tf.metrics.mean_absolute_error( labels, predictions, weights=None, metrics_collections=None, updates_collections=None, name=None )
Computes the mean absolute error between the labels and predictions.
mean_absolute_error function creates two local variables,
count that are used to compute the mean absolute error. This
average is weighted by
weights, and it is ultimately returned as
mean_absolute_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_absolute_error. Internally, an
absolute_errors operation computes the
absolute value of the differences between
total with the reduced sum of the product of
absolute_errors, and it increments
count with the reduced
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_absolute_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.