tf.contrib.layers.crossed_column

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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
)

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).

A _CrossedColumn.

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.