Lab. 8 i 9
This commit is contained in:
Paweł Skórzewski
parent
311d523c10
commit
ca35499814
135
lab/08_Model_neuronowy_typu_word2vec.ipynb
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135
lab/08_Model_neuronowy_typu_word2vec.ipynb
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<h1> Modelowanie Języka</h1>\n",
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"<h2> 9. <i>Model neuronowy typu word2vec</i> [ćwiczenia]</h2> "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Zadania"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Proszę wykonać zadanie 1 lub zadanie 2 (nie oba naraz). Zadanie 3 można zrobić niezależnie."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Zadanie 1\n",
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"\n",
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"Wzorując się na materiałach z wykładu stworzyć 5-gramowy model neuronowy oparty na jednym ze schematów z wykładu, np.\n",
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"\n",
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"\n",
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"![img](bow1.drawio.png \"Model typu worek słów\")\n",
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"\n",
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"\n",
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"Warunkiem koniecznym jest, żeby przewidywać słowo środkowe, np. Mając tekst ['Ala', 'ma', '[MASK]'\n",
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" 'i', 'psa'] chcemy przewidzieć kontekst środkowego słowa (tutaj '[MASK]')\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"Warunki zaliczenia:\n",
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"- wynik widoczny na platformie zarówno dla dev i dla test\n",
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"- wynik dla dev i test lepszy (niższy) niż 6.50 (liczone przy pomocy geval)\n",
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"- deadline do końca dnia 15 maja 2024\n",
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"- 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",
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"- zadania wykonujemy samodzielnie\n",
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"- w nazwie commita podaj nr indeksu\n",
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"- w tagach podaj **neural-network** oraz **5gram**!\n",
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"- zadanie tym razem jest dla polskiego odpowiednika word-gap https://gonito.net/challenge-my-submissions/retro-gap\n",
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"- metryka to LogLossHashed (praktycznie to samo, co PerlpexityHased). Przelicznik, to LogLossHased = log(PerplexityHashed). Podając równe prawd. dla każdego słowa dostaniemy 6.93, bo log(1024) = 6.93\n",
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"\n",
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"Punktacja:\n",
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"- podstawa: 60 punktów\n",
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"- 40 punktów z najlepszy wynik z 2 grup\n",
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"- 20 punktów z 3 kolejno najlepszych wyników z 2 grup\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Jak stworzyć model?\n",
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"- warto bazować na kodzie ze wykładu 7 Zanurzenia słów\n",
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"- elementy, które na pewno trzeba będzie wykorzystać to: nn.Embedding, nn.Linear, nn.Softmax\n",
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"- w odróżnieniu do materiałów z wykładu lepiej nie korzystać z nn.Sequential, tylko wszystki operacje zapisywać w model.forward. Przy użyciu sequential może być problem np. z dodawaniem lub konkatenacją tensorów"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### W jaki sposób uzyskać lepszy wynik?"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"- Po pierwsze proszę stosować sie do rad z poprzednich cwiczeń (trenowanie przez kilka epok i monitorowanie wyniku na zbiorze deweloperskim)\n",
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"- dobry start to zawsze zaczęcie od jak najprostszego modelu (czyli 1 warstwa liniowa, zwykłe dodawanie embeddingów słów) i dopiero później go rozbudowywać monitorując wynik. Jest to rada uniwersalna w uczeniu maszynowym.\n",
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"- Poza tym warto wypróbować przynajmniej kilka modeli z wykładu. Mając zaimplementowany cały kod dla jednego modelu, wystarczy jedynie delikatnie zmienić architekturę modelu i wytrenować go od nowa. Cała reszta kodu zostaje bez zmian.\n",
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"- warto spróbować dodanie np 2 warstw liniowych (lub nawet 3) zamiast jednej warstwy (koniecznie trzeba dodać między nimi funkcję aktywacji, np RELU).\n",
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"- poza tym można zmieniać różne parametry (np. wielkość słownika, wielkość warstwy ukrytej, różne funkcje aktywacji)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Zadanie 2\n",
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"\n",
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"Proszę zrobić parameter Hyperparameter Tuning dla zadania 1 i zaprezentować na forum grupy razem z wnioskami\n",
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"\n",
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"- wymóg wyniku najlepszego modelu, conajwyżej 6.10\n",
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"- wnioski nie muszą być specjalnie rozbudowane, prezentacja może trwać 3-5minut lub dłużej\n",
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"- należy wybrać dla siebie metodę hypermarameter tuningu\n",
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"- należy stworzyć conajmniej 10 modeli, należy pokazać wyniku dla conajmniej paru\n",
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"- oczywiście kod musi być automatyczny (a nie ręcznie zmieniamy paratery), natomiast nie ma wymogu korzystania ze specjalnych bibliotek\n",
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"- podstawa punktów 100\n",
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"- za wynik lepszy (niższy) niż 5.50 +20 punktów\n",
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"- użycie GPU na dowolnym cloud lub od WMI + 30 punktów\n",
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"- deadline do końca dnia 15 maja 2024"
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]
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}
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],
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"metadata": {
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"author": "Jakub Pokrywka",
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"email": "kubapok@wmi.amu.edu.pl",
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"lang": "pl",
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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},
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"subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
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"title": "Ekstrakcja informacji",
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"year": "2021"
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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987
lab/09_Model_neuronowy_rekurencyjny.ipynb
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lab/09_Model_neuronowy_rekurencyjny.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<h1> Modelowanie Języka</h1>\n",
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"<h2> 10. <i>Model neuronowy rekurencyjny</i> [ćwiczenia]</h2> "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch import nn, optim\n",
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"from torch.utils.data import DataLoader\n",
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"import numpy as np\n",
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"from collections import Counter\n",
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"import re"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"device = 'cpu'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--2022-05-08 19:27:04-- https://wolnelektury.pl/media/book/txt/potop-tom-pierwszy.txt\n",
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"Resolving wolnelektury.pl (wolnelektury.pl)... 51.83.143.148, 2001:41d0:602:3294::\n",
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"Connecting to wolnelektury.pl (wolnelektury.pl)|51.83.143.148|:443... connected.\n",
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"HTTP request sent, awaiting response... 200 OK\n",
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"Length: 877893 (857K) [text/plain]\n",
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"Saving to: ‘potop-tom-pierwszy.txt.2’\n",
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"\n",
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"potop-tom-pierwszy. 100%[===================>] 857,32K --.-KB/s in 0,07s \n",
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"\n",
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"2022-05-08 19:27:04 (12,0 MB/s) - ‘potop-tom-pierwszy.txt.2’ saved [877893/877893]\n",
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"\n",
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"--2022-05-08 19:27:04-- https://wolnelektury.pl/media/book/txt/potop-tom-drugi.txt\n",
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"Resolving wolnelektury.pl (wolnelektury.pl)... 51.83.143.148, 2001:41d0:602:3294::\n",
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"Connecting to wolnelektury.pl (wolnelektury.pl)|51.83.143.148|:443... connected.\n",
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"HTTP request sent, awaiting response... 200 OK\n",
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"Length: 1087797 (1,0M) [text/plain]\n",
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"Saving to: ‘potop-tom-drugi.txt.2’\n",
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"\n",
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"potop-tom-drugi.txt 100%[===================>] 1,04M --.-KB/s in 0,08s \n",
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"\n",
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"2022-05-08 19:27:04 (12,9 MB/s) - ‘potop-tom-drugi.txt.2’ saved [1087797/1087797]\n",
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"\n",
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"--2022-05-08 19:27:05-- https://wolnelektury.pl/media/book/txt/potop-tom-trzeci.txt\n",
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"Resolving wolnelektury.pl (wolnelektury.pl)... 51.83.143.148, 2001:41d0:602:3294::\n",
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"Connecting to wolnelektury.pl (wolnelektury.pl)|51.83.143.148|:443... connected.\n",
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"HTTP request sent, awaiting response... 200 OK\n",
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"Length: 788219 (770K) [text/plain]\n",
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"Saving to: ‘potop-tom-trzeci.txt.2’\n",
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"\n",
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"potop-tom-trzeci.tx 100%[===================>] 769,75K --.-KB/s in 0,06s \n",
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"\n",
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"2022-05-08 19:27:05 (12,0 MB/s) - ‘potop-tom-trzeci.txt.2’ saved [788219/788219]\n",
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"\n"
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]
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}
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],
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"source": [
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"! wget https://wolnelektury.pl/media/book/txt/potop-tom-pierwszy.txt\n",
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"! wget https://wolnelektury.pl/media/book/txt/potop-tom-drugi.txt\n",
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"! wget https://wolnelektury.pl/media/book/txt/potop-tom-trzeci.txt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"!cat potop-* > potop.txt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Dataset(torch.utils.data.Dataset):\n",
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" def __init__(\n",
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" self,\n",
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" sequence_length,\n",
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" ):\n",
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" self.sequence_length = sequence_length\n",
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" self.words = self.load()\n",
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" self.uniq_words = self.get_uniq_words()\n",
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"\n",
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" self.index_to_word = {index: word for index, word in enumerate(self.uniq_words)}\n",
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" self.word_to_index = {word: index for index, word in enumerate(self.uniq_words)}\n",
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"\n",
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" self.words_indexes = [self.word_to_index[w] for w in self.words]\n",
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"\n",
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" def load(self):\n",
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" with open('potop.txt', 'r') as f_in:\n",
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" text = [x.rstrip() for x in f_in.readlines() if x.strip()]\n",
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" text = ' '.join(text).lower()\n",
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" text = re.sub('[^a-ząćęłńóśźż ]', '', text) \n",
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" text = text.split(' ')\n",
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" return text\n",
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" \n",
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" \n",
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" def get_uniq_words(self):\n",
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" word_counts = Counter(self.words)\n",
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" return sorted(word_counts, key=word_counts.get, reverse=True)\n",
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"\n",
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" def __len__(self):\n",
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" return len(self.words_indexes) - self.sequence_length\n",
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"\n",
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" def __getitem__(self, index):\n",
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" return (\n",
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" torch.tensor(self.words_indexes[index:index+self.sequence_length]),\n",
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" torch.tensor(self.words_indexes[index+1:index+self.sequence_length+1]),\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"dataset = Dataset(5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(tensor([ 551, 18, 17, 255, 10748]),\n",
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" tensor([ 18, 17, 255, 10748, 34]))"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"dataset[200]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['patrzył', 'tak', 'jak', 'człowiek', 'zbudzony']"
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]
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|
},
|
||||||
|
"execution_count": 8,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"[dataset.index_to_word[x] for x in [ 551, 18, 17, 255, 10748]]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 9,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"['tak', 'jak', 'człowiek', 'zbudzony', 'ze']"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 9,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"[dataset.index_to_word[x] for x in [ 18, 17, 255, 10748, 34]]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 10,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"input_tensor = torch.tensor([[ 551, 18, 17, 255, 10748]], dtype=torch.int32).to(device)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 11,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"#input_tensor = torch.tensor([[ 551, 18]], dtype=torch.int32).to(device)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 12,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"class Model(nn.Module):\n",
|
||||||
|
" def __init__(self, vocab_size):\n",
|
||||||
|
" super(Model, self).__init__()\n",
|
||||||
|
" self.lstm_size = 128\n",
|
||||||
|
" self.embedding_dim = 128\n",
|
||||||
|
" self.num_layers = 3\n",
|
||||||
|
"\n",
|
||||||
|
" self.embedding = nn.Embedding(\n",
|
||||||
|
" num_embeddings=vocab_size,\n",
|
||||||
|
" embedding_dim=self.embedding_dim,\n",
|
||||||
|
" )\n",
|
||||||
|
" self.lstm = nn.LSTM(\n",
|
||||||
|
" input_size=self.lstm_size,\n",
|
||||||
|
" hidden_size=self.lstm_size,\n",
|
||||||
|
" num_layers=self.num_layers,\n",
|
||||||
|
" dropout=0.2,\n",
|
||||||
|
" )\n",
|
||||||
|
" self.fc = nn.Linear(self.lstm_size, vocab_size)\n",
|
||||||
|
"\n",
|
||||||
|
" def forward(self, x, prev_state = None):\n",
|
||||||
|
" embed = self.embedding(x)\n",
|
||||||
|
" output, state = self.lstm(embed, prev_state)\n",
|
||||||
|
" logits = self.fc(output)\n",
|
||||||
|
" return logits, state\n",
|
||||||
|
"\n",
|
||||||
|
" def init_state(self, sequence_length):\n",
|
||||||
|
" return (torch.zeros(self.num_layers, sequence_length, self.lstm_size).to(device),\n",
|
||||||
|
" torch.zeros(self.num_layers, sequence_length, self.lstm_size).to(device))"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 13,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"model = Model(len(dataset)).to(device)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 14,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"y_pred, (state_h, state_c) = model(input_tensor)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 15,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"tensor([[[ 0.0046, -0.0113, 0.0313, ..., 0.0198, -0.0312, 0.0223],\n",
|
||||||
|
" [ 0.0039, -0.0110, 0.0303, ..., 0.0213, -0.0302, 0.0230],\n",
|
||||||
|
" [ 0.0029, -0.0133, 0.0265, ..., 0.0204, -0.0297, 0.0219],\n",
|
||||||
|
" [ 0.0010, -0.0120, 0.0282, ..., 0.0241, -0.0314, 0.0241],\n",
|
||||||
|
" [ 0.0038, -0.0106, 0.0346, ..., 0.0230, -0.0333, 0.0232]]],\n",
|
||||||
|
" grad_fn=<AddBackward0>)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 15,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"y_pred"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 16,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"torch.Size([1, 5, 1187998])"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 16,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"y_pred.shape"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 17,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"def train(dataset, model, max_epochs, batch_size):\n",
|
||||||
|
" model.train()\n",
|
||||||
|
"\n",
|
||||||
|
" dataloader = DataLoader(dataset, batch_size=batch_size)\n",
|
||||||
|
" criterion = nn.CrossEntropyLoss()\n",
|
||||||
|
" optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
|
||||||
|
"\n",
|
||||||
|
" for epoch in range(max_epochs):\n",
|
||||||
|
" for batch, (x, y) in enumerate(dataloader):\n",
|
||||||
|
" optimizer.zero_grad()\n",
|
||||||
|
" x = x.to(device)\n",
|
||||||
|
" y = y.to(device)\n",
|
||||||
|
"\n",
|
||||||
|
" y_pred, (state_h, state_c) = model(x)\n",
|
||||||
|
" loss = criterion(y_pred.transpose(1, 2), y)\n",
|
||||||
|
"\n",
|
||||||
|
" loss.backward()\n",
|
||||||
|
" optimizer.step()\n",
|
||||||
|
"\n",
|
||||||
|
" print({ 'epoch': epoch, 'update in batch': batch, '/' : len(dataloader), 'loss': loss.item() })\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 18,
|
||||||
|
"metadata": {
|
||||||
|
"scrolled": true
|
||||||
|
},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"{'epoch': 0, 'update in batch': 0, '/': 18563, 'loss': 10.717817306518555}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 1, '/': 18563, 'loss': 10.699922561645508}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 2, '/': 18563, 'loss': 10.701103210449219}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 3, '/': 18563, 'loss': 10.700254440307617}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 4, '/': 18563, 'loss': 10.69465160369873}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 5, '/': 18563, 'loss': 10.681333541870117}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 6, '/': 18563, 'loss': 10.668376922607422}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 7, '/': 18563, 'loss': 10.675261497497559}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 8, '/': 18563, 'loss': 10.665823936462402}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 9, '/': 18563, 'loss': 10.655462265014648}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 10, '/': 18563, 'loss': 10.591516494750977}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 11, '/': 18563, 'loss': 10.580559730529785}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 12, '/': 18563, 'loss': 10.524133682250977}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 13, '/': 18563, 'loss': 10.480895042419434}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 14, '/': 18563, 'loss': 10.33996295928955}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 15, '/': 18563, 'loss': 10.345580101013184}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 16, '/': 18563, 'loss': 10.200639724731445}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 17, '/': 18563, 'loss': 10.030133247375488}\n",
|
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|
"{'epoch': 0, 'update in batch': 18, '/': 18563, 'loss': 10.046720504760742}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 19, '/': 18563, 'loss': 10.00318717956543}\n",
|
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|
"{'epoch': 0, 'update in batch': 20, '/': 18563, 'loss': 9.588350296020508}\n",
|
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|
"{'epoch': 0, 'update in batch': 21, '/': 18563, 'loss': 9.780914306640625}\n",
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|
"{'epoch': 0, 'update in batch': 22, '/': 18563, 'loss': 9.36646842956543}\n",
|
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|
"{'epoch': 0, 'update in batch': 23, '/': 18563, 'loss': 9.306387901306152}\n",
|
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|
"{'epoch': 0, 'update in batch': 24, '/': 18563, 'loss': 9.150574684143066}\n",
|
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|
"{'epoch': 0, 'update in batch': 25, '/': 18563, 'loss': 8.89719295501709}\n",
|
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|
"{'epoch': 0, 'update in batch': 26, '/': 18563, 'loss': 8.741975784301758}\n",
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"{'epoch': 0, 'update in batch': 27, '/': 18563, 'loss': 9.36513614654541}\n",
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|
"{'epoch': 0, 'update in batch': 28, '/': 18563, 'loss': 8.840768814086914}\n",
|
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|
"{'epoch': 0, 'update in batch': 29, '/': 18563, 'loss': 8.356801986694336}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 30, '/': 18563, 'loss': 8.274016380310059}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 31, '/': 18563, 'loss': 8.944927215576172}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 32, '/': 18563, 'loss': 8.923280715942383}\n",
|
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|
"{'epoch': 0, 'update in batch': 33, '/': 18563, 'loss': 8.479402542114258}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 34, '/': 18563, 'loss': 8.42425537109375}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 35, '/': 18563, 'loss': 9.487113952636719}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 36, '/': 18563, 'loss': 8.314191818237305}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 37, '/': 18563, 'loss': 8.0274658203125}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 38, '/': 18563, 'loss': 8.725769996643066}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 39, '/': 18563, 'loss': 8.67934799194336}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 40, '/': 18563, 'loss': 8.872161865234375}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 41, '/': 18563, 'loss': 7.883971214294434}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 42, '/': 18563, 'loss': 7.682810306549072}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 43, '/': 18563, 'loss': 7.880677223205566}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 44, '/': 18563, 'loss': 7.807427406311035}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 45, '/': 18563, 'loss': 7.93829870223999}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 46, '/': 18563, 'loss': 7.718912601470947}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 47, '/': 18563, 'loss': 8.309863090515137}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 48, '/': 18563, 'loss': 9.091133117675781}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 49, '/': 18563, 'loss': 9.317312240600586}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 50, '/': 18563, 'loss': 8.517735481262207}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 51, '/': 18563, 'loss': 7.697592258453369}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 52, '/': 18563, 'loss': 6.838181972503662}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 53, '/': 18563, 'loss': 7.967227935791016}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 54, '/': 18563, 'loss': 8.47049331665039}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 55, '/': 18563, 'loss': 8.958921432495117}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 56, '/': 18563, 'loss': 8.316679000854492}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 57, '/': 18563, 'loss': 8.997099876403809}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 58, '/': 18563, 'loss': 8.608811378479004}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 59, '/': 18563, 'loss': 9.377460479736328}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 60, '/': 18563, 'loss': 8.6201171875}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 61, '/': 18563, 'loss': 8.821510314941406}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 62, '/': 18563, 'loss': 8.915961265563965}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 63, '/': 18563, 'loss': 8.222617149353027}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 64, '/': 18563, 'loss': 9.266777992248535}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 65, '/': 18563, 'loss': 8.749354362487793}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 66, '/': 18563, 'loss': 8.311641693115234}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 67, '/': 18563, 'loss': 8.553888320922852}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 68, '/': 18563, 'loss': 8.790258407592773}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 69, '/': 18563, 'loss': 9.090133666992188}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 70, '/': 18563, 'loss': 8.893723487854004}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 71, '/': 18563, 'loss': 8.844594955444336}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 72, '/': 18563, 'loss': 7.771625518798828}\n",
|
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|
"{'epoch': 0, 'update in batch': 73, '/': 18563, 'loss': 8.536479949951172}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 74, '/': 18563, 'loss': 7.300860404968262}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 75, '/': 18563, 'loss': 8.62000846862793}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 76, '/': 18563, 'loss': 8.67784309387207}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 77, '/': 18563, 'loss': 7.319235801696777}\n",
|
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|
"{'epoch': 0, 'update in batch': 78, '/': 18563, 'loss': 8.322186470031738}\n",
|
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|
"{'epoch': 0, 'update in batch': 79, '/': 18563, 'loss': 7.767421722412109}\n",
|
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|
"{'epoch': 0, 'update in batch': 80, '/': 18563, 'loss': 8.817885398864746}\n",
|
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|
"{'epoch': 0, 'update in batch': 81, '/': 18563, 'loss': 8.133109092712402}\n",
|
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|
"{'epoch': 0, 'update in batch': 82, '/': 18563, 'loss': 7.822054862976074}\n",
|
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"{'epoch': 0, 'update in batch': 83, '/': 18563, 'loss': 8.055540084838867}\n",
|
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|
"{'epoch': 0, 'update in batch': 84, '/': 18563, 'loss': 8.053682327270508}\n",
|
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|
"{'epoch': 0, 'update in batch': 85, '/': 18563, 'loss': 8.018306732177734}\n",
|
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|
"{'epoch': 0, 'update in batch': 86, '/': 18563, 'loss': 8.371909141540527}\n",
|
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|
"{'epoch': 0, 'update in batch': 87, '/': 18563, 'loss': 8.057979583740234}\n",
|
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|
"{'epoch': 0, 'update in batch': 88, '/': 18563, 'loss': 8.340703010559082}\n",
|
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|
"{'epoch': 0, 'update in batch': 89, '/': 18563, 'loss': 8.7703857421875}\n",
|
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|
"{'epoch': 0, 'update in batch': 90, '/': 18563, 'loss': 9.714847564697266}\n",
|
||||||
|
"{'epoch': 0, 'update in batch': 91, '/': 18563, 'loss': 8.621702194213867}\n",
|
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{
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{
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{
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||||||
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||||||
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"{'epoch': 0, 'update in batch': 491, '/': 18563, 'loss': 8.659430503845215}\n",
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"{'epoch': 0, 'update in batch': 492, '/': 18563, 'loss': 8.693103790283203}\n",
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"{'epoch': 0, 'update in batch': 493, '/': 18563, 'loss': 8.895064353942871}\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"ename": "KeyboardInterrupt",
|
||||||
|
"evalue": "",
|
||||||
|
"output_type": "error",
|
||||||
|
"traceback": [
|
||||||
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||||
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
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|
"\u001b[0;32m<ipython-input-18-fe996a0be74b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvocab_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniq_words\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
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|
"\u001b[0;32m<ipython-input-17-8d700bc624e3>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(dataset, model, max_epochs, batch_size)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\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[1;32m 18\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\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[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
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|
"\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 361\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 362\u001b[0m inputs=inputs)\n\u001b[0;32m--> 363\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 365\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\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[0;32m~/anaconda3/lib/python3.8/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m 174\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 175\u001b[0m allow_unreachable=True, accumulate_grad=True) # Calls into the C++ engine to run the backward pass\n",
|
||||||
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"model = Model(vocab_size = len(dataset.uniq_words)).to(device)\n",
|
||||||
|
"train(dataset, model, 1, 64)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 19,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"def predict(dataset, model, text, next_words=5):\n",
|
||||||
|
" model.eval()\n",
|
||||||
|
" words = text.split(' ')\n",
|
||||||
|
" state_h, state_c = model.init_state(len(words))\n",
|
||||||
|
"\n",
|
||||||
|
" for i in range(0, next_words):\n",
|
||||||
|
" x = torch.tensor([[dataset.word_to_index[w] for w in words[i:]]]).to(device)\n",
|
||||||
|
" y_pred, (state_h, state_c) = model(x, (state_h, state_c))\n",
|
||||||
|
"\n",
|
||||||
|
" last_word_logits = y_pred[0][-1]\n",
|
||||||
|
" p = torch.nn.functional.softmax(last_word_logits, dim=0).detach().cpu().numpy()\n",
|
||||||
|
" word_index = np.random.choice(len(last_word_logits), p=p)\n",
|
||||||
|
" words.append(dataset.index_to_word[word_index])\n",
|
||||||
|
"\n",
|
||||||
|
" return words"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 22,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/plain": [
|
||||||
|
"['kmicic', 'szedł', 'zwycięzco', 'po', 'do', 'zlituj', 'i']"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 22,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"predict(dataset, model, 'kmicic szedł')"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### ZADANIE\n",
|
||||||
|
"\n",
|
||||||
|
"Stworzyć sieć rekurencyjną GRU dla Challenging America word-gap prediction. Wymogi takie jak zawsze, zadanie widoczne na gonito"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"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
|
||||||
|
}
|
||||||
BIN
lab/bow1.drawio.png
Normal file
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lab/bow1.drawio.png
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|
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lab/devsetppl.png
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lab/devsetppl.png
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|
After Width: | Height: | Size: 61 KiB |
Loading…
Reference in New Issue
Block a user