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tensorflow::
ops::
ScatterMin
#include <state_ops.h>
Reduces sparse updates into a variable reference using the
min
operation.
Summary
This operation computes
# Scalar indices
ref[indices, ...] = min(ref[indices, ...], updates[...])
# Vector indices (for each i)
ref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...])
# High rank indices (for each i, ..., j)
ref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])
This operation outputs
ref
after the update is done. This makes it easier to chain operations that need to use the reset value.
Duplicate entries are handled correctly: if multiple
indices
reference the same location, their contributions combine.
Requires
updates.shape = indices.shape + ref.shape[1:]
or
updates.shape = []
.
Args:
-
scope: A
Scope
object
-
ref: Should be from a
Variable
node.
-
indices: A tensor of indices into the first dimension of
ref
.
-
updates: A tensor of updated values to reduce into
ref
.
Optional attributes (see
Attrs
):
-
use_locking: If True, the update will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.
Returns:
-
Output
: = Same as
ref
. Returned as a convenience for operations that want to use the updated values after the update is done.
Public attributes
Public functions
node
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output
operator::tensorflow::Output() const
Public static functions
UseLocking
Attrs UseLocking(
bool x
)
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Last updated 2021-05-14 UTC.
[null,null,["Last updated 2021-05-14 UTC."],[],[],null,["# tensorflow::ops::ScatterMin Class Reference\n\ntensorflow::\nops::\nScatterMin\n=============================\n\n`\n#include \u003cstate_ops.h\u003e\n`\n\n\nReduces sparse updates into a variable reference using the\n`\nmin\n`\noperation.\n\nSummary\n-------\n\n\nThis operation computes \n\n```transact-sql\n# Scalar indices\nref[indices, ...] = min(ref[indices, ...], updates[...])\n\n# Vector indices (for each i)\nref[indices[i], ...] = min(ref[indices[i], ...], updates[i, ...])\n\n# High rank indices (for each i, ..., j)\nref[indices[i, ..., j], ...] = min(ref[indices[i, ..., j], ...], updates[i, ..., j, ...])\n```\n\n\u003cbr /\u003e\n\n\nThis operation outputs\n`\nref\n`\nafter the update is done. This makes it easier to chain operations that need to use the reset value.\n\n\nDuplicate entries are handled correctly: if multiple\n`\nindices\n`\nreference the same location, their contributions combine.\n\n\nRequires\n`\nupdates.shape = indices.shape + ref.shape[1:]\n`\nor\n`\nupdates.shape = []\n`\n.\n\n\n\u003cbr /\u003e\n\n\nArgs:\n\n- scope: A [Scope](/versions/r2.5/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- ref: Should be from a `\n `[Variable](/versions/r2.5/api_docs/cc/class/tensorflow/ops/variable#classtensorflow_1_1ops_1_1_variable)`\n ` node.\n- indices: A tensor of indices into the first dimension of `\n ref\n ` .\n- updates: A tensor of updated values to reduce into `\n ref\n ` .\n\n\u003cbr /\u003e\n\n\nOptional attributes (see\n`\n`[Attrs](/versions/r2.5/api_docs/cc/struct/tensorflow/ops/scatter-min/attrs#structtensorflow_1_1ops_1_1_scatter_min_1_1_attrs)`\n`\n):\n\n- use_locking: If True, the update will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.\n\n\u003cbr /\u003e\n\n\nReturns:\n\n- `\n `[Output](/versions/r2.5/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output)`\n ` : = Same as `\n ref\n ` . Returned as a convenience for operations that want to use the updated values after the update is done.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| ` `[ScatterMin](#classtensorflow_1_1ops_1_1_scatter_min_1a20672036c077b2295fe3b7f263f1cbbc)` (const :: `[tensorflow::Scope](/versions/r2.5/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` ref, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` indices, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` updates) ` ||\n| ` `[ScatterMin](#classtensorflow_1_1ops_1_1_scatter_min_1a970f0ae60aca198d4ff561223d356190)` (const :: `[tensorflow::Scope](/versions/r2.5/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` ref, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` indices, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` updates, const `[ScatterMin::Attrs](/versions/r2.5/api_docs/cc/struct/tensorflow/ops/scatter-min/attrs#structtensorflow_1_1ops_1_1_scatter_min_1_1_attrs)` & attrs) ` ||\n\n| ### Public attributes ||\n|------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|\n| ` `[operation](#classtensorflow_1_1ops_1_1_scatter_min_1a6b8f4f0a291a0a5110af72304126dfda)` ` | ` `[Operation](/versions/r2.5/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation)` ` |\n| ` `[output_ref](#classtensorflow_1_1ops_1_1_scatter_min_1a246255cec7f7a96a7aaec14b4b44b6b7)` ` | ` :: `[tensorflow::Output](/versions/r2.5/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output)` ` |\n\n| ### Public functions ||\n|---------------------------------------------------------------------------------------------------------------------------|--------------------------|\n| ` `[node](#classtensorflow_1_1ops_1_1_scatter_min_1ad2e63dcee002a733c8a1c255be7b2683)` () const ` | ` ::tensorflow::Node * ` |\n| ` `[operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_scatter_min_1a8938d32752649aeaeba42f68891ababc)` () const ` | ` ` |\n| ` `[operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_scatter_min_1af6605bb1184a6c918a33ba5d6389874b)` () const ` | ` ` |\n\n| ### Public static functions ||\n|---------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|\n| ` `[UseLocking](#classtensorflow_1_1ops_1_1_scatter_min_1a81d4614aac5d7b4ec68e2513034b2970)` (bool x) ` | ` `[Attrs](/versions/r2.5/api_docs/cc/struct/tensorflow/ops/scatter-min/attrs#structtensorflow_1_1ops_1_1_scatter_min_1_1_attrs)` ` |\n\n| ### Structs ||\n|-------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tensorflow:: ops:: ScatterMin:: Attrs](/versions/r2.5/api_docs/cc/struct/tensorflow/ops/scatter-min/attrs) | Optional attribute setters for [ScatterMin](/versions/r2.5/api_docs/cc/class/tensorflow/ops/scatter-min#classtensorflow_1_1ops_1_1_scatter_min) . |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\n### output_ref\n\n```scdoc\n::tensorflow::Output output_ref\n``` \n\nPublic functions\n----------------\n\n### ScatterMin\n\n```gdscript\n ScatterMin(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input ref,\n ::tensorflow::Input indices,\n ::tensorflow::Input updates\n)\n``` \n\n### ScatterMin\n\n```gdscript\n ScatterMin(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input ref,\n ::tensorflow::Input indices,\n ::tensorflow::Input updates,\n const ScatterMin::Attrs & attrs\n)\n``` \n\n### node\n\n```gdscript\n::tensorflow::Node * node() const \n``` \n\n### operator::tensorflow::Input\n\n```gdscript\n operator::tensorflow::Input() const \n``` \n\n### operator::tensorflow::Output\n\n```gdscript\n operator::tensorflow::Output() const \n``` \n\nPublic static functions\n-----------------------\n\n### UseLocking\n\n```text\nAttrs UseLocking(\n bool x\n)\n```"]]
