l2_loss

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l2_loss#

class braintools.metric.l2_loss(predictions, targets=None)#

Calculates the L2 loss for a set of predictions.

Note: the 0.5 term is standard in “Pattern Recognition and Machine Learning” by Bishop, but not “The Elements of Statistical Learning” by Tibshirani.

References

[Chris Bishop, 2006](https://bit.ly/3eeP0ga)

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 shape broadcastable to that of predictions; if not provided then it is assumed to be a vector of zeros.

Return type:

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

Returns:

elementwise squared differences, with same shape as predictions.

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