gdTrain: Train a generative model for a data source

In ganGenerativeData: Generate Generative Data for a Data Source

Train a generative model for a data source

Description

Read a data source from a file, train a generative model that generates generative data for the data source in iterative training steps, write trained generative model and generated data in training steps to a file in binary format. When a higher number of iterations is used the distribution of generated data will get closer to that of the data source. When a name of an existing generative model file is passed training will be continued.

Usage

gdTrain(
  generativeModelFileName,
  generativeDataFileName,
  dataSourceFileName,
  columnIndices,
  trainParameters = gdTrainParameters(numberOfTrainingIterations = 10000,
    numberOfInitializationIterations = 1500, numberOfHiddenLayerUnits = 1024,
    learningRate = 7e-05, dropout = 0.05)
)

Arguments

generativeModelFileName	Name of generative model file
generativeDataFileName	Name of generative data file. When name is NULL or empty string generated data will not be written to a file.
dataSourceFileName	Name of data source file
columnIndices	Vector of two column indices that are used to plot two-dimensional projections of normalized generated generative data and data source for a training step. Indices refer to indices of active columns of data source. Plotting can be disabled by passing NULL or an empty vector.
trainParameters	Generative model training parameters, see function gdTrainParameters().

Value

None

Examples

## Not run: 
trainParameters <- gdTrainParameters(numberOfTrainingIterations = 10000)
gdTrain("gm.bin", "gd.bin", "ds.bin", c(1, 2), trainParameters)
## End(Not run)

ganGenerativeData documentation built on June 22, 2024, 10:01 a.m.