Dividerand - neural network training

2012 6 22
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I'm using a neural network model and for some simulations I've used the function 'dividerand' instead of 'divideind'. Without using the command [net,tr]=train(net,.....) where I could check how the data were randomly separated in training, validation and testing datasets, is it possible to verify how the random separation was executed (analysing the indices)?

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Greg Heath
Greg Heath 2012-6-26

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If you do not include tr as a training output, the only way to obtain the dividerand indices is to call dividerand before calling newff/newfit/fitnet/patternnet/feedforwardnet and nullify it's use within those net creation functions.
However, if all you are concerned about is duplicating a run, just specify the same random number seed before calling the creation function.
Hope this helps.
Greg

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Sean de Wolski
Sean de Wolski 2012-6-22

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I am not sure I understand. You can analyze the indces just by looking at the outputs from dividerannd. Can you please clarify your question a little further and/or provide a short example?
Greg Heath
Greg Heath 2012-6-22

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The structure tr in the double output [net tr ] = train(net,x,t); will contain the train/val/test indices.
Greg
JSousa Sousa
JSousa Sousa 2012-6-25

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Thanks for your prompt answers Sean de Wolski and Greg Heath.
In some simulations I've just used the command net= train(net,...), without specifying an output tr where I could have a structure with the train/val/test indices. In that way, is that any possibility to verify how the data were effectively split in different substets?
I know that to repeat a simulation using the same scenario it must be well known: - the network weights/biases initialization (using for example the command revert to obtain the initial parameters) and - the data separation criteria (difficult to me to recognize without the tr structure).
Thanks for your time and attention
João.
JSousa Sousa
JSousa Sousa 2012-6-26

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I have a saved neural network model with good results that I would like to analyse in more detail. So the point is well understood, I'm interested to duplicate this training process (another similar run). However without a tr structure and also without the random number seed saved for this case I think I won't get exactly the same data division.
Do you know how to recover this seed?
Thanks again
JSousa.

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Greg Heath
Greg Heath 2012-6-26
No.
You should always specify the random number seed/state before calling a function that uses random numbers.
The only thing you can do now is to use a loop to create a lot of designs and try to find one that has a similar performance to the original.
Greg

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