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# tf.keras.metrics.KLDivergence

Computes Kullback-Leibler divergence metric between `y_true` and `y_pred`.

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

#### Usage:

``````m = tf.keras.metrics.KLDivergence()
m.update_state([.4, .9, .2], [.5, .8, .12])
print('Final result: ', m.result().numpy())  # Final result: -0.043
``````

Usage with tf.keras API:

``````model = tf.keras.Model(inputs, outputs)
model.compile('sgd', metrics=[tf.keras.metrics.KLDivergence()])
``````

`fn` The metric function to wrap, with signature `fn(y_true, y_pred, **kwargs)`.
`name` (Optional) string name of the metric instance.
`dtype` (Optional) data type of the metric result.
`**kwargs` The keyword arguments that are passed on to `fn`.

## Methods

### `reset_states`

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Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

### `result`

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Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

### `update_state`

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Accumulates metric statistics.

`y_true` and `y_pred` should have the same shape.

Args
`y_true` The ground truth values.
`y_pred` The predicted values.
`sample_weight` Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true`, and must be broadcastable to `y_true`.

Returns
Update op.