tf.keras.metrics.SparseCategoricalCrossentropy

Computes the crossentropy metric between the labels and predictions.

Inherits From: MeanMetricWrapper, Mean, Metric

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

Used in the notebooks

Used in the guide	Used in the tutorials
Migrate early stopping	Working with tff's ClientData.

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.

Args
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()])

Attributes
dtype
variables

Methods

add_variable

View source

add_variable(
    shape, initializer, dtype=None, aggregation='sum', name=None
)

add_weight

View source

add_weight(
    shape=(), initializer=None, dtype=None, name=None
)

from_config

View source

@classmethod
from_config(
    config
)

get_config

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get_config()

Return the serializable config of the metric.

reset_state

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reset_state()

Reset all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

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result()

Compute the current metric value.

Returns
A scalar tensor, or a dictionary of scalar tensors.

stateless_reset_state

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stateless_reset_state()

stateless_result

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stateless_result(
    metric_variables
)

stateless_update_state

View source

stateless_update_state(
    metric_variables, *args, **kwargs
)

update_state

View source

update_state(
    y_true, y_pred, sample_weight=None
)

Accumulate statistics for the metric.

__call__

View source

__call__(
    *args, **kwargs
)

Call self as a function.