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Bu metin sınıflandırma öğretici bir tren tekrarlayan sinir ağı üzerinde IMDB büyük film yorumu veri kümesi duyguları analiz için.
Kurmak
import numpy as np
import tensorflow_datasets as tfds
import tensorflow as tf
tfds.disable_progress_bar()
İthalat matplotlib ve arsa grafikler için bir yardımcı işlev oluşturun:
import matplotlib.pyplot as plt
def plot_graphs(history, metric):
plt.plot(history.history[metric])
plt.plot(history.history['val_'+metric], '')
plt.xlabel("Epochs")
plt.ylabel(metric)
plt.legend([metric, 'val_'+metric])
Giriş ardışık düzenini ayarla
IMDB büyük film eleştiri veri kümesi bir ikili sınıflandırma veri kümesi-tüm yorumları pozitif ya da negatif duygular ya olması.
Kullanarak veri kümesini indir TFDS . Bkz yükleme metin öğretici elle veri bu tür yüklemek konusunda detaylar için.
dataset, info = tfds.load('imdb_reviews', with_info=True,
as_supervised=True)
train_dataset, test_dataset = dataset['train'], dataset['test']
train_dataset.element_spec
(TensorSpec(shape=(), dtype=tf.string, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))
Başlangıçta bu (metin, etiket çiftleri) bir veri kümesi döndürür:
for example, label in train_dataset.take(1):
print('text: ', example.numpy())
print('label: ', label.numpy())
text: 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." label: 0
Sonraki eğitim için verileri karıştırmak ve bunların oluşturmak partiler (text, label) çiftleri:
BUFFER_SIZE = 10000
BATCH_SIZE = 64
train_dataset = train_dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)
test_dataset = test_dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)
for example, label in train_dataset.take(1):
print('texts: ', example.numpy()[:3])
print()
print('labels: ', label.numpy()[:3])
texts: [b'This is arguably the worst film I have ever seen, and I have quite an appetite for awful (and good) movies. It could (just) have managed a kind of adolescent humour if it had been consistently tongue-in-cheek --\xc3\xa0 la ROCKY HORROR PICTURE SHOW, which was really very funny. Other movies, like PLAN NINE FROM OUTER SPACE, manage to be funny while (apparently) trying to be serious. As to the acting, it looks like they rounded up brain-dead teenagers and asked them to ad-lib the whole production. Compared to them, Tom Cruise looks like Alec Guinness. There was one decent interpretation -- that of the older ghoul-busting broad on the motorcycle.' b"I saw this film in the worst possible circumstance. I'd already missed 15 minutes when I woke up to it on an international flight between Sydney and Seoul. I didn't know what I was watching, I thought maybe it was a movie of the week, but quickly became riveted by the performance of the lead actress playing a young woman who's child had been kidnapped. The premise started taking twist and turns I didn't see coming and by the end credits I was scrambling through the the in-flight guide to figure out what I had just watched. Turns out I was belatedly discovering Do-yeon Jeon who'd won Best Actress at Cannes for the role. I don't know if Secret Sunshine is typical of Korean cinema but I'm off to the DVD store to discover more." b"Hello. I am Paul Raddick, a.k.a. Panic Attack of WTAF, Channel 29 in Philadelphia. Let me tell you about this god awful movie that powered on Adam Sandler's film career but was digitized after a short time.<br /><br />Going Overboard is about an aspiring comedian played by Sandler who gets a job on a cruise ship and fails...or so I thought. Sandler encounters babes that like History of the World Part 1 and Rebound. The babes were supposed to be engaged, but, actually, they get executed by Sawtooth, the meanest cannibal the world has ever known. Adam Sandler fared bad in Going Overboard, but fared better in Big Daddy, Billy Madison, and Jen Leone's favorite, 50 First Dates. Man, Drew Barrymore was one hot chick. Spanglish is red hot, Going Overboard ain't Dooley squat! End of file."] labels: [0 1 0]
Metin kodlayıcıyı oluşturun
Tarafından yüklenen ham metin tfds bir modelde kullanılmadan önce işlenmesi gerekir. Eğitim için işlem metne basit yolu kullanıyor TextVectorization katmanı. Bu katmanın birçok yeteneği vardır, ancak bu öğretici varsayılan davranışa bağlıdır.
Katmanı oluşturun ve katmanın için veri kümesinin metin geçmek .adapt yöntemle:
VOCAB_SIZE = 1000
encoder = tf.keras.layers.TextVectorization(
max_tokens=VOCAB_SIZE)
encoder.adapt(train_dataset.map(lambda text, label: text))
.adapt yöntem katmanın kelime ayarlar. İşte ilk 20 jeton. Doldurma ve bilinmeyen belirteçlerden sonra, frekansa göre sıralanırlar:
vocab = np.array(encoder.get_vocabulary())
vocab[:20]
array(['', '[UNK]', 'the', 'and', 'a', 'of', 'to', 'is', 'in', 'it', 'i',
'this', 'that', 'br', 'was', 'as', 'for', 'with', 'movie', 'but'],
dtype='<U14')
Sözcük bilgisi ayarlandıktan sonra, katman metni dizinlere kodlayabilir. (Bir sabit set sürece indekslerinin tansörleri toplu uzun dizisine 0-yastıklı output_sequence_length ):
encoded_example = encoder(example)[:3].numpy()
encoded_example
array([[ 11, 7, 1, ..., 0, 0, 0],
[ 10, 208, 11, ..., 0, 0, 0],
[ 1, 10, 237, ..., 0, 0, 0]])
Varsayılan ayarlarla, işlem tamamen geri döndürülemez. Bunun üç ana nedeni vardır:
- İçin varsayılan değer
preprocessing.TextVectorization'ınstandardizeargümanı olan"lower_and_strip_punctuation". - Sınırlı kelime boyutu ve karakter tabanlı geri dönüş eksikliği, bazı bilinmeyen belirteçlere neden olur.
for n in range(3):
print("Original: ", example[n].numpy())
print("Round-trip: ", " ".join(vocab[encoded_example[n]]))
print()
Original: b'This is arguably the worst film I have ever seen, and I have quite an appetite for awful (and good) movies. It could (just) have managed a kind of adolescent humour if it had been consistently tongue-in-cheek --\xc3\xa0 la ROCKY HORROR PICTURE SHOW, which was really very funny. Other movies, like PLAN NINE FROM OUTER SPACE, manage to be funny while (apparently) trying to be serious. As to the acting, it looks like they rounded up brain-dead teenagers and asked them to ad-lib the whole production. Compared to them, Tom Cruise looks like Alec Guinness. There was one decent interpretation -- that of the older ghoul-busting broad on the motorcycle.' Round-trip: this is [UNK] the worst film i have ever seen and i have quite an [UNK] for awful and good movies it could just have [UNK] a kind of [UNK] [UNK] if it had been [UNK] [UNK] [UNK] la [UNK] horror picture show which was really very funny other movies like [UNK] [UNK] from [UNK] space [UNK] to be funny while apparently trying to be serious as to the acting it looks like they [UNK] up [UNK] [UNK] and [UNK] them to [UNK] the whole production [UNK] to them tom [UNK] looks like [UNK] [UNK] there was one decent [UNK] that of the older [UNK] [UNK] on the [UNK] Original: b"I saw this film in the worst possible circumstance. I'd already missed 15 minutes when I woke up to it on an international flight between Sydney and Seoul. I didn't know what I was watching, I thought maybe it was a movie of the week, but quickly became riveted by the performance of the lead actress playing a young woman who's child had been kidnapped. The premise started taking twist and turns I didn't see coming and by the end credits I was scrambling through the the in-flight guide to figure out what I had just watched. Turns out I was belatedly discovering Do-yeon Jeon who'd won Best Actress at Cannes for the role. I don't know if Secret Sunshine is typical of Korean cinema but I'm off to the DVD store to discover more." Round-trip: i saw this film in the worst possible [UNK] id already [UNK] [UNK] minutes when i [UNK] up to it on an [UNK] [UNK] between [UNK] and [UNK] i didnt know what i was watching i thought maybe it was a movie of the [UNK] but quickly became [UNK] by the performance of the lead actress playing a young woman whos child had been [UNK] the premise started taking twist and turns i didnt see coming and by the end credits i was [UNK] through the the [UNK] [UNK] to figure out what i had just watched turns out i was [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] best actress at [UNK] for the role i dont know if secret [UNK] is typical of [UNK] cinema but im off to the dvd [UNK] to [UNK] more Original: b"Hello. I am Paul Raddick, a.k.a. Panic Attack of WTAF, Channel 29 in Philadelphia. Let me tell you about this god awful movie that powered on Adam Sandler's film career but was digitized after a short time.<br /><br />Going Overboard is about an aspiring comedian played by Sandler who gets a job on a cruise ship and fails...or so I thought. Sandler encounters babes that like History of the World Part 1 and Rebound. The babes were supposed to be engaged, but, actually, they get executed by Sawtooth, the meanest cannibal the world has ever known. Adam Sandler fared bad in Going Overboard, but fared better in Big Daddy, Billy Madison, and Jen Leone's favorite, 50 First Dates. Man, Drew Barrymore was one hot chick. Spanglish is red hot, Going Overboard ain't Dooley squat! End of file." Round-trip: [UNK] i am paul [UNK] [UNK] [UNK] [UNK] of [UNK] [UNK] [UNK] in [UNK] let me tell you about this god awful movie that [UNK] on [UNK] [UNK] film career but was [UNK] after a short [UNK] br going [UNK] is about an [UNK] [UNK] played by [UNK] who gets a job on a [UNK] [UNK] and [UNK] so i thought [UNK] [UNK] [UNK] that like history of the world part 1 and [UNK] the [UNK] were supposed to be [UNK] but actually they get [UNK] by [UNK] the [UNK] [UNK] the world has ever known [UNK] [UNK] [UNK] bad in going [UNK] but [UNK] better in big [UNK] [UNK] [UNK] and [UNK] [UNK] favorite [UNK] first [UNK] man [UNK] [UNK] was one hot [UNK] [UNK] is red hot going [UNK] [UNK] [UNK] [UNK] end of [UNK]
modeli oluştur
Yukarıdaki modelin bir diyagramıdır.
Bu model olarak inşa olabilir
tf.keras.Sequential.Birinci tabaka
encoderbelirteç endekslerinin bir sekansa metin dönüştürür.Kodlayıcıdan sonra bir gömme katmanı bulunur. Bir gömme katmanı, kelime başına bir vektör depolar. Çağrıldığında, kelime dizinlerinin dizilerini vektör dizilerine dönüştürür. Bu vektörler eğitilebilir. Eğitimden sonra (yeterli veri üzerinde), benzer anlamlara sahip kelimeler genellikle benzer vektörlere sahiptir.
Bu indeks-arama çok daha etkili bir ile bir sıcak kodlanmış vektör geçen benzer bir işlem daha
tf.keras.layers.Densetabakası.Tekrarlayan bir sinir ağı (RNN), öğeler arasında yineleme yaparak dizi girişini işler. RNN'ler, çıktıları bir zaman adımından sonraki zaman adımındaki girdilerine iletir.
tf.keras.layers.Bidirectionalsargı ayrıca, bir RYSA tabaka ile birlikte kullanılabilir. Bu, girdiyi RNN katmanı boyunca ileri ve geri yayar ve ardından son çıktıyı birleştirir.Çift yönlü bir RNN'nin ana avantajı, girişin başlangıcından gelen sinyalin çıkışı etkilemek için her zaman adımında tamamen işlenmesine gerek olmamasıdır.
Çift yönlü bir RNN'nin ana dezavantajı, kelimeler sona eklenirken tahminleri verimli bir şekilde aktaramamanızdır.
RYSA tek bir vektöre dizisi dönüştürülür sonra iki
layers.Densesınıflandırma çıkış olarak tek bir logit için bu vektör gösteriminden bazı son işleme ve dönüştürme yapmak.
Bunu uygulamak için kod aşağıdadır:
model = tf.keras.Sequential([
encoder,
tf.keras.layers.Embedding(
input_dim=len(encoder.get_vocabulary()),
output_dim=64,
# Use masking to handle the variable sequence lengths
mask_zero=True),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1)
])
Lütfen Keras sıralı modelinin burada kullanıldığını unutmayın, çünkü modeldeki tüm katmanlar yalnızca tek girdiye sahiptir ve tek çıktı üretir. Durum bilgisi olan RNN katmanını kullanmak istemeniz durumunda, RNN katman durumlarını alıp yeniden kullanabilmeniz için modelinizi Keras işlevsel API'si veya model alt sınıflaması ile oluşturmak isteyebilirsiniz. Kontrol edin Keras RYSA rehber daha fazla ayrıntı için.
Gömme tabakası maskeleme kullanımları değişen dizi-uzunlukları işlemek için. Sonra tüm katmanları Embedding destek maskeleme:
print([layer.supports_masking for layer in model.layers])
[False, True, True, True, True]
Bunun beklendiği gibi çalıştığını doğrulamak için bir cümleyi iki kez değerlendirin. İlk olarak, maskelenecek dolgu olmaması için tek başına:
# predict on a sample text without padding.
sample_text = ('The movie was cool. The animation and the graphics '
'were out of this world. I would recommend this movie.')
predictions = model.predict(np.array([sample_text]))
print(predictions[0])
[-0.00012211]
Şimdi, daha uzun bir cümle ile toplu olarak tekrar değerlendirin. Sonuç aynı olmalıdır:
# predict on a sample text with padding
padding = "the " * 2000
predictions = model.predict(np.array([sample_text, padding]))
print(predictions[0])
[-0.00012211]
Eğitim sürecini yapılandırmak için Keras modelini derleyin:
model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=['accuracy'])
Modeli eğit
history = model.fit(train_dataset, epochs=10,
validation_data=test_dataset,
validation_steps=30)
Epoch 1/10 391/391 [==============================] - 39s 84ms/step - loss: 0.6454 - accuracy: 0.5630 - val_loss: 0.4888 - val_accuracy: 0.7568 Epoch 2/10 391/391 [==============================] - 30s 75ms/step - loss: 0.3925 - accuracy: 0.8200 - val_loss: 0.3663 - val_accuracy: 0.8464 Epoch 3/10 391/391 [==============================] - 30s 75ms/step - loss: 0.3319 - accuracy: 0.8525 - val_loss: 0.3402 - val_accuracy: 0.8385 Epoch 4/10 391/391 [==============================] - 30s 75ms/step - loss: 0.3183 - accuracy: 0.8616 - val_loss: 0.3289 - val_accuracy: 0.8438 Epoch 5/10 391/391 [==============================] - 30s 75ms/step - loss: 0.3088 - accuracy: 0.8656 - val_loss: 0.3254 - val_accuracy: 0.8646 Epoch 6/10 391/391 [==============================] - 32s 81ms/step - loss: 0.3043 - accuracy: 0.8686 - val_loss: 0.3242 - val_accuracy: 0.8521 Epoch 7/10 391/391 [==============================] - 30s 76ms/step - loss: 0.3019 - accuracy: 0.8696 - val_loss: 0.3315 - val_accuracy: 0.8609 Epoch 8/10 391/391 [==============================] - 32s 76ms/step - loss: 0.3007 - accuracy: 0.8688 - val_loss: 0.3245 - val_accuracy: 0.8609 Epoch 9/10 391/391 [==============================] - 31s 77ms/step - loss: 0.2981 - accuracy: 0.8707 - val_loss: 0.3294 - val_accuracy: 0.8599 Epoch 10/10 391/391 [==============================] - 31s 78ms/step - loss: 0.2969 - accuracy: 0.8742 - val_loss: 0.3218 - val_accuracy: 0.8547
test_loss, test_acc = model.evaluate(test_dataset)
print('Test Loss:', test_loss)
print('Test Accuracy:', test_acc)
391/391 [==============================] - 15s 38ms/step - loss: 0.3185 - accuracy: 0.8582 Test Loss: 0.3184521794319153 Test Accuracy: 0.8581600189208984
plt.figure(figsize=(16, 8))
plt.subplot(1, 2, 1)
plot_graphs(history, 'accuracy')
plt.ylim(None, 1)
plt.subplot(1, 2, 2)
plot_graphs(history, 'loss')
plt.ylim(0, None)
(0.0, 0.6627909764647484)
Yeni bir cümle üzerinde bir tahmin yürütün:
Tahmin >= 0.0 ise pozitif, aksi halde negatiftir.
sample_text = ('The movie was cool. The animation and the graphics '
'were out of this world. I would recommend this movie.')
predictions = model.predict(np.array([sample_text]))
İki veya daha fazla LSTM katmanını istifleyin
Keras tekrarlayan katmanlar tarafından kontrol edilir mevcut iki modu vardır return_sequences yapıcı argüman:
Eğer
Falseher giriş dizisi için sadece son çıkışı verir (şekil (batch_size bir 2D tensörünün output_features)). Bu, önceki modelde kullanılan varsayılandır.Eğer
Trueher timestep için ardışık çıkışların tam dizileri (şekil 3B tensörünün döndürülür(batch_size, timesteps, output_features)).
İşte ne olduğu gibi bilgiler görünüyor akış return_sequences=True :
Bir kullanımı hakkında ilginç şey RNN ile return_sequences=True böyle, başka RYSA katmanına geçirilebilir böylece verimi hala girişi gibi, 3-eksenleri olmasıdır:
model = tf.keras.Sequential([
encoder,
tf.keras.layers.Embedding(len(encoder.get_vocabulary()), 64, mask_zero=True),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences=True)),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dropout(0.5),
tf.keras.layers.Dense(1)
])
model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=['accuracy'])
history = model.fit(train_dataset, epochs=10,
validation_data=test_dataset,
validation_steps=30)
Epoch 1/10 391/391 [==============================] - 71s 149ms/step - loss: 0.6502 - accuracy: 0.5625 - val_loss: 0.4923 - val_accuracy: 0.7573 Epoch 2/10 391/391 [==============================] - 55s 138ms/step - loss: 0.4067 - accuracy: 0.8198 - val_loss: 0.3727 - val_accuracy: 0.8271 Epoch 3/10 391/391 [==============================] - 54s 136ms/step - loss: 0.3417 - accuracy: 0.8543 - val_loss: 0.3343 - val_accuracy: 0.8510 Epoch 4/10 391/391 [==============================] - 53s 134ms/step - loss: 0.3242 - accuracy: 0.8607 - val_loss: 0.3268 - val_accuracy: 0.8568 Epoch 5/10 391/391 [==============================] - 53s 135ms/step - loss: 0.3174 - accuracy: 0.8652 - val_loss: 0.3213 - val_accuracy: 0.8516 Epoch 6/10 391/391 [==============================] - 52s 132ms/step - loss: 0.3098 - accuracy: 0.8671 - val_loss: 0.3294 - val_accuracy: 0.8547 Epoch 7/10 391/391 [==============================] - 53s 134ms/step - loss: 0.3063 - accuracy: 0.8697 - val_loss: 0.3158 - val_accuracy: 0.8594 Epoch 8/10 391/391 [==============================] - 52s 132ms/step - loss: 0.3043 - accuracy: 0.8692 - val_loss: 0.3184 - val_accuracy: 0.8521 Epoch 9/10 391/391 [==============================] - 53s 133ms/step - loss: 0.3016 - accuracy: 0.8704 - val_loss: 0.3208 - val_accuracy: 0.8609 Epoch 10/10 391/391 [==============================] - 54s 136ms/step - loss: 0.2975 - accuracy: 0.8740 - val_loss: 0.3301 - val_accuracy: 0.8651
test_loss, test_acc = model.evaluate(test_dataset)
print('Test Loss:', test_loss)
print('Test Accuracy:', test_acc)
391/391 [==============================] - 26s 65ms/step - loss: 0.3293 - accuracy: 0.8646 Test Loss: 0.329334557056427 Test Accuracy: 0.8646399974822998
# predict on a sample text without padding.
sample_text = ('The movie was not good. The animation and the graphics '
'were terrible. I would not recommend this movie.')
predictions = model.predict(np.array([sample_text]))
print(predictions)
[[-1.6796288]]
plt.figure(figsize=(16, 6))
plt.subplot(1, 2, 1)
plot_graphs(history, 'accuracy')
plt.subplot(1, 2, 2)
plot_graphs(history, 'loss')
Gibi diğer mevcut tekrarlayan katmanları göz atın GRU katmanları .
Özel RNNs bina interestied ediyorsanız, bkz Keras RNN Kılavuzu .