provideMNIST

Folder name, including a trailing slash.

folder

Logical indicating whether download is allowed.

download

This function will, if necessary and allowed, download the compressed MNIST
data set and save it to .RData files using <code><a rd-options="" href="/link/readMNIST?package=darch&version=0.12.0" data-mini-rdoc="darch::readMNIST">readMNIST</a></code>. If the
compressed MNIST archives are available, it will convert them into RData
files loadable from within R. If the RData files are already available,
nothing will be done.

The darch package is built on the basis of the code from G. E.
Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief
nets). This package is for generating neural networks with many layers (deep
architectures) and train them with the method introduced by the publications
"A fast learning algorithm for deep belief nets" (G. E. Hinton, S. Osindero,
Y. W. Teh (2006) <DOI:10.1162/neco.2006.18.7.1527>) and "Reducing the
dimensionality of data with neural networks" (G. E. Hinton, R. R.
Salakhutdinov (2006) <DOI:10.1126/science.1127647>). This method includes a
pre training with the contrastive divergence method published by G.E Hinton
(2002) <DOI:10.1162/089976602760128018> and a fine tuning with common known
training algorithms like backpropagation or conjugate gradients.
Additionally, supervised fine-tuning can be enhanced with maxout and
dropout, two recently developed techniques to improve fine-tuning for deep
learning.

Martin Drees

darch

Package for Deep Architectures and Restricted Boltzmann Machines

provideMNIST function

This function will, if necessary and allowed, download the compressed MNIST
data set and save it to .RData files using <code><a rd-options='' href='readMNIST'>readMNIST</a></code>. If the
compressed MNIST archives are available, it will convert them into RData
files loadable from within R. If the RData files are already available,
nothing will be done.

provideMNIST: Provides MNIST data set in the given folder.

Description

Usage

Arguments

Value

Examples