perceptron_loss

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perceptron_loss#

class braintools.metric.perceptron_loss(predictor_outputs, targets)#

Compute the binary perceptron loss.

The perceptron loss is used in the original perceptron algorithm for binary classification. It penalizes misclassified examples by the magnitude of the prediction error.

The perceptron loss is defined as: max(0, -y * f(x)) where y is the true class label and f(x) is the predicted output.

Parameters:

  • predictor_outputs (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – Scores produced by the model. Real-valued predictions.

  • targets (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – Target values. Must be in the set {-1, 1} for binary classification. Shape must match predictor_outputs.

Returns:

Perceptron loss values with the same shape as the input arrays.

Return type:

Array | ndarray | bool | number | bool | int | float | complex | Quantity

Examples

>>> import jax.numpy as jnp
>>> import braintools
>>> predictions = jnp.array([1.0, -0.5, 2.0])
>>> targets = jnp.array([1, -1, 1])
>>> loss = braintools.metric.perceptron_loss(predictions, targets)
>>> print(loss)
[0.  0.  0. ]

References

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