tf.contrib.metrics.streaming_false_negative_rate_at_thresholds
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Computes various fnr values for different thresholds on predictions.
tf.contrib.metrics.streaming_false_negative_rate_at_thresholds(
predictions, labels, thresholds, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The streaming_false_negative_rate_at_thresholds function creates two
local variables, false_negatives, true_positives, for various values of
thresholds. false_negative_rate[i] is defined as the total weight
of values in predictions above thresholds[i] whose corresponding entry in
labels is False, divided by the total weight of True values in labels
(false_negatives[i] / (false_negatives[i] + true_positives[i])).
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_positive_rate.
If weights is None, weights default to 1. Use weights of 0 to mask values.
Args |
|---|
predictions
|
A floating point Tensor of arbitrary shape and whose values
are in the range [0, 1].
|
labels
|
A bool Tensor whose shape matches predictions.
|
thresholds
|
A python list or tuple of float thresholds in [0, 1].
|
weights
|
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.
|
Returns |
|---|
false_negative_rate
|
A float Tensor of shape [len(thresholds)].
|
update_op
|
An operation that increments the false_negatives and
true_positives variables that are used in the computation of
false_negative_rate.
|
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.
|
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Last updated 2020-10-01 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[],null,["# tf.contrib.metrics.streaming_false_negative_rate_at_thresholds\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/metrics/python/ops/metric_ops.py#L2032-L2100) |\n\nComputes various fnr values for different `thresholds` on `predictions`. \n\n tf.contrib.metrics.streaming_false_negative_rate_at_thresholds(\n predictions, labels, thresholds, weights=None, metrics_collections=None,\n updates_collections=None, name=None\n )\n\nThe `streaming_false_negative_rate_at_thresholds` function creates two\nlocal variables, `false_negatives`, `true_positives`, for various values of\nthresholds. `false_negative_rate[i]` is defined as the total weight\nof values in `predictions` above `thresholds[i]` whose corresponding entry in\n`labels` is `False`, divided by the total weight of `True` values in `labels`\n(`false_negatives[i] / (false_negatives[i] + true_positives[i])`).\n\nFor estimation of the metric over a stream of data, the function creates an\n`update_op` operation that updates these variables and returns the\n`false_positive_rate`.\n\nIf `weights` is `None`, weights default to 1. Use weights of 0 to mask values.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `predictions` | A floating point `Tensor` of arbitrary shape and whose values are in the range `[0, 1]`. |\n| `labels` | A `bool` `Tensor` whose shape matches `predictions`. |\n| `thresholds` | A python list or tuple of float thresholds in `[0, 1]`. |\n| `weights` | `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). |\n| `metrics_collections` | An optional list of collections that `false_negative_rate` should be added to. |\n| `updates_collections` | An optional list of collections that `update_op` should be added to. |\n| `name` | An optional variable_scope name. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|-----------------------|----------------------------------------------------------------------------------------------------------------------------------------------|\n| `false_negative_rate` | A float `Tensor` of shape `[len(thresholds)]`. |\n| `update_op` | An operation that increments the `false_negatives` and `true_positives` variables that are used in the computation of `false_negative_rate`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `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. |\n\n\u003cbr /\u003e"]]
View source on GitHub