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.

tf.contrib.layers.crossed_column

Args

Returns

Raises