From 55fb6276006647c1d523446a3c03b974634e1b61 Mon Sep 17 00:00:00 2001 From: rita Date: Thu, 30 Nov 2023 09:27:04 +0400 Subject: [PATCH] mashkova_margarita_lab_7 ready --- mashkova_margarita_lab_7/1lstm50ep.png | Bin 0 -> 25516 bytes mashkova_margarita_lab_7/1lstm50ep_eng.png | Bin 0 -> 21492 bytes mashkova_margarita_lab_7/2lstm100ep.png | Bin 0 -> 25040 bytes mashkova_margarita_lab_7/2lstm100ep_eng.png | Bin 0 -> 24207 bytes mashkova_margarita_lab_7/2lstm500ep.png | Bin 0 -> 26064 bytes mashkova_margarita_lab_7/2lstm500ep_eng.png | Bin 0 -> 23963 bytes mashkova_margarita_lab_7/2lstm50ep_eng.png | Bin 0 -> 25225 bytes mashkova_margarita_lab_7/2lstm50ep_v1.png | Bin 0 -> 26286 bytes mashkova_margarita_lab_7/2lstm50ep_v2.png | Bin 0 -> 27889 bytes mashkova_margarita_lab_7/README.md | 95 ++++++++++++++++++++ mashkova_margarita_lab_7/english_text.txt | 1 + mashkova_margarita_lab_7/main.py | 80 +++++++++++++++++ mashkova_margarita_lab_7/russian_text.txt | 1 + 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Она предоставляет простой и интуитивно понятный интерфейс для создания +и обучения различных моделей машинного обучения, включая нейронные сети. +> - `numpy` - предоставляет мощные инструменты для работы с многомерными массивами и выполнения математических операций над ними. + +## Описание работы программы + +Т.к. нейронная сеть принимает на вход числа, необходимо закодировать исходные тексты. +Сделать это можно с помощью Tokenizer. Метод `fit_on_texts()` создает словарь вида (слово - индекс) +на основе частоты использования слов. Чем меньше индекс, тем чаще встречается слово. +Метод `texts_to_sequences()` преобразовывает текст в последовательность чисел. +Таким образом каждое слово принимает целочисленное значение. + +![Результат кодирования текста](tokenize.png) + +Затем создаются входные и выходные последовательности, инициализируется, настраивается и компилируется модель. +Модель обучается на данных и генерирует текст на основе начальной фразы. + +## Тесты + +### Варьирование количества слоев и эпох обучения для русскоязычного текста: + +1 слой и 50 эпох обучения: +![1 слой 50 эпох](1lstm50ep.png) +Присутствуют осмысленные словосочетания, например, `преобразившейся природе`, `бодрящей свежести`, +`ветерок поднимающийся изредка пощипывает лицо уши` (хоть это должен быть и морозец, но все же) + +2 слоя и 50 эпох обучения (1 вариант): +![2 слоя 50 эпох_1вариант](2lstm50ep_v1.png) +Присутствуют осмысленные словосочетания, например, `снежинки кружатся`, `наполняет чувством`, +`в воздухе кружатся`, `невольно наполняет`, `морозец он колюч приятен` + +2 слоя и 50 эпох обучения (2 вариант): +![2 слоя 50 эпох_2вариант](2lstm50ep_v2.png) +Помимо некоторых осмысленных словосочетаний, если исключить повторы слов, можно прочесть вполне осмысленную фразу: +`невольно улыбаешься, хочется сказать дружески этому зимнему утру "здравствуй зима"` + +2 слоя и 100 эпох обучения: +![2 слоя 100 эпох](2lstm100ep.png) +В данном случае присутствуют осмысленные словосочетания, и при этом эпитеты стоят рядом с нужными существительными +(подходящим по тематике), +т.е., например, снежинки - `мягкие`, `нежные`, `кружатся`, морозец - `колюч`, магазины - `окна`, +солнце - `переливается слепящим блеском` и т.д. + +2 слоя и 500 эпох обучения: +![2 слоя 500 эпох](2lstm500ep.png) +Увеличение количества эпох снизило качество генерации текста. + +### Варьирование количества слоев и эпох обучения для англоязычного текста: + +1 слой и 50 эпох обучения: +![1 слой 50 эпох](1lstm50ep_eng.png) + +2 слоя и 50 эпох обучения: +![2 слоя 50 эпох](2lstm50ep_eng.png) + +2 слоя и 100 эпох обучения: +![2 слоя 100 эпох](2lstm100ep_eng.png) + +2 слоя и 500 эпох обучения: +![2 слоя 500 эпох](2lstm500ep_eng.png) + +В данном случае видно, что увеличение количество слоев и эпох положительно влияет на генерацию текста. + +**Вывод:** т.к. кодировались в числа не буквы, а слово, для оценки работы модели нужно обращать внимание +на осмысленность условных предложений, а не самих слов. Увелечение количества слоев привело к более хорошему результату +для обоих текстов. + +Для русскоязычного текста увеличение количества эпох обучения не сильно улучшает результат, а иногда даже ухудшает. +Скорее всего, потому что данных не так много и они разнообразные, и размер потерь не сильно уменьшается в каждой +эпохе обучения. + +Для англоязычного текста увеличение количества эпох привело к сильному улучшению результата +(как минимум, сократились повторения одних и тех же слов подряд). +Скорее всего, потому что частота встречаемости слов, таких как, например, `gingerbread`, `cookies`, слишком большая, +и поэтому малообученная модель их часто использует. + +Для русскоязычного текста генерация текста выполнилась сильно лучше, чем для англоязычного. diff --git a/mashkova_margarita_lab_7/english_text.txt b/mashkova_margarita_lab_7/english_text.txt new file mode 100644 index 0000000..6435bee --- /dev/null +++ b/mashkova_margarita_lab_7/english_text.txt @@ -0,0 +1 @@ +Once upon a time there was an old woman who loved baking gingerbread. She would bake gingerbread cookies, cakes, houses and gingerbread people, all decorated with chocolate and peppermint, caramel candies and coloured frosting. She lived with her husband on a farm at the edge of town. The sweet spicy smell of gingerbread brought children skipping and running to see what would be offered that day. Unfortunately the children gobbled up the treats so fast that the old woman had a hard time keeping her supply of flour and spices to continue making the batches of gingerbread. Sometimes she suspected little hands of having reached through her kitchen window because gingerbread pieces and cookies would disappear. One time a whole gingerbread house vanished mysteriously. \ No newline at end of file diff --git a/mashkova_margarita_lab_7/main.py b/mashkova_margarita_lab_7/main.py new file mode 100644 index 0000000..6de837c --- /dev/null +++ b/mashkova_margarita_lab_7/main.py @@ -0,0 +1,80 @@ +import numpy as np +from keras.models import Sequential +from keras.layers import LSTM, Dense, Embedding +from keras.preprocessing.text import Tokenizer +from keras.preprocessing.sequence import pad_sequences + + +# filename = "russian_text.txt" +filename = "english_text.txt" +# Чтение текста из файла +with open(filename, "r", encoding="utf-8") as f: + text = f.read() + +# Создание токенизатора +tokenizer = Tokenizer() +# Создает словарь вида (слово - индекс) на основе частоты использования слов +# чем меньше индекс, тем чаще встречается слово +# т.е. каждое слово принимает целочисленное значение +tokenizer.fit_on_texts([text]) + +# print("Словарь:") +# print(tokenizer.word_index) + +# Преобразование текста в последовательность чисел +sequences = tokenizer.texts_to_sequences([text])[0] +vocab_size = len(tokenizer.word_index) + 1 +# print("\nЗакодированный текст:") +# print(sequences) + +# Длина входных последовательностей +seq_length = 5 + +# Создание входных и выходных последовательностей +X_data = [] +y_data = [] +for i in range(seq_length, len(sequences)): + sequence = sequences[i - seq_length:i] + target = sequences[i] + X_data.append(sequence) + y_data.append(target) + +X = np.array(X_data) +y = np.array(y_data) + +# Создание модели +model = Sequential() +model.add(Embedding(input_dim=vocab_size, output_dim=128, input_length=seq_length)) +model.add(LSTM(256, return_sequences=True)) +model.add(LSTM(256)) +model.add(Dense(vocab_size, activation='softmax')) + +# Компиляция модели +model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy']) + +# Обучение модели +model.fit(X, y, epochs=500, batch_size=64, verbose=1) + +# Начальная фраза для генерации +seed_text = "an old woman" +# Длина генерируемого текста +gen_length = 100 + + +# Функция для генерации текста +def generate_text(seed_text, gen_length): + generated_text = seed_text + for _ in range(gen_length): + sequence = tokenizer.texts_to_sequences([seed_text])[0] + sequence = pad_sequences([sequence], maxlen=seq_length) + prediction = model.predict(sequence, verbose=0) + predicted_index = np.argmax(prediction) + predicted_word = [word for word, index in tokenizer.word_index.items() if index == predicted_index][0] + generated_text += " " + predicted_word + seed_text += " " + predicted_word + return generated_text + + +# Генерация текста +generated_text = generate_text(seed_text, gen_length) +print(generated_text) diff --git a/mashkova_margarita_lab_7/russian_text.txt b/mashkova_margarita_lab_7/russian_text.txt new file mode 100644 index 0000000..71e1d4b --- /dev/null +++ b/mashkova_margarita_lab_7/russian_text.txt @@ -0,0 +1 @@ +Итак, она пришла, долгожданная зима! Хорошо пробежаться по морозцу в первое зимнее утро! Улицы, вчера еще по-осеннему унылые, сплошь покрыты белым снегом, и солнце переливается в нем слепяшим блеском. Причудливый узор мороза лег на витрины магазинов и наглухо закрытые окна домов, иней покрыл ветви тополей. Глянешь ли вдоль улицы, вытянувшейся ровной лентой, вблизи ли вокруг себя посмотришь, везде все то же: снег, снег, снег. Изредка подымающийся ветерок пощипывает лицо и уши, зато как красиво все вокруг! Какие нежные, мягкие снежинки плавно кружатся в воздухе. Как ни колюч морозец, он тоже приятен. Не за то ли все мы любим зиму, что она так же, как весна, наполняет грудь волнующим чувством. Все живо, все ярко в преобразившейся природе, все полно бодрящей свежести. Так легко дышится и так хорошо на душе, что невольно улыбаешься и хочется сказать дружески этому чудному зимнему утру: «Здравствуй, зима!» \ No newline at end of file diff --git a/mashkova_margarita_lab_7/tokenize.png b/mashkova_margarita_lab_7/tokenize.png new file mode 100644 index 0000000000000000000000000000000000000000..e755c156ccba133c73ae2ade2ced729a423b7fe1 GIT binary patch literal 79539 zcmb@tbyQp5*7sYZv}g+jiWeyqFYZu^6?c~)#U(h!3lwMzg#yK`I23m$MT5IriiBVx zKnM^>;PQK)bDp>7-sk>t$9wk}89Q^2ow4R#Yi91X)@OddXsRm`;!)w7AVR zojW+4ckbM^esu5lN>K9G(c9BqPi@7wcPhtdc5jUbb~0)*cka~0;bSauZtcfz?~FX} z+#%}u=W`bfEO~$D&P}0`oQ&=V^MkC%rq7@^U|*k>WT@toyHF`8iRLq)jMiNq0U5PX zj*v7P)V#7v$TfSPGB5wIa>Wc02Xa9CWrOCX=7YOWw@5UUgug8{DhikQ)|3A}euisc>Hpih{qp^Ovig76zo#DmZIc*^ z-Tljcb5Di!FZ0eji`@r*+mA=%NH_h3?s{fZBWxKQg94?GM|fBhhTLPs9{zRT5ioDR zl9UFVS8BBUPsI;KM@~_9c>@+hNTSJ?#HJv$Nzp3{pPyV>VVi+fuw9aq1hV&~{}rf=_ANezi>N2D%i zDR8gJ#iJtP1PhfZCxl*;?>_oQh$@-?50COj=6mrj4C|Wn>g&tP%Zu(7jly*yIMjv5843 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