컬렉션을 사용해 정리하기
내 환경설정을 기준으로 콘텐츠를 저장하고 분류하세요.
#include <nn_ops.h>
입력에 대한 컨볼루션의 기울기를 계산합니다.
요약
인수:
- 범위: 범위 개체
- input_sizes:
input
의 모양을 나타내는 정수 벡터입니다. 여기서 input
4차원 [batch, height, width, channels]
텐서입니다. - 필터:
[filter_height, filter_width, in_channels, out_channels]
모양의 4차원입니다. - out_backprop:
[batch, out_height, out_width, out_channels]
모양의 4D. 그라데이션은 컨볼루션의 출력을 나타냅니다. - strides: 컨볼루션 입력의 각 차원에 대한 슬라이딩 윈도우의 보폭입니다. 형식으로 지정된 측정기준과 동일한 순서여야 합니다.
- padding: 사용할 패딩 알고리즘 유형입니다.
선택적 속성( Attrs
참조):
- explicit_paddings:
padding
이 "EXPLICIT"
인 경우 명시적 패딩 양의 목록입니다. i번째 차원의 경우 차원 앞뒤에 삽입된 패딩 양은 각각 explicit_paddings[2 * i]
및 explicit_paddings[2 * i + 1]
입니다. padding
"EXPLICIT"
가 아닌 경우, explicit_paddings
비어 있어야 합니다. - data_format: 입력 및 출력 데이터의 데이터 형식을 지정합니다. 기본 형식인 "NHWC"를 사용하면 데이터가 [batch, in_height, in_width, in_channels] 순서로 저장됩니다. 또는 형식은 [batch, in_channels, in_height, in_width]의 데이터 저장 순서인 "NCHW"일 수 있습니다.
- 팽창: 길이가 4인 1차원 텐서.
input
의 각 차원에 대한 팽창 인자입니다. k > 1로 설정되면 해당 차원의 각 필터 요소 사이에 k-1개의 건너뛴 셀이 있게 됩니다. 차원 순서는 data_format
값에 따라 결정됩니다. 자세한 내용은 위를 참조하세요. 배치 차원과 깊이 차원의 팽창은 1이어야 합니다.
보고:
-
Output
: [batch, in_height, in_width, in_channels]
모양의 4D. 컨볼루션의 입력에 대한 그라데이션입니다.
생성자와 소멸자 |
---|
Conv2DBackpropInput (const :: tensorflow::Scope & scope, :: tensorflow::Input input_sizes, :: tensorflow::Input filter, :: tensorflow::Input out_backprop, const gtl::ArraySlice< int > & strides, StringPiece padding)
|
Conv2DBackpropInput (const :: tensorflow::Scope & scope, :: tensorflow::Input input_sizes, :: tensorflow::Input filter, :: tensorflow::Input out_backprop, const gtl::ArraySlice< int > & strides, StringPiece padding, const Conv2DBackpropInput::Attrs & attrs) |
공개 속성
공공 기능
공개 정적 함수
달리 명시되지 않는 한 이 페이지의 콘텐츠에는 Creative Commons Attribution 4.0 라이선스에 따라 라이선스가 부여되며, 코드 샘플에는 Apache 2.0 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 Google Developers 사이트 정책을 참조하세요. 자바는 Oracle 및/또는 Oracle 계열사의 등록 상표입니다.
최종 업데이트: 2025-07-27(UTC)
[null,null,["최종 업데이트: 2025-07-27(UTC)"],[],[],null,["# tensorflow::ops::Conv2DBackpropInput Class Reference\n\ntensorflow::ops::Conv2DBackpropInput\n====================================\n\n`#include \u003cnn_ops.h\u003e`\n\nComputes the gradients of convolution with respect to the input.\n\nSummary\n-------\n\nArguments:\n\n- scope: A [Scope](/versions/r2.2/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- input_sizes: An integer vector representing the shape of `input`, where `input` is a 4-D `[batch, height, width, channels]` tensor.\n- filter: 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`.\n- out_backprop: 4-D with shape `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution.\n- strides: The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format.\n- padding: The type of padding algorithm to use.\n\n\u003cbr /\u003e\n\nOptional attributes (see [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/conv2-d-backprop-input/attrs#structtensorflow_1_1ops_1_1_conv2_d_backprop_input_1_1_attrs)):\n\n- explicit_paddings: If `padding` is `\"EXPLICIT\"`, the list of explicit padding amounts. For the ith dimension, the amount of padding inserted before and after the dimension is `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If `padding` is not `\"EXPLICIT\"`, `explicit_paddings` must be empty.\n- data_format: Specify the data format of the input and output data. With the default format \"NHWC\", the data is stored in the order of: \\[batch, in_height, in_width, in_channels\\]. Alternatively, the format could be \"NCHW\", the data storage order of: \\[batch, in_channels, in_height, in_width\\].\n- dilations: 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k \\\u003e 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r2.2/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): 4-D with shape `[batch, in_height, in_width, in_channels]`. Gradient w.r.t. the input of the convolution.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [Conv2DBackpropInput](#classtensorflow_1_1ops_1_1_conv2_d_backprop_input_1aa5357992b64dbb43b51d35c084d442d8)`(const ::`[tensorflow::Scope](/versions/r2.2/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` input_sizes, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` filter, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` out_backprop, const gtl::ArraySlice\u003c int \u003e & strides, StringPiece padding)` ||\n| [Conv2DBackpropInput](#classtensorflow_1_1ops_1_1_conv2_d_backprop_input_1a01da97aaaf681a4f6f45d3bda57f0f82)`(const ::`[tensorflow::Scope](/versions/r2.2/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` input_sizes, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` filter, ::`[tensorflow::Input](/versions/r2.2/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` out_backprop, const gtl::ArraySlice\u003c int \u003e & strides, StringPiece padding, const `[Conv2DBackpropInput::Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/conv2-d-backprop-input/attrs#structtensorflow_1_1ops_1_1_conv2_d_backprop_input_1_1_attrs)` & attrs)` ||\n\n| ### Public attributes ||\n|----------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_conv2_d_backprop_input_1aebb0f66b81bb602fa8600e2e32f621b2) | [Operation](/versions/r2.2/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [output](#classtensorflow_1_1ops_1_1_conv2_d_backprop_input_1a53bf3bf2eb2af62764981f62c794fbe2) | `::`[tensorflow::Output](/versions/r2.2/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n\n| ### Public functions ||\n|----------------------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_conv2_d_backprop_input_1acf62af3e404315cfe9622e3d1295033b)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_conv2_d_backprop_input_1a94315c7d6148fb6451deb58f91955405)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_conv2_d_backprop_input_1a1bced60701935dddacef1af9398879df)`() const ` | ` ` ` ` |\n\n| ### Public static functions ||\n|-----------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|\n| [DataFormat](#classtensorflow_1_1ops_1_1_conv2_d_backprop_input_1ac762988224740afda86e2a852ef11774)`(StringPiece x)` | [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/conv2-d-backprop-input/attrs#structtensorflow_1_1ops_1_1_conv2_d_backprop_input_1_1_attrs) |\n| [Dilations](#classtensorflow_1_1ops_1_1_conv2_d_backprop_input_1a01b3b905a6bba3d7c7e61238d45109e4)`(const gtl::ArraySlice\u003c int \u003e & x)` | [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/conv2-d-backprop-input/attrs#structtensorflow_1_1ops_1_1_conv2_d_backprop_input_1_1_attrs) |\n| [ExplicitPaddings](#classtensorflow_1_1ops_1_1_conv2_d_backprop_input_1a4f19fe8f8ae4c3b237038489ba58a721)`(const gtl::ArraySlice\u003c int \u003e & x)` | [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/conv2-d-backprop-input/attrs#structtensorflow_1_1ops_1_1_conv2_d_backprop_input_1_1_attrs) |\n| [UseCudnnOnGpu](#classtensorflow_1_1ops_1_1_conv2_d_backprop_input_1a6df425d872077ec66d9eb2e2b42f767b)`(bool x)` | [Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/conv2-d-backprop-input/attrs#structtensorflow_1_1ops_1_1_conv2_d_backprop_input_1_1_attrs) |\n\n| ### Structs ||\n|------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tensorflow::ops::Conv2DBackpropInput::Attrs](/versions/r2.2/api_docs/cc/struct/tensorflow/ops/conv2-d-backprop-input/attrs) | Optional attribute setters for [Conv2DBackpropInput](/versions/r2.2/api_docs/cc/class/tensorflow/ops/conv2-d-backprop-input#classtensorflow_1_1ops_1_1_conv2_d_backprop_input). |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\n### output\n\n```text\n::tensorflow::Output output\n``` \n\nPublic functions\n----------------\n\n### Conv2DBackpropInput\n\n```gdscript\n Conv2DBackpropInput(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input_sizes,\n ::tensorflow::Input filter,\n ::tensorflow::Input out_backprop,\n const gtl::ArraySlice\u003c int \u003e & strides,\n StringPiece padding\n)\n``` \n\n### Conv2DBackpropInput\n\n```gdscript\n Conv2DBackpropInput(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input_sizes,\n ::tensorflow::Input filter,\n ::tensorflow::Input out_backprop,\n const gtl::ArraySlice\u003c int \u003e & strides,\n StringPiece padding,\n const Conv2DBackpropInput::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### DataFormat\n\n```text\nAttrs DataFormat(\n StringPiece x\n)\n``` \n\n### Dilations\n\n```gdscript\nAttrs Dilations(\n const gtl::ArraySlice\u003c int \u003e & x\n)\n``` \n\n### ExplicitPaddings\n\n```gdscript\nAttrs ExplicitPaddings(\n const gtl::ArraySlice\u003c int \u003e & x\n)\n``` \n\n### UseCudnnOnGpu\n\n```text\nAttrs UseCudnnOnGpu(\n bool x\n)\n```"]]