tf.keras.losses.KLDivergence | TensorFlow v2.16.1 (original) (raw)
tf.keras.losses.KLDivergence
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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'
)
Formula:
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" or None. |
| name | Optional name for the loss instance. |
Methods
call
call(
y_true, y_pred
)
from_config
@classmethod
from_config( config )
get_config
get_config()
__call__
__call__(
y_true, y_pred, sample_weight=None
)
Call self as a function.
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Last updated 2024-06-07 UTC.