tf.keras.losses.Tversky

Computes the Tversky loss value between y_true and y_pred.

Inherits From: Loss

tf.keras.losses.Tversky(
    alpha=0.5,
    beta=0.5,
    reduction='sum_over_batch_size',
    name='tversky',
    axis=None,
    dtype=None
)

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
`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"`, `"mean"`, `"mean_with_sample_weight"` or `None`. `"sum"` sums the loss, `"sum_over_batch_size"` and `"mean"` sum the loss and divide by the sample size, and `"mean_with_sample_weight"` sums the loss and divides by the sum of the sample weights. `"none"` and `None` perform no aggregation. Defaults to `"sum_over_batch_size"`.
`name`	Optional name for the loss instance.
`dtype`	The dtype of the loss's computations. Defaults to `None`, which means using `keras.backend.floatx()`. `keras.backend.floatx()` is a `"float32"` unless set to different value (via `keras.backend.set_floatx()`). If a `keras.DTypePolicy` is provided, then the `compute_dtype` will be utilized.

Returns
Tversky loss value.

Attributes
`dtype`

Attributes

dtype

call(
    y_true, y_pred
)

@classmethod
from_config(
    config
)

get_config()

__call__(
    y_true, y_pred, sample_weight=None
)

Call self as a function.