MachulinaDV
/
is_dnn
форкнуто от main/is_dnn
1
0
Форкнуть 0
Код Задачи Запросы на слияние Пакеты Проекты Релизы Вики Активность

Загрузил(а) файлы в 'labworks/LW4'

Просмотр исходного кода
main
MachulinaDV 2 недель назад
Родитель a2eede0abc
Сommit a03211b59b
2 изменённых файлов: 377 добавлений и 0 удалений
Показать статистику Скачать Patch файл Скачать Diff файл

Двоичные данные
labworks/LW4/1.png
Просмотреть файл

Двоичный файл не отображается.
Режим "рядом"

После

Ширина:  |  Высота:  |  Размер: 21 KiB

377
labworks/LW4/Report.md
Unescape Просмотреть файл

@ -0,0 +1,377 @@
# Отчёт по лабораторной работе №3
## по теме: "Распознавание изображений"
---
### Выполнили: Бригада 2, Мачулина Д.В., Бирюкова А.С., А-02-22
---
### 1. Создание блокнота и настройка среды. Настройка блокнота для работы с аппаратным ускорителем GPU
```python
from google.colab import drive
drive.mount('/content/drive')
import os
os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab4')
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import ConfusionMatrixDisplay
```
```python
import tensorflow as tf
device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
raise SystemError('GPU device not found')
print('Found GPU at: {}'.format(device_name))
```
---
### 2. Загрузка данных IMBd. Настройка набора данных (4*2 - 1)
```python
# загрузка датасета
from keras.datasets import imdb
vocabulary_size = 5000
index_from = 3
(X_train, y_train), (X_test, y_test) = imdb.load_data(path="imdb.npz",
num_words=vocabulary_size,
skip_top=0,
maxlen=None,
seed=7,
start_char=1,
oov_char=2,
index_from=index_from
)
print('Shape of X train:', X_train.shape)
print('Shape of y train:', y_train.shape)
print('Shape of X test:', X_test.shape)
print('Shape of y test:', y_test.shape)
```
Shape of X train: (25000,) <br>
Shape of y train: (25000,) <br>
Shape of X test: (25000,) <br>
Shape of y test: (25000,) <br>
---
### 3. Вывод отзыва из обучающего множества в виде списка индекса слов. Преобразование списка индексов в текст и вывод отзыва в виде текста. Вывод длины отзыва, метки класса и названия.
```python
# создание словаря для перевода индексов в слова
# заргузка словаря "слово:индекс"
word_to_id = imdb.get_word_index()
# уточнение словаря
word_to_id = {key:(value + index_from) for key,value in word_to_id.items()}
word_to_id["<PAD>"] = 0
word_to_id["<START>"] = 1
word_to_id["<UNK>"] = 2
word_to_id["<UNUSED>"] = 3
# создание обратного словаря "индекс:слово"
id_to_word = {value:key for key,value in word_to_id.items()}
```
```python
#Вывод отзыва из обучающего множества в виде списка индексов слов
some_number = 192
review_indices = X_train[some_number]
print("Список индексов слов:")
print(review_indices)
#Преобразование списка индексов в текст
review_as_text = ' '.join(id_to_word[id] for id in X_train[some_number])
print("\nОтзыв в виде текста:")
print(review_as_text)
```
Список индексов слов:
[1, 225, 164, 433, 74, 2753, 35, 2188, 20, 5, 397, 35, 298, 20, 585, 305, 10, 10, 45, 64, 61, 652, 21, 6, 52, 708, 9, 2, 725, 4, 2, 7, 1451, 1089, 105, 17, 230, 17, 2, 9, 1947, 4, 3238, 11, 135, 422, 26, 6, 2, 1021, 378, 1780, 224, 472, 36, 26, 379, 724, 2607, 387, 178, 1582, 4, 771, 36, 2, 112, 2, 34, 6, 2, 10, 10, 300, 103, 6, 2, 2, 8, 516, 25, 30, 252, 8, 376, 90, 51, 1455, 335, 4010, 33, 54, 25, 2440, 90, 125, 10, 10, 241, 1559, 4, 609, 46, 7, 4, 2, 11, 3826, 2, 5, 11, 1011, 7, 4193, 7, 4684, 2, 3498, 90, 8, 3534, 2, 7, 4779, 10, 10, 342, 92, 1414, 979, 4, 568, 44, 4, 2, 5, 331, 2237, 18, 57, 684, 52, 282, 15, 4, 1788, 71, 2, 34, 90, 10, 10, 470, 137, 269, 8, 1090, 387, 129, 761, 46, 7, 129, 1682, 17, 76, 17, 614, 8, 2, 15, 4, 2, 2, 41, 10, 10, 457, 103, 397, 339, 39, 294, 8, 169, 4, 2, 103, 2, 129, 322, 30, 252, 8, 2222, 98, 245, 17, 515, 17, 614, 38, 25, 70, 393, 90, 31, 23, 31, 57, 213, 11, 112, 2, 208, 10, 10, 150, 474, 115, 535, 15, 101, 415, 62, 30, 2, 8, 231, 6, 171, 2497, 467, 134, 2, 2, 21, 4, 105, 11, 135, 422, 26, 38, 2, 5, 97, 38, 111, 1297, 2497, 15, 45, 2620, 1167, 18, 4, 529, 8, 459, 44, 68, 4369, 237, 36, 26, 1484, 7, 68, 205, 399, 14, 1098, 4, 2, 7, 4, 436, 22, 10, 10, 11, 420, 25, 71, 1535, 4, 2, 161, 570, 19, 2, 2, 105, 237, 36, 533, 26, 1348, 2, 2, 18, 487, 14, 2, 36, 872, 8, 97, 1186, 38, 2, 2270, 15, 32, 281, 7, 635, 271, 46, 4, 2054, 10, 10, 300, 4, 2, 1098, 6, 1002, 1004, 6, 568, 1709, 475, 137, 4, 2311, 9, 2359, 57, 53, 74, 747, 2194, 245, 10, 10, 241, 4, 2, 2, 11, 32, 2580, 7, 2, 4983, 11, 3826, 2, 5, 187, 3400, 7, 84, 246, 57, 31, 85, 74, 4, 1021, 378, 186, 8, 1495, 27, 1032, 2003, 10, 10, 342, 4, 2, 2, 35, 1755, 1166, 7, 567, 15, 62, 28, 556, 101, 406, 112, 10, 10, 470, 4, 836, 139, 69, 57, 1546, 1689, 11, 192, 49, 139, 71, 1504, 1677, 2, 39, 298, 102, 10, 10, 4, 64, 1123, 9, 4, 2, 751, 4, 130, 63, 16, 6, 184, 1770, 136, 237, 12, 16, 2, 725, 4, 322, 45, 99, 78, 4, 1057, 1477, 12, 56, 19, 35, 2, 379, 277, 15, 266, 46, 7, 317, 1845, 10, 10, 371, 4, 2, 496, 4, 231, 7, 135, 422, 144, 30, 3013, 7, 533, 128, 246, 36, 144, 43, 847, 8, 2642, 5, 193, 2, 19, 84, 37, 97, 102, 19, 6, 729, 2, 18, 489, 5, 1663]
Отзыв в виде текста:
<START> there's nothing worse than renting an asian movie and getting an american movie experience instead br br it's only my opinion but a good thriller is <UNK> upon the <UNK> of likable intelligent characters as far as <UNK> is concerned the protagonists in say yes are a <UNK> married couple nicely done unfortunately they are stupid beyond belief let us count the ways they <UNK> being <UNK> by a <UNK> br br 1 after a <UNK> <UNK> to kill you be sure to tell him what hotel you're staying at when you drop him off br br 2 beat the hell out of the <UNK> in broad <UNK> and in front of dozens of witnesses <UNK> allowing him to press <UNK> of assault br br 3 don't bother telling the police about the <UNK> and simply assume for no apparently good reason that the cops were <UNK> by him br br 4 while trying to escape let your lady out of your sight as much as possible to <UNK> that the <UNK> <UNK> her br br 5 after getting help from someone to find the <UNK> after <UNK> your wife be sure to send them away as soon as possible so you can face him one on one no point in being <UNK> right br br now i'd never expect that any person would be <UNK> to making a few mistakes under these <UNK> <UNK> but the characters in say yes are so <UNK> and make so many unbelievable mistakes that it's effectively impossible for the viewer to care about their safety since they are victims of their own doing this kills the <UNK> of the entire film br br in case you were wondering the <UNK> didn't stop with <UNK> <UNK> characters since they themselves are surely <UNK> <UNK> for writing this <UNK> they decided to make situations so <UNK> unrealistic that all sense of reality goes out the window br br 1 the <UNK> kills a cop inside a police station – while the protagonist is asleep no more than ten feet away br br 2 the <UNK> <UNK> in all sorts of <UNK> activities in broad <UNK> and around tons of people yet no one other than the married couple seems to notice his odd behavior br br 3 the <UNK> <UNK> an absurd amount of violence that would have killed any human being br br 4 the suspense scenes had no imagination whatsoever in fact some scenes were direct rip <UNK> from american movies br br the only positive is the <UNK> near the end which was a pretty brutal scene since it was <UNK> upon the wife it's too bad the filmmakers followed it up with an <UNK> stupid ending that comes out of left field br br truly the <UNK> behind the making of say yes should be ashamed of themselves better yet they should just move to california and take <UNK> with people who make movies with a similar <UNK> for quality and intelligence
```python
#Вывод метки и названия класса
class_label = y_train[some_number]
class_name = "Positive" if class_label == 1 else "Negative"
print(f"\nМетка класса: {class_label} - {class_name}")
```
Метка класса: 0 - Negative
---
### 4. Вывод максимальной и минимальной длины отзыва в обучающем множестве
```python
#Вывод длины отзыва
max_review_length = len(max(X_train, key=len))
print(f"\nМаксимальная длина отзыва: {max_review_length}")
min_review_length = len(min(X_train, key=len))
print(f"\nМинимальная длина отзыва: {min_review_length}")
review_length = len(review_indices)
print(f"\nДлина отзыва: {review_length}")
```
Максимальная длина отзыва: 2494 <br>
Минимальная длина отзыва: 11 <br>
Длина отзыва: 502
---
### 5. Предобработка данных
```python
# предобработка данных
from tensorflow.keras.utils import pad_sequences
max_words = 500
X_train = pad_sequences(X_train, maxlen=max_words, value=0, padding='pre', truncating='post')
X_test = pad_sequences(X_test, maxlen=max_words, value=0, padding='pre', truncating='post')
```
---
### 6. Повтор пунктов 4-3. Вывод о том, как преобразовался отзыв после предобработки
```python
#Вывод длины отзыва
max_review_length = len(max(X_train, key=len))
print(f"\nМаксимальная длина отзыва: {max_review_length}")
min_review_length = len(min(X_train, key=len))
print(f"\nМинимальная длина отзыва: {min_review_length}")
review_length = len(review_indices)
print(f"\nДлина отзыва: {review_length}")
```
Максимальная длина отзыва: 500 <br>
Минимальная длина отзыва: 500 <br>
Длина отзыва: 502
```python
#Вывод отзыва из обучающего множества в виде списка индексов слов
some_number = 132
review_indices = X_train[some_number]
print("Список индексов слов:")
print(review_indices)
#Преобразование списка индексов в текст
review_as_text = ' '.join(id_to_word[id] for id in X_train[some_number])
print("\nОтзыв в виде текста:")
print(review_as_text)
```
Список индексов слов:
[ 1 225 164 433 74 2753 35 2188 20 5 397 35 298 20
585 305 10 10 45 64 61 652 21 6 52 708 9 2
725 4 2 7 1451 1089 105 17 230 17 2 9 1947 4
3238 11 135 422 26 6 2 1021 378 1780 224 472 36 26
379 724 2607 387 178 1582 4 771 36 2 112 2 34 6
2 10 10 300 103 6 2 2 8 516 25 30 252 8
376 90 51 1455 335 4010 33 54 25 2440 90 125 10 10
241 1559 4 609 46 7 4 2 11 3826 2 5 11 1011
7 4193 7 4684 2 3498 90 8 3534 2 7 4779 10 10
342 92 1414 979 4 568 44 4 2 5 331 2237 18 57
684 52 282 15 4 1788 71 2 34 90 10 10 470 137
269 8 1090 387 129 761 46 7 129 1682 17 76 17 614
8 2 15 4 2 2 41 10 10 457 103 397 339 39
294 8 169 4 2 103 2 129 322 30 252 8 2222 98
245 17 515 17 614 38 25 70 393 90 31 23 31 57
213 11 112 2 208 10 10 150 474 115 535 15 101 415
62 30 2 8 231 6 171 2497 467 134 2 2 21 4
105 11 135 422 26 38 2 5 97 38 111 1297 2497 15
45 2620 1167 18 4 529 8 459 44 68 4369 237 36 26
1484 7 68 205 399 14 1098 4 2 7 4 436 22 10
10 11 420 25 71 1535 4 2 161 570 19 2 2 105
237 36 533 26 1348 2 2 18 487 14 2 36 872 8
97 1186 38 2 2270 15 32 281 7 635 271 46 4 2054
10 10 300 4 2 1098 6 1002 1004 6 568 1709 475 137
4 2311 9 2359 57 53 74 747 2194 245 10 10 241 4
2 2 11 32 2580 7 2 4983 11 3826 2 5 187 3400
7 84 246 57 31 85 74 4 1021 378 186 8 1495 27
1032 2003 10 10 342 4 2 2 35 1755 1166 7 567 15
62 28 556 101 406 112 10 10 470 4 836 139 69 57
1546 1689 11 192 49 139 71 1504 1677 2 39 298 102 10
10 4 64 1123 9 4 2 751 4 130 63 16 6 184
1770 136 237 12 16 2 725 4 322 45 99 78 4 1057
1477 12 56 19 35 2 379 277 15 266 46 7 317 1845
10 10 371 4 2 496 4 231 7 135 422 144 30 3013
7 533 128 246 36 144 43 847 8 2642 5 193 2 19
84 37 97 102 19 6 729 2 18 489]
Отзыв в виде текста:
<START> there's nothing worse than renting an asian movie and getting an american movie experience instead br br it's only my opinion but a good thriller is <UNK> upon the <UNK> of likable intelligent characters as far as <UNK> is concerned the protagonists in say yes are a <UNK> married couple nicely done unfortunately they are stupid beyond belief let us count the ways they <UNK> being <UNK> by a <UNK> br br 1 after a <UNK> <UNK> to kill you be sure to tell him what hotel you're staying at when you drop him off br br 2 beat the hell out of the <UNK> in broad <UNK> and in front of dozens of witnesses <UNK> allowing him to press <UNK> of assault br br 3 don't bother telling the police about the <UNK> and simply assume for no apparently good reason that the cops were <UNK> by him br br 4 while trying to escape let your lady out of your sight as much as possible to <UNK> that the <UNK> <UNK> her br br 5 after getting help from someone to find the <UNK> after <UNK> your wife be sure to send them away as soon as possible so you can face him one on one no point in being <UNK> right br br now i'd never expect that any person would be <UNK> to making a few mistakes under these <UNK> <UNK> but the characters in say yes are so <UNK> and make so many unbelievable mistakes that it's effectively impossible for the viewer to care about their safety since they are victims of their own doing this kills the <UNK> of the entire film br br in case you were wondering the <UNK> didn't stop with <UNK> <UNK> characters since they themselves are surely <UNK> <UNK> for writing this <UNK> they decided to make situations so <UNK> unrealistic that all sense of reality goes out the window br br 1 the <UNK> kills a cop inside a police station – while the protagonist is asleep no more than ten feet away br br 2 the <UNK> <UNK> in all sorts of <UNK> activities in broad <UNK> and around tons of people yet no one other than the married couple seems to notice his odd behavior br br 3 the <UNK> <UNK> an absurd amount of violence that would have killed any human being br br 4 the suspense scenes had no imagination whatsoever in fact some scenes were direct rip <UNK> from american movies br br the only positive is the <UNK> near the end which was a pretty brutal scene since it was <UNK> upon the wife it's too bad the filmmakers followed it up with an <UNK> stupid ending that comes out of left field br br truly the <UNK> behind the making of say yes should be ashamed of themselves better yet they should just move to california and take <UNK> with people who make movies with a similar <UNK> for quality
Отбросили два последних слова, чтобы длина отзыва стала равна заданному значению (500)
---
### 7. Вывод предобработанных массивов обучающих и тестовых данных и их размерностей
```python
# вывод данных
print('X train: \n',X_train)
print('X train: \n',X_test)
# вывод размерностей
print('Shape of X train:', X_train.shape)
print('Shape of X test:', X_test.shape)
```
X train: <br>
[[ 0 0 0 ... 104 545 7] <br>
[ 0 0 0 ... 2 262 372] <br>
[ 0 0 0 ... 758 10 10] <br>
... <br>
[ 0 0 0 ... 2 27 375] <br>
[ 0 0 0 ... 11 111 531] <br>
[ 0 0 0 ... 152 1833 12]] <br> <br>
X train: <br>
[[ 0 0 0 ... 2 126 3849] <br>
[ 0 0 0 ... 25 1833 12] <br>
[ 0 0 0 ... 129 249 4262] <br>
... <br>
[ 0 0 0 ... 2 24 1178] <br>
[ 0 0 0 ... 61 278 145] <br>
[ 0 0 0 ... 12 5 358]] <br>
Shape of X train: (25000, 500) <br>
Shape of X test: (25000, 500)
---
### 8. Реализация модели рекуррентной нейронной сети, состоящей из слоев Embedding, LSTM, Dropout, Dense и её обучение. Вывод информации об архитектуре нейронной сети.
```python
model = Sequential()
model.add(layers.Embedding(input_dim=vocabulary_size, output_dim=32, input_length=max_words, input_shape=(max_words,)))
model.add(layers.LSTM(76))
model.add(layers.Dropout(0.3))
model.add(layers.Dense(1, activation='sigmoid'))
model.summary()
```
<table>
<thead>
<tr>
<th>Layer (type)</th>
<th>Output Shape</th>
<th>Param #</th>
</tr>
</thead>
<tbody>
<tr>
<td>embedding_1 (Embedding)</td>
<td>(None, 500, 32)</td>
<td>160,000</td>
</tr>
<tr>
<td>lstm_1 (LSTM)</td>
<td>(None, 76) </td>
<td>33,136</td>
</tr>
<tr>
<td>dropout_1 (Dropout)</td>
<td>(None, 76) </td>
<td>0</td>
</tr>
<tr>
<td>dense_1 (Dense)</td>
<td>(None, 1)</td>
<td>77</td>
</tr>
</tbody>
</table>
Total params: 193,213 (754.74 KB) <br>
Trainable params: 193,213 (754.74 KB) <br>
Non-trainable params: 0 (0.00 B)
```python
batch_size = 64
epochs = 5
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.2)
```
```python
test_loss, test_acc = model.evaluate(X_test, y_test)
print(f"\nTest accuracy: {test_acc}")
```
Test accuracy: 0.8670799732208252
---
### 9. Оценить качество обучения на тестовых данных.
```python
y_score = model.predict(X_test)
y_pred = [1 if y_score[i,0]>=0.5 else 0 for i in range(len(y_score))]
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred, labels = [0, 1], target_names=['Negative', 'Positive']))
```
<table>
<thead>
<tr>
<th> </th>
<th>precision</th>
<th>recall</th>
<th>f1-score</th>
<th>support</th>
</tr>
</thead>
<tbody>
<tr>
<td>Negative</td>
<td>0.86</td>
<td>0.88</td>
<td>0.87</td>
<td>12500</td>
</tr>
<tr>
<td>Positive</td>
<td>0.88</td>
<td>0.86</td>
<td>0.87</td>
<td>12500</td>
</tr>
<tr>
<td colspan = 5> </td>
</tr>
<tr>
<td>accuracy</td>
<td> </td>
<td> </td>
<td>0.87</td>
<td>25000</td>
</tr>
<tr>
<td>macro avg</td>
<td>0.87</td>
<td>0.87</td>
<td>0.87</td>
<td>25000</td>
</tr>
<tr>
<td>weighted avg</td>
<td>0.87</td>
<td>0.87</td>
<td>0.87</td>
<td>25000</td>
</tr>
</tbody>
</table>
```python
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
fpr, tpr, thresholds = roc_curve(y_test, y_score)
plt.plot(fpr, tpr)
plt.grid()
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC')
plt.show()
print('Area under ROC is', auc(fpr, tpr))
```
Area under ROC is 0.936850272
**Вывод**
<table>
<thead>
<tr>
<th>Модель</th>
<th>Количество настраиваемых параметров</th>
<th>Количество эпох обучения</th>
<th>Качество классификации тестовой выборки</th>
</tr>
</thead>
<tbody>
<tr>
<td>Рекуррентная</td>
<td><center>193,213</center></td>
<td><center>3</center></td>
<td>accuracy: 0.867<br> loss: 0.331 <br> ROC: 0.936</td>
</tr>
</tbody>
</table>
В ходе лабораторной работы было изучено применение рекуррентной нейронной сети. Исходя из анализа полученных результатов, представленных в таблице, делаем вывод, что модель хорошо справилась с задачей определения тональности текста: accuracy = 0.867, loss = 0.331. Показатель точности превышает требуемый порог в 0,8. Значение ROC превышает 0,9, что свидетельствует о способности модели хорошо различать два класса - отрицательные и положительные отзывы
Редактирование Предпросмотр
Загрузка…
Отмена
Сохранить