Hi there,
I have a dnn with BatchNormalization layers. I did [Uf, statef] = forward(dnn_net, XT); "statef" returned from forward call should contain the learned mean and variance of the BN layers. I then update the dnn_net.State = statef so that the dnn_net.State is updated with the learned mean and var from the forward call. Then I did Up = predict(dnn_net,XT) [on the same data XT as the forward call]. Then I compare the mse(Up,Uf) and it turns out to be quite large (e.g 2.xx). This is totally not expected. Please help
I have created a simple test script to show the problem below:
numLayer = 9;
numNeurons = 80;
layers = featureInputLayer(2);% 2 input features
for i = 1:numLayer-1
layers = [
layers
fullyConnectedLayer(numNeurons)
batchNormalizationLayer
geluLayer
];
end
layers = [
layers
fullyConnectedLayer(4)];
dnn_net = dlnetwork(layers,’Initialize’,true);
dnn_net = expandLayers(dnn_net);% expand the layers in residual blocks
dnn_net = dlupdate(@double,dnn_net);
X = 2*pi*dlarray(randn(1,1000),"CB");
T = 2*pi*dlarray(randn(1,1000),"CB");
XT = cat(1,X,T);
% compute DNN output using forward function, statef contains
% batchnormlayers (learned means and learned var)
[Uf,statef] = forward(dnn_net,XT);
dnn_net.State = statef;% update the dnn_net.State (so that it has the BN layers updated learned means and var
Up = predict(dnn_net,XT);
% DNN output between predict call and forward call shoudl be the same in
% this case (becausae the dnn_net.State is updated with the same learned mean/var from forward calls.
% However, it is not the case. The err is quite large
plot(Uf(1,:),’r-‘);hold on; plot(Up(1,:),’b-‘);
err = mse(Uf(1,:),Up(1,:))Hi there,
I have a dnn with BatchNormalization layers. I did [Uf, statef] = forward(dnn_net, XT); "statef" returned from forward call should contain the learned mean and variance of the BN layers. I then update the dnn_net.State = statef so that the dnn_net.State is updated with the learned mean and var from the forward call. Then I did Up = predict(dnn_net,XT) [on the same data XT as the forward call]. Then I compare the mse(Up,Uf) and it turns out to be quite large (e.g 2.xx). This is totally not expected. Please help
I have created a simple test script to show the problem below:
numLayer = 9;
numNeurons = 80;
layers = featureInputLayer(2);% 2 input features
for i = 1:numLayer-1
layers = [
layers
fullyConnectedLayer(numNeurons)
batchNormalizationLayer
geluLayer
];
end
layers = [
layers
fullyConnectedLayer(4)];
dnn_net = dlnetwork(layers,’Initialize’,true);
dnn_net = expandLayers(dnn_net);% expand the layers in residual blocks
dnn_net = dlupdate(@double,dnn_net);
X = 2*pi*dlarray(randn(1,1000),"CB");
T = 2*pi*dlarray(randn(1,1000),"CB");
XT = cat(1,X,T);
% compute DNN output using forward function, statef contains
% batchnormlayers (learned means and learned var)
[Uf,statef] = forward(dnn_net,XT);
dnn_net.State = statef;% update the dnn_net.State (so that it has the BN layers updated learned means and var
Up = predict(dnn_net,XT);
% DNN output between predict call and forward call shoudl be the same in
% this case (becausae the dnn_net.State is updated with the same learned mean/var from forward calls.
% However, it is not the case. The err is quite large
plot(Uf(1,:),’r-‘);hold on; plot(Up(1,:),’b-‘);
err = mse(Uf(1,:),Up(1,:)) Hi there,
I have a dnn with BatchNormalization layers. I did [Uf, statef] = forward(dnn_net, XT); "statef" returned from forward call should contain the learned mean and variance of the BN layers. I then update the dnn_net.State = statef so that the dnn_net.State is updated with the learned mean and var from the forward call. Then I did Up = predict(dnn_net,XT) [on the same data XT as the forward call]. Then I compare the mse(Up,Uf) and it turns out to be quite large (e.g 2.xx). This is totally not expected. Please help
I have created a simple test script to show the problem below:
numLayer = 9;
numNeurons = 80;
layers = featureInputLayer(2);% 2 input features
for i = 1:numLayer-1
layers = [
layers
fullyConnectedLayer(numNeurons)
batchNormalizationLayer
geluLayer
];
end
layers = [
layers
fullyConnectedLayer(4)];
dnn_net = dlnetwork(layers,’Initialize’,true);
dnn_net = expandLayers(dnn_net);% expand the layers in residual blocks
dnn_net = dlupdate(@double,dnn_net);
X = 2*pi*dlarray(randn(1,1000),"CB");
T = 2*pi*dlarray(randn(1,1000),"CB");
XT = cat(1,X,T);
% compute DNN output using forward function, statef contains
% batchnormlayers (learned means and learned var)
[Uf,statef] = forward(dnn_net,XT);
dnn_net.State = statef;% update the dnn_net.State (so that it has the BN layers updated learned means and var
Up = predict(dnn_net,XT);
% DNN output between predict call and forward call shoudl be the same in
% this case (becausae the dnn_net.State is updated with the same learned mean/var from forward calls.
% However, it is not the case. The err is quite large
plot(Uf(1,:),’r-‘);hold on; plot(Up(1,:),’b-‘);
err = mse(Uf(1,:),Up(1,:)) predict and forward gives inconsistent outputs MATLAB Answers — New Questions

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Hello, I would appreaciate any suggestion to estimate the total average of angles based on the location of the quadrants. The length of the matrix of the angles to assess the average is variable and for these examples I am assuming that the length of the matrices are constant:
angles=[2;355;0;6]
I should have the following answer for the average:
average_angle=0.75;
% Another case:
angles=[-2;350;0;-3 ]
average=356.25;
% another case
angles=[5;36;45;-5]
average=110.25
% another case
angles=[35;36;45;50]
average=41.5
% another case
angles=[180;150;-8;130]
average=203
I would appreciate the help if there is a quick command in Matlab to do these operations without having too many conditions.Hello, I would appreaciate any suggestion to estimate the total average of angles based on the location of the quadrants. The length of the matrix of the angles to assess the average is variable and for these examples I am assuming that the length of the matrices are constant:
angles=[2;355;0;6]
I should have the following answer for the average:
average_angle=0.75;
% Another case:
angles=[-2;350;0;-3 ]
average=356.25;
% another case
angles=[5;36;45;-5]
average=110.25
% another case
angles=[35;36;45;50]
average=41.5
% another case
angles=[180;150;-8;130]
average=203
I would appreciate the help if there is a quick command in Matlab to do these operations without having too many conditions. Hello, I would appreaciate any suggestion to estimate the total average of angles based on the location of the quadrants. The length of the matrix of the angles to assess the average is variable and for these examples I am assuming that the length of the matrices are constant:
angles=[2;355;0;6]
I should have the following answer for the average:
average_angle=0.75;
% Another case:
angles=[-2;350;0;-3 ]
average=356.25;
% another case
angles=[5;36;45;-5]
average=110.25
% another case
angles=[35;36;45;50]
average=41.5
% another case
angles=[180;150;-8;130]
average=203
I would appreciate the help if there is a quick command in Matlab to do these operations without having too many conditions. average estimation of angles bases on quadrants MATLAB Answers — New Questions

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I have to evaluate the symmetry of this nevus, you can see his binary image.
I have found the centroid, would you give me an advise for the code to find and show the inertia axis of the image which match each other in the centroid?
I would have to align them with x and y axis of the image with a rotation then.
Thank youI have to evaluate the symmetry of this nevus, you can see his binary image.
I have found the centroid, would you give me an advise for the code to find and show the inertia axis of the image which match each other in the centroid?
I would have to align them with x and y axis of the image with a rotation then.
Thank you I have to evaluate the symmetry of this nevus, you can see his binary image.
I have found the centroid, would you give me an advise for the code to find and show the inertia axis of the image which match each other in the centroid?
I would have to align them with x and y axis of the image with a rotation then.
Thank you inertia, axis MATLAB Answers — New Questions

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I have MATLAB 2024a installed (it has update 2) — it shows that no updates are available when I check for updates in the GUI. I just received an email from MATHWORKS that I bug I reported has been fixed in UPDATE 6. When I go to the licenses to install this update, it seems to be saying that I need to uninstall the previous version and start over. Is this really what I need to do? Seems unreasonable…..I have MATLAB 2024a installed (it has update 2) — it shows that no updates are available when I check for updates in the GUI. I just received an email from MATHWORKS that I bug I reported has been fixed in UPDATE 6. When I go to the licenses to install this update, it seems to be saying that I need to uninstall the previous version and start over. Is this really what I need to do? Seems unreasonable….. I have MATLAB 2024a installed (it has update 2) — it shows that no updates are available when I check for updates in the GUI. I just received an email from MATHWORKS that I bug I reported has been fixed in UPDATE 6. When I go to the licenses to install this update, it seems to be saying that I need to uninstall the previous version and start over. Is this really what I need to do? Seems unreasonable….. update installation MATLAB Answers — New Questions

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I want to make V<=0.001 at a position where Wn=1, but the non-linear constraint cannot do it. I am sure there is this answer. The following is my program。
https://drive.mathworks.com/sharing/df23a873-b7f8-420c-b085-dedccef7860cI want to make V<=0.001 at a position where Wn=1, but the non-linear constraint cannot do it. I am sure there is this answer. The following is my program。
https://drive.mathworks.com/sharing/df23a873-b7f8-420c-b085-dedccef7860c I want to make V<=0.001 at a position where Wn=1, but the non-linear constraint cannot do it. I am sure there is this answer. The following is my program。
https://drive.mathworks.com/sharing/df23a873-b7f8-420c-b085-dedccef7860c multi-objective optimization MATLAB Answers — New Questions

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What is the maximum size of *.mat file?
What is the maximum size of HDF5 file?What is the maximum size of *.mat file?
What is the maximum size of HDF5 file? What is the maximum size of *.mat file?
What is the maximum size of HDF5 file? mat file MATLAB Answers — New Questions

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Hello, this is my first post in this form and I am new to MatLab. I would like to take the inverse of a polynomial in factored form and express the answer in polynomial form. For example, I wish to convert 10/((x+3)^2(x+5)) in regular polynomial form.Hello, this is my first post in this form and I am new to MatLab. I would like to take the inverse of a polynomial in factored form and express the answer in polynomial form. For example, I wish to convert 10/((x+3)^2(x+5)) in regular polynomial form. Hello, this is my first post in this form and I am new to MatLab. I would like to take the inverse of a polynomial in factored form and express the answer in polynomial form. For example, I wish to convert 10/((x+3)^2(x+5)) in regular polynomial form. polynomial MATLAB Answers — New Questions

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Goodafternoon ‘u all,
I’m actually working with simscape and I was wondering if any way exist to get from a multibody model acting forces components, ad function of kinematic conditions. In simpler words, how can I get centrifugal, coriolis, gravitations, and accelerative forces components action on a mass or body of the system? Let’s assume as first approximation we’re working with rigid body (but I imagine would be in any case a good approximation also for other cases). I don’t see any evident methd for getting them easily, unless repeating all required onerous calculations (futile to say I’m not going for :-D). In robotic toolbox all these components are directly derived from internal jacobians and kinematcs conditions. I was guessing any similar feature also exist in simscape. Remarks, I’m currently using simscape under simulink environment.
Suggestions?
Best regardsGoodafternoon ‘u all,
I’m actually working with simscape and I was wondering if any way exist to get from a multibody model acting forces components, ad function of kinematic conditions. In simpler words, how can I get centrifugal, coriolis, gravitations, and accelerative forces components action on a mass or body of the system? Let’s assume as first approximation we’re working with rigid body (but I imagine would be in any case a good approximation also for other cases). I don’t see any evident methd for getting them easily, unless repeating all required onerous calculations (futile to say I’m not going for :-D). In robotic toolbox all these components are directly derived from internal jacobians and kinematcs conditions. I was guessing any similar feature also exist in simscape. Remarks, I’m currently using simscape under simulink environment.
Suggestions?
Best regards Goodafternoon ‘u all,
I’m actually working with simscape and I was wondering if any way exist to get from a multibody model acting forces components, ad function of kinematic conditions. In simpler words, how can I get centrifugal, coriolis, gravitations, and accelerative forces components action on a mass or body of the system? Let’s assume as first approximation we’re working with rigid body (but I imagine would be in any case a good approximation also for other cases). I don’t see any evident methd for getting them easily, unless repeating all required onerous calculations (futile to say I’m not going for :-D). In robotic toolbox all these components are directly derived from internal jacobians and kinematcs conditions. I was guessing any similar feature also exist in simscape. Remarks, I’m currently using simscape under simulink environment.
Suggestions?
Best regards simscape, multibody, forces MATLAB Answers — New Questions

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