o3de-extras/Gems/MachineLearning/Code/Source/Nodes/SupervisedLearning.ScriptCanvasNodeable.xml

<?xml version="1.0" encoding="utf-8"?>

<ScriptCanvas Include="Nodes/SupervisedLearning.h" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <Class Name="SupervisedLearning"
           QualifiedName="MachineLearning::SupervisedLearning"
           PreferredClassName="Supervised learning"
           Category="MachineLearning"
           Description="Performs a fully supervised training session of a neural network using stochastic gradient descent.">

        <Input Name="In" DisplayGroup="In" Description="Parameters controlling model training">
            <Parameter Name="Model" Type="MachineLearning::INeuralNetworkPtr" Description="The model to perform a gradient descent step on."/>
            <Parameter Name="TrainingData" Type="MachineLearning::ILabeledTrainingDataPtr" Description="The set of labeled training data to use."/>
            <Parameter Name="TestData" Type="MachineLearning::ILabeledTrainingDataPtr" Description="The set of labeled test data to evaluate against."/>
            <!-- Can't enable this until script canvas supports enumeration-type pins -->
            <!-- Parameter Name="CostFunction" Type="MachineLearning::LossFunctions" Description="The loss function to use to compute the cost."/ -->
            <Parameter Name="CostFunction" Type="AZStd::size_t" Description="The loss function to use to compute the cost."/>
            <Parameter Name="TotalIterations" Type="AZStd::size_t" Description="The total number of times to iterate (epochs) when training."/>
            <Parameter Name="BatchSize" Type="AZStd::size_t" Description="The batch size to use."/>
            <Parameter Name="LearningRate" Type="float" Description="The learning rate to use."/>
            <Parameter Name="LearningRateDecay" Type="float" Description="The decay factor to use after each iteration (epoch) of training."/>
            <Parameter Name="EarlyStopCost" Type="float" Description="If the total cost of the model drops below this value, training will halt. 0 will always complete the whole training cycle."/>
            <Return Name="Model" Type="MachineLearning::INeuralNetworkPtr" Shared="true"/>
        </Input>
    </Class>
</ScriptCanvas>