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Bookmark or Share- sql - Bulk/batch update/upsert in PostgreSQL - Stack OverflowFew bigger queries are much better. For example it's better to use large batch inserts (say 100 rows at once) instead of 100 one-liners. Now, from what I can see, SQL doesn't really supply any statement to perform a batch update on a table. The term seems to be confusing so, I'll explain what I mean by that. I have an array of arbitrary data ...
- How does batch size affect convergence of SGD and why?At the other extreme (minibatch=one line) you have full per line SGD. Per line SGD is better anyway, but bigger minibatches are suited for more efficient parallelization. At the end of the convergence process, SGD becomes less precise than (batch) GD. But at this point, things become (usually) a sort of uselessly precise fitting.
- python - How big should batch size and number of epochs be when fitting ...I got best results with a batch size of 32 and epochs = 100 while training a Sequential model in Keras with 3 hidden layers. Generally batch size of 32 or 25 is good, with epochs = 100 unless you have large dataset. in case of large dataset you can go with batch size of 10 with epochs b/w 50 to 100. Again the above mentioned figures have worked ...
- How to calculate optimal batch size? - Stack OverflowTraining with such a small batch size might require a small learning rate to maintain stability because of the high variance in the estimate of the gradient. The total runtime can be very high as a result of the need to make more steps, both because of the reduced learning rate and because it takes more steps to observe the entire training set.
- How should the learning rate change as the batch size change?For reference, I was discussing with someone, and it was said that, when batch size is increased, the learning rate should be decreased by some extent. My understanding is when I increase batch size, computed average gradient will be less noisy and so I either keep same learning rate or increase it.
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