Hello everyone
Matlab loop calls the LSTM network that has been trained slowly. I plan to extract the weight parameters and calculate them according to the formula, but the prediction results are always wrong. I have checked the normalization and structural parameters. What are the other possible reasons? There is another operation when matlab calls net.
HiddenLayersNum=128;
LSTM_R1=net.Layers(2,1).RecurrentWeights;
LSTM_W1=net.Layers(2,1).InputWeights;
LSTM_b1=net.Layers(2,1).Bias;
LSTM_R2=net.Layers(4,1).RecurrentWeights;
LSTM_W2=net.Layers(4,1).InputWeights;
LSTM_b2=net.Layers(4,1).Bias;
FC_Weights=net.Layers(6,1).Weights;
FC_Bias=net.Layers(6,1).Bias;
W.Wi1=LSTM_W1(1:HiddenLayersNum,:);
W.Wf1=LSTM_W1(HiddenLayersNum+1:2*HiddenLayersNum,:);
W.Wg1=LSTM_W1(2*HiddenLayersNum+1:3*HiddenLayersNum,:);
W.Wo1=LSTM_W1(3*HiddenLayersNum+1:4*HiddenLayersNum,:);
R.Ri1=LSTM_R1(1:HiddenLayersNum,:);
R.Rf1=LSTM_R1(HiddenLayersNum+1:2*HiddenLayersNum,:);
R.Rg1=LSTM_R1(2*HiddenLayersNum+1:3*HiddenLayersNum,:);
R.Ro1=LSTM_R1(3*HiddenLayersNum+1:4*HiddenLayersNum,:);
b.bi1=LSTM_b1(1:HiddenLayersNum,:);
b.bf1=LSTM_b1(HiddenLayersNum+1:2*HiddenLayersNum,:);
b.bg1=LSTM_b1(2*HiddenLayersNum+1:3*HiddenLayersNum,:);
b.bo1=LSTM_b1(3*HiddenLayersNum+1:4*HiddenLayersNum,:);
W.Wi2=LSTM_W2(1:HiddenLayersNum,:);
W.Wf2=LSTM_W2(HiddenLayersNum+1:2*HiddenLayersNum,:);
W.Wg2=LSTM_W2(2*HiddenLayersNum+1:3*HiddenLayersNum,:);
W.Wo2=LSTM_W2(3*HiddenLayersNum+1:4*HiddenLayersNum,:);
R.Ri2=LSTM_R2(1:HiddenLayersNum,:);
R.Rf2=LSTM_R2(HiddenLayersNum+1:2*HiddenLayersNum,:);
R.Rg2=LSTM_R2(2*HiddenLayersNum+1:3*HiddenLayersNum,:);
R.Ro2=LSTM_R2(3*HiddenLayersNum+1:4*HiddenLayersNum,:);
b.bi2=LSTM_b2(1:HiddenLayersNum,:);
b.bf2=LSTM_b2(HiddenLayersNum+1:2*HiddenLayersNum,:);
b.bg2=LSTM_b2(2*HiddenLayersNum+1:3*HiddenLayersNum,:);
b.bo2=LSTM_b2(3*HiddenLayersNum+1:4*HiddenLayersNum,:);
%LSTM - Layer
h_prev=zeros(HiddenLayersNum,1);%Output gate initial values (t-1)
c_prev=zeros(HiddenLayersNum,1);
%归一化
test_in = Test_input(:,2:end)';
P_train = (test_in-Train_data_meanVal')./Train_data_stdVal';
for i=1:length(P_train)
%LSTM1
%Input Gate
z=W.Wi1*P_train(:,i)+R.Ri1*h_prev+b.bi1;
I = 1.0 ./ (1.0 + exp(-z));%Input gate
%Forget Gate
f=W.Wf1*P_train(:,i)+R.Rf1*h_prev+b.bf1;
F = 1.0 ./ (1.0 + exp(-f));%Forget gate
%Layer Input
g=W.Wg1*P_train(:,i)+R.Rg1*h_prev+b.bg1;%Layer input
G=tanh(g);
%Output Layer
o=W.Wo1*P_train(:,i)+R.Ro1*h_prev+b.bo1;
O = 1.0 ./ (1.0 + exp(-o));%Output Gate
%Cell State
c=F.*c_prev+I.*G;%Cell Gate
c_prev=c;
% Output (Hidden) State
h=O.*tanh(c);%Output State
h_prev=h;
L1 = relu(h);
%LSTM2
%Input Gate
z=W.Wi2*L1+R.Ri2*h_prev+b.bi2;
I = 1.0 ./ (1.0 + exp(-z));%Input gate
%Forget Gate
f=W.Wf2*L1+R.Rf2*h_prev+b.bf2;
F = 1.0 ./ (1.0 + exp(-f));%Forget gate
%Layer Input
g=W.Wg2*L1+R.Rg2*h_prev+b.bg2;%Layer input
G=tanh(g);
%Output Layer
o=W.Wo2*L1+R.Ro2*h_prev+b.bo2;
O = 1.0 ./ (1.0 + exp(-o));%Output Gate
%Cell State
c=F.*c_prev+I.*G;%Cell Gate
c_prev=c;
% Output (Hidden) State
h=O.*tanh(c);%Output State
h_prev=h;
L2 = relu(h);
% Fully Connected Layers
fc=FC_Weights*L2+FC_Bias;
Predict_value(i) = fc.*Train_label_stdVal+Train_label_meanVal;
end
function output = relu(x)
output = max(0, x);
end
