Epoch

In machine learning, an epoch refers to one complete pass of the entire training dataset through the neural network model. During each epoch, the model is exposed to all of the training examples and makes adjustments to its internal parameters in order to minimize the loss function. The number of epochs is a hyperparameter that can be tuned to optimize the performance of the model.

How does it work?

Training a model on multiple epochs means repeatedly exposing it to the entire training dataset. Each time the model sees the entire dataset, it is called an epoch. The goal of training on multiple epochs is to allow the model to learn the patterns and relationships in the data more effectively.

Here is how training on multiple epochs works in more detail:

1. The model is initialized with random weights.
2. The training dataset is divided into batches.
3. The model is fed a batch of data and its predictions are compared to the true labels.
4. The error between the predictions and the true labels is calculated.
5. The model’s weights are updated to minimize the error.
6. Steps 3-5 are repeated until all batches have been processed.
7. This competes one epoch.
8. Steps 2-7 are repeated for the desired number of epochs.

As the model sees the training data more times, it is able to learn the patterns and relationships in the data more effectively. This leads to improved performance on the training data and, eventually, on unseen data.

Optimization

In general, more epochs will lead to better performance, but there is a point of diminishing returns. If too many epochs are used, the model may begin to overfit the training data, which means that it will perform well on the training data but poorly on unseen data.

The optimal number of epochs for a particular model will depend on the complexity of the model, the size of the training dataset, and the specific task at hand. It is often a good idea to experiment with different values of the epoch hyperparameter to find the best value for a given model and task. Start with a small number of epochs and then increase the number of epochs if the model is not performing well.

Here is an example of how epochs are used in practice:

Suppose we are training a neural network model to classify images of handwriting digits. We have a training dataset of 10,000 images, each of which is labeled with the correct digit. We start by training the model for 10 epochs. At the end of each epoch, we evaluate the model’s performance on a validation dataset of 1,000 images. We find that the model’s accuracy is initially low, but it gradually improves with each epochs. After 10 epochs, the model’s accuracy has reached 95%.

We could continue training the model for more epochs, but we might start to see overfitting. If we train the model for 20 epochs, its accuracy on the validation dataset might actually start to decrease. This is because the model is starting to memorize the training data and is not generalizing well to unseen data.

In this case, the optimal number of epochs is 10. The model has learned to classify the digits accurately without overfitting the training data.