tf.keras.metrics.LogCoshError

Computes the logarithm of the hyperbolic cosine of the prediction error.

Inherits From: MeanMetricWrapper, Mean, Metric

tf.keras.metrics.LogCoshError(
    name='logcosh', dtype=None
)

Formula:

error = y_pred - y_true
logcosh = mean(log((exp(error) + exp(-error))/2), axis=-1)

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

Example:

Example:

m = keras.metrics.LogCoshError()
m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
m.result()
0.10844523
m.reset_state()
m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
               sample_weight=[1, 0])
m.result()
0.21689045

Usage with compile() API:

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

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

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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

View source

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.