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Text prediction with LSTMs

During the following exercises you will build a toy LSTM model that is able to predict the next word using a small text dataset. This dataset consist of cleaned quotes from the The Lord of the Ring movies. You can find them in the text variable.

You will turn this text into sequences of length 4 and make use of the Keras Tokenizer to prepare the features and labels for your model!

The Keras Tokenizer is already imported for you to use. It assigns a unique number to each unique word, and stores the mappings in a dictionary. This is important since the model deals with numbers but we later will want to decode the output numbers back into words.

Cet exercice fait partie du cours

Introduction to Deep Learning with Keras

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Instructions

  • Split the text into an array of words using .split().
  • Make sentences of 4 words each, moving one word at a time.
  • Instantiate a Tokenizer(), then fit it on the sentences with .fit_on_texts().
  • Turn sentences into a sequence of numbers calling .texts_to_sequences().

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Split text into an array of words 
words = ____.____

# Make sentences of 4 words each, moving one word at a time
sentences = []
for i in range(4, len(words)):
  sentences.append(' '.join(words[i-____:i]))

# Instantiate a Tokenizer, then fit it on the sentences
tokenizer = ____
tokenizer.____(____)

# Turn sentences into a sequence of numbers
sequences = tokenizer.____(____)
print("Sentences: \n {} \n Sequences: \n {}".format(sentences[:5],sequences[:5]))
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