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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(...)
string or a
string. If it is
None, all of the
dict is forwarded to the
predictions. If it is a
string, only given key is forwarded. If it is a
list of strings, all
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.