mlp_get_w_abs_idx

mlp_get_w_idx

mlp_net-absolute-weight-indices

layer

nidx

integer value (vector), index of the neuron in the previous
layer determining connection from neuron <code>nidx</code>
in <code>layer</code>, 0 denotes bias of neuron <code>nidx</code> in <code>layer</code>

nplidx

integer value (vector), weight index (indices) within active ones


In some situations absolute weight index (i.e. index within all weights
including inactive ones) needs to be computed based on information
about connected neurons' indices or weight index within actives ones.
The latter functionality is especially useful in implementation of pruning
algorithms.


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-absolute-weight-indices: Retrieving absolute weight index

Description

Usage

Arguments

Value