148 lines
7.1 KiB
TeX
148 lines
7.1 KiB
TeX
\documentclass[tikz,border=10pt]{standalone}
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\usepackage{tikz}
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\usepackage{amsfonts}
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\usetikzlibrary{positioning, shapes.geometric, fit, arrows, arrows.meta, backgrounds}
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% Define box styles
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\tikzset{
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databox/.style={rectangle, align=center, draw=black, fill=blue!50, thick, rounded corners},%, inner sep=4},
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procbox/.style={rectangle, align=center, draw=black, fill=orange!30, thick, rounded corners},
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hyperbox/.style={rectangle, align=center, draw=black, fill=green!30, thick, rounded corners},
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stepsbox/.style={rectangle, align=left, draw=black,fill=white, rounded corners, minimum width=5.2cm, minimum height=1.5cm, font=\small},
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outputbox/.style={rectangle, align=center, draw=red!80, fill=red!20, rounded corners, minimum width=5.2cm, minimum height=1.5cm, font=\small},
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hlabelbox/.style={rectangle, align=center, draw=black,fill=white, rounded corners, minimum width=5.2cm, minimum height=1.5cm, font=\small},
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vlabelbox/.style={rectangle, align=center, draw=black,fill=white, rounded corners, minimum width=3cm, minimum height=1.8cm, font=\small},
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arrow/.style={-{Latex[length=3mm]}},
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arrowlabel/.style={fill=white,inner sep=2pt,midway}
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}
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\newcommand{\boxtitle}[1]{\textbf{#1}\\[.4em]}
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\pgfdeclarelayer{background}
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\pgfdeclarelayer{foreground}
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\pgfsetlayers{background,main,foreground}
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\begin{document}
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\begin{tikzpicture}[node distance=1cm and 2cm]
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\node (data) {Data};
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\node[right=4.9 of data] (process) {Procedure};
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\node[right=4.1 of process] (hyper) {Hyperparameters};
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\begin{pgfonlayer}{foreground}
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\node[hlabelbox, below=1.29 of data] (unlabeled) {\boxtitle{Unlabeled Data} Significantly more normal than \\ anomalous samples required};
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\node[hlabelbox, below=.1 of unlabeled] (labeled) {\boxtitle{Labeled Data} No requirement regarding ratio \\ +1 = normal, -1 = anomalous};
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\end{pgfonlayer}
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\begin{pgfonlayer}{background}
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\node[databox, fit=(unlabeled) (labeled), label={[label distance = 1, name=traindatalabel]above:{\textbf{Training Data}}}] (traindata) {};
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\end{pgfonlayer}
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%\draw[arrow] (latent.east) -- node{} (autoenc.west);
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\begin{pgfonlayer}{foreground}
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\node[stepsbox, below=of process] (pretrainproc) {Train Autoencoder $\mathcal{\phi}_{AE}$ \\ optimize Autoencoding Objective \\ for $E_A$ Epochs \\ with $L_A$ Learning Rate \\ No Labels Used / Required};
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\node[outputbox, below=.1 of pretrainproc] (pretrainout) {\boxtitle{Outputs} $\mathcal{\phi}$: Encoder / DeepSAD Network \\ $\mathcal{W}_E$: Encoder Network Weights};
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\end{pgfonlayer}
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\begin{pgfonlayer}{background}
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\node[procbox, fit=(pretrainproc) (pretrainout), label={[label distance = 1, name=pretrainlab]above:{\textbf{Pre-Training of Autoencoder}}}] (pretrain) {};
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\end{pgfonlayer}
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\begin{pgfonlayer}{foreground}
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\node[hlabelbox, below=1.26 of hyper] (autoencarch) {\boxtitle{Autoencoder Architecture} $\mathcal{\phi}_{AE}$: Autoencoder Network \\ $\mathbb{R}^d$: Latent Space Size };
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\node[hlabelbox, below=.1 of autoencarch] (pretrainhyper) {\boxtitle{Hyperparameters} $E_A$: Number of Epochs \\ $L_A$: Learning Rate AE};
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\end{pgfonlayer}
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\begin{pgfonlayer}{background}
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\node[hyperbox, fit=(autoencarch) (pretrainhyper), label={[label distance = 1, name=autoenclabel]above:{\textbf{Pre-Training Hyperparameters}}}] (pretrainhyp) {};
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\end{pgfonlayer}
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\draw[arrow] (pretrainhyp.west) -- (pretrain.east);
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%\draw[arrow] (node cs:name=traindata,angle=10) -- node[arrowlabel]{data type} (node cs:name=autoenc,angle=177);
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% \draw[arrow] (node cs:name=autoenc,angle=196) |- (node cs:name=pretrain,angle=5);
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\begin{pgfonlayer}{foreground}
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\node[stepsbox, below=1.4 of pretrain] (calccproc) {Init Network $\mathcal{\phi}$ with $\mathcal{W}_E$ \\ Forward Pass on all data \\ Hypersphere center $\mathbf{c}$ is mean \\ of all Latent Representation};
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\node[outputbox, below=.1 of calccproc] (calccout) {\boxtitle{Outputs} $\mathbf{c}$: Hypersphere Center};
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\end{pgfonlayer}
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\begin{pgfonlayer}{background}
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\node[procbox, fit=(calccproc) (calccout), label={[label distance = 1, name=calcclab]above:{\textbf{Calculate Hypersphere Center}}}] (calcc) {};
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\end{pgfonlayer}
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\draw[arrow] (pretrain.south) -- (calcclab.north);
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\draw[arrow] (traindata.east) -- (pretrain.west);
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\draw[arrow] (traindata.south) |- (calcc.west);
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%\draw[arrow] (node cs:name=traindata,angle=45) |- node[arrowlabel]{all training data, labels removed} (node cs:name=pretrain,angle=160);
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%\draw[arrow] (node cs:name=traindata,angle=-45) |- node[arrowlabel]{all training data, labels removed} (node cs:name=calcc,angle=200);
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\begin{pgfonlayer}{foreground}
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\node[stepsbox, below=1.4 of calcc] (maintrainproc) {Init Network $\mathcal{\phi}$ with $\mathcal{W}_E$ \\ Train Network $\mathcal{\phi}$ \\ optimize DeepSAD Objective\\ for $E_M$ Epochs \\ with $L_M$ Learning Rate \\ Considers Labels with $\eta$ strength};
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\node[outputbox, below=.1 of maintrainproc] (maintrainout) {\boxtitle{Outputs} $\mathcal{\phi}$: DeepSAD Network \\ $\mathcal{W}$: DeepSAD Network Weights \\ $\mathbf{c}$: Hypersphere Center};
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\end{pgfonlayer}
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\begin{pgfonlayer}{background}
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\node[procbox, fit=(maintrainproc) (maintrainout), label={[label distance = 1, name=maintrainlab]above:{\textbf{Main Training}}}] (maintrain) {};
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\end{pgfonlayer}
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\begin{pgfonlayer}{foreground}
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\node[hlabelbox, below=12.48 of hyper] (maintrainhyper) {$E_M$: Number of Epochs \\ $L_M$: Learning Rate \\ $\eta$: Weight Labeled/Unlabeled};
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\end{pgfonlayer}
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\begin{pgfonlayer}{background}
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\node[hyperbox, fit=(maintrainhyper), label={[label distance = 1, name=autoenclabel]above:{\textbf{Main-Training Hyperparameters}}}] (maintrainhyp) {};
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\end{pgfonlayer}
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\draw[arrow] (node cs:name=pretrain,angle=-50) |- +(1.5, -0.55) -- +(1.5,-5.4) -| (node cs:name=maintrain,angle=50);
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%\draw[arrow] (pretrainoutput.south) -- (node cs:name=maintrain,angle=22);
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\draw[arrow] (calcc.south) -- (maintrainlab.north);
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\draw[arrow] (traindata.south) |- (maintrain.west);
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%\draw[arrow] (node cs:name=traindata,angle=-135) |- node[arrowlabel]{all training data, including labels} (maintrain.west);
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\draw[arrow] (maintrainhyp.west) -- (maintrain.east);
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\begin{pgfonlayer}{foreground}
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\node[stepsbox, below=1.4 of maintrain] (inferenceproc) {Init Network $\mathcal{\phi}$ with $\mathcal{W}$ \\Forward Pass on sample = $\mathbf{p}$ \\ Calculate Distance $\mathbf{p} \rightarrow \mathbf{c}$ \\ Distance = Anomaly Score};
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\node[outputbox, below=.1 of inferenceproc] (inferenceout) {\boxtitle{Outputs} Anomaly Score (Analog Value) \\ Higher for Anomalies};
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\end{pgfonlayer}
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\begin{pgfonlayer}{background}
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\node[procbox, fit=(inferenceproc) (inferenceout), label={[label distance = 1, name=inferencelab]above:{\textbf{Inference}}}] (inference) {};
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\end{pgfonlayer}
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\begin{pgfonlayer}{foreground}
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\node[hlabelbox, below=13.32 of traindata] (newdatasample) {\boxtitle{New Data Sample} Same data type as training data};
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\end{pgfonlayer}
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\begin{pgfonlayer}{background}
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\node[databox, fit=(newdatasample), label={[label distance = 1] above:{\textbf{Unseen Data}}}] (newdata) {};
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\end{pgfonlayer}
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\draw[arrow] (maintrain.south) -- (inferencelab.north);
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\draw[arrow] (newdata.east) -- (inference.west);
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\end{tikzpicture}
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\end{document}
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