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Sum the weights of true_negatives.
tf.metrics.true_negatives( labels, predictions, weights=None, metrics_collections=None, updates_collections=None, name=None )
None, weights default to 1. Use weights of 0 to mask values.
labels: The ground truth values, a
Tensorwhose dimensions must match
predictions. Will be cast to
predictions: The predicted values, a
Tensorof arbitrary dimensions. Will be cast to
Tensorwhose rank is either 0, or the same rank as
labels, and must be broadcastable to
labels(i.e., all dimensions must be either
1, or the same as the corresponding
metrics_collections: An optional list of collections that the metric value variable should be added to.
updates_collections: An optional list of collections that the metric update ops should be added to.
name: An optional variable_scope name.
Tensorrepresenting the current value of the metric.
update_op: An operation that accumulates the error from a batch of data.
labelshave mismatched shapes, or if
Noneand its shape doesn't match
predictions, or if either
updates_collectionsare not a list or tuple.
RuntimeError: If eager execution is enabled.