Training parameter explanation in caffe

Repost

1. Batch Size
   Batch size in mainly depended to your memory in GPU/RAM. Most time it is used power of two (64,128,256). I always try to choose 256, because it works better with SGD. But for bigger network I use 64.

2. Number of Iterations
   Number if iterations set number of epoch of learning. Here I will use MNIST example to explain it to you:
   Training: 60k, batch size: 64, maximum_iterations= 10k. So, there will be 10k*64 = 640k images of learning. This mean, that there will be 10.6 of epochs.(Number if epochs is hard to set, you should stop when net does not learn any more, or it is overfitting)
   Val: 10k, batch size: 100, test_iterations: 100, So, 100*100: 10K, exacly all images from validation base.