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author | Yifei Feng <yifeif@google.com> | 2018-04-23 21:19:14 -0700 |
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committer | TensorFlower Gardener <gardener@tensorflow.org> | 2018-04-23 21:21:38 -0700 |
commit | 22f3a97b8b089202f60bb0c7697feb0c8e0713cc (patch) | |
tree | d16f95826e4be15bbb3b0f22bed0ca25d3eb5897 /tensorflow/python/ops/embedding_ops.py | |
parent | 24b7c9a800ab5086d45a7d83ebcd6218424dc9e3 (diff) |
Merge changes from github.
PiperOrigin-RevId: 194031845
Diffstat (limited to 'tensorflow/python/ops/embedding_ops.py')
-rw-r--r-- | tensorflow/python/ops/embedding_ops.py | 26 |
1 files changed, 15 insertions, 11 deletions
diff --git a/tensorflow/python/ops/embedding_ops.py b/tensorflow/python/ops/embedding_ops.py index f0120f2957..9e46739bc1 100644 --- a/tensorflow/python/ops/embedding_ops.py +++ b/tensorflow/python/ops/embedding_ops.py @@ -331,11 +331,11 @@ def embedding_lookup_sparse(params, representing sharded embedding tensors. Alternatively, a `PartitionedVariable`, created by partitioning along dimension 0. Each element must be appropriately sized for the given `partition_strategy`. - sp_ids: N x M SparseTensor of int64 ids (typically from FeatureValueToId), + sp_ids: N x M `SparseTensor` of int64 ids (typically from FeatureValueToId), where N is typically batch size and M is arbitrary. - sp_weights: either a SparseTensor of float / double weights, or None to - indicate all weights should be taken to be 1. If specified, sp_weights - must have exactly the same shape and indices as sp_ids. + sp_weights: either a `SparseTensor` of float / double weights, or `None` to + indicate all weights should be taken to be 1. If specified, `sp_weights` + must have exactly the same shape and indices as `sp_ids`. partition_strategy: A string specifying the partitioning strategy, relevant if `len(params) > 1`. Currently `"div"` and `"mod"` are supported. Default is `"mod"`. See `tf.nn.embedding_lookup` for more details. @@ -351,39 +351,43 @@ def embedding_lookup_sparse(params, Returns: A dense tensor representing the combined embeddings for the - sparse ids. For each row in the dense tensor represented by sp_ids, the op + sparse ids. For each row in the dense tensor represented by `sp_ids`, the op looks up the embeddings for all ids in that row, multiplies them by the corresponding weight, and combines these embeddings as specified. In other words, if - shape(combined params) = [p0, p1, ..., pm] + `shape(combined params) = [p0, p1, ..., pm]` and - shape(sp_ids) = shape(sp_weights) = [d0, d1, ..., dn] + `shape(sp_ids) = shape(sp_weights) = [d0, d1, ..., dn]` then - shape(output) = [d0, d1, ..., dn-1, p1, ..., pm]. + `shape(output) = [d0, d1, ..., dn-1, p1, ..., pm]`. For instance, if params is a 10x20 matrix, and sp_ids / sp_weights are + ```python [0, 0]: id 1, weight 2.0 [0, 1]: id 3, weight 0.5 [1, 0]: id 0, weight 1.0 [2, 3]: id 1, weight 3.0 + ``` with `combiner`="mean", then the output will be a 3x20 matrix where + ```python output[0, :] = (params[1, :] * 2.0 + params[3, :] * 0.5) / (2.0 + 0.5) output[1, :] = (params[0, :] * 1.0) / 1.0 output[2, :] = (params[1, :] * 3.0) / 3.0 + ``` Raises: - TypeError: If sp_ids is not a SparseTensor, or if sp_weights is neither - None nor SparseTensor. - ValueError: If combiner is not one of {"mean", "sqrtn", "sum"}. + TypeError: If `sp_ids` is not a `SparseTensor`, or if `sp_weights` is + neither `None` nor `SparseTensor`. + ValueError: If `combiner` is not one of {"mean", "sqrtn", "sum"}. """ if combiner is None: logging.warn("The default value of combiner will change from \"mean\" " |