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Update probabilistic_metrics.py #20660

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Update probabilistic_metrics.py #20660

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16 changes: 4 additions & 12 deletions keras/src/metrics/probabilistic_metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,9 +69,7 @@ class Poisson(reduction_metrics.MeanMetricWrapper):
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.

Example:

Example:
Examples:

>>> m = keras.metrics.Poisson()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
Expand Down Expand Up @@ -119,9 +117,7 @@ class BinaryCrossentropy(reduction_metrics.MeanMetricWrapper):
e.g. `label_smoothing=0.2` means that we will use
a value of 0.1 for label "0" and 0.9 for label "1".

Example:

Example:
Examples:

>>> m = keras.metrics.BinaryCrossentropy()
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
Expand Down Expand Up @@ -195,9 +191,7 @@ class CategoricalCrossentropy(reduction_metrics.MeanMetricWrapper):
axis: (Optional) Defaults to `-1`.
The dimension along which entropy is computed.

Example:

Example:
Examples:

>>> # EPSILON = 1e-7, y = y_true, y` = y_pred
>>> # y` = clip_ops.clip_by_value(output, EPSILON, 1. - EPSILON)
Expand Down Expand Up @@ -282,9 +276,7 @@ class SparseCategoricalCrossentropy(reduction_metrics.MeanMetricWrapper):
axis: (Optional) Defaults to `-1`.
The dimension along which entropy is computed.

Example:

Example:
Examples:

>>> # y_true = one_hot(y_true) = [[0, 1, 0], [0, 0, 1]]
>>> # logits = log(y_pred)
Expand Down
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