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,
    ignore_class=None,
    axis=-1
)

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.
ignore_class	(Optional) Integer. Class to ignore. If None, no class is ignored.
axis	(Optional) Defaults to -1. The dimension along which entropy is computed.

Examples:

# 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

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@classmethod
from_config(
    config
)

get_config

View source

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

View source

stateless_reset_state()

stateless_result

View source

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.