Aprenda o que há de mais recente em aprendizado de máquina, IA generativa e muito mais no WiML Symposium 2023
Registre-se
Mantenha tudo organizado com as coleções
Salve e categorize o conteúdo com base nas suas preferências.
#include <nn_ops.h> Calcula o gradiente de dilatação 2-D morfológica em relação à entrada.
Resumo
Argumentos:
- escopo: um objeto Scope
- entrada: 4-D com forma
[batch, in_height, in_width, depth] . - filtro: 3-D com forma
[filter_height, filter_width, depth] . - out_backprop: 4-D com forma
[batch, out_height, out_width, depth] . - passadas: 1-D de comprimento 4. A passada da janela deslizante para cada dimensão do tensor de entrada. Deve ser:
[1, stride_height, stride_width, 1] . - taxas: 1-D de comprimento 4. A passada de entrada para dilatação morfológica atrosa. Deve ser:
[1, rate_height, rate_width, 1] . - preenchimento: o tipo de algoritmo de preenchimento a ser usado.
Retorna:
-
Output : 4-D com forma [batch, in_height, in_width, depth] .
Atributos públicos
Funções públicas
Exceto em caso de indicação contrária, o conteúdo desta página é licenciado de acordo com a Licença de atribuição 4.0 do Creative Commons, e as amostras de código são licenciadas de acordo com a Licença Apache 2.0. Para mais detalhes, consulte as políticas do site do Google Developers. Java é uma marca registrada da Oracle e/ou afiliadas.
Última atualização 2020-04-20 UTC.
[null,null,["Última atualização 2020-04-20 UTC."],[],[],null,["# tensorflow::ops::Dilation2DBackpropInput Class Reference\n\ntensorflow::ops::Dilation2DBackpropInput\n========================================\n\n`#include \u003cnn_ops.h\u003e`\n\nComputes the gradient of morphological 2-D dilation 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: 4-D with shape `[batch, in_height, in_width, depth]`.\n- filter: 3-D with shape `[filter_height, filter_width, depth]`.\n- out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`.\n- strides: 1-D of length 4. The stride of the sliding window for each dimension of the input tensor. Must be: `[1, stride_height, stride_width, 1]`.\n- rates: 1-D of length 4. The input stride for atrous morphological dilation. Must be: `[1, rate_height, rate_width, 1]`.\n- padding: The type of padding algorithm to use.\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, depth]`.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [Dilation2DBackpropInput](#classtensorflow_1_1ops_1_1_dilation2_d_backprop_input_1a656665a13419870e177ca6b67a869f31)`(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, ::`[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, const gtl::ArraySlice\u003c int \u003e & rates, StringPiece padding)` ||\n\n| ### Public attributes ||\n|----------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|\n| [in_backprop](#classtensorflow_1_1ops_1_1_dilation2_d_backprop_input_1a40353e552051aa8801ef9f9ad4efc15c) | `::`[tensorflow::Output](/versions/r2.2/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n| [operation](#classtensorflow_1_1ops_1_1_dilation2_d_backprop_input_1a02412d298e84e41cd1bd2a21b9a064f4) | [Operation](/versions/r2.2/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n\n| ### Public functions ||\n|--------------------------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_dilation2_d_backprop_input_1ab58e7a7ffead7f2dd0dcc062204f47f8)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_dilation2_d_backprop_input_1a4ad020555d5b8ffc10bdf49956232358)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_dilation2_d_backprop_input_1a8029c3562d2413ed5a8b249933690e32)`() const ` | ` ` ` ` |\n\nPublic attributes\n-----------------\n\n### in_backprop\n\n```scdoc\n::tensorflow::Output in_backprop\n``` \n\n### operation\n\n```text\nOperation operation\n``` \n\nPublic functions\n----------------\n\n### Dilation2DBackpropInput\n\n```gdscript\n Dilation2DBackpropInput(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input filter,\n ::tensorflow::Input out_backprop,\n const gtl::ArraySlice\u003c int \u003e & strides,\n const gtl::ArraySlice\u003c int \u003e & rates,\n StringPiece padding\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```"]]
