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# tf.keras.losses.KLDivergence

## Class `KLDivergence`

Computes Kullback Leibler divergence loss between `y_true` and `y_pred`.

### Aliases:

• Class `tf.compat.v1.keras.losses.KLDivergence`
• Class `tf.compat.v2.keras.losses.KLDivergence`
• Class `tf.compat.v2.losses.KLDivergence`

`loss = y_true * log(y_true / y_pred)`

#### Usage:

``````k = tf.keras.losses.KLDivergence()
loss = k([.4, .9, .2], [.5, .8, .12])
print('Loss: ', loss.numpy())  # Loss: -0.043
``````

Usage with tf.keras API:

``````model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss=tf.keras.losses.KLDivergence())
``````

## `__init__`

View source

``````__init__(
reduction=losses_utils.ReductionV2.AUTO,
name='kullback_leibler_divergence'
)
``````

## Methods

### `__call__`

View source

``````__call__(
y_true,
y_pred,
sample_weight=None
)
``````

Invokes the `Loss` instance.

#### Args:

• `y_true`: Ground truth values.
• `y_pred`: The predicted values.
• `sample_weight`: Optional `Tensor` whose rank is either 0, or the same rank as `y_true`, or is broadcastable to `y_true`. `sample_weight` acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If `sample_weight` is a tensor of size `[batch_size]`, then the total loss for each sample of the batch is rescaled by the corresponding element in the `sample_weight` vector. If the shape of `sample_weight` matches the shape of `y_pred`, then the loss of each measurable element of `y_pred` is scaled by the corresponding value of `sample_weight`.

#### Returns:

Weighted loss float `Tensor`. If `reduction` is `NONE`, this has the same shape as `y_true`; otherwise, it is scalar.

#### Raises:

• `ValueError`: If the shape of `sample_weight` is invalid.

### `from_config`

View source

``````from_config(
cls,
config
)
``````

Instantiates a `Loss` from its config (output of `get_config()`).

#### Args:

• `config`: Output of `get_config()`.

#### Returns:

A `Loss` instance.

### `get_config`

View source

``````get_config()
``````