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"
)
Arguments
- y_true
tensor of true targets.
- y_pred
tensor of predicted targets.
- ...
For forward/backward compatability.
- alpha
coefficient controlling incidence of false positives.
- beta
coefficient controlling incidence of false negatives.
- 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"
orNULL
.- name
String, name for the object
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_log_cosh()
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()
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()