Loss
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Known Direct Subclasses
BinaryCrossentropy,
CategoricalCrossentropy,
CategoricalHinge,
CosineSimilarity,
Hinge,
Huber,
KLDivergence,
LogCosh,
MeanAbsoluteError,
MeanAbsolutePercentageError,
MeanSquaredError,
MeanSquaredLogarithmicError,
Poisson,
SparseCategoricalCrossentropy,
SquaredHinge
| BinaryCrossentropy |
Computes the cross-entropy loss between true labels and predicted labels. |
| CategoricalCrossentropy |
Computes the crossentropy loss between the labels and predictions. |
| CategoricalHinge |
Computes the categorical hinge loss between labels and predictions. |
| CosineSimilarity |
Computes the cosine similarity between labels and predictions. |
| Hinge |
Computes the hinge loss between labels and predictions. |
| Huber |
Computes the Huber loss between labels and predictions. |
| KLDivergence |
Computes Kullback-Leibler divergence loss between labels and predictions. |
| LogCosh |
Computes Computes the logarithm of the hyperbolic cosine of the prediction error. |
| MeanAbsoluteError |
Computes the mean of absolute difference between labels and predictions. |
| MeanAbsolutePercentageError |
Computes the mean absolute percentage error between labels and predictions. |
| MeanSquaredError |
Computes the mean of squares of errors between labels and predictions. |
| MeanSquaredLogarithmicError |
Computes the mean squared logarithmic errors between labels and predictions. |
| Poisson |
Computes the Poisson loss between labels and predictions. |
| SparseCategoricalCrossentropy |
Computes the crossentropy loss between labels and predictions. |
| SquaredHinge |
Computes the squared hinge loss between labels and predictions. |
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Inherited Methods
From class
java.lang.Object
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boolean
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equals(Object arg0)
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final
Class<?>
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getClass()
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int
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hashCode()
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final
void
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notify()
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final
void
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notifyAll()
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String
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toString()
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final
void
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wait(long arg0, int arg1)
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final
void
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wait(long arg0)
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final
void
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wait()
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Fields
public
static
final
Reduction
REDUCTION_DEFAULT
Public Methods
public
abstract
Operand<T>
call
(Operand<? extends TNumber> labels, Operand<T> predictions, Operand<T> sampleWeights)
Generates an Operand that calculates the loss.
Parameters
| labels |
the truth values or labels |
| predictions |
the predictions |
| sampleWeights |
Optional sampleWeights acts as a coefficient for the loss. If a scalar is
provided, then the loss is simply scaled by the given value. If SampleWeights is a tensor
of size [batch_size], then the total loss for each sample of the batch is rescaled by the
corresponding element in the SampleWeights vector. If the shape of SampleWeights is
[batch_size, d0, .. dN-1] (or can be broadcast to this shape), then each loss element of
predictions is scaled by the corresponding value of SampleWeights. (Note on dN-1: all loss
functions reduce by 1 dimension, usually axis=-1.) |
Parameters
| labels |
the truth values or labels |
| predictions |
the predictions |
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Last updated 2021-11-29 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2021-11-29 UTC."],[],[]]
