minimizeClassifier: Conjugate gradient for a classification network

In darch: Package for Deep Architectures and Restricted Boltzmann Machines

Description

This function trains a DArch classifier network with the conjugate gradient method.

Usage

minimizeClassifier(darch, trainData, targetData,
  cg.length = getParameter(".cg.length"),
  cg.switchLayers = getParameter(".cg.length"),
  dropout = getParameter(".darch.dropout"),
  dropConnect = getParameter(".darch.dropout.dropConnect"),
  matMult = getParameter(".matMult"), debugMode = getParameter(".debug"),
  ...)

Arguments

darch	A instance of the class DArch.
trainData	The training data matrix.
targetData	The labels for the training data.
cg.length	Numbers of line search
cg.switchLayers	Indicates when to train the full network instead of only the upper two layers
dropout	See darch.dropout parameter of darch.
dropConnect	See darch.dropout.dropConnect parameter of darch.
matMult	Matrix multiplication function, internal parameter.
debugMode	Whether debug mode is enabled, internal parameter.
...	Further parameters.

Details

This function is build on the basis of the code from G. Hinton et. al. (http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html - last visit 2016-04-30) for the fine tuning of deep belief nets. The original code is located in the files 'backpropclassify.m', 'CG_MNIST.m' and 'CG_CLASSIFY_INIT.m'. It implements the fine tuning for a classification net with backpropagation using a direct translation of the minimize function from C. Rassmussen (available at http://www.gatsby.ucl.ac.uk/~edward/code/minimize/ - last visit 2016-04-30) to R. The parameter cg.switchLayers is for the switch between two training type. Like in the original code, the top two layers can be trained alone until epoch is equal to epochSwitch. Afterwards the entire network will be trained.

minimizeClassifier supports dropout but does not use the weight update function as defined via the darch.weightUpdateFunction parameter of darch, so that weight decay, momentum etc. are not supported.

Value

The trained DArch object.

See Also

darch, fineTuneDArch

Other fine-tuning functions: backpropagation, minimizeAutoencoder, rpropagation

Examples

## Not run: 
data(iris)
model <- darch(Species ~ ., iris,
 preProc.params = list(method = c("center", "scale")),
 darch.unitFunction = c("sigmoidUnit", "softmaxUnit"),
 darch.fineTuneFunction = "minimizeClassifier",
 cg.length = 3, cg.switchLayers = 5)

## End(Not run)

darch documentation built on May 29, 2017, 8:14 p.m.