Matlab Shallow Network Mini Batch Training
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Lynn Marciano on 12 Jul 2018
Commented: Jingjun Liu on 23 Nov 2019
Hello, I have been training my data through the patternnet provided by matlab and really like it's functionality and I've been seeing great results using it. I have a problem however, I want to start investigating all the functions that can be adjusted under the hood of the default patternnet, but I have such a large data size, that even though I'm connected to a cluster, my model takes about 10 hours at minimum to train. I know there are capabilities with training on the GPU but after several attempts, it says I have no memory for training. I know having a minibatch might be able to compensate for this, but I'm not entirely sure if I have to create a datastore for the minibatch to be effective. If anyone has input a minibatch into the shallow network inputs and trained on GPU, please give me some insight on the right direction to go with this. Thanks in advance.
Jingjun Liu on 23 Nov 2019
The mini-batch does not work for sequenceInputLayer. That's what I found.
Greg Heath on 20 Dec 2018
If you have a huge dataset, it is often rewarding to just randomly divide it into m subsets. Then design with 1 and test on m-1. If the subsets are sufficiently large. it is not necessary to use m-fold cross-validation. However, you may want to design more than one net.
Hope this helps.
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