tf.contrib.layers.crossed_column

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.

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.