tf.contrib.layers.scattered_embedding_column( column_name, size, dimension, hash_key, combiner='mean', initializer=None )
Creates an embedding column of a sparse feature using parameter hashing.
This is a useful shorthand when you have a sparse feature you want to use an embedding for, but also want to hash the embedding's values in each dimension to a variable based on a different hash.
Specifically, the i-th embedding component of a value v is found by retrieving an embedding weight whose index is a fingerprint of the pair (v,i).
An embedding column with sparse_column_with_hash_bucket such as
embedding_column( sparse_column_with_hash_bucket(column_name, bucket_size), dimension)
could be replaced by
scattered_embedding_column( column_name, size=bucket_size * dimension, dimension=dimension, hash_key=tf.contrib.layers.SPARSE_FEATURE_CROSS_DEFAULT_HASH_KEY)
for the same number of embedding parameters. This should hopefully reduce the impact of collisions, but adds the cost of slowing down training.
column_name: A string defining sparse column name.
size: An integer specifying the number of parameters in the embedding layer.
dimension: An integer specifying dimension of the embedding.
hash_key: Specify the hash_key that will be used by the
FingerprintCat64function to combine the crosses fingerprints on SparseFeatureCrossOp.
combiner: A string specifying how to reduce if there are multiple entries in a single row. Currently "mean", "sqrtn" and "sum" are supported, with "mean" 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 features in the column
- "mean": do l1 normalization on features in the column
- "sqrtn": do l2 normalization on features in the column
For more information:
initializer: A variable initializer function to be used in embedding variable initialization. If not specified, defaults to
tf.truncated_normal_initializerwith mean 0 and standard deviation 0.1.
ValueError: if dimension or size is not a positive integer; or if combiner is not supported.