Preparing the output text

In this exercise, you will prepare the output texts to be used on the translation model. Apart from transforming the text to sequences of indexes, you also need to one-hot encode each index.

The English texts are loaded on the en_sentences variable, the fitted tokenizer on the output_tokenizer variable and the English vocabulary size on en_vocab_size.

Also, a function to perform the first steps of transforming the output language (transformation of texts into sequence of indexes) is already created. The function is loaded on the environment as transform_text_to_sequences() and has two parameters: sentences that expect a list of sentences in English and tokenizer that expects a fitted Tokenizer object from the keras.preprocessing.text module.

numpy is loaded as np.

This exercise is part of the course

Recurrent Neural Networks (RNNs) for Language Modeling with Keras

Exercise instructions

Pass the en_sentences and output_tokenizer variables to the transform_text_to_sequences() function to initialize the Y variable.
Use the to_categorical() function to one-hot encode the sentences. Use the en_vocab_size variable as number of classes.
Transform the temporary list to numpy array and reshape to have shape equal to (num_sentences, sentences_len, en_vocab_size).
Print the raw text and the transformed one.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Initialize the variable
Y = transform_text_to_sequences(____, ____)

# Temporary list
ylist = list()
for sequence in Y:
  	# One-hot encode sentence and append to list
    ylist.append(____(sequence, num_classes=____))

# Update the variable
Y = np.array(ylist).reshape(____, Y.shape[1], en_vocab_size)

# Print the raw sentence and its transformed version
print("Raw sentence: {0}\nTransformed: {1}".format(____, Y[0]))

Edit and Run Code

This exercise is part of the course

Recurrent Neural Networks (RNNs) for Language Modeling with Keras

AdvancedSkill Level

4.8+

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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

Current Exercise

Exercise 15: Translate Portuguese to English Exercise 16: Congratulations!