케라스와 텐서플로 허브를 사용한 영화 리뷰 텍스트 분류하기
- TensorFlow Hub 및 Keras를 사용한 전이 학습의 기본적인 응용
- “영화 리뷰를 사용한 텍스트 분류”와 동일한 IMDB 데이터이나 keras.dataset가 아닌 tensorflow_datasets 사용
- TFHub로부터 훈련된 모델을 로드하기 위한 라이브러리인 tensorflow_hub를 사용
중요 모듈 import
import os
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_datasets as tfds
print("Version: ", tf.__version__)
print("Eager mode: ", tf.executing_eagerly())
print("Hub version: ", hub.__version__)
print("GPU is", "available" if tf.config.list_physical_devices("GPU") else "NOT AVAILABLE")
Version: 2.7.0
Eager mode: True
Hub version: 0.12.0
GPU is available
IMDB 데이터셋 다운로드
- 전체 학습 데이터의 40%를 검증용 데이터 세트로 사용
- tfds.load는 tf.data.Dataset 타입을 반환
# Split the training set into 60% and 40% to end up with 15,000 examples
# for training, 10,000 examples for validation and 25,000 examples for testing.
train_data, validation_data, test_data = tfds.load(
name="imdb_reviews",
split=('train[:60%]', 'train[60%:]', 'test'),
as_supervised=True)
Metal device set to: Apple M1
2022-02-04 10:01:29.439089: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.
2022-02-04 10:01:29.439310: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)
type(train_data)
tensorflow.python.data.ops.dataset_ops.PrefetchDataset
데이터 탐색
- 데이터 세트는 전처리된 정수 배열
- 정수는 영화 리뷰에 나타나는 단어
- 레이블은 0(부정적) 또는 1(긍정적)
- tf.data.Dataset.batch 함수는 전체 데이터 세트를 파라미터로 전달한 batch_size 크기만큼 나뉘어진 Dataset로 만들어준다.
- python의 iter 함수는 iterator를 반환하고 next 함수는 호출할 때마다 iterator를 순회하면서 실행한다.
아래 코드는 train_data를 10개씩 나누어 저장시킨 후 iterator로 반들어 첫 번째(next를 한 번만 호출했으므로) 10개를 불러오는 코드다.
train_examples_batch, train_labels_batch = next(iter(train_data.batch(10)))
train_examples_batch
2022-02-04 10:01:29.515366: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz
2022-02-04 10:01:29.539297: W tensorflow/core/kernels/data/cache_dataset_ops.cc:768] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.
<tf.Tensor: shape=(10,), dtype=string, numpy=
array([b"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions. Maria Conchita Alonso appeared phony, and her pseudo-love affair with Walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning. I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it.",
b'I have been known to fall asleep during films, but this is usually due to a combination of things including, really tired, being warm and comfortable on the sette and having just eaten a lot. However on this occasion I fell asleep because the film was rubbish. The plot development was constant. Constantly slow and boring. Things seemed to happen, but with no explanation of what was causing them or why. I admit, I may have missed part of the film, but i watched the majority of it and everything just seemed to happen of its own accord without any real concern for anything else. I cant recommend this film at all.',
b'Mann photographs the Alberta Rocky Mountains in a superb fashion, and Jimmy Stewart and Walter Brennan give enjoyable performances as they always seem to do. <br /><br />But come on Hollywood - a Mountie telling the people of Dawson City, Yukon to elect themselves a marshal (yes a marshal!) and to enforce the law themselves, then gunfighters battling it out on the streets for control of the town? <br /><br />Nothing even remotely resembling that happened on the Canadian side of the border during the Klondike gold rush. Mr. Mann and company appear to have mistaken Dawson City for Deadwood, the Canadian North for the American Wild West.<br /><br />Canadian viewers be prepared for a Reefer Madness type of enjoyable howl with this ludicrous plot, or, to shake your head in disgust.',
b'This is the kind of film for a snowy Sunday afternoon when the rest of the world can go ahead with its own business as you descend into a big arm-chair and mellow for a couple of hours. Wonderful performances from Cher and Nicolas Cage (as always) gently row the plot along. There are no rapids to cross, no dangerous waters, just a warm and witty paddle through New York life at its best. A family film in every sense and one that deserves the praise it received.',
b'As others have mentioned, all the women that go nude in this film are mostly absolutely gorgeous. The plot very ably shows the hypocrisy of the female libido. When men are around they want to be pursued, but when no "men" are around, they become the pursuers of a 14 year old boy. And the boy becomes a man really fast (we should all be so lucky at this age!). He then gets up the courage to pursue his true love.',
b"This is a film which should be seen by anybody interested in, effected by, or suffering from an eating disorder. It is an amazingly accurate and sensitive portrayal of bulimia in a teenage girl, its causes and its symptoms. The girl is played by one of the most brilliant young actresses working in cinema today, Alison Lohman, who was later so spectacular in 'Where the Truth Lies'. I would recommend that this film be shown in all schools, as you will never see a better on this subject. Alison Lohman is absolutely outstanding, and one marvels at her ability to convey the anguish of a girl suffering from this compulsive disorder. If barometers tell us the air pressure, Alison Lohman tells us the emotional pressure with the same degree of accuracy. Her emotional range is so precise, each scene could be measured microscopically for its gradations of trauma, on a scale of rising hysteria and desperation which reaches unbearable intensity. Mare Winningham is the perfect choice to play her mother, and does so with immense sympathy and a range of emotions just as finely tuned as Lohman's. Together, they make a pair of sensitive emotional oscillators vibrating in resonance with one another. This film is really an astonishing achievement, and director Katt Shea should be proud of it. The only reason for not seeing it is if you are not interested in people. But even if you like nature films best, this is after all animal behaviour at the sharp edge. Bulimia is an extreme version of how a tormented soul can destroy her own body in a frenzy of despair. And if we don't sympathise with people suffering from the depths of despair, then we are dead inside.",
b'Okay, you have:<br /><br />Penelope Keith as Miss Herringbone-Tweed, B.B.E. (Backbone of England.) She\'s killed off in the first scene - that\'s right, folks; this show has no backbone!<br /><br />Peter O\'Toole as Ol\' Colonel Cricket from The First War and now the emblazered Lord of the Manor.<br /><br />Joanna Lumley as the ensweatered Lady of the Manor, 20 years younger than the colonel and 20 years past her own prime but still glamourous (Brit spelling, not mine) enough to have a toy-boy on the side. It\'s alright, they have Col. Cricket\'s full knowledge and consent (they guy even comes \'round for Christmas!) Still, she\'s considerate of the colonel enough to have said toy-boy her own age (what a gal!)<br /><br />David McCallum as said toy-boy, equally as pointlessly glamourous as his squeeze. Pilcher couldn\'t come up with any cover for him within the story, so she gave him a hush-hush job at the Circus.<br /><br />and finally:<br /><br />Susan Hampshire as Miss Polonia Teacups, Venerable Headmistress of the Venerable Girls\' Boarding-School, serving tea in her office with a dash of deep, poignant advice for life in the outside world just before graduation. Her best bit of advice: "I\'ve only been to Nancherrow (the local Stately Home of England) once. I thought it was very beautiful but, somehow, not part of the real world." Well, we can\'t say they didn\'t warn us.<br /><br />Ah, Susan - time was, your character would have been running the whole show. They don\'t write \'em like that any more. Our loss, not yours.<br /><br />So - with a cast and setting like this, you have the re-makings of "Brideshead Revisited," right?<br /><br />Wrong! They took these 1-dimensional supporting roles because they paid so well. After all, acting is one of the oldest temp-jobs there is (YOU name another!)<br /><br />First warning sign: lots and lots of backlighting. They get around it by shooting outdoors - "hey, it\'s just the sunlight!"<br /><br />Second warning sign: Leading Lady cries a lot. When not crying, her eyes are moist. That\'s the law of romance novels: Leading Lady is "dewy-eyed."<br /><br />Henceforth, Leading Lady shall be known as L.L.<br /><br />Third warning sign: L.L. actually has stars in her eyes when she\'s in love. Still, I\'ll give Emily Mortimer an award just for having to act with that spotlight in her eyes (I wonder . did they use contacts?)<br /><br />And lastly, fourth warning sign: no on-screen female character is "Mrs." She\'s either "Miss" or "Lady."<br /><br />When all was said and done, I still couldn\'t tell you who was pursuing whom and why. I couldn\'t even tell you what was said and done.<br /><br />To sum up: they all live through World War II without anything happening to them at all.<br /><br />OK, at the end, L.L. finds she\'s lost her parents to the Japanese prison camps and baby sis comes home catatonic. Meanwhile (there\'s always a "meanwhile,") some young guy L.L. had a crush on (when, I don\'t know) comes home from some wartime tough spot and is found living on the street by Lady of the Manor (must be some street if SHE\'s going to find him there.) Both war casualties are whisked away to recover at Nancherrow (SOMEBODY has to be "whisked away" SOMEWHERE in these romance stories!)<br /><br />Great drama.',
b'The film is based on a genuine 1950s novel.<br /><br />Journalist Colin McInnes wrote a set of three "London novels": "Absolute Beginners", "City of Spades" and "Mr Love and Justice". I have read all three. The first two are excellent. The last, perhaps an experiment that did not come off. But McInnes\'s work is highly acclaimed; and rightly so. This musical is the novelist\'s ultimate nightmare - to see the fruits of one\'s mind being turned into a glitzy, badly-acted, soporific one-dimensional apology of a film that says it captures the spirit of 1950s London, and does nothing of the sort.<br /><br />Thank goodness Colin McInnes wasn\'t alive to witness it.',
b'I really love the sexy action and sci-fi films of the sixties and its because of the actress\'s that appeared in them. They found the sexiest women to be in these films and it didn\'t matter if they could act (Remember "Candy"?). The reason I was disappointed by this film was because it wasn\'t nostalgic enough. The story here has a European sci-fi film called "Dragonfly" being made and the director is fired. So the producers decide to let a young aspiring filmmaker (Jeremy Davies) to complete the picture. They\'re is one real beautiful woman in the film who plays Dragonfly but she\'s barely in it. Film is written and directed by Roman Coppola who uses some of his fathers exploits from his early days and puts it into the script. I wish the film could have been an homage to those early films. They could have lots of cameos by actors who appeared in them. There is one actor in this film who was popular from the sixties and its John Phillip Law (Barbarella). Gerard Depardieu, Giancarlo Giannini and Dean Stockwell appear as well. I guess I\'m going to have to continue waiting for a director to make a good homage to the films of the sixties. If any are reading this, "Make it as sexy as you can"! I\'ll be waiting!',
b'Sure, this one isn\'t really a blockbuster, nor does it target such a position. "Dieter" is the first name of a quite popular German musician, who is either loved or hated for his kind of acting and thats exactly what this movie is about. It is based on the autobiography "Dieter Bohlen" wrote a few years ago but isn\'t meant to be accurate on that. The movie is filled with some sexual offensive content (at least for American standard) which is either amusing (not for the other "actors" of course) or dumb - it depends on your individual kind of humor or on you being a "Bohlen"-Fan or not. Technically speaking there isn\'t much to criticize. Speaking of me I find this movie to be an OK-movie.'],
dtype=object)>
train_labels_batch
<tf.Tensor: shape=(10,), dtype=int64, numpy=array([0, 0, 0, 1, 1, 1, 0, 0, 0, 0])>
모델 구성
- 고려사항
- 텍스트를 어떻게 표현할 것인가
- 모델에서 얼마나 많은 층을 사용할 것인가
- 각 층에서는 얼마나 많은 은닉 유닛을 사용할 것인가
- 텍스트를 표현하는 방법 중 한가지는 임베딩 벡터로 변환하는 것
- 임베딩 레이어는 첫 번째 레이어로만 사용 가능
- 임베딩의 장점
- 텍스트 전처리에 대해 걱정할 필요가 없다.
- 전이 학습에 따른 이점이 있다.
- 임베딩 벡터는 고정 크기이기 때문에 처리 과정이 단순해진다.
- 이 예에서는 google/nnlm-en-dim50/2라고 하는 TensorFlow Hub에서 사전 훈련된 텍스트 임베딩 모델을 사용
embedding = "https://tfhub.dev/google/nnlm-en-dim50/2"
hub_layer = hub.KerasLayer(embedding, input_shape=[],
dtype=tf.string, trainable=True)
hub_layer(train_examples_batch[:3])
2022-02-04 10:09:21.383914: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
2022-02-04 10:09:21.564423: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
<tf.Tensor: shape=(3, 50), dtype=float32, numpy=
array([[ 0.5423195 , -0.0119017 , 0.06337538, 0.06862972, -0.16776837,
-0.10581174, 0.16865303, -0.04998824, -0.31148055, 0.07910346,
0.15442263, 0.01488662, 0.03930153, 0.19772711, -0.12215476,
-0.04120981, -0.2704109 , -0.21922152, 0.26517662, -0.80739075,
0.25833532, -0.3100421 , 0.28683215, 0.1943387 , -0.29036492,
0.03862849, -0.7844411 , -0.0479324 , 0.4110299 , -0.36388892,
-0.58034706, 0.30269456, 0.3630897 , -0.15227164, -0.44391504,
0.19462997, 0.19528408, 0.05666234, 0.2890704 , -0.28468323,
-0.00531206, 0.0571938 , -0.3201318 , -0.04418665, -0.08550783,
-0.55847436, -0.23336391, -0.20782952, -0.03543064, -0.17533456],
[ 0.56338924, -0.12339553, -0.10862679, 0.7753425 , -0.07667089,
-0.15752277, 0.01872335, -0.08169781, -0.3521876 , 0.4637341 ,
-0.08492756, 0.07166859, -0.00670817, 0.12686075, -0.19326553,
-0.52626437, -0.3295823 , 0.14394785, 0.09043556, -0.5417555 ,
0.02468163, -0.15456742, 0.68333143, 0.09068331, -0.45327246,
0.23180096, -0.8615696 , 0.34480393, 0.12838456, -0.58759046,
-0.4071231 , 0.23061076, 0.48426893, -0.27128142, -0.5380916 ,
0.47016326, 0.22572741, -0.00830663, 0.2846242 , -0.304985 ,
0.04400365, 0.25025874, 0.14867121, 0.40717036, -0.15422426,
-0.06878027, -0.40825695, -0.3149215 , 0.09283665, -0.20183425],
[ 0.7456154 , 0.21256861, 0.14400336, 0.5233862 , 0.11032254,
0.00902788, -0.3667802 , -0.08938274, -0.24165542, 0.33384594,
-0.11194605, -0.01460047, -0.0071645 , 0.19562712, 0.00685216,
-0.24886718, -0.42796347, 0.18620004, -0.05241098, -0.66462487,
0.13449019, -0.22205497, 0.08633006, 0.43685386, 0.2972681 ,
0.36140734, -0.7196889 , 0.05291241, -0.14316116, -0.1573394 ,
-0.15056328, -0.05988009, -0.08178931, -0.15569411, -0.09303783,
-0.18971172, 0.07620788, -0.02541647, -0.27134508, -0.3392682 ,
-0.10296468, -0.27275252, -0.34078008, 0.20083304, -0.26644835,
0.00655449, -0.05141488, -0.04261917, -0.45413622, 0.20023568]],
dtype=float32)>
- 전체 모델 생성
model = tf.keras.Sequential()
model.add(hub_layer)
model.add(tf.keras.layers.Dense(16, activation='relu'))
model.add(tf.keras.layers.Dense(1))
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
keras_layer (KerasLayer) (None, 50) 48190600
dense (Dense) (None, 16) 816
dense_1 (Dense) (None, 1) 17
=================================================================
Total params: 48,191,433
Trainable params: 48,191,433
Non-trainable params: 0
_________________________________________________________________
- TensorFlow Hub 레이어: 이 레이어는 사전 훈련된 저장된 모델을 사용하여 문장을 임베딩 벡터에 매핑합니다. 사용 중인 사전 훈련된 텍스트 임베딩 모델(google/nnlm-en-dim50/2)은 문장을 토큰으로 분할하고 각 토큰을 임베딩한 다음 임베딩을 결합합니다. 결과적인 차원은 (num_examples, embedding_dimension)입니다. 이 NNLM 모델의 경우에는 embedding_dimension은 50입니다.
- Dense 레이어: 16개의 은닉 유닛(hidden unit)을 가진 완전 연결 층(Dense)
- Dense 레이어: 최종 출력층, 하나의 출력 노드를 가진 완전 연결 층, sigmoid 활성화 함수를 사용하므로 확률 또는 신뢰도 수준을 표현하는 0~1 사이의 실수가 출력됩.
손실 함수와 옵티마이저
- 모델이 로짓을 출력하므로 binary_crossentropy를 손실함수로 사용 (확률을 다루는데 적합)
model.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
모델 훈련
- 512개 샘플의 미니 배치에서 10개 epoch 동안 모델을 훈련
history = model.fit(train_data.shuffle(10000).batch(512),
epochs=10,
validation_data=validation_data.batch(512),
verbose=1)
Epoch 1/10
2022-02-04 10:21:20.764825: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
30/30 [==============================] - ETA: 0s - loss: 0.6803 - accuracy: 0.5118
2022-02-04 10:21:27.136092: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.
30/30 [==============================] - 7s 188ms/step - loss: 0.6803 - accuracy: 0.5118 - val_loss: 0.6540 - val_accuracy: 0.5409
Epoch 2/10
30/30 [==============================] - 5s 179ms/step - loss: 0.6085 - accuracy: 0.6159 - val_loss: 0.5665 - val_accuracy: 0.6712
Epoch 3/10
30/30 [==============================] - 5s 177ms/step - loss: 0.4878 - accuracy: 0.7595 - val_loss: 0.4546 - val_accuracy: 0.7806
Epoch 4/10
30/30 [==============================] - 5s 174ms/step - loss: 0.3515 - accuracy: 0.8579 - val_loss: 0.3646 - val_accuracy: 0.8282
Epoch 5/10
30/30 [==============================] - 5s 176ms/step - loss: 0.2452 - accuracy: 0.9085 - val_loss: 0.3227 - val_accuracy: 0.8598
Epoch 6/10
30/30 [==============================] - 5s 174ms/step - loss: 0.1771 - accuracy: 0.9399 - val_loss: 0.3058 - val_accuracy: 0.8680
Epoch 7/10
30/30 [==============================] - 5s 174ms/step - loss: 0.1294 - accuracy: 0.9608 - val_loss: 0.3023 - val_accuracy: 0.8716
Epoch 8/10
30/30 [==============================] - 5s 180ms/step - loss: 0.0944 - accuracy: 0.9749 - val_loss: 0.3077 - val_accuracy: 0.8732
Epoch 9/10
30/30 [==============================] - 5s 176ms/step - loss: 0.0667 - accuracy: 0.9848 - val_loss: 0.3178 - val_accuracy: 0.8724
Epoch 10/10
30/30 [==============================] - 5s 175ms/step - loss: 0.0475 - accuracy: 0.9908 - val_loss: 0.3304 - val_accuracy: 0.8715
모델 평가
results = model.evaluate(test_data.batch(512), verbose=2)
for name, value in zip(model.metrics_names, results):
print("%s: %.3f" % (name, value))
49/49 - 1s - loss: 0.3563 - accuracy: 0.8543 - 1s/epoch - 24ms/step
loss: 0.356
accuracy: 0.854