How to classify data in a hierarchical neural network (training, validation, testing)

I don't know how to classify (train, validate, test) data in a hierarchical neural network.

I can classify the data with a double array, but I can't classify it well with a cell array.

net = network; % ネットワーク初期化

net.numInputs = 2; % 入力層の数を指定 (ユニットではなくグループ数)

net.numLayers = 3; % 隠れ層(2)と出力層(1)の数

net.layers{1}.size = 10; % 隠れ層1のユニット数(10)

net.layers{2}.size = 5; % 隠れ層2のユニット数(5)

net.layers{3}.size = 3; % 出力層のユニット数

net.biasConnect = [1;1;1];

net.inputConnect = [1 0;0 1;0 0]; % 入力層から直接接続される隠れ層/出力層設定

net.LayerConnect = [0 0 0;1 0 0;0 1 0]; % 隠れ層同士の接続状況

net.outputConnect = [0 0 1]; % 出力への接続状況

net.trainFcn = 'trainlm'; % 学習関数

% 伝達関数指定

net.layers{1}.transferFcn = 'logsig';

net.layers{2}.transferFcn = 'logsig';

net.layers{3}.transferFcn = 'purelin';

net.plotFcns = {'plotperform','plottrainstate'};

net.initFcn = 'initlay';

net.performFcn = 'mse';

net.divideFcn = 'dividerand';

% 入出力データ設定

X = rand(105,1000);

T = rand(3,1000);

X = con2seq(mat2cell(X,[100 5]))

T = con2seq(T);

% net.initFcn = 'initlay';

% net.performFcn = 'mse';

% net.divideFcn = 'dividerand';

% 学習

% net = feedforwardnet(5);

[net,tr] = train(net,X,T);

outputs = net(X);

errors = gsubtract(T, outputs);

performance = perform(net, T, outputs)

tInd = tr.testInd;

tstOutputs = net(X(:, tInd));

tstPerform = perform(net, T(tInd), tstOutputs)

view(net)

figure, plotperform(tr)

figure, plottrainstate(tr)

figure, plotfit(net,X,T)

figure, plotregression(T,outputs)

figure, ploterrhist(errors)

Answer 1

[Naoya]

Please change the net.DivideMode value from "sample" to "time".

You can divide the data into three items: training, validation, and testing, for 1000 samples.

net.plotFcns = {'plotperform','plottrainstate'};
net.initFcn = 'initlay';
net.performFcn = 'mse';
net.divideMode = 'time'; % add