tfma.metrics.BinaryCrossEntropy

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.