tf.contrib.layers.safe_embedding_lookup_sparse

tf.contrib.layers.safe_embedding_lookup_sparse(
    embedding_weights,
    sparse_ids,
    sparse_weights=None,
    combiner=None,
    default_id=None,
    name=None,
    partition_strategy='div',
    max_norm=None
)

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

See the guide: Layers (contrib) > Higher level ops for building neural network layers

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.

Args:

• 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.

Returns:

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

Raises:

• ValueError: if embedding_weights is empty.