tf.keras.metrics.KLDivergence     View source on GitHub    Computes Kullback-Leibler divergence metric between y_true and y_pred.  View aliases  Compat aliases for migration 
See Migration guide for more details. tf.compat.v1.keras.metrics.KLDivergence, `tf.compat.v2.keras.metrics.KLDivergence`, `tf.compat.v2.metrics.KLDivergence`  
tf.keras.metrics.KLDivergence(
    name='kullback_leibler_divergence', dtype=None
)
 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()])

 


 Args
  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 View source 
reset_states()
 Resets all of the metric state variables. This function is called between epochs/steps, when a metric is evaluated during training. result View source 
result()
 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 View source 
update_state(
    y_true, y_pred, sample_weight=None
)
 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.  
  

    
{'path': 'public/docs/tensorflow~1.15/keras/metrics/kldivergence.html', 'source': 'DevDocs.IO'}