pinnx.metrics module

This module provides metrics for evaluating model performance and solution accuracy. These metrics can be used to assess how well the neural network approximates the true solution.

Metrics

accuracy: Classification accuracy.

l2_relative_error: Relative L2 error between predicted and true values.

nanl2_relative_error: L2 relative error ignoring NaN values.

mean_l2_relative_error: Mean relative L2 error across multiple outputs.

mean_squared_error: Mean squared error (MSE).

mean_absolute_percentage_error: Mean absolute percentage error (MAPE).

max_absolute_percentage_error: Maximum absolute percentage error.

absolute_percentage_error_std: Standard deviation of absolute percentage error.

accuracy	Computes accuracy across nested structures of labels and predictions.
l2_relative_error	Computes L2 relative error across nested structures of labels and predictions.
nanl2_relative_error	Computes L2 relative error across nested structures of labels and predictions.
mean_l2_relative_error	Computes mean L2 relative error across nested structures of labels and predictions.
mean_squared_error	Computes mean squared error across nested structures of labels and predictions.
mean_absolute_percentage_error	Computes mean absolute percentage error across nested structures of labels and predictions.
max_absolute_percentage_error
absolute_percentage_error_std	Computes standard deviation of absolute percentage error across nested structures of labels and predictions.