tf.compat.v1.metrics.average_precision_at

Computes the total number of false negatives.

tf.compat.v1.metrics.false_negatives(
    labels, predictions, weights=None, metrics_collections=None,
    updates_collections=None, name=None
)

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.
`RuntimeError`	If eager execution is enabled.

TensorFlow

tf.compat / v1 / v1.metrics.average_precision_at_k

Args

Returns

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TensorFlow

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tf.compat / v1 / v1.metrics.average_precision_at_k

Args

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