tf.contrib.layers.sparse_column_with_keys(column_name, keys, default_value=-1, combiner=None)

tf.contrib.layers.sparse_column_with_keys(column_name, keys, default_value=-1, combiner=None)

See the guide: Layers (contrib) > Feature columns

Creates a _SparseColumn with keys.

Look up logic is as follows: lookup_id = index_of_feature_in_keys if feature in keys else default_value

Args:

  • column_name: A string defining sparse column name.
  • keys: a string list defining vocabulary.
  • default_value: The value to use for out-of-vocabulary feature values. Default is -1.
  • combiner: A string specifying how to reduce if the sparse column is multivalent. Currently "mean", "sqrtn" and "sum" are supported, with "sum" the default:
    • "sum": do not normalize features in the column
    • "mean": do l1 normalization on features in the column
    • "sqrtn": do l2 normalization on features in the column For more information: tf.embedding_lookup_sparse.

Returns:

A _SparseColumnKeys with keys configuration.

Defined in tensorflow/contrib/layers/python/layers/feature_column.py.