From 879a1c5730d9c911db5e632d9127c2269a99637b Mon Sep 17 00:00:00 2001 From: Orthodoxal Date: Sat, 28 Oct 2023 01:43:58 +0400 Subject: [PATCH] lab7 done --- ilbekov_dmitriy_lab_7/README.md | 33 ++++++++++++ ilbekov_dmitriy_lab_7/console_eng.jpg | Bin 0 -> 59104 bytes ilbekov_dmitriy_lab_7/console_rus.jpg | Bin 0 -> 93669 bytes ilbekov_dmitriy_lab_7/lab7.py | 71 +++++++++++++++++++++++++ ilbekov_dmitriy_lab_7/text_english.txt | 11 ++++ ilbekov_dmitriy_lab_7/text_russian.txt | 23 ++++++++ 6 files changed, 138 insertions(+) create mode 100644 ilbekov_dmitriy_lab_7/README.md create mode 100644 ilbekov_dmitriy_lab_7/console_eng.jpg create mode 100644 ilbekov_dmitriy_lab_7/console_rus.jpg create mode 100644 ilbekov_dmitriy_lab_7/lab7.py create mode 100644 ilbekov_dmitriy_lab_7/text_english.txt create mode 100644 ilbekov_dmitriy_lab_7/text_russian.txt diff --git a/ilbekov_dmitriy_lab_7/README.md b/ilbekov_dmitriy_lab_7/README.md new file mode 100644 index 0000000..7a8ed1b --- /dev/null +++ b/ilbekov_dmitriy_lab_7/README.md @@ -0,0 +1,33 @@ +# Лабораторная работа 7 +### Вариант 10 + +### Задание: +- Выбрать художественный текст (четные варианты – русскоязычный, нечетные – англоязычный)и обучить на нем рекуррентную нейронную сеть для решения задачи генерации. Подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату. +### Запуск +- Запустить файл lab7.py (Для английского языка необходимо в коде выбрать соответсвующий файл и начальную строку) + +### Технологии +- Язык - 'Python' +- Библиотеки numpy, keras, tensorflow + +### Что делает +- Программа читает текст из файла .txt +- Программа использует `Tokenizer` с настройкой `char_level=True`, что позволяет упразднить использование `Embedding` слоя далее +- Программа подготавливает исходный текст и последовательности для обучения модели +- Далее строится последовательная рекуррентная нейронная сеть `Sequential` (использует LSTM-ячейки) с двумя слоями LSTM. `LSTM` (Long Short-Term Memory) является типом рекуррентной нейронной сети, который хорошо подходит для работы с последовательными данными, такими как текст. Использование двух слоев позволяет модели иметь больше гибкости и уловить более сложные зависимости в тексте. Слой `Dense` с функцией активации softmax используется для предсказания следующего слова +- Модель компилируется с использованием функции потерь `sparse_categorical_crossentropy` и оптимизатора `Adam`. +- После чего происходит обучение модели на 100 эпохах (оптимальный вариант) +- После обучения программа, используя начальный текст генерирует текст размером 250 (по умолчанию) символами + +### Пример работы +Пример работы представлен в виде скриншотов: + +Сгенерированный текст: +- (RUS) Уходили в тайгу в середине октября, когда выпадал первый снег. это было чудесное время. за одну ночь природа преображалась: все вокруг светлело. остывший воздух становился упругим и звонким. всюду на снегу виднелись следы зверьков — так тайга перед человеком открыв +- (ENG) I had last night. but was too drunk to remember to shut the popholes. with the ring of light from his lantern dancing from side to side, he lurched across the yard, kicked off his boots at the back door. when major saw that they should all meet in the bedroom went o + +Скриншоты: +![Console](console_rus.jpg) +![Graphics](console_eng.jpg) + +- В результате программа генерирует вполне себе осмысленный текст как на русском, так и на английском языках. \ No newline at end of file diff --git a/ilbekov_dmitriy_lab_7/console_eng.jpg b/ilbekov_dmitriy_lab_7/console_eng.jpg new file mode 100644 index 0000000000000000000000000000000000000000..0cb27b95e66669a6b370d108ffbde8af803c8a5a GIT binary patch literal 59104 zcmeFZcU%)&w=g^*q4(aKfFMPB?;uT-W&uG!dhab@AQYt+1wjP`1QZ1U3q_FL5$OmD zQW8*5KoV+z5Wd0Fp7)&dKKI=3cfb43_YCaJ%9_1b+k4htd-j?feLR{6=+5dJ=>rf5 z02qRQz!3`2(t~@r0>Jt6fG7X}WB??=1Au`LSOwq!3jh!m68(musw{;51_RZ{7mv`i z!$!Kg_GT8Q`bOvUev+nbb@2E1CZYoXAK&0W3j-ZKt1H%gq)Q-84uA>}0|XtMg8a43 z%+4KC`0acQ|8eY0{FDF~lQ^dJ2mT*H^v*6pPM`{AU~Na|ASWLX2Bn2PaPkif1^|*{ zyg*p6|1k^}fGH3Z5QGztVYgr42gk7EFYt?>buL=yf^|AUBT+dx1iAqL%`k}P3w3e> zY0zLmSjyMg!xsQ(`9N61$H&hZgs*|H2w2A-gpcVfIsOYBw|{{h92|eA>EPh@JN%0- 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