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hyperparam section & setup rework

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This commit is contained in:
Jan Kowalczyk
2025-09-28 18:58:03 +02:00
parent 1e71600102
commit fe45de00ca
6 changed files with 86 additions and 28 deletions
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thesis/diagrams/deepsad_procedure/deepsad_procedure.pdf
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thesis/diagrams/deepsad_procedure/deepsad_procedure.tex
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@@ -40,15 +40,15 @@
%\draw[arrow] (latent.east) -- node{} (autoenc.west);
\begin{pgfonlayer}{foreground}
\node[stepsbox, below=of process] (pretrainproc) {Train Autoencoder $\mathcal{W}_{E}$ \\ optimize Autoencoding Objective \\ for $E_A$ Epochs \\ with $L_A$ Learning Rate \\ No Labels Used / Required};
\node[outputbox, below=.1 of pretrainproc] (pretrainout) {\boxtitle{Outputs} $\mathcal{W}$: Encoder / DeepSAD Network \\ $\mathbf{w_{E}}$: Encoder Network Weights};
\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};
\node[outputbox, below=.1 of pretrainproc] (pretrainout) {\boxtitle{Outputs} $\mathcal{\phi}$: Encoder / DeepSAD Network \\ $\mathcal{W}_E$: Encoder 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=1.26 of hyper] (autoencarch) {\boxtitle{Autoencoder Architecture} $\mathcal{W}_{E}$: Autoencoder Network \\ $\mathbb{R}^d$: Latent Space Size };
\node[hlabelbox, below=1.26 of hyper] (autoencarch) {\boxtitle{Autoencoder Architecture} $\mathcal{\phi}_{AE}$: Autoencoder Network \\ $\mathbb{R}^d$: Latent Space Size };
\node[hlabelbox, below=.1 of autoencarch] (pretrainhyper) {\boxtitle{Hyperparameters} $E_A$: Number of Epochs \\ $L_A$: Learning Rate AE};
\end{pgfonlayer}
\begin{pgfonlayer}{background}
@@ -62,7 +62,7 @@
% \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) {Init Network $\mathcal{W}$ with $\mathbf{w_{E}}$ \\ Forward Pass on all data \\ Hypersphere center $\mathbf{c}$ is mean \\ of all Latent Representation};
\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};
\node[outputbox, below=.1 of calccproc] (calccout) {\boxtitle{Outputs} $\mathbf{c}$: Hypersphere Center};
\end{pgfonlayer}
\begin{pgfonlayer}{background}
@@ -77,8 +77,8 @@
%\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) {Init Network $\mathcal{W}$ with $\mathbf{w_{E}}$ \\ Train Network $\mathcal{W}$ \\ optimize DeepSAD Objective\\ for $E_M$ Epochs \\ with $L_M$ Learning Rate \\ Considers Labels with $\eta$ strength};
\node[outputbox, below=.1 of maintrainproc] (maintrainout) {\boxtitle{Outputs} $\mathcal{W}$: DeepSAD Network \\ $\mathbf{w}$: DeepSAD Network Weights \\ $\mathbf{c}$: Hypersphere Center};
\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};
\node[outputbox, below=.1 of maintrainproc] (maintrainout) {\boxtitle{Outputs} $\mathcal{\phi}$: DeepSAD Network \\ $\mathcal{W}$: DeepSAD 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) {};
@@ -102,7 +102,7 @@
\begin{pgfonlayer}{foreground}
\node[stepsbox, below=1.4 of maintrain] (inferenceproc) {Init Network $\mathcal{W}$ with $\mathbf{w}$ \\Forward Pass on sample = $\mathbf{p}$ \\ Calculate Distance $\mathbf{p} \rightarrow \mathbf{c}$ \\ Distance = Anomaly Score};
\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};
\node[outputbox, below=.1 of inferenceproc] (inferenceout) {\boxtitle{Outputs} Anomaly Score (Analog Value) \\ Higher for Anomalies};
\end{pgfonlayer}
\begin{pgfonlayer}{background}