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  3. Machine Translation with Keras
  • 1

    Introduction to Machine Translation

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    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 1: Understanding GRU models

Did you know these models can remember even up to thousands of time steps compared to standard recurrent neural networks which can usually remember less than hundred time steps only. Understanding GRU models is essential to use them effectively to implement machine translation models.

In this exercise, you will implement a simple model that has an input layer and a GRU layer. You will then use the model to produce output values for a random input array.

Don't be discouraged that you are using random data. The objective of this exercise is to understand the shape of the outputs produced by the GRU layer. In later chapters, you will feed in actual sentences to GRU layers to perform translation.

Instructions 1/3

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  • Define a Keras input layer with batch size 2, sequence length 3 and input dimensionality 4. Remember that you can define an input layer using the keras.layers.Input(<argument>=<value>) syntax.