Defined in tensorflow/python/ops/

Lookup embedding results, accounting for invalid IDs and empty features.

The partitioned embedding in embedding_weights must all be the same shape except for the first dimension. The first dimension is allowed to vary as the vocabulary size is not necessarily a multiple of P. embedding_weights may be a PartitionedVariable as returned by using tf.get_variable() with a partitioner.

Invalid IDs (< 0) are pruned from input IDs and weights, as well as any IDs with non-positive weight. For an entry with no features, the embedding vector for default_id is returned, or the 0-vector if default_id is not supplied.

The ids and weights may be multi-dimensional. Embeddings are always aggregated along the last dimension.


  • embedding_weights: A list of P float Tensors or values representing partitioned embedding Tensors. Alternatively, a PartitionedVariable created by partitioning along dimension 0. The total unpartitioned shape should be [e_0, e_1, ..., e_m], where e_0 represents the vocab size and e_1, ..., e_m are the embedding dimensions.
  • sparse_ids: SparseTensor of shape [d_0, d_1, ..., d_n] containing the ids. d_0 is typically batch size.
  • sparse_weights: SparseTensor of same shape as sparse_ids, containing float weights corresponding to sparse_ids, or None if all weights are be assumed to be 1.0.
  • combiner: A string specifying how to combine embedding results for each entry. Currently "mean", "sqrtn" and "sum" are supported, with "mean" the default.
  • default_id: The id to use for an entry with no features.
  • name: A name for this operation (optional).
  • partition_strategy: A string specifying the partitioning strategy. Currently "div" and "mod" are supported. Default is "div".
  • max_norm: If not None, all embeddings are l2-normalized to max_norm before combining.


Dense Tensor of shape [d_0, d_1, ..., d_{n-1}, e_1, ..., e_m].


  • ValueError: if embedding_weights is empty.