Я изучаю НЛП с Керасом и прохожу учебное пособие. Код следующий:
import tensorflow_datasets as tfds
imdb, info = tfds.load("imdb_reviews", with_info=True, as_supervised=True)
import numpy as np
train_data, test_data = imdb['train'], imdb['test']
training_sentences = []
training_labels = []
testing_sentences = []
testing_labels = []
# str(s.tonumpy()) is needed in Python3 instead of just s.numpy()
for s,l in train_data:
training_sentences.append(str(s.numpy()))
training_labels.append(l.numpy())
training_labels_final = np.array(training_labels)
testing_labels_final = np.array(testing_labels)
vocab_size = 10000
embedding_dim = 16
max_length = 120
trunc_type='post'
oov_tok = "<OOV>"
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
# CREATE AN INSTANCE OF THE Tokenizer. WE DECIDE HOW MANY WORDS THE TOKENIZER WILL READ.
tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)
# FIT THE TOKENIZER ON THE TEXTS. NOW THE TOKENIZER HAS SEEN THE WORDS IN THE TEXT.
tokenizer.fit_on_texts(training_sentences) # the training_sentences is a list of words. Each word is considered a token.
# CREATE A DICTIONARY THAT INCLUDES ALL THE WORDS IN THE TEXT (UP TO THE MAXIMUM NUMBER DEFINED WHEN CREATING THE INSTANCE
# OF THE TOKENIZER)
word_index = tokenizer.word_index # the tokenizer creates a word_index with the words encountered in the text. Each word
# is assigned an integer which is the key while the word is the value.
# CONVERT THE SEQUENCES OF WORDS TO SEQUENCES OF INTEGERS
sequences = tokenizer.texts_to_sequences(training_sentences) # the texts_to_sequences method converts the sequences of
# words to sequences of integers using the key of each word in the dictionary
# PAD THE SEQUENCES OR TRUNCATE THEM ACCORDINGLY SO THAT ALL HAVE THE GIVEN max_length. NOW ALL SEQUENCES HAVE THE SAME LENGTH.
padded = pad_sequences(sequences,maxlen=max_length, truncating=trunc_type)
# THE SAME FOR THE SEQUENCES WHICH WILL BE USED FOR TESTING
testing_sequences = tokenizer.texts_to_sequences(testing_sentences)
testing_padded = pad_sequences(testing_sequences,maxlen=max_length)
# REVERSE THE DICTIONARY, MAKING KEYS THE WORDS AND VALUES THE INTEGERS WHICH REPRESENT THE WORDS
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
# CREATE A FUNCTION THAT TURNS THE INTEGERS COMPRISING A SEQUENCE TO WORDS THUS DECODING THE SEQUENCE AND CONVERTING IT TO
# NATURAL LANGUAGE
def decode_review(text):
return ' '.join([reverse_word_index.get(i, '?') for i in text])
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
# The Embedding layer gets as first argumnet the vocab_size which has been set to 10,000 and which was the value
# passed to the Tokenizer. On the other hand the vocabulary that was created using the training text was less
# than vocab_size, it was 86539
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(6, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
model.summary()
num_epochs = 10
model.fit(padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final))
К концу первой эпохи модель зависает и не продвигается дальше:
Когда я отбросил последние 1000 предложений и повторил процесс, я получил ту же ситуацию, но теперь на более раннем этапе:
Я перезагрузил компьютер(Windows 10), но это не решило проблему.
Затем я удалил tenorflow и переустановил. Затем я запускаю следующий код, найденный в официальной документации tenorflow 2.0:
Но когда я снова запускаю код НЛП, модель зависла при установкеданные:
num_epochs = 10
model.fit(padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final))
Train on 24000 samples
Epoch 1/10
23968/24000 [============================>.] - ETA: 6:09 - loss: 0.6878 - accuracy: 0.53 - ETA: 1:22 - loss: 0.6904 - accuracy: 0.56 - ETA: 50s - loss: 0.6927 - accuracy: 0.5069 - ETA: 37s - loss: 0.6932 - accuracy: 0.495 - ETA: 31s - loss: 0.6925 - accuracy: 0.492 - ETA: 26s - loss: 0.6923 - accuracy: 0.492 - ETA: 24s - loss: 0.6925 - accuracy: 0.490 - ETA: 21s - loss: 0.6925 - accuracy: 0.493 - ETA: 20s - loss: 0.6929 - accuracy: 0.490 - ETA: 19s - loss: 0.6931 - accuracy: 0.487 - ETA: 18s - loss: 0.6929 - accuracy: 0.490 - ETA: 17s - loss: 0.6929 - accuracy: 0.492 - ETA: 16s - loss: 0.6930 - accuracy: 0.489 - ETA: 15s - loss: 0.6927 - accuracy: 0.494 - ETA: 15s - loss: 0.6925 - accuracy: 0.498 - ETA: 14s - loss: 0.6925 - accuracy: 0.501 - ETA: 14s - loss: 0.6924 - accuracy: 0.504 - ETA: 14s - loss: 0.6925 - accuracy: 0.502 - ETA: 13s - loss: 0.6926 - accuracy: 0.503 - ETA: 13s - loss: 0.6925 - accuracy: 0.503 - ETA: 13s - loss: 0.6924 - accuracy: 0.506 - ETA: 12s - loss: 0.6926 - accuracy: 0.506 - 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