Zakończono omówienie procesu generowania muzyki

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docs/document.tex

@@ -1325,12 +1325,151 @@ W ten sposób otrzymujemy trzy zestawy danych potrzebne do przeprowadzenia proce
 
 Warto zwrócić uwagę, że przed opisaną transformacją sekwencje były różnej długości a po niej, rozmiar sekwencji został rozszerzony do rozmiaru sekwencji posiadającej najwięcej elementów. Taki zabieg był niezbędny ponieważ rozmiar sieci neuronowej jest stały dla wszystkich prób ze zbioru uczacego. Nie wpływa to jednak na same sekwencję ponieważ podczas predykcję kończymy w momencie wygenerowania tokenu \pyth{<EOS>}.
 
-\section{Uczenie modelu}
+\section{Ekperyment}
 
-\section{Generowanie muzyki przy pomocy wytrenowanego modelu}
+W tej części pokażę jak wykorzystać oprogramowanie, które stworzyłem aby wygenerować muzykę na przykładzie. Omówię cały proces, następnie zademonstruję wyniki.
+
+\subsection{Oprogramowanie}
+Stworzone przeze mnie oprogramowanie, składa się z 5 skryptów pythona.
+
+\begin{itemize}
+	\item \pyth{midi_procesing.py} - zawiera funkcję potrzebne do pracy w plikami midi.
+	\item \pyth{model.py} - zawiera definicję modelu sieci neuronowej
+	\item \pyth{extract.py} - służy do wydobycia w plików midi zbioru danych w postacji sekwencji.
+	\item \pyth{train.py} - wykonując ten skrypt wykorzystujemy wygenerowane dane, aby wytrenować zestaw sieci neuronych.
+	\item \pyth{generate.py} - wykorzystuje wytrenowane modele aby wygenerować ostatecznie plik midi.
+\end{itemize}
+
+\subsection{Zbiór danych}
+W omawianym przykładzie wykorzystałem zbiór wybranych utworów midi zespołu The Offstring. Został on skompletowany ze źródeł dostępnych na stronie internetowej https://www.midiworld.com/. Składa się z 7 utworów.
+
+\begin{itemize}
+	\item The Offspring - All I Want.mid
+	\item The Offspring - Change the World.mid
+	\item The Offspring - Nitro.mid
+	\item The Offspring - Original Prankster.mid
+	\item The Offspring - Self Esteem.mid
+	\item The Offspring - The Kids Arent Alright.mid
+	\item The Offspring - Why Dont You Get a Job.mid
+\end{itemize}
+
+\subsection{Wydobycie danych}
+Aby wydobyć dane z plików midi wykorzystamy skypt \pyth{extract.py}. Można uzyć flagi \pyth{-a}, aby najpierw zapoznać się z zawartością muzyczną zbioru plików midi.
+
+% 100\%|███████████████████████████████████| 7/7 [00:01<00:00,  3.67it/s]
+\begin{python}
+	>>> python extract.py offspring -a
+	1098 of Drums
+	1037 of Guitar
+	704 of Melody
+	528 of Bass
+	1 of Organ
+\end{python}
+	
+	Dzięki temu możemy zaobserować że w procesowanym zbiorze danych jest 1098 taktów perkusji, 1037 taktów gitar, 704 melodii, 528 basu oraz 1 takt organ.
+	
+	Na tym etapie musimy zdecydować, który instrment będzie generatorem oraz jakie będą zalezności między partiami w zespole sieci neuronowych.
+	
+	W tym przykładzie, zdecydowałem że gitara będzie generowana na podstawie losowego wektora, a melodia, bass oraz perkusja będe tworzone na podstawie gitary.
+	
+	Uruchamiajać skrypt \pyth{extract.py} bez flagi \pyth{-a} zdefiniujemy omawiany przepływ za pomocą prostego konfiguratora.
+
+\begin{python}
+>>> python extract.py offspring
+Please specify number of instruments
+4
+Please specify a workflow step 
+>>> Guitar m
+Please specify a workflow step 
+>>> Melody Guitar a
+Please specify a workflow step 
+>>> Drums Guitar a
+Please specify a workflow step 
+>>> Bass Guitar a
+
+Exporting: 'Guitar'
+Exporting: ('Guitar', 'Melody')
+Exporting: ('Guitar', 'Drums')
+Exporting: ('Guitar', 'Bass')
+Done.
+
+\end{python}
+%Exporting: Guitar: 100\%||███████████████████████████████████| 7/7 [00:01<00:00,  3.82it/s]
+%Exporting: ('Guitar', 'Melody'): 100\%||███████████████████████████████████| 7/7 [00:01<00:00,  3.81it/s]
+%Exporting: ('Guitar', 'Drums'): 100\%||███████████████████████████████████| 7/7 [00:01<00:00,  3.79it/s]
+%Exporting: ('Guitar', 'Bass'): 100\%||███████████████████████████████████| 7/7 [00:01<00:00,  3.81it/s]
+
+Po tym etapie zostały utworzony pliki zawierające oczyszczone pary sekwencji dla każdej sieci neuronowej. Zostało wygenerowanych
+
+\begin{itemize}
+	\item 263 próby dla modelu gitary,
+	\item 622 próby par melodii i gitary,
+	\item 948 prób par perkusji i gitary,
+	\item 385 prób par basu i gitary.
+\end{itemize}
+
+Na podstawie takiego zbioru danych, w nastepnym kroku zostaną wytrenowane czrety sieci neuronowe, po jednej dla każdego intrumentu.
+
+\section{Trenowanie modelu}
+
+Uzywając skypt \pyth{train.py} możemy w prosty sposób wytrenować wszystkie modele, a wagi zapiszą i będzie można je wykorzystać w celu generowania, lub w celu dalszego uczenia.
+
+\begin{python}
+>>> python train.py offspring --e 1
+
+Using TensorFlow backend.
+
+Training: Guitar
+Train on 210 samples, validate on 53 samples
+Epoch 1/1
+210/210 [==============================] 
+	- 2s 10ms/step - loss: 2.1553 - val_loss: 2.0384
+Training: Melody
+Train on 497 samples, validate on 125 samples
+Epoch 1/1
+497/497 [==============================] 
+	- 6s 11ms/step - loss: 1.7045 - val_loss: 1.6693
+Training: Drums
+Train on 758 samples, validate on 190 samples
+Epoch 1/1
+758/758 [==============================] 
+	- 9s 13ms/step - loss: 2.2218 - val_loss: 2.1255
+Training: Bass
+Train on 308 samples, validate on 77 samples
+Epoch 1/1
+308/308 [==============================] 
+	- 4s 12ms/step - loss: 2.2721 - val_loss: 1.7813
+\end{python}
+
+Na potrzeby badań trenowałem i generowałem klipy muzyczne dla epok 1, 10, 25, 50, 75, 100, 150. Dzięki temu mogę porównać ze sobą poszczególne etapy treningu sieci neuronowych. Wykresy funkcji kosztów dla zbioru uczącego oraz testowego zaprezentowane zostały na rysunku~\ref{fig:losses}.
+
+\begin{figure}[!htb]
+	\centering
+	\includegraphics[width=\linewidth]{images/training_losses.png}
+	\caption[]{Wartości kosztu dla poszczególnych modeli.}
+	\label{fig:losses}
+\end{figure}
+
+\section{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}).
+
+\begin{python}
+>>> python generate.py offstring --i 10 --m from_state
+
+Using TensorFlow backend.
+Loading models...
+Generating music...
+Done.
+\end{python}
+
+Parametr \pyth{--i} służy do okreslenia liczby wygenerowanych utworów a parametr \pyth{--m} pozwala zdefiniować metodę generowania, omówioną wyżej. W tym momencie pomyślnie zostały wygenerowane fragmenty muzyczne przy pomocy głębokiego uczenia.
 
 \section{Wyniki}
 
+
+
+
 \section{Wnioski}
 
 \chapter{Podsumowanie}

docs/document.toc

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