# FCNN4R v0.6.2

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## Fast Compressed Neural Networks for R

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.

## Functions in FCNN4R

Name | Description | |

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 | |

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## Last month downloads

## Details

Type | Package |

Date | 2016-03-08 |

License | GPL (>= 2) |

NeedsCompilation | yes |

Packaged | 2016-03-08 19:52:41 UTC; chaos |

Repository | CRAN |

Date/Publication | 2016-03-09 00:57:57 |

depends | graphics , methods , R (>= 3.0) , Rcpp , stats |

Contributors | Grzegorz Klima |

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