elegy/losses/mean_absolute_percentage_error.py
def call(
        self,
        y_true: jnp.ndarray,
        y_pred: jnp.ndarray,
        sample_weight: tp.Optional[
            jnp.ndarray
        ] = None,  # not used, __call__ handles it, left for documentation purposes.
    ) -> jnp.ndarray:
        """
        Invokes the `MeanAbsolutePercentageError` instance.

        Arguments:
            y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`, except
                sparse loss functions such as sparse categorical crossentropy where
                shape = `[batch_size, d0, .. dN-1]`
            y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`
            sample_weight: Optional `sample_weight` acts as a
                coefficient for the loss. If a scalar is provided, then the loss is
                simply scaled by the given value. If `sample_weight` is a tensor of size
                `[batch_size]`, then the total loss for each sample of the batch is
                rescaled by the corresponding element in the `sample_weight` vector. If
                the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be
                broadcasted to this shape), then each loss element of `y_pred` is scaled
                by the corresponding value of `sample_weight`. (Note on`dN-1`: all loss
                functions reduce by 1 dimension, usually axis=-1.)

        Returns:
            Weighted loss float `Tensor`. If `reduction` is `NONE`, this has
                shape `[batch_size, d0, .. dN-1]`; otherwise, it is scalar. (Note `dN-1`
                because all loss functions reduce by 1 dimension, usually axis=-1.)

        Raises:
            ValueError: If the shape of `sample_weight` is invalid.
        """
        return mean_percentage_absolute_error(y_true, y_pred)

        Invokes the `MeanAbsolutePercentageError` instance.

        Arguments:
            y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`, except
                sparse loss functions such as sparse categorical crossentropy where
                shape = `[batch_size, d0, .. dN-1]`
            y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`
            sample_weight: Optional `sample_weight` acts as a
                coefficient for the loss. If a scalar is provided, then the loss is
                simply scaled by the given value. If `sample_weight` is a tensor of size
                `[batch_size]`, then the total loss for each sample of the batch is
                rescaled by the corresponding element in the `sample_weight` vector. If
                the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be
                broadcasted to this shape), then each loss element of `y_pred` is scaled
                by the corresponding value of `sample_weight`. (Note on`dN-1`: all loss
                functions reduce by 1 dimension, usually axis=-1.)

        Returns:
            Weighted loss float `Tensor`. If `reduction` is `NONE`, this has
                shape `[batch_size, d0, .. dN-1]`; otherwise, it is scalar. (Note `dN-1`
                because all loss functions reduce by 1 dimension, usually axis=-1.)

        Raises:
            ValueError: If the shape of `sample_weight` is invalid.
        
Invokes the `MeanAbsolutePercentageError` instance.
Invokes the ` MeanAbsolutePercentageError ` instance .
def call(
        self,
        y_true: jnp.ndarray,
        y_pred: jnp.ndarray,
        sample_weight: tp.Optional[
            jnp.ndarray
        ] = None,  
    ) -> jnp.ndarray:
        return mean_percentage_absolute_error(y_true, y_pred)
def call ( self , y_true : jnp . ndarray , y_pred : jnp . ndarray , sample_weight : tp . Optional [ jnp . ndarray ] = None , ) -> jnp . ndarray : return mean_percentage_absolute_error ( y_true , y_pred )
Invokes the `MeanAbsolutePercentageError` instance.
Invokes the ` MeanAbsolutePercentageError ` instance .
# not used, __call__ handles it, left for documentation purposes. """
        Invokes the `MeanAbsolutePercentageError` instance.

        Arguments:
            y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`, except
                sparse loss functions such as sparse categorical crossentropy where
                shape = `[batch_size, d0, .. dN-1]`
            y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`
            sample_weight: Optional `sample_weight` acts as a
                coefficient for the loss. If a scalar is provided, then the loss is
                simply scaled by the given value. If `sample_weight` is a tensor of size
                `[batch_size]`, then the total loss for each sample of the batch is
                rescaled by the corresponding element in the `sample_weight` vector. If
                the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be
                broadcasted to this shape), then each loss element of `y_pred` is scaled
                by the corresponding value of `sample_weight`. (Note on`dN-1`: all loss
                functions reduce by 1 dimension, usually axis=-1.)

        Returns:
            Weighted loss float `Tensor`. If `reduction` is `NONE`, this has
                shape `[batch_size, d0, .. dN-1]`; otherwise, it is scalar. (Note `dN-1`
                because all loss functions reduce by 1 dimension, usually axis=-1.)

        Raises:
            ValueError: If the shape of `sample_weight` is invalid.
        """
Weighted loss float `Tensor`. If `reduction` is `NONE`, this has
shape `[batch_size, d0, .. dN-1]`; otherwise, it is scalar. (Note `dN-1`
because all loss functions reduce by 1 dimension, usually axis=-1.)
Weighted loss float ` Tensor ` . If ` reduction ` is ` NONE ` this has shape ` [ batch_size d0 .. dN - 1 ] ` ; otherwise it is scalar . ( Note ` dN - 1 ` because all loss functions reduce by 1 dimension usually axis = - 1 . )
If the shape of `sample_weight` is invalid.
If the shape of ` sample_weight ` is invalid .
ValueError
self
y_true
jnp.ndarray
Ground truth values. shape = `[batch_size, d0, .. dN]`, except
sparse loss functions such as sparse categorical crossentropy where
shape = `[batch_size, d0, ..
Ground truth values . shape = ` [ batch_size d0 .. dN ] ` except sparse loss functions such as sparse categorical crossentropy where shape = ` [ batch_size d0 ..
y_pred
jnp.ndarray
The predicted values. shape = `[batch_size, d0, .. dN]`
The predicted values . shape = ` [ batch_size d0 .. dN ] `
sample_weight
tp.Optional[
            jnp.ndarray
        ]
Optional `sample_weight` acts as a
coefficient for the loss. If a scalar is provided, then the loss is
simply scaled by the given value. If `sample_weight` is a tensor of size
`[batch_size]`, then the total loss for each sample of the batch is
rescaled by the corresponding element in the `sample_weight` vector. If
the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be
broadcasted to this shape), then each loss element of `y_pred` is scaled
by the corresponding value of `sample_weight`. (Note on`dN-1`: all loss
functions reduce by 1 dimension, usually axis=-1.)
Optional ` sample_weight ` acts as a coefficient for the loss . If a scalar is provided then the loss is simply scaled by the given value . If ` sample_weight ` is a tensor of size ` [ batch_size ] ` then the total loss for each sample of the batch is rescaled by the corresponding element in the ` sample_weight ` vector . If the shape of ` sample_weight ` is ` [ batch_size d0 .. dN - 1 ] ` ( or can be broadcasted to this shape ) then each loss element of ` y_pred ` is scaled by the corresponding value of ` sample_weight ` . ( Note on ` dN - 1 ` : all loss functions reduce by 1 dimension usually axis = - 1 . )