tf.keras.metrics.TrueNegatives

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Calculates the number of true negatives.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.metrics.TrueNegatives, `tf.compat.v2.keras.metrics.TrueNegatives`, `tf.compat.v2.metrics.TrueNegatives`

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

For example, if y_true is [0, 1, 0, 0] and y_pred is [1, 1, 0, 0] then the true negatives value is 2. If the weights were specified as [0, 0, 1, 0] then the true negatives value would be 1.

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.

Usage:

m = tf.keras.metrics.TrueNegatives()
m.update_state([0, 1, 0, 0], [1, 1, 0, 0])
print('Final result: ', m.result().numpy())  # Final result: 2

Usage with tf.keras API:

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

Args
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.

Methods

reset_states

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reset_states()

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 metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

update_state

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update_state(
    y_true, y_pred, sample_weight=None
)

Accumulates the given confusion matrix condition 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.