tf.contrib.metrics.streaming_pearson_correlation( predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None )
Defined in tensorflow/contrib/metrics/python/ops/metric_ops.py
.
See the guide: Metrics (contrib) > Metric Ops
Computes Pearson correlation coefficient between predictions
, labels
.
The streaming_pearson_correlation
function delegates to streaming_covariance
the tracking of three [co]variances:
streaming_covariance(predictions, labels)
, i.e. covariancestreaming_covariance(predictions, predictions)
, i.e. variancestreaming_covariance(labels, labels)
, i.e. varianceThe product-moment correlation ultimately returned is an idempotent operation cov(predictions, labels) / sqrt(var(predictions) * var(labels))
. To facilitate correlation computation across multiple batches, the function groups the update_op
s of the underlying streaming_covariance and returns an update_op
.
If weights
is not None, then it is used to compute a weighted correlation. NOTE: these weights are treated as "frequency weights", as opposed to "reliability weights". See discussion of the difference on https://wikipedia.org/wiki/Weighted_arithmetic_mean#Weighted_sample_variance
predictions
: A Tensor
of arbitrary size.labels
: A Tensor
of the same size as predictions.weights
: Optional Tensor
indicating the frequency with which an example is sampled. Rank must be 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.pearson_r
: A Tensor
representing the current Pearson product-moment correlation coefficient, the value of cov(predictions, labels) / sqrt(var(predictions) * var(labels))
.update_op
: An operation that updates the underlying variables appropriately.ValueError
: If labels
and predictions
are of different sizes, or if weights
is the wrong size, or if either metrics_collections
or updates_collections
are not a list
or tuple
.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/metrics/streaming_pearson_correlation