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DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC
構造体サイズ=各フィールドのバイト数(x64/x86 で異なる場合は x64/x86 と併記)。x64/x86 列=フィールドのバイトオフセット(HSPで dupptr / lpoke / wpoke 等に使用)。
フィールド
| フィールド | 型 | サイズ | x64 | x86 | 説明 |
|---|---|---|---|---|---|
| InputTensor | DML_TENSOR_DESC* | 8/4 | +0 | +0 | バッチ正規化順方向の入力値を保持する入力テンソルへのポインタ。 |
| InputGradientTensor | DML_TENSOR_DESC* | 8/4 | +8 | +4 | 上流から逆伝播してきた勾配を保持する入力テンソルへのポインタ。 |
| MeanTensor | DML_TENSOR_DESC* | 8/4 | +16 | +8 | 順方向で算出した平均を保持する入力テンソルへのポインタ。 |
| VarianceTensor | DML_TENSOR_DESC* | 8/4 | +24 | +12 | 順方向で算出した分散を保持する入力テンソルへのポインタ。 |
| ScaleTensor | DML_TENSOR_DESC* | 8/4 | +32 | +16 | スケール(ガンマ)パラメータを保持する入力テンソルへのポインタ。 |
| OutputGradientTensor | DML_TENSOR_DESC* | 8/4 | +40 | +20 | 入力に対する勾配を格納する出力テンソルへのポインタ。 |
| OutputScaleGradientTensor | DML_TENSOR_DESC* | 8/4 | +48 | +24 | スケールパラメータに対する勾配を格納する出力テンソルへのポインタ。 |
| OutputBiasGradientTensor | DML_TENSOR_DESC* | 8/4 | +56 | +28 | バイアス(ベータ)パラメータに対する勾配を格納する出力テンソルへのポインタ。 |
| Epsilon | FLOAT | 4 | +64 | +32 | ゼロ除算を防ぐため分散に加える微小定数(単精度)。 |
各言語での定義
#include <windows.h>
// DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC (x64 72 / x86 36 バイト)
typedef struct DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC {
DML_TENSOR_DESC* InputTensor;
DML_TENSOR_DESC* InputGradientTensor;
DML_TENSOR_DESC* MeanTensor;
DML_TENSOR_DESC* VarianceTensor;
DML_TENSOR_DESC* ScaleTensor;
DML_TENSOR_DESC* OutputGradientTensor;
DML_TENSOR_DESC* OutputScaleGradientTensor;
DML_TENSOR_DESC* OutputBiasGradientTensor;
FLOAT Epsilon;
} DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC;using System;
using System.Runtime.InteropServices;
[StructLayout(LayoutKind.Sequential, CharSet = CharSet.Unicode)]
public struct DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC
{
public IntPtr InputTensor;
public IntPtr InputGradientTensor;
public IntPtr MeanTensor;
public IntPtr VarianceTensor;
public IntPtr ScaleTensor;
public IntPtr OutputGradientTensor;
public IntPtr OutputScaleGradientTensor;
public IntPtr OutputBiasGradientTensor;
public float Epsilon;
}Imports System.Runtime.InteropServices
<StructLayout(LayoutKind.Sequential, CharSet:=CharSet.Unicode)>
Public Structure DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC
Public InputTensor As IntPtr
Public InputGradientTensor As IntPtr
Public MeanTensor As IntPtr
Public VarianceTensor As IntPtr
Public ScaleTensor As IntPtr
Public OutputGradientTensor As IntPtr
Public OutputScaleGradientTensor As IntPtr
Public OutputBiasGradientTensor As IntPtr
Public Epsilon As Single
End Structureimport ctypes
from ctypes import wintypes
class DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC(ctypes.Structure):
_fields_ = [
("InputTensor", ctypes.c_void_p),
("InputGradientTensor", ctypes.c_void_p),
("MeanTensor", ctypes.c_void_p),
("VarianceTensor", ctypes.c_void_p),
("ScaleTensor", ctypes.c_void_p),
("OutputGradientTensor", ctypes.c_void_p),
("OutputScaleGradientTensor", ctypes.c_void_p),
("OutputBiasGradientTensor", ctypes.c_void_p),
("Epsilon", ctypes.c_float),
]#[repr(C)]
pub struct DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC {
pub InputTensor: *mut core::ffi::c_void,
pub InputGradientTensor: *mut core::ffi::c_void,
pub MeanTensor: *mut core::ffi::c_void,
pub VarianceTensor: *mut core::ffi::c_void,
pub ScaleTensor: *mut core::ffi::c_void,
pub OutputGradientTensor: *mut core::ffi::c_void,
pub OutputScaleGradientTensor: *mut core::ffi::c_void,
pub OutputBiasGradientTensor: *mut core::ffi::c_void,
pub Epsilon: f32,
}import "golang.org/x/sys/windows"
type DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC struct {
InputTensor uintptr
InputGradientTensor uintptr
MeanTensor uintptr
VarianceTensor uintptr
ScaleTensor uintptr
OutputGradientTensor uintptr
OutputScaleGradientTensor uintptr
OutputBiasGradientTensor uintptr
Epsilon float32
}type
DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC = record
InputTensor: Pointer;
InputGradientTensor: Pointer;
MeanTensor: Pointer;
VarianceTensor: Pointer;
ScaleTensor: Pointer;
OutputGradientTensor: Pointer;
OutputScaleGradientTensor: Pointer;
OutputBiasGradientTensor: Pointer;
Epsilon: Single;
end;const DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC = extern struct {
InputTensor: ?*anyopaque,
InputGradientTensor: ?*anyopaque,
MeanTensor: ?*anyopaque,
VarianceTensor: ?*anyopaque,
ScaleTensor: ?*anyopaque,
OutputGradientTensor: ?*anyopaque,
OutputScaleGradientTensor: ?*anyopaque,
OutputBiasGradientTensor: ?*anyopaque,
Epsilon: f32,
};type
DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC {.bycopy.} = object
InputTensor: pointer
InputGradientTensor: pointer
MeanTensor: pointer
VarianceTensor: pointer
ScaleTensor: pointer
OutputGradientTensor: pointer
OutputScaleGradientTensor: pointer
OutputBiasGradientTensor: pointer
Epsilon: float32struct DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC
{
void* InputTensor;
void* InputGradientTensor;
void* MeanTensor;
void* VarianceTensor;
void* ScaleTensor;
void* OutputGradientTensor;
void* OutputScaleGradientTensor;
void* OutputBiasGradientTensor;
float Epsilon;
}HSP用 定義
HSP3.7/3.8 は構造体機能が無いため4byte整数配列(dim)+peek/poke で操作(32/64bitでサイズ・位置が異なる場合はタブで分割)。IronHSP は NSTRUCT(#defstruct/stdim/->)で32/64bit共通。
; HSP3.7/3.8 は構造体機能が無いため、4byte整数の配列変数で操作します。(x86 レイアウト)
; DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC サイズ: 36 バイト(x86)
dim st, 9 ; 4byte整数×9(構造体サイズ 36 / 4 切り上げ)
; InputTensor : DML_TENSOR_DESC* (+0, 4byte) varptr(st)+0 を基点に操作(4byte:入れ子/配列)
; InputGradientTensor : DML_TENSOR_DESC* (+4, 4byte) varptr(st)+4 を基点に操作(4byte:入れ子/配列)
; MeanTensor : DML_TENSOR_DESC* (+8, 4byte) varptr(st)+8 を基点に操作(4byte:入れ子/配列)
; VarianceTensor : DML_TENSOR_DESC* (+12, 4byte) varptr(st)+12 を基点に操作(4byte:入れ子/配列)
; ScaleTensor : DML_TENSOR_DESC* (+16, 4byte) varptr(st)+16 を基点に操作(4byte:入れ子/配列)
; OutputGradientTensor : DML_TENSOR_DESC* (+20, 4byte) varptr(st)+20 を基点に操作(4byte:入れ子/配列)
; OutputScaleGradientTensor : DML_TENSOR_DESC* (+24, 4byte) varptr(st)+24 を基点に操作(4byte:入れ子/配列)
; OutputBiasGradientTensor : DML_TENSOR_DESC* (+28, 4byte) varptr(st)+28 を基点に操作(4byte:入れ子/配列)
; Epsilon : FLOAT (+32, 4byte) st.8 = 値 / 値 = st.8 (lpoke/lpeek も可)
; ※4byte境界の整数は添字 st.N(N=オフセット/4)で読み書き可。それ以外は peek/poke 系を使用。; HSP3.7/3.8 は構造体機能が無いため、4byte整数の配列変数で操作します。(x64 レイアウト)
; DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC サイズ: 72 バイト(x64)
dim st, 18 ; 4byte整数×18(構造体サイズ 72 / 4 切り上げ)
; InputTensor : DML_TENSOR_DESC* (+0, 8byte) varptr(st)+0 を基点に操作(8byte:入れ子/配列)
; InputGradientTensor : DML_TENSOR_DESC* (+8, 8byte) varptr(st)+8 を基点に操作(8byte:入れ子/配列)
; MeanTensor : DML_TENSOR_DESC* (+16, 8byte) varptr(st)+16 を基点に操作(8byte:入れ子/配列)
; VarianceTensor : DML_TENSOR_DESC* (+24, 8byte) varptr(st)+24 を基点に操作(8byte:入れ子/配列)
; ScaleTensor : DML_TENSOR_DESC* (+32, 8byte) varptr(st)+32 を基点に操作(8byte:入れ子/配列)
; OutputGradientTensor : DML_TENSOR_DESC* (+40, 8byte) varptr(st)+40 を基点に操作(8byte:入れ子/配列)
; OutputScaleGradientTensor : DML_TENSOR_DESC* (+48, 8byte) varptr(st)+48 を基点に操作(8byte:入れ子/配列)
; OutputBiasGradientTensor : DML_TENSOR_DESC* (+56, 8byte) varptr(st)+56 を基点に操作(8byte:入れ子/配列)
; Epsilon : FLOAT (+64, 4byte) st.16 = 値 / 値 = st.16 (lpoke/lpeek も可)
; ※4byte境界の整数は添字 st.N(N=オフセット/4)で読み書き可。それ以外は peek/poke 系を使用。; IronHSP は NSTRUCT(構造体)をサポート。32bit/64bit どちらでも同じコードで動作します。
#defstruct global DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC
#field intptr InputTensor
#field intptr InputGradientTensor
#field intptr MeanTensor
#field intptr VarianceTensor
#field intptr ScaleTensor
#field intptr OutputGradientTensor
#field intptr OutputScaleGradientTensor
#field intptr OutputBiasGradientTensor
#field float Epsilon
#endstruct
stdim st, DML_BATCH_NORMALIZATION_GRAD_OPERATOR_DESC ; NSTRUCT 変数を確保
st->Epsilon = 100
mes "Epsilon=" + st->Epsilon