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 is0
, 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
- 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
andivy.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
andivy.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 is0
, 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
- 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 is0
, 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
- 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
andivy.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
andivy.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]) }