tf.keras.losses.MeanAbsolutePercentageError

Computes the mean absolute percentage error between y_true & y_pred.

Inherits From: Loss

tf.keras.losses.MeanAbsolutePercentageError(
    reduction='sum_over_batch_size',
    name='mean_absolute_percentage_error',
    dtype=None
)

Formula:

loss = 100 * mean(abs((y_true - y_pred) / y_true))

reduction Type of reduction to apply to the loss. In almost all cases this should be "sum_over_batch_size". Supported options are "sum", "sum_over_batch_size", "mean", "mean_with_sample_weight" or None. "sum" sums the loss, "sum_over_batch_size" and "mean" sum the loss and divide by the sample size, and "mean_with_sample_weight" sums the loss and divides by the sum of the sample weights. "none" and None perform no aggregation. Defaults to "sum_over_batch_size".
name Optional name for the loss instance.
dtype The dtype of the loss's computations. Defaults to None, which means using keras.backend.floatx(). keras.backend.floatx() is a "float32" unless set to different value (via keras.backend.set_floatx()). If a keras.DTypePolicy is provided, then the compute_dtype will be utilized.

Examples:

y_true = keras.ops.array([100.0, 200.0, 300.0])
y_pred = keras.ops.array([90.0, 210.0, 310.0])
loss = keras.losses.MeanAbsolutePercentageError()
loss(y_true, y_pred)
6.111111

dtype

Methods

call

View source

call(
    y_true, y_pred
)

from_config

View source

@classmethod
from_config(
    config
)

get_config

View source

get_config()

__call__

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__call__(
    y_true, y_pred, sample_weight=None
)

Call self as a function.