tf.keras.metrics.FalsePositives

Calculates the number of false positives.

Inherits From: Metric, Layer, Module

tf.keras.metrics.FalsePositives(
    thresholds=None, name=None, dtype=None
)

If sample_weight is given, calculates the sum of the weights of false positives. This metric creates one local variable, accumulator that is used to keep track of the number of false positives.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

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. Defaults to 0.5.
name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.

Standalone usage:

m = tf.keras.metrics.FalsePositives()
m.update_state([0, 1, 0, 0], [0, 0, 1, 1])
m.result().numpy()
2.0

m.reset_state()
m.update_state([0, 1, 0, 0], [0, 0, 1, 1], sample_weight=[0, 0, 1, 0])
m.result().numpy()
1.0

Usage with compile() API:

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

Usage with a loss with from_logits=True:

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

Methods

merge_state

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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

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reset_state()

Resets 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()

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

View source

update_state(
    y_true, y_pred, sample_weight=None
)

Accumulates the metric statistics.

Args
y_true The ground truth values.
y_pred The predicted values.
sample_weight Optional weighting of each example. Can be a Tensor whose rank is either 0, or the same rank as y_true, and must be broadcastable to y_true. Defaults to 1.

Returns
Update op.