Can’t train a custom neural network
Hello,
I am trying to create a neural network of the type shown in the picture. This is a pre-structured network that mimics a certain equation. However, I am having difficulties with creating the network structure using the network function for custom networks.
Here is the code I currently have:
clear; clc; rng(0);
%% 1) Simulink’ten verileri al
simOut = sim(‘MSD_Model’); % Simulink modeli
u_data = simOut.get(‘u’); % To Workspace: u (vektör)
y_data = simOut.get(‘y’); % To Workspace: y (vektör)
u_data = u_data(:)’; % 1xN
y_data = y_data(:)’; % 1xN
% Hücre dizileri (1xN, her hücre skalar)
U = con2seq(u_data);
Y = con2seq(y_data);
%% 2) SSNN-benzeri özel RNN mimarisi
n = 1; % input
s = 2; % state (Layer 2)
h = 4; % hidden S (Layer 1)
h2 = 2; % hidden O (Layer 3)
m = 1; % output
net = network;
net.numInputs = 1;
net.numLayers = 4;
% Bağlantı topolojisi
% L1 <- Input
% L1 <- L2(z^-1) (x̂(t-1) geri besleme)
% L2 <- L1
% L3 <- L2
% L4 <- L3 (çıktı)
net.inputConnect = [1;0;0;0];
net.layerConnect = logical([ …
0 1 0 0; % L1 <- L2 (delayed)
1 0 0 0; % L2 <- L1
0 1 0 0; % L3 <- L2
0 0 1 0]);% L4 <- L3
net.outputConnect = [false false false true];
% Katman boyutları
net.inputs{1}.size = n;
net.layers{1}.size = h; % Hidden S
net.layers{2}.size = s; % State layer (x̂)
net.layers{3}.size = h2; % Hidden O
net.layers{4}.size = m; % Output
% Transfer fonksiyonları (SSNN uyumlu)
net.layers{1}.transferFcn = ‘tansig’;
net.layers{2}.transferFcn = ‘purelin’;
net.layers{3}.transferFcn = ‘tansig’;
net.layers{4}.transferFcn = ‘purelin’;
% Gecikmeler
net.layerWeights{1,2}.delays = 1; % x̂(t-1) -> Hidden S
net.layerWeights{2,1}.delays = 0;
net.layerWeights{3,2}.delays = 1;
net.layerWeights{4,3}.delays = 0;
% Biaslar
net.biasConnect = [1;1;1;1];
% Performans ve eğitim
net.performFcn = ‘mse’;
net.trainFcn = ‘trainlm’;
net.trainParam.epochs = 100;
net.trainParam.goal = 1e-6;
net.trainParam.max_fail = 12;
net.trainParam.showWindow = true;
% Zaman serisi bölme (blok halinde)
net.divideFcn = ‘dividetrain’; % tüm veri train, erken durdurma yok
% Ön-konfigürasyon & init
net = configure(net, U, Y);
net = init(net);
%% 3) Eğitim
[net, tr] = train(net, U, Y);
%% 4) Tahmin ve performans
Y_pred = net(U);
mse_train = perform(net, Y(tr.trainInd), Y_pred(tr.trainInd));
fprintf(‘MSE (train): %.3en’, mse_train);
% Diyagnostik grafikler
figure; plotperform(tr);
figure; plotregression(cell2mat(Y), cell2mat(Y_pred), ‘Regression’);
% Zaman serisi karşılaştırma
figure; hold on; grid on;
plot(cell2mat(Y)’, ‘LineWidth’,1.5);
plot(cell2mat(Y_pred)’, ‘–‘, ‘LineWidth’,1.5);
xlabel(‘Zaman (örnek)’); ylabel(‘Çıkış’);
legend(‘Gerçek Y(t)’,’Tahmin Ŷ(t)’); title(‘SSNN-RNN Zaman Serisi Modelleme’);
%% 5) Kaydet & Simulink bloğu üret
save(‘trained_SSNN.mat’,’net’);
Ts = 0.01; % model örnekleme zamanınla eşleştir
gensim(net, Ts); % Simulink bloğu üretir (mask altından I/O=1×1)
The problem is that I cannot start the training properly — it always stops at the second epoch. I also tried using a Layered Recurrent Network (layrecnet), but the problem remains the same.
Could you please help me understand what I am doing wrong?
Also, are there any good resources or documentation on how to create and train custom neural networks in MATLAB (beyond the standard predefined types)?
Thank you in advance!Hello,
I am trying to create a neural network of the type shown in the picture. This is a pre-structured network that mimics a certain equation. However, I am having difficulties with creating the network structure using the network function for custom networks.
Here is the code I currently have:
clear; clc; rng(0);
%% 1) Simulink’ten verileri al
simOut = sim(‘MSD_Model’); % Simulink modeli
u_data = simOut.get(‘u’); % To Workspace: u (vektör)
y_data = simOut.get(‘y’); % To Workspace: y (vektör)
u_data = u_data(:)’; % 1xN
y_data = y_data(:)’; % 1xN
% Hücre dizileri (1xN, her hücre skalar)
U = con2seq(u_data);
Y = con2seq(y_data);
%% 2) SSNN-benzeri özel RNN mimarisi
n = 1; % input
s = 2; % state (Layer 2)
h = 4; % hidden S (Layer 1)
h2 = 2; % hidden O (Layer 3)
m = 1; % output
net = network;
net.numInputs = 1;
net.numLayers = 4;
% Bağlantı topolojisi
% L1 <- Input
% L1 <- L2(z^-1) (x̂(t-1) geri besleme)
% L2 <- L1
% L3 <- L2
% L4 <- L3 (çıktı)
net.inputConnect = [1;0;0;0];
net.layerConnect = logical([ …
0 1 0 0; % L1 <- L2 (delayed)
1 0 0 0; % L2 <- L1
0 1 0 0; % L3 <- L2
0 0 1 0]);% L4 <- L3
net.outputConnect = [false false false true];
% Katman boyutları
net.inputs{1}.size = n;
net.layers{1}.size = h; % Hidden S
net.layers{2}.size = s; % State layer (x̂)
net.layers{3}.size = h2; % Hidden O
net.layers{4}.size = m; % Output
% Transfer fonksiyonları (SSNN uyumlu)
net.layers{1}.transferFcn = ‘tansig’;
net.layers{2}.transferFcn = ‘purelin’;
net.layers{3}.transferFcn = ‘tansig’;
net.layers{4}.transferFcn = ‘purelin’;
% Gecikmeler
net.layerWeights{1,2}.delays = 1; % x̂(t-1) -> Hidden S
net.layerWeights{2,1}.delays = 0;
net.layerWeights{3,2}.delays = 1;
net.layerWeights{4,3}.delays = 0;
% Biaslar
net.biasConnect = [1;1;1;1];
% Performans ve eğitim
net.performFcn = ‘mse’;
net.trainFcn = ‘trainlm’;
net.trainParam.epochs = 100;
net.trainParam.goal = 1e-6;
net.trainParam.max_fail = 12;
net.trainParam.showWindow = true;
% Zaman serisi bölme (blok halinde)
net.divideFcn = ‘dividetrain’; % tüm veri train, erken durdurma yok
% Ön-konfigürasyon & init
net = configure(net, U, Y);
net = init(net);
%% 3) Eğitim
[net, tr] = train(net, U, Y);
%% 4) Tahmin ve performans
Y_pred = net(U);
mse_train = perform(net, Y(tr.trainInd), Y_pred(tr.trainInd));
fprintf(‘MSE (train): %.3en’, mse_train);
% Diyagnostik grafikler
figure; plotperform(tr);
figure; plotregression(cell2mat(Y), cell2mat(Y_pred), ‘Regression’);
% Zaman serisi karşılaştırma
figure; hold on; grid on;
plot(cell2mat(Y)’, ‘LineWidth’,1.5);
plot(cell2mat(Y_pred)’, ‘–‘, ‘LineWidth’,1.5);
xlabel(‘Zaman (örnek)’); ylabel(‘Çıkış’);
legend(‘Gerçek Y(t)’,’Tahmin Ŷ(t)’); title(‘SSNN-RNN Zaman Serisi Modelleme’);
%% 5) Kaydet & Simulink bloğu üret
save(‘trained_SSNN.mat’,’net’);
Ts = 0.01; % model örnekleme zamanınla eşleştir
gensim(net, Ts); % Simulink bloğu üretir (mask altından I/O=1×1)
The problem is that I cannot start the training properly — it always stops at the second epoch. I also tried using a Layered Recurrent Network (layrecnet), but the problem remains the same.
Could you please help me understand what I am doing wrong?
Also, are there any good resources or documentation on how to create and train custom neural networks in MATLAB (beyond the standard predefined types)?
Thank you in advance! Hello,
I am trying to create a neural network of the type shown in the picture. This is a pre-structured network that mimics a certain equation. However, I am having difficulties with creating the network structure using the network function for custom networks.
Here is the code I currently have:
clear; clc; rng(0);
%% 1) Simulink’ten verileri al
simOut = sim(‘MSD_Model’); % Simulink modeli
u_data = simOut.get(‘u’); % To Workspace: u (vektör)
y_data = simOut.get(‘y’); % To Workspace: y (vektör)
u_data = u_data(:)’; % 1xN
y_data = y_data(:)’; % 1xN
% Hücre dizileri (1xN, her hücre skalar)
U = con2seq(u_data);
Y = con2seq(y_data);
%% 2) SSNN-benzeri özel RNN mimarisi
n = 1; % input
s = 2; % state (Layer 2)
h = 4; % hidden S (Layer 1)
h2 = 2; % hidden O (Layer 3)
m = 1; % output
net = network;
net.numInputs = 1;
net.numLayers = 4;
% Bağlantı topolojisi
% L1 <- Input
% L1 <- L2(z^-1) (x̂(t-1) geri besleme)
% L2 <- L1
% L3 <- L2
% L4 <- L3 (çıktı)
net.inputConnect = [1;0;0;0];
net.layerConnect = logical([ …
0 1 0 0; % L1 <- L2 (delayed)
1 0 0 0; % L2 <- L1
0 1 0 0; % L3 <- L2
0 0 1 0]);% L4 <- L3
net.outputConnect = [false false false true];
% Katman boyutları
net.inputs{1}.size = n;
net.layers{1}.size = h; % Hidden S
net.layers{2}.size = s; % State layer (x̂)
net.layers{3}.size = h2; % Hidden O
net.layers{4}.size = m; % Output
% Transfer fonksiyonları (SSNN uyumlu)
net.layers{1}.transferFcn = ‘tansig’;
net.layers{2}.transferFcn = ‘purelin’;
net.layers{3}.transferFcn = ‘tansig’;
net.layers{4}.transferFcn = ‘purelin’;
% Gecikmeler
net.layerWeights{1,2}.delays = 1; % x̂(t-1) -> Hidden S
net.layerWeights{2,1}.delays = 0;
net.layerWeights{3,2}.delays = 1;
net.layerWeights{4,3}.delays = 0;
% Biaslar
net.biasConnect = [1;1;1;1];
% Performans ve eğitim
net.performFcn = ‘mse’;
net.trainFcn = ‘trainlm’;
net.trainParam.epochs = 100;
net.trainParam.goal = 1e-6;
net.trainParam.max_fail = 12;
net.trainParam.showWindow = true;
% Zaman serisi bölme (blok halinde)
net.divideFcn = ‘dividetrain’; % tüm veri train, erken durdurma yok
% Ön-konfigürasyon & init
net = configure(net, U, Y);
net = init(net);
%% 3) Eğitim
[net, tr] = train(net, U, Y);
%% 4) Tahmin ve performans
Y_pred = net(U);
mse_train = perform(net, Y(tr.trainInd), Y_pred(tr.trainInd));
fprintf(‘MSE (train): %.3en’, mse_train);
% Diyagnostik grafikler
figure; plotperform(tr);
figure; plotregression(cell2mat(Y), cell2mat(Y_pred), ‘Regression’);
% Zaman serisi karşılaştırma
figure; hold on; grid on;
plot(cell2mat(Y)’, ‘LineWidth’,1.5);
plot(cell2mat(Y_pred)’, ‘–‘, ‘LineWidth’,1.5);
xlabel(‘Zaman (örnek)’); ylabel(‘Çıkış’);
legend(‘Gerçek Y(t)’,’Tahmin Ŷ(t)’); title(‘SSNN-RNN Zaman Serisi Modelleme’);
%% 5) Kaydet & Simulink bloğu üret
save(‘trained_SSNN.mat’,’net’);
Ts = 0.01; % model örnekleme zamanınla eşleştir
gensim(net, Ts); % Simulink bloğu üretir (mask altından I/O=1×1)
The problem is that I cannot start the training properly — it always stops at the second epoch. I also tried using a Layered Recurrent Network (layrecnet), but the problem remains the same.
Could you please help me understand what I am doing wrong?
Also, are there any good resources or documentation on how to create and train custom neural networks in MATLAB (beyond the standard predefined types)?
Thank you in advance! deep learning, custom neural network, neural networks, modelling MATLAB Answers — New Questions
