tf.nn.embedding_lookup
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Looks up ids in a list of embedding tensors.
tf.nn.embedding_lookup(
params, ids, max_norm=None, name=None
)
This function is used to perform parallel lookups on the list of
tensors in params. It is a generalization of
tf.gather, where params is
interpreted as a partitioning of a large embedding tensor. params may be
a PartitionedVariable as returned by using tf.compat.v1.get_variable()
with a
partitioner.
If len(params) > 1, each element id of ids is partitioned between
the elements of params according to the partition_strategy.
In all strategies, if the id space does not evenly divide the number of
partitions, each of the first (max_id + 1) % len(params) partitions will
be assigned one more id.
The partition_strategy is always "div" currently. This means that we
assign ids to partitions in a contiguous manner. For instance, 13 ids are
split across 5 partitions as:
[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10], [11, 12]]
The results of the lookup are concatenated into a dense
tensor. The returned tensor has shape shape(ids) + shape(params)[1:].
Args |
|---|
params
|
A single tensor representing the complete embedding tensor, or a
list of P 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 'div' partition_strategy.
|
ids
|
A Tensor with type int32 or int64 containing the ids to be looked
up in params.
|
max_norm
|
If not None, each embedding is clipped if its l2-norm is larger
than this value.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor with the same type as the tensors in params.
|
Raises |
|---|
ValueError
|
If params is empty.
|
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Last updated 2020-10-01 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[],null,["# tf.nn.embedding_lookup\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------|\n| [TensorFlow 1 version](/versions/r1.15/api_docs/python/tf/nn/embedding_lookup) | [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.0.0/tensorflow/python/ops/embedding_ops.py#L320-L364) |\n\nLooks up `ids` in a list of embedding tensors. \n\n tf.nn.embedding_lookup(\n params, ids, max_norm=None, name=None\n )\n\nThis function is used to perform parallel lookups on the list of\ntensors in `params`. It is a generalization of\n[`tf.gather`](../../tf/gather), where `params` is\ninterpreted as a partitioning of a large embedding tensor. `params` may be\na `PartitionedVariable` as returned by using [`tf.compat.v1.get_variable()`](../../tf/compat/v1/get_variable)\nwith a\npartitioner.\n\nIf `len(params) \u003e 1`, each element `id` of `ids` is partitioned between\nthe elements of `params` according to the `partition_strategy`.\nIn all strategies, if the id space does not evenly divide the number of\npartitions, each of the first `(max_id + 1) % len(params)` partitions will\nbe assigned one more id.\n\nThe `partition_strategy` is always `\"div\"` currently. This means that we\nassign ids to partitions in a contiguous manner. For instance, 13 ids are\nsplit across 5 partitions as:\n`[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10], [11, 12]]`\n\nThe results of the lookup are concatenated into a dense\ntensor. The returned tensor has shape `shape(ids) + shape(params)[1:]`.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `params` | A single tensor representing the complete embedding tensor, or a list of P 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 'div' `partition_strategy`. |\n| `ids` | A `Tensor` with type `int32` or `int64` containing the ids to be looked up in `params`. |\n| `max_norm` | If not `None`, each embedding is clipped if its l2-norm is larger than this value. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor` with the same type as the tensors in `params`. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|-----------------------|\n| `ValueError` | If `params` is empty. |\n\n\u003cbr /\u003e"]]
TensorFlow 1 version
View source on GitHub