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Computes the false negative rate of predictions with respect to labels.

The false_negative_rate function creates two local variables, false_negatives and true_positives, that are used to compute the false positive rate. This value is ultimately returned as false_negative_rate, an idempotent operation that simply divides false_negatives by the sum of false_negatives and true_positives.

For estimation of the metric over a stream of data, the function creates an update_op operation that updates these variables and returns the false_negative_rate. update_op weights each prediction by the corresponding value in weights.

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

predictions The predicted values, a Tensor of arbitrary dimensions. Will be cast to bool.
labels The ground truth values, a Tensor whose dimensions must match predictions. Will be cast to bool.
weights Optional Tensor whose 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 labels dimension).
metrics_collections An optional list of collections that false_negative_rate should be added to.
updates_collections An optional list of collections that update_op should be added to.
name An optional variable_scope name.

false_negative_rate Scalar float Tensor with the value of false_negatives divided by the sum of false_negatives and true_positives.
update_op Operation that increments false_negatives and true_positives variables appropriately and whose value matches false_negative_rate.

ValueError If predictions and labels have mismatched shapes, or if weights is not None and its shape doesn't match predictions, or if either metrics_collections or updates_collections are not a list or tuple.