linearUnitFunc

A instance of the class <code><a rd-options="" href="/link/RBM?package=darch&version=0.10.0" data-mini-rdoc="darch::RBM">RBM</a></code>.

A list with the data matrices for the calculations.

dataList

biases

The weight matrix for the calculations

weights

Parameters which indicates the status of the training.

runParams

Calculates the linear neuron output with no transfer function from real value
input saved in the first entry of the list <code>dataList</code>.

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; doi: 10.1162/neco.2006.18.7.1527) and "Reducing the
dimensionality of data with neural networks" (G. E. Hinton, R. R.
Salakhutdinov; 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

linearUnitFunc function

A instance of the class <code><a rd-options='' href='RBM'>RBM</a></code>.

linearUnitFunc: Calculates the linear neuron output no transfer function

Description

Usage

Arguments

Value

Details

See Also