Keras 3 API documentation Keras 2 API documentation
Models API Layers API Callbacks API Optimizers Metrics Accuracy metrics Probabilistic metrics Regression metrics Classification metrics based on True/False positives & negatives Image segmentation metrics Hinge metrics for "maximum-margin" classification Losses Data loading Built-in small datasets Keras Applications Mixed precision Utilities
► Keras 2 API documentation / Metrics / Probabilistic metrics

Probabilistic metrics

[source]

BinaryCrossentropy class

tf_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".

Standalone usage:

>>> m = tf.keras.metrics.BinaryCrossentropy()
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.result().numpy()
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().numpy()
0.9162905

Usage with compile() API:

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

[source]

CategoricalCrossentropy class

tf_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). Here we assume that labels are given as a one_hot representation. eg., When labels values are [2, 0, 1], y_true = [[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) -1 is the dimension along which entropy is computed. Defaults to -1.

Standalone usage:

>>> # 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 = tf.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().numpy()
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=tf.constant([0.3, 0.7]))
>>> m.result().numpy()
1.6271976

Usage with compile() API:

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

[source]

SparseCategoricalCrossentropy class

tf_keras.metrics.SparseCategoricalCrossentropy(
    name: str = "sparse_categorical_crossentropy",
    dtype: Union[str, tensorflow.python.framework.dtypes.DType, NoneType] = None,
    from_logits: bool = False,
    ignore_class: Optional[int] = None,
    axis: int = -1,
)

Computes the crossentropy metric between the labels and predictions.

Use this crossentropy metric when there are two or more label classes. We expect labels to be provided as integers. If you want to provide labels using one-hot representation, please use CategoricalCrossentropy metric. There should be # classes floating point values per feature for y_pred and a single floating point value per feature for y_true.

In the snippet below, there is a single floating point value per example for y_true and # classes floating pointing values per example for y_pred. The shape of y_true is [batch_size] and the shape of y_pred is [batch_size, num_classes].

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.
  • ignore_class: Optional integer. The ID of a class to be ignored during metric computation. This is useful, for example, in segmentation problems featuring a "void" class (commonly -1 or 255) in segmentation maps. By default (ignore_class=None), all classes are considered.
  • axis: (Optional) The dimension along which entropy is computed. Defaults to -1.

Standalone usage:

>>> # 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 = tf.keras.metrics.SparseCategoricalCrossentropy()
>>> m.update_state([1, 2],
...                [[0.05, 0.95, 0], [0.1, 0.8, 0.1]])
>>> m.result().numpy()
1.1769392

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

Usage with compile() API:

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

[source]

KLDivergence class

tf_keras.metrics.KLDivergence(name="kullback_leibler_divergence", dtype=None)

Computes Kullback-Leibler divergence metric between y_true and y_pred.

metric = y_true * log(y_true / y_pred)

Arguments

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

Standalone usage:

>>> m = tf.keras.metrics.KLDivergence()
>>> m.update_state([[0, 1], [0, 0]], [[0.6, 0.4], [0.4, 0.6]])
>>> m.result().numpy()
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().numpy()
0.9162892

Usage with compile() API:

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

[source]

Poisson class

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

Computes the Poisson score between y_true and y_pred.

🐟 🐟 🐟

It is defined as: poisson_score = y_pred - y_true * log(y_pred).

Arguments

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

Standalone usage:

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

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

Usage with compile() API:

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