tf.metrics.false_negatives(labels, predictions, weights=None, metrics_collections=None, updates_collections=None, name=None)

tf.metrics.false_negatives(labels, predictions, weights=None, metrics_collections=None, updates_collections=None, name=None)

Computes the total number of false positives.

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

Args:

• labels: The ground truth values, a Tensor whose dimensions must match predictions. Will be cast to bool.
• predictions: The predicted values, a Tensor of arbitrary dimensions. 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 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.

Returns:

• value_tensor: A Tensor representing the current value of the metric.
• update_op: An operation that accumulates the error from a batch of data.

Raises:

• ValueError: If weights is not None and its shape doesn't match values, or if either metrics_collections or updates_collections are not a list or tuple.

Defined in tensorflow/python/ops/metrics_impl.py.