tf.keras.metrics.TopKCategoricalAccuracy

Computes how often targets are in the top K predictions.

Inherits From: MeanMetricWrapper, Mean, Metric

k (Optional) Number of top elements to look at for computing accuracy. Defaults to 5.
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.

Standalone usage:

m = keras.metrics.TopKCategoricalAccuracy(k=1)
m.update_state([[0, 0, 1], [0, 1, 0]],
               [[0.1, 0.9, 0.8], [0.05, 0.95, 0]])
m.result()
0.5
m.reset_state()
m.update_state([[0, 0, 1], [0, 1, 0]],
               [[0.1, 0.9, 0.8], [0.05, 0.95, 0]],
               sample_weight=[0.7, 0.3])
m.result()
0.3

Usage with compile() API:

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

dtype

variables

Methods

add_variable

View source

add_weight

View source

from_config

View source

get_config

View source

Return the serializable config of the metric.

reset_state

View source

Reset all of the metric state variables.

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

result

View source

Compute the current metric value.

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

stateless_reset_state

View source

stateless_result

View source

stateless_update_state

View source

update_state

View source

Accumulate statistics for the metric.

__call__

View source

Call self as a function.