tf.keras.losses.KLDivergence

Computes Kullback-Leibler divergence loss between y_true & y_pred.

Inherits From: Loss

tf.keras.losses.KLDivergence(
    reduction='sum_over_batch_size',
    name='kl_divergence',
    dtype=None
)

loss = y_true * log(y_true / y_pred)

y_true and y_pred are expected to be probability distributions, with values between 0 and 1. They will get clipped to the [0, 1] range.

Args
`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.

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.