Function safe_embedding_lookup_sparse

tensorflow/python/ops/embedding_ops.py:1174–1304

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_w

(embedding_weights,
                                 sparse_ids,
                                 sparse_weights=None,
                                 combiner="mean",
                                 default_id=None,
                                 name=None,
                                 partition_strategy="div",
                                 max_norm=None,
                                 prune=True)

@tf_export(v1=["nn.safe_embedding_lookup_sparse"])
def safe_embedding_lookup_sparse(embedding_weights,
                                 sparse_ids,
                                 sparse_weights=None,
                                 combiner="mean",
                                 default_id=None,
                                 name=None,
                                 partition_strategy="div",
                                 max_norm=None,
                                 prune=True):
  """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.compat.v1.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 `Tensor`s or values representing
      partitioned embedding `Tensor`s.  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.
  """
  if embedding_weights is None:
    raise ValueError("Missing embedding_weights %s." % embedding_weights)
  if isinstance(embedding_weights, variables.PartitionedVariable):
    embedding_weights = list(embedding_weights)  # get underlying Variables.
  if not isinstance(embedding_weights, list):
    embedding_weights = [embedding_weights]
  if len(embedding_weights) < 1:
    raise ValueError("Missing embedding_weights %s." % embedding_weights)

  dtype = sparse_weights.dtype if sparse_weights is not None else None
  tmp_embedding_weights = []
  for w in embedding_weights: