I want to publish a 2-dimensional array from a MATLAB function in Simulink to a ROS topic.
I have followed this tutorial where an example is given for a geometry_msgs/Point. However, I want to use std_msgs/Float32MultiArray which is a bit more complex:
I cannot even make it compile with Data and DataOffset only:
I get the following error:
Error in port widths or dimensions. Output port 1 of '.../MATLAB Function' is a [1x10] matrix.
Error in port widths or dimensions. Input port 2 of '.../Bus Assignment' is a one dimensional vector with 128 elements.
Related
I have two sets of 58 images each which I have to feed as input to a multiple-input network.
To do so, I first read each of the sets onto a different Image Datastore, as shown on the images
Reading the first dataset:
And then, the second dataset:
Then I merge the two datastores by using 'combine'. However, according to the documentation available here, I should be reading a three-column cell array, wherein the third column would be the labels. I get only two columns when reading though:
If I try to give this combined dataset as input to my network (which has two inputs and one output) I get the following error message:
>> net = trainNetwork(imdsCombined,lgraph_1,options);
Error using trainNetwork (line 170)
Invalid training data for multiple-input network. For a network with 2 inputs and 1 output, the datastore read function must
return an M-by-3 cell array, but it returns an M-by-2 cell array.
I am using R2019b by the way.
So, is there a way to insert the labels column onto the cell arrays read from the image datastore? Or is there something else I need to do?
Thanks for all the help.
Regards,
I'm trying to optimise and validate a neural network using Netlab on Matlab
I'd like to find the error value for each iteration, so I can see convergence on a plot. This can be done by storing the errors presented in the command window which is done by setting options(1) to 1 using errlog is a netopt output.
However these errors are not the same as mlperr which gives an error value of 0.5*(sum of squares error) for the last iteration. I can't really validly use them if I don't know how they're calculated.
Does anybody know what the errors displayed in the command window represent (I'm using scaled conjugate gradient as my optimisation algorithm)?
Is there a way of storing the mlperr for each iteration that the network
runs?
Any help is greatly appreciated, many thanks!
NB:
I have tried doing something similar to this :
ftp://ftp.dcs.shef.ac.uk/home/spc/com336/neural-lab-wk6.html
However it gives different results to running the network with the number of iterations specified under options(14) rather than k for some reason.
Yes certainly,
The ERRLOG vector, created as an output to the network optimisation function netopt with the following syntax
[NET, OPTIONS, ERRLOG] = netopt(NET, OPTIONS, X, T, ALG)
Each row of ERRLOG gives 0.5*SSE (sum of squares error) for the corresponding iteration of network optimisation. This error is calculated between the predicted outputs (y) and the target outputs (t).
The MLPERR function, hast the following syntax
E = mlperr(NET, X, T)
It also gives 0.5*SSE between predicted outputs (y) and target outputs (t), but as the network parameters are constant (NET should be pre-trained), E is a singular value.
If netopt was run with an ERRLOG output, and then MLPERR was run with the same network and variables, E should be the same value as value of the final row of ERRLOG (the error after the final iteration of network optimisation).
Hope this is of some use to someone!
I need to implement a if/else in simulink to find out if a input is a scalar value or a matrix. Please see, the diagram below :
Given:
Block(1) - is a input that can be a scalar "1" or a matrix "[[0 15];[5 10]]"
Block(2) - must return the signal dimension of the input. Ex: 1 for scalar and >1 for a matrix
The requirements are:
Everything must work interpreted or compiled (Simulink coder)
The final output of blocks (4) and (5) are scalars
I have average understanding of CMexSFunctions. So if I need to implement one to solve the problem it is ok
So far, I have had the following problems:
I don't at all if what I am planning to do is feasible
I don't know how to implement Block(2) to work on compiled mode
Even though there is a if/else, simulink performs a pre-check before running to verify if all signal dimensions are ok. During this check, it gives a error saying ex: that Block(5) has a input of matrix
Any Clues?
Block(2) is the easiest part which can be implemented using the "Probe" block in Simulink library. Your Input at port 1 must be variable sized signal since you are expecting a scalar or matrix.
I assume you are feeding Input(1) to blocks 4 and 5. At model compile time Simulink does not know which one of these blocks are going to run based on the input size. So Simulink needs to assume both blocks may get scalar or matrix. You need to make blocks 4 and 5 not throw error for both scalar and matrix even though they will be used only for one type at run-time.
If you are not able to do this, for the scalar case a simple work around is to place a Selector before block 5 that selects the first sample always. This will let Simulink know that the input to block 5 is always a scalar.
I'm teaching myself classification, I read and understood the MatLab online help of the simple LDA classifier which uses the fisher iris dataset.
I have now moved to SVM. But even though I use the exact syntax from the help page I get an error of either not enough or too many input arguments.
I have made trained my SVMClassifier using svmtrain via the command:
SVMStruct = svmtrain(training,labels);
Where training is a 207 by 900 training matrix. There are 207 samples and 900 HoG descriptors or features. Similarly labels is a 207 by 1 column vector consisting of either +1 or -1 for their respective samples.
I then wanted to test it and see if this works by calling:
Group = svmclassify(SVMStruct,sample,'Showplot',true)
Where sample is a 2 by 900 matrix containing 2 test samples. I was expecting to get +1 and -1 as these are what the test samples should be labelled. But I get the error:
Too many input arguments.
And when I use the command
Group = svmclassify(SVMStruct,sample)
I get the error
Not enough input arguments.
You might have overloaded svmclassify function.
try
>> which svmclassify
to verify that you are actually calling the right function.
In case that you overloaded the function (that is, created a different function with the same name svmclassify) and it is located higher in your path then you'll need to rename the overloaded function and run svmclassify again.
I have a 90×8 dataset that I feature-extracted (by summing 1's in every 10×10 cell) from 90 character images i.e. digits 1-9. Every row represents an image.
I am trying to use following code to train a neural network and recognize new input images(that are digits between 1 and 9 inclusive):
net.trainFcn='traingdx';
net.performFcn='sse';
net.trainParam.goal=0.1;
net.trainParam.show=20;
net.trainParam.epochs=5000;
net.trainParam.mc=0.95;
net =newff(minmax(datasetNormalized'),[20 9],{'logsig' 'logsig'});
T=reshape(repmat([1:9],10,1),1,90);
[net,tr]=train(net,datasetNormalized,T);
Afterwards I want to use the following to recognize new images using the trained network. m is an image character that has also been feature extracted.
[a,m]=max(sim(net,m));
disp(b);
I am getting the following errors and I don't have any idea how to solve it:
Error using trainlm (line 109)
Inputs and targets have different numbers of samples.
Error in network/train (line 106) [net,tr] =
feval(net.trainFcn,net,X,T,Xi,Ai,EW,net.trainParam);
Error in Neural (line 55) [net,tr]=train(net,datasetNormalized,T);
Note: datasetNormalized is my dataset normalized in [0,1].
Which part causes the problem?
Inputs and targets have different numbers of samples. it seems to be the problem
T=reshape(repmat([1:9],10,1),1,90) --> T=reshape(repmat([1:9],10,1),90,1)
[net,tr]=train(net,datasetNormalized,T); --> [net,tr]=train(net,datasetNormalized',T);
T is to be used as target for the network; Therefore, following a friend's advice, I defined T as a 9*90 array in such a way that the first 10 columns have 1 in their first row-other rows being zero, the second 10 columns have 1 in their second row, and so on
T=zeros(9,90);
for j=1:90
i=ceil(j/10);
T(i,j)=1;
end
[net,tr]=train(net,datasetNormalized',T);
This solved the error I was getting upon training network, though I'm not still sure how it's going to be mapped to input characters and determine them.