tf.keras.metrics.MeanAbsoluteError

Computes the mean absolute error between the labels and predictions.

Inherits From: MeanMetricWrapper, Mean, Metric

tf.keras.metrics.MeanAbsoluteError(
    name='mean_absolute_error', dtype=None
)

Used in the notebooks

Used in the tutorials

Formula:

loss = mean(abs(y_true - y_pred))

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

Examples:

m = keras.metrics.MeanAbsoluteError()
m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]])
m.result()
0.25
m.reset_state()
m.update_state([[0, 1], [0, 0]], [[1, 1], [0, 0]],
               sample_weight=[1, 0])
m.result()
0.5

Usage with compile() API:

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

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

View source

get_config()

Return the serializable config of the metric.

reset_state

View source

reset_state()

Reset all of the metric state variables.

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

result

View source

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.