mlp_get_weights

mlp_net-weights-access

mlp_set_weights

numeric vector of new active weights' values

weights


One of FCNN's design objectives (and main advantages) is the complete separation
of teaching (and pruning) algorithms from internal network structure workings.
This goal is achieved through fast access to (active) weights vector facilitated
by FCNN's `compressed' network representation. The following two functions
allow users to efficiently retrieve and set network (active) weights vector.


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.

mlp_net-weights-access: Set and retrieve (active) weights' values

Description

Usage

Arguments

Value