From 3a868e5545f6fbd1552a53f9e70d45427c4a5cac Mon Sep 17 00:00:00 2001 From: DmitriyAntonov Date: Thu, 12 Oct 2023 21:19:26 +0400 Subject: [PATCH 1/3] =?UTF-8?q?=D1=80=D0=B5=D0=B0=D0=B4=D0=B81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- antonov_dmitry_lab_7/README.md | 84 +++++++++++++++++++++++++ antonov_dmitry_lab_7/for_test.py | 34 ++++++++++ antonov_dmitry_lab_7/lab7.py | 55 ++++++++++++++++ antonov_dmitry_lab_7/my_model.h5 | Bin 0 -> 2346864 bytes antonov_dmitry_lab_7/rus.txt | 13 ++++ antonov_dmitry_lab_7/screens/img.png | Bin 0 -> 40777 bytes antonov_dmitry_lab_7/screens/img_1.png | Bin 0 -> 33062 bytes antonov_dmitry_lab_7/screens/img_2.png | Bin 0 -> 45535 bytes antonov_dmitry_lab_7/screens/img_3.png | Bin 0 -> 12886 bytes antonov_dmitry_lab_7/screens/img_4.png | Bin 0 -> 47603 bytes antonov_dmitry_lab_7/small.txt | 16 +++++ 11 files changed, 202 insertions(+) create mode 100644 antonov_dmitry_lab_7/README.md create mode 100644 antonov_dmitry_lab_7/for_test.py create mode 100644 antonov_dmitry_lab_7/lab7.py create mode 100644 antonov_dmitry_lab_7/my_model.h5 create mode 100644 antonov_dmitry_lab_7/rus.txt create mode 100644 antonov_dmitry_lab_7/screens/img.png create mode 100644 antonov_dmitry_lab_7/screens/img_1.png create mode 100644 antonov_dmitry_lab_7/screens/img_2.png create mode 100644 antonov_dmitry_lab_7/screens/img_3.png create mode 100644 antonov_dmitry_lab_7/screens/img_4.png create mode 100644 antonov_dmitry_lab_7/small.txt diff --git a/antonov_dmitry_lab_7/README.md b/antonov_dmitry_lab_7/README.md new file mode 100644 index 0000000..93a515e --- /dev/null +++ b/antonov_dmitry_lab_7/README.md @@ -0,0 +1,84 @@ +# Лаб 7 RNN + +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. Далее +разбиться на пары четный-нечетный вариант, обменяться разработанными +сетями и проверить, как архитектура товарища справляется с вашим текстом. +В завершении подобрать компромиссную архитектуру, справляющуюся +достаточно хорошо с обоими видами текстов. + +# Вариант 3 + +Рекуррентная нейронная сеть и задача +генерации текста + +# Запуск + +Выполнением скрипта файла (вывод в консоль). + +# Описание модели: + +Использованы библиотеки: +* numpy (np): популярная библиотека для научных вычислений. +* tensorflow (tf): библиотека для тренировки нейросетей. +* Sequential: тип Keras модель которая позволяет создавать нейросети слой за слоем. +* Embedding, LSTM, Dense: различные типы слоев в нейросетях. +* Tokenizer: класс для конвертации слов в числовой понятный для нейросети формат. +

+Каждая строка текста переводится в числа с помощью Tokernizer. +Класс Tokenizer в Keras - это утилита обработки текста, которая преобразует текст в +последовательность целых чисел. Он присваивает уникальное целое число (индекс) каждому слову +в тексте и создает словарь, который сопоставляет каждое слово с соответствующим индексом. +Это позволяет вам работать с текстовыми данными в формате, который может быть передан в нейронную сеть. +Все это записывается в input_sequences. + +Строим RNN модель используя Keras: + +* Embedding: Этот слой превращает числа в векторы плотности фиксированного размера. Так же известного +как "word embeddings". Вложения слов - это плотные векторные представления слов в непрерывном +векторном пространстве.Они позволяют нейронной сети изучать и понимать взаимосвязи между словами +на основе их контекста в содержании текста. +* LSTM: это тип рекуррентной нейронной сети (RNN), которая предназначена для обработки +зависимостей в последовательностях. +* Dense: полносвязный слой с множеством нейронов, нейронов столько же сколько и уникальных слов. +Он выводит вероятность следующего слова. + +* Модель обучаем на разном количестве эпох, по умолчанию epochs = 100 (итераций по всему набору данных). + +Определеяем функцию generate_text которая принимает стартовое слово, а также, число слов для генерации. +Модель генерирует текст путем многократного предсказания следующего слова на основе предыдущих слов в +начальном тексте. + +* В конце мы получаем сгенерированную на основе текста последовательность. + +# Задача генерации англоязычного текста +На вход подаем историю с похожими повторяющимися слова. Историю сохраняем в файл. +Задача проверить насколько сеть не станет повторять текст, а будет действительно генерировать +относительно новый текст. + +# Результаты +Тестируется английский текст, приложенный в репозитории. +* на 50 эпохах ответ на I want +* I want to soar high up in the sky like to glide through the clouds feeling the wind beneath my wings i want to fly i want to fly i want to fly i want to fly i want to fly i want to fly i want to fly i want to +* на 100 эпох ответ на I want +* I want to fly i want to soar high up in the sky like a bird to glide through the clouds feeling the wind beneath my wings i want to fly i want to fly i want to spread my wings and soar into the open sky to glide far above the +* на 150 эпохах ответ на I want +* I want to fly i want to spread my wings and soar into the open sky to glide far above the earth unbounded by gravity i want to fly i want to fly i want to fly i want to soar high up in the sky like a bird to glide through +* на 220 эпохах ответ на I want +* I want to fly i want to soar high up in the sky like a bird to glide through the clouds feeling the wind beneath my wings i want to fly i want to fly i want to fly i want to fly i want to fly i want to fly i + +* На 220 эпохах результаты хуже, это произошло скорее всего из-за переобучения(грубый повтор). +* На 50 эпохах нейронная сеть плохо обучена (из 1 места плюс повтор) +* На 100 эпохах средний результат (из 2 мест) +* На 150 эпохах нейронная сеть показывает наилучший результат (из 3 разных мест без повтора) +

+

Обучение
+ +

+ +

+

Результат
+ +

\ No newline at end of file diff --git a/antonov_dmitry_lab_7/for_test.py b/antonov_dmitry_lab_7/for_test.py new file mode 100644 index 0000000..c6b099e --- /dev/null +++ b/antonov_dmitry_lab_7/for_test.py @@ -0,0 +1,34 @@ +import numpy as np +from keras.models import load_model +from keras_preprocessing.sequence import pad_sequences +from keras_preprocessing.text import Tokenizer + +from antonov_dmitry_lab_7.lab7 import tokenizer, max_sequence_length + +# Step 3: Load the pre-trained model +model = load_model('my_model.h5') # Replace with the actual path to your model file + +# Recreate the Tokenizer and compile the model (in case the model was not compiled before saving) +with open('small.txt', 'r') as file: + text = file.read() + +tokenizer = Tokenizer() +tokenizer.fit_on_texts([text]) +total_words = len(tokenizer.word_index) + 1 +def generate_text(seed_text, next_words, model, max_sequence_length): + for _ in range(next_words): + token_list = tokenizer.texts_to_sequences([seed_text])[0] + token_list = pad_sequences([token_list], maxlen=max_sequence_length - 1, padding='pre') + predicted = np.argmax(model.predict(token_list), axis=-1) + output_word = "" + for word, index in tokenizer.word_index.items(): + if index == predicted: + output_word = word + break + seed_text += " " + output_word + return seed_text + + +# Generate text using the loaded model (same as before) +generated_text = generate_text("Once upon a", 50, model, max_sequence_length) +print(generated_text) diff --git a/antonov_dmitry_lab_7/lab7.py b/antonov_dmitry_lab_7/lab7.py new file mode 100644 index 0000000..71f8815 --- /dev/null +++ b/antonov_dmitry_lab_7/lab7.py @@ -0,0 +1,55 @@ +import numpy as np +from keras.models import Sequential +from keras.layers import Embedding, LSTM, Dense +from keras.preprocessing.text import Tokenizer +from keras_preprocessing.sequence import pad_sequences + +# загрузка текста +with open('rus.txt', encoding='utf-8') as file: + text = file.read() + +tokenizer = Tokenizer() +tokenizer.fit_on_texts([text]) +total_words = len(tokenizer.word_index) + 1 + +input_sequences = [] +for line in text.split('\n'): + token_list = tokenizer.texts_to_sequences([line])[0] + for i in range(1, len(token_list)): + n_gram_sequence = token_list[:i + 1] + input_sequences.append(n_gram_sequence) + +max_sequence_length = max([len(x) for x in input_sequences]) +input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_length, padding='pre') + +predictors, labels = input_sequences[:, :-1], input_sequences[:, -1] + +# создание RNN модели +model = Sequential() +model.add(Embedding(total_words, 100, input_length=max_sequence_length - 1)) +model.add(LSTM(150)) +model.add(Dense(total_words, activation='softmax')) + +model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) + +# тренировка модели +model.fit(predictors, labels, epochs=200, verbose=1) + + +# генерация текста на основе модели +def generate_text(seed_text, next_words, model, max_sequence_length): + for _ in range(next_words): + token_list = tokenizer.texts_to_sequences([seed_text])[0] + token_list = pad_sequences([token_list], maxlen=max_sequence_length - 1, padding='pre') + predicted = np.argmax(model.predict(token_list), axis=-1) + output_word = "" + for word, index in tokenizer.word_index.items(): + if index == predicted: + output_word = word + break + seed_text += " " + output_word + return seed_text + + +generated_text = generate_text("Я хочу", 50, model, max_sequence_length) +print(generated_text) diff --git a/antonov_dmitry_lab_7/my_model.h5 b/antonov_dmitry_lab_7/my_model.h5 new file mode 100644 index 0000000000000000000000000000000000000000..f3d25533d3328e3ce54701afbced38cb0a0b7cde GIT binary patch literal 2346864 zcmeEud03BKw{Qb04H`v*CMu*7&G%j;i6V`P=HZu0lO~CzqDiG9qJai7XROq{S4d>a zkeP&#A@eN5_iK8d=XIXpR~c=iILAx9`2y+H0@9*4k^Yz4x8;nbT*84wW0q z)AI-l@(kh;?|mKk`PKh~UsUS_4b%tlUi~k;{bgEzxr49og_matPY-THKU}<@zcPOx 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