Summary

This article discusses the importance of choosing the right batch size for a neural network to achieve optimal training speed, accuracy, and efficient use of computer resources.

Abstract

The article explains that the batch size is a crucial hyperparameter in deep learning models, as it directly impacts the model's performance and training time. The batch size refers to the number of training instances in a batch, and it is specified using the batch_size argument in the model.fit() method in Keras. The acceptable values for the batch size are 16, 32, 64, 128, 256, 512, and 1024, which are limited by the memory requirements of the GPU and the architecture of the CPU. The article also discusses the three variants of the gradient descent optimization algorithm, which differ in the batch size chosen. General guidelines for choosing the right batch size are provided, including starting with the default batch size of 32, trying smaller batch sizes first, and considering the nature of the dataset, network architecture, and type of optimizer.

Opinions

The minimum acceptable value for the batch size is 1, while the maximum acceptable value is the size of the full training dataset.
The default value for the batch size is 32, which is specified by batch_size=None.
It is a good practice to start with the default batch size of 32 and then try other values if not satisfied with the default value.
A large batch size typically requires a lot of computational resources to complete an epoch but requires fewer epochs to converge.
A small batch size typically requires less computational resources to complete an epoch but requires a high number of epochs to converge.
The nature of the dataset, neural network architecture, network structure, and type of optimizer have a direct impact on the batch size.
There is a strong positive correlation between the batch size and the learning rate. When the learning rate is high, larger batch sizes give better results and vice versa.

Determining the Right Batch Size for a Neural Network to Get Better and Faster Results

Guidelines for choosing the right batch size to maintain optimal training speed and accuracy while saving computer resources

In a previous post, we discussed that many hyperparameters need to be adjusted before training a natural network.

The learning rate is the most important hyperparameter that we need to configure before training.

The batch size is another important hyperparameter that we need to adjust before the training process because it directly impacts on the model’s performance and training time.

Beginners in deep learning always ask:

How to determine the right batch size that will help a neural network to achieve the highest performance in the shortest period of time.

In this post, I will address this question in depth by providing more details about the batch size.

What are batches and why do we need them?

Deep learning models require very large datasets to achieve high performance. When the dataset has thousands of millions of rows (observations/instances), it would be almost impossible to fit the entire dataset in the computer’s memory and each training step would be time-consuming and computationally expensive if we use the entire dataset for each gradient update during the training process. To address these issues, we use batches which are parts/portions of the entire dataset.

Batch size refers to the number of training instances in the batch — Source: All You Need to Know about Batch Size, Epochs and Training Steps in a Neural Network

In Keras, the batch size is specified using the batch_sizeargument in the model.fit()method.

Acceptable values for the batch size hyperparameter

The minimum acceptable value for the batch size is 1 which uses each training instance in the dataset to complete one training step (perform one gradient update).

The maximum acceptable value for the batch size is m which is the size of the full training dataset.

The default value for the batch size is 32 which is specified by batch_size=None.

Minimum value = 1
Maximum value = m
Default value = 32

We can literally use any integer value between 1 and m for the batch size. But we are limited to using the batch sizes with the power of 2 starting from 16 until 1024. This is because the batch size needs to fit the memory requirements of the GPU and the architecture of the CPU.

So, the acceptable values for the batch size are 16, 32, 64, 128, 256, 512 and 1024!

Batch size and variants of gradient descent

The three variants of the gradient descent optimization algorithm differ in the batch size that we choose.

Batch gradient descent: batch size = m (size of the full training dataset)
Stochastic gradient descent: batch size = 1
Mini-batch gradient descent: 1 < batch size < m

General guidelines for choosing the right batch size

The following guidelines will help you to choose the right batch size for your neural network model to maintain optimal training speed and accuracy while utilizing available computer resources.

Always use the above acceptable values for the batch size.
It is a good practice to start with the default batch size of 32 and then try other values if you’re not satisfied with the default value.
It is better to try smaller batch sizes first.
A large batch size typically requires a lot of computational resources to complete an epoch but requires fewer epochs to converge.
A small batch size typically requires less computational resources to complete an epoch but requires a high number of epochs to converge.
So, we should increase the number of epochs significantly when the batch size is too small.
A small batch size with a low learning rate will generally give better performance but will take longer to converge.
Too large batch sizes will take longer to converge without any additional performance gain.
The nature (size and complexity) of the dataset, neural network architecture (e.g. MLP, CNN), network structure (width and depth) and type of optimizer have a direct impact on the batch size.
There is a strong positive correlation between the batch size and the learning rate. When the learning rate is high, larger batch sizes give better results and vice versa.

This is the end of today’s post.

Please let me know if you’ve any questions or feedback.

Thank you so much for your continuous support! See you in the next article. Happy learning to everyone!

Rukshan Pramoditha 2022–09–26