Keras-modellen

In deze oefening ga je werken met twee klassen uit de module keras.models. Je maakt één model met de Sequential-klasse en een ander model met de Model-klasse.

De Sequential-klasse is makkelijker te gebruiken omdat de lagen in volgorde worden geplaatst, terwijl de Model-klasse flexibeler is en meerdere inputs, meerdere outputs en gedeelde lagen (gedeelde gewichten) toestaat.

Bij de Model-klasse moet je de inputlaag expliciet definiëren, terwijl dit in de Sequential-klasse gebeurt met de parameter input_shape.

De objecten en modules Sequential, Model, Dense, Input, LSTM en np (numpy) zijn al geladen in de omgeving.

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.

# Instantiate the class
model = ____(name="sequential_model")

# One LSTM layer (defining the input shape because it is the 
# initial layer)
model.add(____(128, input_shape=(None, 10), name="LSTM"))

# Add a dense layer with one unit
model.add(____(1, activation="sigmoid", name="output"))

# The summary shows the layers and the number of parameters 
# that will be trained
model.____

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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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Begin de cursus 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: Introductie van de cursus Exercise 2: Het aantal parameters van RNN en ANN vergelijken Exercise 3: Sentimentanalyse Exercise 4: Sequence-to-sequence-modellen Exercise 5: Introductie tot taalmodellen Exercise 6: Wennen aan tekstdata Exercise 7: Tekstgegevens voorbereiden voor modelinvoer Exercise 8: Nieuwe tekst transformeren Exercise 9: Introductie tot RNN in Keras Exercise 10: Keras-modellen

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Exercise 11: Keras-preprocessing Exercise 12: Je eerste 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 Exercise 16: Congratulations!