Predict next character

In this exercise, you will code the function to predict the next character given a trained model. You will use the past 20 chars to predict the next one. You will learn how to train the model in the next lesson, as this step is integral before model training.

This is the initial step to create rules for generating sentences, paragraphs, short texts or other blocks of text as needed.

The variables n_vocab, chars_window and the dictionary index_to_char are already loaded in the environment. Also, the functions below are already created for you:

initialize_X(): Transforms the text input into a sequence of index numbers with the correct shape.
predict_next_char(): Gets the next character using the .predict() method of the model class and the index_to_char dictionary.

Este ejercicio forma parte del curso

Recurrent Neural Networks (RNNs) for Language Modeling with Keras

Instrucciones del ejercicio

Define the function get_next_char() and add the parameters initial_text and chars_window without default values.
Use initialize_X() function and pass variable char_to_index to obtain a vector of zeros to be used for prediction.
Use the predict_next_char() function to obtain the prediction and store it in the next_char variable.
Print the predicted character by applying the defined function on the given initial_text.

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def ____(model, ____, ____, char_to_index, index_to_char):
  	# Initialize the X vector with zeros
    X = ____(initial_text, chars_window, ____)
    
    # Get next character using the model
    ____ = predict_next_char(model, X, ____)
	
    return next_char

# Define context sentence and print the generated text
initial_text = "I am not insane, "
print("Next character: {0}".format(____(model, ____, 20, char_to_index, index_to_char)))

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Este ejercicio forma parte del curso

Recurrent Neural Networks (RNNs) for Language Modeling with Keras

AvanzadoNivel de habilidad

4.8+

Comienza el curso gratis

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

Ejercicio actual

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!