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meridian.backend.broadcast_dynamic_shape
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Computes the shape of a broadcast given symbolic shapes.
meridian
.
backend
.
broadcast_dynamic_shape
(
shape_x
,
shape_y
)
When shape_x
and shape_y
are Tensors representing shapes (i.e. the result
of calling tf.shape on another Tensor) this computes a Tensor which is the
shape of the result of a broadcasting op applied in tensors of shapes shape_x
and shape_y
.
This is useful when validating the result of a broadcasting operation when the
tensors do not have statically known shapes.
Example:
>>>
shape_x
=
(
1
,
2
,
3
)
>>>
shape_y
=
(
5
,
1
,
3
)
>>>
tf
.
broadcast_dynamic_shape
(
shape_x
,
shape_y
)
< tf
.
Tensor
:
shape
=(
3
,),
dtype
=
int32
,
numpy
=
array
([
5
,
2
,
3
],
...
>
A rank 1 integer Tensor
, representing the shape of x.
A rank 1 integer Tensor
, representing the shape of y.
A rank 1 integer Tensor
representing the broadcasted shape.
If the two shapes are incompatible for
broadcasting.
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, and code samples are licensed under the Apache 2.0 License
. For details, see the Google Developers Site Policies
. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-09-05 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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-09-05 UTC."],[],[],null,["\u003cbr /\u003e\n\nComputes the shape of a broadcast given symbolic shapes. \n\n meridian.backend.broadcast_dynamic_shape(\n shape_x, shape_y\n )\n\nWhen `shape_x` and `shape_y` are Tensors representing shapes (i.e. the result\nof calling tf.shape on another Tensor) this computes a Tensor which is the\nshape of the result of a broadcasting op applied in tensors of shapes\n`shape_x` and `shape_y`.\n\nThis is useful when validating the result of a broadcasting operation when the\ntensors do not have statically known shapes.\n\nExample: \n\n \u003e\u003e\u003e shape_x = (1, 2, 3)\n \u003e\u003e\u003e shape_y = (5, 1, 3)\n \u003e\u003e\u003e tf.broadcast_dynamic_shape(shape_x, shape_y)\n \u003ctf.Tensor: shape=(3,), dtype=int32, numpy=array([5, 2, 3], ...\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|-----------|---------------------------------------------------------|\n| `shape_x` | A rank 1 integer `Tensor`, representing the shape of x. |\n| `shape_y` | A rank 1 integer `Tensor`, representing the shape of y. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| A rank 1 integer `Tensor` representing the broadcasted shape. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ||\n|------------------------|------------------------------------------------------|\n| `InvalidArgumentError` | If the two shapes are incompatible for broadcasting. |\n\n\u003cbr /\u003e"]]
