Ctrl+K

sparse_cross_entropy#

ivy.sparse_cross_entropy(true, pred, /, *, axis=-1, epsilon=1e-07, reduction='mean', out=None)[source]#

Compute sparse cross entropy between logits and labels.

Parameters:

  • true (Union[Array, NativeArray]) – input array containing the true labels as class indices.

  • pred (Union[Array, NativeArray]) – input array containing the predicted labels as logits.

  • axis (int, default: -1) – the axis along which to compute the cross-entropy. If axis is -1, the cross-entropy will be computed along the last dimension. Default: -1.

  • epsilon (float, default: 1e-07) – a float in [0.0, 1.0] specifying the amount of smoothing when calculating the loss. If epsilon is 0, no smoothing will be applied. Default: 1e-7.

  • reduction (str, default: 'mean') – 'none': No reduction will be applied to the output. 'mean': The output will be averaged. 'sum': The output will be summed. Default: 'mean'.

  • out (Optional[Array], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – The sparse cross-entropy loss between the given distributions

Examples

With ivy.Array input:

>>> x = ivy.array([2])
>>> y = ivy.array([0.1, 0.1, 0.7, 0.1])
>>> print(ivy.sparse_cross_entropy(x, y))
ivy.array(0.9732134)

With ivy.NativeArray input:

>>> x = ivy.native_array([4])
>>> y = ivy.native_array([0.1, 0.2, 0.1, 0.1, 0.5])
>>> print(ivy.sparse_cross_entropy(x, y))
ivy.array(1.32223)

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([4]))
>>> y = ivy.Container(a=ivy.array([0.1, 0.2, 0.1, 0.1, 0.5]))
>>> print(ivy.sparse_cross_entropy(x, y))
{
    a: ivy.array(1.32223)
}

With a mix of ivy.Array and ivy.NativeArray inputs:

>>> x = ivy.array([0])
>>> y = ivy.native_array([0.1, 0.2, 0.6, 0.1])
>>> print(ivy.sparse_cross_entropy(x, y))
ivy.array(1.5589634)

With a mix of ivy.Array and ivy.Container inputs:

>>> x = ivy.array([0])
>>> y = ivy.Container(a=ivy.array([0.1, 0.2, 0.6, 0.1]))
>>> print(ivy.sparse_cross_entropy(x, y))
{
    a: ivy.array(1.5589634)
}

Instance Method Examples

With ivy.Array input:

>>> x = ivy.array([2])
>>> y = ivy.array([0.1, 0.1, 0.7, 0.1])
>>> print(x.sparse_cross_entropy(y))
ivy.array(0.9732134)

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([2]))
>>> y = ivy.Container(a=ivy.array([0.1, 0.1, 0.7, 0.1]))
>>> print(x.sparse_cross_entropy(y))
{
    a: ivy.array(0.9732134)
}
Array.sparse_cross_entropy(self, pred, /, *, axis=-1, epsilon=1e-07, reduction='mean', out=None)[source]#

ivy.Array instance method variant of ivy.sparse_cross_entropy. This method simply wraps the function, and so the docstring for ivy.sparse_cross_entropy also applies to this method with minimal changes.

Parameters:

  • self (Array) – input array containing the true labels as logits.

  • pred (Union[Array, NativeArray]) – input array containing the predicted labels as logits.

  • axis (int, default: -1) – the axis along which to compute the cross-entropy. If axis is -1, the cross-entropy will be computed along the last dimension. Default: -1. epsilon a float in [0.0, 1.0] specifying the amount of smoothing when calculating the loss. If epsilon is 0, no smoothing will be applied. Default: 1e-7.

  • epsilon (float, default: 1e-07) – a float in [0.0, 1.0] specifying the amount of smoothing when calculating the loss. If epsilon is 0, no smoothing will be applied. Default: 1e-7.

  • out (Optional[Array], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – The sparse cross-entropy loss between the given distributions.

Examples

>>> x = ivy.array([1 , 1, 0])
>>> y = ivy.array([0.7, 0.8, 0.2])
>>> z = x.sparse_cross_entropy(y)
>>> print(z)
ivy.array(0.9309097)
Container.sparse_cross_entropy(self, pred, /, *, axis=-1, epsilon=1e-07, reduction='mean', key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False, out=None)[source]#

ivy.Container instance method variant of ivy.sparse_cross_entropy. This method simply wraps the function, and so the docstring for ivy.sparse_cross_entropy also applies to this method with minimal changes.

Parameters:

  • self (Container) – input container containing the true labels as logits.

  • pred (Union[Container, Array, NativeArray]) – input array or container containing the predicted labels as logits.

  • axis (Union[int, Container], default: -1) – the axis along which to compute the cross-entropy. If axis is -1, the cross-entropy will be computed along the last dimension. Default: -1. epsilon a float in [0.0, 1.0] specifying the amount of smoothing when calculating the loss. If epsilon is 0, no smoothing will be applied. Default: 1e-7.

  • key_chains (Optional[Union[List[str], Dict[str, str], Container]], default: None) – The key-chains to apply or not apply the method to. Default is None.

  • to_apply (Union[bool, Container], default: True) – If True, the method will be applied to key_chains, otherwise key_chains will be skipped. Default is True.

  • prune_unapplied (Union[bool, Container], default: False) – Whether to prune key_chains for which the function was not applied. Default is False.

  • map_sequences (Union[bool, Container], default: False) – Whether to also map method to sequences (lists, tuples). Default is False.

  • out (Optional[Container], default: None) – optional output container, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Container

Returns:

ret – The sparse cross-entropy loss between the given distributions.

Examples

>>> x = ivy.Container(a=ivy.array([1, 0, 0]),b=ivy.array([0, 0, 1]))
>>> y = ivy.Container(a=ivy.array([0.6, 0.2, 0.3]),b=ivy.array([0.8, 0.2, 0.2]))
>>> z = x.sparse_cross_entropy(y)
>>> print(z)
{
    a: ivy.array(1.0134323),
    b: ivy.array(0.9408049)
}
On this page