995 lines
60 KiB
Plaintext
995 lines
60 KiB
Plaintext
{
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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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"## Modelowanie języka – laboratoria\n",
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"### 24 kwietnia 2024\n",
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"# 9. Model neuronowy rekurencyjny"
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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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},
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"execution_count": 8,
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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.index_to_word[x] for x in [ 551, 18, 17, 255, 10748]]"
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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": 9,
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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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"['tak', 'jak', 'człowiek', 'zbudzony', 'ze']"
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]
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},
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"execution_count": 9,
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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.index_to_word[x] for x in [ 18, 17, 255, 10748, 34]]"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"input_tensor = torch.tensor([[ 551, 18, 17, 255, 10748]], dtype=torch.int32).to(device)"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"#input_tensor = torch.tensor([[ 551, 18]], dtype=torch.int32).to(device)"
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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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Model(nn.Module):\n",
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" def __init__(self, vocab_size):\n",
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" super(Model, self).__init__()\n",
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" self.lstm_size = 128\n",
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" self.embedding_dim = 128\n",
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" self.num_layers = 3\n",
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"\n",
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" self.embedding = nn.Embedding(\n",
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" num_embeddings=vocab_size,\n",
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" embedding_dim=self.embedding_dim,\n",
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" )\n",
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" self.lstm = nn.LSTM(\n",
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" input_size=self.lstm_size,\n",
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" hidden_size=self.lstm_size,\n",
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" num_layers=self.num_layers,\n",
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" dropout=0.2,\n",
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" )\n",
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" self.fc = nn.Linear(self.lstm_size, vocab_size)\n",
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"\n",
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" def forward(self, x, prev_state = None):\n",
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" embed = self.embedding(x)\n",
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" output, state = self.lstm(embed, prev_state)\n",
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" logits = self.fc(output)\n",
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" return logits, state\n",
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"\n",
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" def init_state(self, sequence_length):\n",
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" return (torch.zeros(self.num_layers, sequence_length, self.lstm_size).to(device),\n",
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" torch.zeros(self.num_layers, sequence_length, self.lstm_size).to(device))"
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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": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = Model(len(dataset)).to(device)"
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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": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"y_pred, (state_h, state_c) = model(input_tensor)"
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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": 15,
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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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"tensor([[[ 0.0046, -0.0113, 0.0313, ..., 0.0198, -0.0312, 0.0223],\n",
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" [ 0.0039, -0.0110, 0.0303, ..., 0.0213, -0.0302, 0.0230],\n",
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" [ 0.0029, -0.0133, 0.0265, ..., 0.0204, -0.0297, 0.0219],\n",
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" [ 0.0010, -0.0120, 0.0282, ..., 0.0241, -0.0314, 0.0241],\n",
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" [ 0.0038, -0.0106, 0.0346, ..., 0.0230, -0.0333, 0.0232]]],\n",
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" grad_fn=<AddBackward0>)"
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]
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},
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"execution_count": 15,
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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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"y_pred"
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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": 16,
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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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"torch.Size([1, 5, 1187998])"
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]
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},
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"execution_count": 16,
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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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"y_pred.shape"
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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": 17,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"def train(dataset, model, max_epochs, batch_size):\n",
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" model.train()\n",
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"\n",
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" dataloader = DataLoader(dataset, batch_size=batch_size)\n",
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" criterion = nn.CrossEntropyLoss()\n",
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" optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
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"\n",
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" for epoch in range(max_epochs):\n",
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" for batch, (x, y) in enumerate(dataloader):\n",
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" optimizer.zero_grad()\n",
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" x = x.to(device)\n",
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" y = y.to(device)\n",
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"\n",
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" y_pred, (state_h, state_c) = model(x)\n",
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" loss = criterion(y_pred.transpose(1, 2), y)\n",
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"\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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"\n",
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" print({ 'epoch': epoch, 'update in batch': batch, '/' : len(dataloader), 'loss': loss.item() })\n"
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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": 18,
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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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"{'epoch': 0, 'update in batch': 0, '/': 18563, 'loss': 10.717817306518555}\n",
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"{'epoch': 0, 'update in batch': 1, '/': 18563, 'loss': 10.699922561645508}\n",
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||
"{'epoch': 0, 'update in batch': 2, '/': 18563, 'loss': 10.701103210449219}\n",
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"{'epoch': 0, 'update in batch': 3, '/': 18563, 'loss': 10.700254440307617}\n",
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"{'epoch': 0, 'update in batch': 4, '/': 18563, 'loss': 10.69465160369873}\n",
|
||
"{'epoch': 0, 'update in batch': 5, '/': 18563, 'loss': 10.681333541870117}\n",
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"{'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",
|
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"{'epoch': 0, 'update in batch': 9, '/': 18563, 'loss': 10.655462265014648}\n",
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"{'epoch': 0, 'update in batch': 10, '/': 18563, 'loss': 10.591516494750977}\n",
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||
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"{'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",
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"{'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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||
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||
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||
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"{'epoch': 0, 'update in batch': 25, '/': 18563, 'loss': 8.89719295501709}\n",
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"{'epoch': 0, 'update in batch': 27, '/': 18563, 'loss': 9.36513614654541}\n",
|
||
"{'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",
|
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"{'epoch': 0, 'update in batch': 30, '/': 18563, 'loss': 8.274016380310059}\n",
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|
||
"{'epoch': 0, 'update in batch': 32, '/': 18563, 'loss': 8.923280715942383}\n",
|
||
"{'epoch': 0, 'update in batch': 33, '/': 18563, 'loss': 8.479402542114258}\n",
|
||
"{'epoch': 0, 'update in batch': 34, '/': 18563, 'loss': 8.42425537109375}\n",
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||
"{'epoch': 0, 'update in batch': 35, '/': 18563, 'loss': 9.487113952636719}\n",
|
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"{'epoch': 0, 'update in batch': 36, '/': 18563, 'loss': 8.314191818237305}\n",
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"{'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",
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"{'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",
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"{'epoch': 0, 'update in batch': 43, '/': 18563, 'loss': 7.880677223205566}\n",
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"{'epoch': 0, 'update in batch': 44, '/': 18563, 'loss': 7.807427406311035}\n",
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{
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"name": "stdout",
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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: "
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||
]
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}
|
||
],
|
||
"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:\n",
|
||
"\n",
|
||
"https://gonito.csi.wmi.amu.edu.pl/challenge/challenging-america-word-gap-prediction\n",
|
||
"\n",
|
||
"Punktacja: **100 punktów**\n",
|
||
"\n",
|
||
"Deadline: **29 maja 2024** przed zajęciami"
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
}
|