generateWeightsGlorotNormal: Glorot normal weight initialization

In darch: Package for Deep Architectures and Restricted Boltzmann Machines

Description

This function is used to generate random weights and biases using Glorot normal weight initialization as described in Glorot & Bengio, AISTATS 2010.

Usage

generateWeightsGlorotNormal(numUnits1, numUnits2,
  weights.mean = getParameter(".weights.mean", 0, ...), ...)

Arguments

numUnits1	Number of units in the lower layer.
numUnits2	Number of units in the upper layer.
weights.mean	mean parameter to the rnorm function.
...	Additional parameters, used for parameter resolution and passed to generateWeightsNormal.

Value

Weight matrix.

References

Glorot, Xavier and Yoshua Bengio (2010). "Understanding the difficulty of training deep feedforward neural networks". In: International conference on artificial intelligence and statistics, pp. 249-256

See Also

Other weight generation functions: generateWeightsGlorotUniform, generateWeightsHeNormal, generateWeightsHeUniform, generateWeightsNormal, generateWeightsUniform

Examples

## Not run: 
data(iris)
model <- darch(Species ~ ., iris, generateWeightsFunction = "generateWeightsGlorotNormal",
 weights.mean = .1)

## End(Not run)

darch documentation built on May 29, 2017, 8:14 p.m.