I have some problems with understanding of batch concept and batch size. I messed something up. First i start it consider based on convolutional neural network I heard two versions: 1. When batch size is set on 50 - 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). 2. When batch size is set on 50, one of 50 neurons is recalculated in learning process on single image.
Both of this explanations seems 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 would i learn recurrent neural network. Let's say, that I would like to learn a neural network to predict weather next day. 1. I would take a weather data from expected area from last 30.000 days. 2. I would assume, that my prediction would be based on measurements from last 365 days. 3. I would take data from day 1 to 365 - feed RNN with it and learn. 4. Then i would take data from day 2 to 366 => feed + learn 5. Then day 3 to 367 => feed + learn 6. And so on.
Is this 365 measurement concept a batch size?