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Losses#

class ivy.data_classes.array.losses._ArrayWithLosses[source]#

Bases: ABC

_abc_impl = <_abc._abc_data object>#
binary_cross_entropy(pred, /, *, from_logits=False, epsilon=0.0, reduction='mean', pos_weight=None, axis=None, out=None)[source]#

ivy.Array instance method variant of ivy.binary_cross_entropy. This method simply wraps the function, and so the docstring for ivy.binary_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 Predicted labels.

  • from_logits (bool, default: False) – Whether pred is expected to be a logits tensor. By default, we assume that pred encodes a probability distribution.

  • epsilon (float, default: 0.0) – 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: 0.

  • 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: 'none'.

  • pos_weight (Optional[Union[Array, NativeArray]], default: None) – a weight for positive examples. Must be an array with length equal to the number of classes.

  • axis (Optional[int], default: None) – Axis along which to compute crossentropy.

  • 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 binary cross entropy between the given distributions.

Examples

>>> x = ivy.array([1 , 1, 0])
>>> y = ivy.array([0.7, 0.8, 0.2])
>>> z = x.binary_cross_entropy(y)
>>> print(z)
ivy.array(0.26765382)
cross_entropy(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)
sparse_cross_entropy(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])

This should have hopefully given you an overview of the losses submodule, if you have any questions, please feel free to reach out on our discord!

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