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aliran tensor:: operasi:: Siaran Ke
#include <array_ops.h> Siarkan array untuk bentuk yang kompatibel.
Ringkasan
Penyiaran adalah proses membuat array memiliki bentuk yang kompatibel untuk operasi aritmatika. Dua bentuk dikatakan kompatibel jika untuk setiap pasangan dimensi keduanya sama atau salah satunya adalah satu. Saat mencoba menyiarkan Tensor ke suatu bentuk, Tensor dimulai dengan dimensi tambahan, dan terus berlanjut.
Misalnya,
x = tf.constant([1, 2, 3]) y = tf.broadcast_to(x, [3, 3]) print(y) tf.Tensor( [[1 2 3] [1 2 3] [1 2 3]], bentuk=(3, 3), tiped=int32)
Pada contoh di atas, Tensor masukan berbentuk [1, 3] disiarkan ke Tensor keluaran berbentuk [3, 3] .
Saat melakukan operasi penyiaran seperti mengalikan tensor dengan skalar, penyiaran (biasanya) memberikan manfaat waktu atau ruang, karena tensor yang disiarkan tidak pernah terwujud.
Namun, broadcast_to tidak memberikan manfaat apa pun. Tensor yang baru dibuat mengambil memori penuh dari bentuk yang disiarkan. (Namun, dalam konteks grafik, broadcast_to mungkin digabungkan ke operasi berikutnya dan kemudian dioptimalkan.)
Argumen:
- ruang lingkup: Objek Lingkup
- input: Tensor yang akan disiarkan.
- bentuk: Tensor
int 1-D . Bentuk keluaran yang diinginkan.
Pengembalian:
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::BroadcastTo Class Reference\n\ntensorflow::ops::BroadcastTo\n============================\n\n`#include \u003carray_ops.h\u003e`\n\nBroadcast an array for a compatible shape.\n\nSummary\n-------\n\nBroadcasting is the process of making arrays to have compatible shapes for arithmetic operations. Two shapes are compatible if for each dimension pair they are either equal or one of them is one. When trying to broadcast a [Tensor](/versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) to a shape, it starts with the trailing dimensions, and works its way forward.\n\nFor example,\n\nx = tf.constant(\\[1, 2, 3\\]) y = tf.broadcast_to(x, \\[3, 3\\]) print(y) tf.Tensor( \\[\\[1 2 3\\] \\[1 2 3\\] \\[1 2 3\\]\\], shape=(3, 3), dtype=int32)\n\nIn the above example, the input [Tensor](/versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) with the shape of `[1, 3]` is broadcasted to output [Tensor](/versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) with shape of `[3, 3]`.\n\nWhen doing broadcasted operations such as multiplying a tensor by a scalar, broadcasting (usually) confers some time or space benefit, as the broadcasted tensor is never materialized.\n\nHowever, `broadcast_to` does not carry with it any such benefits. The newly-created tensor takes the full memory of the broadcasted shape. (In a graph context, `broadcast_to` might be fused to subsequent operation and then be optimized away, however.)\n\nArguments:\n\n- scope: A [Scope](/versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- input: A [Tensor](/versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) to broadcast.\n- shape: An 1-D `int`[Tensor](/versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor). The shape of the desired output.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): A [Tensor](/versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor).\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [BroadcastTo](#classtensorflow_1_1ops_1_1_broadcast_to_1a37bf1f8b63e588def9b3805017209ee6)`(const ::`[tensorflow::Scope](/versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` input, ::`[tensorflow::Input](/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` shape)` ||\n\n| ### Public attributes ||\n|------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_broadcast_to_1abb152ff71cda1cf3af84a7c656faac03) | [Operation](/versions/r2.3/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [output](#classtensorflow_1_1ops_1_1_broadcast_to_1aaa451e1fc17fe438aa744a2880efca62) | `::`[tensorflow::Output](/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n\n| ### Public functions ||\n|------------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_broadcast_to_1a2c429236acfd549d2252190a63a446f0)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_broadcast_to_1a21be2705c2eba98f1cf7560295561b58)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_broadcast_to_1a43222f4482f5ccb868548380633ce7f5)`() 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### BroadcastTo\n\n```gdscript\n BroadcastTo(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input shape\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```"]]
