Accuracy and Loss Don’t Change in CNN. Is It Over-Fitting?

Answer : No, if accuracy and loss don’t change, it’s more indicative of underfitting or learning issues, not overfitting.

When training a Convolutional Neural Network (CNN), encountering a situation where both accuracy and loss remain constant over epochs does not typically suggest overfitting. Instead, this scenario is often indicative of underfitting or other learning-related challenges. Overfitting is characterized by a high training accuracy with a significant drop in validation accuracy, whereas constant accuracy and loss suggest that the model is not learning effectively from the training data.

Key Indicators and Their Implications:

Indicator	Implication
Constant Loss	The model is not improving its predictions.
Constant Accuracy	The model is not learning from the training data.
High Training Accuracy, Low Validation Accuracy	Overfitting (not the case here).

Potential Causes:

• Inadequate Model Complexity: The model may be too simple to capture the underlying patterns in the data.
• Improper Learning Rate: Too high a learning rate can cause the model to overshoot the optimal, while too low a rate may result in minimal updates to weights.
• Faulty Data Processing: Errors in data preprocessing or augmentation can lead to ineffective training.
• Suboptimal Initialization: Poor initialization of weights can hinder the learning process.

Conclusion:

Stagnant accuracy and loss in a CNN signal that the model is not learning effectively, pointing towards underfitting or other learning issues rather than overfitting. Addressing this requires revisiting the model architecture, learning rate, data preprocessing, and initialization strategy to ensure the model can learn and generalize from the training data.