Description Usage Format Information
Default hyperparameter experimental test design for a single-hidden layer undercomplete autoencoder neural netowrk.
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A dataset with 600 rows and 12 features; 9-test design features, 1-response variable feature, and 1-design point index feature
Subset_Split: Categorical 3-level factor; describes the subset to use in the Autoencoder
Design_Point: An index of each test point
Activatino_Function: Categroical 2-level factor; describes the activation function within each neuron in the neural network
Input_DO_Rate: Continuous multi-level factor; describes the Dropout rate of the input layer neurons
Hidden_DO_Rate: Continuous multi-level factor; describes the Dropout rate of the hidden layer neurons
Initial_Weight_Distribution: Categorical 3-level factor; describes the distribution by which the initial weights of the autoencoder neural network are generated
Data_Scale: Categorical 3-level factor; describes the range to which the training and test subset data is scaled.
Rho: Continuous multi-level factor; describes the value of the rho parameter for the ADADELTA learning procedure
Epsilon: Continuous multi-level factor; describes the value of the epsilon parameter for the ADADELTA learning procedure
Shuffle_Train_Data: Categorical 2-level factor; boolean value indicating if the training data should be randomly shuffled during neural network training
Y: Placeholder for the response value measurement
A .rda dataset containing the factors and levels of 600-test trial designed experiment generated in JMP Pro v12.1. The designed experiment is a flexible space filling design for 9 test design factors.
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