From 7d5463198aa935381c57268f43eab80eac035e2f Mon Sep 17 00:00:00 2001 From: enuementtt Date: Mon, 25 Dec 2023 01:19:51 +0400 Subject: [PATCH] romanova_adelina_lab_7 is ready --- romanova_adelina_lab_7/README.md | 52 + romanova_adelina_lab_7/generate.py | 46 + romanova_adelina_lab_7/generated_text.png | Bin 0 -> 43523 bytes romanova_adelina_lab_7/model.py | 16 + romanova_adelina_lab_7/single-char.pth | Bin 0 -> 1114928 bytes romanova_adelina_lab_7/train.py | 86 + romanova_adelina_lab_7/train_process.png | Bin 0 -> 46013 bytes romanova_adelina_lab_7/wonderland.txt | 3375 +++++++++++++++++++++ 8 files changed, 3575 insertions(+) create mode 100644 romanova_adelina_lab_7/README.md create mode 100644 romanova_adelina_lab_7/generate.py create mode 100644 romanova_adelina_lab_7/generated_text.png create mode 100644 romanova_adelina_lab_7/model.py create mode 100644 romanova_adelina_lab_7/single-char.pth create mode 100644 romanova_adelina_lab_7/train.py create mode 100644 romanova_adelina_lab_7/train_process.png create mode 100644 romanova_adelina_lab_7/wonderland.txt diff --git a/romanova_adelina_lab_7/README.md b/romanova_adelina_lab_7/README.md new file mode 100644 index 0000000..fa60334 --- /dev/null +++ b/romanova_adelina_lab_7/README.md @@ -0,0 +1,52 @@ +# Лабораторная работа №7. Вариант 21 + +## Тема + +Рекуррентная нейронная сеть и задача генерации текста + +## Задание + +- Выбрать художественный текст и обучить на нем рекуррентную нейронную сеть для решения задачи генерации. + +- Подобрать архитектуру и параметры так, чтобы приблизиться к максимально осмысленному результату. + +## Используемые ресурсы + + 1.  Художественный текст на английском языке ```wonderland.txt``` + + 2.  Python-скрипты: ```generate.py```, ```model.py```, ```train.py```. + +## Описание работы + +### Подготовка данных: +В файле ```train.py``` реализована функция ```get_data```, которая загружает художественный текст, приводит его к нижнему регистру, и создает сопоставление символов числовым значениям. + +Текст разбивается на последовательности фиксированной длины ```seq_length```, и каждая последовательность связывается с символом, следующим за ней. + +Данные приводятся к тензорам PyTorch и нормализуются для обучения модели. + +### Архитектура модели: + +В файле ```model.py``` определен класс ```CharModel```, наследуемый от ```nn.Module``` и представляющий собой рекуррентную нейронную сеть. + +Архитектура модели включает в себя один слой LSTM с размером скрытого состояния 256, слой dropout для регуляризации и линейный слой для вывода результатов. + +### Обучение модели: + +В файле ```train.py``` реализован скрипт для обучения модели. Выбрана оптимизация Adam, функция потерь - ```CrossEntropyLoss```. + +Обучение происходит на GPU, если он доступен. Обучение проводится в течение нескольких эпох, с валидацией на каждой эпохе. Сохраняется лучшая модель. + +Процесс обучения модели: + +![](train_process.png "") + +### Генерация текста: + +В файле ```generate.py``` модель загружается из сохраненного состояния. Генерируется случайный промпт из исходного текста, и модель используется для предсказания следующего символа в цикле. + +## Вывод: + +![](generated_text.png "") + +В сгенерированном тексте можно найти осмысленные участки, поэтому можно сделать вывод, что модель действительно хорошо обучилась. diff --git a/romanova_adelina_lab_7/generate.py b/romanova_adelina_lab_7/generate.py new file mode 100644 index 0000000..af35dff --- /dev/null +++ b/romanova_adelina_lab_7/generate.py @@ -0,0 +1,46 @@ +import torch +from model import CharModel +import numpy as np + +if __name__ == "__main__": + best_model, char_to_int = torch.load("single-char.pth") + n_vocab = len(char_to_int) + int_to_char = dict((i, c) for c, i in char_to_int.items()) + + + model = CharModel() + model.load_state_dict(best_model) + + # randomly generate a prompt + filename = "wonderland.txt" + seq_length = 100 + raw_text = open(filename, 'r', encoding='utf-8').read() + raw_text = raw_text.lower() + + start = np.random.randint(0, len(raw_text)-seq_length) + prompt = raw_text[start:start+seq_length] + pattern = [char_to_int[c] for c in prompt] + + model.eval() + print(f'Prompt:\n{prompt}') + print("==="*15, "Сгенерированный результ", "==="*15, sep=" ") + + with torch.no_grad(): + for i in range(1000): + # format input array of int into PyTorch tensor + x = np.reshape(pattern, (1, len(pattern), 1)) / float(n_vocab) + x = torch.tensor(x, dtype=torch.float32) + # generate logits as output from the model + prediction = model(x) + # convert logits into one character + index = int(prediction.argmax()) + result = int_to_char[index] + print(result, end="") + # append the new character into the prompt for the next iteration + pattern.append(index) + pattern = pattern[1:] + + print() + print("==="*30) + print("Done.") + \ No newline at end of file diff --git a/romanova_adelina_lab_7/generated_text.png b/romanova_adelina_lab_7/generated_text.png new file mode 100644 index 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diff --git a/romanova_adelina_lab_7/model.py b/romanova_adelina_lab_7/model.py new file mode 100644 index 0000000..55d92b8 --- /dev/null +++ b/romanova_adelina_lab_7/model.py @@ -0,0 +1,16 @@ +import torch.nn as nn + + +class CharModel(nn.Module): + def __init__(self, n_vocab): + super().__init__() + self.lstm = nn.LSTM(input_size=1, hidden_size=256, num_layers=1, batch_first=True) + self.dropout = nn.Dropout(0.2) + self.linear = nn.Linear(256, n_vocab) + def forward(self, x): + x, _ = self.lstm(x) + # take only the last output + x = x[:, -1, :] + # produce output + x = self.linear(self.dropout(x)) + return x \ No newline at end of file diff --git a/romanova_adelina_lab_7/single-char.pth b/romanova_adelina_lab_7/single-char.pth new file mode 100644 index 0000000000000000000000000000000000000000..be73ac988bb9ee7c50b22171043902b2ea180d2a GIT binary patch literal 1114928 zcmZ^~30TeF*Z1E%iAqS36jEs-g*tof%1|7VG9(SsY^aV#La7LuMMa^6P)SMD*=t7< 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