Answers.Body: <p>Whilst minibatch size can make a difference to eventual neural network performance, when combined with changes to other hyperparameters such as learning rate, in my experience the impact is relatively small. It's not worth a lot of time investment to tune this parameter for an end result metric (e.g. accuracy).</p>
  <p>It is fairly common training strategy for minibatch size to focus on the impact to training time instead, and use the largest minibatch that safely fits all training calculations into available GPU RAM. This should allow the training code to take as much advantage of parallel processing as possible.</p>
  <p>As a side effect, this can also improve eventual results because faster training can be repeated more often with changes to <em>other</em> hyperparameters, so you can perform more experiments to tune those.</p>
  <p>Unfortunately no-one can give you an ironclad statement about the exact size of minibatch that will give the best results. But knowing very little else about your project, and not having trained a chess evaluator before, I would still recommend starting with as large a minibatch as your hardware can comfortably handle.</p>
  <blockquote>
  <p>Or do you think I should test each batch size with a smaller dataset and choose the one that has the best validation loss?</p>
  </blockquote>
  <p>This is a reasonable strategy for tuning other hyperparameters.</p>
  <p>You can also try training any given architecture several times, scaling up the training set size, and plot a learning curve of your target metric against dataset size. This will give you a sense of whether your network has the capability to take advantage of more data.</p>
  <p>Don't over-tune on these data scaling tests though, especially for regularization hyperparameters, because stronger regularisation may squeeze better generalisation out of smaller datasets, at the expense of reducing the capacity to learn from larger ones.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-08-23T09:04:26.727Z
Answers.Text: thanks for the reply! I also noticed that there wasn’t a big difference with batch sizes when I trained it with around 10 million positions. However, I wasn’t sure if it would be the same with a larger dataset. But now, I'll go with 128 as the batch size. Thanks!
Answers.UserDisplayName: Semih Özalp
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-08-23T08:49:13.183Z
Answers.LastActivityDate: 2024-08-23T08:56:44.650Z
Answers.LastEditDate: 2024-08-23T08:56:44.650Z
Answers.UserDisplayName: Neil Slater
Body: <p>I am currently training a neural network to evaluate chess positions. Everything so far works great, but I am unsure which batch size to choose. I am considering batch sizes of 32, 64, or 128. Obviously 32 is the slowest and 128 is the fastest. Is it worth training with a smaller batch size, even if it takes more than 3x (32 takes 3x longer than 128) longer?</p>
  <p>Given that I am training with around 37 million positions and validating with around 3.6 million positions, which batch size would you recommend? Or do you think I should test each batch size with a smaller dataset and choose the one that has the best validation loss?</p>
  <p>Thanks in advance!</p>
  
Tags: <neural-networks><machine-learning><training>
Title: Which batch size is optimal for my neural network?
UserDisplayName: Semih Özalp
ThreadText: # Which batch size is optimal for my neural network?
  
  (Asked by: username_0 on 2024-08-23)
  
  I am currently training a neural network to evaluate chess positions. Everything so far works great, but I am unsure which batch size to choose. I am considering batch sizes of 32, 64, or 128. Obviously 32 is the slowest and 128 is the fastest. Is it worth training with a smaller batch size, even if it takes more than 3x (32 takes 3x longer than 128) longer?
  
  Given that I am training with around 37 million positions and validating with around 3.6 million positions, which batch size would you recommend? Or do you think I should test each batch size with a smaller dataset and choose the one that has the best validation loss?
  
  Thanks in advance!
  
  **Tags:** neural-networks, machine-learning, training
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-08-23. Score: 1 | Accepted answer
  
  Whilst minibatch size can make a difference to eventual neural network performance, when combined with changes to other hyperparameters such as learning rate, in my experience the impact is relatively small. It's not worth a lot of time investment to tune this parameter for an end result metric (e.g. accuracy).
  
  It is fairly common training strategy for minibatch size to focus on the impact to training time instead, and use the largest minibatch that safely fits all training calculations into available GPU RAM. This should allow the training code to take as much advantage of parallel processing as possible.
  
  As a side effect, this can also improve eventual results because faster training can be repeated more often with changes to *other* hyperparameters, so you can perform more experiments to tune those.
  
  Unfortunately no-one can give you an ironclad statement about the exact size of minibatch that will give the best results. But knowing very little else about your project, and not having trained a chess evaluator before, I would still recommend starting with as large a minibatch as your hardware can comfortably handle.
  
  > Or do you think I should test each batch size with a smaller dataset and choose the one that has the best validation loss?
  
  This is a reasonable strategy for tuning other hyperparameters.
  
  You can also try training any given architecture several times, scaling up the training set size, and plot a learning curve of your target metric against dataset size. This will give you a sense of whether your network has the capability to take advantage of more data.
  
  Don't over-tune on these data scaling tests though, especially for regularization hyperparameters, because stronger regularisation may squeeze better generalisation out of smaller datasets, at the expense of reducing the capacity to learn from larger ones.
  
  > **Comments:**
  > **username_0**: thanks for the reply! I also noticed that there wasn’t a big difference with batch sizes when I trained it with around 10 million positions. However, I wasn’t sure if it would be the same with a larger dataset. But now, I'll go with 128 as the batch size. Thanks!  
  
  ---
  
  