I am trying to implement code for this loss function for a classification task for a subset image data using several pre-trained models provided by MatConvNet but not sure how to implement it.
average cross-entropy loss formula
I am working on speaker identification project in matlab which my goal is to check whether a test speaker is my target speaker or not.
I used mfcc and lpcc and pitch as my features in this project and I used libsvm for single class classifier to train my model but my model accuracy even when I test it on my train data is quite low.
I use pre-implement mfcc and lpcc function which I am sure of correctness of this two features so I thought this might be a problem with classifier so I decide to use Gaussian Mixture Model as my classifier in this project however how can use Gaussian Mixture Model for single class classification?
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.
Using nntool(Neural Network Manager) in Matlab, we have created a neural network named network1, the network type is Feed Forward backprop. Training function is TRAINLM, learning function is LEARNGDM, performance function is MSE. No. of layers are 2 and transfer function is TRANSIG. No. of Inputs is 2.
We have trained it using known datasets.
Now, we want to use this trained Neural Network on real time values(dynamically one by one) to get the output.
We are unable to use the network on real time values.
So, please guide us through the steps to use trained neural network on real time values.
if you created a ann via
network1 = feedforwardnet;
or something of that kind and then trained it with your known data, you should be able to use said net via
outputs = network1(inputs);
You can create a function from the neural network that you have trained and use it as regular MATLAB functions.
You can either create it with genFun command or using the GUI in neural network toolbox.
genFunction(net,pathname)
If you want the function to accept only matrix elements you should use this command:
genFunction(net,pathname,'MatrixOnly','yes')
I'm currently trying to build an AR model to approximate the error process in a sensor system. And I'm comparing the different parameter estimators in MATLAB. I have sets of data that i'm trying to match a model to, but I'm not too sure on the benefits/disadvantages of the algorithms available in the signal processing toolbox.
arburg: Autoregressive (AR) all-pole model parameters estimated using Burg method
arcov: Estimate AR model parameters using covariance method
armcov: Estimate AR model parameters using modified covariance method
aryule: Estimate autoregressive (AR) all-pole model using Yule-Walker method
If someone could give a more detailed description comparing the different algorithm and which one would best model existing data that would be very helpful.