Yeni metni dönüştürme

Bu egzersizde, daha önce oluşturulan sözlüklerdeki sayısal indeks dizilerine yeni bir metni dönüştüreceksin.

Bu, zaten eğitilmiş bir modelin varsa ve onu yeni bir veri kümesine uygulamak istediğinde kullanışlıdır. Modelin tahmin/sınıflandırma yapabilmesi için, eğitim verisinde yapılan ön işleme adımları yeni metne de uygulanmalıdır.

Burada ayrıca, söz varlığında olmayan kelimeleri temsil etmek için özel bir '<UKN/>' belirteci kullanacaksın. Genellikle bu özel belirteçler sözlüklerin ilk indeksleridir, yani 0 konumu.

word_to_index, index_to_word ve vocabulary değişkenleri ortama zaten yüklendi. Ayrıca yeni metni içeren değişken de new_text olarak yüklendi. Göz atman için yeni metin yazdırıldı.

Bu egzersiz

Keras ile Dil Modellemesi için Yinelenen Sinir Ağları (RNN)

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Cümleleri içeren new_text listesi üzerinde döngü kur.
Kelime sözlükte bulunamazsa indeksi 0 olarak ayarla.
İndeksli cümleyi new_text_split değişkenine ekle.
İndeksleri index_to_word sözlüğünü kullanarak tekrar metne çevir.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Loop through the sentences and get indexes
new_text_split = []
for sentence in ____:
    sent_split = []
    for wd in sentence.split(' '):
        index = word_to_index.get(wd, ____)
        sent_split.append(index)
    new_text_split.append(____)

# Print the first sentence's indexes
print(new_text_split[0])

# Print the sentence converted using the dictionary
print(' '.join([index_to_word[____] for index in new_text_split[0]]))

Kodu Düzenle ve Çalıştır

Bu egzersiz

Keras ile Dil Modellemesi için Yinelenen Sinir Ağları (RNN)

kursunun bir parçasıdır

AvançadoNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

In this chapter, you will learn the foundations of Recurrent Neural Networks (RNN). Starting with some prerequisites, continuing to understanding how information flows through the network and finally seeing how to implement such models with Keras in the sentiment classification task.

Exercise 1: Kursa giriş Exercise 2: RNN ve ANN'in parametre sayılarını karşılaştırma Exercise 3: Duygu analizi Exercise 4: Diziden diziye modeller Exercise 5: Dil modellerine giriş Exercise 6: Metin verisine alışma Exercise 7: Model girişi için metin verisini hazırlama Exercise 8: Yeni metni dönüştürme

Geçerli Egzersiz

Exercise 9: Keras içinde RNN’e Giriş Exercise 10: Keras modelleri Exercise 11: Keras ön işleme Exercise 12: İlk RNN modelin

You will learn about the vanishing and exploding gradient problems, often occurring in RNNs, and how to deal with them with the GRU and LSTM cells. Furthermore, you'll create embedding layers for language models and revisit the sentiment classification task.

Exercise 1: Vanishing and exploding gradients Exercise 2: Exploding gradient problem Exercise 3: Vanishing gradient problem Exercise 4: GRU and LSTM cells Exercise 5: GRU cells are better than simpleRNN Exercise 6: Stacking RNN layers Exercise 7: The Embedding layer Exercise 8: Number of parameters comparison Exercise 9: Transfer learning Exercise 10: Embeddings improves performance Exercise 11: Sentiment classification revisited Exercise 12: Better sentiment classification Exercise 13: Using the CNN layer

Next, in this chapter you will learn how to prepare data for the multi-class classification task, as well as the differences between multi-class classification and binary classification (sentiment analysis). Finally, you will learn how to create models and measure their performance with Keras.

Exercise 1: Data pre-processing Exercise 2: Prepare label vectors Exercise 3: Pre-process data Exercise 4: Transfer learning for language models Exercise 5: Transfer learning starting point Exercise 6: Word2Vec Exercise 7: Multi-class classification models Exercise 8: Exploring 20 News Groups dataset Exercise 9: Classifying news articles Exercise 10: Assessing the model's performance Exercise 11: Precision-Recall trade-off Exercise 12: Precision or Recall, that is the question Exercise 13: Performance on multi-class classification

This chapter introduces you to two applications of RNN models: Text Generation and Neural Machine Translation. You will learn how to prepare the text data to the format needed by the models. The Text Generation model is used for replicating a character's way of speech and will have some fun mimicking Sheldon from The Big Bang Theory. Neural Machine Translation is used for example by Google Translate in a much more complex model. In this chapter, you will create a model that translates Portuguese small phrases into English.

Exercise 1: Sequence to Sequence Models Exercise 2: Text generation examples Exercise 3: NMT example Exercise 4: The Text Generating Function Exercise 5: Predict next character Exercise 6: Generate sentence with context Exercise 7: Change the probability scale Exercise 8: Text Generation Models Exercise 9: Create vectors of sentences and next characters Exercise 10: Preparing the data for training Exercise 11: Creating the text generation model Exercise 12: Neural Machine Translation Exercise 13: Preparing the input text Exercise 14: Preparing the output text Exercise 15: Translate Portuguese to English Exercise 16: Congratulations!