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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: Understanding GRU models

You will now see how you can use Keras models to accept arbitrary sized batches of inputs. The ability to accept arbitrary sized batches is important for many reasons. For example, this allows you to define a single Keras model and experiment with different batch sizes during the model training stage, without having to change anything in the model.

For this exercise, keras and numpy (as np) have already been imported.

Instructions

100 XP
  • Define an input layer that accepts an arbitrary sized batch of data having sequence length 3 and input size 4.
  • Define a GRU layer with 10 hidden units that consumes the previous input and produces an output.
  • Define a Model called model that takes the input layer as the input and produces the output of the GRU layer as the output. Remember that you can use the keras.models.Model(<argument>=<value>) syntax to define a model.
  • Predict the model output for both x1 and x2.