deep_logistic_local: Deep Learning Classification with Automated Parameter Tuning...
In tianwei-zhang/easyAI: Democratize AI

Usage Arguments

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deep_logistic_local(x, y, num_layer, max_units, start_unit, max_dropout,
  min_dropout, max_lr, min_lr, iteration_per_layer, validation_split, num_epoch,
  num_patience)

`x`	training feature matrix
`y`	target matrix
`num_layer`	a vector of integers indicating the number of hidden layers to test. Default to seq(1,5,1)
`max_units`	the maximum number of hidden units in a layer. Default to an optimized value based on data
`start_unit`	the minimum number of hiddent units in a layer. Default to 5
`max_dropout`	A number between 0 and 1 indicating the maximum dropoff rate in a layer. Default to 0.2
`min_dropout`	A number between 0 and 1 indicating the minimum dropoff rate in a layer. Default to 0
`max_lr`	maximum learning rate in a run. Default to 0.2
`min_lr`	minimum learning rate in a run. Default to 0.001
`iteration_per_layer`	Number of parameter randomizations for a given number of hidden layers. More iterations will explore a larger parameter space
`validation_split`	\item num_epochnumber of epoches to go through during training \item num_patiencenumber of patience in early stopping criteria

returns a list object with two values: train_performance: A table with parameters and model performance metrics best_model: a keras_model object with the optimal parameters Deep Learning Classification with Automated Parameter Tuning by Random Search

tianwei-zhang/easyAI documentation built on May 14, 2019, 12:48 p.m.