View source on GitHub |
Computes best precision where recall is >= specified value.
Inherits From: Metric
, Layer
, Module
tf.keras.metrics.PrecisionAtRecall(
recall, num_thresholds=200, name=None, dtype=None
)
This metric creates four local variables, true_positives
, true_negatives
,
false_positives
and false_negatives
that are used to compute the
precision at the given recall. The threshold for the given recall
value is computed and used to evaluate the corresponding precision.
If sample_weight
is None
, weights default to 1.
Use sample_weight
of 0 to mask values.
Args | |
---|---|
recall
|
A scalar value in range [0, 1] .
|
num_thresholds
|
(Optional) Defaults to 200. The number of thresholds to use for matching the given recall. |
name
|
(Optional) string name of the metric instance. |
dtype
|
(Optional) data type of the metric result. |
Standalone usage:
m = tf.keras.metrics.PrecisionAtRecall(0.5)
m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8])
m.result().numpy()
0.5
m.reset_states()
m.update_state([0, 0, 0, 1, 1], [0, 0.3, 0.8, 0.3, 0.8],
sample_weight=[2, 2, 2, 1, 1])
m.result().numpy()
0.33333333
Usage with compile()
API:
model.compile(
optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.PrecisionAtRecall(recall=0.8)])
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 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. |