Losses

Collection of Ivy loss functions.

ivy.binary_cross_entropy(true, pred, /, *, epsilon=1e-07, out=None)[source]

Computes the binary cross entropy loss.

Parameters

true (Union[Array, NativeArray]) – input array containing true labels.
pred (Union[Array, NativeArray]) – input array containing Predicted labels.
epsilon (float) – a float in [0.0, 1.0] specifying the amount of smoothing when calculating the (default: 1e-07) loss. If epsilon is 0, no smoothing will be applied. Default: 1e-7.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape (default: None) that the inputs broadcast to.

Return type

Array

Returns

ret – The binary cross entropy between the given distributions.

Functional Examples

With ivy.Array input:

>>> x = ivy.array([0, 1, 0, 0])
>>> y = ivy.array([0.2, 0.8, 0.3, 0.8])
>>> z = ivy.binary_cross_entropy(x, y)
>>> print(z)
ivy.array([0.223,0.223,0.357,1.61])

>>> x = ivy.array([[0, 1, 0, 0]])
>>> y = ivy.array([[0.6, 0.2, 0.7, 0.3]])
>>> z = ivy.binary_cross_entropy(x, y, epsilon=1e-3)
>>> print(z)
ivy.array([[0.916,1.61,1.2,0.357]])

With ivy.NativeArray input:

>>> x = ivy.native_array([0, 1, 0, 1])
>>> y = ivy.native_array([0.2, 0.7, 0.2, 0.6])
>>> z = ivy.binary_cross_entropy(x, y)
>>> print(z)
ivy.array([0.223,0.357,0.223,0.511])

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

>>> x = ivy.array([0, 0, 1, 1])
>>> y = ivy.native_array([0.1, 0.2, 0.8, 0.6])
>>> z = ivy.binary_cross_entropy(x, y)
>>> print(z)
ivy.array([0.105,0.223,0.223,0.511])

With ivy.Container input:

>>> 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 = ivy.binary_cross_entropy(x, y)
>>> print(z)
{a:ivy.array([0.511,0.223,0.357]),b:ivy.array([1.61,0.223,1.61])}

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

>>> x = ivy.array([1 , 1, 0])
>>> y = ivy.Container(a=ivy.array([0.7, 0.8, 0.2]))
>>> z = ivy.binary_cross_entropy(x, y)
>>> print(z)
{
   a: ivy.array([0.357, 0.223, 0.223])
}

Instance Method Examples

Using ivy.Array instance method:

>>> x = ivy.array([1, 0, 0, 0])
>>> y = ivy.array([0.8, 0.2, 0.2, 0.2])
>>> z = ivy.binary_cross_entropy(x, y)
>>> print(z)
ivy.array([0.223, 0.223, 0.223, 0.223])

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

Computes 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 (int) – the axis along which to compute the cross-entropy. If axis is -1, (default: -1) the cross-entropy will be computed along the last dimension. Default: -1.
epsilon (float) – a float in [0.0, 1.0] specifying the amount of smoothing when calculating (default: 1e-07) the loss. If epsilon is 0, no smoothing will be applied. Default: 1e-7.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape (default: None) 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(1.3862944)

>>> z = ivy.array([0.1, 0.1, 0.7, 0.1])
>>> print(ivy.cross_entropy(x, z))
ivy.array(0.35667497)

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

Computes 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) – the axis along which to compute the cross-entropy. If axis is -1, the (default: -1) cross-entropy will be computed along the last dimension. Default: -1.
epsilon (float) – a float in [0.0, 1.0] specifying the amount of smoothing when calculating the (default: 1e-07) loss. If epsilon is 0, no smoothing will be applied. Default: 1e-7.
out (Optional[Array]) – optional output array, for writing the result to. It must have a shape (default: None) that the inputs broadcast to.

Return type

Array

Returns

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

Functional 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([0.357])

>>> x = ivy.array([3])
>>> y = ivy.array([0.1, 0.1, 0.7, 0.1])
>>> print(ivy.cross_entropy(x, y))
ivy.array(21.793291)

>>> x = ivy.array([2,3])
>>> y = ivy.array([0.1, 0.1])
>>> print(ivy.cross_entropy(x, y))
ivy.array(11.512926)

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([0.693])

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([0.693])
}

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([2.3])

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([2.3])
}

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([0.357])

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([0.357])
}