tfma.metrics.BinaryCrossEntropy
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Calculates the binary cross entropy.
Inherits From: Metric
tfma.metrics.BinaryCrossEntropy(
name: str = BINARY_CROSSENTROPY_NAME,
from_logits: bool = False,
label_smoothing: float = 0.0
)
The metric computes the cross entropy when there are only two label classes
(0 and 1). See definition at: https://en.wikipedia.org/wiki/Cross_entropy
Args |
name
|
The name of the metric.
|
from_logits
|
(Optional) Whether output is expected to be a logits tensor.
By default, we consider that output encodes a probability distribution.
|
label_smoothing
|
Float in [0, 1]. If > 0 then smooth the labels by
squeezing them towards 0.5 That is, using 1. - 0.5 * label_smoothing
for the target class and 0.5 * label_smoothing for the non-target
class.
|
Attributes |
compute_confidence_interval
|
Whether to compute confidence intervals for this metric.
Note that this may not completely remove the computational overhead
involved in computing a given metric. This is only respected by the
jackknife confidence interval method.
|
Methods
computations
View source
computations(
eval_config: Optional[tfma.EvalConfig
] = None,
schema: Optional[schema_pb2.Schema] = None,
model_names: Optional[List[str]] = None,
output_names: Optional[List[str]] = None,
sub_keys: Optional[List[Optional[SubKey]]] = None,
aggregation_type: Optional[AggregationType] = None,
class_weights: Optional[Dict[int, float]] = None,
example_weighted: bool = False,
query_key: Optional[str] = None
) -> tfma.metrics.MetricComputations
Creates computations associated with metric.
from_config
View source
@classmethod
from_config(
config: Dict[str, Any]
) -> 'Metric'
get_config
View source
get_config() -> Dict[str, Any]
Returns serializable config.
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Last updated 2024-04-26 UTC.
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{"lastModified": "Last updated 2024-04-26 UTC."}
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