Prestaties bij multi-class classificatie

In deze oefening bereken je de prestatiecijfers voor modellen met de module sklearn.metrics.

Het model is al getraind en staat in de variabele model. Ook de variabelen X_test en y_true zijn geladen, samen met de functies confusion_matrix() en classification_report() uit het pakket sklearn.metrics.

Je berekent eerst de confusion matrix van het model. Daarna vat je de modelprestaties samen door de precision, recall en F1-score te berekenen met de functie classification_report(). In deze functie kun je optioneel een list met de klassennamen (die staan in de variabele news_cat) doorgeven aan de parameter target_names om het rapport beter leesbaar te maken.

Deze oefening maakt deel uit van de cursus

Recurrent Neural Networks (RNN's) voor taalmodellen met Keras

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Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Use the model to predict on new data
____ = model.____(X_test)

# Choose the class with higher probability 
y_pred = np.____(predicted, axis=1)

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Deze oefening maakt deel uit van de cursus

Recurrent Neural Networks (RNN's) voor taalmodellen met Keras

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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 preprocessing Exercise 2: Labelvectoren voorbereiden Exercise 3: Data pre-processen Exercise 4: Transfer learning voor taalmodellen Exercise 5: Startpunt voor transfer learning Exercise 6: Word2Vec Exercise 7: Modellen voor multiclass-classificatie Exercise 8: De 20 Newsgroups-gegevensset verkennen Exercise 9: Nieuwsartikelen classificeren Exercise 10: De prestatie van het model beoordelen Exercise 11: Trade-off tussen precisie en recall Exercise 12: Precision of Recall, dát is de vraag Exercise 13: Prestaties bij multi-class classificatie

Huidige oefening

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!