View source on GitHub |
Computes the precision of the predictions with respect to the labels.
Inherits From: Metric
, Layer
, Module
tf.keras.metrics.Precision(
thresholds=None, top_k=None, class_id=None, name=None, dtype=None
)
The metric creates two local variables, true_positives
and
false_positives
that are used to compute the precision. This value is
ultimately returned as precision
, an idempotent operation that simply
divides true_positives
by the sum of true_positives
and
false_positives
.
If sample_weight
is None
, weights default to 1.
Use sample_weight
of 0 to mask values.
If top_k
is set, we'll calculate precision as how often on average a class
among the top-k classes with the highest predicted values of a batch entry
is correct and can be found in the label for that entry.
If class_id
is specified, we calculate precision by considering only the
entries in the batch for which class_id
is above the threshold and/or in
the top-k highest predictions, and computing the fraction of them for which
class_id
is indeed a correct label.
Standalone usage:
m = tf.keras.metrics.Precision()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
m.result().numpy()
0.6666667
m.reset_state()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
m.result().numpy()
1.0
# With top_k=2, it will calculate precision over y_true[:2]
# and y_pred[:2]
m = tf.keras.metrics.Precision(top_k=2)
m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
m.result().numpy()
0.0
# With top_k=4, it will calculate precision over y_true[:4]
# and y_pred[:4]
m = tf.keras.metrics.Precision(top_k=4)
m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
m.result().numpy()
0.5
Usage with compile()
API:
model.compile(optimizer='sgd',
loss='mse',
metrics=[tf.keras.metrics.Precision()])
Usage with a loss with from_logits=True
:
model.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.Precision(thresholds=0)])
Methods
merge_state
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([[1], [2]], [[0], [2]])
m2 = tf.keras.metrics.Accuracy()
_ = m2.update_state([[3], [4]], [[3], [4]])
m2.merge_state([m1])
m2.result().numpy()
0.75
Args | |
---|---|
metrics
|
an iterable of metrics. The metrics must have compatible state. |
Raises | |
---|---|
ValueError
|
If the provided iterable does not contain metrics matching the metric's required specifications. |
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 scalar metric value tensor or a dict of scalars.
Result computation is an idempotent operation that simply calculates the metric value using the state variables.
Returns | |
---|---|
A scalar tensor, or a dictionary of scalar tensors. |
update_state
update_state(
y_true, y_pred, sample_weight=None
)
Accumulates true positive and false positive 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. |