What is epoch in machine learning? Understanding its role and importance

Machine learning is the science of developing algorithms that perform tasks without explicit instructions. These algorithms use statistical models to identify patterns in existing data and make an inference or prediction for new data. For example, ML models analyze an existing data set of prelabelled cat and dog images. Then, they predict whether a previously unknown image (not found in the initial training data set) is a cat or dog.

Most machine learning models are internally made of neural networks. Neural networks are layers of interconnected software components called nodes that work together to process data. Each node looks at different data aspects or features—like eye shape, ear shape, or nose shape and performs mathematical calculations. Results from individual nodes are combined to find the final solution.

You must understand the training process to learn more about machine learning and neural networks. This article covers terms like epoch, batch, iterations, and stochastic gradient descent so you can get started with your own ML projects in your organization.

What is an epoch?What is an epoch?

An epoch in machine learning occurs when the machine learning model completes one pass through the training dataset. You can think of this process as going through your entire study material once. Every time you read through to learn, you complete an epoch in machine language terms.

Data engineers repeatedly feed the same training data to the model so it can identify new patterns and gain a deeper understanding of the data. After every epoch, the model updates its internal mathematical calculations based on the data it was just fed. It mainly adjusts weights and biases, mathematical factors that determine how different neurons influence each other’s output.

What happens in an epochWhat happens in an epoch

Here is a closer look at what occurs during an epoch.

• Forward pass. Each sample in the training dataset is passed through the network to compute the output. This involves using the current values of the network’s weights and biases to calculate the output for each input sample.
• Loss calculation. After the output is obtained, a loss (or cost) function calculates the prediction error by comparing it to the expected output. This error provides a measure of the network’s performance.
• Backward pass. The error is then propagated back through the network, updating the weights and biases. This step is critical as it helps to minimize the loss by adjusting the model parameters (weights and biases).
• Parameter update. The weights and biases are updated. The specific adjustments are determined by the gradients of the loss function with respect to each parameter.

Number of epochsNumber of epochs

It’s important to mention that the number of epochs needed for model training can vary and is set by the data engineer. In most cases, it depends on the data’s complexity and the desired accuracy level. This means training can run for tens, hundreds, or even thousands of epochs until the model generates accurate predictions for new data.

Generally, increasing the number of epochs leads to better model performance because it learns more complex patterns in the data. But be careful; too many epochs may cause the model to overfit! Accuracy drops if the unknown data is too different from the training data set. For example, if the training data contained only images of cats and dogs in a park, the model may not be able to identify a cat on a beach.

Batch, batch size, iterationBatch, batch size, iteration

A batch is a smaller portion of the entire training dataset. A large training dataset is usually split into smaller groups called batches or mini-batches for efficient model training. The model can process data in smaller chunks without problems like insufficient storage space. The batch determines how many samples will pass before updating the model’s weights.

The batch size shows the number of training examples in a single batch. For example, 10,000 data samples can be divided into ten batches of batch size 1000. The process of breaking down the dataset is called batch processing.

IterationIteration

Every time an algorithm processes a batch, it updates its internal parameters based on that data in preparation for the next batch. This update helps the model improve its performance on the learning task and reduce errors. This is also called an iteration. Multiple iterations make up an epoch.
So, if:

• N = Total number of examples
• B = Batch size
• I = Iteration

Then,

• I = N/B
• In 10000 samples of batch size 1000, I = 10000/1000
• Therefore, I = 10
• Therefore, it’ll take ten iterations to complete one epoch.

What is the difference between epoch and batch?What is the difference between epoch and batch?

As discussed above, a batch is a subsection of the complete training dataset. The quantity of samples in each batch is the batch size. On the other hand, an epoch is when all batches complete one pass through the algorithm.

Here’s the difference between epoch and batch in a simple table:

Feature	Epoch	Batch
Concept	An epoch happens when all the training data has passed through the algorithm once.	A batch is when all the training data has been divided into smaller sections for easier management.
Role in training	Epochs offer a macro view of the training process.	Batches provide a micro view.
Purpose	Epoch in machine learning does not directly address computational efficiency and is particularly about the learning regimen.	Batches are used to manage computational load and memory, ensuring models are trained and large datasets that can’t fit into memory at once.
Value range	The number of epochs can be any value from 1 to infinity. This number determines how many times the model sees the entire dataset.	The batch size has a minimum size of 1 and a maximum size that’s less than or equal to the number of samples in the training dataset.
Who specifies	The number of epochs is a hyperparameter that the data engineer must specify to the algorithm.	The batch size is also a hyperparameter that the data engineer sets.

How are parameters updated in an epoch?How are parameters updated in an epoch?

Parameters are updated when a batch is processed. There are three modes of batch processing:

— Batch mode
The entire training dataset is considered a single batch (B = N). This means the model processes the entire dataset at once before updating its internal parameters.

One major advantage of this type of batch processing is that the model can reach convergence (optimal performance) in fewer epochs than other modes.

On the flip side, this type of processing is computationally expensive, particularly for complex models. It’ll also require a lot of memory to hold the entire dataset. There are also no frequent parameter updates, unlike in other modes.

— Mini-batch mode
This is the most common type of batch processing where the training data is broken down into smaller, manageable groups called mini-batches. The model processes one mini-batch at a time and updates its parameters after each mini-batch.

With the mini-batch mode, you’ll rarely run into memory usage problems, meaning more speed. There’s also the potential for more frequent parameter updates. However, it’ll require more epochs to converge. You may also need to experiment a few times to find the optimal mini-batch size.

— Stochastic mode
The stochastic mode uses unique batch sizing, which means the gradient and parameters are changed after each sample. Stochastic gradient descent (SGD) is the optimization algorithm used to identify the set of internal model parameters that yield the closest match between predicted and actual outputs.

The SGD algorithm leverages the concept of an error gradient to achieve convergence. This essentially means it follows the slope of the error surface with respect to model parameters. It guides the parameter optimization process towards the minimum error level by iteratively descending this slope. Predictions are generated at each step using a specific sample and the current parameter set. The discrepancy between predictions and expected results is quantified as the error. Subsequently, the internal parameters are adjusted to minimize this error in the next iteration.

Example of epoch in machine learningExample of epoch in machine learning

To better understand the concept of an epoch in machine learning, let’s quickly discuss a specific example of mini-batch processing. Let’s imagine you’re training a model to predict movie ratings. Your entire dataset has information on 5000 movies (samples) with details like genre, cast, producer, and director. You could set the training to run for 20 epochs and choose a batch size of 100.

Now, here’s how everything plays out:

In the first epoch, the data is shuffled and then split into 50 batches of 100 movies each. The model takes each batch, analyzes the features of the 100 movies, compares its predictions to actual ratings, and adjusts its internal weights. Thus, the model adjusts its weights 50 times across all 50 batches or iterations.

Then, data engineers shuffle the data, batch it again, and repeat the training process for 19 more epochs. By the end, the model has been exposed to the entire dataset 20 times (completing 20 epochs). During this process, its weights were updated a total of 1000 times as 50 batches/epoch X 20 epochs = 1000 iterations.

Why is epoch essential in machine learning?Why is epoch essential in machine learning?

Epochs are important when training machine learning models. An epoch helps to identify the model that best fits the training data. Here are a few more reasons why this is a crucial concept in ML.

• Improved model performance. The number of epochs is an essential hyperparameter during training. Setting too few epochs can cause underfitting, where the model doesn’t adequately learn the patterns in the data. On the flip side, too many epochs can lead to overfitting, where the model becomes overly tuned to the training data’s noise, causing poor generalization to new and unseen data. We can find a sweet spot where the model learns the underlying patterns without memorizing noise by choosing the right number of epochs.
• Simplifying early stopping. Monitoring performance metrics across epochs is important for techniques like early stopping. This method halts training when the model’s performance on a validation set stops improving or starts to decline. Early stopping can also prevent overfitting and save computational resources.
• Better insights into learning dynamics. As epochs progress, users often get more valuable insights into the model’s learning dynamics. For example, by observing how quickly the model learns and when it begins to plateau, users can make informed decisions about adjusting the learning rate, batch size, and other model parameters to improve performance.

How to choose the number of epochs?How to choose the number of epochs?

Although more epochs can improve the model’s accuracy, it also increases the training time. At the same time, a single epoch is usually not enough to optimally modify the model’s weight because of the need for learning optimization. This makes it important to find a sweet spot to avoid underfitting or overfitting the data.

There are a handful of effective techniques for obtaining the optimal number of epochs.

• Early stopping halts training when the model’s performance on a validation set starts to decline, preventing overfitting.
• Cross-validation splits the training data into multiple folds and trains the model with different epoch values on each fold. The epoch value that performs best on average across the validation folds is then chosen for the final model.
• Transfer learning leverages pre-trained models to achieve good results with fewer epochs needed for training compared to training from scratch.

ConclusionConclusion

Epochs and batch sizes are essential hyperparameters in training machine learning models. Epochs represent the number of times the entire training dataset passed through the algorithm, while batch size determines the number of training samples processed before updating the model’s parameters. A batch is a small fraction of the entire dataset that was broken down to make it computationally efficient to feed the model without losing memory.