问题
When training a net does it matter if the number of samples in the epoch is not an exact multiple of the batch size? My training code doesnt seem to mind if this is the case, though my loss curve is pretty noisy at the moment (in case that is a related issue).
This would be useful to know, as if it is not an issue it saves on messing around with the dataset to make it quantized by batch size. It may also be less wasteful of captured data.
回答1:
does it matter if the number of samples in the epoch is not an exact multiple of the batch size
No, it does not. Your number of samples can be say 1000, and your batch size can be 400.
You can decide the total number of iterations (where each iteration = sampling a batch and doing gradient descent) based on the overall number of epochs you want to cover. Say, you want to have roughly 5 epochs, then roughly your number of iterations >= 5 * 1000 / 400 = 13. So you will sample a random batch 13 times to get roughly 5 epochs.
回答2:
In the context of Convolution Neural Networks (CNN), Batch size is the number of examples that are fed to the algorithm at a time. This is normally some small power of 2 like 32,64,128 etc. During training an optimization algorithm computes the average cost over a batch then runs backpropagation to update the weights. In a single epoch the algorithm is run with $n_{batches} = {n_{examples} \over batchsize} $ times. Generally the algorithm needs to train for several epochs to achieve convergence of weight values. Every batch is normally sampled randomly from the whole example set.
The idea is this: mini-batch optimization wrt (x1,..., xn) is equivalent to consecutive optimization steps wrt x1, ..., xn inputs, because the gradient is a linear operator. This means that mini-batch update equals to the sum of its individual updates. Important note here: I assume that NN doesn't apply batch-norm or any other layer that adds an explicit variation to the inference model (in this case the math is a bit more hairy).
So the batch size can be seen as a pure computational idea that speeds up the optimization through vectorization and parallel computing. Assuming that one can afford arbitrarily long training and the data are properly shuffled, the batch size can be set to any value. But it isn't automatically true for all hyperparameters, for example very high learning rate can easily force the optimization to diverge, so don't make a mistake thinking hyperparamer tuning isn't important in general.
来源：https://stackoverflow.com/questions/50424938/does-epoch-size-need-to-be-an-exact-mutiple-of-batch-size