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doIf doIfElse doReport doStopThis doRun fork evaluate reportPipe doTellTo reportAskFor receiveOnClone createClone newClone removeClone doPauseAll doSwitchToScene receiveUserEdit doDefineBlock doDeleteBlock doSetBlockAttribute reportBlockAttribute reportEnvironment receiveSlotEvent doSetSlot reportTouchingObject reportTouchingColor reportColorIsTouchingColor doAsk getLastAnswer reportMousePosition reportMouseX reportMouseY reportMouseDown reportKeyPressed reportRelationTo reportAspect doResetTimer getTimer reportDate reportAttributeOf reportObject reportURL reportAudio reportVideo doSetVideoTransparency reportGlobalFlag doSetGlobalFlag reifyScript reifyPredicate reportVariadicSum reportDifference reportVariadicProduct reportQuotient reportPower reportModulus reportVariadicMin reportVariadicMax reportRound reportMonadic reportAtan2 reportRandom reportVariadicLessThan reportVariadicGreaterThan reportVariadicAnd reportVariadicOr reportNot reportBoolean reportTextSplit reportLetter 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64

de:resample _ auf _ Punkte 64lastlast1

de:Länge von Pfad _

de:Entfernung von _ nach _

0

centeredlengthscaledheightstage22

unrecognized

de:animiere _ draw a list of numbers as vertical lines distributed evenly across the stage.

de:male Balken _ _ ca:dibuixa amb barres _ _ 0.8 0 1posslicewidthstagelengthpen sizesizeleftstage2Report a copy of the given table in which the numerical data of each column is distributed between 0 and 1 using the column's min and max values (feature scaling).

de:normalisiere Tabelle _ ca:_ normalitzada Report a function (ring) that can be called with a single row (record) in the form of the given data set to normalize it using the sample's min and max values. Use this reporter to create a normalization function from a training set that can be applied to validation or live data.

de:Normalisierung für Tabelle _ ca:normalització per a la taula _

inoutsolvesamplelearnerrorreshuffledeltathingadjust weightslearning rate0.1listobjecttopologyset learning ratealphapredictsampleclassifysamplefitlayerabssampletrainsetvalidatelengthsetget modelweightsset modelmodelshuffleget topology2dimensionsweights111dimensionsweightsGenerate a new predicate block in the sensing category that reports whether a given sample data record classifies as the given tag (name) based on an example dataset in the form of a binary truth table. The generated predicate block offers its estimated accuracy in its help screen / comment and can then be exported and shared. By default training happens all automatically using a neural network with one hidden layer, observing the learning progress and partitioning the dataset internally into a training set and validation set. Optionally you can specify an exact number of epochs (0 = automatic), a partitioning fraction (0 = automatic, 1 = none), and none to 8 hidden layers with arbitrary neurons (0 = no hidden layers). You can abort / shorten the training process manually by positioning the mouse pointer near the stage center and pressing the mouse button down. Running the command again updates any previously generated block, i.e. you can optimize existing blocks by re-training them with different parameters.

de:generiere Prädikat für _ in _ _ $_tag0 0.8 51initsampleblocks222213323423get model1normblocks22labelide_translate(text)is _ ?def1commentpredict whether the data sample classifies as , estimated accuracy: 210001099.9%.oldcustom?type3labelblockspredicateCreate and report a new neural network with the specified topology representing the number of inputs, arbitrary hidden layers, and output(s).

de:neues neuronales Netzwerk _ ca:nova xarxa neuronal _ 5 1Predict and report the classification of a given data sample - a list of numbers representing a single record in a dataset - using the specified neural network instance. The result is a list of numbers representing the neural network's output layer.

de:klassifiziere _ mit Netzwerk _ ca:classifica _ amb la xarxa _ Train a single epoch or validate a neural model on a truth-table dataset (a list of number-vectors with the expected classification in the last column). For "train" this reports the accumulated activation error over the epoch. For "validate" this reports the overall classification accuracy of the dataset.

de:_ Netzwerk _ mit _ ca:_ xarxa _ amb _ $_traintrain=$_train validate=$_validateQuery the model or the learning rate of a given neural network. For "model" this reports a list of weight-matrices representing the neural network's hidden and output layers. Models can be exported and shared among projects. For "learning rate" this reports a single number representing the neural network's eagerness to adjust its weights when learning.

de:_ von Netzwerk _ ca:_ de la xarxa _ $_get modelget model=$_get model get learning rate=$_get learning rate get topology=$_get topologyDraw a picture of the specified model of a neural network where each layer is represented as a vertical line of dots and each weight as a line between 2 neuron-dots. The line width represents the weight's absolute value, negative values can be rendered in another color.

de:male neurales Modell _ _ ca:renderitza el model neuronal _ _ 1 1 rgba(214,49,0,255)flat line endsfalseabsbottomstage100flat line endsSplit a list into 2 sets by randomly assigning its elements to each partition at the given ratio, reports a 2-item list containing the shuffled and split data. Use this block to create training and validation data sets.

de:teile _ im Verhältnis _ ca:partició de _ per _ 0.8

de:Objekt _ ca:objecte _ $_field $_thing11

de:klone _ _ ca:clon _ _ $_field $_thing11

de:blockifiziere _

0 0.8 5topology1length2303length.251lengthcolumnsaiepochs0donefalselastlengthlogscale110rendererTurtle spriteflat linesflat line endsflat line endstruereadout1last100010%flat line endsaccuracyvalidatereadout100010%Assign a pre-trained model to the given neural network or change its learning rate.

de:_ von Netzwerk _ auf _ ca:_ de la xarxa _ a _ $_set modelset model=$_set model set learning rate=$_set learning rateCreate and report a new sprite that can serve as a layer in a neural network of sprites. It responds to 3 events / methods: 1) setup Initializes the layer with a list of 2 numbers representing the number of inputs and the number of desired output neurons. 2) predict Reports the result of a forward pass of a single sample / record with the precision of the activation function, i.e. not rounded for classification. If you wish to use this answer to classify the record you must also sum and round the list of results. 3) learn Adjusts the layer's weights depending on the given delta vector and reports a new delta vector to be backpropagated to the previous layer, if any. You can either use a single sprite as a SLP (perceptron), or clone it several time to create additional hidden layers for a deep neural network by using a forward PIPE for prediction and a backward PIPE for learning.

de:neues Perzeptron Objekt var_declare(scope, name)globallearning ratelearning ratepredict whether the data sample classifies as heart, estimated accuracy: 99.9%.

1-8.392304589068967-28.56159499819441-40.236358309273825-50.44124784664666-57.99461679191652-68.76431977439212-74.43790246937394-70.17369533522836-66.0375110254218-63.92484499730688-54.604742803248996-45.5419889265026-39.066317561703315-34.714762323839295-26.134416393578082-20.30201336249433-10.325629413641462-2.34996250638714826.45058342491581336.02737338510367-179.8016998190928-90-90-90-90-90-87.39248198216224-84.78100449350589-87.39004814112216-154.07221539040907-64.23067237566129-67.41704264388981-69.01791573477483-52.4216775409903664.4547218428492373.42598729416746039.01145066421343312.24894749786027914.783405666048612.80880277163495300-4.519157467621269-7.141703689211965-112.87237872334775-121.62660203888441-120.27241683929955-121.41291481260338-138.29091005068807-120.0666834713838-126.04210765197485-131.37297747998193-168.26347110300946-153.58010958414167-137.57693696346232-125.72361418888688-121.11275646397591-121.06945530979685-124.95124736059657-123.99077941252126-131.8787763296995-153.28075698767458-135.70716831137312-19.687116198125864028.24816753328304555.126646175272470.230690836694380.886559359954388.7960627680533699.56576575052895105.1364876354094106.61200795249779100.9929676036684888.6299779410563578.50762382969417222.88509020517958213.71049251884813201.56132615369566190.75714522594154176.68328111373918165.110007326986148.6598949804074121.02746337853415116.36169820198124342.17894085646697250.42132488122772155.72059611389483171.92980226040447180198.103018626147201.3549709567405208.20923396607384222.25717432943924294.8144653221562212.60962933858553230.95339948607824242.30716905656158223.59487836710412120.9306558810928129.90691479618158124.31921983707495108.59081661902096100.1218926233353785.2080535782471587.563351753189869096.38762542580088116.11479456243622237.80810691658496265.2239310536138269.6060140166529280.91678566005544305.65179411531693292.90544003518863302.72583655680836311.3729774799819653.6792089880527939.72674304331619205.37220856876664197.77967516892107207.3513469835247211.06945530979687214.95124736059657213.99077941252125219.45949339088685240.48712999076588231.2508345850747666.057567215925471predict whether the data sample classifies as house, estimated accuracy: 99.9%.

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