tf.keras.metrics.TrueNegatives

Calculates the number of true negatives.

Inherits From: Metric, Layer, Module

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

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

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

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

Standalone usage:

m = tf.keras.metrics.TrueNegatives()
m.update_state([0, 1, 0, 0], [1, 1, 0, 0])
m.result().numpy()
2.0
 
m.reset_state()
m.update_state([0, 1, 0, 0], [1, 1, 0, 0], sample_weight=[0, 0, 1, 0])
m.result().numpy()
1.0

Usage with compile() API:

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

Methods

reset_state

View source

reset_state()

Resets all of the metric state variables.

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

result

View source

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

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. 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.