loss_mean_squared_logarithmic_error: Computes the mean squared logarithmic error between 'y_true'...

In keras3: R Interface to 'Keras'

Computes the mean squared logarithmic error between y_true and y_pred.

Description

Note that y_pred and y_true cannot be less or equal to 0. Negative values and 0 values will be replaced with config_epsilon() (default to 1e-7).

Formula:

loss <- mean(square(log(y_true + 1) - log(y_pred + 1)))

Usage

loss_mean_squared_logarithmic_error(
  y_true,
  y_pred,
  ...,
  reduction = "sum_over_batch_size",
  name = "mean_squared_logarithmic_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", "mean", "mean_with_sample_weight" or NULL. "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 NULL 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 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 squared logarithmic 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_squared_logarithmic_error(y_true, y_pred)

See Also

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

Other losses:
Loss()
loss_binary_crossentropy()
loss_binary_focal_crossentropy()
loss_categorical_crossentropy()
loss_categorical_focal_crossentropy()
loss_categorical_hinge()
loss_circle()
loss_cosine_similarity()
loss_ctc()
loss_dice()
loss_hinge()
loss_huber()
loss_kl_divergence()
loss_log_cosh()
loss_mean_absolute_error()
loss_mean_absolute_percentage_error()
loss_mean_squared_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()

keras3 documentation built on June 8, 2025, 1:36 p.m.