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70 KiB
70 KiB
1) В среде Google Colab создали новый блокнот (notebook). Импортировали необходимые для работы библиотеки и модули. Настроили блокнот для работы с аппаратным ускорителем GPU.
from google.colab import drive
drive.mount('/content/drive')Mounted at /content/drive
# импорт модулей
import os
os.mkdir('/content/drive/MyDrive/Colab Notebooks/is_lab4')
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 npimport 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))Found GPU at: /device:GPU:0
2) Загрузили набор данных IMDb, содержащий оцифрованные отзывы на фильмы, размеченные на два класса: позитивные и негативные. При загрузке набора данных параметр seed выбрали равным значению (4k – 1)=15, где k=4 – номер бригады. Вывели размеры полученных обучающих и тестовых массивов данных.
# 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=15,
start_char=1,
oov_char=2,
index_from=index_from
)
# вывод размерностей
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)Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz
17464789/17464789 ━━━━━━━━━━━━━━━━━━━━ 2s 0us/step
Shape of X train: (25000,)
Shape of y train: (25000,)
Shape of X test: (25000,)
Shape of y test: (25000,)
3) Вывели один отзыв из обучающего множества в виде списка индексов слов. Преобразовали список индексов в текст и вывели отзыв в виде текста. Вывели длину отзыва. Вывели метку класса данного отзыва и название класса (1 – Positive, 0 – Negative).
# 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()}Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json
1641221/1641221 ━━━━━━━━━━━━━━━━━━━━ 1s 1us/step
print(X_train[26])
print('len:',len(X_train[26]))[1, 608, 17, 316, 47, 3381, 46, 14, 22, 9, 6, 601, 912, 8, 49, 2461, 14, 9, 88, 12, 16, 6, 2207, 2, 22, 15, 69, 6, 176, 7, 819, 2, 42, 2, 180, 8, 751, 2, 8, 1090, 4, 2, 1745, 675, 21, 4, 22, 47, 111, 85, 1508, 17, 73, 10, 10, 8, 895, 19, 4, 2, 186, 8, 28, 188, 27, 2, 5, 2109, 1849, 56, 4, 2, 11, 14, 22, 26, 2, 5, 92, 40, 3390, 21, 11, 175, 85, 1161, 36, 4486, 40, 2109, 150, 25, 43, 191, 81, 15, 19, 6, 2136, 512, 509, 874, 188, 8, 1231, 8, 4, 2269, 7, 4, 512, 42, 4, 451, 79, 32, 1471, 5, 3222, 34, 2, 2793, 11, 4, 355, 155, 11, 192, 4, 226, 1499, 5, 862, 1353, 114, 9, 142, 15, 47, 460, 77, 224, 18, 2109, 21, 152, 97, 101, 281, 11, 6, 1985, 20, 10, 10, 4129, 4, 1985, 1352, 26, 4, 2, 25, 28, 126, 110, 1814, 11, 4, 1985, 20, 970, 3882, 8, 124, 15, 4, 1985, 1352, 5, 2, 26, 142, 4, 451, 2, 2, 246, 49, 7, 134, 2, 26, 43, 1044, 2968, 10, 10, 50, 26, 6, 378, 7, 1076, 52, 1801, 13, 165, 179, 423, 4, 603, 409, 28, 1046, 2, 2, 2, 5, 10, 10, 1361, 48, 141, 6, 155, 70, 1778, 10, 10, 13, 82, 179, 423, 4, 1347, 18, 2, 4, 2, 2, 50, 26, 38, 111, 189, 102, 15, 2, 23, 105, 2, 2, 21, 11, 14, 420, 36, 86, 2, 6, 55, 2, 5, 1134, 1210, 1985, 2, 5, 140, 4682, 4, 1939, 13, 384, 25, 70, 516, 2, 19, 3390, 3589, 5, 75, 28, 49, 184, 976, 2, 134, 504, 1616, 30, 99, 254, 8, 276, 107, 5, 107, 295, 2, 21, 11, 801, 405, 14, 20, 271, 120, 4, 350, 5, 1608, 49, 85, 55, 2, 5, 1139, 1210, 2, 2872]
len: 323
review_as_text = ' '.join(id_to_word[id] for id in X_train[26])
print(review_as_text)
print('len:',len(review_as_text))<START> ok as everyone has pointed out this film is a complete dog to some degree this is because it was a gory <UNK> film that had a lot of material <UNK> or <UNK> down to near <UNK> to escape the <UNK> x rating but the film has many other flaws as well br br to begin with the <UNK> seems to have got his <UNK> and vampires mixed up the <UNK> in this film are <UNK> and don't like silver but in every other respect they behave like vampires now you just can't do that with a crappy genre flick you've got to stick to the rules of the genre or the fans get all confused and annoyed by <UNK> disbelief in the wrong thing in fact the whole confusing and poorly presented plot is something that has already been done for vampires but doesn't make any sense in a werewolf movie br br secondly the werewolf costumes are the <UNK> you have ever seen anybody in the werewolf movie business ought to know that the werewolf costumes and <UNK> are something the fans <UNK> <UNK> yet some of these <UNK> are just plain goofy br br there are a couple of slightly good bits i actually quite liked the score others have mentioned <UNK> <UNK> <UNK> and br br spoiler if such a thing can exist br br i also quite liked the plan for <UNK> the <UNK> <UNK> there are so many horror movies that <UNK> on characters <UNK> <UNK> but in this case they first <UNK> a very <UNK> and effective anti werewolf <UNK> and go slaughter the monsters i mean you can kill <UNK> with silver bullets and we have some pretty powerful <UNK> these days shouldn't be too hard to put two and two together <UNK> but in typical style this movie goes over the top and adds some other very <UNK> and amusing anti <UNK> weapons
len: 1682
4) Вывели максимальную и минимальную длину отзыва в обучающем множестве.
print('MAX Len: ',len(max(X_train, key=len)))
print('MIN Len: ',len(min(X_train, key=len)))MAX Len: 2494
MIN Len: 11
5) Провели предобработку данных. Выбрали единую длину, к которой будут приведены все отзывы. Короткие отзывы дополнили спецсимволами, а длинные обрезали до выбранной длины.
# 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) Повторили пункт 4.
print('MAX Len: ',len(max(X_train, key=len)))
print('MIN Len: ',len(min(X_train, key=len)))MAX Len: 500
MIN Len: 500
7) Повторили пункт 3. Сделали вывод о том, как отзыв преобразовался после предобработки.
print(X_train[26])
print('len:',len(X_train[26]))[ 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1 608 17 316 47
3381 46 14 22 9 6 601 912 8 49 2461 14 9 88
12 16 6 2207 2 22 15 69 6 176 7 819 2 42
2 180 8 751 2 8 1090 4 2 1745 675 21 4 22
47 111 85 1508 17 73 10 10 8 895 19 4 2 186
8 28 188 27 2 5 2109 1849 56 4 2 11 14 22
26 2 5 92 40 3390 21 11 175 85 1161 36 4486 40
2109 150 25 43 191 81 15 19 6 2136 512 509 874 188
8 1231 8 4 2269 7 4 512 42 4 451 79 32 1471
5 3222 34 2 2793 11 4 355 155 11 192 4 226 1499
5 862 1353 114 9 142 15 47 460 77 224 18 2109 21
152 97 101 281 11 6 1985 20 10 10 4129 4 1985 1352
26 4 2 25 28 126 110 1814 11 4 1985 20 970 3882
8 124 15 4 1985 1352 5 2 26 142 4 451 2 2
246 49 7 134 2 26 43 1044 2968 10 10 50 26 6
378 7 1076 52 1801 13 165 179 423 4 603 409 28 1046
2 2 2 5 10 10 1361 48 141 6 155 70 1778 10
10 13 82 179 423 4 1347 18 2 4 2 2 50 26
38 111 189 102 15 2 23 105 2 2 21 11 14 420
36 86 2 6 55 2 5 1134 1210 1985 2 5 140 4682
4 1939 13 384 25 70 516 2 19 3390 3589 5 75 28
49 184 976 2 134 504 1616 30 99 254 8 276 107 5
107 295 2 21 11 801 405 14 20 271 120 4 350 5
1608 49 85 55 2 5 1139 1210 2 2872]
len: 500
review_as_text = ' '.join(id_to_word[id] for id in X_train[26])
print(review_as_text)
print('len:',len(review_as_text))<PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <PAD> <START> ok as everyone has pointed out this film is a complete dog to some degree this is because it was a gory <UNK> film that had a lot of material <UNK> or <UNK> down to near <UNK> to escape the <UNK> x rating but the film has many other flaws as well br br to begin with the <UNK> seems to have got his <UNK> and vampires mixed up the <UNK> in this film are <UNK> and don't like silver but in every other respect they behave like vampires now you just can't do that with a crappy genre flick you've got to stick to the rules of the genre or the fans get all confused and annoyed by <UNK> disbelief in the wrong thing in fact the whole confusing and poorly presented plot is something that has already been done for vampires but doesn't make any sense in a werewolf movie br br secondly the werewolf costumes are the <UNK> you have ever seen anybody in the werewolf movie business ought to know that the werewolf costumes and <UNK> are something the fans <UNK> <UNK> yet some of these <UNK> are just plain goofy br br there are a couple of slightly good bits i actually quite liked the score others have mentioned <UNK> <UNK> <UNK> and br br spoiler if such a thing can exist br br i also quite liked the plan for <UNK> the <UNK> <UNK> there are so many horror movies that <UNK> on characters <UNK> <UNK> but in this case they first <UNK> a very <UNK> and effective anti werewolf <UNK> and go slaughter the monsters i mean you can kill <UNK> with silver bullets and we have some pretty powerful <UNK> these days shouldn't be too hard to put two and two together <UNK> but in typical style this movie goes over the top and adds some other very <UNK> and amusing anti <UNK> weapons
len: 2744
После обработки в начало отзыва добавилось необходимое количество токенов , чтобы отзыв был длинной в 500 индексов.
8) Вывели предобработанные массивы обучающих и тестовых данных и их размерности.
# 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:
[[ 0 0 0 ... 4 86 273]
[ 0 0 0 ... 705 9 150]
[ 0 0 0 ... 44 12 32]
...
[ 0 0 0 ... 22 8 377]
[ 0 0 0 ... 4 2554 647]
[ 0 0 0 ... 2 4 2]]
X train:
[[ 0 0 0 ... 106 14 31]
[ 0 0 0 ... 458 168 52]
[ 0 0 0 ... 22 6 31]
...
[ 0 0 0 ... 38 76 128]
[ 0 0 0 ... 73 290 12]
[ 0 0 0 ... 12 38 76]]
Shape of X train: (25000, 500)
Shape of X test: (25000, 500)
9) Реализовали модель рекуррентной нейронной сети, состоящей из слоев Embedding, LSTM, Dropout, Dense, и обучили ее на обучающих данных с выделением части обучающих данных в качестве валидационных. Вывели информацию об архитектуре нейронной сети. Добились качества обучения по метрике accuracy не менее 0.8.
embed_dim = 32
lstm_units = 64
model = Sequential()
model.add(layers.Embedding(input_dim=vocabulary_size, output_dim=embed_dim, input_length=max_words, input_shape=(max_words,)))
model.add(layers.LSTM(lstm_units))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
model.summary()/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/embedding.py:97: UserWarning: Argument `input_length` is deprecated. Just remove it.
warnings.warn(
/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/embedding.py:100: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
super().__init__(**kwargs)
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ │ embedding (Embedding) │ (None, 500, 32) │ 160,000 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ lstm (LSTM) │ (None, 64) │ 24,832 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dropout (Dropout) │ (None, 64) │ 0 │ ├─────────────────────────────────┼────────────────────────┼───────────────┤ │ dense (Dense) │ (None, 1) │ 65 │ └─────────────────────────────────┴────────────────────────┴───────────────┘
Total params: 184,897 (722.25 KB)
Trainable params: 184,897 (722.25 KB)
Non-trainable params: 0 (0.00 B)
# компилируем и обучаем модель
batch_size = 64
epochs = 3
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)Epoch 1/3
313/313 ━━━━━━━━━━━━━━━━━━━━ 12s 23ms/step - accuracy: 0.6328 - loss: 0.6075 - val_accuracy: 0.7826 - val_loss: 0.4588
Epoch 2/3
313/313 ━━━━━━━━━━━━━━━━━━━━ 6s 21ms/step - accuracy: 0.8121 - loss: 0.4143 - val_accuracy: 0.8628 - val_loss: 0.3359
Epoch 3/3
313/313 ━━━━━━━━━━━━━━━━━━━━ 10s 21ms/step - accuracy: 0.8905 - loss: 0.2795 - val_accuracy: 0.8506 - val_loss: 0.3324
<keras.src.callbacks.history.History at 0x7a8b2b9509e0>test_loss, test_acc = model.evaluate(X_test, y_test)
print(f"\nTest accuracy: {test_acc}")
print(f"\nTest loss: {test_loss}")782/782 ━━━━━━━━━━━━━━━━━━━━ 8s 10ms/step - accuracy: 0.8544 - loss: 0.3396
Test accuracy: 0.8564800024032593
Test loss: 0.33131280541419983
10) Оценили качество обучения на тестовых данных:
- вывели значение метрики качества классификации на тестовых данных
- вывели отчет о качестве классификации тестовой выборки
- построили ROC-кривую по результату обработки тестовой выборки и вычислили площадь под ROC-кривой (AUC ROC)
#значение метрики качества классификации на тестовых данных
print(f"\nTest accuracy: {test_acc}")
Test accuracy: 0.8564800024032593
#отчет о качестве классификации тестовой выборки
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']))782/782 ━━━━━━━━━━━━━━━━━━━━ 7s 8ms/step
precision recall f1-score support
Negative 0.83 0.90 0.86 12500
Positive 0.89 0.82 0.85 12500
accuracy 0.86 25000
macro avg 0.86 0.86 0.86 25000
weighted avg 0.86 0.86 0.86 25000
#построение ROC-кривой и AUC ROC
from sklearn.metrics import roc_curve, auc
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('AUC ROC:', auc(fpr, tpr))
AUC ROC: 0.9356664896
11) Сделали выводы по результатам применения рекуррентной нейронной сети для решения задачи определения тональности текста.
Таблица1:
| Модель | Количество настраиваемых параметров | Количество эпох обучения | Качество классификации тестовой выборки |
|---|---|---|---|
| Рекуррентная | 184 897 | 3 | accuracy:0.8564 ; loss:0.3313 ; AUC ROC:0.9357 |
По результатам применения рекуррентной нейронной сети можно сделать вывод, что модель остаточно неплохо справилась с задачей определения тональности текста. Показатель accuracy = 0.8564 превышает требуемый порог 0.8. Значение AUC ROC = 0.9357 (> 0.9) говорит о высокой способности модели различать два класса на положительные и отрицательные области.