I have some problems with understanding of the batch concept and batch size. I messed something up. First I start it consider based on convolutional neural network I heard two versions:
- When the batch size is set to 50, the first network is fed with 50 images and then learned / recalculated (it doesn't make sense to me, because in this case the network learns one of 50 images).
- When the batch size is set to 50, one of 50 neurons is recalculated in the learning process on a single image.
Both of these explanations seem to be wrong to me, so I assume, that I completely don't understand this. What is batch / batch size in RNN? Could you show any example?
I can tell you how I would teach a recurrent neural network. Let's say, that I would like to teach a neural network to predict the weather the next day:
- I would take weather data from an expected area from the last 30,000 days.
- I would assume that my prediction would be based on measurements from the last 365 days.
- I would take data from day 1 to 365 - feed RNN with it and learn.
- Then I would take data from day 2 to 366 => feed + learn
- Then day 3 to 367 => feed + learn
- And so on.
Is this 365 measurement concept a batch size?