tf.metrics.mean_squared_error( labels, predictions, weights=None, metrics_collections=None, updates_collections=None, name=None )
Computes the mean squared error between the labels and predictions.
mean_squared_error function creates two local variables,
count that are used to compute the mean squared error.
This average is weighted by
weights, and it is ultimately returned as
mean_squared_error: an idempotent operation that simply divides
For estimation of the metric over a stream of data, the function creates an
update_op operation that updates these variables and returns the
mean_squared_error. Internally, a
squared_error operation computes the
element-wise square of the difference between
total with the reduced sum of the product of
squared_error, and it increments
count with the reduced sum
None, weights default to 1. Use weights of 0 to mask values.
Tensorof the same shape as
Tensorof arbitrary shape.
Tensorwhose 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
metrics_collections: An optional list of collections that
mean_squared_errorshould be added to.
updates_collections: An optional list of collections that
update_opshould be added to.
name: An optional variable_scope name.
Tensorrepresenting the current mean, the value of
update_op: An operation that increments the
countvariables appropriately and whose value matches
labelshave mismatched shapes, or if
Noneand its shape doesn't match
predictions, or if either
updates_collectionsare not a list or tuple.
RuntimeError: If eager execution is enabled.