Computes the Dice loss value between y_true and y_pred. — loss_dice • keras3

Formula:

loss = 1 - (2 * sum(y_true * y_pred)) / (sum(y_true) + sum(y_pred))

Formula:

loss = 1 - (2 * sum(y_true * y_pred)) / (sum(y_true) + sum(y_pred))

Usage

loss_dice(
  y_true,
  y_pred,
  ...,
  reduction = "sum_over_batch_size",
  name = "dice"
)

Arguments

y_true: tensor of true targets.
y_pred: tensor of predicted targets.
...: For forward/backward compatability.
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: String, name for the object

Value

if y_true and y_pred are provided, Dice loss value. Otherwise, a Loss() instance.

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_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()
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()