager*payoff*RandomVariate@BernoulliDistribution[odds]; Print[{i, bankroll}]; ... |
| I don't know hidden option for Accumulate. But you can also speed up calculations even more using data[[;;,1]] instead of QuantityMagnitude. You can use this if you know that all data points are in the same unit. Quantity[Accumulate[data[[;; ,... |