Answer: An ‘epoch’ represents one pass through the entire training dataset, while an ‘iteration’ corresponds to one update of the model’s parameters using a mini-batch of data during training.
Epoch:
- An epoch signifies the completion of one full cycle through the entire training dataset.
- During each epoch, the model processes all training examples once, adjusting its weights and biases based on the observed errors.
- Multiple epochs are often required to sufficiently train the model and optimize its performance.
- The number of epochs is a hyperparameter that needs to be determined based on the dataset’s size, complexity, and convergence characteristics.
Iteration:
- An iteration occurs each time the model updates its parameters using a mini-batch of data.
- Instead of processing the entire dataset at once, training occurs in smaller batches to improve efficiency and convergence.
- After processing each mini-batch, the model calculates gradients and updates its parameters using optimization algorithms such as gradient descent.
- The number of iterations per epoch depends on the batch size, where one epoch consists of multiple iterations until all batches are processed.
‘Epoch’ vs ‘Iteration’ in Training Neural Networks:Comparison
| Aspect | Epoch | Iteration |
|---|---|---|
| Definition | One pass through the entire training dataset | One update of model parameters using a mini-batch |
| Processing | Entire dataset processed once | Mini-batches of data processed iteratively |
| Duration | Longer duration per epoch | Shorter duration per iteration |
| Control | Determines the number of complete passes through the dataset | Influences the convergence of the model |
Conclusion:
Understanding the distinction between epochs and iterations is crucial for effectively monitoring and controlling the training process of neural networks, optimizing model performance, and managing computational resources.