fineTuneDArch: Fine tuning function for the deep architecture.

Description

The fine tuning function for deep architectures. This function use the function saved in the attribute fineTuneFunction to train the deep architecture.

Usage

fineTuneDArch(darch, trainData, targetData, ..., maxEpoch = 1,
  isBin = FALSE, isClass = TRUE, validData = NULL, validTargets = NULL,
  testData = NULL, testTargets = NULL, stopErr = -Inf,
  stopClassErr = 101, stopValidErr = -Inf, stopValidClassErr = 101)
## S3 method for class 'DArch':
fineTuneDArch(darch, trainData, targetData, ...,
  maxEpoch = 1, isBin = FALSE, isClass = TRUE, validData = NULL,
  validTargets = NULL, testData = NULL, testTargets = NULL,
  stopErr = -Inf, stopClassErr = 101, stopValidErr = -Inf,
  stopValidClassErr = 101)

Arguments

darch

A instance of the class DArch.

trainData

The training data matrix

targetData

The expected output matrix for the training data

...

Additional parameters for the training function

maxEpoch

The number of training iterations

isBin

Indicates whether the output data must be interpreted as boolean value. Default is FALSE. If it is true, every value over 0.5 is interpreted as 1 and under as 0.

isClass

Indicates whether the training is for a classification net. When TRUE then statistics for classification will be determind. Default is TRUE

validData

Data for validating the network. Default is NULL

validTargets

The expected output for the training data Default is NULL

testData

Data for testing the network. Default is NULL

testTargets

The expected output for the training data Default is NULL

stopErr

Stop criteria for the error on the train data. Default is -Inf

stopClassErr

Stop criteria for the classification error on the train data. Default is 101

stopValidErr

Stop criteria for the error on the validation data. Default is -Inf.

stopValidClassErr

Stop criteria for the classification error on the validation data. Default is 101 .

Details

The function trains the given network darch with the function saved in the attribute fineTuneFunction of the DArch-Object. The data (trainData, validData, testData) and belonging classes of the data (targetData, validTargets, testTargets) can be hand over either as matrix or as ff-matrix (see package ff for details). The data and classes for validation and testing are optional. If they are provided the network will be executed with this datasets and statistics will be calculated. This statistics are saved in the stats attribute (see Net). The attribue isBin indicates whether the output data must be interpreted as binary value. If true every value over 0.5 is interpreted as 1 otherwise as 0. Also it is possible to set stop criteria for the training on the error (stopErr, stopValidErr) or the correct classifications (stopClassErr, stopValidClassErr) of the training or validation dataset.

Description

Usage

Arguments

Details

See Also