tf.raw_ops.TPUEmbeddingActivations
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An op enabling differentiation of TPU Embeddings.
tf.raw_ops.TPUEmbeddingActivations(
embedding_variable, sliced_activations, table_id, lookup_id, name=None
)
This op simply returns its first input, which is assumed to have been sliced
from the Tensors returned by TPUEmbeddingDequeueActivations. The presence of
this op, and its first argument being a trainable Variable, enables automatic
differentiation of graphs containing embeddings via the TPU Embedding Python
libraries.
Args |
|---|
embedding_variable
|
A Tensor of type float32.
A trainable variable, enabling optimizers to find this op.
|
sliced_activations
|
A Tensor of type float32.
The embedding activations Tensor to return.
|
table_id
|
An int that is >= 0.
The id of the table in the embedding layer configuration from which
these activations were computed.
|
lookup_id
|
An int that is >= 0.
Identifier of the set of embedding indices which produced these
activations.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor of type float32.
|
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tf.raw_ops.TPUEmbeddingActivations\n\n\u003cbr /\u003e\n\nAn op enabling differentiation of TPU Embeddings.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.TPUEmbeddingActivations`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/TPUEmbeddingActivations)\n\n\u003cbr /\u003e\n\n tf.raw_ops.TPUEmbeddingActivations(\n embedding_variable, sliced_activations, table_id, lookup_id, name=None\n )\n\nThis op simply returns its first input, which is assumed to have been sliced\nfrom the Tensors returned by TPUEmbeddingDequeueActivations. The presence of\nthis op, and its first argument being a trainable Variable, enables automatic\ndifferentiation of graphs containing embeddings via the TPU Embedding Python\nlibraries.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------------|-------------------------------------------------------------------------------------------------------------------------------|\n| `embedding_variable` | A `Tensor` of type `float32`. A trainable variable, enabling optimizers to find this op. |\n| `sliced_activations` | A `Tensor` of type `float32`. The embedding activations Tensor to return. |\n| `table_id` | An `int` that is `\u003e= 0`. The id of the table in the embedding layer configuration from which these activations were computed. |\n| `lookup_id` | An `int` that is `\u003e= 0`. Identifier of the set of embedding indices which produced these activations. |\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` of type `float32`. ||\n\n\u003cbr /\u003e"]]
