► Keras 3 API documentation / Metrics / Probabilistic metrics

Probabilistic metrics

[source]

BinaryCrossentropy class

keras.metrics.BinaryCrossentropy(
    name="binary_crossentropy", dtype=None, from_logits=False, label_smoothing=0
)

Computes the crossentropy metric between the labels and predictions.

This is the crossentropy metric class to be used when there are only two label classes (0 and 1).

Arguments

  • name: (Optional) string name of the metric instance.
  • dtype: (Optional) data type of the metric result.
  • from_logits: (Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution.
  • label_smoothing: (Optional) Float in [0, 1]. When > 0, label values are smoothed, meaning the confidence on label values are relaxed. 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

>>> m = keras.metrics.BinaryCrossentropy()
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.result()
0.81492424

>>> m.reset_state()
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
...                sample_weight=[1, 0])
>>> m.result()
0.9162905

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[keras.metrics.BinaryCrossentropy()])

[source]

CategoricalCrossentropy class

keras.metrics.CategoricalCrossentropy(
    name="categorical_crossentropy",
    dtype=None,
    from_logits=False,
    label_smoothing=0,
    axis=-1,
)

Computes the crossentropy metric between the labels and predictions.

This is the crossentropy metric class to be used when there are multiple label classes (2 or more). It assumes that labels are one-hot encoded, e.g., when labels values are [2, 0, 1], then y_true is [[0, 0, 1], [1, 0, 0], [0, 1, 0]].

Arguments

  • name: (Optional) string name of the metric instance.
  • dtype: (Optional) data type of the metric result.
  • from_logits: (Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution.
  • label_smoothing: (Optional) Float in [0, 1]. When > 0, label values are smoothed, meaning the confidence on label values are relaxed. 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".
  • axis: (Optional) Defaults to -1. The dimension along which entropy is computed.

Example

Example

>>> # EPSILON = 1e-7, y = y_true, y` = y_pred
>>> # y` = clip_ops.clip_by_value(output, EPSILON, 1. - EPSILON)
>>> # y` = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]
>>> # xent = -sum(y * log(y'), axis = -1)
>>> #      = -((log 0.95), (log 0.1))
>>> #      = [0.051, 2.302]
>>> # Reduced xent = (0.051 + 2.302) / 2
>>> m = keras.metrics.CategoricalCrossentropy()
>>> m.update_state([[0, 1, 0], [0, 0, 1]],
...                [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
>>> m.result()
1.1769392

>>> m.reset_state()
>>> m.update_state([[0, 1, 0], [0, 0, 1]],
...                [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],
...                sample_weight=np.array([0.3, 0.7]))
>>> m.result()
1.6271976

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[keras.metrics.CategoricalCrossentropy()])

[source]

SparseCategoricalCrossentropy class

keras.metrics.SparseCategoricalCrossentropy(
    name="sparse_categorical_crossentropy", dtype=None, from_logits=False, axis=-1
)

Computes the crossentropy metric between the labels and predictions.

Use this crossentropy metric when there are two or more label classes. It expects labels to be provided as integers. If you want to provide labels that are one-hot encoded, please use the CategoricalCrossentropy metric instead.

There should be num_classes floating point values per feature for y_pred and a single floating point value per feature for y_true.

Arguments

  • name: (Optional) string name of the metric instance.
  • dtype: (Optional) data type of the metric result.
  • from_logits: (Optional) Whether output is expected to be a logits tensor. By default, we consider that output encodes a probability distribution.
  • axis: (Optional) Defaults to -1. The dimension along which entropy is computed.

Example

Example

>>> # y_true = one_hot(y_true) = [[0, 1, 0], [0, 0, 1]]
>>> # logits = log(y_pred)
>>> # softmax = exp(logits) / sum(exp(logits), axis=-1)
>>> # softmax = [[0.05, 0.95, EPSILON], [0.1, 0.8, 0.1]]
>>> # xent = -sum(y * log(softmax), 1)
>>> # log(softmax) = [[-2.9957, -0.0513, -16.1181],
>>> #                [-2.3026, -0.2231, -2.3026]]
>>> # y_true * log(softmax) = [[0, -0.0513, 0], [0, 0, -2.3026]]
>>> # xent = [0.0513, 2.3026]
>>> # Reduced xent = (0.0513 + 2.3026) / 2
>>> m = keras.metrics.SparseCategoricalCrossentropy()
>>> m.update_state([1, 2],
...                [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
>>> m.result()
1.1769392

>>> m.reset_state()
>>> m.update_state([1, 2],
...                [[0.05, 0.95, 0], [0.1, 0.8, 0.1]],
...                sample_weight=np.array([0.3, 0.7]))
>>> m.result()
1.6271976

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[keras.metrics.SparseCategoricalCrossentropy()])

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KLDivergence class

keras.metrics.KLDivergence(name="kl_divergence", dtype=None)

Computes Kullback-Leibler divergence metric between y_true and y_pred.

Formula:

metric = y_true * log(y_true / y_pred)

y_true and y_pred are expected to be probability distributions, with values between 0 and 1. They will get clipped to the [0, 1] range.

Arguments

  • name: (Optional) string name of the metric instance.
  • dtype: (Optional) data type of the metric result.

Examples

>>> m = keras.metrics.KLDivergence()
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.result()
0.45814306

>>> m.reset_state()
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]],
...                sample_weight=[1, 0])
>>> m.result()
0.9162892

Usage with compile() API:

model.compile(optimizer='sgd',
              loss='mse',
              metrics=[keras.metrics.KLDivergence()])

[source]

Poisson class

keras.metrics.Poisson(name="poisson", dtype=None)

Computes the Poisson metric between y_true and y_pred.

Formula:

metric = y_pred - y_true * log(y_pred)

Arguments

  • name: (Optional) string name of the metric instance.
  • dtype: (Optional) data type of the metric result.

Example

Example

>>> m = keras.metrics.Poisson()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
>>> m.result()
0.49999997

>>> m.reset_state()
>>> m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
...                sample_weight=[1, 0])
>>> m.result()
0.99999994

Usage with compile() API:

model.compile(optimizer='sgd',
              loss='mse',
              metrics=[keras.metrics.Poisson()])
Probabilistic metrics
BinaryCrossentropy class
CategoricalCrossentropy class
SparseCategoricalCrossentropy class
KLDivergence class
Poisson class