How to format the inputs and targets for a feedforward neural network?

조회 수: 21(최근 30일)
I have to create a feedforward neural netweok in order to classify some signal data in matlab. Below is the dataset I have been given. I'm not sure what parameters from this dataset I should use as my inputs and also how to create the targets from this as well. I am using MatLab 2020a and the deep learning toolbox.
rng(2);
sample_rate = 6700; % Samples measured per second
T = 1; % Time in seconds
num_classes = 3;
tt = 0:1/sample_rate:T-1/sample_rate;
num_samples = length(tt);
m = 1;
k = 400:50:90000;
num_freq = length(k);
A = randperm(num_freq) / num_freq;
phi = 0;
% Generate U1ndamped (c = 0)
U1 = zeros([num_samples, 1, 1, num_freq]);
c = 0;
gamma = c / (2 * m);
omega0 = sqrt(k / m);
omega1 = sqrt(omega0.^2 - gamma^2);
for i = 1:num_freq
U1(:,:,:,i) = A(i) * exp(-1 * gamma * tt) .* cos(omega1(i) * tt - phi) + 0.01*randn(1,num_samples);
end
figure;
plot(tt,U1(:,:,:,1));
hold on;
plot(tt,U1(:,:,:,end));
% Generate D1amped
D1 = zeros([num_samples, 1, 1, num_freq]);
for i = 1:num_freq
maxx_c = 2*sqrt(k(i));
c = 2*sqrt(k(i))- maxx_c + 10;
gamma = c / (2 * m);
omega0 = sqrt(k(i) / m);
omega1 = sqrt(omega0.^2 - gamma^2);
D1(:,:,:,i) = A(i) * exp(-1 * gamma * tt) .* cos(omega1 * tt - phi) + 0.01*randn(1,num_samples);
end
figure;
plot(tt,D1(:,:,:,1));
hold on;
plot(tt,D1(:,:,:,end));
% Generate C1ritically D1amped
C1 = zeros([num_samples, 1, 1, num_freq]);
for i = 1:num_freq
maxx_c = 2*sqrt(k(i));
c = maxx_c;
gamma = c / (2 * m);
omega0 = sqrt(k(i) / m);
omega1 = sqrt(omega0.^2 - gamma^2);
C1(:,:,:,i) = A(i) * exp(-1 * gamma * tt) .* cos(omega1 * tt - phi) + 0.01*randn(1,num_samples);
end
figure;
plot(tt,C1(:,:,:,1));
hold on;
plot(tt,C1(:,:,:,end));
% Generate U1ndamped (c = 0)
U2 = zeros([num_samples, 1, 1, num_freq]);
c = 0;
gamma = c / (2 * m);
omega0 = sqrt(k / m);
omega1 = sqrt(omega0.^2 - gamma^2);
for i = 1:num_freq
U2(:,:,:,i) = A(i) * exp(-1 * gamma * tt) .* cos(omega1(i) * tt - phi) + 0.05*randn(1,num_samples);
end
figure;
plot(tt,U2(:,:,:,1));
hold on;
plot(tt,U2(:,:,:,end));
% Generate D1amped
D2 = zeros([num_samples, 1, 1, num_freq]);
for i = 1:num_freq
maxx_c = 2*sqrt(k(i));
c = 2*sqrt(k(i))- maxx_c + 10;
gamma = c / (2 * m);
omega0 = sqrt(k(i) / m);
omega1 = sqrt(omega0.^2 - gamma^2);
D2(:,:,:,i) = A(i) * exp(-1 * gamma * tt) .* cos(omega1 * tt - phi) + 0.05*randn(1,num_samples);
end
figure;
plot(tt,D2(:,:,:,1));
hold on;
plot(tt,D2(:,:,:,end));
% Generate C1ritically D1amped
C2 = zeros([num_samples, 1, 1, num_freq]);
for i = 1:num_freq
maxx_c = 2*sqrt(k(i));
c = maxx_c;
gamma = c / (2 * m);
omega0 = sqrt(k(i) / m);
omega1 = sqrt(omega0.^2 - gamma^2);
C2(:,:,:,i) = A(i) * exp(-1 * gamma * tt) .* cos(omega1 * tt - phi) + 0.05*randn(1,num_samples);
end
figure;
plot(tt,C2(:,:,:,1));
hold on;
plot(tt,C2(:,:,:,end));
% Generate U1ndamped (c = 0)
U3 = zeros([num_samples, 1, 1, num_freq]);
c = 0;
gamma = c / (2 * m);
omega0 = sqrt(k / m);
omega1 = sqrt(omega0.^2 - gamma^2);
for i = 1:num_freq
U3(:,:,:,i) = A(i) * exp(-1 * gamma * tt) .* cos(omega1(i) * tt - phi) + 0.1*randn(1,num_samples);
end
figure;
plot(tt,U3(:,:,:,1));
hold on;
plot(tt,U3(:,:,:,end));
% Generate D1amped
D3 = zeros([num_samples, 1, 1, num_freq]);
for i = 1:num_freq
maxx_c = 2*sqrt(k(i));
c = 2*sqrt(k(i))- maxx_c + 10;
gamma = c / (2 * m);
omega0 = sqrt(k(i) / m);
omega1 = sqrt(omega0.^2 - gamma^2);
D3(:,:,:,i) = A(i) * exp(-1 * gamma * tt) .* cos(omega1 * tt - phi) + 0.1*randn(1,num_samples);
end
figure;
plot(tt,D3(:,:,:,1));
hold on;
plot(tt,D3(:,:,:,end));
% Generate C1ritically D1amped
C3 = zeros([num_samples, 1, 1, num_freq]);
for i = 1:num_freq
maxx_c = 2*sqrt(k(i));
c = maxx_c;
gamma = c / (2 * m);
omega0 = sqrt(k(i) / m);
omega1 = sqrt(omega0.^2 - gamma^2);
C3(:,:,:,i) = A(i) * exp(-1 * gamma * tt) .* cos(omega1 * tt - phi) + 0.1*randn(1,num_samples);
end
figure;
plot(tt,C3(:,:,:,1));
hold on;
plot(tt,C3(:,:,:,end));
% C1ombine D1ata
x_data = cat(4,U1,U2,U3,D1,D2,D3,C1,C2,C3);
y_data = [zeros(1, 3*length(k)) ones(1,3*length(k)) 2*ones(1,3*length(k))];
y_data = categorical(y_data);
p = randperm(num_classes*3*num_freq);
x = x_data(:,:,:,p);
y = y_data(p);
figure;
hist(y);
N = length(y);
train_split = round(0.8 * N, 0);
test_split = round(0.1 * N, 0);
x_train = x(:,:,:,1:train_split);
x_val = x(:,:,:,train_split:train_split+test_split);
x_test = x(:,:,:,train_split+test_split:end);
y_train = y(1:train_split);
y_val = y(train_split:train_split+test_split);
y_test = y(train_split+test_split:end);
figure;
subplot(1,3,1)
hist(y_train);
xlabel('Train');
subplot(1,3,2)
hist(y_val);
xlabel('Val');
subplot(1,3,3)
hist(y_test);
xlabel('Test');
save('ff_cnn_data.mat', 'x_train', 'y_train', 'x_val', 'y_val', 'x_test', 'y_test');

답변(1개)

Swetha Polemoni
Swetha Polemoni 2021년 3월 29일
Hi
It is my understanding that you are doing classification. As it is clearly mentioned in the code x_train and y_tran are for traning i.e., x_train is input to the network and y_train values are target values. You may find the following link helpful for classification in Matlab.

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