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forward turn turnLeft setHeading doFaceTowards gotoXY doGotoObject doGlide changeXPosition setXPosition changeYPosition setYPosition bounceOffEdge getPosition xPosition yPosition direction doWearNextCostume getCostumeIdx doSayFor bubble doThinkFor doThink reportGetImageAttribute reportNewCostumeStretched reportNewCostumeSkewed reportNewCostume changeEffect setEffect clearEffects getEffect changeScale setScale getScale show hide reportShown goToLayer goBack playSound doPlaySoundUntilDone doStopAllSounds doPlaySoundAtRate reportNewSoundFromSamples doRest doPlayNote doSetInstrument doChangeTempo doSetTempo getTempo changeVolume setVolume getVolume changePan setPan getPan playFreq stopFreq down up getPenDown setColor changePenColorDimension setPenColorDimension getPenAttribute changeSize setSize doStamp floodFill write reportPenTrailsAsCostume doPasteOn doCutFrom reportColor reportColorAttribute reportNewColor receiveGo receiveKey receiveInteraction receiveConditionEvent receiveMessage doBroadcast doBroadcastAndWait reportPoll doWarp doWait doWaitUntil doRepeat doFor doIf doIfElse reportIfElse 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 reportGet reportObject reportURL reportVideo doSetVideoTransparency reportGlobalFlag doSetGlobalFlag reifyScript reifyPredicate reportVariadicSum reportDifference reportQuotient reportPower reportModulus reportVariadicMin reportVariadicMax reportRound reportMonadic reportAtan2 reportRandom reportVariadicEquals reportVariadicGreaterThan reportVariadicAnd reportVariadicOr reportNot reportBoolean reportJoinWords reportTextSplit reportLetter reportTextAttribute reportUnicode reportUnicodeAsLetter reportIsA reportVariadicIsIdentical doChangeVar doShowVar doHideVar doDeleteAttr reportCONS reportCDR reportListIndex reportListContainsItem reportListIsEmpty reportKeep reportFindFirst reportCombine doForEach doDeleteFromList doInsertInList doReplaceInList reportCrossproduct reifyReporter reportJSFunction

inoutsolvesamplelearnerrorreshuffledeltathingadjust weightslearning rate0.1topologyset learning ratealphapredictsampleclassifysamplefitlayersampletrainsetvalidatesetget modelweightsset modelmodelshuffleget topology2weights111weights

500

draw a list of numbers as vertical lines distributed evenly across the stage.

de:male Balken _ _ ca:dibuixa amb barres _ _ 0.8 0 1posslicepen size2

Report 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 _

Generate 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 51initsample222213323423

norm

22labelide_translate(text)is _ ?def1

commentpredict whether the data sample classifies as , estimated accuracy: 210001099.9%.old3

Create 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 1

Predict 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=$_validate

Query 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 topology

Draw 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)false100

Split 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 5

topology12303.251ai

epochs0donefalselastlogscale110rendererflat linestruereadout1100010%accuracy

readout100010%

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 rate

Create 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 rate

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