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Computes the root mean squared error between the labels and predictions. (deprecated)
tf.contrib.metrics.streaming_root_mean_squared_error(
predictions, labels, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The streaming_root_mean_squared_error
function creates two local variables,
total
and count
that are used to compute the root mean squared error.
This average is weighted by weights
, and it is ultimately returned as
root_mean_squared_error
: an idempotent operation that takes the square root
of the division of total
by count
.
For estimation of the metric over a stream of data, the function creates an
update_op
operation that updates these variables and returns the
root_mean_squared_error
. Internally, a squared_error
operation computes
the element-wise square of the difference between predictions
and labels
.
Then update_op
increments total
with the reduced sum of the product of
weights
and squared_error
, and it increments count
with the reduced sum
of weights
.
If weights
is None
, weights default to 1. Use weights of 0 to mask values.
Args | |
---|---|
predictions
|
A Tensor of arbitrary shape.
|
labels
|
A Tensor of the same shape 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
root_mean_squared_error 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 | |
---|---|
root_mean_squared_error
|
A Tensor representing the current mean, the value
of total divided by count .
|
update_op
|
An operation that increments the total and count variables
appropriately and whose value matches root_mean_squared_error .
|
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.
|