TrainEspecieNN.m

function [redeNeuronal data timers precision input target imagens] = TrainEspecieNN(data_file, path_to_files, scale)
addpath(genpath('./Functions'));

%override for test's
path_to_files = './Tema 1 - RN\Folhas_1';
scale = 175;
data_file = '.\Tema 1 - RN\ClassificaçãoFolhas.xlsx';
    
%Data Extration -----------------------------------------------------------
    %- Images
tstart = tic;%timer
imagens = LoadImages(path_to_files, scale);
imagens = UpdateImages(imagens,data_file, scale);
    %- Input and Output Generation
input = inputfromImageExtration(imagens, scale);
target = targetCodigoSubEspecie(imagens);
data_extration = toc(tstart); %timer

%Run Especie NN -----------------------------------------------------------
nstart = tic;

NNparam.topologia = 'cascadeforwardnet';
NNparam.fAtivacao = 'tansig';
NNparam.fTreino = 'trainrp';
NNparam.neuronios = 10;
NNparam.trainRatio = 0.7;
NNparam.valRatio = 0.15;
NNparam.testRatio = 0.15;
NNparam.max_fail = 25;

[redeNeuronal data pTotal pTeste] = NeuralNetwork(NNparam, input, target);
cnn_extration = toc(nstart);
%view(redeNeuronal);

timers = {data_extration;cnn_extration};
precision = {pTotal; pTeste};

% % %---------------------------------------------------

end