tf.contrib.layers.crossed_column(
columns,
hash_bucket_size,
combiner='sum',
ckpt_to_load_from=None,
tensor_name_in_ckpt=None,
hash_key=None
)
Defined in tensorflow/contrib/layers/python/layers/feature_column.py
.
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 iftensor_name_in_ckpt
is not None.tensor_name_in_ckpt
: (Optional). Name of theTensor
in the provided checkpoint from which to restore the column weights. Required ifckpt_to_load_from
is not None.hash_key
: Specify the hash_key that will be used by theFingerprintCat64
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.