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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:
ckpt_to_load_from: (Optional). String representing checkpoint name/pattern to restore the column weights. Required if
tensor_name_in_ckptis not None.
tensor_name_in_ckpt: (Optional). Name of the
Tensorin the provided checkpoint from which to restore the column weights. Required if
ckpt_to_load_fromis not None.
hash_key: Specify the hash_key that will be used by the
FingerprintCat64function to combine the crosses fingerprints on SparseFeatureCrossOp (optional).
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.