This article attempts to demystify batch size, answering all your questions, including: "What is batchsize?", "Why is batch size important?", "Can batch size affect behavior?", "Can batch size be too large?, "Can batch size be too small?", and more. Information is presented here in question/answer format.
How to choose batch size
This is best answered by a tweet from Yann Lecun, succinctly the answer is: 32. For more background, read Revisiting Small Batch Training for Deep Neural Networks [Masters, Luschi, 2018].
Batch normalization is a technique used in deep learning to normalize the input layer by adjusting and scaling the activations. Batch normalization can help to speed up the training process and sometimes allows for using larger batch sizes.
Various research studies have shown that using a batch size of 32 can lead to faster convergence and better generalization performance compared to using larger batch sizes (see resources below). The optimal batch size depends on the specific problem and the resources available. It can also depend on the number of parameters in the model, the data distribution, and the optimization algorithm used. It is common practice to experiment with different batch sizes to find the best one for a given task.
What is batch size?
Batch size is the number of samples used in one iteration of training. The samples are processed in a batch, rather than one at a time. The batch size can be a fixed number or a variable, depending on the implementation.
Why is batch size important?
Batch size can affect the performance and stability of the training process.
Can batch size influence behavior (model accuracy, overfitting, training time)?
These aspects can be influenced by batch size as follows:
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Model accuracy: gradients are based on the average of the samples in the batch. A larger batch size can lead to a less optimal model because the gradients are based on the average of the samples in the batch, which can cause the optimizer to converge to a suboptimal solution. A smaller batch size can provide more accurate gradients but can be computationally expensive.
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Overfitting: Overfitting can occur when the model is too complex for the given task and it starts to memorize the training data instead of generalizing to new examples. Using smaller batch sizes can help to reduce overfitting as it provides more accurate gradients and make the optimization process more stable, but it also increases the number of iterations needed to cover the same amount of data. Techniques such as regularization, dropout or data augmentation can also help to reduce overfitting, but they may not replace the need to use small batch sizes.
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Training time: Larger batch sizes can lead to faster training times because more samples are processed at once, which can take advantage of parallel processing capabilities of modern hardware such as GPUs. However, this can also increase the memory requirements, as the larger batch sizes require more memory to store the activations and gradients.
Can batch size be too large?
Larger batch sizes can lead to faster training times because more samples are processed at once, which can take advantage of parallel processing capabilities of modern hardware such as GPUs. However, using a larger batch size can also lead to a less optimal model because the gradients are based on the average of the samples in the batch, which can cause the optimizer to converge to a suboptimal solution.
Can batch size be too small?
Using a smaller batch size allows for more frequent updates to the model's parameters. In deep learning, the goal is to minimize the loss function, which measures the difference between the model's predictions and the true values. By updating the model's parameters more frequently, the model can make small adjustments to the parameters, which can lead to a more precise optimization of the loss function.
For this reason, smaller batch sizes can lead to slower training, but can result in a more optimal model. The gradients are based on fewer samples, which can reduce the noise and make the optimization process more stable. However, this also means that the optimizer takes more steps to cover the same amount of data, which can be computationally expensive.
Resources:
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"The Batch Normalization Training Trick": https://arxiv.org/abs/1502.03167
Introduces the batch normalization technique, which is used to improve the stability and performance of neural networks by normalizing the activations of each layer. The authors found that using a batch size of 32 led to faster convergence and better performance compared to using larger batch sizes. -
"Don't Decay the Learning Rate, Increase the Batch Size": https://arxiv.org/abs/1711.00489
Shows that increasing the batch size can lead to faster convergence and better generalization performance compared to using a larger learning rate. The authors found that using a batch size of 32 led to the best performance for various models and datasets. -
"On the Effects of Batch Size on Training Dynamics and Generalization Error": https://arxiv.org/abs/1905.05719
Examines the effects of batch size on the training dynamics and generalization performance of neural networks. The authors found that using a batch size of 32 led to faster convergence and better generalization performance compared to using larger batch sizes. -
"Large Batch Training of Neural Networks": https://arxiv.org/abs/1708.03888
Shows that large batch training can lead to better generalization performance and faster convergence compared to small batch training. The authors found that using a batch size of 32 led to the best performance for various models and datasets