Computes the Tversky loss value between y_true and y_pred.

This loss function is weighted by the alpha and beta coefficients that penalize false positives and false negatives.

With alpha=0.5 and beta=0.5, the loss value becomes equivalent to Dice Loss.

This loss function is weighted by the alpha and beta coefficients that penalize false positives and false negatives.

With alpha=0.5 and beta=0.5, the loss value becomes equivalent to Dice Loss.

Usage

loss_tversky(
  y_true,
  y_pred,
  ...,
  alpha = 0.5,
  beta = 0.5,
  reduction = "sum_over_batch_size",
  name = "tversky",
  dtype = NULL
)

y_true: tensor of true targets.
y_pred: tensor of predicted targets.
...: For forward/backward compatability.
alpha: The coefficient controlling incidence of false positives. Defaults to 0.5.
beta: The coefficient controlling incidence of false negatives. Defaults to 0.5.
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. (string)
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.

Tversky loss value.