# tf.contrib.layers.crossed_column

tf.contrib.layers.crossed_column(
columns,
hash_bucket_size,
combiner='sum',
tensor_name_in_ckpt=None,
hash_key=None
)


See the guide: Layers (contrib) > Feature columns

Creates a _CrossedColumn for performing feature crosses.

#### Args:

• columns: An iterable of _FeatureColumn. Items can be an instance of _SparseColumn, _CrossedColumn, or _BucketizedColumn.
• hash_bucket_size: An int that is > 1. The number of buckets.
• combiner: A string specifying how to reduce if there are multiple entries in a single row. Currently "mean", "sqrtn" and "sum" are supported, with "sum" the default. "sqrtn" often achieves good accuracy, in particular with bag-of-words columns. Each of this can be thought as example level normalizations on the column::
• "sum": do not normalize
• "mean": do l1 normalization
• "sqrtn": do l2 normalization For more information: tf.embedding_lookup_sparse.
• ckpt_to_load_from: (Optional). String representing checkpoint name/pattern to restore the column weights. Required if tensor_name_in_ckpt is not None.
• tensor_name_in_ckpt: (Optional). Name of the Tensor in the provided checkpoint from which to restore the column weights. Required if ckpt_to_load_from is not None.
• hash_key: Specify the hash_key that will be used by the FingerprintCat64 function to combine the crosses fingerprints on SparseFeatureCrossOp (optional).

#### Returns:

A _CrossedColumn.

#### Raises:

• TypeError: if any item in columns is not an instance of _SparseColumn, _CrossedColumn, or _BucketizedColumn, or hash_bucket_size is not an int.
• ValueError: if hash_bucket_size is not > 1 or len(columns) is not > 1.