runDArch

A instance of the class <code><a href="/link/?package=darch&version=0.12.0" darch'="" data-mini-rdoc="darch::"></a>'>DArch</code>.

darch

The input data to execute the darch on.

data

Into which layer the given data is to be fed. Absolute
number starting at 1 for the input layer.

inputLayer

The output of which layer is to be returned, absolute
number starting a 0 for the input layer (i.e. pre-processed data is
returned).

outputLayer

Function to use for matrix multiplication.

matMult

Runs the <code>DArch</code> in a feed-forward manner and returns the output.

internal

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

Package for Deep Architectures and Restricted Boltzmann Machines

runDArch function

A instance of the class <code><a href='<a href='DArch'></a>'>DArch</a></code>.

runDArch: Forward-propagates data through the network

Description

Usage

Arguments

Value

Details

See Also