tf.contrib.layers.crossed_column

Creates a _CrossedColumn for performing feature crosses.

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

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.