DeepNetworkTrain: Training a simple deep network.

In RcppShark: R Interface to the Shark Machine Learning Library

Description

This will train a simple 'deep' neural network with two hidden layers. It will use an autoencoder for pretraining. For more information refer to the Shark tutorial at http://image.diku.dk/shark/sphinx_pages/build/html/rest_sources/tutorials/algorithms/deep_denoising_autoencoder_network.html

Usage

DeepNetworkTrain(x, y, nHidden1 = 8L, nHidden2 = 8L,
  unsupRegularisation = 0.001, noiseStrength = 0.3,
  unsupIterations = 100L, regularisation = 1e-04, iterations = 200L,
  verbose = FALSE)

Arguments

x	matrix with input data
y	vector with labels
nHidden1	number of nodes of first hidden layer (part of network model)
nHidden2	number of nodes of second hidden layer (part of network model)
unsupRegularisation	regularization factor of supervised training
noiseStrength	noise strength for unsupervised training
unsupIterations	iteration number for unsupervised training
regularisation	regularisation factor for supervised training
iterations	iteration number for supervised training
verbose	print extra information?

Examples

	x = as.matrix(iris[,1:4])
	y = as.vector(as.numeric(iris[,5]))
	y = replace(y, y == 2, 0)
	y = replace(y, y == 3, 0)
	model = DeepNetworkTrain (x, y, nHidden1 = 32, nHidden2 = 32)
	results = DeepNetworkPredict (x, model)
	networkPrediction = apply (results$prediction, 1, which.max) - 1
	errors = sum(abs(y - networkPrediction))/length(y)
	cat("Network produced ", errors, "errors.\n")

Example output

RcppShark v3.1.1 loaded.
Network produced  0 errors.

RcppShark documentation built on May 30, 2017, 6:20 a.m.