tf.compat.v1.metrics.false

Computes false negatives at provided threshold values.

tf.compat.v1.metrics.false_negatives_at_thresholds(
    labels, predictions, thresholds, 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`	A `Tensor` whose shape matches `predictions`. Will be cast to `bool`.
`predictions`	A floating point `Tensor` of arbitrary shape and whose values are in the range `[0, 1]`.
`thresholds`	A python list or tuple of float thresholds in `[0, 1]`.
`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_negatives` should be added to.
`updates_collections`	An optional list of collections that `update_op` should be added to.
`name`	An optional variable_scope name.

Returns
`false_negatives`	A float `Tensor` of shape `[len(thresholds)]`.
`update_op`	An operation that updates the `false_negatives` variable and returns its current value.

Raises
`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.
`RuntimeError`	If eager execution is enabled.

TensorFlow

tf.compat / v1 / v1.metrics.false_negatives

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TensorFlow

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tf.compat / v1 / v1.metrics.false_negatives

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