Need to create Custom Loss Functions

Loss function is considered as a fundamental component of deep learning as it is helpful in error minimization. Loss is computed by comparing predicted values and actual values for a given set of inputs. Loss functions vary depending on the task. The need to create custom loss functions is discussed below:

• The loss functions vary depending on the machine learning task, there might be some cases where the standard loss functions provided by Keras might not be suitable for a given assignment. This issue can be addressed by introducing custom loss functions.
• These custom loss functions are helpful not only when standard loss functions are inadequate but also when data is imbalanced. Class imbalance can be addressed by employing a custom loss function when the dataset is extremely imbalanced (one class is significantly more abundant than others).
• By assigning minority classes greater weight, custom loss functions can avoid bias in the model’s favour of the dominant class. These custom loss functions can be implemented with Keras.

Creating a custom loss function in Keras is crucial for optimizing deep learning models. The article aims to learn how to create a custom loss function.