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Computes the logarithm of the hyperbolic cosine of the prediction error.

Source: R/losses.R
loss_log_cosh.Rd

Formula:

loss <- mean(log(cosh(y_pred - y_true)), axis=-1)

Note that log(cosh(x)) is approximately equal to (x ** 2) / 2 for small x and to abs(x) - log(2) for large x. This means that 'logcosh' works mostly like the mean squared error, but will not be so strongly affected by the occasional wildly incorrect prediction.

Usage

loss_log_cosh(
  y_true,
  y_pred,
  ...,
  reduction = "sum_over_batch_size",
  name = "log_cosh"
)

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 loss. Options are "sum", "sum_over_batch_size" or NULL. Defaults to "sum_over_batch_size".

name

Optional name for the instance.

Value

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

Examples

y_true <- rbind(c(0., 1.), c(0., 0.))
y_pred <- rbind(c(1., 1.), c(0., 0.))
loss <- loss_log_cosh(y_true, y_pred)
# 0.108

See also

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_mean_absolute_error()
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()