how to design custom ANN using Deep Network designer app MATLAB
2 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
Med Future
am 28 Feb. 2022
Kommentiert: yanqi liu
am 1 Mär. 2022
Hello Everyone, I Hope you are doing well.
I want to create a simple ANN using Deep network Designer app or using code.
ANN contain input layer, 10 neurons in hidden layer with sigmoid activation and output layer with classifciaton and softmax layer
I have the dataset of shape 250x1000. I have attached the dataset below. which contain label as well.
I also want to be label in catogorical form . Like 1 name as 'Class1'
How can i do it in MATLAB
Akzeptierte Antwort
yanqi liu
am 1 Mär. 2022
clc; clear all; close all;
load Datasetn
layers = [
imageInputLayer([1000 1 1])
fullyConnectedLayer(10)
reluLayer
fullyConnectedLayer(5)
softmaxLayer
classificationLayer];
opts = trainingOptions('adam', ...
'MaxEpochs',200, ...
'Shuffle','every-epoch', ...
'Plots','training-progress', ...
'Verbose',false, ...
'ExecutionEnvironment', 'cpu', ...
'ValidationPatience',Inf);
xc = reshape(dataset', [1000,1,1,250]);
% 'Class1'
yc = [];
for i = 1 : length(label)
yc{i,1} = ['Class' num2str(double(label(i)))];
end
yc = categorical(yc);
disp(yc)
net = trainNetwork(xc,yc,layers,opts);
2 Kommentare
yanqi liu
am 1 Mär. 2022
yes,sir
clc; clear all; close all;
load Datasetn
idx = randperm(length(label)) ;
dataset = dataset(idx,:);
label = label(idx,:);
m = round(length(label)*0.8) ;
dataset1 = dataset(1:m,:); label1 = label(1:m,:);
dataset2 = dataset(1+m:end,:); label2 = label(1+m:end,:);
layers = [
imageInputLayer([1000 1 1])
fullyConnectedLayer(10)
reluLayer
fullyConnectedLayer(5)
softmaxLayer
classificationLayer];
opts = trainingOptions('adam', ...
'MaxEpochs',200, ...
'Shuffle','every-epoch', ...
'Plots','training-progress', ...
'Verbose',false, ...
'ExecutionEnvironment', 'cpu', ...
'ValidationPatience',Inf);
xc = reshape(dataset1', [1000,1,1,m]);
% 'Class1'
yc = [];
for i = 1 : length(label1)
yc{i,1} = ['Class' num2str(double(label1(i)))];
end
yc = categorical(yc);
disp(yc)
net = trainNetwork(xc,yc,layers,opts);
Weitere Antworten (0)
Siehe auch
Kategorien
Mehr zu Pattern Recognition and Classification finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!