Text generation examples

In this exercise, you are going to experiment on two pre-trained models for text generation.

The first model will generate one phrase based on the character Sheldon of The Big Bang Theory TV show, and the second model will generate a Shakespeare poems up to 400 characters.

The models are loaded on the sheldon_model and poem_model variables. Also, two custom functions to help generate text are available: generate_sheldon_phrase() and generate_poem(). Both receive the pre-trained model and a context string as parameters.

Este ejercicio forma parte del curso

Recurrent Neural Networks (RNNs) for Language Modeling with Keras

Instrucciones del ejercicio

Use pre-defined function generate_sheldon_phrase() with parameters sheldon_model and sheldon_context and store the output in the sheldon_phrase variable.
Print the obtained phrase.
Store the given text into the poem_context variable.
Print the poem generated by applying the function generate_poem() with the poem_model and poem_context parameters.

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# Context for Sheldon phrase
sheldon_context = "I’m not insane, my mother had me tested. "

# Generate one Sheldon phrase
sheldon_phrase = ____(sheldon_model, sheldon_context)

# Print the phrase
print(____)

# Context for poem
____ = "May thy beauty forever remain"

# Print the poem
print(generate_poem(____, poem_context))

Editar y ejecutar código

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

Ejercicio actual

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!