Custom filedatastore for deep learning

2020 11 17
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更新时间：2020 11 27
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I started to implement a Deep Learning network to classify the modulation of RF signals.
I use a 2D image input layer with dimensions 1x1024x2. I converted the dataset into variables which are saved in mat files. The variables have a dimension of 1x1024x2xN, where N is the number of signals which is 24*4096. The labels are stored in separate mat files and the variable is an Nx1 categorical cell array.
I have multiple mat files for the training data but I am unable to create a filedatastore which will read all of the signals along the 4th dimension from one file and then do the same for the rest of the files.

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Bence Cserkuti
Bence Cserkuti 2020-11-27

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Thank Mahesh,
I ended up writing a custom Datastore class. In case others face the same problem, I recommend reading these pages carefully:
https://uk.mathworks.com/help/matlab/import_export/develop-custom-datastore.html
https://uk.mathworks.com/help/matlab/import_export/testing-guidelines-for-custom-datastores.html
In my case, when I initialise my custom class, I pass two arguments: the file names of my signals and the file names of the corresponding labels. I then create a filedatastore for each dataset. What was a key observation is that your custom read function must return 1 observation at a time by default (this can be increased by implementing the ReadSize property). In this case that means then if I call ds.preview it will return a row that contains a 1x1024x2 signal and the corresponding categorical array.

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Mahesh Taparia
Mahesh Taparia 2020-11-21

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Hi
In this case, you can create a custom ReadFcn while creating a file datastore. For more information, you can refer this documentation. The custom read function will load the file and read the data as per required.Hope it will help!

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Bence Cserkuti
Bence Cserkuti 2020-11-27
Thank Mahesh,
I ended up writing a custom Datastore class. In case others face the same problem, I recommend reading these pages carefully:
https://uk.mathworks.com/help/matlab/import_export/develop-custom-datastore.html
https://uk.mathworks.com/help/matlab/import_export/testing-guidelines-for-custom-datastores.html
In my case, when I initialise my custom class, I pass two arguments: the file names of my signals and the file names of the corresponding labels. I then create a filedatastore for each dataset. What was a key observation is that your custom read function must return 1 observation at a time by default (this can be increased by implementing the ReadSize property). In this case that means then if I call ds.preview it will return a row that contains a 1x1024x2 signal and the corresponding categorical array.

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