tf.contrib.layers.sparse_column_with_integerized_feature(column_name, bucket_size, combiner=None, dtype=tf.int64)

tf.contrib.layers.sparse_column_with_integerized_feature(column_name, bucket_size, combiner=None, dtype=tf.int64)

See the guide: Layers (contrib) > Feature columns

Creates an integerized _SparseColumn.

Use this when your features are already pre-integerized into int64 IDs. output_id = input_feature

Args:

• column_name: A string defining sparse column name.
• bucket_size: An int that is > 1. The number of buckets. It should be bigger than maximum feature. In other words features in this column should be an int64 in range [0, bucket_size)
• 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.
• dtype: Type of features. It should be an integer type. Default value is dtypes.int64.

Returns:

An integerized _SparseColumn definition.

Raises:

• ValueError: bucket_size is not greater than 1.
• ValueError: dtype is not integer.

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