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tfa.seq2seq.tile_batch
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Tiles the batch dimension of a (possibly nested structure of) tensor(s).
tfa.seq2seq.tile_batch(
t: tfa.types.TensorLike
,
multiplier: int,
name: Optional[str] = None
) -> tf.Tensor
Used in the notebooks
For each tensor t in a (possibly nested structure) of tensors,
this function takes a tensor t shaped [batch_size, s0, s1, ...]
composed
of minibatch entries t[0], ..., t[batch_size - 1]
and tiles it to have a
shape [batch_size * multiplier, s0, s1, ...]
composed of minibatch
entries t[0], t[0], ..., t[1], t[1], ...
where each minibatch entry is
repeated multiplier
times.
Args |
t
|
Tensor shaped [batch_size, ...] .
|
multiplier
|
Python int.
|
name
|
Name scope for any created operations.
|
Returns |
A (possibly nested structure of) Tensor shaped
[batch_size * multiplier, ...] .
|
Raises |
ValueError
|
if tensor(s) t do not have a statically known rank or
the rank is < 1.
|
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Last updated 2023-05-25 UTC.
[null,null,["Last updated 2023-05-25 UTC."],[],[],null,["# tfa.seq2seq.tile_batch\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/addons/blob/v0.20.0/tensorflow_addons/seq2seq/beam_search_decoder.py#L124-L148) |\n\nTiles the batch dimension of a (possibly nested structure of) tensor(s). \n\n tfa.seq2seq.tile_batch(\n t: ../../tfa/types/TensorLike,\n multiplier: int,\n name: Optional[str] = None\n ) -\u003e tf.Tensor\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [TensorFlow Addons Networks : Sequence-to-Sequence NMT with Attention Mechanism](https://www.tensorflow.org/addons/tutorials/networks_seq2seq_nmt) |\n\nFor each tensor t in a (possibly nested structure) of tensors,\nthis function takes a tensor t shaped `[batch_size, s0, s1, ...]` composed\nof minibatch entries `t[0], ..., t[batch_size - 1]` and tiles it to have a\nshape `[batch_size * multiplier, s0, s1, ...]` composed of minibatch\nentries `t[0], t[0], ..., t[1], t[1], ...` where each minibatch entry is\nrepeated `multiplier` times.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------------|----------------------------------------|\n| `t` | `Tensor` shaped `[batch_size, ...]`. |\n| `multiplier` | Python int. |\n| `name` | Name scope for any created operations. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A (possibly nested structure of) `Tensor` shaped `[batch_size * multiplier, ...]`. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|---------------------------------------------------------------------------|\n| `ValueError` | if tensor(s) `t` do not have a statically known rank or the rank is \\\u003c 1. |\n\n\u003cbr /\u003e"]]