# cross_entropy#

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

Compute cross-entropy between predicted and true discrete distributions.

Parameters:
• true (`Union`[`Array`, `NativeArray`]) – input array containing true labels.

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

• axis (`Optional`[`int`], default: `None`) – 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`.

• 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 cross-entropy loss between the given distributions

Examples

```>>> x = ivy.array([0, 0, 1, 0])
>>> y = ivy.array([0.25, 0.25, 0.25, 0.25])
>>> print(ivy.cross_entropy(x, y))
ivy.array(0.34657359)
```
```>>> z = ivy.array([0.1, 0.1, 0.7, 0.1])
>>> print(ivy.cross_entropy(x, z))
ivy.array(0.08916873)
```
Array.cross_entropy(self, pred, /, *, axis=-1, epsilon=1e-07, reduction='mean', out=None)[source]#

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

Parameters:
• self (`Array`) – input array containing true labels.

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

• 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`.

• 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 cross-entropy loss between the given distributions.

Examples

```>>> x = ivy.array([0, 0, 1, 0])
>>> y = ivy.array([0.25, 0.25, 0.25, 0.25])
>>> z = x.cross_entropy(y)
>>> print(z)
ivy.array(0.34657359)
```
Container.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.cross_entropy. This method simply wraps the function, and so the docstring for ivy.cross_entropy also applies to this method with minimal changes.

Parameters:
• self (`Container`) – input container containing true labels.

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

• 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 (`Union`[`float`, `Container`], 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`.

• 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 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.cross_entropy(y)
>>> print(z)
{
a: ivy.array(0.17027519),
b: ivy.array(0.53647931)
}
```