Monitoring the training process
Monitoring the training process
Monitoring the training process is essential for several reasons:
- Model Performance Evaluation: It helps assess how well the model is learning from the training data and generalising to unseen data.
- Preventing Overfitting: It aids in detecting overfitting, a situation where the model performs well on the training data but fails to generalise to new, unseen data.
- Hyperparameter Tuning: It provides insights into the effectiveness of different hyperparameters and model architectures.
Accuracy
Accuracy measures the proportion of correctly classified instances out of the total instances For classification tasks, it’s a commonly used metric Higher accuracy indicates better performance, but it might not be sufficient for imbalanced datasets For example, in a binary classification problem where 95% of the data belongs to one class, a 95% accuracy can be achieved by predicting that class all the time, ignoring the minority class importance of balanced training and validation sets!
Loss
Loss represents the error between the predicted values and the actual values - Examples: Cross-Entropy Loss for classification tasks and MSE for regression tasks. Lower loss values indicate better model performance. During training, the goal is to minimize the loss function.
Training Phase:
- The model is trained iteratively on batches of training data
- During each iteration, the model’s parameters are updated to minimize the training loss
- Accuracy and loss on the training set are calculated and logged after each epoch
Validation Phase:
- After each training epoch, the model’s performance is evaluated on the validation set (unseen during training)
- Accuracy and loss on the validation set are calculated
- If the training accuracy is significantly higher than the validation accuracy, or if the training loss continues to decrease while the validation loss increases, it indicates overfitting
Early stopping
Early stopping is a regularization technique used in machine learning to prevent overfitting and improve the generalisation of a model. The basic idea behind early stopping is to monitor the performance of the model on a validation dataset during training and stop training once the performance starts to degrade
How it works
- Validation Dataset: Early stopping requires a dataset separate from the training data, called the validation dataset. This dataset is not used for training the model but is used to evaluate the model’s performance during training.
- Monitoring Metrics: Typically, a metric such as accuracy, loss, or any other relevant metric is chosen to monitor the model’s performance on the validation dataset.
- Training Process: The model is trained iteratively on the training dataset. After each training iteration (epoch), the model’s performance is evaluated on the validation dataset using the chosen metric.
- Early Stopping Criteria: If the performance metric on the validation dataset starts to degrade (e.g., accuracy decreases or loss increases) after a certain number of epochs, early stopping is triggered.
- Stopping the Training: Once early stopping is triggered, the training process is halted, and the current model is considered the final model. The number of epochs to wait before triggering early stopping, often called patience, is a hyperparameter that can be tuned.
Benefits of early stopping
- Prevents Overfitting: Early stopping helps prevent overfitting by stopping the training process before the model becomes too specific to the training data and loses its ability to generalize to unseen data.
- Saves Time and Resources: It saves time and computational resources by avoiding unnecessary training epochs, especially in deep learning where training can be time-consuming.