nz::nodes::loss Namespace Reference

Contains loss function nodes for computing various loss metrics in a machine learning model. More...

Classes
class	BinaryCrossEntropyNode
	Represents the Binary Cross-Entropy (BCE) loss function node in a computational graph. More...
class	MeanSquaredErrorNode
	Represents the Mean Squared Error (MSE) loss function node in a computational graph. More...

Detailed Description

Contains loss function nodes for computing various loss metrics in a machine learning model.

The Loss namespace provides a collection of nodes that represent different loss functions used during the training of machine learning models. These loss functions are used to evaluate the model's performance by calculating the difference between the predicted output and the true values.

This namespace includes:

• Regression Loss Functions: Nodes like MeanSquaredErrorNode that compute loss for regression tasks.
• Classification Loss Functions: Nodes such as BinaryCrossEntropyNode for computing loss in binary classification problems.

Loss function nodes perform key roles in the optimization process by guiding the model to minimize errors during training. These nodes integrate with the model’s computational graph, where they compute the forward pass loss and its gradients during the backward pass.

Note

• The nodes in this namespace are specifically designed to handle loss calculations in supervised learning tasks.
• These loss functions are typically combined with optimization algorithms like Gradient Descent during model training.
• Ensure that the input tensors are compatible in terms of shape for proper loss computation.

Author

Mgepahmge (https://github.com/Mgepahmge)

Date

2024/12/07