Arguments - Aster Analytics

Teradata Aster Analytics Foundation User Guide
Product
Aster Analytics
Release Number
6.21
Published
November 2016
Language
English (United States)
Last Update
2018-04-14
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kiu1466024880662.ditamap
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AA-notempfilter_pdf_output.ditaval
dita:id
B700-1021
lifecycle
previous
Product Category
Software
Argument Category Description
InputTable Required Specifies the table containing the input data to be trained.
OutputTable Required Specifies the table to which to output the trained network weight data.
WeightTable Optional Specifies the table that lists the starting values for the neural network weights. If you do not specify a weight table, the function assigns the initial weights for the neural network randomly.
InputColumns Required Specifies the name of the columns of the InputTable that contains the numerical predictor variables (x1, x2, x3, and so on).
ResponseColumns Required Specifies the names of the input table columns of that contain the numerical dependent variables (y1, y2, y3, and so on).
GroupByColumns Optional Specifies the weight table columns in which to output different neural networks for different groups.
HiddenLayers Optional Specifies the number of hidden neurons in each layer, from left to right. The default is 1 layer, 1 neuron. For example, HiddenLayers('5','5') produces a 3-layer network with 5 neurons in each hidden layer, while HiddenLayers('3') produces the network shown in Introduction to Neural Networks.
Threshold Optional Specifies the threshold for the partial derivatives of the error function as stopping criteria. The default value is 0.01.
MaxIterNum Optional Specifies the maximum number of steps for the training of the neural network. The default value is 1.
LearningRate Optional Specifies the learning rate used by traditional backpropagation. The default value is 0.001.
ActivationFunction Optional Specifies the name of the differentiable function that the function applies to the result of the cross-product of the neurons and the weights.:
  • 'logistic': logistic (default)
  • 'tanh': hyperbolic tangent
ErrorFunction Optional Specifies the name of the differentiable function with which the function calculates the error:
  • 'sse': sum of squared errors (default)
  • 'ce': cross-entropy
Algorithms Optional Specifies the algorithm type that the function uses to calculate the neural network, 'backprop'.
LinearOutput Optional Specifies whether to apply the ActivationFunction to the output neurons.
OverwriteOutput Optional Specifies whether to overwrite the output table.
Seed Optional Specifies the seed with which to initialize the model, an INTEGER. Given the same seed, cluster configuration, and input table, the function generates the same model. By default, the function initializes the model randomly.