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Add new figures in Tikz

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Signed-off-by: Riccardo Finotello <riccardo.finotello@gmail.com>
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Riccardo
2020-10-16 19:01:42 +02:00
parent 0f00e28fe5
commit 6f6c909341
45 changed files with 823 additions and 727 deletions
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6
sec/part3/introduction.tex
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@@ -59,7 +59,7 @@ Thus getting also \hodge{2}{1} from \ml techniques is an important first step to
Finally regression is also more useful for extrapolating results: a classification approach assumes that we already know all the possible values of the Hodge numbers and has difficulties to predict labels which do not appear in the training set.
This is necessary when we move to a dataset for which not all topological quantities have been computed, for instance CY constructed from the Kreuzer--Skarke list of polytopes~\cite{Kreuzer:2000:CompleteClassificationReflexive}.
The data analysis and \ml are programmed in Python using open-source packages: \texttt{pandas}~\cite{WesMcKinney:2010:DataStructuresStatistical}, \texttt{matplotlib}~\cite{Hunter:2007:Matplotlib2DGraphics}, \texttt{seaborn}~\cite{Waskom:2020:MwaskomSeabornV0}, \texttt{scikit-learn}~\cite{Pedregosa:2011:ScikitlearnMachineLearning}, \texttt{scikit-optimize}~\cite{Head:2020:ScikitoptimizeScikitoptimize}, \texttt{tensorflow}~\cite{Abadi:2015:TensorFlowLargescaleMachine} (and its high level API \emph{Keras}).
The data analysis and \ml are programmed in Python using known open-source packages such as \texttt{pandas}~\cite{WesMcKinney:2010:DataStructuresStatistical}, \texttt{matplotlib}~\cite{Hunter:2007:Matplotlib2DGraphics}, \texttt{seaborn}~\cite{Waskom:2020:MwaskomSeabornV0}, \texttt{scikit-learn}~\cite{Pedregosa:2011:ScikitlearnMachineLearning}, \texttt{scikit-optimize}~\cite{Head:2020:ScikitoptimizeScikitoptimize}, \texttt{tensorflow}~\cite{Abadi:2015:TensorFlowLargescaleMachine} (and its high level API \emph{Keras}).
Code is available on \href{https://thesfinox.github.io/ml-cicy/}{Github}.
@@ -192,14 +192,14 @@ Below we show a list of the \cicy properties and of their configuration matrices
\begin{figure}[tbp]
\centering
\begin{subfigure}[c]{.45\linewidth}
\begin{subfigure}[b]{.45\linewidth}
\centering
\includegraphics[width=\linewidth, trim={0 0.45in 6in 0}, clip]{img/label-distribution_orig}
\caption{\hodge{1}{1}}
\label{fig:data:hist-h11}
\end{subfigure}
\hfill
\begin{subfigure}[c]{.45\linewidth}
\begin{subfigure}[b]{.45\linewidth}
\centering
\includegraphics[width=\linewidth, trim={6in 0.45in 0 0}, clip]{img/label-distribution_orig}
\caption{\hodge{2}{1}}

8
sec/part3/ml.tex
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@@ -1020,7 +1020,7 @@ Using the same network we also achieve \SI{97}{\percent} of accuracy in the favo
\centering
\begin{subfigure}[c]{0.475\linewidth}
\centering
\includegraphics[width=\linewidth]{img/fc}
\import{tikz}{fc.pgf}
\caption{Architecture of the network.}
\label{fig:nn:dense}
\end{subfigure}
@@ -1099,7 +1099,7 @@ The convolution layers have $180$, $100$, $40$ and $20$ units each.
\begin{figure}[tbp]
\centering
\includegraphics[width=0.75\linewidth]{img/ccnn}
\import{tikz}{ccnn.pgf}
\caption{%
Pure convolutional neural network for redicting \hodge{1}{1}.
It is made of $4$ modules composed by convolutional layer, ReLU activation, batch normalisation (in this order), followed by a dropout layer, a flatten layer and the output layer (in this order).
@@ -1204,7 +1204,7 @@ The callbacks helped to contain the training time (without optimisation) under 5
\begin{figure}[tbp]
\centering
\includegraphics[width=0.9\linewidth]{img/icnn}
\resizebox{\linewidth}{!}{\import{tikz}{icnn.pgf}}
\caption{%
In each concatenation module (here shown for the ``old'' dataset) we operate with separate convolution operations over rows and columns, then concatenate the results.
The overall architecture is composed of 3 ``inception'' modules made by two separate convolutions, a concatenation layer and a batch normalisation layer (strictly in this order), followed by a dropout layer, a flatten layer and the output layer with ReLU activation (in this order).
@@ -1374,7 +1374,7 @@ Another reason is that the different algorithms may perform similarly well in th
\begin{figure}[tbp]
\centering
\includegraphics[width=0.65\linewidth]{img/stacking}
\resizebox{0.65\linewidth}{!}{\import{tikz}{stacking.pgf}}
\caption{Stacking ensemble learning with two level learning.}
\label{fig:stack:def}
\end{figure}