Computes the mean absolute percentage error between y_true and y_pred. — loss_mean_absolute_percentage_error • keras3

Formula:

loss <- 100 * op_mean(op_abs((y_true - y_pred) / y_true),
                      axis=-1)

Division by zero is prevented by dividing by max(y_true, epsilon) where epsilon = config_epsilon() (default to 1e-7).

Usage

loss_mean_absolute_percentage_error(
  y_true,
  y_pred,
  ...,
  reduction = "sum_over_batch_size",
  name = "mean_absolute_percentage_error"
)

Arguments

y_true: Ground truth values with shape = [batch_size, d0, .. dN].
y_pred: The predicted values with shape = [batch_size, d0, .. dN].
...: For forward/backward compatability.
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" or NULL.
name: Optional name for the loss instance.

Value

Mean absolute percentage error values with shape = [batch_size, d0, ..dN-1].

Examples

y_true <- random_uniform(c(2, 3))
y_pred <- random_uniform(c(2, 3))
loss <- loss_mean_absolute_percentage_error(y_true, y_pred)

See also

https://keras.io/api/losses/regression_losses#meanabsolutepercentageerror-class

Other losses:
Loss()
loss_binary_crossentropy()
loss_binary_focal_crossentropy()
loss_categorical_crossentropy()
loss_categorical_focal_crossentropy()
loss_categorical_hinge()
loss_cosine_similarity()
loss_ctc()
loss_dice()
loss_hinge()
loss_huber()
loss_kl_divergence()
loss_log_cosh()
loss_mean_absolute_error()
loss_mean_squared_error()
loss_mean_squared_logarithmic_error()
loss_poisson()
loss_sparse_categorical_crossentropy()
loss_squared_hinge()
loss_tversky()
metric_binary_crossentropy()
metric_binary_focal_crossentropy()
metric_categorical_crossentropy()
metric_categorical_focal_crossentropy()
metric_categorical_hinge()
metric_hinge()
metric_huber()
metric_kl_divergence()
metric_log_cosh()
metric_mean_absolute_error()
metric_mean_absolute_percentage_error()
metric_mean_squared_error()
metric_mean_squared_logarithmic_error()
metric_poisson()
metric_sparse_categorical_crossentropy()
metric_squared_hinge()