Keras is a powerful and widely-used library for building deep learning models. However, many developers encounter a common issue during the training of their neural networks: the loss keeps increasing instead of decreasing, as one would expect. This issue can arise from various factors, ranging from data-related problems to model configuration errors. In this article, we’ll explore these factors, providing technical explanations and examples, and offer solutions to troubleshoot the problem.

Understanding Loss

Function

In supervised learning, the loss function quantifies how well your model's predictions align with the actual target values. For most optimization problems, the aim is to minimize the loss function. An increasing loss typically indicates that the optimization process is not converging. The choice of a loss function can depend on the type of problem:

• Categorical Cross-Entropy: Often used for multi-class classification problems.
• Binary Cross-Entropy: Suitable for binary classification tasks.
• Mean Squared Error (MSE): Commonly used for regression problems.

Reasons for Increasing Loss

Let's delve into some common reasons why the loss function might increase during training:

1. Learning Rate Issues

The learning rate is a crucial hyperparameter. If it's set too high, the model might overshoot the optimal parameters during training, causing the loss to increase:

• Adjust the Learning Rate: Use a lower learning rate or employ learning rate schedules that adaptively modify it during training.
• Overfitting: Model performs well on training data but poorly on validation data, causing loss to remain high.
• Underfitting: Model cannot capture the underlying data pattern, leading to high loss.
• Architecture Tuning: Modify the number of layers or units in each layer to suit your dataset.
• Regularization: Apply techniques like dropout or L1/L2 regularization to prevent overfitting.
• Noisy Labels: Inaccurate labels can confuse the model, increasing the loss.
• Feature Scaling: Unnormalized data can affect learning, especially when features have different scales.
• Data Cleaning: Remove outliers and correct inaccurate labels.
• Data Augmentation: Increase your dataset size with transformations like rotation, scaling, etc.
• Feature Scaling: Normalize or standardize data to ensure all features contribute equally.
• Proper Initialization: Use initialization strategies like he_normal or glorot_uniform provided in Keras.
• Activation Choice: Utilize advanced activations such as ReLU for hidden layers, which mitigate gradient-related issues.