treex.losses.Reduction

Types of loss reduction.

Contains the following values: * NONE: Weighted losses with one dimension reduced (axis=-1, or axis specified by loss function). When this reduction type used with built-in Keras training loops like fit/evaluate, the unreduced vector loss is passed to the optimizer but the reported loss will be a scalar value. * SUM: Scalar sum of weighted losses. * SUM_OVER_BATCH_SIZE: Scalar SUM divided by number of elements in losses.

Source code in treex/losses/loss.py

class Reduction(Enum):
    """
    Types of loss reduction.

    Contains the following values:
    * `NONE`: Weighted losses with one dimension reduced (axis=-1, or axis
        specified by loss function). When this reduction type used with built-in
        Keras training loops like `fit`/`evaluate`, the unreduced vector loss is
        passed to the optimizer but the reported loss will be a scalar value.
    * `SUM`: Scalar sum of weighted losses.
    * `SUM_OVER_BATCH_SIZE`: Scalar `SUM` divided by number of elements in losses.
    """

    # AUTO = "auto"
    NONE = "none"
    SUM = "sum"
    SUM_OVER_BATCH_SIZE = "sum_over_batch_size"

    @classmethod
    def all(cls):
        return (
            # cls.AUTO,
            cls.NONE,
            cls.SUM,
            cls.SUM_OVER_BATCH_SIZE,
        )

    @classmethod
    def validate(cls, key):
        if key not in cls.all():
            raise ValueError("Invalid Reduction Key %s." % key)