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aliran tensor:: operasi:: Dilatasi2D
#include <nn_ops.h> Menghitung dilatasi skala abu-abu dari input 4-D dan tensor filter 3-D.
Ringkasan
Tensor input berbentuk [batch, in_height, in_width, depth] dan tensor filter berbentuk [filter_height, filter_width, depth] , yaitu, setiap saluran masukan diproses secara independen satu sama lain dengan fungsi penataannya sendiri. Tensor output memiliki bentuk [batch, out_height, out_width, depth] . Dimensi spasial tensor keluaran bergantung pada algoritma padding . Saat ini kami hanya mendukung data_format "NHWC" default.
Secara rinci, dilatasi 2-D morfologi skala abu-abu adalah korelasi jumlah maksimal (untuk konsistensi dengan conv2d , kami menggunakan filter yang tidak dicerminkan):
output[b, y, x, c] =
max_{dy, dx} input[b,
strides[1] * y + rates[1] * dy,
strides[2] * x + rates[2] * dx,
c] +
filter[dy, dx, c] Max-pooling adalah kasus khusus ketika filter memiliki ukuran yang sama dengan ukuran kernel pooling dan berisi semua angka nol.
Catatan tentang dualitas: Pelebaran input oleh filter sama dengan negasi erosi -input oleh filter yang dipantulkan.
Argumen:
- ruang lingkup: Objek Lingkup
- masukan: 4-D dengan bentuk
[batch, in_height, in_width, depth] . - filter: 3-D dengan bentuk
[filter_height, filter_width, depth] . - langkah: Langkah jendela geser untuk setiap dimensi tensor masukan. Harus:
[1, stride_height, stride_width, 1] . - rate: Langkah masukan untuk dilatasi morfologi yang atrous. Harus:
[1, rate_height, rate_width, 1] . - padding: Jenis algoritma padding yang akan digunakan.
Pengembalian:
-
Output : 4-D dengan bentuk [batch, out_height, out_width, depth] .
Atribut publik
Fungsi publik
simpul
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Keluaran
operator::tensorflow::Output() const
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Terakhir diperbarui pada 2025-07-26 UTC.
[null,null,["Terakhir diperbarui pada 2025-07-26 UTC."],[],[],null,["# tensorflow::ops::Dilation2D Class Reference\n\ntensorflow::ops::Dilation2D\n===========================\n\n`#include \u003cnn_ops.h\u003e`\n\nComputes the grayscale dilation of 4-D `input` and 3-D `filter` tensors.\n\nSummary\n-------\n\nThe `input` tensor has shape `[batch, in_height, in_width, depth]` and the `filter` tensor has shape `[filter_height, filter_width, depth]`, i.e., each input channel is processed independently of the others with its own structuring function. The `output` tensor has shape `[batch, out_height, out_width, depth]`. The spatial dimensions of the output tensor depend on the `padding` algorithm. We currently only support the default \"NHWC\" `data_format`.\n\nIn detail, the grayscale morphological 2-D dilation is the max-sum correlation (for consistency with `conv2d`, we use unmirrored filters): \n\n```scdoc\noutput[b, y, x, c] =\n max_{dy, dx} input[b,\n strides[1] * y + rates[1] * dy,\n strides[2] * x + rates[2] * dx,\n c] +\n filter[dy, dx, c]\n```\n\n\u003cbr /\u003e\n\nMax-pooling is a special case when the filter has size equal to the pooling kernel size and contains all zeros.\n\nNote on duality: The dilation of `input` by the `filter` is equal to the negation of the erosion of `-input` by the reflected `filter`.\n\nArguments:\n\n- scope: A [Scope](/versions/r1.15/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- strides: The stride of the sliding window for each dimension of the input tensor. Must be: `[1, stride_height, stride_width, 1]`.\n- rates: 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/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): 4-D with shape `[batch, out_height, out_width, depth]`.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [Dilation2D](#classtensorflow_1_1ops_1_1_dilation2_d_1aa7fba6ab148288fc230175c265483ff3)`(const ::`[tensorflow::Scope](/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` input, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` filter, const gtl::ArraySlice\u003c int \u003e & strides, const gtl::ArraySlice\u003c int \u003e & rates, StringPiece padding)` ||\n\n| ### Public attributes ||\n|-----------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_dilation2_d_1a50e0232a57e1ec03bc56803f2e2ae58e) | [Operation](/versions/r1.15/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [output](#classtensorflow_1_1ops_1_1_dilation2_d_1ae68b7f48c9fc91ffdef4e18bd34625f0) | `::`[tensorflow::Output](/versions/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n\n| ### Public functions ||\n|-----------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_dilation2_d_1a965eb807b87a9ac84ef8533e6da72768)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_dilation2_d_1ae1a45a55c3092ef319c027a697e3e667)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_dilation2_d_1a451d294669723935c6618946259273c3)`() const ` | ` ` ` ` |\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### Dilation2D\n\n```gdscript\n Dilation2D(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input filter,\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```"]]
