TensorFlow 2 version | View source on GitHub |
Computes the precision of the predictions with respect to the labels.
Inherits From: Metric
tf.keras.metrics.Precision(
thresholds=None, top_k=None, class_id=None, name=None, dtype=None
)
For example, if y_true
is [0, 1, 1, 1] and y_pred
is [1, 0, 1, 1]
then the precision value is 2/(2+1) ie. 0.66. If the weights were specified as
[0, 0, 1, 0] then the precision value would be 1.
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.
Usage:
m = tf.keras.metrics.Precision()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
print('Final result: ', m.result().numpy()) # Final result: 0.66
Usage with tf.keras API:
model = tf.keras.Model(inputs, outputs)
model.compile('sgd', loss='mse', metrics=[tf.keras.metrics.Precision()])
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 precision with thresholds=0.5 .
|
top_k
|
(Optional) Unset by default. An int value specifying the top-k predictions to consider when calculating 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. |
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 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. |