tf.contrib.layers.scattered_embedding_column(column_name, size, dimension, hash_key, combiner=None, initializer=None)

tf.contrib.layers.scattered_embedding_column(column_name, size, dimension, hash_key, combiner=None, initializer=None)

See the guide: Layers (contrib) > Feature columns

Creates an embedding column of a sparse feature using parameter hashing.

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 and hopefully reduced impact of collisions with a 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 the FingerprintCat64 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. 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 to tf.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.

Defined in tensorflow/contrib/layers/python/layers/feature_column.py.