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Jakub Pokrywka 2022-07-05 11:24:56 +02:00
parent 13869a4d2d
commit 85d14a1c10
18 changed files with 9185 additions and 8439 deletions
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7
cw/00_Informacje_na_temat_przedmiotu.ipynb
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@ -70,13 +70,6 @@
"\n",
"**Żeby zaliczyć przedmiot należy pojawiać się na laboratoriach. Maksymalna liczba nieobecności to 3. Obecność będę sprawdzał co zajęcia. Jeżeli kogoś nie będzie więcej niż 3 razy, to nie będzie miał zaliczonego przedmiotu** \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

9
cw/01_Kodowanie_tekstu.ipynb
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@ -7,7 +7,7 @@
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Ekstrakcja informacji </h1>\n",
"<h2> 0. <i>Kodowanie tekstu</i> [ćwiczenia]</h2> \n",
"<h2> 1. <i>Kodowanie tekstu</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
@ -733,13 +733,6 @@
"- następnie wygeneruj z notebooka PDF (File → Download As → PDF via Latex).\n",
"- notebook z kodem oraz PDF zamieść w zakładce zadań w MS TEAMS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

7547
cw/02_Jezyk.ipynb
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176
cw/03_statystyczny_model_językowy.ipynb
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@ -1,176 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Ekstrakcja informacji </h1>\n",
"<h2> 0. <i>Jezyk</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
"![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
]
},
{
"cell_type": "code",
"execution_count": 278,
"metadata": {},
"outputs": [],
"source": [
"NR_INDEKSU = 375985"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://web.stanford.edu/~jurafsky/slp3/3.pdf"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"class Model():\n",
" \n",
" def __init__(self, vocab_size=30_000, UNK_token= '<UNK>'):\n",
" pass\n",
" \n",
" def train(corpus:list) -> None:\n",
" pass\n",
" \n",
" def get_conditional_prob_for_word(text: list, word: str) -> float:\n",
" pass\n",
" \n",
" def get_prob_for_text(text: list) -> float:\n",
" pass\n",
" \n",
" def most_probable_next_word(text:list) -> str:\n",
" 'nie powinien zwracań nigdy <UNK>'\n",
" pass\n",
" \n",
" def high_probable_next_word(text:list) -> str:\n",
" 'nie powinien zwracań nigdy <UNK>'\n",
" pass\n",
" \n",
" def generate_text(text_beggining:list, length: int, greedy: bool) -> list:\n",
" 'nie powinien zwracań nigdy <UNK>'\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"def get_ppl(text: list) -> float:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"def get_entropy(text: list) -> float:\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- wybierz tekst w dowolnym języku (10_000_000 słów)\n",
"- podziel zbiór na train/test w proporcji 90/100\n",
"- stworzyć unigramowy model językowy\n",
"- stworzyć bigramowy model językowy\n",
"- stworzyć trigramowy model językowy\n",
"- wymyśl 5 krótkich zdań. Policz ich prawdopodobieństwo\n",
"- napisz włąsnoręcznie funkcję, która liczy perplexity na korpusie i policz perplexity na każdym z modeli dla train i test\n",
"- wygeneruj tekst, zaczynając od wymyślonych 5 początków. Postaraj się, żeby dla obu funkcji, a przynajmniej dla high_probable_next_word teksty były orginalne. Czy wynik będzię sie róźnił dla tekstów np.\n",
"`We sketch how Loomis–Whitney follows from this: Indeed, let X be a uniformly distributed random variable with values` oraz `random variable with values`?\n",
"- stwórz model dla korpusu z ZADANIE 1 i policz perplexity dla każdego z tekstów (zrób split 90/10) dla train i test\n",
"\n",
"- klasyfikacja za pomocą modelu językowego\n",
"- wygładzanie metodą laplace'a"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### START ZADANIA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### KONIEC ZADANIA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- znajdź duży zbiór danych dla klasyfikacji binarnej, wytrenuj osobne modele dla każdej z klas i użyj dla klasyfikacji. Warunkiem zaliczenia jest uzyskanie wyniku większego niż baseline (zwracanie zawsze bardziej licznej klasy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## WYKONANIE ZADAŃ\n",
"Zgodnie z instrukcją 01_Kodowanie_tekstu.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Teoria informacji"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wygładzanie modeli językowych"
]
}
],
"metadata": {
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"lang": "pl",
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
},
"subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
"title": "Ekstrakcja informacji",
"year": "2021"
},
"nbformat": 4,
"nbformat_minor": 4
}

246
cw/04_statystyczny_model_językowy.ipynb
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@ -16,7 +16,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 278,
"metadata": {},
"outputs": [],
"source": [
@ -32,25 +32,40 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"class Model():\n",
" \n",
" def __init__(self, vocab_size, UNK_token= '<UNK>'):\n",
" def __init__(self, vocab_size=30_000, UNK_token= '<UNK>'):\n",
" pass\n",
" \n",
" def train(corpus:list) -> None:\n",
" pass\n",
" \n",
" def predict(text: list, probs: str) -> float:\n",
" def get_conditional_prob_for_word(text: list, word: str) -> float:\n",
" pass\n",
" \n",
" def get_prob_for_text(text: list) -> float:\n",
" pass\n",
" \n",
" def most_probable_next_word(text:list) -> str:\n",
" 'nie powinien zwracań nigdy <UNK>'\n",
" pass\n",
" \n",
" def high_probable_next_word(text:list) -> str:\n",
" 'nie powinien zwracań nigdy <UNK>'\n",
" pass\n",
" \n",
" def generate_text(text_beggining:list, length: int, greedy: bool) -> list:\n",
" 'nie powinien zwracań nigdy <UNK>'\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
@ -60,186 +75,75 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"text = 'Pani Ala ma kota oraz ładnego pieska i 3 chomiki'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"text_splitted = text.split(' ')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"['Pani', 'Ala', 'ma', 'kota', 'oraz', 'ładnego', 'pieska', 'i', '3', 'chomiki']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text_splitted"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"text_masked = text_splitted[:4] + ['<MASK>'] + text_splitted[5:]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Pani',\n",
" 'Ala',\n",
" 'ma',\n",
" 'kota',\n",
" '<MASK>',\n",
" 'ładnego',\n",
" 'pieska',\n",
" 'i',\n",
" '3',\n",
" 'chomiki']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text_masked"
"def get_entropy(text: list) -> float:\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"trigram_model działa na ['ma', 'kota', <'MASK>']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"trigram_model.predict(['ma', 'kota']) → 'i:0.55 oraz:0.25 czarnego:0.1 :0.1'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ZADANIE:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"g1 = [470618, 415366, 434695, 470611, 470607]\n",
"g2 = [440054, 434742, 434760, 434784, 434788]\n",
"g3 = [434804, 430705, 470609, 470619, 434704]\n",
"g4 = [434708, 470629, 434732, 434749, 426206]\n",
"g5 = [434766, 470628, 437622, 434780, 470627, 440058]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"model trigramowy odwrotny\n"
]
}
],
"source": [
"if NR_INDEKSU in g1:\n",
" print('model bigramowy standardowy')\n",
"elif NR_INDEKSU in g2:\n",
" print('model bigramowy odwrotny')\n",
"elif NR_INDEKSU in g3:\n",
" print('model trigramowy')\n",
"elif NR_INDEKSU in g4:\n",
" print('model trigramowy odwrotny')\n",
"elif NR_INDEKSU in g5:\n",
" print('model trigramowy ze zgadywaniem środka')\n",
"else:\n",
" print('proszę zgłosić się do prowadzącego')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### gonito:\n",
"- zapisanie do achievmentu przez start working\n",
"- send to review"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ZADANIE\n",
"- wybierz tekst w dowolnym języku (10_000_000 słów)\n",
"- podziel zbiór na train/test w proporcji 90/100\n",
"- stworzyć unigramowy model językowy\n",
"- stworzyć bigramowy model językowy\n",
"- stworzyć trigramowy model językowy\n",
"- wymyśl 5 krótkich zdań. Policz ich prawdopodobieństwo\n",
"- napisz włąsnoręcznie funkcję, która liczy perplexity na korpusie i policz perplexity na każdym z modeli dla train i test\n",
"- wygeneruj tekst, zaczynając od wymyślonych 5 początków. Postaraj się, żeby dla obu funkcji, a przynajmniej dla high_probable_next_word teksty były orginalne. Czy wynik będzię sie róźnił dla tekstów np.\n",
"`We sketch how Loomis–Whitney follows from this: Indeed, let X be a uniformly distributed random variable with values` oraz `random variable with values`?\n",
"- stwórz model dla korpusu z ZADANIE 1 i policz perplexity dla każdego z tekstów (zrób split 90/10) dla train i test\n",
"\n",
"Proszę stworzyć rozwiązanie modelu (komórka wyżej) dla https://gonito.net/challenge/challenging-america-word-gap-prediction i umieścić je na platformie gonito\n",
" \n",
"Warunki zaliczenia:\n",
"- wynik widoczny na platformie zarówno dla dev i dla test\n",
"- wynik dla dev i test lepszy (niższy) od 1024.00\n",
"- deadline do końca dnia 27.04\n",
"- commitując rozwiązanie proszę również umieścić rozwiązanie w pliku /run.py (czyli na szczycie katalogu). Można przekonwertować jupyter do pliku python przez File → Download as → Python. Rozwiązanie nie musi być w pythonie, może być w innym języku.\n",
"- zadania wykonujemy samodzielnie\n",
"- w nazwie commita podaj nr indeksu\n",
"- w tagach podaj \"n-grams\" (należy zatwierdzić przecinkiem po wybraniu tagu)!\n",
"\n",
"Uwagi:\n",
"\n",
"- warto wymyślić jakąś metodę wygładazania, bez tego może być bardzo kiepski wynik\n",
"- nie trzeba korzystać z całego zbioru trenującego\n",
"- zadanie to 50 punktów, za najlepsze rozwiązanie w swojej grupie (g1,g2,g3,g4,g5), przyznaję dodatkowo 40 punktów\n",
"- punkty będą przyznane na gonito\n",
"- warto monitorować RAM, próbować z różnym vocab_size, można skorzystać z pythonowego Counter\n",
"- warto sobie zrobić dodatkowo model unigramowy w ramach ćwiczenia"
"- klasyfikacja za pomocą modelu językowego\n",
"- wygładzanie metodą laplace'a"
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": []
"source": [
"#### START ZADANIA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### KONIEC ZADANIA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- znajdź duży zbiór danych dla klasyfikacji binarnej, wytrenuj osobne modele dla każdej z klas i użyj dla klasyfikacji. Warunkiem zaliczenia jest uzyskanie wyniku większego niż baseline (zwracanie zawsze bardziej licznej klasy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## WYKONANIE ZADAŃ\n",
"Zgodnie z instrukcją 01_Kodowanie_tekstu.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Teoria informacji"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wygładzanie modeli językowych"
]
}
],
"metadata": {

272
cw/05_statystyczny_model_językowy_część_2.ipynb Normal file
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@ -0,0 +1,272 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Ekstrakcja informacji </h1>\n",
"<h2> 5. <i>Statystyczny model językowy część 2</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
"![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"NR_INDEKSU = 375985"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://web.stanford.edu/~jurafsky/slp3/3.pdf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"class Model():\n",
" \n",
" def __init__(self, vocab_size, UNK_token= '<UNK>'):\n",
" pass\n",
" \n",
" def train(corpus:list) -> None:\n",
" pass\n",
" \n",
" def predict(text: list, probs: str) -> float:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def get_ppl(text: list) -> float:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"text = 'Pani Ala ma kota oraz ładnego pieska i 3 chomiki'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"text_splitted = text.split(' ')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"['Pani', 'Ala', 'ma', 'kota', 'oraz', 'ładnego', 'pieska', 'i', '3', 'chomiki']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text_splitted"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"text_masked = text_splitted[:4] + ['<MASK>'] + text_splitted[5:]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Pani',\n",
" 'Ala',\n",
" 'ma',\n",
" 'kota',\n",
" '<MASK>',\n",
" 'ładnego',\n",
" 'pieska',\n",
" 'i',\n",
" '3',\n",
" 'chomiki']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text_masked"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"trigram_model działa na ['ma', 'kota', <'MASK>']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"trigram_model.predict(['ma', 'kota']) → 'i:0.55 oraz:0.25 czarnego:0.1 :0.1'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ZADANIE:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"g1 = [470618, 415366, 434695, 470611, 470607]\n",
"g2 = [440054, 434742, 434760, 434784, 434788]\n",
"g3 = [434804, 430705, 470609, 470619, 434704]\n",
"g4 = [434708, 470629, 434732, 434749, 426206]\n",
"g5 = [434766, 470628, 437622, 434780, 470627, 440058]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"model trigramowy odwrotny\n"
]
}
],
"source": [
"if NR_INDEKSU in g1:\n",
" print('model bigramowy standardowy')\n",
"elif NR_INDEKSU in g2:\n",
" print('model bigramowy odwrotny')\n",
"elif NR_INDEKSU in g3:\n",
" print('model trigramowy')\n",
"elif NR_INDEKSU in g4:\n",
" print('model trigramowy odwrotny')\n",
"elif NR_INDEKSU in g5:\n",
" print('model trigramowy ze zgadywaniem środka')\n",
"else:\n",
" print('proszę zgłosić się do prowadzącego')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### gonito:\n",
"- zapisanie do achievmentu przez start working\n",
"- send to review"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ZADANIE\n",
"\n",
"Proszę stworzyć rozwiązanie modelu (komórka wyżej) dla https://gonito.net/challenge/challenging-america-word-gap-prediction i umieścić je na platformie gonito\n",
" \n",
"Warunki zaliczenia:\n",
"- wynik widoczny na platformie zarówno dla dev i dla test\n",
"- wynik dla dev i test lepszy (niższy) od 1024.00\n",
"- deadline do końca dnia 27.04\n",
"- commitując rozwiązanie proszę również umieścić rozwiązanie w pliku /run.py (czyli na szczycie katalogu). Można przekonwertować jupyter do pliku python przez File → Download as → Python. Rozwiązanie nie musi być w pythonie, może być w innym języku.\n",
"- zadania wykonujemy samodzielnie\n",
"- w nazwie commita podaj nr indeksu\n",
"- w tagach podaj \"n-grams\" (należy zatwierdzić przecinkiem po wybraniu tagu)!\n",
"\n",
"Uwagi:\n",
"\n",
"- warto wymyślić jakąś metodę wygładazania, bez tego może być bardzo kiepski wynik\n",
"- nie trzeba korzystać z całego zbioru trenującego\n",
"- zadanie to 70 punktów, za najlepsze rozwiązanie w swojej grupie przyznaję dodatkowo 40 punktów\n",
"- punkty będą przyznane na gonito\n",
"- warto monitorować RAM, próbować z różnym vocab_size, można skorzystać z pythonowego Counter\n",
"- warto sobie zrobić dodatkowo model unigramowy w ramach ćwiczenia"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"lang": "pl",
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
},
"subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
"title": "Ekstrakcja informacji",
"year": "2021"
},
"nbformat": 4,
"nbformat_minor": 4
}

2
cw/05_wygładzanie_modeli_językowych.ipynb → cw/06_wygładzanie_modeli_językowych.ipynb
View File

@ -7,7 +7,7 @@
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka</h1>\n",
"<h2> 5. <i>Wygłazanie modeli językowych</i> [ćwiczenia]</h2> \n",
"<h2> 6. <i>Wygładzanie modeli językowych</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",

2
cw/06_biblioteki_STM.ipynb → cw/07_biblioteki_STM.ipynb
View File

@ -7,7 +7,7 @@
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka</h1>\n",
"<h2> 6. <i>biblioteki LM</i> [ćwiczenia]</h2> \n",
"<h2> 7. <i>biblioteki STM</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",

2
cw/07_neuronowe_modele_językowe.ipynb → cw/08_neuronowe_modele_językowe.ipynb
View File

@ -7,7 +7,7 @@
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka</h1>\n",
"<h2> 7. <i>Model neuronowy ff</i> [ćwiczenia]</h2> \n",
"<h2> 8. <i>Neuronowe modele językowe</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",

6
cw/08_Model_neuronowy_typu_word2vec.ipynb → cw/09_Model_neuronowy_typu_word2vec.ipynb
View File

@ -7,7 +7,7 @@
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka</h1>\n",
"<h2> 8. <i>Model neuronowy typu word2vec</i> [ćwiczenia]</h2> \n",
"<h2> 9. <i>Model neuronowy typu word2vec</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
@ -133,7 +133,7 @@
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
@ -148,7 +148,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
"version": "3.8.3"
},
"subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
"title": "Ekstrakcja informacji",

34
cw/09_Model_neuronowy_rekurencyjny.ipynb → cw/10_Model_neuronowy_rekurencyjny.ipynb
View File

@ -7,7 +7,7 @@
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka</h1>\n",
"<h2> 9. <i>Model neuronowy rekurencyjny</i> [ćwiczenia]</h2> \n",
"<h2> 10. <i>Model neuronowy rekurencyjny</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
@ -952,6 +952,38 @@
"source": [
"predict(dataset, model, 'kmicic szedł')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ZADANIE 1\n",
"\n",
"Stworzyć sieć rekurencyjną GRU dla Challenging America word-gap prediction. Wymogi takie jak zawsze, zadanie widoczne na gonito"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ZADANIE 2\n",
"\n",
"Podjąć wyzwanie na https://gonito.net/challenge/precipitation-pl i/lub https://gonito.net/challenge/book-dialogues-pl\n",
"\n",
"\n",
"**KONIECZNIE** należy je zgłosić do końca następnego piątku, czyli 20 maja!. Za późniejsze zgłoszenia (nawet minutę) nieprzyznaję punktów.\n",
" \n",
"Za każde zgłoszenie lepsze niż baseline przyznaję 40 punktów.\n",
"\n",
"Zamiast tych 40 punktów za najlepsze miejsca:\n",
"- 1. miejsce 150 punktów\n",
"- 2. miejsce 100 punktów\n",
"- 3. miejsce 70 punktów\n",
"\n",
"Można brać udział w 2 wyzwaniach jednocześnie.\n",
"\n",
"Zadania nie będą widoczne w gonito w achievements. Nie trzeba udostępniać kodu, należy jednak przestrzegać regulaminu wyzwań."
]
}
],
"metadata": {

517
cw/11_Model_rekurencyjny_z_atencją.ipynb
View File

@ -1,517 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka</h1>\n",
"<h2> 10. <i>Model rekurencyjny z atencją</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
"![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"notebook na podstawie:\n",
"\n",
"# https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import unicode_literals, print_function, division\n",
"from io import open\n",
"import unicodedata\n",
"import string\n",
"import re\n",
"import random\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch import optim\n",
"import torch.nn.functional as F\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"SOS_token = 0\n",
"EOS_token = 1\n",
"\n",
"class Lang:\n",
" def __init__(self):\n",
" self.word2index = {}\n",
" self.word2count = {}\n",
" self.index2word = {0: \"SOS\", 1: \"EOS\"}\n",
" self.n_words = 2 # Count SOS and EOS\n",
"\n",
" def addSentence(self, sentence):\n",
" for word in sentence.split(' '):\n",
" self.addWord(word)\n",
"\n",
" def addWord(self, word):\n",
" if word not in self.word2index:\n",
" self.word2index[word] = self.n_words\n",
" self.word2count[word] = 1\n",
" self.index2word[self.n_words] = word\n",
" self.n_words += 1\n",
" else:\n",
" self.word2count[word] += 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pairs = []\n",
"with open('data/eng-pol.txt') as f:\n",
" for line in f:\n",
" eng_line, pol_line = line.lower().rstrip().split('\\t')\n",
"\n",
" eng_line = re.sub(r\"([.!?])\", r\" \\1\", eng_line)\n",
" eng_line = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", eng_line)\n",
"\n",
" pol_line = re.sub(r\"([.!?])\", r\" \\1\", pol_line)\n",
" pol_line = re.sub(r\"[^a-zA-Z.!?ąćęłńóśźżĄĆĘŁŃÓŚŹŻ]+\", r\" \", pol_line)\n",
"\n",
" pairs.append([eng_line, pol_line])\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pairs[1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"MAX_LENGTH = 10\n",
"eng_prefixes = (\n",
" \"i am \", \"i m \",\n",
" \"he is\", \"he s \",\n",
" \"she is\", \"she s \",\n",
" \"you are\", \"you re \",\n",
" \"we are\", \"we re \",\n",
" \"they are\", \"they re \"\n",
")\n",
"\n",
"pairs = [p for p in pairs if len(p[0].split(' ')) < MAX_LENGTH and len(p[1].split(' ')) < MAX_LENGTH]\n",
"pairs = [p for p in pairs if p[0].startswith(eng_prefixes)]\n",
"\n",
"eng_lang = Lang()\n",
"pol_lang = Lang()\n",
"\n",
"for pair in pairs:\n",
" eng_lang.addSentence(pair[0])\n",
" pol_lang.addSentence(pair[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pairs[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pairs[1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pairs[2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"eng_lang.n_words"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pol_lang.n_words"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class EncoderRNN(nn.Module):\n",
" def __init__(self, input_size, embedding_size, hidden_size):\n",
" super(EncoderRNN, self).__init__()\n",
" self.embedding_size = 200\n",
" self.hidden_size = hidden_size\n",
"\n",
" self.embedding = nn.Embedding(input_size, self.embedding_size)\n",
" self.gru = nn.GRU(self.embedding_size, hidden_size)\n",
"\n",
" def forward(self, input, hidden):\n",
" embedded = self.embedding(input).view(1, 1, -1)\n",
" output = embedded\n",
" output, hidden = self.gru(output, hidden)\n",
" return output, hidden\n",
"\n",
" def initHidden(self):\n",
" return torch.zeros(1, 1, self.hidden_size, device=device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class DecoderRNN(nn.Module):\n",
" def __init__(self, embedding_size, hidden_size, output_size):\n",
" super(DecoderRNN, self).__init__()\n",
" self.embedding_size = embedding_size\n",
" self.hidden_size = hidden_size\n",
"\n",
" self.embedding = nn.Embedding(output_size, self.embedding_size)\n",
" self.gru = nn.GRU(self.embedding_size, hidden_size)\n",
" self.out = nn.Linear(hidden_size, output_size)\n",
" self.softmax = nn.LogSoftmax(dim=1)\n",
"\n",
" def forward(self, input, hidden):\n",
" output = self.embedding(input).view(1, 1, -1)\n",
" output = F.relu(output)\n",
" output, hidden = self.gru(output, hidden)\n",
" output = self.softmax(self.out(output[0]))\n",
" return output, hidden\n",
"\n",
" def initHidden(self):\n",
" return torch.zeros(1, 1, self.hidden_size, device=device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class AttnDecoderRNN(nn.Module):\n",
" def __init__(self, embedding_size, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):\n",
" super(AttnDecoderRNN, self).__init__()\n",
" self.embedding_size = embedding_size\n",
" self.hidden_size = hidden_size\n",
" self.output_size = output_size\n",
" self.dropout_p = dropout_p\n",
" self.max_length = max_length\n",
"\n",
" self.embedding = nn.Embedding(self.output_size, self.embedding_size)\n",
" self.attn = nn.Linear(self.hidden_size + self.embedding_size, self.max_length)\n",
" self.attn_combine = nn.Linear(self.hidden_size + self.embedding_size, self.embedding_size)\n",
" self.dropout = nn.Dropout(self.dropout_p)\n",
" self.gru = nn.GRU(self.embedding_size, self.hidden_size)\n",
" self.out = nn.Linear(self.hidden_size, self.output_size)\n",
"\n",
" def forward(self, input, hidden, encoder_outputs):\n",
" embedded = self.embedding(input).view(1, 1, -1)\n",
" embedded = self.dropout(embedded)\n",
"\n",
" attn_weights = F.softmax(\n",
" self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1)\n",
" attn_applied = torch.bmm(attn_weights.unsqueeze(0),\n",
" encoder_outputs.unsqueeze(0))\n",
" #import pdb; pdb.set_trace()\n",
"\n",
" output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
" output = self.attn_combine(output).unsqueeze(0)\n",
"\n",
" output = F.relu(output)\n",
" output, hidden = self.gru(output, hidden)\n",
"\n",
" output = F.log_softmax(self.out(output[0]), dim=1)\n",
" return output, hidden, attn_weights\n",
"\n",
" def initHidden(self):\n",
" return torch.zeros(1, 1, self.hidden_size, device=device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def tensorFromSentence(sentence, lang):\n",
" indexes = [lang.word2index[word] for word in sentence.split(' ')]\n",
" indexes.append(EOS_token)\n",
" return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"teacher_forcing_ratio = 0.5\n",
"\n",
"def train_one_batch(input_tensor, target_tensor, encoder, decoder, optimizer, criterion, max_length=MAX_LENGTH):\n",
" encoder_hidden = encoder.initHidden()\n",
"\n",
"\n",
" optimizer.zero_grad()\n",
"\n",
" input_length = input_tensor.size(0)\n",
" target_length = target_tensor.size(0)\n",
"\n",
" encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
"\n",
" loss = 0\n",
"\n",
" for ei in range(input_length):\n",
" encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)\n",
" encoder_outputs[ei] = encoder_output[0, 0]\n",
"\n",
" decoder_input = torch.tensor([[SOS_token]], device=device)\n",
"\n",
" decoder_hidden = encoder_hidden\n",
"\n",
" use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False\n",
"\n",
" if use_teacher_forcing:\n",
" for di in range(target_length):\n",
" decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)\n",
" loss += criterion(decoder_output, target_tensor[di])\n",
" decoder_input = target_tensor[di] # Teacher forcing\n",
"\n",
" else:\n",
" for di in range(target_length):\n",
" decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)\n",
" topv, topi = decoder_output.topk(1)\n",
" decoder_input = topi.squeeze().detach() # detach from history as input\n",
"\n",
" loss += criterion(decoder_output, target_tensor[di])\n",
" if decoder_input.item() == EOS_token:\n",
" break\n",
"\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
"\n",
" return loss.item() / target_length"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def trainIters(encoder, decoder, n_iters, print_every=1000, learning_rate=0.01):\n",
" print_loss_total = 0 # Reset every print_every\n",
" encoder.train()\n",
" decoder.train()\n",
"\n",
" optimizer = optim.SGD(list(encoder.parameters()) + list(decoder.parameters()), lr=learning_rate)\n",
" \n",
" training_pairs = [random.choice(pairs) for _ in range(n_iters)]\n",
" training_pairs = [(tensorFromSentence(p[0], eng_lang), tensorFromSentence(p[1], pol_lang)) for p in training_pairs]\n",
" \n",
" criterion = nn.NLLLoss()\n",
"\n",
" for i in range(1, n_iters + 1):\n",
" training_pair = training_pairs[i - 1]\n",
" input_tensor = training_pair[0]\n",
" target_tensor = training_pair[1]\n",
"\n",
" loss = train_one_batch(input_tensor,\n",
" target_tensor,\n",
" encoder,\n",
" decoder,\n",
" optimizer,\n",
"\n",
" criterion)\n",
" \n",
" print_loss_total += loss\n",
"\n",
" if i % print_every == 0:\n",
" print_loss_avg = print_loss_total / print_every\n",
" print_loss_total = 0\n",
" print(f'iter: {i}, loss: {print_loss_avg}')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):\n",
" encoder.eval()\n",
" decoder.eval()\n",
" with torch.no_grad():\n",
" input_tensor = tensorFromSentence(sentence, eng_lang)\n",
" input_length = input_tensor.size()[0]\n",
" encoder_hidden = encoder.initHidden()\n",
"\n",
" encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
"\n",
" for ei in range(input_length):\n",
" encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)\n",
" encoder_outputs[ei] += encoder_output[0, 0]\n",
"\n",
" decoder_input = torch.tensor([[SOS_token]], device=device)\n",
"\n",
" decoder_hidden = encoder_hidden\n",
"\n",
" decoded_words = []\n",
" decoder_attentions = torch.zeros(max_length, max_length)\n",
"\n",
" for di in range(max_length):\n",
" decoder_output, decoder_hidden, decoder_attention = decoder(\n",
" decoder_input, decoder_hidden, encoder_outputs)\n",
" decoder_attentions[di] = decoder_attention.data\n",
" topv, topi = decoder_output.data.topk(1)\n",
" if topi.item() == EOS_token:\n",
" decoded_words.append('<EOS>')\n",
" break\n",
" else:\n",
" decoded_words.append(pol_lang.index2word[topi.item()])\n",
"\n",
" decoder_input = topi.squeeze().detach()\n",
"\n",
" return decoded_words, decoder_attentions[:di + 1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def evaluateRandomly(encoder, decoder, n=10):\n",
" for i in range(n):\n",
" pair = random.choice(pairs)\n",
" print('>', pair[0])\n",
" print('=', pair[1])\n",
" output_words, attentions = evaluate(encoder, decoder, pair[0])\n",
" output_sentence = ' '.join(output_words)\n",
" print('<', output_sentence)\n",
" print('')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"embedding_size = 200\n",
"hidden_size = 256\n",
"encoder1 = EncoderRNN(eng_lang.n_words, embedding_size, hidden_size).to(device)\n",
"attn_decoder1 = AttnDecoderRNN(embedding_size, hidden_size, pol_lang.n_words, dropout_p=0.1).to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"trainIters(encoder1, attn_decoder1, 10_000, print_every=50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"evaluateRandomly(encoder1, attn_decoder1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## ZADANIE\n",
"\n",
"Gonito \"WMT2017 Czech-English machine translation challenge for news \"\n",
"\n",
"Proszę wytrenować najpierw model german -> english, a później dotrenować na czech-> english.\n",
"Można wziąć inicjalizować enkoder od nowa lub nie. Proszę w każdym razie użyć wytrenowanego dekodera."
]
}
],
"metadata": {
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"lang": "pl",
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
},
"subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
"title": "Ekstrakcja informacji",
"year": "2021"
},
"nbformat": 4,
"nbformat_minor": 4
}

126
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@ -0,0 +1,126 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka</h1>\n",
"<h2> 11. <i>Regularyzacja modeli neuronowych</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
"![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Overfitting modeli"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Trenując model uczenia maszynowego zależy nam, aby model miał dobrą zdolność predykcji. Zdolności predykcyjne powinny być wysokie na jakichkolwiek danych, a nie wyłącznie na tych, na których model się uczył. \n",
"\n",
"\n",
"Zjawiskiem overfittingu modeli nazywamy nadmierne dopasowanie modelu do zbioru trenującego. Skutkuje to tym, że model świetnie działa na zbiorze trenującym, ale źle dla innych danych, na których się nie uczył.\n",
"\n",
"Overfitting modelu łatwo sprawdzić jako różnicę w metrykach między zbiorem trenującym a zbiorem deweloperskim/testowym. Nim większa jest ta różnica, tym większy overfitting modelu.\n",
"\n",
"Zazwyczaj overfitting będzie występował do pewnego stopnia. Nie należy się tym przejmować. Najważniejsze jest, aby model miał jak najlepszy wynik metryki na zbiorze deweloperskim/testowym. Nawet kosztem overfittingu.\n",
"\n",
"Aby zmniejszyć overfitting (a tym samym zwiększyć wyniki modelu na zbiorze deweloperskim/testowym), korzysta się z metod regularyzacji.\n",
"\n",
"## Regularyzacja modelu\n",
"\n",
"Najbardziej powszechne metody regularyzacji to:\n",
"\n",
"- regularyzacja L1\n",
"- regularyzacja L2\n",
"- dropout"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### regularyzacja L1\n",
"\n",
"Czynnik regularyzacyjny to $\\lambda \\sum_{i=1}^{N}|w_i|$, gdzie $0<\\lambda$ to parametr, a $w_i$ to parametry modelu.\n",
"\n",
"Wtedy funkcja kosztu powinna wyglądać: $L(x) = Error(y,\\bar{y}) + \\lambda \\sum_{i=1}^{N}|w_i|$.\n",
"\n",
"\n",
"### regularyzacja L2\n",
"\n",
"\n",
"Czynnik regularyzacyjny to $\\lambda \\sum_{i=1}^{N}(w_i)^2$, gdzie $0<\\lambda$ to parametr, a $w_i$ to parametry modelu.\n",
"\n",
"Wtedy funkcja kosztu powinna wyglądać: $L(x) = Error(y,\\bar{y}) + \\lambda \\sum_{i=1}^{N}(w_i)^2$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dropout\n",
"\n",
"Dropout to technika polegająca na losowym wygaszania wyjściu z neuronów (przyjmowanie wartości $0$) podczas treningu. Prawpodopobieństwo ignorowania to parametr $p$. Podczas inferencji nie wygasza sie wyjścia, natomiast wszystkie wartości przemnaża się przez $1-p$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Zadanie 1 \n",
"\n",
"Wzorując się na poprzednich zajęciach zaimplementować powyższe metody reguluryzacji i zgłosić na gonito.\n",
"\n",
"Warunki zaliczenia:\n",
"- wynik widoczny na platformie zarówno dla dev i dla test\n",
"- wynik dla dev i test lepszy (niższy) niż 1024.00 (liczone przy pomocy geval)\n",
"- deadline do końca dnia 24.04\n",
"- commitując rozwiązanie proszę również umieścić rozwiązanie w pliku /run.py (czyli na szczycie katalogu). Można przekonwertować jupyter do pliku python przez File → Download as → Python. Rozwiązanie nie musi być w pythonie, może być w innym języku.\n",
"- zadania wykonujemy samodzielnie\n",
"- w nazwie commita podaj nr indeksu\n",
"- w tagach podaj **neural-network** oraz **bigram**!\n",
"- uwaga na specjalne znaki \\\\n w pliku 'in.tsv' oraz pierwsze kolumny pliku in.tsv (które należy usunąć)\n",
"\n",
"Punktacja:\n",
"- 50 punktów z najlepszy wynik z 2 grup\n"
]
}
],
"metadata": {
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"lang": "pl",
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
},
"subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
"title": "Ekstrakcja informacji",
"year": "2021"
},
"nbformat": 4,
"nbformat_minor": 4
}

6
cw/10_Ensemble_oraz_Model_neuronowy_rekurencyjny2.ipynb → cw/12_Ensemble_modeli.ipynb
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@ -7,7 +7,7 @@
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka</h1>\n",
"<h2> 10. <i>Model neuronowy rekurencyjny</i> [ćwiczenia]</h2> \n",
"<h2> 12. <i>Model neuronowy rekurencyjny</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
@ -308,7 +308,7 @@
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
@ -323,7 +323,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
"version": "3.8.3"
},
"subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
"title": "Ekstrakcja informacji",

59
cw/13_Model_neuronowy_rekurencyjny_część_2.ipynb Normal file
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@ -0,0 +1,59 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka</h1>\n",
"<h2> 13. <i>Model neuronowy rekurencyjny część 2</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
"![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ZADANIE\n",
"\n",
"Proszę zrobić model 1 model rekurencyjny dwuwarstwowy BiLSTM z rekurencyjnym dropoutem oraz analogiczny model GRU.\n",
"Proszę zaimplementować early stopping i wykorzystać do treningu. Następnie proszę zrobić ensemble tych 2 modeli.\n",
"\n",
"Zadanie widoczne na gonito\n",
"\n",
"\n"
]
}
],
"metadata": {
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"lang": "pl",
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
},
"subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
"title": "Ekstrakcja informacji",
"year": "2021"
},
"nbformat": 4,
"nbformat_minor": 4
}

955
cw/14_Model_rekurencyjny_z_atencją.ipynb Normal file
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@ -0,0 +1,955 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka</h1>\n",
"<h2> 14. <i>Model rekurencyjny z atencją</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
"![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"notebook na podstawie:\n",
"\n",
"# https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import unicode_literals, print_function, division\n",
"from io import open\n",
"import unicodedata\n",
"import string\n",
"import re\n",
"import random\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch import optim\n",
"import torch.nn.functional as F\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"SOS_token = 0\n",
"EOS_token = 1\n",
"\n",
"class Lang:\n",
" def __init__(self):\n",
" self.word2index = {}\n",
" self.word2count = {}\n",
" self.index2word = {0: \"SOS\", 1: \"EOS\"}\n",
" self.n_words = 2 # Count SOS and EOS\n",
"\n",
" def addSentence(self, sentence):\n",
" for word in sentence.split(' '):\n",
" self.addWord(word)\n",
"\n",
" def addWord(self, word):\n",
" if word not in self.word2index:\n",
" self.word2index[word] = self.n_words\n",
" self.word2count[word] = 1\n",
" self.index2word[self.n_words] = word\n",
" self.n_words += 1\n",
" else:\n",
" self.word2count[word] += 1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"pairs = []\n",
"with open('data/eng-pol.txt') as f:\n",
" for line in f:\n",
" eng_line, pol_line = line.lower().rstrip().split('\\t')\n",
"\n",
" eng_line = re.sub(r\"([.!?])\", r\" \\1\", eng_line)\n",
" eng_line = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", eng_line)\n",
"\n",
" pol_line = re.sub(r\"([.!?])\", r\" \\1\", pol_line)\n",
" pol_line = re.sub(r\"[^a-zA-Z.!?ąćęłńóśźżĄĆĘŁŃÓŚŹŻ]+\", r\" \", pol_line)\n",
"\n",
" pairs.append([eng_line, pol_line])\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['hi .', 'cześć .']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs[1]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"MAX_LENGTH = 10\n",
"eng_prefixes = (\n",
" \"i am \", \"i m \",\n",
" \"he is\", \"he s \",\n",
" \"she is\", \"she s \",\n",
" \"you are\", \"you re \",\n",
" \"we are\", \"we re \",\n",
" \"they are\", \"they re \"\n",
")\n",
"\n",
"pairs = [p for p in pairs if len(p[0].split(' ')) < MAX_LENGTH and len(p[1].split(' ')) < MAX_LENGTH]\n",
"pairs = [p for p in pairs if p[0].startswith(eng_prefixes)]\n",
"\n",
"eng_lang = Lang()\n",
"pol_lang = Lang()\n",
"\n",
"for pair in pairs:\n",
" eng_lang.addSentence(pair[0])\n",
" pol_lang.addSentence(pair[1])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['i m ok .', 'ze mną wszystko w porządku .']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs[0]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['i m up .', 'wstałem .']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs[1]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['i m tom .', 'jestem tom .']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs[2]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1828"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eng_lang.n_words"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2883"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pol_lang.n_words"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"class EncoderRNN(nn.Module):\n",
" def __init__(self, input_size, embedding_size, hidden_size):\n",
" super(EncoderRNN, self).__init__()\n",
" self.embedding_size = 200\n",
" self.hidden_size = hidden_size\n",
"\n",
" self.embedding = nn.Embedding(input_size, self.embedding_size)\n",
" self.gru = nn.GRU(self.embedding_size, hidden_size)\n",
"\n",
" def forward(self, input, hidden):\n",
" embedded = self.embedding(input).view(1, 1, -1)\n",
" output = embedded\n",
" output, hidden = self.gru(output, hidden)\n",
" return output, hidden\n",
"\n",
" def initHidden(self):\n",
" return torch.zeros(1, 1, self.hidden_size, device=device)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"class DecoderRNN(nn.Module):\n",
" def __init__(self, embedding_size, hidden_size, output_size):\n",
" super(DecoderRNN, self).__init__()\n",
" self.embedding_size = embedding_size\n",
" self.hidden_size = hidden_size\n",
"\n",
" self.embedding = nn.Embedding(output_size, self.embedding_size)\n",
" self.gru = nn.GRU(self.embedding_size, hidden_size)\n",
" self.out = nn.Linear(hidden_size, output_size)\n",
" self.softmax = nn.LogSoftmax(dim=1)\n",
"\n",
" def forward(self, input, hidden):\n",
" output = self.embedding(input).view(1, 1, -1)\n",
" output = F.relu(output)\n",
" output, hidden = self.gru(output, hidden)\n",
" output = self.softmax(self.out(output[0]))\n",
" return output, hidden\n",
"\n",
" def initHidden(self):\n",
" return torch.zeros(1, 1, self.hidden_size, device=device)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"class AttnDecoderRNN(nn.Module):\n",
" def __init__(self, embedding_size, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):\n",
" super(AttnDecoderRNN, self).__init__()\n",
" self.embedding_size = embedding_size\n",
" self.hidden_size = hidden_size\n",
" self.output_size = output_size\n",
" self.dropout_p = dropout_p\n",
" self.max_length = max_length\n",
"\n",
" self.embedding = nn.Embedding(self.output_size, self.embedding_size)\n",
" self.attn = nn.Linear(self.hidden_size + self.embedding_size, self.max_length)\n",
" self.attn_combine = nn.Linear(self.hidden_size + self.embedding_size, self.embedding_size)\n",
" self.dropout = nn.Dropout(self.dropout_p)\n",
" self.gru = nn.GRU(self.embedding_size, self.hidden_size)\n",
" self.out = nn.Linear(self.hidden_size, self.output_size)\n",
"\n",
" def forward(self, input, hidden, encoder_outputs):\n",
" embedded = self.embedding(input).view(1, 1, -1)\n",
" embedded = self.dropout(embedded)\n",
"\n",
" attn_weights = F.softmax(\n",
" self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1)\n",
" attn_applied = torch.bmm(attn_weights.unsqueeze(0),\n",
" encoder_outputs.unsqueeze(0))\n",
" import pdb; pdb.set_trace()\n",
"\n",
" output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
" output = self.attn_combine(output).unsqueeze(0)\n",
"\n",
" output = F.relu(output)\n",
" output, hidden = self.gru(output, hidden)\n",
"\n",
" output = F.log_softmax(self.out(output[0]), dim=1)\n",
" return output, hidden, attn_weights\n",
"\n",
" def initHidden(self):\n",
" return torch.zeros(1, 1, self.hidden_size, device=device)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def tensorFromSentence(sentence, lang):\n",
" indexes = [lang.word2index[word] for word in sentence.split(' ')]\n",
" indexes.append(EOS_token)\n",
" return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"teacher_forcing_ratio = 0.5\n",
"\n",
"def train_one_batch(input_tensor, target_tensor, encoder, decoder, optimizer, criterion, max_length=MAX_LENGTH):\n",
" encoder_hidden = encoder.initHidden()\n",
"\n",
"\n",
" optimizer.zero_grad()\n",
"\n",
" input_length = input_tensor.size(0)\n",
" target_length = target_tensor.size(0)\n",
"\n",
" encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
"\n",
" loss = 0\n",
"\n",
" for ei in range(input_length):\n",
" encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)\n",
" encoder_outputs[ei] = encoder_output[0, 0]\n",
"\n",
" decoder_input = torch.tensor([[SOS_token]], device=device)\n",
"\n",
" decoder_hidden = encoder_hidden\n",
"\n",
" use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False\n",
"\n",
" if use_teacher_forcing:\n",
" for di in range(target_length):\n",
" decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)\n",
" loss += criterion(decoder_output, target_tensor[di])\n",
" decoder_input = target_tensor[di] # Teacher forcing\n",
"\n",
" else:\n",
" for di in range(target_length):\n",
" decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)\n",
" topv, topi = decoder_output.topk(1)\n",
" decoder_input = topi.squeeze().detach() # detach from history as input\n",
"\n",
" loss += criterion(decoder_output, target_tensor[di])\n",
" if decoder_input.item() == EOS_token:\n",
" break\n",
"\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
"\n",
" return loss.item() / target_length"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def trainIters(encoder, decoder, n_iters, print_every=1000, learning_rate=0.01):\n",
" print_loss_total = 0 # Reset every print_every\n",
" encoder.train()\n",
" decoder.train()\n",
"\n",
" optimizer = optim.SGD(list(encoder.parameters()) + list(decoder.parameters()), lr=learning_rate)\n",
" \n",
" training_pairs = [random.choice(pairs) for _ in range(n_iters)]\n",
" training_pairs = [(tensorFromSentence(p[0], eng_lang), tensorFromSentence(p[1], pol_lang)) for p in training_pairs]\n",
" \n",
" criterion = nn.NLLLoss()\n",
"\n",
" for i in range(1, n_iters + 1):\n",
" training_pair = training_pairs[i - 1]\n",
" input_tensor = training_pair[0]\n",
" target_tensor = training_pair[1]\n",
"\n",
" loss = train_one_batch(input_tensor,\n",
" target_tensor,\n",
" encoder,\n",
" decoder,\n",
" optimizer,\n",
"\n",
" criterion)\n",
" \n",
" print_loss_total += loss\n",
"\n",
" if i % print_every == 0:\n",
" print_loss_avg = print_loss_total / print_every\n",
" print_loss_total = 0\n",
" print(f'iter: {i}, loss: {print_loss_avg}')\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):\n",
" encoder.eval()\n",
" decoder.eval()\n",
" with torch.no_grad():\n",
" input_tensor = tensorFromSentence(sentence, eng_lang)\n",
" input_length = input_tensor.size()[0]\n",
" encoder_hidden = encoder.initHidden()\n",
"\n",
" encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
"\n",
" for ei in range(input_length):\n",
" encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)\n",
" encoder_outputs[ei] += encoder_output[0, 0]\n",
"\n",
" decoder_input = torch.tensor([[SOS_token]], device=device)\n",
"\n",
" decoder_hidden = encoder_hidden\n",
"\n",
" decoded_words = []\n",
" decoder_attentions = torch.zeros(max_length, max_length)\n",
"\n",
" for di in range(max_length):\n",
" decoder_output, decoder_hidden, decoder_attention = decoder(\n",
" decoder_input, decoder_hidden, encoder_outputs)\n",
" decoder_attentions[di] = decoder_attention.data\n",
" topv, topi = decoder_output.data.topk(1)\n",
" if topi.item() == EOS_token:\n",
" decoded_words.append('<EOS>')\n",
" break\n",
" else:\n",
" decoded_words.append(pol_lang.index2word[topi.item()])\n",
"\n",
" decoder_input = topi.squeeze().detach()\n",
"\n",
" return decoded_words, decoder_attentions[:di + 1]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def evaluateRandomly(encoder, decoder, n=10):\n",
" for i in range(n):\n",
" pair = random.choice(pairs)\n",
" print('>', pair[0])\n",
" print('=', pair[1])\n",
" output_words, attentions = evaluate(encoder, decoder, pair[0])\n",
" output_sentence = ' '.join(output_words)\n",
" print('<', output_sentence)\n",
" print('')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"embedding_size = 200\n",
"hidden_size = 256\n",
"encoder1 = EncoderRNN(eng_lang.n_words, embedding_size, hidden_size).to(device)\n",
"attn_decoder1 = AttnDecoderRNN(embedding_size, hidden_size, pol_lang.n_words, dropout_p=0.1).to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"> \u001b[0;32m/tmp/ipykernel_41821/2519748186.py\u001b[0m(27)\u001b[0;36mforward\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 25 \u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 26 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m---> 27 \u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membedded\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattn_applied\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 28 \u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattn_combine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 29 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
"ipdb> embedded\n",
"tensor([[[-0.7259, 0.0000, 2.2112, 1.1947, -0.1261, -1.0427, -1.4295,\n",
" 0.1567, -0.3949, -1.0815, 1.1206, 2.0630, 2.8148, -1.8538,\n",
" -1.5486, -0.4900, -0.0000, 0.0000, -1.5046, 2.0329, -0.5872,\n",
" 1.5764, -0.0000, 1.1447, -0.4200, -0.1560, 0.1723, 1.5950,\n",
" 1.2955, -0.5796, -0.0000, -0.8989, 0.4737, 1.7037, 0.8787,\n",
" -0.2064, 1.9589, 2.0400, -1.0883, 1.0515, 0.0540, 0.1436,\n",
" 1.2383, 0.4912, -1.7719, 1.6435, 1.5523, 2.3576, 0.0000,\n",
" 0.4063, -0.0821, -1.2872, 0.8372, -0.5638, 0.0706, 0.4151,\n",
" -0.0000, 1.1651, 1.7333, -0.1684, -0.0000, -0.8560, -0.0000,\n",
" 2.7717, -0.4485, -0.8488, 0.8165, 2.1787, -1.0720, -0.3146,\n",
" 1.5798, -0.6788, 0.0000, 0.5609, 0.7415, -0.5585, 2.0659,\n",
" 0.7054, 1.3791, -0.2697, -0.0458, 1.6028, -0.0304, -0.6326,\n",
" -1.3258, -0.8370, 0.6533, 2.2756, -0.5393, 0.4752, 0.4479,\n",
" -0.0186, -0.7785, -1.7858, 0.2345, 1.9794, -0.0314, -0.8594,\n",
" -0.0000, 0.0596, -2.6836, -1.9927, 0.2714, -1.4617, -0.8142,\n",
" -0.7790, 0.5029, -0.6001, -0.7932, 1.3418, 0.1305, -0.0000,\n",
" -1.2961, -2.7107, -2.3360, -0.7960, 0.5207, 1.6896, 0.9285,\n",
" 0.0000, 1.8187, -0.0000, 1.5908, 0.2745, -0.2589, 0.4066,\n",
" -0.0000, -1.3145, -0.5903, 0.3696, -1.9539, -1.9995, -0.8219,\n",
" 0.3937, -0.6068, 0.7947, 1.3940, 0.5513, 0.7498, 1.4578,\n",
" -0.0000, -0.5037, -0.6856, 0.7723, -0.6553, 1.0936, -0.2788,\n",
" -1.9658, 1.5950, 0.8480, 1.1166, 1.3168, -0.0000, 0.2597,\n",
" 1.0813, 0.1827, -1.6485, 0.5743, -0.4952, 0.7176, -0.4468,\n",
" -1.7915, -0.6303, 0.2046, 0.7791, 0.1586, 0.2322, -2.3935,\n",
" 1.3643, -1.2023, -1.6792, 0.5582, -2.0117, -0.6245, 2.4039,\n",
" 2.3736, 0.0559, 0.9173, 0.6446, -0.2068, -0.8805, -0.3070,\n",
" 0.7318, 1.9806, 1.9318, -1.1276, -0.1307, 0.0243, 0.8480,\n",
" 0.4865, -1.5352, 0.8082, 1.7595, -0.2168, 2.0735, -1.0444,\n",
" -0.0000, 1.0729, -0.2194, 0.5439]]], grad_fn=<MulBackward0>)\n",
"ipdb> embedded.shape\n",
"torch.Size([1, 1, 200])\n",
"ipdb> attn_weights\n",
"tensor([[0.0817, 0.1095, 0.1425, 0.1611, 0.0574, 0.0546, 0.0374, 0.0621, 0.0703,\n",
" 0.2234]], grad_fn=<SoftmaxBackward0>)\n",
"ipdb> attn_applied\n",
"tensor([[[ 0.0354, -0.0156, -0.0048, -0.0936, 0.0637, 0.1516, 0.1419,\n",
" 0.1106, 0.0511, 0.0235, -0.0622, 0.0725, 0.0709, -0.0624,\n",
" 0.1407, -0.0069, -0.1602, -0.1883, -0.1707, -0.1528, -0.0296,\n",
" -0.0500, 0.2115, 0.0705, -0.1385, -0.0487, -0.0165, -0.0128,\n",
" -0.0594, 0.0209, -0.1081, 0.0509, 0.0655, 0.1314, -0.0455,\n",
" -0.0049, -0.1527, -0.1900, -0.0019, 0.0295, -0.0308, 0.0886,\n",
" 0.1369, -0.1571, 0.0518, -0.0991, -0.0310, -0.1781, -0.0290,\n",
" 0.0558, 0.0585, -0.1045, -0.0027, -0.0476, -0.0377, -0.1026,\n",
" 0.0481, 0.0398, -0.0956, 0.0655, -0.1449, 0.0193, -0.0380,\n",
" 0.0401, 0.0491, -0.1925, 0.0669, 0.0774, 0.0604, 0.1187,\n",
" -0.0401, 0.1094, 0.0706, 0.0474, 0.0178, -0.0888, -0.0632,\n",
" 0.1180, -0.0257, -0.0180, -0.0807, 0.0867, -0.0428, -0.0982,\n",
" -0.0129, 0.1326, -0.0868, -0.0118, 0.0923, -0.0634, -0.1758,\n",
" -0.0835, -0.2328, 0.0578, 0.0184, 0.0602, -0.1132, -0.1089,\n",
" -0.1371, -0.0996, -0.0758, -0.1615, 0.0474, -0.0595, 0.1130,\n",
" -0.1329, 0.0068, -0.0485, -0.0376, 0.0170, 0.0743, 0.0284,\n",
" -0.1708, 0.0283, -0.0161, 0.1138, -0.0223, -0.0504, -0.0068,\n",
" 0.1297, 0.0962, 0.1806, -0.1773, -0.1658, 0.1612, 0.0569,\n",
" 0.0703, -0.0321, -0.1741, -0.0983, -0.0848, 0.0342, 0.1021,\n",
" -0.1319, 0.1122, -0.0467, 0.0927, -0.0528, -0.0696, 0.0227,\n",
" 0.0445, 0.0268, 0.1563, 0.0008, 0.0296, 0.0112, -0.0863,\n",
" -0.1705, -0.0137, -0.0336, -0.0533, 0.0015, -0.0134, -0.0530,\n",
" 0.0995, 0.0445, -0.1190, -0.1675, 0.1295, -0.1072, 0.0954,\n",
" 0.0559, 0.0572, 0.1595, 0.0054, -0.1020, 0.0309, -0.0821,\n",
" 0.0230, -0.1480, -0.0815, -0.0013, -0.0012, 0.1046, 0.0248,\n",
" 0.1121, 0.0055, 0.1006, -0.0891, -0.0237, -0.0231, -0.0891,\n",
" 0.0234, 0.0164, -0.0080, -0.0431, -0.0041, 0.2627, -0.2110,\n",
" 0.1026, -0.0049, 0.0077, -0.1126, 0.0161, 0.0039, 0.0700,\n",
" 0.0353, -0.0941, 0.0770, 0.1015, -0.1124, -0.1738, 0.0232,\n",
" 0.1839, -0.2329, 0.0488, 0.0791, 0.2002, 0.0389, -0.0985,\n",
" -0.0744, 0.1392, 0.0052, 0.1119, 0.0851, -0.1062, -0.0948,\n",
" 0.0718, 0.0308, 0.0136, 0.2036, -0.0510, 0.0615, 0.1164,\n",
" 0.0242, -0.0717, 0.0955, -0.0796, 0.0856, 0.0040, -0.1370,\n",
" -0.1614, 0.0605, -0.1396, -0.0286, 0.0295, 0.0515, -0.0880,\n",
" 0.0249, -0.2263, 0.0048, -0.0381, -0.0019, 0.0186, -0.0209,\n",
" -0.0929, -0.1371, 0.0052, -0.1237, -0.1090, -0.0606, 0.0524,\n",
" 0.0351, 0.0283, 0.0264, 0.0866]]], grad_fn=<BmmBackward0>)\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> attn_weights.shape\n",
"torch.Size([1, 10])\n",
"ipdb> encoder_outputs.shape\n",
"torch.Size([10, 256])\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> attn_applied\n",
"tensor([[[ 0.0354, -0.0156, -0.0048, -0.0936, 0.0637, 0.1516, 0.1419,\n",
" 0.1106, 0.0511, 0.0235, -0.0622, 0.0725, 0.0709, -0.0624,\n",
" 0.1407, -0.0069, -0.1602, -0.1883, -0.1707, -0.1528, -0.0296,\n",
" -0.0500, 0.2115, 0.0705, -0.1385, -0.0487, -0.0165, -0.0128,\n",
" -0.0594, 0.0209, -0.1081, 0.0509, 0.0655, 0.1314, -0.0455,\n",
" -0.0049, -0.1527, -0.1900, -0.0019, 0.0295, -0.0308, 0.0886,\n",
" 0.1369, -0.1571, 0.0518, -0.0991, -0.0310, -0.1781, -0.0290,\n",
" 0.0558, 0.0585, -0.1045, -0.0027, -0.0476, -0.0377, -0.1026,\n",
" 0.0481, 0.0398, -0.0956, 0.0655, -0.1449, 0.0193, -0.0380,\n",
" 0.0401, 0.0491, -0.1925, 0.0669, 0.0774, 0.0604, 0.1187,\n",
" -0.0401, 0.1094, 0.0706, 0.0474, 0.0178, -0.0888, -0.0632,\n",
" 0.1180, -0.0257, -0.0180, -0.0807, 0.0867, -0.0428, -0.0982,\n",
" -0.0129, 0.1326, -0.0868, -0.0118, 0.0923, -0.0634, -0.1758,\n",
" -0.0835, -0.2328, 0.0578, 0.0184, 0.0602, -0.1132, -0.1089,\n",
" -0.1371, -0.0996, -0.0758, -0.1615, 0.0474, -0.0595, 0.1130,\n",
" -0.1329, 0.0068, -0.0485, -0.0376, 0.0170, 0.0743, 0.0284,\n",
" -0.1708, 0.0283, -0.0161, 0.1138, -0.0223, -0.0504, -0.0068,\n",
" 0.1297, 0.0962, 0.1806, -0.1773, -0.1658, 0.1612, 0.0569,\n",
" 0.0703, -0.0321, -0.1741, -0.0983, -0.0848, 0.0342, 0.1021,\n",
" -0.1319, 0.1122, -0.0467, 0.0927, -0.0528, -0.0696, 0.0227,\n",
" 0.0445, 0.0268, 0.1563, 0.0008, 0.0296, 0.0112, -0.0863,\n",
" -0.1705, -0.0137, -0.0336, -0.0533, 0.0015, -0.0134, -0.0530,\n",
" 0.0995, 0.0445, -0.1190, -0.1675, 0.1295, -0.1072, 0.0954,\n",
" 0.0559, 0.0572, 0.1595, 0.0054, -0.1020, 0.0309, -0.0821,\n",
" 0.0230, -0.1480, -0.0815, -0.0013, -0.0012, 0.1046, 0.0248,\n",
" 0.1121, 0.0055, 0.1006, -0.0891, -0.0237, -0.0231, -0.0891,\n",
" 0.0234, 0.0164, -0.0080, -0.0431, -0.0041, 0.2627, -0.2110,\n",
" 0.1026, -0.0049, 0.0077, -0.1126, 0.0161, 0.0039, 0.0700,\n",
" 0.0353, -0.0941, 0.0770, 0.1015, -0.1124, -0.1738, 0.0232,\n",
" 0.1839, -0.2329, 0.0488, 0.0791, 0.2002, 0.0389, -0.0985,\n",
" -0.0744, 0.1392, 0.0052, 0.1119, 0.0851, -0.1062, -0.0948,\n",
" 0.0718, 0.0308, 0.0136, 0.2036, -0.0510, 0.0615, 0.1164,\n",
" 0.0242, -0.0717, 0.0955, -0.0796, 0.0856, 0.0040, -0.1370,\n",
" -0.1614, 0.0605, -0.1396, -0.0286, 0.0295, 0.0515, -0.0880,\n",
" 0.0249, -0.2263, 0.0048, -0.0381, -0.0019, 0.0186, -0.0209,\n",
" -0.0929, -0.1371, 0.0052, -0.1237, -0.1090, -0.0606, 0.0524,\n",
" 0.0351, 0.0283, 0.0264, 0.0866]]], grad_fn=<BmmBackward0>)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ipdb> attn_weights.shape\n",
"torch.Size([1, 10])\n",
"ipdb> encoder_outputs.shape\n",
"torch.Size([10, 256])\n",
"ipdb> embedded.shape\n",
"torch.Size([1, 1, 200])\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
"ipdb> output.shape\n",
"torch.Size([1, 456])\n",
"ipdb> output = self.attn_combine(output).unsqueeze(0)\n",
"ipdb> output.shape\n",
"torch.Size([1, 1, 200])\n",
"ipdb> attn_weights\n",
"tensor([[0.0817, 0.1095, 0.1425, 0.1611, 0.0574, 0.0546, 0.0374, 0.0621, 0.0703,\n",
" 0.2234]], grad_fn=<SoftmaxBackward0>)\n",
"ipdb> attn_weights.shape\n",
"torch.Size([1, 10])\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> attn_applied\n",
"tensor([[[ 0.0354, -0.0156, -0.0048, -0.0936, 0.0637, 0.1516, 0.1419,\n",
" 0.1106, 0.0511, 0.0235, -0.0622, 0.0725, 0.0709, -0.0624,\n",
" 0.1407, -0.0069, -0.1602, -0.1883, -0.1707, -0.1528, -0.0296,\n",
" -0.0500, 0.2115, 0.0705, -0.1385, -0.0487, -0.0165, -0.0128,\n",
" -0.0594, 0.0209, -0.1081, 0.0509, 0.0655, 0.1314, -0.0455,\n",
" -0.0049, -0.1527, -0.1900, -0.0019, 0.0295, -0.0308, 0.0886,\n",
" 0.1369, -0.1571, 0.0518, -0.0991, -0.0310, -0.1781, -0.0290,\n",
" 0.0558, 0.0585, -0.1045, -0.0027, -0.0476, -0.0377, -0.1026,\n",
" 0.0481, 0.0398, -0.0956, 0.0655, -0.1449, 0.0193, -0.0380,\n",
" 0.0401, 0.0491, -0.1925, 0.0669, 0.0774, 0.0604, 0.1187,\n",
" -0.0401, 0.1094, 0.0706, 0.0474, 0.0178, -0.0888, -0.0632,\n",
" 0.1180, -0.0257, -0.0180, -0.0807, 0.0867, -0.0428, -0.0982,\n",
" -0.0129, 0.1326, -0.0868, -0.0118, 0.0923, -0.0634, -0.1758,\n",
" -0.0835, -0.2328, 0.0578, 0.0184, 0.0602, -0.1132, -0.1089,\n",
" -0.1371, -0.0996, -0.0758, -0.1615, 0.0474, -0.0595, 0.1130,\n",
" -0.1329, 0.0068, -0.0485, -0.0376, 0.0170, 0.0743, 0.0284,\n",
" -0.1708, 0.0283, -0.0161, 0.1138, -0.0223, -0.0504, -0.0068,\n",
" 0.1297, 0.0962, 0.1806, -0.1773, -0.1658, 0.1612, 0.0569,\n",
" 0.0703, -0.0321, -0.1741, -0.0983, -0.0848, 0.0342, 0.1021,\n",
" -0.1319, 0.1122, -0.0467, 0.0927, -0.0528, -0.0696, 0.0227,\n",
" 0.0445, 0.0268, 0.1563, 0.0008, 0.0296, 0.0112, -0.0863,\n",
" -0.1705, -0.0137, -0.0336, -0.0533, 0.0015, -0.0134, -0.0530,\n",
" 0.0995, 0.0445, -0.1190, -0.1675, 0.1295, -0.1072, 0.0954,\n",
" 0.0559, 0.0572, 0.1595, 0.0054, -0.1020, 0.0309, -0.0821,\n",
" 0.0230, -0.1480, -0.0815, -0.0013, -0.0012, 0.1046, 0.0248,\n",
" 0.1121, 0.0055, 0.1006, -0.0891, -0.0237, -0.0231, -0.0891,\n",
" 0.0234, 0.0164, -0.0080, -0.0431, -0.0041, 0.2627, -0.2110,\n",
" 0.1026, -0.0049, 0.0077, -0.1126, 0.0161, 0.0039, 0.0700,\n",
" 0.0353, -0.0941, 0.0770, 0.1015, -0.1124, -0.1738, 0.0232,\n",
" 0.1839, -0.2329, 0.0488, 0.0791, 0.2002, 0.0389, -0.0985,\n",
" -0.0744, 0.1392, 0.0052, 0.1119, 0.0851, -0.1062, -0.0948,\n",
" 0.0718, 0.0308, 0.0136, 0.2036, -0.0510, 0.0615, 0.1164,\n",
" 0.0242, -0.0717, 0.0955, -0.0796, 0.0856, 0.0040, -0.1370,\n",
" -0.1614, 0.0605, -0.1396, -0.0286, 0.0295, 0.0515, -0.0880,\n",
" 0.0249, -0.2263, 0.0048, -0.0381, -0.0019, 0.0186, -0.0209,\n",
" -0.0929, -0.1371, 0.0052, -0.1237, -0.1090, -0.0606, 0.0524,\n",
" 0.0351, 0.0283, 0.0264, 0.0866]]], grad_fn=<BmmBackward0>)\n",
"ipdb> torch.cat((embedded[0], attn_applied[0]), 1)\n",
"tensor([[-7.2585e-01, 0.0000e+00, 2.2112e+00, 1.1947e+00, -1.2609e-01,\n",
" -1.0427e+00, -1.4295e+00, 1.5669e-01, -3.9488e-01, -1.0815e+00,\n",
" 1.1206e+00, 2.0630e+00, 2.8148e+00, -1.8538e+00, -1.5486e+00,\n",
" -4.8997e-01, -0.0000e+00, 0.0000e+00, -1.5046e+00, 2.0329e+00,\n",
" -5.8720e-01, 1.5764e+00, -0.0000e+00, 1.1447e+00, -4.2003e-01,\n",
" -1.5600e-01, 1.7233e-01, 1.5950e+00, 1.2955e+00, -5.7964e-01,\n",
" -0.0000e+00, -8.9891e-01, 4.7372e-01, 1.7037e+00, 8.7866e-01,\n",
" -2.0642e-01, 1.9589e+00, 2.0400e+00, -1.0883e+00, 1.0515e+00,\n",
" 5.3959e-02, 1.4358e-01, 1.2383e+00, 4.9123e-01, -1.7719e+00,\n",
" 1.6435e+00, 1.5523e+00, 2.3576e+00, 0.0000e+00, 4.0628e-01,\n",
" -8.2075e-02, -1.2872e+00, 8.3723e-01, -5.6378e-01, 7.0637e-02,\n",
" 4.1508e-01, -0.0000e+00, 1.1651e+00, 1.7333e+00, -1.6842e-01,\n",
" -0.0000e+00, -8.5601e-01, -0.0000e+00, 2.7717e+00, -4.4849e-01,\n",
" -8.4885e-01, 8.1650e-01, 2.1787e+00, -1.0720e+00, -3.1463e-01,\n",
" 1.5798e+00, -6.7880e-01, 0.0000e+00, 5.6090e-01, 7.4153e-01,\n",
" -5.5849e-01, 2.0659e+00, 7.0539e-01, 1.3791e+00, -2.6968e-01,\n",
" -4.5789e-02, 1.6028e+00, -3.0432e-02, -6.3259e-01, -1.3258e+00,\n",
" -8.3697e-01, 6.5333e-01, 2.2756e+00, -5.3934e-01, 4.7520e-01,\n",
" 4.4788e-01, -1.8612e-02, -7.7847e-01, -1.7858e+00, 2.3452e-01,\n",
" 1.9794e+00, -3.1421e-02, -8.5938e-01, -0.0000e+00, 5.9576e-02,\n",
" -2.6836e+00, -1.9927e+00, 2.7139e-01, -1.4617e+00, -8.1419e-01,\n",
" -7.7900e-01, 5.0293e-01, -6.0008e-01, -7.9323e-01, 1.3418e+00,\n",
" 1.3053e-01, -0.0000e+00, -1.2961e+00, -2.7107e+00, -2.3360e+00,\n",
" -7.9603e-01, 5.2071e-01, 1.6896e+00, 9.2845e-01, 0.0000e+00,\n",
" 1.8187e+00, -0.0000e+00, 1.5908e+00, 2.7451e-01, -2.5888e-01,\n",
" 4.0663e-01, -0.0000e+00, -1.3145e+00, -5.9031e-01, 3.6964e-01,\n",
" -1.9539e+00, -1.9995e+00, -8.2193e-01, 3.9374e-01, -6.0678e-01,\n",
" 7.9467e-01, 1.3940e+00, 5.5134e-01, 7.4983e-01, 1.4578e+00,\n",
" -0.0000e+00, -5.0368e-01, -6.8556e-01, 7.7229e-01, -6.5534e-01,\n",
" 1.0936e+00, -2.7885e-01, -1.9658e+00, 1.5950e+00, 8.4796e-01,\n",
" 1.1166e+00, 1.3168e+00, -0.0000e+00, 2.5968e-01, 1.0813e+00,\n",
" 1.8274e-01, -1.6485e+00, 5.7433e-01, -4.9516e-01, 7.1760e-01,\n",
" -4.4680e-01, -1.7915e+00, -6.3027e-01, 2.0462e-01, 7.7905e-01,\n",
" 1.5859e-01, 2.3222e-01, -2.3935e+00, 1.3643e+00, -1.2023e+00,\n",
" -1.6792e+00, 5.5823e-01, -2.0117e+00, -6.2452e-01, 2.4039e+00,\n",
" 2.3736e+00, 5.5896e-02, 9.1725e-01, 6.4464e-01, -2.0675e-01,\n",
" -8.8049e-01, -3.0703e-01, 7.3178e-01, 1.9806e+00, 1.9318e+00,\n",
" -1.1276e+00, -1.3072e-01, 2.4253e-02, 8.4797e-01, 4.8654e-01,\n",
" -1.5352e+00, 8.0822e-01, 1.7595e+00, -2.1682e-01, 2.0735e+00,\n",
" -1.0444e+00, -0.0000e+00, 1.0729e+00, -2.1940e-01, 5.4391e-01,\n",
" 3.5435e-02, -1.5585e-02, -4.8357e-03, -9.3600e-02, 6.3727e-02,\n",
" 1.5162e-01, 1.4191e-01, 1.1063e-01, 5.1059e-02, 2.3501e-02,\n",
" -6.2207e-02, 7.2538e-02, 7.0922e-02, -6.2352e-02, 1.4066e-01,\n",
" -6.8974e-03, -1.6019e-01, -1.8832e-01, -1.7067e-01, -1.5275e-01,\n",
" -2.9574e-02, -5.0036e-02, 2.1154e-01, 7.0534e-02, -1.3852e-01,\n",
" -4.8703e-02, -1.6496e-02, -1.2794e-02, -5.9357e-02, 2.0857e-02,\n",
" -1.0812e-01, 5.0935e-02, 6.5458e-02, 1.3136e-01, -4.5476e-02,\n",
" -4.8890e-03, -1.5270e-01, -1.9004e-01, -1.9268e-03, 2.9531e-02,\n",
" -3.0820e-02, 8.8608e-02, 1.3690e-01, -1.5715e-01, 5.1807e-02,\n",
" -9.9062e-02, -3.0984e-02, -1.7808e-01, -2.8995e-02, 5.5791e-02,\n",
" 5.8522e-02, -1.0453e-01, -2.7097e-03, -4.7650e-02, -3.7730e-02,\n",
" -1.0258e-01, 4.8142e-02, 3.9797e-02, -9.5571e-02, 6.5458e-02,\n",
" -1.4489e-01, 1.9339e-02, -3.8005e-02, 4.0136e-02, 4.9097e-02,\n",
" -1.9247e-01, 6.6852e-02, 7.7364e-02, 6.0379e-02, 1.1870e-01,\n",
" -4.0057e-02, 1.0945e-01, 7.0648e-02, 4.7377e-02, 1.7824e-02,\n",
" -8.8779e-02, -6.3218e-02, 1.1804e-01, -2.5733e-02, -1.7959e-02,\n",
" -8.0674e-02, 8.6741e-02, -4.2754e-02, -9.8244e-02, -1.2859e-02,\n",
" 1.3257e-01, -8.6784e-02, -1.1774e-02, 9.2331e-02, -6.3417e-02,\n",
" -1.7581e-01, -8.3526e-02, -2.3277e-01, 5.7765e-02, 1.8407e-02,\n",
" 6.0199e-02, -1.1321e-01, -1.0885e-01, -1.3705e-01, -9.9638e-02,\n",
" -7.5838e-02, -1.6146e-01, 4.7433e-02, -5.9514e-02, 1.1298e-01,\n",
" -1.3286e-01, 6.7797e-03, -4.8545e-02, -3.7572e-02, 1.7049e-02,\n",
" 7.4291e-02, 2.8442e-02, -1.7075e-01, 2.8328e-02, -1.6143e-02,\n",
" 1.1376e-01, -2.2335e-02, -5.0417e-02, -6.8320e-03, 1.2967e-01,\n",
" 9.6223e-02, 1.8056e-01, -1.7727e-01, -1.6582e-01, 1.6121e-01,\n",
" 5.6873e-02, 7.0338e-02, -3.2107e-02, -1.7414e-01, -9.8330e-02,\n",
" -8.4751e-02, 3.4170e-02, 1.0213e-01, -1.3191e-01, 1.1224e-01,\n",
" -4.6743e-02, 9.2736e-02, -5.2760e-02, -6.9552e-02, 2.2712e-02,\n",
" 4.4459e-02, 2.6758e-02, 1.5629e-01, 8.4847e-04, 2.9560e-02,\n",
" 1.1163e-02, -8.6294e-02, -1.7045e-01, -1.3690e-02, -3.3578e-02,\n",
" -5.3289e-02, 1.4815e-03, -1.3354e-02, -5.3049e-02, 9.9541e-02,\n",
" 4.4520e-02, -1.1904e-01, -1.6747e-01, 1.2955e-01, -1.0718e-01,\n",
" 9.5381e-02, 5.5950e-02, 5.7216e-02, 1.5949e-01, 5.4154e-03,\n",
" -1.0203e-01, 3.0928e-02, -8.2072e-02, 2.2982e-02, -1.4800e-01,\n",
" -8.1458e-02, -1.3399e-03, -1.2277e-03, 1.0457e-01, 2.4771e-02,\n",
" 1.1215e-01, 5.4644e-03, 1.0059e-01, -8.9117e-02, -2.3669e-02,\n",
" -2.3117e-02, -8.9104e-02, 2.3379e-02, 1.6435e-02, -8.0299e-03,\n",
" -4.3092e-02, -4.1300e-03, 2.6272e-01, -2.1100e-01, 1.0265e-01,\n",
" -4.9496e-03, 7.7325e-03, -1.1258e-01, 1.6118e-02, 3.8591e-03,\n",
" 6.9952e-02, 3.5275e-02, -9.4110e-02, 7.6992e-02, 1.0149e-01,\n",
" -1.1243e-01, -1.7381e-01, 2.3158e-02, 1.8389e-01, -2.3291e-01,\n",
" 4.8788e-02, 7.9070e-02, 2.0018e-01, 3.8932e-02, -9.8458e-02,\n",
" -7.4388e-02, 1.3917e-01, 5.1577e-03, 1.1188e-01, 8.5138e-02,\n",
" -1.0618e-01, -9.4835e-02, 7.1822e-02, 3.0813e-02, 1.3624e-02,\n",
" 2.0363e-01, -5.0962e-02, 6.1539e-02, 1.1643e-01, 2.4200e-02,\n",
" -7.1730e-02, 9.5475e-02, -7.9572e-02, 8.5584e-02, 3.9502e-03,\n",
" -1.3701e-01, -1.6142e-01, 6.0496e-02, -1.3962e-01, -2.8607e-02,\n",
" 2.9515e-02, 5.1506e-02, -8.7967e-02, 2.4942e-02, -2.2634e-01,\n",
" 4.7778e-03, -3.8064e-02, -1.9145e-03, 1.8559e-02, -2.0943e-02,\n",
" -9.2896e-02, -1.3714e-01, 5.1929e-03, -1.2374e-01, -1.0901e-01,\n",
" -6.0571e-02, 5.2448e-02, 3.5082e-02, 2.8269e-02, 2.6405e-02,\n",
" 8.6625e-02]], grad_fn=<CatBackward0>)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ipdb> torch.cat((embedded[0], attn_applied[0]), 1).shape\n",
"torch.Size([1, 456])\n",
"ipdb> attnn_weights\n",
"*** NameError: name 'attnn_weights' is not defined\n",
"ipdb> attn_weights.shape\n",
"torch.Size([1, 10])\n",
"ipdb> attn_applied\n",
"tensor([[[ 0.0354, -0.0156, -0.0048, -0.0936, 0.0637, 0.1516, 0.1419,\n",
" 0.1106, 0.0511, 0.0235, -0.0622, 0.0725, 0.0709, -0.0624,\n",
" 0.1407, -0.0069, -0.1602, -0.1883, -0.1707, -0.1528, -0.0296,\n",
" -0.0500, 0.2115, 0.0705, -0.1385, -0.0487, -0.0165, -0.0128,\n",
" -0.0594, 0.0209, -0.1081, 0.0509, 0.0655, 0.1314, -0.0455,\n",
" -0.0049, -0.1527, -0.1900, -0.0019, 0.0295, -0.0308, 0.0886,\n",
" 0.1369, -0.1571, 0.0518, -0.0991, -0.0310, -0.1781, -0.0290,\n",
" 0.0558, 0.0585, -0.1045, -0.0027, -0.0476, -0.0377, -0.1026,\n",
" 0.0481, 0.0398, -0.0956, 0.0655, -0.1449, 0.0193, -0.0380,\n",
" 0.0401, 0.0491, -0.1925, 0.0669, 0.0774, 0.0604, 0.1187,\n",
" -0.0401, 0.1094, 0.0706, 0.0474, 0.0178, -0.0888, -0.0632,\n",
" 0.1180, -0.0257, -0.0180, -0.0807, 0.0867, -0.0428, -0.0982,\n",
" -0.0129, 0.1326, -0.0868, -0.0118, 0.0923, -0.0634, -0.1758,\n",
" -0.0835, -0.2328, 0.0578, 0.0184, 0.0602, -0.1132, -0.1089,\n",
" -0.1371, -0.0996, -0.0758, -0.1615, 0.0474, -0.0595, 0.1130,\n",
" -0.1329, 0.0068, -0.0485, -0.0376, 0.0170, 0.0743, 0.0284,\n",
" -0.1708, 0.0283, -0.0161, 0.1138, -0.0223, -0.0504, -0.0068,\n",
" 0.1297, 0.0962, 0.1806, -0.1773, -0.1658, 0.1612, 0.0569,\n",
" 0.0703, -0.0321, -0.1741, -0.0983, -0.0848, 0.0342, 0.1021,\n",
" -0.1319, 0.1122, -0.0467, 0.0927, -0.0528, -0.0696, 0.0227,\n",
" 0.0445, 0.0268, 0.1563, 0.0008, 0.0296, 0.0112, -0.0863,\n",
" -0.1705, -0.0137, -0.0336, -0.0533, 0.0015, -0.0134, -0.0530,\n",
" 0.0995, 0.0445, -0.1190, -0.1675, 0.1295, -0.1072, 0.0954,\n",
" 0.0559, 0.0572, 0.1595, 0.0054, -0.1020, 0.0309, -0.0821,\n",
" 0.0230, -0.1480, -0.0815, -0.0013, -0.0012, 0.1046, 0.0248,\n",
" 0.1121, 0.0055, 0.1006, -0.0891, -0.0237, -0.0231, -0.0891,\n",
" 0.0234, 0.0164, -0.0080, -0.0431, -0.0041, 0.2627, -0.2110,\n",
" 0.1026, -0.0049, 0.0077, -0.1126, 0.0161, 0.0039, 0.0700,\n",
" 0.0353, -0.0941, 0.0770, 0.1015, -0.1124, -0.1738, 0.0232,\n",
" 0.1839, -0.2329, 0.0488, 0.0791, 0.2002, 0.0389, -0.0985,\n",
" -0.0744, 0.1392, 0.0052, 0.1119, 0.0851, -0.1062, -0.0948,\n",
" 0.0718, 0.0308, 0.0136, 0.2036, -0.0510, 0.0615, 0.1164,\n",
" 0.0242, -0.0717, 0.0955, -0.0796, 0.0856, 0.0040, -0.1370,\n",
" -0.1614, 0.0605, -0.1396, -0.0286, 0.0295, 0.0515, -0.0880,\n",
" 0.0249, -0.2263, 0.0048, -0.0381, -0.0019, 0.0186, -0.0209,\n",
" -0.0929, -0.1371, 0.0052, -0.1237, -0.1090, -0.0606, 0.0524,\n",
" 0.0351, 0.0283, 0.0264, 0.0866]]], grad_fn=<BmmBackward0>)\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> torch.cat((embedded[0], attn_applied[0]), 1).shape\n",
"torch.Size([1, 456])\n",
"ipdb> self.attn_combine(output).unsqueeze(0).shape\n",
"*** RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x200 and 456x200)\n",
"ipdb> output = self.attn_combine(output).unsqueeze(0)\n",
"*** RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x200 and 456x200)\n",
"ipdb> output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
"ipdb> output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
"ipdb> c\n",
"> \u001b[0;32m/tmp/ipykernel_41821/2519748186.py\u001b[0m(27)\u001b[0;36mforward\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 25 \u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 26 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m---> 27 \u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membedded\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattn_applied\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 28 \u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattn_combine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 29 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
"ipdb> output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
"ipdb> attn_weights.shape\n",
"torch.Size([1, 10])\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> output.shape\n",
"torch.Size([1, 456])\n",
"ipdb> self.attn_combine(output).unsqueeze(0).shape\n",
"torch.Size([1, 1, 200])\n"
]
}
],
"source": [
"trainIters(encoder1, attn_decoder1, 10_000, print_every=50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"evaluateRandomly(encoder1, attn_decoder1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## ZADANIE\n",
"\n",
"Gonito \"WMT2017 Czech-English machine translation challenge for news \"\n",
"\n",
"Proszę wytrenować najpierw model german -> english, a później dotrenować na czech-> english.\n",
"Można wziąć inicjalizować enkoder od nowa lub nie. Proszę w każdym razie użyć wytrenowanego dekodera."
]
}
],
"metadata": {
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"lang": "pl",
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
},
"subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
"title": "Ekstrakcja informacji",
"year": "2021"
},
"nbformat": 4,
"nbformat_minor": 4
}

6
cw/12_Model_transformer_autoregresywny.ipynb → cw/15_Model_transformer_autoregresywny.ipynb
View File

@ -7,7 +7,7 @@
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka</h1>\n",
"<h2> 12. <i>Model rekurencyjny z atencją</i> [ćwiczenia]</h2> \n",
"<h2> 15. <i>Model transformer autoregresywny</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
@ -4638,7 +4638,7 @@
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
@ -4653,7 +4653,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
"version": "3.8.3"
},
"subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
"title": "Ekstrakcja informacji",