If users keep data in TensorFlow Example format, they need to call
with a proper feature spec. There are two main things that this utility
Users need to combine parsing spec of features with labels and
weights (if any) since they are all parsed from same
This utility combines these specs.
It is difficult to map expected label by
a classifier such as
dnn_classifier to corresponding
This utility encodes it by getting related information from users (key,
1 2 3 4 5 6 7
An iterable containing all feature columns. All items
should be instances of classes derived from
A string identifying the label. It means
used as label if label_key does not exist in given
A string or a numeric column created by
A dict mapping each feature key to a
ValueError: If label is used in
ValueError: If weight_column is used in
ValueError: If any of the given
feature_columns is not a feature column instance.
weight_column is not a numeric column instance.
ValueError: if label_key is
Other parsing utilities:
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