Translate Portuguese to English

This is the last exercise of the course, congratulations on getting here!

You will learn how to use NMT models for making translations.

A model that encodes Portuguese small phrases and decodes them into English small phrases was pre-trained and is loaded in the model variable.

Also, the function predict_one() is already loaded, use help() for details and the dataset is available on the test (raw text) and X_test (tokenized) variables.

You will define a function to translate a list of sentences. In the parameters, sentences is a list of phrases to be translated, index_to_word is a dict containing numerical indexes as keys and words as values for the English language, loaded in the en_index_to_word variable.

The model summary has been printed for your consideration.

Diese Übung ist Teil des Kurses

Recurrent Neural Networks (RNNs) for Language Modeling with Keras

Anleitung zur Übung

Loop over the enumerated iterator of the phrases.
Use the pre-loaded function predict_one() to translate one phrase.
Print the translation result.
Call the defined function to translate the initial 10 phrases of the X_test variable.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Function to predict many phrases
def predict_many(model, sentences, index_to_word, raw_dataset):
    for i, sentence in ____(sentences):
        # Translate the Portuguese sentence
        translation = ____(model, sentence, index_to_word)
        
        # Get the raw Portuguese and English sentences
        raw_target, raw_src = raw_dataset[i]
        
        # Print the correct Portuguese and English sentences and the predicted
        print('src=[%s], target=[%s], predicted=[%s]' % (raw_src, raw_target, ____))

____(model, X_test[____], en_index_to_word, test)

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

Recurrent Neural Networks (RNNs) for Language Modeling with Keras

Hohe SchwierigkeitSchwierigkeitsgrad

4.8+

Kurs kostenlos starten

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: Introduction to the course Exercise 2: Comparing the number of parameter of RNN and ANN Exercise 3: Sentiment analysis Exercise 4: Sequence to sequence models Exercise 5: Introduction to language models Exercise 6: Getting used to text data Exercise 7: Preparing text data for model input Exercise 8: Transforming new text Exercise 9: Introduction to RNN inside Keras Exercise 10: Keras models Exercise 11: Keras preprocessing Exercise 12: Your first RNN model

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

Aktuelle Übung

Exercise 16: Congratulations!