Why doesn't validation loss decrease after certain epochs?

Key takeaways:

  1. Training loss measures model performance on training data, while validation loss assesses performance on unseen data, helping to identify overfitting.

  2. Common causes of plateau are:

    1. Insufficient data: Small datasets hinder model generalization.

    2. Data duality: Poor-quality data can lead to increased validation error.

    3. Learning rate: An inappropriate learning rate can prevent convergence.

    4. Model complexity: Overly complex models are prone to overfitting.

    5. Overfitting: A model may perform well on training data but poorly on validation data.

  3. The techniques to address plateau are:

    1. Early stopping: Monitors validation loss to prevent overfitting.

    2. Data augmentation: Expands datasets to improve training.

    3. Regularization: Helps control model weights and reduce overfitting.

    4. Dropout: Randomly disregards certain nodes to combat overfitting.

    5. Hyperparameter tuning: Adjusting parameters significantly affects training outcomes.

During the training of a machine learning model, we calculate different types of losses to evaluate the performance of a machine learning model. Training and validation losses are two common types, as discussed below:

  • Training loss: It measures the model performance on training data after each iteration. It is calculated based on the difference between the predicted and actual value of training data.

  • Validation loss: It tests the machine learning model on unseen data after each epoch. It aims to avoid model overfitting on training data and performs well on new unseen data.

Graph example of training and validation loss graphs

Common causes for validation loss plateau

There are numerous reasons for the validation loss of a machine learning model not to decrease after a certain number of epochs. Some of them are explained below:

  • Insufficient data: Validation loss does not decrease if the model is trained on a small dataset. The model does not generalize well because it does not have enough information.

  • Data quality: Poor quality data can lead to poor performance of the model and cause the validation error to increase. When collecting or gathering a dataset for training a model, we must ensure good-quality data and that the data is standardized and normalized.

  • Learning rate: The learning rate can cause the validation error to not decrease. If the learning rate is too high, the model may overshoot the optimal value and cause a high loss value. If the learning rate is too low, it might converge very slowly or get stuck at local minima.

  • Complexity of the model: If the model is too complex, it might overfit the data and not perform well on new data.

  • Overfitting: This is another common issue where the model performs well on the training data but does not generalize new data, causing the validation error to increase.

Techniques to avoid validation loss plateau

  • Early stopping: It is a technique used to monitor the validation loss during training when the loss increases or stops improving. It can help to save the model from overfitting and reduce training time. Here is an example where we use the mnist dataset and employ the early stopping technique to avoid the validation loss to increase. 

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Example of the early stopping technique

  • Data augmentation: This is another technique used when dealing with image datasets when we don’t have enough data to train our model. We can increase the size of our dataset by applying the data augmentation technique.

  • Regularization: This technique is used to penalize the large weights of the model and help control the validation loss by reducing the model overfitting or underfitting.

  • Dropout: This is another useful technique to avoid overfitting of the model. It randomly disregards some nodes in a layer. Here is an example of how we can import and use the dropout technique using the TensorFlow library:

from tensorflow.keras.layers import Dense, Dropout
# Dropout with a 40% probability
model.add(Dense(128, activation='relu', input_shape=(train.shape[1],)))
model.add(Dropout(0.4))  
# Dropout with a 20% probability
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.2))
Example of the dropout technique using TensorFlow

  • Hyperparameters tuning: Tuning hyperparameters such as batch size, learning rate, optimizer choice, number of epochs, weight initialization, etc., greatly impact model training and validation loss.

Conclusion

The stagnation of validation loss after a certain number of epochs can stem from various factors, including data limitations, model complexity, and inappropriate learning rates. By implementing strategies such as early stopping, data augmentation, regularization, dropout, and hyperparameter tuning, practitioners can improve their model’s performance and avoid the pitfalls of overfitting. Addressing these issues proactively can lead to a more robust machine learning model capable of generalizing well to unseen data.

Frequently asked questions

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What if the validation loss is not decreasing?

If the validation loss is not decreasing, it may be due to insufficient data, poor data quality, an inappropriate learning rate, or model complexity leading to overfitting.

Why does the validation error increases when the number of epochs are increased?

Typically, too many epochs can cause your model to overfit the training data, suggesting that it is memorizing the data instead of genuinely learning from it.

Is 100 epochs too much?

Whether 100 epochs is too much depends on factors like model complexity, dataset size, and learning rate. Monitor training and validation loss; if validation loss plateaus or increases, consider reducing epochs or using early stopping.

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