dela
3a322aa8dc
Add the ledger schema, class router, lint codes, ingest/build pipeline, and three work-tree examples: align derivation, superfig delegation, and supertensor delegation.
48 lines
1.5 KiB
TeX
48 lines
1.5 KiB
TeX
% superfig golden example 1 -- one horizontal paper-figure pipeline.
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% Main path is input -> model -> prediction. Loss is a side object, not a
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% station on the forward path.
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% ../scripts/build.sh pipeline.tex
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\documentclass[border=10pt]{standalone}
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\usepackage[cjk]{superfig}
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\sfsetrole{input}{sfTeal}
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\sfsetrole{model}{sfOrange}
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\sfsetrole{loss}{sfCoral}
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\sfsetrole{output}{sfViolet}
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\begin{document}
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\begin{tikzpicture}
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\sfstage{SA}{推理流程：一次前向}
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\sfrow{R1}{16mm}
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\sfnode[role=input]{x}{输入 $x$}{16mm}{12mm}
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\sfconn{e1}{预处理}
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\sfnode[role=model]{f}{模型 $f_\theta$}{18mm}{12mm}
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\sfconn{e2}{logits}
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\sfnode[role=output]{y}{预测 $\hat y$}{16mm}{12mm}
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\sfrowend
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% Loss compares the prediction with the target; it is not on the main path.
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% Hang it below ŷ with enough shaft that no caption sits on the arrow.
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\sfnode[role=loss, at={($(y.south)+(0,-22mm)$)}]{s}{损失 $L$}{14mm}{12mm}
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\sfarrowlabel{y.south}{s.north}{$L(\hat y,y)$}
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\sflane{R1}
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\sfcaption{x}{$x$}{原始输入}
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\sfcaption{f}{$f_\theta$}{可学习参数}
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\sfnolane
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\sfcaption{s}{$L$}{与真值比较}
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\sfbbox{all}
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\sftopformula{F}{%
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$x \;\xrightarrow{\;f_\theta\;}\; \hat y,\qquad
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\min_\theta\; L\bigl(f_\theta(x),\,y\bigr)$}
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\sfmeaningbox{mb}{96mm}{all}
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{一次从输入到预测的前向；损失在预测之后单独计算}
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{数据、模型参数、预测、损失}
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{预处理后送入模型；模型产生 logits 得到预测；损失比较预测与真值，梯度再回到参数}
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\sfsignature{推理流程示意}{mb}
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\end{tikzpicture}
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\end{document}
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