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  3. Machine Translation with Keras
  • 1

    Introduction to Machine Translation

    Free

    In this chapter, you'll understand what the encoder-decoder architecture is and how it is used for machine translation. You will also learn about Gated Recurrent Units (GRUs) and how they are used in the encoder-decoder architecture.

  • 2

    Implementing an Encoder-Decoder Model with Keras

    In this chapter, you will implement the encoder-decoder model with the Keras functional API. While doing so, you will learn several useful Keras layers such as RepeatVector and TimeDistributed layers.

  • 3

    Training and Generating Translations

    In this chapter, you will train the previously defined model and then use a well-trained model to generate translations. You will see that our model does a good job when translating sentences.

  • 4

    Teacher Forcing and Word Embeddings

    In this chapter, you will learn about a technique known as Teacher Forcing, which enables translation models to be trained better and faster. Then you will learn how you can use word embeddings to make the model even better.

Initializing

Exercise

Part 2: Text reversing model - Encoder

You will now implement the rest of the encoder of the text reversing model. The encoder feeds on the one-hot vectors produced by the words2onehot() function you implemented previously.

Here you will be implementing the encoder() function. The encoder() function takes in a set of one-hot vectors and converts them to a list of word ids.

For this exercise, the words2onehot() function and the word2index dictionary (having the words We, like and dogs) have been provided.

Instructions

100 XP
  • Convert onehot to an array of word IDs using np.argmax() function and return the word IDs.
  • Define a list of words with words We, like, dogs.
  • Convert the list of words to onehot vectors using the words2onehot() function. Remember that words2onehot() takes a list of words and a Python dictionary as its arguments.
  • Get the context vector of the onehot vectors using the encoder() function.