View source on GitHub |
Computes the Tversky loss value between y_true
and y_pred
.
tf.keras.losses.tversky(
y_true, y_pred, alpha=0.5, beta=0.5
)
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.
Args | |
---|---|
y_true
|
tensor of true targets. |
y_pred
|
tensor of predicted targets. |
alpha
|
coefficient controlling incidence of false positives. |
beta
|
coefficient controlling incidence of false negatives. |
Returns | |
---|---|
Tversky loss value. |