autoencoder: Sparse Autoencoder for Automatic Learning of Representative Features from Unlabeled Data
Version 1.1

Implementation of the sparse autoencoder in R environment, following the notes of Andrew Ng (http://www.stanford.edu/class/archive/cs/cs294a/cs294a.1104/sparseAutoencoder.pdf). The features learned by the hidden layer of the autoencoder (through unsupervised learning of unlabeled data) can be used in constructing deep belief neural networks.

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AuthorEugene Dubossarsky (project leader, chief designer), Yuriy Tyshetskiy (design, implementation, testing)
Date of publication2015-07-02 09:09:12
MaintainerYuriy Tyshetskiy <yuriy.tyshetskiy@nicta.com.au>
LicenseGPL-2
Version1.1
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("autoencoder")

Man pages

autoencode: Train a sparse autoencoder using unlabeled data
autoencoder_Ninput=100_Nhidden=100_rho=1e-2: A trained autoencoder example with 100 hidden units
autoencoder_Ninput=100_Nhidden=25_rho=1e-2: A trained autoencoder example with 25 hidden units
autoencoder-package: Implementation of sparse autoencoder for automatic learning...
predict.autoencoder: Predict outputs of a sparse autoencoder
training_matrix_N=1e4_Ninput=100: An example training set of images for training sparse...
visualize.hidden.units: Visualize features learned by a sparse autoencoder

Functions

autoencode Man page Source code
autoencoder Man page
autoencoder-package Man page
autoencoder.object Man page
autoencoder.object.1 Man page
predict.autoencoder Man page Source code
training.matrix Man page
visualize.hidden.units Man page Source code

Files

NAMESPACE
data
data/autoencoder_Ninput=100_Nhidden=25_rho=1e-2.rda
data/autoencoder_Ninput=100_Nhidden=100_rho=1e-2.rda
data/datalist
data/training_matrix_N=5e3_Ninput=100.rda
R
R/autoencoder.R
MD5
DESCRIPTION
man
man/autoencode.Rd
man/autoencoder_Ninput=100_Nhidden=25_rho=1e-2.Rd
man/autoencoder_Ninput=100_Nhidden=100_rho=1e-2.Rd
man/visualize.hidden.units.Rd
man/autoencoder-package.Rd
man/training_matrix_N=1e4_Ninput=100.Rd
man/predict.autoencoder.Rd
autoencoder documentation built on May 19, 2017, 4:42 p.m.