tf.contrib.metrics.streaming_pearson_correlation( predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None )
See the guide: Metrics (contrib) > Metric
Computes Pearson correlation coefficient between
streaming_pearson_correlation function delegates to
streaming_covariance the tracking of three [co]variances:
streaming_covariance(predictions, labels), i.e. covariance
streaming_covariance(predictions, predictions), i.e. variance
streaming_covariance(labels, labels), i.e. variance
The 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_ops of the underlying streaming_covariance and returns an
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
Tensorof arbitrary size.
Tensorof the same size as predictions.
Tensorindicating 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
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.
Tensorrepresenting 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.
predictionsare of different sizes, or if
weightsis the wrong size, or if either
updates_collectionsare not a
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Code samples licensed under the Apache 2.0 License.