Understanding Epochs in Neural Networks

In a neural network epoch is known as a complete pass through the data of the training set, where the weights of the network are updated.

However, perhaps the most important term associated with training neural network is the ‘epoch’. In other words, an epoch should be understood as one complete cycle in the entire data set used in training. Such periods are essential in enhancing the efficiency of neural networks during training. Epochs are an essential part of training a model, and this article will help to understand what epochs are, where they are used in the training process, and even provide the fragment of code with examples of epochs properly used.

What Is an Epoch?

An epoch is a full one pass through the training data set or the total number of iterations when all the examples in the training set are processed once through the network. In one cycle, the model moves through all the examples in the training set and adjusts the model parameters to reflect the new information seen in every example. In most cases, it is done in multiple epochs in which the neural network continues to feed on the dataset in attempts to adjust its weight parameters.

Training a neural network involves several key steps:

Forward Pass: Data from a given input provides the input to a particular network, and predictions are produced.

Loss Calculation: The actual values are compared to the predictions based on the model and the result as the loss is determined.

Backward Pass: The values of loss are then used to back propagate through the network in order to modify the weights of the model.

Parameter Update: The weights values and bias are then adjusted using the derivatives that were calculated during the back propagation step.

These steps are done for each of the batches of data in the dataset that are being used in the current loop. When all the batches have been traversed, it is referred to as an epoch, meaning that all the images have gone through the model once. Chain formation is then reset as the process begins anew to form the next epoch.

Importance of Epochs

Learning: They enable the use of multiple epochs which in a way, teach the model to subsequently make adjustments to its parameters.

Convergence: There are cases the model performs better with increased epochs since it gradually closes the gap and arrives at the nadir optimum loss.

Overfitting: Having too many epochs on the other hand can cause over fitting, this is a condition in which the model performs well on training data set, but poorly on the new set of data that is never encountered before.

Training a Neural Network with Epochs

Let’s look at an example using Python and TensorFlow to train a simple neural network on the MNIST dataset.

Step 1: Import Libraries and Load Data

Step 2: Define the Model

Step 3: Train the Model with Epochs

Monitoring Performance Across Epochs

TensorFlow’s fit method returns a history object that contains the loss and accuracy for each epoch. You can use this to monitor the model’s performance.

These plots can help you understand how the model’s performance evolves over epochs. If you notice that the training accuracy continues to improve while the validation accuracy plateaus or decreases, your model might be overfitting.

Practical Considerations for Epochs

1.Early Stopping: To prevent overfitting, you can use early stopping, which halts training when the validation loss stops improving.

2.Batch Size: The size of the mini-batches can affect the number of epochs needed for convergence.

3.Learning Rate: Adjusting the learning rate can help the model converge faster, reducing the number of required epochs.

Early Stopping Example

In this example, training will stop if the validation loss does not improve for 3 consecutive epochs.

Conclusion

Epochs are concepts that have to be fully understood when training artificial neural networks. So through attenuation of epochs, it is possible to put off underfitting or overfitting, obtaining reliable outcomes pertaining to new data. We can enhance deeper learning by applying techniques like early stoppage of the training process and checking different performance measures.

References :

1.DeepAI. (2020, June 25). Epoch. DeepAI. https://deepai.org/machine-learning-glossary-and-terms/epoch

2.GeeksforGeeks. (2024, March 20). Epoch in machine learning. GeeksforGeeks. https://www.geeksforgeeks.org/epoch-in-machine-learning/

3.Daniel. (2023, October 30). Epoch : An essential notion in real-time programming. Data Science Courses | DataScientest. https://datascientest.com/en/epoch-an-essential-notion