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Computes best recall where precision is >= specified value.
Inherits From: Metric
, Layer
, Module
tf.keras.metrics.RecallAtPrecision(
precision, num_thresholds=200, class_id=None, name=None, dtype=None
)
For a given score-label-distribution the required precision might not be achievable, in this case 0.0 is returned as recall.
This metric creates four local variables, true_positives
, true_negatives
,
false_positives
and false_negatives
that are used to compute the
recall at the given precision. The threshold for the given precision
value is computed and used to evaluate the corresponding recall.
If sample_weight
is None
, weights default to 1.
Use sample_weight
of 0 to mask values.
If class_id
is specified, we calculate precision by considering only the
entries in the batch for which class_id
is above the threshold predictions,
and computing the fraction of them for which class_id
is indeed a correct
label.
Args | |
---|---|
precision
|
A scalar value in range [0, 1] .
|
num_thresholds
|
(Optional) Defaults to 200. The number of thresholds to use for matching the given precision. |
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.RecallAtPrecision(0.8)
m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
m.result().numpy()
0.5
m.reset_state()
m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9],
sample_weight=[1, 0, 0, 1])
m.result().numpy()
1.0
Usage with compile()
API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.RecallAtPrecision(precision=0.8)])
Methods
reset_state
reset_state()
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 confusion matrix statistics.
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. |