Dokończono definicję modelu

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CezaryPukownik 2020-06-16 10:16:52 +02:00
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@ -730,35 +730,6 @@ note_on channel=0 note=60 velocity=0 time=0
\section{Wstępne przygotowanie danych do treningu}
% Aby móc przedstawić sieci neuronowej muzykę, aby mogła być ona przez niego zrozumiana należy zamienić ją na liczby, wektory i macierze. Istnieje wiele technik umożliwiających osiągnięcie tego celu. W mojej pracy wykorzystałam technikę o nazwie one-hot encoding. Enkodowanie One-Hot jest wykorzystywane w uczeniu maszynowym aby nadać liczbową wartość danych kategorycznych. Polega ona stworzeniu słownika, w którym każde słowo otrzyma swój unikatowy identyfikator, następnie zostanie utworzony wektor o wymiarze słów w słowniku, gdzie na pozycji odpowiadającej indeksowi słowa będzie wartość 1 a na pozostałych będzie wartość zero \cite{onehot}. %\footnote{https://deepai.org/machine-learning-glossary-and-terms/one-hot-encoding 28 maja 2020 12:24}
% \subsection{Przykład działania One-Hot Encoding}
% Weźmy sekwencję liter w słowie MATEMATYKA. Znajdźmy unikatowe elementy tej sekwencji, oraz nadajmy im unikatowy identyfikator. Kolejność nie ma znaczenia.
% \begin{center}
% M - 0,
% A - 1,
% T - 2,
% E - 3,
% Y - 4,
% K - 5.
% \end{center}
% Kodując słowo MATEMATYKA, otrzymalibyśmy macierz
% \[
% \mathbb{\boldsymbol{
% \begin{bmatrix}
% 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 \\
% 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\
% 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\
% 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\
% 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\
% 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\
% \end{bmatrix} }}
% \]
\subsection{Muzyczne ,,słowo''}
Na potrzeby dostosowania danych muzycznych do koncepcji słów w zdaniu zakodowałem pojedyncze słowo muzyczne jako
@ -1061,12 +1032,303 @@ Funkcja sprawdza liczbę pojedynczych nut i akordów w ścieżce oraz zagęszcze
\section{Definicja modelu}
Wszystkie modele sieci neuonowych zastosowane w tej pracy, zostały napisane z wykorzystaniem środowiska Keras. Keras jest to środowisko wyższego poziomu, służące do tworzenia modelu głębokiego uczenia.
Model sequence-to-sequence, jest to model składający się z dwóch mniejszych sieci neuronowych, enkodera i dekodera. Dodatkowo inaczej definiuje się model aby go uczyć, a inaczej aby dokowyać predycji.
\subsection{Model w trybie uczenia}
\subsubsection{Zdefiniowanie warstw enkodera}
Zadaniem enkodera jest wydobycie z przetwarzanej sekwekcji kontekstu, skompresowanej informacji o danych. W tym celu zastosowana została jedna warstwa wejściowa o rozmiarze słownika wejściowego, oraz warstwa LSTM. Definiowane warstwy są atrybutami całej klasy modelu, dlatego w prezentowanym kodzie występują przedrostek \pyth{self.}.
\begin{python}
self.encoder_inputs =
Input(shape=(None, self.transformer.x_vocab_size))
self.encoder =
LSTM(latent_dim, return_state=True, dropout=enc_dropout)
self.encoder_outputs, self.state_h, self.state_c =
self.encoder(self.encoder_inputs)
self.encoder_states = [self.state_h, self.state_c]
\end{python}
Ostatecznie jako dane wyjsciowe otrzymujemy wektor danych ukrytych \pyth{encoder_outputs} o wymiarze równym ustalonej zmiennej \pyth{latent_dim}. Dodatkowo uzywając parametru \pyth{return_state=True}, warstwa LSTM zwraca wektory stanu komórek $h$ oraz $c$. Te wektory są pamięcią wartswy LSTM i posłużą jako stan wejściowy dla modelu dekodera.
Oba wekory zostały zapisane do jednej zmiennej \pyth{self.encoder_states}.
\subsubsection{Zdefiniowanie warstw dekodera}
W części dekodera sieci sequance-to-sequence, definiujemy warstwę wejściową o rozmiarze słownika wynikowego i jedną warstwę LSTM o stanie komórek $h$ i $c$ w enkodera. Na końcu została zdefiniowana prosta warstwa typu \pyth{Dense} z funkcją aktywacji \pyth{softmax} w celu rozszerzenia wymiaru do wielkości słownika wyjściowego.
\begin{python}
self.decoder_inputs =
Input(shape=(None, self.transformer.y_vocab_size))
self.decoder_lstm =
LSTM(latent_dim,
return_sequences=True,
return_state=True,
dropout=dec_dropout)
self.decoder_outputs, _, _ =
self.decoder_lstm(self.decoder_inputs,
initial_state=self.encoder_states)
self.decoder_dense =
Dense(self.transformer.y_vocab_size, activation='softmax')
self.decoder_outputs = self.decoder_dense(self.decoder_outputs)
\end{python}
Jako wynik propagacji modelu otrzymujemy wektor o rozmiarze słownika wyjściowego \pyth{self.transformer.y_vocab_size}, który przedstawia zakodowany element sekwencji.
\subsubsection{Definicja modelu w trybie uczenia}
Ostatnim krokiem, aby otrzymać model gotowy do procesu uczenia należy połączyć ze sobą enkoder i dekoder, tworząc pełny model. Po jego skompilowaniu model mozna przedstawić jako graf tak jak pokazano to na rysunku~\ref{fig:training-model}
\begin{figure}[!htb]
\centering
\includegraphics[width=\linewidth]{images/training-model-graph.png}
\caption[]{Model seq2seq w trybie uczenia\footnotemark}
\label{fig:training-model}
\end{figure}
\footnotetext{machinelearningmastery.com 16 czerwca 2020 06:24}
\begin{python}
self.train_model =
Model([self.encoder_inputs, self.decoder_inputs],
self.decoder_outputs)
self.train_model.compile(optimizer='rmsprop',
loss='categorical_crossentropy')
\end{python}
Możemy zauważyć, że w procesie uczenia musimy zaprezentować modelowi dwa wektory danych wejsciowych oraz jednego wektora danych wyjściowych. W pracy zostały one opisane jako:
\begin{itemize}
\item \pyth{encoder_input_data}, zawierające sekwencję elementów wejściowych. Te dane będą zasilać encoder aby na ich podstawie został wygenerowany kontekst.
\item \pyth{decoder_input_data}, zawierające sekwencje elementów wyjściowych opóźnione o jeden element w czasie. Te dane będą zasilały dekoder.
\item \pyth{decoder_target_data}, zawierające sekwencje elementów wyjściowych, które bedą celem.
\end{itemize}
Jest to ciekawe, że musimy dostarczyć do modelu dane, które tak naprawdę chcemy otrzymać. Jednak te dane są przesunięte o jeden element w sekwencji do przodu i pierwszy element tej sekwencji nie zawiera treści. Dekoder uczy się jak na podstawie aktualnego stanu enkodera oraz aktualnego elementu sekwencji wyjściowej jaki powinien być następny element w sekwecji docelowej. Warto też zauważyć, że z definicji modelu nie wynika aby model uczył się przetwarzania całych sekwencji, tylko każdego elementu sekwencji z osobna.
\subsection{Model w trybie wnioskowania}
\label{section:inference-model}
To co czyni model seq2seq zdolnym do przetwarzania całych sekwencji jest sposób zdefiniowania metody wnioskowania (\textit{ang. inference}). Na tym etapie model w sposób rekurencyjny będzie przewidywał następny element sekwencji, na podstawie tego co przewidział wcześniej oraz aktualizowanych wektorów stanu warstwy LSTM. Musimy w tym celu zdefiniować dekoder tak, aby mógł przyjąć swoje wyjście jako nowe wejście. Dlatego definicja modelu wtedy wygląda nastepująco, a graf przedstawiający dekoder w trybie uczenia zostal przedstawiony na rysunku~\ref{fig:inference-decoder}.
\begin{python}
self.decoder_state_input_h = Input(shape=(self.latent_dim,))
self.decoder_state_input_c = Input(shape=(self.latent_dim,))
self.decoder_states_inputs = [self.decoder_state_input_h,
self.decoder_state_input_c]
self.decoder_outputs, self.state_h, self.state_c =
self.decoder_lstm(
self.decoder_inputs,
initial_state = self.decoder_states_inputs
)
self.decoder_states = [self.state_h, self.state_c]
self.decoder_outputs = self.decoder_dense(self.decoder_outputs)
self.decoder_model = Model(
[self.decoder_inputs] + self.decoder_states_inputs,
[self.decoder_outputs] + self.decoder_states)
\end{python}
\begin{figure}[!htb]
\centering
\includegraphics[width=\linewidth]{images/inference-decoder-graph.png}
\caption[]{Model seq2seq w trybie uczenia\footnotemark}
\label{fig:inference-decoder}
\end{figure}
\footnotetext{machinelearningmastery.com 16 czerwca 2020 06:24}
Dzięki takiej definicji, jesteśmy w stanie w pętli przewidywać następne elementy do momentu osiągnięcia warunku stopu. Implementacja tego procesu wygląda tak:
\begin{python}
def predict(self, input_seq=None, mode=None):
if not self.has_predict_model:
self.init_predict_model()
self.has_predict_model = True
if mode == 'generate':
h = np.random.rand(1,self.latent_dim)*2 - 1
c = np.random.rand(1,self.latent_dim)*2 - 1
states_value = [h, c]
else:
states_value = self.encoder_model.predict(input_seq)
target_seq =
np.zeros((1, 1, self.transformer.y_vocab_size)
target_seq[0, 0, self.transformer.y_transform_dict['<GO>']] =
1
stop_condition = False
decoded_sentence = []
while not stop_condition:
output_tokens, h, c = self.decoder_model.predict(
[target_seq] + states_value)
sampled_token_index =
np.argmax(output_tokens[0, -1, :])
sampled_char =
self.transformer.y_reverse_dict[sampled_token_index]
decoded_sentence.append(sampled_char)
if (sampled_char == '<EOS>' or
len(decoded_sentence) > self.transformer.y_max_seq_length):
stop_condition = True
target_seq = np.zeros((1, 1, self.transformer.y_vocab_size))
target_seq[0, 0, sampled_token_index] = 1.
states_value = [h, c]
return decoded_sentence
\end{python}
Encoder w tym trybie ma za zadanie tylko dostarczyć do dekodera wektory kontekstu dla zainicjowania procesu. Dekoder na podstawie wektorów pamięci $h$ i $c$ przewidzi pierwszy element sekwencji i tym samym zainicjuje proces wnioskowania.
W tym miejscu mamy kontrolę twórczą nad dekoderem. Aby zainicjować reakcję wystarczą wektory pamięci, możemy wtedy podać je w dowolny sposób i zobaczyć co wygeneruje dekoder. W mojej pracy próbowałem dwóch sposób na zainicjowanie procesu predykcji z wektorów losowych. Pierwszy polega na wygenerowaniu losowej sekwencji dla encodera i nazywam tę metodę \pyth{from_seq}. Druga metoda polega na bezpośrednim definiowaniu stanu $h$ i $c$ \pyth{states_value} w sposób losowy tak aby wartości były z przedziału [-1, 1].
\section{Transformacja danych dla modelu}
\section{Generowanie muzyki przy pomocy wytrenowanego modelu}
Na podstawie definicji modelu wiemy, że należy przygotować trzy zestawy danych aby móc wytrenowac model. Dwa zestawy danych wejściowych i jeden zestaw danych wyjściowych. Musimy również zakodować sekwencje elementów w taki sposób aby można było wykonywac na nich obliczenia.
\subsection{Enkodowanie one-hot}
Kazdy element sekwencji jest osobną kategorią, dlatego dane należy potraktować tak jak dane kategorycznym. Wykorzystamy w tym celu enkodowanie one-hot (\textit{one-hot encoding}) Enkodowanie One-Hot jest wykorzystywane w uczeniu maszynowym aby nadać liczbową wartość danych kategorycznych. Polega ona stworzeniu słownika, w którym każde słowo otrzyma swój unikatowy identyfikator, następnie zostanie utworzony wektor o wymiarze słów w słowniku, gdzie na pozycji odpowiadającej indeksowi słowa będzie wartość 1 a na pozostałych będzie wartość zero \cite{onehot}.
\subsubsection{Przykład działania One-Hot Encoding}
Weźmy sekwencję liter w słowie MATEMATYKA. Znajdźmy unikatowe elementy tej sekwencji, oraz nadajmy im unikatowy identyfikator. Kolejność nie ma znaczenia.
\begin{center}
M - 0,
A - 1,
T - 2,
E - 3,
Y - 4,
K - 5.
\end{center}
Kodując słowo MATEMATYKA, otrzymalibyśmy macierz
\[
\mathbb{\boldsymbol{
\begin{bmatrix}
1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 \\
0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\
0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\
0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\
\end{bmatrix} }}
\]
\subsection{Słownik}
Aby zakodować sekwencje musimy utowrzyć słownik, w którym każdy element będzie posiadał unikatowy indeks.
\begin{python}
x_vocab = set([note for seq in x_train for note in seq])
y_vocab = set([note for seq in y_train for note in seq])
\end{python}
Do słownika dodajemy dwa dodatkowe elementy, które opiszę w następnym rozdziale.
\begin{python}
self.x_vocab = sorted(list(x_vocab))
self.y_vocab = ['<GO>','<EOS>']
self.y_vocab.extend(sorted(list(y_vocab)))
\end{python}
Następnie tworzymy obiekty typu \pyth{dictionary}, aby zmapować elementy sekwncji na liczby. W tym momencie tworzymy również słowniki odwrotne, aby móc później otrzymane wyniki zamienić na z powrotem na muzyczne słowa.
\begin{python}
self.x_transform_dict = dict(
[(char, i) for i, char in enumerate(self.x_vocab)])
self.y_transform_dict = dict(
[(char, i) for i, char in enumerate(self.y_vocab)])
self.x_reverse_dict = dict(
(i, char) for char, i in self.x_transform_dict.items())
self.y_reverse_dict = dict(
(i, char) for char, i in self.y_transform_dict.items())
\end{python}
\subsection{Elementy specjalne}
Dodaje się dwóch elementów specjalnych. Te elementy mają za zadanie oznaczenie początku i końca sekwencji. Token początku senwekcji zazwyczaj oznaczany jest jako \pyth{<SOS>} (\textit{ang. start of sequance}). Można spotkać również inne oznaczenia,np. \pyth{<GO>} lub \pyth{<s>}. Ten element ma na celu wypełnienie przestrzenie pierwszego pustego miejsca w danych wejściowych dla dekodera. Token końca sekwecji oznaczany jako \pyth{<EOS>} (\textit{ang. start of sequance}) lub \pyth{</s>}. Ten element jest wykorzystywany aby wywołać warunek stopu w procesie predykcji opisanym w rozdziale~\ref{section:inference-model}.
\begin{python}
_y_train = []
for i, seq in enumerate(y_train):
_y_train.append([])
_y_train[i].append('<GO>')
for note in seq:
_y_train[i].append(note)
_y_train[i].append('<EOS>')
\end{python}
\subsection{Zakodowanie sekwencji}
Ostatecznie tworzymy tensory o rozmiarach $n$ x $s$ x $p$, gdzie $n$ jest liczbą obserwacji \pyth{len(x_train)}, $s$ jest maksymalną liczbą elementów w sekwencji \pyth{self.x_max_seq_length} a $p$ jest rozmiarem słownika, czyli liczbą cech \pyth{self.x_vocab_size}.
\begin{python}
self.x_max_seq_length =
max([len(seq) for seq in x_train])
self.y_max_seq_length =
max([len(seq) for seq in y_train])
encoder_input_data = np.zeros(
(len(x_train),
self.x_max_seq_length,
self.x_vocab_size),
dtype='float32')
decoder_input_data = np.zeros(
(len(x_train),
self.y_max_seq_length,
self.y_vocab_size),
dtype='float32')
decoder_target_data = np.zeros(
(len(x_train),
self.y_max_seq_length,
self.y_vocab_size),
dtype='float32')
for i, (x_train, y_train) in enumerate(zip(x_train, y_train)):
for t, char in enumerate(x_train):
encoder_input_data[i, t, self.x_transform_dict[char]] = 1.
for t, char in enumerate(y_train):
decoder_input_data[i, t, self.y_transform_dict[char]] = 1.
if t > 0:
decoder_target_data[i, t - 1, self.y_transform_dict[char]] = 1.
\end{python}
W ten sposób otrzymujemy trzy zestawy danych potrzebne do przeprowadzenia procesu uczenia modelu.
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{Generowanie muzyki przy pomocy wytrenowanego modelu}
\section{Wyniki}
\section{Wnioski}
@ -1095,6 +1357,10 @@ Funkcja sprawdza liczbę pojedynczych nut i akordów w ścieżce oraz zagęszcze
\bibitem{deep_learning_2} Zocca, V., Spacagna, G., Slater, D., Roelants, P. (2018): {\em Deep Learning. Uczenie głębokie z językiem Python. Helion.}
\bibitem{seq2seq-keras} Brownlee, J. (2017): {\em How to Develop a Seq2Seq Model for Neural Machine Translation in Keras, machinelearningmastery.com}
% https://machinelearningmastery.com/define-encoder-decoder-sequence-sequence-model-neural-machine-translation-keras/
% \footnote{Statystyka, Mieczysław Sobczyk s.179}
% \footnote{źródlo: Analiza matematyczna, Krysicki Włodarski, s.187 }
% \footnote{Deep Learning techniques for music geneation - A survay s.44}

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@ -1,40 +1,46 @@
\contentsline {chapter}{Streszczenie}{7}%
\contentsline {chapter}{Abstract}{9}%
\contentsline {chapter}{Wst\IeC {\k e}p}{11}%
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 1\relax .\leavevmode@ifvmode \kern .5em Wprowadzenie do sieci neuronowych}{13}%
\contentsline {section}{\numberline {1.1\relax .\leavevmode@ifvmode \kern .5em }Regresja liniowa}{13}%
\contentsline {section}{\numberline {1.2\relax .\leavevmode@ifvmode \kern .5em }Uczenie modelu}{14}%
\contentsline {subsection}{\numberline {1.2.1\relax .\leavevmode@ifvmode \kern .5em }Funkcja kosztu}{15}%
\contentsline {subsection}{\numberline {1.2.2\relax .\leavevmode@ifvmode \kern .5em }Znajdowanie minimum funkcji}{15}%
\contentsline {subsection}{\numberline {1.2.3\relax .\leavevmode@ifvmode \kern .5em }Metody gradientowe}{15}%
\contentsline {section}{\numberline {1.3\relax .\leavevmode@ifvmode \kern .5em }Regresja liniowa jako model sieci neuronowej}{17}%
\contentsline {section}{\numberline {1.4\relax .\leavevmode@ifvmode \kern .5em }Funkcje aktywacji}{19}%
\contentsline {section}{\numberline {1.5\relax .\leavevmode@ifvmode \kern .5em }Wielowarstwowe sieci neuronowe}{19}%
\contentsline {subsection}{\numberline {1.5.1\relax .\leavevmode@ifvmode \kern .5em }Jednokierunkowe sieci neuronowe}{20}%
\contentsline {subsection}{\numberline {1.5.2\relax .\leavevmode@ifvmode \kern .5em }Autoencoder}{21}%
\contentsline {subsection}{\numberline {1.5.3\relax .\leavevmode@ifvmode \kern .5em }Rekurencyjne sieci neuronowe}{22}%
\contentsline {subsection}{\numberline {1.5.4\relax .\leavevmode@ifvmode \kern .5em }LSTM}{23}%
\contentsline {subsection}{\numberline {1.5.5\relax .\leavevmode@ifvmode \kern .5em }Sequence-to-sequence}{26}%
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 2\relax .\leavevmode@ifvmode \kern .5em Wprowadzenie do teorii muzyki}{27}%
\contentsline {section}{\numberline {2.1\relax .\leavevmode@ifvmode \kern .5em }Podstawowe koncepcje muzyczne}{27}%
\contentsline {subsection}{\numberline {2.1.1\relax .\leavevmode@ifvmode \kern .5em }D\IeC {\'z}wi\IeC {\k e}k muzyczny}{27}%
\contentsline {subsection}{\numberline {2.1.2\relax .\leavevmode@ifvmode \kern .5em }Sygna\IeC {\l } d\IeC {\'z}wi\IeC {\k e}kowy}{27}%
\contentsline {subsection}{\numberline {2.1.3\relax .\leavevmode@ifvmode \kern .5em }Zapis nutowy}{27}%
\contentsline {section}{\numberline {2.2\relax .\leavevmode@ifvmode \kern .5em }Cyfrowa reprezentacja muzyki symbolicznej}{31}%
\contentsline {subsection}{\numberline {2.2.1\relax .\leavevmode@ifvmode \kern .5em }Standard MIDI}{31}%
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 3\relax .\leavevmode@ifvmode \kern .5em Projekt}{35}%
\contentsline {section}{\numberline {3.1\relax .\leavevmode@ifvmode \kern .5em }Koncepcja}{35}%
\contentsline {section}{\numberline {3.2\relax .\leavevmode@ifvmode \kern .5em }Wst\IeC {\k e}pne przygotowanie danych do treningu}{36}%
\contentsline {subsection}{\numberline {3.2.1\relax .\leavevmode@ifvmode \kern .5em }Muzyczne ,,s\IeC {\l }owo''}{36}%
\contentsline {subsection}{\numberline {3.2.2\relax .\leavevmode@ifvmode \kern .5em }Konwersja MIDI na sekwencje s\IeC {\l }\IeC {\'o}w muzycznych}{36}%
\contentsline {subsection}{\numberline {3.2.3\relax .\leavevmode@ifvmode \kern .5em }Inne aspekty przygotowania danych}{39}%
\contentsline {subsection}{\numberline {3.2.4\relax .\leavevmode@ifvmode \kern .5em }Podzia\IeC {\l } danych na dane wej\IeC {\'s}ciowe i wyj\IeC {\'s}ciowe}{40}%
\contentsline {subsection}{\numberline {3.2.5\relax .\leavevmode@ifvmode \kern .5em }Inne aspekty przygotowania zbioru ucz\IeC {\k a}cego}{42}%
\contentsline {section}{\numberline {3.3\relax .\leavevmode@ifvmode \kern .5em }Definicja modelu}{44}%
\contentsline {section}{\numberline {3.4\relax .\leavevmode@ifvmode \kern .5em }Transformacja danych dla modelu}{44}%
\contentsline {section}{\numberline {3.5\relax .\leavevmode@ifvmode \kern .5em }Generowanie muzyki przy pomocy wytrenowanego modelu}{44}%
\contentsline {section}{\numberline {3.6\relax .\leavevmode@ifvmode \kern .5em }Uczenie modelu}{44}%
\contentsline {section}{\numberline {3.7\relax .\leavevmode@ifvmode \kern .5em }Wyniki}{44}%
\contentsline {section}{\numberline {3.8\relax .\leavevmode@ifvmode \kern .5em }Wnioski}{44}%
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 4\relax .\leavevmode@ifvmode \kern .5em Podsumowanie}{45}%
\contentsline {chapter}{Bibliografia}{47}%
\contentsline {chapter}{Wstęp}{11}%
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 1\relax .\enspace Wprowadzenie do sieci neuronowych}{13}%
\contentsline {section}{\numberline {1.1\relax .\enspace }Regresja liniowa}{13}%
\contentsline {section}{\numberline {1.2\relax .\enspace }Uczenie modelu}{14}%
\contentsline {subsection}{\numberline {1.2.1\relax .\enspace }Funkcja kosztu}{15}%
\contentsline {subsection}{\numberline {1.2.2\relax .\enspace }Znajdowanie minimum funkcji}{15}%
\contentsline {subsection}{\numberline {1.2.3\relax .\enspace }Metody gradientowe}{15}%
\contentsline {section}{\numberline {1.3\relax .\enspace }Regresja liniowa jako model sieci neuronowej}{17}%
\contentsline {section}{\numberline {1.4\relax .\enspace }Funkcje aktywacji}{19}%
\contentsline {section}{\numberline {1.5\relax .\enspace }Wielowarstwowe sieci neuronowe}{19}%
\contentsline {subsection}{\numberline {1.5.1\relax .\enspace }Jednokierunkowe sieci neuronowe}{20}%
\contentsline {subsection}{\numberline {1.5.2\relax .\enspace }Autoencoder}{21}%
\contentsline {subsection}{\numberline {1.5.3\relax .\enspace }Rekurencyjne sieci neuronowe}{22}%
\contentsline {subsection}{\numberline {1.5.4\relax .\enspace }LSTM}{23}%
\contentsline {subsection}{\numberline {1.5.5\relax .\enspace }Sequence-to-sequence}{26}%
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 2\relax .\enspace Wprowadzenie do teorii muzyki}{27}%
\contentsline {section}{\numberline {2.1\relax .\enspace }Podstawowe koncepcje muzyczne}{27}%
\contentsline {subsection}{\numberline {2.1.1\relax .\enspace }Dźwięk muzyczny}{27}%
\contentsline {subsection}{\numberline {2.1.2\relax .\enspace }Sygnał dźwiękowy}{27}%
\contentsline {subsection}{\numberline {2.1.3\relax .\enspace }Zapis nutowy}{27}%
\contentsline {section}{\numberline {2.2\relax .\enspace }Cyfrowa reprezentacja muzyki symbolicznej}{31}%
\contentsline {subsection}{\numberline {2.2.1\relax .\enspace }Standard MIDI}{31}%
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 3\relax .\enspace Projekt}{35}%
\contentsline {section}{\numberline {3.1\relax .\enspace }Koncepcja}{35}%
\contentsline {section}{\numberline {3.2\relax .\enspace }Wstępne przygotowanie danych do treningu}{36}%
\contentsline {subsection}{\numberline {3.2.1\relax .\enspace }Muzyczne ,,słowo''}{36}%
\contentsline {subsection}{\numberline {3.2.2\relax .\enspace }Konwersja MIDI na sekwencje słów muzycznych}{36}%
\contentsline {subsection}{\numberline {3.2.3\relax .\enspace }Inne aspekty przygotowania danych}{39}%
\contentsline {subsection}{\numberline {3.2.4\relax .\enspace }Podział danych na dane wejściowe i wyjściowe}{40}%
\contentsline {subsection}{\numberline {3.2.5\relax .\enspace }Inne aspekty przygotowania zbioru uczącego}{42}%
\contentsline {section}{\numberline {3.3\relax .\enspace }Definicja modelu}{44}%
\contentsline {subsection}{\numberline {3.3.1\relax .\enspace }Model w trybie uczenia}{44}%
\contentsline {subsection}{\numberline {3.3.2\relax .\enspace }Model w trybie wnioskowania}{46}%
\contentsline {section}{\numberline {3.4\relax .\enspace }Transformacja danych dla modelu}{48}%
\contentsline {subsection}{\numberline {3.4.1\relax .\enspace }Enkodowanie one-hot}{48}%
\contentsline {subsection}{\numberline {3.4.2\relax .\enspace }Słownik}{49}%
\contentsline {subsection}{\numberline {3.4.3\relax .\enspace }Elementy specjalne}{50}%
\contentsline {subsection}{\numberline {3.4.4\relax .\enspace }Zakodowanie sekwencji}{50}%
\contentsline {section}{\numberline {3.5\relax .\enspace }Uczenie modelu}{51}%
\contentsline {section}{\numberline {3.6\relax .\enspace }Generowanie muzyki przy pomocy wytrenowanego modelu}{51}%
\contentsline {section}{\numberline {3.7\relax .\enspace }Wyniki}{51}%
\contentsline {section}{\numberline {3.8\relax .\enspace }Wnioski}{51}%
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 4\relax .\enspace Podsumowanie}{53}%
\contentsline {chapter}{Bibliografia}{55}%

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