RestrictedBolzmannMachine: Initialize a Restricted Bolzman Machine

Description Usage Arguments Value Copying/Cloning See Also Examples

View source: R/rbm.R

Description

Creates a Restricted Bolzman Machine (RBM), precisely a RestrictedBolzmannMachine object, with the given specifications. It is typically stacked in a DeepBeliefNet.

Usage

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RestrictedBolzmannMachine(input, output, weights = NULL, initialize = c("0",
  "uniform"))

Arguments

input, output

Layer objects

weights

optional starting weights. If NULL, weights will be initialized to 0

initialize

whether to initialize weights and biases with 0 or random uniform values. Ignored if weights are provided.

Value

an object of class RestrictedBolzmannMachine containing the following elements:

Copying/Cloning

For performance purposes, the weights are stored in an environment. This means that when you copy the RestrictedBolzmannMachine with an assignment, you do not copy the weights and any modification you make to the new object will be propagated to the original one, and reciprocally. Use clone to control this and make a copy of the weights whenever you need it. Note that all the functions defined in the package do this by default.

See Also

DeepBeliefNet, which makes use of RestrictedBolzmannMachine objects, Layer.

Examples

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rbm <- RestrictedBolzmannMachine(Layer(784, "continuous"), Layer(1000, "binary"))
print(rbm)
methods(class="RestrictedBolzmannMachine")

xrobin/DeepLearning documentation built on May 17, 2018, 3:51 a.m.