Formula:

Formula:

## 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()`