Early Stopping Based on Validation Loss Plateau
Validation & Tuning DS practice problem on Onlearn.
Difficulty: medium.
Topics: Understanding Early Stopping Mechanisms in Neural Network Training, Patience Parameter, Minimum Delta, Validation Loss Tracking, Best Model State Retention, Training Convergence, Machine Learning, Supervised Learning, Deep Learning, Optimization Algorithms, Model Regularization, Validation Sets, Overfitting Mitigation, Hyperparameter Tuning, Training Loops, Generalization Error.
Implement an EarlyStopping class that monitors validation loss. The class should track the 'best' loss observed so far. If the loss does not improve by at least 'min delta' for 'patience' consecutive epochs, it should signal that training should stop. Provide a method 'step(val loss)' that returns a boolean indicating whether to stop training.