I trained CNN using spectrograms of audio data (Matlab). Now, I want to do the same research, however, to use a recurrent neural network. I have a question for you, what data should be submitted to the recurrent network input? And how is it possible to get this data based on either a spectrogram or an audio file?
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I am a new Matlab user and I would be grateful if you help me. I have converted a set of time series into pictural presentations using CWT (continuous wavelet transform) and trained a deep learning network with quite a reasonable accuracy. I have made use of classify to check the trained network performance for the output of a single image. Now I am going to use it for a series of images consecutively feeding on the main time series, so how do I have to use classify in this issue?
regards
I am new to learning and training neural networks. I have the task of recognizing emotions based on voice data (audio data). I'm trying to create the simplest recurrent network in Matlab. I am submitting images to the input in the form of spectrograms. However, the accuracy of the network is about 40%. In fact, I understand that the result obtained is not acceptable. I ask you for advice and recommendations on how to dig in which direction to achieve a more significant result.
I'm trying to create a 1x1x198 input for my CNN from a 100x100x198 data(image).
I'm using Hyperspectral Imaging data (jasper), however, if I want to process and plot their endmembers through a Convolution Neural Network I have to modify the data in 1x1x198 kernel sizes.
I am trying to write the Matlab code for the following paper. I will appreciate any help regarding the paper's Matlab code. IEEE paper: Hyperspectral unmixing via deep convolutional neural networks
Thank you
I have trained the neural network for a particular time series in MATLAB. After that I have saved the network. So if I want to simulate the network using different parameters like changing the number of neurons,number of hidden layer, transfer functions, learning ratio,momentum coefficient, Can I do it without again training the network?
If not what is the criteria to select the best parameter for my neural network?
How should I configure my neural network in MATLAB to do all these?
No because you save whole model to file, with including weights + activation function and whole structure (layers). You can train few networks, and save to file if you want to check in future on real data (validation data) which networks is better.
Check this also ;) http://people.cs.umass.edu/~btaylor/publications/PSI000008.pdf
I am using the neural network toolbox that Matlab provides. I trained a NARX neural network for time series problems. I am trying to predict future values using the inputs I am giving to the neural network.
I am able to see the error graphs and the response for the testing and validation samples, but how do I test new samples? How can I make a prediction using the trained neural network? I could not find any documentation.
This was my attempt
>> net(input2')
ans =
[917.9814]
But no matter what the inputs are, I am getting the exact same output always....