neuronales Netzwerk mit Sigmoid

Question

Ich versuche, ein neuronales Netzwerk mit Sigmoid-Funktion zu implementieren Aber der folgende Code funktioniert nicht Dies ist der Trainingsteil des neuronalen Netzes. Die Gewichte werden nicht korrekt aktualisiert. Was ist in diesem Code falsch?

clc; clear all; close all; 
% load train_data1 
train_data1=[-0.498800000000000,-0.257500000000000;-0.492800000000000,-0.274300000000000;-0.470300000000000,-0.282600000000000;-0.427400000000000,-0.474000000000000;-0.420400000000000,-0.518000000000000;-0.326300000000000,-1.13230000000000;-0.317300000000000,-0.875300000000000;-0.295000000000000,-1.02770000000000;-0.267600000000000,-0.882800000000000;-0.260500000000000,-0.976500000000000;-0.216100000000000,-0.970400000000000;-0.207000000000000,-0.813800000000000;-0.164000000000000,-0.696600000000000;-0.159900000000000,-0.793300000000000;-0.122000000000000,-0.764400000000000;-0.0729000000000000,-0.435300000000000;-0.00640000000000000,-0.0546000000000000;0.132200000000000,0.710300000000000;0.137100000000000,0.587000000000000;0.160300000000000,0.819200000000000;0.230600000000000,0.989200000000000;0.286800000000000,0.737700000000000;0.334000000000000,0.943500000000000;0.375200000000000,0.688200000000000;0.429700000000000,0.567800000000000]; 
train_data1 = sortrows(train_data1); 
% normalize data to [0,1] 
data1=[train_data1]; 
max1=max(max(data1)); 
min1=min(min(data1)); 
train_data2 = (train_data1 - min1)/(max1 - min1); 

x = train_data2(:,1); % train input data 
r = train_data2(:,2); % train output data 


hidden_neurons = 2; 
maxepochcount = 1000; 

datacount1 = size(x,1); 
% add a bias as an input 
bias = ones(datacount1,1); 
% x = [x bias]; 
% read how many inputs 
inputcount = size(x,2); 
% ---------- data loaded ----------- 
% ---------- set weights ----------- 
% set initial random weights 
WI = (randn(inputcount,hidden_neurons) - 0.5)/10; 
WO = (randn(1,hidden_neurons) - 0.5)/10; 
%----------------------------------- 
%--- Learning Starts Here! --------- 
%----------------------------------- 
eta1 = 0.5; 
eta2 = eta1/5; 
% do a number of epochs 
for iter = 1:maxepochcount 
% loop through the data 
    for j = 1:datacount1 
     % read the current sample 
     I = x(j,:); 
     D = r(j,1); 
     % calculate the error for this sample 
     H = (sigmoid(I * WI))'; 
     O = H' * WO'; 
     error = D-O; 
     % adjust weight between hidden & output 
     delta_i = O.*(1-O).*(D-O); % D actual, O calculated output 
     % Calculate error for each node in layer_(n-1) 
     delta_j = H.*(1-H).*(WO.'*delta_i); % H.' is the output of hidden layer 
     % Adjust weights in matrices sequentially 
     WO = WO + eta2.*delta_i*(H.') % H.' is the output of hidden layer 
     WI = WI + eta1.*(delta_j*(I))' % I.' is the inputs 

%   % adjust weight between hidden & output 
%   delta_HO = error.*eta2 .* hidden_val; 
%   WO = WO - delta_HO'; 
%   % adjust the weights between input & hidden 
%   delta_IH = eta1 .* error .* WO' .* (1 - (H .^ 2)) * I; 
%   WI = WI - delta_IH'; 

    end 
    O = sigmoid(WO*sigmoid(x * WI)'); 
%  error(iter) = (sum(error .^ 2))^0.5; 
    if rem(iter,100)==0  % Every 100 epochs, show how training is doing 
    plot(x,O, 'color','red','linewidth',2); hold on;  
    drawnow; 
    iter 

    end 

% return 
end 

Answer 1

Nur die Ausgabewerte müssen auf die Aktivierungsfunktion skaliert werden. Wenn wir tanh verwenden, müssen wir sie auf [-1,1] skalieren, im Falle von Sigmoid [0,1]. Der Code funktioniert gut, aber manchmal braucht es mehr Epochen.