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 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 (`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

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([0.08916873])

```>>> x = ivy.array([3])
>>> y = ivy.array([0.1, 0.1, 0.7, 0.1])
>>> print(ivy.cross_entropy(x, y))
ivy.array(5.44832274)
```
```>>> x = ivy.array([2,3])
>>> y = ivy.array([0.1, 0.1])
>>> print(ivy.cross_entropy(x, y))
ivy.array(5.75646281)
```

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([0.13862944])
```

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([0.13862944])
}
```

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([0.57564628])
```

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([0.57564628])
}
```

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([0.08916873])
```

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([0.08916873])
}
```
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([0.07438118, 0.07438118, 0.11889165])
```
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([0.53647929, 0.1702752, 0.1702752]),
b: ivy.array([0.07438118, 0.07438118, 0.53647929])
}
```