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Your scenario is common. The most straightforward approach is to subsample your data randomly. Unless your data or your model has strong bias, your performance to the smaller data set should be comparable. The accuracy might be lower, but the purpose is to do quick sanity check.


This might work for your case but isn't necessarily true and depends on how much data the network goes through in an iteration. You should be able to test this by making a small change and training until 100 iterations and seeing if the performance significantly changes and if it can be predicted from the 20th iteration. Another way which may work for you ...


Decrease of loss does not essentially lead to increase of accuracy (most of the time it happens but sometime it may not happen). To know why, you can have a look at this question. The network cares about decreasing the loss and it does not care about the accuracy at all. So it's no surprise to see what you presented. Additional note: If you use batch ...

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