Computes the mean of absolute difference between labels and predictions.

Formula:

loss <- mean(abs(y_true - y_pred))

Usage

loss_mean_absolute_error(
  y_true,
  y_pred,
  ...,
  reduction = "sum_over_batch_size",
  name = "mean_absolute_error",
  dtype = NULL
)

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.

dtype

The dtype of the loss's computations. Defaults to NULL, which means using config_floatx(). config_floatx() is a "float32" unless set to different value (via config_set_floatx()). If a keras$DTypePolicy is provided, then the compute_dtype will be utilized.

Value

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

Examples

y_true <- random_uniform(c(2, 3), 0, 2)
y_pred <- random_uniform(c(2, 3))
loss <- loss_mean_absolute_error(y_true, y_pred)

See also

• https://keras.io/api/losses/regression_losses#meanabsoluteerror-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_percentage_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()