I've seen similar conclusion from many discussions, that as the minibatch size gets larger the convergence of SGD actually gets harder/worse, for example this paper and this answer. Also I've heard of people using tricks like small learning rates or batch sizes in the early stage to address this difficulty with large batch sizes.

However it seems counter-intuitive as the average loss of a minibatch can be thought of as an approximation to the expected loss over the data distribution, $$\frac{1}{|X|}\sum_{x\in X} l(x,w)\approx E_{x\sim p_{data}}[l(x,w)]$$ the larger the batch size the more accurate it's supposed to be. Why in practice is it not the case?

Here are some of my (probably wrong) thoughts that try to explain.

The parameters of the model highly depend on each other, when the batch gets too large it will affect too many parameters at once, such that its hard for the parameters to reach a stable inherent dependency? (like the internal covariate shift problem mentioned in the batch normalization paper)

Or when nearly all the parameters are responsible in every iteration they will tend to learn redundant implicit patterns hence reduces the capacity of the model? (I mean say for digit classification problems some patterns should be responsible for dots, some for edges, but when this happens every pattern tries to be responsible for all shapes).

Or is it because the when the batches size gets closer to the scale of the training set, the minibatches can no longer be seen as i.i.d from the data distribution, as there will be a large probability for correlated minibatches?

As pointed out in Benoit Sanchez's answer one important reason is that large minibatches require more computation to complete one update, and most of the analyses use a fix amount of training epochs for comparison.

However this paper (Wilson and Martinez, 2003) shows that a larger batch size is still slightly disadvantageous even given enough amount of training epochs. Is that generally the case? enter image description here


4 Answers 4


Sure one update with a big minibatch is "better" (in terms of accuracy) than one update with a small minibatch. This can be seen in the table you copied in your question (call $N$ the sample size):

  • batch size 1: number of updates $27N$
  • batch size 20,000: number of updates $8343\times\frac{N}{20000}\approx 0.47N$

You can see that with bigger batches you need much fewer updates for the same accuracy.

But it can't be compared because it's not processing the same amount of data. I'm quoting the first article:

"We compare the effect of executing $k$ SGD iterations with small minibatches $B_j$ versus a single iteration with a large minibatch $\displaystyle\bigcup_{1\leq j\leq k} B_j$"

Here it's about processing the same amount of data and while there is small overhead for multiple mini-batches, this takes comparable processing resources.

There are several ways to understand why several updates is better (for the same amount of data being read). It's the key idea of stochastic gradient descent vs. gradient descent. Instead of reading everything and then correct yourself at the end, you correct yourself on the way, making the next reads more useful since you correct yourself from a better guess. Geometrically, several updates is better because you are drawing several segments, each in the direction of the (approximated) gradient at the start of each segment. while a single big update is a single segment from the very start in the direction of the (exact) gradient. It's better to change direction several times even if the direction is less precise.

The size of mini-batches is essentially the frequency of updates: the smaller minibatches the more updates. At one extreme (minibatch=dataset) you have gradient descent. 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. While you get a slightly smaller loss function on the training set, you don't get real predictive power. You are only looking for the very precise optimum but it does not help. If the loss function is correctly regularized (which prevents over-fitting) you don't exactly "over"-fit, you just uselessly "hyper"-fit. This shows as a non significant change in accuracy on the test set.

  • 1
    $\begingroup$ thanks, it makes great sense. So basically if doing the same amount of updates, then larger batch size will be at least as good right? $\endgroup$
    – dontloo
    Commented Dec 1, 2017 at 3:09
  • $\begingroup$ do you happen to know any published experiments on that (comparing different batch sizes with fixed number of updates)? $\endgroup$
    – dontloo
    Commented Dec 1, 2017 at 3:09
  • $\begingroup$ Yes for the same number of updates, bigger batches is always better. I don't know of a publication, if I ever find one, I'll post it. $\endgroup$ Commented Dec 1, 2017 at 9:44
  • $\begingroup$ I read the rest of your question (the table). Interestingly it shows results on a test set while the objective of gradient decent is to optimize on the training set. It is possible small batches avoid a certain kind of minor overfitting by randomizing the optimum. It's a subtle thing I have no intuitive catch about. $\endgroup$ Commented Dec 1, 2017 at 10:08
  • $\begingroup$ According to the article the difference in accuracy is not significant. They just want to point out that the accuracy is essentially the same. What they mainly want to point out is that SGD with small batches is much faster. $\endgroup$ Commented Dec 1, 2017 at 10:49

To add to Curtis White's answer (and adding some more references):

Yes SGD works as a type of regularization. This is important because otherwise, it's hard to explain why DNNs do not always overfit, because they can.

The reason, as I understand, is that SGD causes 'hopping around' in parameter space, so during training the parameters cannot stay in a narrow minimum, only in (or close to) wider ones. And these wider ones apparently [1] generalize better (aka, less overfitting).

More references:

  • Here's [2] another paper that formalizes this (or tries to, I didn't follow everything through, check for yourself!)
  • This paper [3] claims that there is a phase of "stochastic relaxation, or random diffusion" where the stochasticity inherent in SGD leads to "maximiz[ation of] the conditional entropy of the layer".

Both sort of say that SGD corresponds to an entropy regularization term.

There could definitely be other ways in which batch size influences convergence; this is the one I know of.

Editing in another reference, to show where people in practice observe that a smaller batch size improves generalization: [5]

[1] Example: "A Bayesian Perspective on Generalization and Stochastic Gradient Descent", Smith, Le, 2018. From the abstract: "We propose that the noise introduced by small mini-batches drives the parameters towards minima whose evidence is large."

[2] "Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks", Chaudhari, Soatto 2017

[3] "Opening the black box of Deep Neural Networks via Information" Schwartz-Ziv, Tishby, 2017

[4] "Understanding deep learning requires rethinking generalization", C. Zhang etc. 2016

[5] "On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima", N. S. Keskar et al 2016

  • $\begingroup$ (+1) Good references. btw, the first author on [4] is C. Zhang $\endgroup$
    – user20160
    Commented Feb 13, 2019 at 3:02
  • $\begingroup$ Oh, you're right! Edited it, thanks for the correction. $\endgroup$
    – dasWesen
    Commented Feb 13, 2019 at 18:15

A too large batch size can prevent convergence at least when using SGD and training MLP using Keras. As for why, I am not 100% sure whether it has to do with averaging of the gradients or that smaller updates provides greater probability of escaping the local minima.

See here.


From my experience with tensorflow and keras (which is confirmed by this article):

  • Higher batchsize results lower accuracy
  • Accuracy can be kept up if learning rate is increased when batchsize is high

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