data:image/png;base64,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draw a list of numbers as vertical lines distributed evenly across the stage.de:male Balken _ _
ca:dibuixa amb barres _ _
0.8
0
1posslicepen size2Report 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 rate0.1topologyset learning ratealphapredictsampleclassifysamplefitlayersampletrainsetvalidatesetget modelweightsset modelmodelshuffleget topology2weights111weightsGenerate 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 _ _
$_tag§_dynamicMenu$_auto
0.8
$_autovar name_AI: var getteraivar_declare(scope, name)global1initsample2222133234231norm22labelide_translate(text)is _?def1commentpredict whether the data sample classifies as , estimated accuracy: 210001099.9%.old301Create 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)false100Split 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 _
0.8de:Objekt _
ca:objecte _
$_field
$_thing11de:klone _ _
ca:clon _ _
$_field
$_thing11de:blockifiziere _
$_auto
0.8
$_autotopology123auto303.251aiepochs0donefalselastlogscale110rendererflat linestruereadout1100010%accuracyreadout100010%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 ratede:fange _ _
ca:agafa _ _
es:atrapar _ _
fr:attrape _ _
pt:captura _ _
pl:przechwyć _ _
contde:wirf _
ca:llança _
es:lanzar _
fr:lance _
pt:lança _
pl:wyrzuć _
catchtagde:fange _ _
ca:agafa _ _
es:atrapar _ _
fr:attrape _ _
pt:captura _ _
pl:przechwyć _ _
contde:wirf _ _
ca:llança _ _
es:lanzar _ _
fr:lance _ _
pt:lança _ _
pl:wyrzuć _ _
catchtagThis is a breakpoint block, to be used in debugging.
The Boolean input specifies the condition under which
the program should pause. (Use True for an unconditional breakpoint.) The intent is that the script should have
SHOW VARIABLE blocks for the process's local variables.
After the pause, this block automatically hides all temporary variables.de:falls _ dann _ und pause $pause-1-255-220-0
ca:si _ fes _ i atura-ho tot $pause-1-255-220-0
es:si _ haz _ y páralo todo $pause-1-255-220-0
fr:si _ faire _ et mettre tout en pause $pause-1-255-220-0
pt:se _ , então _ e faz pausa em tudo $pause-1-255-220-0
pl:jeżeli _ wykonaj _ i pauzuj wszystko $pause-1-255-220-0
This block does nothing.
Use it when you are calling a reporter
for its side effect and don't care about
its return value.de:ignoriere _
ca:ignora _
es:ignorar _
fr:ignore _
pt:ignora _
pl:ignoruj _
Call f(f(f(...(f(x))))) n times where the three input slots are n, f, and x from left to right. The # variable can be used inside f to represent how many times f has been called.pt:o resultado de _ invocações em cascata de _ com argumento inicial _ _
ca:en cascada _ vegades _ _ _
pl:kaskada _ razy _ _ _
Call f(f(f(...(f(x))))) until condition is true, where the three input slots are condition, f, and x from left to right. The # variable can be used inside f or condition to indicate how many times f has been called.pt:o resultado da invocação em cascata até que _ de _ com argumento inicial _ _
ca:en cascada fins _ _ _ _
pl:kaskada aż _ _ _ _
Returns the function x↦f(g(x)) where f and g are the two inputs.pt:a composição de _ com _
ca:composa _ _
pl:skomponuj _ _
Like the built-in REPEAT UNTIL block, except that the ending condition is not tested until the script has been run the first time. So the script is run at least once.pt:repete _ até que _ $loop-0.7
ca:repeteix _ fins _
pl:powtarzaj _ aż _
Run the script repeatedly, as long as the given condition is true. Runs the script at least once before testing the condition.pt:repete _ enquanto _ $loop-0.7
ca:repeteix _ mentre _
pl:powtarzaj _ gdy _
Runs the script repeatedly, as long as the condition is true. Tests the condition before the first time the script is run. Like the built in REPEAT UNTIL except that in this block the condition must be true, not false.pt:enquanto _ , repete _ $loop-0.7
pl:powtarzaj gdy _ _
Runs the script the specified number of times, like the built-in REPEAT block, but this one provides the # variable that can be used inside the script. Try
REPEAT (200)
MOVE (#) STEPS
RIGHT 92
with the pen down.pt:repete _ vezes _ _ $loop-0.7
ca:repeteix _ _ _
pl:powtórz _ _ _
The primitive FOR block uses an implicit step of ±1 depending on which of the starting and ending values is larger. This version allows you to provide an explicit step value. If the sign of the step input is incompatible with the ordering of the starting and ending values, the script will not be run at all.pt:para _ de _ com passo _ a _ _ $loop-0.7
ca:per _ = _ incrementant _ fins _ _
pl:dla _ = _ co _ do _ _
1110Provides LOOP as a function of one input that runs
the body of the LET with A set to the function input,
so the body can run itself recursively.
See COPY block in Variables for an example of use.pt:tendo _ o valor inicial _ , executa _ definido como _
ca:fes que _ sigui _ al _ _
pl:ustaw _ na _ w _ _
new valuede:führe _ mit Continuation aus
de:rufe _ mit Continuation auf
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learning ratelearning rate1setuptopologyset learning ratealphapredictsampleclassifysamplefitsamplelayertrainsetvalidatesetget modelweightsset modelmodelshuffleget topology2weights111weightsinputs,thingweights,thingsetupinoutsolvesamplelearnerrorreshuffledelta,thingadjust weightslearning 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