unignored pdfs, added network arch diagrams

thesis/diagrams/deepsad_procedure/deepsad_procedure.tex

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+\documentclass[tikz,border=10pt]{standalone}
+\usepackage{tikz}
+\usetikzlibrary{positioning, shapes.geometric, fit, arrows, arrows.meta, backgrounds}
+
+% Define box styles
+\tikzset{
+  databox/.style={rectangle, align=center, draw=black, fill=blue!50, thick, rounded corners},%, inner sep=4},
+  procbox/.style={rectangle, align=center, draw=black, fill=orange!30, thick, rounded corners},
+  hyperbox/.style={rectangle, align=center, draw=black, fill=green!30, thick, rounded corners},
+  stepsbox/.style={rectangle, align=left, draw=black,fill=white, rounded corners, minimum width=6cm, minimum height=1.5cm, font=\small},
+  outputbox/.style={rectangle, align=center, draw=red!80, fill=red!20, rounded corners, minimum width=6cm, minimum height=1.5cm, font=\small},
+  hlabelbox/.style={rectangle, align=center, draw=black,fill=white, rounded corners, minimum width=6cm, minimum height=1.5cm, font=\small},
+  vlabelbox/.style={rectangle, align=center, draw=black,fill=white, rounded corners, minimum width=3cm, minimum height=1.8cm, font=\small},
+  arrow/.style={-{Latex[length=3mm]}},
+  arrowlabel/.style={fill=white,inner sep=2pt,midway}
+}
+
+\newcommand{\boxtitle}[1]{\textbf{#1}\\[.4em]}
+
+\pgfdeclarelayer{background}
+\pgfdeclarelayer{foreground}
+\pgfsetlayers{background,main,foreground}
+
+\begin{document}
+\begin{tikzpicture}[node distance=1cm and 2cm]
+
+	\node (data) {Data};
+	\node[right=7 of data] (process) {Procedure};
+	\node[right=7 of process] (hyper) {Hyperparameters};
+
+	\begin{pgfonlayer}{foreground}
+		\node[hlabelbox, below=of data] (unlabeled) {\boxtitle{Unlabeled Data} More normal than \\ anomalous samples required};
+		\node[hlabelbox, below=.1 of unlabeled] (labeled) {\boxtitle{Labeled Data} No requirement regarding ratio \\ +1 = normal, -1 = anomalous};
+	\end{pgfonlayer}
+	\begin{pgfonlayer}{background}
+		\node[databox, fit=(unlabeled) (labeled), label={[label distance = 1, name=traindatalabel]above:{\textbf{Training Data}}}] (traindata) {};
+	\end{pgfonlayer}
+
+	%\draw[arrow] (latent.east) -- node{} (autoenc.west);
+
+	\begin{pgfonlayer}{foreground}
+		\node[stepsbox, below=of process] (pretrainproc) {Train Autoencoder for $E_A$ Epochs \\ with $L_A$ Learning Rate \\ No Labels Used};
+		\node[outputbox, below=.1 of pretrainproc] (pretrainout) {\boxtitle{Outputs} Encoder Network \\ $\mathbf{w}$: Network Weights};
+	\end{pgfonlayer}
+	\begin{pgfonlayer}{background}
+		\node[procbox, fit=(pretrainproc) (pretrainout), label={[label distance = 1, name=pretrainlab]above:{\textbf{Pre-Training of Autoencoder}}}] (pretrain) {};
+	\end{pgfonlayer}
+
+	\begin{pgfonlayer}{foreground}
+    \node[hlabelbox, below=of hyper] (autoencarch) {\boxtitle{Autoencoder Architecture} Choose based on data type \\ Latent Space Size (based on complexity)};
+		\node[hlabelbox, below=.1 of autoencarch] (pretrainhyper) {\boxtitle{Hyperparameters} $E_A$: Number of Epochs  \\ $L_A$: Learning Rate};
+	\end{pgfonlayer}
+	\begin{pgfonlayer}{background}
+		\node[hyperbox, fit=(autoencarch) (pretrainhyper), label={[label distance = 1, name=autoenclabel]above:{\textbf{Pre-Training Hyperparameters}}}] (pretrainhyp) {};
+	\end{pgfonlayer}
+
+	\draw[arrow] (pretrainhyp.west) -- (pretrain.east);
+
+	%\draw[arrow] (node cs:name=traindata,angle=10) -- node[arrowlabel]{data type} (node cs:name=autoenc,angle=177);
+
+	%	\draw[arrow] (node cs:name=autoenc,angle=196) |- (node cs:name=pretrain,angle=5);
+
+	\begin{pgfonlayer}{foreground}
+		\node[stepsbox, below=1.4 of pretrain] (calccproc) {1. Init Encoder with $\mathbf{w}$ \\ 2. Forward Pass on all data \\ 3. $\mathbf{c}$ = Mean Latent Representation};
+		\node[outputbox, below=.1 of calccproc] (calccout) {\boxtitle{Outputs} $\mathbf{c}$: Hypersphere Center};
+	\end{pgfonlayer}
+	\begin{pgfonlayer}{background}
+		\node[procbox, fit=(calccproc) (calccout), label={[label distance = 1, name=calcclab]above:{\textbf{Calculate Hypersphere Center}}}] (calcc) {};
+	\end{pgfonlayer}
+
+	\draw[arrow] (pretrain.south) -- (calcclab.north);
+	\draw[arrow] (traindata.east) -- (pretrain.west);
+	\draw[arrow] (traindata.south) |- (calcc.west);
+
+	%\draw[arrow] (node cs:name=traindata,angle=45) |- node[arrowlabel]{all training data, labels removed} (node cs:name=pretrain,angle=160);
+	%\draw[arrow] (node cs:name=traindata,angle=-45) |- node[arrowlabel]{all training data, labels removed} (node cs:name=calcc,angle=200);
+
+	\begin{pgfonlayer}{foreground}
+		\node[stepsbox, below=1.4 of calcc] (maintrainproc) {Train Network for $E_M$ Epochs \\ with $L_M$ Learning Rate \\ Considers Labels with $\eta$ strength};
+		\node[outputbox, below=.1 of maintrainproc] (maintrainout) {\boxtitle{Outputs} Encoder Network \\ $\mathbf{w}$: Network Weights  \\ $\mathbf{c}$: Hypersphere Center};
+	\end{pgfonlayer}
+	\begin{pgfonlayer}{background}
+		\node[procbox, fit=(maintrainproc) (maintrainout), label={[label distance = 1, name=maintrainlab]above:{\textbf{Main Training}}}] (maintrain) {};
+	\end{pgfonlayer}
+
+	\begin{pgfonlayer}{foreground}
+		\node[hlabelbox, below=11.25 of hyper] (maintrainhyper) {$E_M$: Number of Epochs  \\ $L_M$: Learning Rate  \\ $\eta$: Strength Labeled/Unlabeled};
+	\end{pgfonlayer}
+	\begin{pgfonlayer}{background}
+		\node[hyperbox, fit=(maintrainhyper), label={[label distance = 1, name=autoenclabel]above:{\textbf{Main-Training Hyperparameters}}}] (maintrainhyp) {};
+	\end{pgfonlayer}
+
+  \draw[arrow] (node cs:name=pretrain,angle=-20) -- +(1, 0) |- (node cs:name=maintrain,angle=20);
+
+
+	%\draw[arrow] (pretrainoutput.south) -- (node cs:name=maintrain,angle=22);
+	\draw[arrow] (calcc.south) -- (maintrainlab.north);
+	\draw[arrow] (traindata.south) |- (maintrain.west);
+	%\draw[arrow] (node cs:name=traindata,angle=-135) |- node[arrowlabel]{all training data, including labels} (maintrain.west);
+	\draw[arrow] (maintrainhyp.west) -- (maintrain.east);
+
+
+	\begin{pgfonlayer}{foreground}
+		\node[stepsbox, below=1.4 of maintrain] (inferenceproc) {Forward Pass through Network = $\mathbf{p}$ \\ Calculate Geometric Distance $\mathbf{p} \rightarrow \mathbf{c}$ \\ Anomaly Score = Geometric Distance};
+		\node[outputbox, below=.1 of inferenceproc] (inferenceout) {\boxtitle{Outputs} Anomaly Score (Analog Value) \\ Higher for Anomalies};
+	\end{pgfonlayer}
+	\begin{pgfonlayer}{background}
+		\node[procbox, fit=(inferenceproc) (inferenceout), label={[label distance = 1, name=inferencelab]above:{\textbf{Inference}}}] (inference) {};
+	\end{pgfonlayer}
+
+	\begin{pgfonlayer}{foreground}
+		\node[hlabelbox, below=11.8 of traindata] (newdatasample) {\boxtitle{New Data Sample} Same data type as training data};
+	\end{pgfonlayer}
+	\begin{pgfonlayer}{background}
+		\node[databox, fit=(newdatasample),  label={[label distance = 1] above:{\textbf{Unseen Data}}}] (newdata) {};
+	\end{pgfonlayer}
+
+	\draw[arrow] (maintrain.south) -- (inferencelab.north);
+	\draw[arrow] (newdata.east) -- (inference.west);
+
+\end{tikzpicture}
+\end{document}
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