tf.keras.losses.tversky

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.

Reference:

• Salehi et al., 2017