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Połączenie pracy z dwóch źródłem #4

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s444337 wants to merge 1 commits from examples into master
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merge into: s444337:master
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@ -1455,7 +1455,7 @@ Na potrzeby badań trenowałem i generowałem klipy muzyczne dla epok 1, 10, 25,
\label{fig:losses} \label{fig:losses}
\end{figure} \end{figure}
\section{Generowanie muzyki przy pomocy wytrenowanych modeli} \subsection{Generowanie muzyki przy pomocy wytrenowanych modeli}
Gdy zdefiniowane modele zostaną wytrenowane możemy wykorzystać skrypt \pyth{generate.py}, wtedy generująca sieć neuonowa zostanie zasilona losowym wekorem aby wygenerować partię. W tym przykładzie gitary a wygenerowana partia posłuży jako dane wejściowe na pozostałych modeli. Ostatecznie otrzymane sekwencje zostaną skompilowane do pliku MIDI. W tym momencie zostają wykorzystane informacje o programach dla każdego z intrmentów, a tempo utworu domyślnie ustawione jest na 120 BPM. Możemy również zdecydować, czy zasilenie dekodera modelu generującego odbędzie się za pomocą losowej sekwencji elementów ze słownika (\textit{from\textunderscore seq}), czy losowy wektor zasili bezpośrednio stany wewnętrzne dekodera $h$ i $c$ (\textit{from\textunderscore state}). Gdy zdefiniowane modele zostaną wytrenowane możemy wykorzystać skrypt \pyth{generate.py}, wtedy generująca sieć neuonowa zostanie zasilona losowym wekorem aby wygenerować partię. W tym przykładzie gitary a wygenerowana partia posłuży jako dane wejściowe na pozostałych modeli. Ostatecznie otrzymane sekwencje zostaną skompilowane do pliku MIDI. W tym momencie zostają wykorzystane informacje o programach dla każdego z intrmentów, a tempo utworu domyślnie ustawione jest na 120 BPM. Możemy również zdecydować, czy zasilenie dekodera modelu generującego odbędzie się za pomocą losowej sekwencji elementów ze słownika (\textit{from\textunderscore seq}), czy losowy wektor zasili bezpośrednio stany wewnętrzne dekodera $h$ i $c$ (\textit{from\textunderscore state}).
@ -1512,14 +1512,60 @@ generated_midi.save(save_path)
W ten sposób pomyślnie zostały wygenerowane fragmenty muzyczne przy pomocy głębokiego uczenia. W ten sposób pomyślnie zostały wygenerowane fragmenty muzyczne przy pomocy głębokiego uczenia.
\section{Wyniki} \subsection{Wyniki}
Przedstawię wybrane fragmenty wygenerowanych utworów w zapisie nutowym dla kolejnych etapów uczenia. Krótko scharakteryzuję każdy fragment oraz opowiem jakie ulepszenia pojawiały się wraz z postępem treningu.
\begin{figure}[!htb]
\centering
\includegraphics[width=\linewidth]{images/score_1.png}
\caption[]{Wygenerowany fragment po jednej epoce.}
\label{fig:score1}
\end{figure}
\begin{figure}[!htb]
\centering
\includegraphics[width=\linewidth]{images/score_10.png}
\caption[]{Wygenerowany fragment po dziesięciu epokach.}
\label{fig:score10}
\end{figure}
\section{Wnioski} \begin{figure}[!htb]
\centering
\includegraphics[width=\linewidth]{images/score_25.png}
\caption[]{Wygenerowany fragment po dwudziestu pięciu epokach.}
\label{fig:score25}
\end{figure}
\chapter{Podsumowanie} \begin{figure}[!htb]
\centering
\includegraphics[width=\linewidth]{images/score_50.png}
\caption[]{Wygenerowany fragment po pięćdziesięciu epokach.}
\label{fig:score50}
\end{figure}
\begin{figure}[!htb]
\centering
\includegraphics[width=\linewidth]{images/score_75.png}
\caption[]{Wygenerowany fragment po siedemdziesięciu pięciu epokach}
\label{fig:score75}
\end{figure}
\begin{figure}[!htb]
\centering
\includegraphics[width=\linewidth]{images/score_100.png}
\caption[]{Wygenerowany fragment po stu epokach}
\label{fig:score100}
\end{figure}
\begin{figure}[!htb]
\centering
\includegraphics[width=\linewidth]{images/score_150.png}
\caption[]{Wygenerowany fragment po stu pięćdziesięciu epokach}
\label{fig:score150}
\end{figure}
\chapter*{Podsumowanie}
Ostateczne wnioski, czy muzyka generowana komputerowa da się lubić? Czy to pozytywnie wpłynie na przemysł muzyczny? Tak i nie. Może służyć jako inspiracja dla muzyków, proces wspierający. Z drugiej strony może obniży koszty produkowania muzyki pop, która i tak jest już bardzo powtarzalna. Czy sieci neuronowe nauczą się produkować Hity? Ostateczne wnioski, czy muzyka generowana komputerowa da się lubić? Czy to pozytywnie wpłynie na przemysł muzyczny? Tak i nie. Może służyć jako inspiracja dla muzyków, proces wspierający. Z drugiej strony może obniży koszty produkowania muzyki pop, która i tak jest już bardzo powtarzalna. Czy sieci neuronowe nauczą się produkować Hity?

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@ -43,8 +43,7 @@
\contentsline {subsection}{\numberline {3.5.2\relax .\enspace }Zbiór danych}{52}% \contentsline {subsection}{\numberline {3.5.2\relax .\enspace }Zbiór danych}{52}%
\contentsline {subsection}{\numberline {3.5.3\relax .\enspace }Wydobycie danych}{52}% \contentsline {subsection}{\numberline {3.5.3\relax .\enspace }Wydobycie danych}{52}%
\contentsline {section}{\numberline {3.6\relax .\enspace }Trenowanie modelu}{53}% \contentsline {section}{\numberline {3.6\relax .\enspace }Trenowanie modelu}{53}%
\contentsline {section}{\numberline {3.7\relax .\enspace }Generowanie muzyki przy pomocy wytrenowanych modeli}{54}% \contentsline {subsection}{\numberline {3.6.1\relax .\enspace }Generowanie muzyki przy pomocy wytrenowanych modeli}{54}%
\contentsline {section}{\numberline {3.8\relax .\enspace }Wyniki}{56}% \contentsline {subsection}{\numberline {3.6.2\relax .\enspace }Wyniki}{56}%
\contentsline {section}{\numberline {3.9\relax .\enspace }Wnioski}{56}% \contentsline {chapter}{Podsumowanie}{61}%
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 4\relax .\enspace Podsumowanie}{57}% \contentsline {chapter}{Bibliografia}{63}%
\contentsline {chapter}{Bibliografia}{59}%

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