View source on GitHub |
Forward features to predictions dictionary.
tf.contrib.estimator.forward_features(
estimator, keys=None, sparse_default_values=None
)
In some cases, user wants to see some of the features in estimators prediction output. As an example, consider a batch prediction service: The service simply runs inference on the users graph and returns the results. Keys are essential because there is no order guarantee on the outputs so they need to be rejoined to the inputs via keys or transclusion of the inputs in the outputs. Example:
def input_fn():
features, labels = ...
features['unique_example_id'] = ...
features, labels
estimator = tf.estimator.LinearClassifier(...)
estimator = tf.contrib.estimator.forward_features(
estimator, 'unique_example_id')
estimator.train(...)
assert 'unique_example_id' in estimator.predict(...)
Args:
estimator: A tf.estimator.Estimator
object.
keys: A string
or a list
of string
. If it is None
, all of the
features
in dict
is forwarded to the predictions
. If it is a
string
, only given key is forwarded. If it is a list
of strings, all
the given keys
are forwarded.
sparse_default_values: A dict of str
keys mapping the name of the sparse
features to be converted to dense, to the default value to use. Only
sparse features indicated in the dictionary are converted to dense and the
provided default value is used.
Returns | |
---|---|
A new tf.estimator.Estimator which forwards features to predictions.
|
Raises | |
---|---|
ValueError
|
|
TypeError
|
if keys type is not one of string or list/tuple of string .
|