tf.compat.v1.nn.relu

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

tf.compat.v1.nn.safe_embedding_lookup_sparse(
    embedding_weights, sparse_ids, sparse_weights=None, combiner='mean',
    default_id=None, name=None, partition_strategy='div', max_norm=None
)

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 single tensor representing the complete embedding tensor, or a list tensors all of same shape except for the first dimension, representing sharded embedding tensors. Alternatively, a `PartitionedVariable`, created by partitioning along dimension 0. Each element must be appropriately sized for the given `partition_strategy`.
`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
A dense tensor representing the combined embeddings for the 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 embedding_weights) = [p0, p1, ..., pm]` and `shape(sparse_ids) = shape(sparse_weights) = [d0, d1, ..., dn]` then `shape(output) = [d0, d1, ... dn-1, p1, ..., pm]`. For instance, if params is a 10x20 matrix, and sp_ids / sp_weights are `[0, 0]: id 1, weight 2.0 [0, 1]: id 3, weight 0.5 [1, 0]: id -1, weight 1.0 [2, 3]: id 1, weight 3.0` `default_id` is 0. with `combiner`="mean", then the output will be a 3x20 matrix where `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`

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 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 embedding_weights) = [p0, p1, ..., pm]

and

shape(sparse_ids) = shape(sparse_weights) = [d0, d1, ..., dn]

then

shape(output) = [d0, d1, ... dn-1, p1, ..., pm].

For instance, if params is a 10x20 matrix, and sp_ids / sp_weights are

[0, 0]: id 1, weight 2.0
[0, 1]: id 3, weight 0.5
[1, 0]: id -1, weight 1.0
[2, 3]: id 1, weight 3.0

default_id is 0.

with combiner="mean", then the output will be a 3x20 matrix where

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
`ValueError`	if `embedding_weights` is empty.

TensorFlow

tf.compat / v1 / v1.nn.relu_layer

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TensorFlow

tf

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tf.compat / v1 / v1.nn.relu_layer

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