deeplearning: An Implementation of Deep Neural Network for Regression and Classification
Version 0.1.0

An implementation of deep neural network with rectifier linear units trained with stochastic gradient descent method and batch normalization. A combination of these methods have achieved state-of-the-art performance in ImageNet classification by overcoming the gradient saturation problem experienced by many deep architecture neural network models in the past. In addition, batch normalization and dropout are implemented as a means of regularization. The deeplearning package is inspired by the darch package and uses its class DArch.

Browse man pages Browse package API and functions Browse package files

AuthorZhi Ruan [aut, cre], Martin Drees [cph]
Date of publication2016-04-11 18:09:06
MaintainerZhi Ruan <ryan.zhiruan@gmail.com>
LicenseGPL (>= 2)
Version0.1.0
URL https://github.com/rz1988/deeplearning
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("deeplearning")

Man pages

applyDropoutMask: Applies the given dropout mask to the given data row-wise.
AR: Calculates the Accuracy Ratio of a classifier
AR.DArch: Calculates the Accruacy Ratio of a given set of probability
AR.default: Calculates the Accruacy Ratio of a given set of probability
AR.numeric: Calculates the Accruacy Ratio of a given set of probability
backpropagate_delta_bn: Calculates the delta functions using backpropagation
batch_normalization: Batch Normalization Function that normalizes the input before...
batch_normalization_differential: Function that calcualtes the differentials in the batch...
calcualte_population_mu_sigma: Calculates the mu and sigmas of a darch instance
classification_error: Calculates the classification error
convert_categorical: Data proprosess function that covnerts a categorical input to...
crossEntropyErr: Calculates the cross entropy error
finetune_SGD_bn: Updates a deep neural network's parameters using stochastic...
generateDropoutMask: Generates the dropout mask for the deep neural network
generateDropoutMasksForDarch: Generates dropout masks for dnn
matMult: Calculates the outer product of two matricies
meanSquareErr: Calculates the mean squared error
new_dnn: Creats a new instance of darch class
print_weight: Prints out the weight of a deep neural network
rectified_linear_unit_function: Rectified Linear Unit Function
reset_population_mu_sigma: Resets the mu and sigmas of a darch instance to 0 and 1
rsq: Calculate the RSQ of a regression model Utilitiy function...
rsq.DArch: Utilitiy function that calcualtes RSQ of a DArch instance
rsq.lm: Utilitiy function that calcualtes RSQ of a linear model
run_dnn: Execution function that runs in the batch normalization mode
train_dnn: Train a deep neural network
verticalize: Creates a matrix by repeating a row vector N times

Functions

AR Man page
AR.DArch Man page
AR.default Man page
AR.numeric Man page
applyDropoutMask Man page
backpropagate_delta_bn Man page
batch_normalization Man page
batch_normalization_differential Man page
calcualte_population_mu_sigma Man page
classification_error Man page
convert_categorical Man page
crossEntropyErr Man page
finetune_SGD_bn Man page
generateDropoutMask Man page
generateDropoutMasksForDarch Man page
matMult Man page
meanSquareErr Man page
new_dnn Man page
print_weight Man page
rectified_linear_unit_function Man page
reset_population_mu_sigma Man page
rsq Man page
rsq.DArch Man page
rsq.lm Man page
run_dnn Man page
train_dnn Man page
verticalize Man page

Files

deeplearning
deeplearning/inst
deeplearning/inst/test_batch_normalization_differential.R
deeplearning/inst/test_new_dnn.R
deeplearning/inst/test_ReLU.R
deeplearning/inst/test_finetune_SGD_bn.R
deeplearning/inst/test_train_dnn.R
deeplearning/inst/test_fineTuneFunctions.R
deeplearning/inst/test_run_dnn.R
deeplearning/inst/examples_regression.R
deeplearning/inst/examples_classification.R
deeplearning/NAMESPACE
deeplearning/R
deeplearning/R/new_dnn.R
deeplearning/R/AR.R
deeplearning/R/error_functions.R
deeplearning/R/finetune_SGD.R
deeplearning/R/calculate_mu_sigma.R
deeplearning/R/run_dnn.R
deeplearning/R/dropout.R
deeplearning/R/backpropagate_delta.R
deeplearning/R/train_dnn.R
deeplearning/R/batch_normalization.R
deeplearning/R/rsq.R
deeplearning/R/rectified_linear_unit_function.R
deeplearning/R/util.R
deeplearning/README.md
deeplearning/MD5
deeplearning/DESCRIPTION
deeplearning/man
deeplearning/man/convert_categorical.Rd
deeplearning/man/AR.numeric.Rd
deeplearning/man/AR.DArch.Rd
deeplearning/man/rsq.Rd
deeplearning/man/AR.Rd
deeplearning/man/matMult.Rd
deeplearning/man/generateDropoutMask.Rd
deeplearning/man/train_dnn.Rd
deeplearning/man/classification_error.Rd
deeplearning/man/rsq.lm.Rd
deeplearning/man/batch_normalization_differential.Rd
deeplearning/man/new_dnn.Rd
deeplearning/man/verticalize.Rd
deeplearning/man/calcualte_population_mu_sigma.Rd
deeplearning/man/finetune_SGD_bn.Rd
deeplearning/man/rectified_linear_unit_function.Rd
deeplearning/man/reset_population_mu_sigma.Rd
deeplearning/man/meanSquareErr.Rd
deeplearning/man/rsq.DArch.Rd
deeplearning/man/backpropagate_delta_bn.Rd
deeplearning/man/run_dnn.Rd
deeplearning/man/batch_normalization.Rd
deeplearning/man/applyDropoutMask.Rd
deeplearning/man/generateDropoutMasksForDarch.Rd
deeplearning/man/print_weight.Rd
deeplearning/man/AR.default.Rd
deeplearning/man/crossEntropyErr.Rd
deeplearning documentation built on Jan. 15, 2017, 9:52 a.m.