|TensorFlow 1 version||View source on GitHub|
Computes the precision of the predictions with respect to the labels.
See Migration guide for more details.
Used in the tutorials:
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,
that are used to compute the precision. This value is ultimately returned as
precision, an idempotent operation that simply divides
by the sum of
None, weights default to 1.
sample_weight of 0 to mask values.
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.
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.
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()])
__init__( thresholds=None, top_k=None, class_id=None, name=None, dtype=None )
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
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_classesis the last dimension of predictions.
name: (Optional) string name of the metric instance.
dtype: (Optional) data type of the metric result.
@staticmethod __new__( cls, *args, **kwargs )
Create and return a new object. See help(type) for accurate signature.
Resets all of the metric state variables.
This function is called between epochs/steps, when a metric is evaluated during training.
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( y_true, y_pred, sample_weight=None )
Accumulates true positive and false positive statistics.
y_true: The ground truth values, with the same dimensions as
y_pred. Will be cast to
y_pred: The predicted values. Each element must be in the range
sample_weight: Optional weighting of each example. Defaults to 1. Can be a
Tensorwhose rank is either 0, or the same rank as
y_true, and must be broadcastable to