I have a problem in applying Super-Resolution Convolutional Neural Networks (SRCNN). In SRCNN code, the 'Readme.txt' file tells
1) Place the "SRCNN" folder into "($Caffe_Dir)/examples/"
2) Open MATLAB and direct to ($Caffe_Dir)/example/SRCNN, run "generate_train.m" and "generate_test.m" to generate training and test data.
3)To train our SRCNN, run ./build/tools/caffe train --solver examples/SRCNN/SRCNN_solver.prototxt
4) After training, you can extract parameters from the caffe model and save them in the format that can
be used in our test package (SRCNN_v1). To do this, you need to
install mat-caffe first, then open MATLAB and direct to ($Caffe_Dir)
and run "saveFilters.m". The "($Caffe_Dir)/examples/SRCNN/x3.mat" will
be there for you.
In number 3, I don't know how to run prototxt file in Matlab. So there is anyone to help me running prototxt file in Maytlab? I want some example code to run prototxt file in Matlab. Thank you for reading.
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I would train an alexnet DNN (given by MATLAB function alexnet) from scratch (i.e. without pretraining on ImageNet given by alexnet function). I could to manually set weights but I don't know the from what distribution I can sample my initial weights. Is there a built-in MATLAB option that make it for me?
For example, I've read that Python's library has the option pre-training=off but I don't find a similar option in MATLAB.
This is my proposed project:
- I have developed a classifier in Weka after some experimentation.
- Now I want to develop a project in Matlab which will take input
attributes from users.
- This input will be given to Weka after loading saved classification model.
- Weka will predict the class for that instance.
- Finally I want to pick this predicted class and display it on screen developed by Matlab.
Now I don't know how to import and export data from Matlab to Weka. Immediate help will be appreciated.
Some thoughts on your problem.
As far as i know matlab has a machine learning toolbox(haven't used it). So you can build your system entirely on matlab.
If you want to stick with Weka and use it programmatically with Java, then you can call Java methods from matlab so as to get the classification result. For how to call java methods from matlab check here
If you use Weka from GUI then you should be able to save the classification result to a file straight from GUI. Then you can load this file to matlab, retrieve the result and visualize it in matlab.
Hope this helps.
i am trying to train a classifier of an object. I have tried to train it in MATLAB and i am getting good results. But the generated output .xml file can't be used in open-cv. so can anyone tell me how can i use the MATLAB generated .xml file in open-cv or can anyone give me some link to app where i can directly put my positive and negative images and it can do all the training and give me out .xml file in open-cv.
Thanks in advance
Here you can find several .exe that will help you to train your OpenCV classifier.
And here you can find a tutorial. Hope it helps!
I am training a neural network using the FANN library and I find the library pretty impressive. The problem is that I when I tried to "export" (manually) the weights and the formation of the network so I could simulate it in Matlab something is going wrong... While FANN tells me I have an mse of 4-5% when I try to simulate it in matlab it's around 80%!
I believe I'm missing something when I'm trying to translate/map the attributes of the network from the saved file. Can somebody have a look and please help me?
The saved .net file which fann produces, the .xls files into which I put the weights as well as the matlab scripts in case you want to test it are all here : http://users.isc.tuc.gr/~spapagrigoriou/network/ .
I'm doing some preliminary testing with 2 classes of vectors, trying to separate them with libsvm. I get a 78.2% correct ID rate in Matlab and at the cmd line (using libsvm), but in Weka I get around 95%.
No cross-validation was done in Weka; just trained model and then read in test dataset and classified it.
Can anyone offer an explanation? Thanks in advance.
If you didn't provide a separate Test Data , the validation Folds should be set, 10 or desired value. however, be sure that the same SVMType and kerneltype are being used in both program. by default Weka uses C-SVC with radial basis function.