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tensorflow::
ops::
TensorArraySplit
#include <data_flow_ops.h>
Split the data from the input value into
TensorArray
elements.
Summary
Assuming that
lengths
takes on values
(n0, n1, ..., n(T-1))
and that `value` has shape
(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)```,
this splits values into a
TensorArray
with T tensors.
TensorArray
index t will be the subtensor of values with starting position
```(n0 + n1 + ... + n(t-1), 0, 0, ...)
and having size
nt x d0 x d1 x ...```
Args:
-
scope: A
Scope
object
-
handle: The handle to a
TensorArray
.
-
value: The concatenated tensor to write to the
TensorArray
.
-
lengths: The vector of lengths, how to split the rows of value into the
TensorArray
.
-
flow_in: A float scalar that enforces proper chaining of operations.
Returns:
-
Output
: A float scalar that enforces proper chaining of operations.
Public attributes
Public functions
node
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output
operator::tensorflow::Output() const
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, 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. Some content is licensed under the numpy license.
Last updated 2021-05-14 UTC.
[null,null,["Last updated 2021-05-14 UTC."],[],[],null,["# tensorflow::ops::TensorArraySplit Class Reference\n\ntensorflow::\nops::\nTensorArraySplit\n===================================\n\n`\n#include \u003cdata_flow_ops.h\u003e\n`\n\n\nSplit the data from the input value into\n[TensorArray](/versions/r2.5/api_docs/cc/class/tensorflow/ops/tensor-array#classtensorflow_1_1ops_1_1_tensor_array)\nelements.\n\nSummary\n-------\n\n\nAssuming that\n`\nlengths\n`\ntakes on values\n\n\n(n0, n1, ..., n(T-1)) \n\n```text\n\n```\n\n\u003cbr /\u003e\n\n\n```mysql\nand that `value` has shape\n```\n\n\u003cbr /\u003e\n\n\n```text\n\n```\n(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)\\`\\`\\`,\n\n\u003cbr /\u003e\n\n\nthis splits values into a\n[TensorArray](/versions/r2.5/api_docs/cc/class/tensorflow/ops/tensor-array#classtensorflow_1_1ops_1_1_tensor_array)\nwith T tensors.\n\n\n[TensorArray](/versions/r2.5/api_docs/cc/class/tensorflow/ops/tensor-array#classtensorflow_1_1ops_1_1_tensor_array)\nindex t will be the subtensor of values with starting position\n\n\n\\`\\`\\`(n0 + n1 + ... + n(t-1), 0, 0, ...) \n\n```text\n\n```\n\n\u003cbr /\u003e\n\n\n```text\nand having size\n```\n\n\u003cbr /\u003e\n\n\n```text\n\n```\nnt x d0 x d1 x ...\\`\\`\\`\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- handle: The handle to a [TensorArray](/versions/r2.5/api_docs/cc/class/tensorflow/ops/tensor-array#classtensorflow_1_1ops_1_1_tensor_array) .\n- value: The concatenated tensor to write to the [TensorArray](/versions/r2.5/api_docs/cc/class/tensorflow/ops/tensor-array#classtensorflow_1_1ops_1_1_tensor_array) .\n- lengths: The vector of lengths, how to split the rows of value into the [TensorArray](/versions/r2.5/api_docs/cc/class/tensorflow/ops/tensor-array#classtensorflow_1_1ops_1_1_tensor_array) .\n- flow_in: A float scalar that enforces proper chaining of operations.\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 ` : A float scalar that enforces proper chaining of operations.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| ` `[TensorArraySplit](#classtensorflow_1_1ops_1_1_tensor_array_split_1ae33a80f5f64f1d0ce47cb9ba380ee6bb)` (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)` handle, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` value, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` lengths, :: `[tensorflow::Input](/versions/r2.5/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` flow_in) ` ||\n\n| ### Public attributes ||\n|------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------|\n| ` `[flow_out](#classtensorflow_1_1ops_1_1_tensor_array_split_1a6a6beee076f43e4045b8327c9a8f0be9)` ` | ` :: `[tensorflow::Output](/versions/r2.5/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output)` ` |\n| ` `[operation](#classtensorflow_1_1ops_1_1_tensor_array_split_1a1cf9133d6b7032ba48abeff356547a58)` ` | ` `[Operation](/versions/r2.5/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation)` ` |\n\n| ### Public functions ||\n|----------------------------------------------------------------------------------------------------------------------------------|--------------------------|\n| ` `[node](#classtensorflow_1_1ops_1_1_tensor_array_split_1ad03cc93202545e0234d90faee0425ed9)` () const ` | ` ::tensorflow::Node * ` |\n| ` `[operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_tensor_array_split_1ac2029be4ba96df5da32f6bd0fc3fb8b1)` () const ` | ` ` |\n| ` `[operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_tensor_array_split_1ab90a5c257e9a8df6209a663ade45e3fc)` () const ` | ` ` |\n\nPublic attributes\n-----------------\n\n### flow_out\n\n```scdoc\n::tensorflow::Output flow_out\n``` \n\n### operation\n\n```text\nOperation operation\n``` \n\nPublic functions\n----------------\n\n### TensorArraySplit\n\n```gdscript\n TensorArraySplit(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input handle,\n ::tensorflow::Input value,\n ::tensorflow::Input lengths,\n ::tensorflow::Input flow_in\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```"]]
