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",
dtype = NULL
)
```

## Arguments

- y_true
tensor of true targets.

- y_pred
tensor of predicted targets.

- ...
For forward/backward compatability.

- alpha
The coefficient controlling incidence of false positives. Defaults to

`0.5`

.- beta
The coefficient controlling incidence of false negatives. Defaults to

`0.5`

.- 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
Optional name for the loss instance. (string)

- dtype
The dtype of the loss's computations. Defaults to

`NULL`

, which means using`config_floatx()`

.`config_floatx()`

is a`"float32"`

unless set to different value (via`config_set_floatx()`

).

## 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()`