Training deep learning models using epochs (sampling batches from data without replacement) is a common practice for several reasons:
Diverse Data Exposure: By using epochs, you ensure that the model sees the entire dataset multiple times during training. This exposure to diverse data samples helps the model generalize better. If you sample data with replacement, some data points might be seen more frequently than others, potentially leading to overfitting on those data points and poorer generalization.
Convergence and Stability: Training deep learning models can be a complex optimization process. By presenting the data in different orders during each epoch, you can help the model converge more efficiently and achieve a more stable optimization process. This is because the order of data points and the initial model weights can affect the optimization trajectory, and cycling through epochs can help the model escape local minima.
Data Augmentation: In many deep learning tasks, data augmentation techniques are used to artificially increase the size of the training dataset. These techniques generate new samples from existing data through transformations (e.g., rotations, flips, and crops in image data). When using epochs, data augmentation can be applied differently to each epoch, further increasing the diversity of the data the model is exposed to.
Monitoring Progress: When you train a model using epochs, you can monitor the model's performance (e.g., loss and accuracy) on a validation set at the end of each epoch. This allows you to track how the model's performance evolves over time and make decisions such as early stopping if the performance plateaus or degrades.
Shuffling Data: To prevent any potential bias introduced by the order of the data, it's common to shuffle the dataset before each epoch. Shuffling ensures that the model doesn't see correlated patterns in the same order, which can help it learn more robust representations.
However, there may be scenarios where you might consider sampling data with replacement:
Large Datasets: If you have an extremely large dataset and computational resources are limited, it may not be feasible to train on the entire dataset in each epoch. In such cases, you might consider random sampling with replacement to reduce the training time while still benefiting from diversity.
Stochastic Gradient Descent (SGD): In some optimization algorithms, like stochastic gradient descent (SGD), sampling data points with replacement is an inherent part of the algorithm. SGD updates the model's weights after each data point or batch, and it doesn't require going through the entire dataset in each epoch.
In summary, using epochs without replacement is a common practice in deep learning because it promotes better generalization, convergence, and model stability. However, the choice between using epochs and sampling with replacement depends on factors like the dataset size, available computational resources, and the specific optimization algorithm being used.