|TensorFlow 1 version||View source on GitHub|
Computes the recall of the predictions with respect to the labels.
See Migration guide for more details.
tf.keras.metrics.Recall( thresholds=None, top_k=None, class_id=None, name=None, dtype=None )
Used in the notebooks
|Used in the tutorials|
This metric creates two local variables,
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
None, weights default to 1.
sample_weight of 0 to mask values.
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.
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
(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
||(Optional) Unset by default. An int value specifying the top-k predictions to consider when calculating recall.|
(Optional) Integer class ID for which we want binary metrics.
This must be in the half-open interval
||(Optional) string name of the metric instance.|
||(Optional) data type of the metric result.|
m = tf.keras.metrics.Recall()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
model.compile(optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.Recall()])
merge_state( metrics )
Merges the state from one or more metrics.
This method can be used by distributed systems to merge the state computed by different metric instances. Typically the state will be stored in the form of the metric's weights. For example, a tf.keras.metrics.Mean metric contains a list of two weight values: a total and a count. If there were two instances of a tf.keras.metrics.Accuracy that each independently aggregated partial state for an overall accuracy calculation, these two metric's states could be combined as follows:
m1 = tf.keras.metrics.Accuracy()
_ = m1.update_state([, ], [, ])
m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([, ], [, ])
||an iterable of metrics. The metrics must have compatible state.|
||If the provided iterable does not contain metrics matching the metric's required specifications.|
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
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( y_true, y_pred, sample_weight=None )
Accumulates true positive and false negative statistics.
The ground truth values, with the same dimensions as
The predicted values. Each element must be in the range
Optional weighting of each example. Defaults to 1. Can be a