clara/pytorch-ai
1
0
Fork 0
Code Issues 1 Pull requests Projects Releases Packages Wiki Activity Actions

finisdhed regression chapter

Browse source
This commit is contained in:
Clemens Dautermann 2020-01-22 13:06:35 +01:00
parent cf9b534127
commit 1a830d0832
9 changed files with 712 additions and 136 deletions
Download patch file Download diff file Expand all files Collapse all files

80
doc/Grundlagen_des_maschinellen_lernens.aux
View file

@ -30,6 +30,8 @@
\providecommand*\caption@xref[2]{\@setref\relax\@undefined{#1}}
\newlabel{Classification}{{1}{4}{Binärklassifizierung\relax }{figure.caption.2}{}}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {1.2}Regressionsprobleme}{4}{subsection.1.2}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Regression\relax }}{4}{figure.caption.3}\protected@file@percent }
\newlabel{Regression}{{2}{4}{Regression\relax }{figure.caption.3}{}}
\abx@aux@cite{2}
\abx@aux@segm{0}{0}{2}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {1.3}Gefahren von maschinellem Lernen}{5}{subsection.1.3}\protected@file@percent }
@ -42,59 +44,59 @@
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {2.3}Best\IeC {\"a}rkendes Lernen}{5}{subsection.2.3}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {3}Neuronale Netze}{5}{section.3}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Maschinelles Lernen und menschliches Lernen}{5}{subsection.3.1}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Neuron \newline Quelle: simple.wikipedia.org/wiki/File:Neuron.svg\newline Copyright: CC Attribution-Share Alike von Nutzer Dhp1080,\newline bearbeitet}}{6}{figure.caption.3}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces Neuron \newline Quelle: simple.wikipedia.org/wiki/File:Neuron.svg\newline Copyright: CC Attribution-Share Alike von Nutzer Dhp1080,\newline bearbeitet}}{6}{figure.caption.4}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Der Aufbau eines neuronalen Netzes}{6}{subsection.3.2}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Berechnung des Ausgabevektors}{6}{subsection.3.3}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces Ein einfaches neuronales Netz\relax }}{7}{figure.caption.4}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces Der Plot der Sigmoid Funktion $\sigma (x)=\frac {e^x}{e^x+1}$\relax }}{8}{figure.caption.5}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces Formel zur Berechnung eines Ausgabevektors aus einem Eingabevektor durch ein Layer Neuronen. \relax }}{9}{figure.caption.6}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces Ein einfaches neuronales Netz\relax }}{7}{figure.caption.5}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Berechnung des Ausgabevektors}{7}{subsection.3.3}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces Der Plot der Sigmoid Funktion $\sigma (x)=\frac {e^x}{e^x+1}$\relax }}{8}{figure.caption.6}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces Formel zur Berechnung eines Ausgabevektors aus einem Eingabevektor durch ein Layer Neuronen. \relax }}{9}{figure.caption.7}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {3.4}Der Lernprozess}{9}{subsection.3.4}\protected@file@percent }
\abx@aux@cite{3}
\abx@aux@segm{0}{0}{3}
\abx@aux@segm{0}{0}{3}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {3.5}Fehlerfunktionen}{10}{subsection.3.5}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.5.1}MSE -- Durchschnittlicher quadratischer Fehler}{10}{subsubsection.3.5.1}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces Die Gleichung f\IeC {\"u}r den durchschnittlichen quadratischen Fehler\relax }}{10}{figure.caption.7}\protected@file@percent }
\newlabel{MSE_equation}{{6}{10}{Die Gleichung für den durchschnittlichen quadratischen Fehler\relax }{figure.caption.7}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces Die Gleichung f\IeC {\"u}r den durchschnittlichen quadratischen Fehler\relax }}{10}{figure.caption.8}\protected@file@percent }
\newlabel{MSE_equation}{{7}{10}{Die Gleichung für den durchschnittlichen quadratischen Fehler\relax }{figure.caption.8}{}}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.5.2}MAE -- Durchschnitztlicher absoluter Fehler}{10}{subsubsection.3.5.2}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces Die Gleichung f\IeC {\"u}r den durchschnittlichen absoluten Fehler\relax }}{10}{figure.caption.8}\protected@file@percent }
\newlabel{MAE_equation}{{7}{10}{Die Gleichung für den durchschnittlichen absoluten Fehler\relax }{figure.caption.8}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces Die Gleichung f\IeC {\"u}r den durchschnittlichen absoluten Fehler\relax }}{10}{figure.caption.9}\protected@file@percent }
\newlabel{MAE_equation}{{8}{10}{Die Gleichung für den durchschnittlichen absoluten Fehler\relax }{figure.caption.9}{}}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.5.3}Kreuzentropiefehler}{11}{subsubsection.3.5.3}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces Der Graph der Kreuzentropie Fehlerfunktion wenn das tats\IeC {\"a}chliche Label 1 ist\relax }}{11}{figure.caption.9}\protected@file@percent }
\newlabel{CEL_Graph}{{8}{11}{Der Graph der Kreuzentropie Fehlerfunktion wenn das tatsächliche Label 1 ist\relax }{figure.caption.9}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces Die Gleichung f\IeC {\"u}r den Kreuzentropiefehler\relax }}{12}{figure.caption.10}\protected@file@percent }
\newlabel{CEL_Function}{{9}{12}{Die Gleichung für den Kreuzentropiefehler\relax }{figure.caption.10}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces Die Gleichung f\IeC {\"u}r den durchschnittlichen absoluten Fehler\relax }}{12}{figure.caption.11}\protected@file@percent }
\newlabel{CEL_Finction_cummulative}{{10}{12}{Die Gleichung für den durchschnittlichen absoluten Fehler\relax }{figure.caption.11}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces Der Graph der Kreuzentropie Fehlerfunktion wenn das tats\IeC {\"a}chliche Label 1 ist\relax }}{11}{figure.caption.10}\protected@file@percent }
\newlabel{CEL_Graph}{{9}{11}{Der Graph der Kreuzentropie Fehlerfunktion wenn das tatsächliche Label 1 ist\relax }{figure.caption.10}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces Die Gleichung f\IeC {\"u}r den Kreuzentropiefehler\relax }}{12}{figure.caption.11}\protected@file@percent }
\newlabel{CEL_Function}{{10}{12}{Die Gleichung für den Kreuzentropiefehler\relax }{figure.caption.11}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {11}{\ignorespaces Die Gleichung f\IeC {\"u}r den durchschnittlichen absoluten Fehler\relax }}{12}{figure.caption.12}\protected@file@percent }
\newlabel{CEL_Finction_cummulative}{{11}{12}{Die Gleichung für den durchschnittlichen absoluten Fehler\relax }{figure.caption.12}{}}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {3.6}Gradientenverfahren und Backpropagation}{12}{subsection.3.6}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {11}{\ignorespaces Die Gleichung f\IeC {\"u}r den Gradienten der Fehlerfunktion\relax }}{12}{figure.caption.12}\protected@file@percent }
\newlabel{Gradient_Function}{{11}{12}{Die Gleichung für den Gradienten der Fehlerfunktion\relax }{figure.caption.12}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {12}{\ignorespaces Die Gleichung f\IeC {\"u}r den Gradienten der Fehlerfunktion\relax }}{12}{figure.caption.13}\protected@file@percent }
\newlabel{Gradient_Function}{{12}{12}{Die Gleichung für den Gradienten der Fehlerfunktion\relax }{figure.caption.13}{}}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.6.1}Lernrate}{13}{subsubsection.3.6.1}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {12}{\ignorespaces Die Gleichung f\IeC {\"u}r die Anpassung eines einzelnen Parameters\relax }}{13}{figure.caption.13}\protected@file@percent }
\newlabel{Learning_Rate_Function}{{12}{13}{Die Gleichung für die Anpassung eines einzelnen Parameters\relax }{figure.caption.13}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {13}{\ignorespaces $\eta $ ist hier zu gro\IeC {\ss } gew\IeC {\"a}hlt\relax }}{13}{figure.caption.14}\protected@file@percent }
\newlabel{Learning_Rate_Graphic}{{13}{13}{$\eta $ ist hier zu groß gewählt\relax }{figure.caption.14}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {13}{\ignorespaces Die Gleichung f\IeC {\"u}r die Anpassung eines einzelnen Parameters\relax }}{13}{figure.caption.14}\protected@file@percent }
\newlabel{Learning_Rate_Function}{{13}{13}{Die Gleichung für die Anpassung eines einzelnen Parameters\relax }{figure.caption.14}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {14}{\ignorespaces $\eta $ ist hier zu gro\IeC {\ss } gew\IeC {\"a}hlt\relax }}{13}{figure.caption.15}\protected@file@percent }
\newlabel{Learning_Rate_Graphic}{{14}{13}{$\eta $ ist hier zu groß gewählt\relax }{figure.caption.15}{}}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {3.7}Verschiedene Layerarten}{14}{subsection.3.7}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.7.1}Convolutional Layers}{14}{subsubsection.3.7.1}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {14}{\ignorespaces Eine Verbildlichung der Vorg\IeC {\"a}nge in einem convolutional Layer\newline Aus einer Animation von\newline https://github.com/vdumoulin/conv\_arithmetic/blob/master/README.md Vincent Dumoulin, Francesco Visin - A guide to convolution arithmetic for deep learning (BibTeX)}}{14}{figure.caption.15}\protected@file@percent }
\newlabel{Convolution_illustration}{{14}{14}{Eine Verbildlichung der Vorgänge in einem convolutional Layer\newline Aus einer Animation von\newline https://github.com/vdumoulin/conv\_arithmetic/blob/master/README.md\\ Vincent Dumoulin, Francesco Visin - A guide to convolution arithmetic for deep learning (BibTeX)}{figure.caption.15}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {15}{\ignorespaces Erkennt obere horizontale Kanten\relax }}{15}{figure.caption.16}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {16}{\ignorespaces Erkennt linke vertikale Kanten\relax }}{15}{figure.caption.16}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {17}{\ignorespaces Erkennt untere horizontale Kanten\relax }}{15}{figure.caption.16}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {18}{\ignorespaces Erkennt rechte vertikale Kanten\relax }}{15}{figure.caption.16}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {19}{\ignorespaces Das Beispielbild aus dem Mnist Datensatz\relax }}{15}{figure.caption.17}\protected@file@percent }
\newlabel{Filter_Example_raw}{{19}{15}{Das Beispielbild aus dem Mnist Datensatz\relax }{figure.caption.17}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {20}{\ignorespaces Die jeweils oben stehenden Filter wurden auf das Beispielbild angewandt.\relax }}{15}{figure.caption.18}\protected@file@percent }
\newlabel{Filter_output dargestellt}{{20}{15}{Die jeweils oben stehenden Filter wurden auf das Beispielbild angewandt.\relax }{figure.caption.18}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {21}{\ignorespaces Beispiele f\IeC {\"u}r low- mid- und high-level Features in Convolutional Neural Nets\newline Quelle: https://tvirdi.github.io/2017-10-29/cnn/}}{16}{figure.caption.19}\protected@file@percent }
\newlabel{HL_features_conv}{{21}{16}{Beispiele für low- mid- und high-level Features in Convolutional Neural Nets\newline Quelle: https://tvirdi.github.io/2017-10-29/cnn/}{figure.caption.19}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {15}{\ignorespaces Eine Verbildlichung der Vorg\IeC {\"a}nge in einem convolutional Layer\newline Aus einer Animation von\newline https://github.com/vdumoulin/conv\_arithmetic/blob/master/README.md Vincent Dumoulin, Francesco Visin - A guide to convolution arithmetic for deep learning (BibTeX)}}{14}{figure.caption.16}\protected@file@percent }
\newlabel{Convolution_illustration}{{15}{14}{Eine Verbildlichung der Vorgänge in einem convolutional Layer\newline Aus einer Animation von\newline https://github.com/vdumoulin/conv\_arithmetic/blob/master/README.md\\ Vincent Dumoulin, Francesco Visin - A guide to convolution arithmetic for deep learning (BibTeX)}{figure.caption.16}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {16}{\ignorespaces Erkennt obere horizontale Kanten\relax }}{15}{figure.caption.17}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {17}{\ignorespaces Erkennt linke vertikale Kanten\relax }}{15}{figure.caption.17}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {18}{\ignorespaces Erkennt untere horizontale Kanten\relax }}{15}{figure.caption.17}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {19}{\ignorespaces Erkennt rechte vertikale Kanten\relax }}{15}{figure.caption.17}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {20}{\ignorespaces Das Beispielbild aus dem Mnist Datensatz\relax }}{15}{figure.caption.18}\protected@file@percent }
\newlabel{Filter_Example_raw}{{20}{15}{Das Beispielbild aus dem Mnist Datensatz\relax }{figure.caption.18}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {21}{\ignorespaces Die jeweils oben stehenden Filter wurden auf das Beispielbild angewandt.\relax }}{15}{figure.caption.19}\protected@file@percent }
\newlabel{Filter_output dargestellt}{{21}{15}{Die jeweils oben stehenden Filter wurden auf das Beispielbild angewandt.\relax }{figure.caption.19}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {22}{\ignorespaces Beispiele f\IeC {\"u}r low- mid- und high-level Features in Convolutional Neural Nets\newline Quelle: https://tvirdi.github.io/2017-10-29/cnn/}}{16}{figure.caption.20}\protected@file@percent }
\newlabel{HL_features_conv}{{22}{16}{Beispiele für low- mid- und high-level Features in Convolutional Neural Nets\newline Quelle: https://tvirdi.github.io/2017-10-29/cnn/}{figure.caption.20}{}}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsubsection}{\numberline {3.7.2}Pooling Layers}{16}{subsubsection.3.7.2}\protected@file@percent }
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {22}{\ignorespaces Max Pooling mit $2\times 2$ gro\IeC {\ss }en Submatritzen\newline Quelle: https://computersciencewiki.org/index.php/Max-pooling\_/\_Pooling CC BY NC SA Lizenz}}{17}{figure.caption.20}\protected@file@percent }
\newlabel{Maxpool}{{22}{17}{Max Pooling mit $2\times 2$ großen Submatritzen\newline Quelle: https://computersciencewiki.org/index.php/Max-pooling\_/\_Pooling\\ CC BY NC SA Lizenz}{figure.caption.20}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {23}{\ignorespaces Average Pooling mit $2\times 2$ gro\IeC {\ss }en Submatritzen\newline Aus: Dominguez-Morales, Juan Pedro. (2018). Neuromorphic audio processing through real-time embedded spiking neural networks. Abbildung 33}}{17}{figure.caption.21}\protected@file@percent }
\newlabel{AvgPool}{{23}{17}{Average Pooling mit $2\times 2$ großen Submatritzen\newline Aus: Dominguez-Morales, Juan Pedro. (2018). Neuromorphic audio processing through real-time embedded spiking neural networks. Abbildung 33}{figure.caption.21}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {24}{\ignorespaces Gegen\IeC {\"u}berstellung von Max und Average Pooling\relax }}{18}{figure.caption.22}\protected@file@percent }
\newlabel{Pooling_Mnist}{{24}{18}{Gegenüberstellung von Max und Average Pooling\relax }{figure.caption.22}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {23}{\ignorespaces Max Pooling mit $2\times 2$ gro\IeC {\ss }en Submatritzen\newline Quelle: https://computersciencewiki.org/index.php/Max-pooling\_/\_Pooling CC BY NC SA Lizenz}}{17}{figure.caption.21}\protected@file@percent }
\newlabel{Maxpool}{{23}{17}{Max Pooling mit $2\times 2$ großen Submatritzen\newline Quelle: https://computersciencewiki.org/index.php/Max-pooling\_/\_Pooling\\ CC BY NC SA Lizenz}{figure.caption.21}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {24}{\ignorespaces Average Pooling mit $2\times 2$ gro\IeC {\ss }en Submatritzen\newline Aus: Dominguez-Morales, Juan Pedro. (2018). Neuromorphic audio processing through real-time embedded spiking neural networks. Abbildung 33}}{17}{figure.caption.22}\protected@file@percent }
\newlabel{AvgPool}{{24}{17}{Average Pooling mit $2\times 2$ großen Submatritzen\newline Aus: Dominguez-Morales, Juan Pedro. (2018). Neuromorphic audio processing through real-time embedded spiking neural networks. Abbildung 33}{figure.caption.22}{}}
\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {25}{\ignorespaces Gegen\IeC {\"u}berstellung von Max und Average Pooling\relax }}{18}{figure.caption.23}\protected@file@percent }
\newlabel{Pooling_Mnist}{{25}{18}{Gegenüberstellung von Max und Average Pooling\relax }{figure.caption.23}{}}
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{\numberline {4}PyTorch}{19}{section.4}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {4.1}Datenvorbereitung}{19}{subsection.4.1}\protected@file@percent }
\@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}Definieren des Netzes}{19}{subsection.4.2}\protected@file@percent }