tf.keras.metrics.Precision

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
)

Used in the notebooks

Used in the tutorials

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.

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). If used with a loss function that sets from_logits=True (i.e. no sigmoid applied to predictions), thresholds should be set to 0. 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.

Example:

m = keras.metrics.Precision()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1])
m.result()
0.6666667
 
m.reset_state()
m.update_state([0, 1, 1, 1], [1, 0, 1, 1], sample_weight=[0, 0, 1, 0])
m.result()
1.0
 
# With top_k=2, it will calculate precision over y_true[:2]
# and y_pred[:2]
m = keras.metrics.Precision(top_k=2)
m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
m.result()
0.0
 
# With top_k=4, it will calculate precision over y_true[:4]
# and y_pred[:4]
m = keras.metrics.Precision(top_k=4)
m.update_state([0, 0, 1, 1], [1, 1, 1, 1])
m.result()
0.5

Usage with compile() API:

model.compile(optimizer='sgd',
              loss='binary_crossentropy',
              metrics=[keras.metrics.Precision()])

Usage with a loss with from_logits=True:

model.compile(optimizer='adam',
              loss=keras.losses.BinaryCrossentropy(from_logits=True),
              metrics=[keras.metrics.Precision(thresholds=0)])

dtype

variables

Methods

add_variable

View source

add_variable(
    shape, initializer, dtype=None, aggregation='sum', name=None
)

add_weight

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add_weight(
    shape=(), initializer=None, dtype=None, name=None
)

from_config

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@classmethod
from_config(
    config
)

get_config

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get_config()

Return the serializable config of the metric.

reset_state

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reset_state()

Reset all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

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result()

Compute the current metric value.

Returns
A scalar tensor, or a dictionary of scalar tensors.

stateless_reset_state

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stateless_reset_state()

stateless_result

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stateless_result(
    metric_variables
)

stateless_update_state

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stateless_update_state(
    metric_variables, *args, **kwargs
)

update_state

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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.

__call__

View source

__call__(
    *args, **kwargs
)

Call self as a function.