İlk RNN modelin

Bu egzersizde, ilk RNN modelini kurmak ve onu film yorumlarında duygu sınıflandırması yapmak için Keras modüllerini pratiğe dökeceksin.

Bu ilk model, vanilya RNN hücresi olan SimpleRNN ile bir yinelemeli katmana ve iki olası değer üreten bir çıktı katmanına sahip: 0 olumsuz duyguyu, 1 olumlu duyguyu temsil eder.

keras.datasets içinde yer alan IMDB veri kümesini kullanacaksın. Bir model önceden eğitildi ve ağırlıkları model_weights.h5 dosyasında saklandı. Modelin mimarisini kuracak ve performansını kontrol etmek için önceden yüklenmiş x_test ve y_test değişkenlerini kullanacaksın.

Bu egzersiz

Keras ile Dil Modellemesi için Yinelenen Sinir Ağları (RNN)

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

128 birimle SimpleRNN hücresini ekle.
Duygu sınıflandırması için bir birimli bir Dense katmanı ekle.
İkili sınıflandırma için uygun kayıp fonksiyonunu kullan.
Modeli önceden eğitilmiş doğrulama kümesinde değerlendir: (x_test, y_test).

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Build model
model = Sequential()
model.add(____(units=____, input_shape=(None, 1)))
model.add(Dense(____, activation='sigmoid'))
model.compile(loss='____', 
              optimizer='adam',
              metrics=['accuracy'])

# Load pre-trained weights
model.load_weights('model_weights.h5')

# Method '.evaluate()' shows the loss and accuracy
loss, acc = model.evaluate(____, ____, verbose=0)
print("Loss: {0} \nAccuracy: {1}".format(loss, acc))

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

Geçerli Egzersiz

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!