What Does Batch Size Mean in Deep Learning? An In-Depth Guide

Batch size in machine learning and deep learning is an important hyperparameter that determines how fast you can train a model. It is the number of samples passed through the neural network at a single time, referred to as an epoch. Finding the optimal batch size is important because you want to train your network to be as fast as possible while maintaining accuracy in the output. The amount of computational resources, such as graphical processing units (GPUs) you have available often limits your batch size.

Answer the question “What does batch size mean in deep learning?” as you learn about its impact on training dynamics, the types of batch processing available, how to optimize your batch size, and how to start in deep learning. 

What does batch size mean in deep learning?

In deep learning, the batch size is the number of training samples that pass forward and backward through a neural network in one epoch. Determining the correct batch size is crucial to the training process, as it helps determine the learning rate of the model. Research in deep learning continues to search for the optimal batch size for training, as some studies advocate for the largest batch size possible while others think that smaller batch sizes are better. In training a model, researchers typically find the optimal batch size by trial and error and usually identify a size between two and 128. 

Impact on training dynamics

Batch size impacts training dynamics in multiple ways since it affects both the training time and resource consumption of training a deep learning model. Batch size also impacts training dynamics in the following ways:

• With proper hyperparameter re-tuning, increasing batch size decreases the number of steps to reach the intended performance.

• Increasing the batch size may require the purchasing of new hardware, such as additional GPUs.

• If you’re using a cloud provider, increasing batch size may increase the usage costs billed to you by your provider.

• As you increase your batch size, other hyperparameters, such as learning rate and regularization, need retuning, which is time-consuming and potentially complex.

Types of batch processing

Different types of batch processing or gradient descent exist depending on the needs of your deep learning model. Three popular options include:

• Batch gradient descent

• Stochastic gradient descent 

• Mini-batch gradient descent

Each type of batch processing deals with data differently. Explore more about each type below. 

Batch gradient descent

Batch gradient descent, sometimes called gradient descent, performs error calculations for each sample in the training set. However, the algorithm prediction only updates parameters after the entire data set has gone through an iteration. This makes the batch size equal to the total number of training samples in the data set. Batch gradient descent is an efficient batch type at the risk of not always achieving the most accurate model. 

Stochastic gradient descent

Stochastic gradient descent (SGD) updates its parameters after each training sample goes through the model. This means that the batch size is set to one. This makes SGD sometimes faster and more accurate than batch gradient descent. This speed and accuracy come at the cost of computational efficiency and can lead to noisy gradients as the error rate frequency jumps around with the constant updates. 

Mini-batch gradient descent

Mini-batch gradient descent takes the best of both batch gradient descent and SGD and combines them into one method to get a blend of computational efficiency and accuracy. To do this, mini-batch gradient descent takes the entire data set and splits it into smaller batches, runs those batches through the model, and then performs updates to the parameters after each smaller batch. The batch size for this method is higher than one but less than the total number of samples in the dataset. 

Since deep learning models use very large datasets to train, mini-batch gradient descent is the most common method to use when training a neural network. 

Optimizing batch size for performance

The optimal batch size when training a deep learning model is usually the largest one your computer hardware can support. By optimizing the batch size, you control the speed and stability of the neural network learning performance. However, batch size is not something you want to tune itself because, for every batch size you test, you need to tune the hyperparameters around it, such as learning rate and regularization. 

Finding the optimal batch size

Finding the optimal batch size when training your deep learning model is a process of trial and error since calculating which batch size fits in your memory is difficult. Explore the following steps to help you find the optimal batch size when training a neural network:

1. Create a set of batch size experiments that increase by the power of two (2, 4, 8, 16, 32, 64…) until you go beyond your hardware memory. 

2. Consider the training throughput. Even if your hardware supports a larger batch size, once your training throughput no longer increases as batch size increases, use that as your maximum batch size. 

3. Consider the training time of different batch sizes. If increasing batch size no longer reduces the number of training steps, then you use that as your maximum since increasing it provides diminishing returns. 

4. Ensure that you properly re-tune other hyperparameters as you experiment with different batch sizes to achieve optimal model performance. Important hyperparameters to always re-tune for each batch size are the learning rate, momentum, and regularization. 

Finding the optimal batch size is an important early step in developing your deep learning model, as it becomes expensive, difficult, and time-consuming to re-tune each hyperparameter later on.

How to get started in deep learning

Deep learning builds on the principles of machine learning to create in-depth neural networks that function similarly to the human brain. It requires a basic understanding of linear algebra, data science, and programming. Once you have these basics down, consider these steps to enhance your knowledge of deep learning:

1. Take an online course in deep learning such as the Introduction to Deep Learning & Neural Networks with Keras from IBM on Coursera.

2. Learn about different deep learning frameworks such as TensorFlow, PyTorch, and Keras.

3. Consider a guided project such as the Deep Learning with PyTorch: Siamese Network on Coursera or create your own project from an online example. 

4. Practice regularly and find an online community to stay consistent.

Learn more about batch size in deep learning on Coursera

Batch size in deep learning is an important hyperparameter to monitor as you build neural networks. If you’re just starting in deep learning, try the Deep Learning Specialization from DeepLearning.AI on Coursera. You may develop skills in building and training neural networks as well as optimizing parameters. For a more advanced look, try the TensorFlow: Advanced Techniques Specialization also from DeepLearning.AI on Coursera.