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FCNN4R (version 0.6.2)

Fast Compressed Neural Networks for R

Description

Provides an interface to kernel routines from the FCNN C++ library. FCNN is based on a completely new Artificial Neural Network representation that offers unmatched efficiency, modularity, and extensibility. FCNN4R provides standard teaching (backpropagation, Rprop, simulated annealing, stochastic gradient) and pruning algorithms (minimum magnitude, Optimal Brain Surgeon), but it is first and foremost an efficient computational engine. Users can easily implement their algorithms by taking advantage of fast gradient computing routines, as well as network reconstruction functionality (removing weights and redundant neurons, reordering inputs, merging networks). Networks can be exported to C functions in order to integrate them into virtually any software solution.

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Version

Install

install.packages('FCNN4R')

Monthly Downloads

60

Version

0.6.2

License

GPL (>= 2)

Maintainer

Grzegorz Klima

Last Published

March 8th, 2016

Functions in FCNN4R (0.6.2)

FCNN4R-package

Fast Compressed Neural Networks for R
mlp_net-absolute-weight-indices

Retrieving absolute weight index
mlp_actvfunc2str

Return character string representing activation function
is.mlp_net

Is it an object of mlp_net class?
mlp_net-accessing-individual-weights

Setting and retrieving status (on/off) and value of individual weight(s)
mlp_check_w

Check validity of weight index
mlp_net-class

An S4 class representing Multilayer Perception Network.
mlp_eval

Evaluation
mlp_check_inout

Check validity of inputs and outputs
mlp_export_C

Export multilayer perceptron network to a C function
mlp_net-weights-access

Set and retrieve (active) weights' values
mlp_net-manipulating-network-inputs

Manipulating network inputs
mlp_plot

Plotting multilayer perceptron network
mlp_net-export-import

Export and import multilayer perceptron network to/from a text file in FCNN format
mlp_net-general-information

General information about network
mlp_net

Create objects of mlp_net class
mlp_net-MSE-gradients

Computing mean squared error, its gradient, and output derivatives
mlp_net-combining-two-networks

Combining two networks into one
mlp_net-names

Get and set network names
mlp_net-display

Displaying networks (objects of mlp_net class)
mlp_teach_rprop

Rprop teaching
mlp_teach_bp

Backpropagation (batch) teaching
mlp_teach_grprop

Rprop teaching - minimising arbitrary objective function
mlp_prune_mag

Minimum magnitude pruning
mlp_prune_obs

Optimal Brain Surgeon pruning
mlp_teach_sa

Teaching networks using Simulated Annealing
mlp_set_activation

Set network activation functions
mlp_teach_sgd

Stochastic gradient descent with (optional) RMS weights scaling, weight decay, and momentum
mlp_rm_neurons

Remove redundant neurons in a multilayer perceptron network
mlp_rnd_weights

This function sets network weights to random values drawn from uniform distribution.
read-write-fcnndataset

Reading and writing datasets in the FCNN format