binSigmoidUnit

The data matrix for the calculation

data

The weight and bias matrix for the calculation

weights

The function calculates the activation and the output from the sigmoid
transfer function. It returns a binary matrix where a entry is 1 if the value
is bigger than a random number generated with <code><a rd-options="" href="/link/runif?package=darch&version=0.10.0" data-mini-rdoc="darch::runif">runif</a></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

binSigmoidUnit function

The function calculates the activation and the output from the sigmoid
transfer function. It returns a binary matrix where a entry is 1 if the value
is bigger than a random number generated with <code><a rd-options='' href='runif'>runif</a></code>.

binSigmoidUnit: Binary sigmoid unit function.

Description

Usage

Arguments

Value

See Also