l2_norm

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l2_norm#

class braintools.metric.l2_norm(predictions, targets=None, axis=None)#

Compute the L2 norm of the difference between predictions and targets.

Parameters:

  • predictions (Array | ndarray | bool | number | bool | int | float | complex | Quantity) – A vector of arbitrary shape [...].

  • targets (Array | ndarray | bool | number | bool | int | float | complex | Quantity | None) – A vector with the same shape as predictions (shape equality is asserted; no broadcasting). If not provided then it is assumed to be a vector of zeros.

  • axis (int | tuple[int, ...] | None) – The dimensions to reduce. If None, the norm is computed over the whole (flattened) array and reduced to a scalar.

Returns:

The L2 norm of the differences along axis.

Return type:

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

Examples

>>> import jax.numpy as jnp
>>> import braintools
>>> predictions = jnp.array([3.0, 4.0])
>>> braintools.metric.l2_norm(predictions)
Array(5., dtype=float32)
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