tf.keras.metrics.Recall     View source on GitHub    Computes the recall of the predictions with respect to the labels. Inherits From: Metric, Layer, Module  View aliases  Main aliases 
tf.metrics.Recall Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.metrics.Recall  
tf.keras.metrics.Recall(
    thresholds=None, top_k=None, class_id=None, name=None, dtype=None
)
 This metric creates two local variables, true_positives and false_negatives, that are used to compute the recall. This value is ultimately returned as recall, an idempotent operation that simply divides true_positives by the sum of true_positives and false_negatives. If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values. If top_k is set, recall will be computed as how often on average a class among the labels of a batch entry is in the top-k predictions. If class_id is specified, we calculate recall by considering only the entries in the batch for which class_id is in the label, and computing the fraction of them for which class_id is above the threshold and/or in the top-k predictions.
 


 Args
  thresholds   (Optional) A float value or a python list/tuple of float threshold values in [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). One metric value is generated for each threshold value. If neither thresholds nor top_k are set, the default is to calculate recall with thresholds=0.5.  
  top_k   (Optional) Unset by default. An int value specifying the top-k predictions to consider when calculating recall.  
  class_id   (Optional) Integer class ID for which we want binary metrics. This must be in the half-open interval [0, num_classes), where num_classes is the last dimension of predictions.  
  name   (Optional) string name of the metric instance.  
  dtype   (Optional) data type of the metric result.    Standalone usage: 
m = tf.keras.metrics.Recall()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
m.result().numpy()
0.6666667
 
m.reset_states()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
m.result().numpy()
1.0
 Usage with compile() API: model.compile(optimizer='sgd',
              loss='mse',
              metrics=[tf.keras.metrics.Recall()])
 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 true positive and false negative statistics.
 


 Args
  y_true   The ground truth values, with the same dimensions as y_pred. Will be cast to bool.  
  y_pred   The predicted values. Each element must be in the range [0, 1].  
  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.