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Rprop

Version 1.3.0.0

by Roberto Calandra

Rprop training for Artificial Neural Networks

class2lab(nn,class) - CLASS2LAB Convert RNN classes back to labels

computelayer(inputs,Weights,typelayer) - COMPUTELAYER Compute the output of a NN layer

opt_rprop(nn,train_in,train_out,test_in,test_out) - OPT_RPROP Optimize a NN using Rprop

class2matrix(in,n_classes) - CLASS2MATRIX Convert classes to a binary matrix

Demo_Rprop_2(batch) - DEMO_RPROP_2 All 4 Rprop on MNIST-small dataset

class_stat(inputs,targets,n_class) - CLASS_STAT Compute various classification statistics

lab2class(labels,nn) - LAB2CLASS Convert labels to RNN classes

init_nn(size_layer,nn) - INIT_NN Initialize a NN

shuffledata(matrix,index) - SHUFFLEDATA Shuffle the rows of a matrix

computenetwork(nn,inputs,fullout) - COMPUTENETWORK Compute the output of a NN

indent(verbose) - INDENT Indent text

Demo_Rprop_1(batch) - DEMO_RPROP_1 IRprop- on MNIST-small dataset

MSE(inputs,targets) - MSE Compute the Mean Square Error (MSE)

Community

1.3K Downloads

Updated 23 Aug 2011

cell2num

Version 1.1.0.0

by Roberto Calandra

Convert N-dimensional cells of K-dimensional matrices to N*K matrices.

Demo_cell2num.m - DEMO_CELL2NUM Demonstrate some features of the NUM2CELL function

cell2num(input,parameters) - CELL2NUM Convert a cell into a matrix

linearize(X) - LINEARIZE Linearize a matrix to a vector

Community

519 Downloads

Updated 5 Jun 2012