tf.contrib.layers.scattered_embedding_column(
column_name,
size,
dimension,
hash_key,
combiner='mean',
initializer=None
)
Defined in tensorflow/contrib/layers/python/layers/feature_column.py
.
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.
Args:
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 theFingerprintCat64
function 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:
tf.embedding_lookup_sparse
.
initializer
: A variable initializer function to be used in embedding variable initialization. If not specified, defaults totf.truncated_normal_initializer
with mean 0 and standard deviation 0.1.
Returns:
A _ScatteredEmbeddingColumn.
Raises:
ValueError
: if dimension or size is not a positive integer; or if combiner is not supported.