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TensorFlow 1 version
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View source on GitHub
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Computes the recall of the predictions with respect to the labels.
Inherits From: Metric, Layer, Module
tf.keras.metrics.Recall(
thresholds=None, top_k=None, class_id=None, name=None, dtype=None
)
Used in the notebooks
| Used in the tutorials |
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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.
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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
reset_states()
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
result
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
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.
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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.
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| Returns | |
|---|---|
| Update op. |