Part 2: Defining the full model

Did you know that it took around 6 days and 96 GPUs to train a variant of the Google Neural Machine Translator just on the English to French translation task.

In this exercise you will define a similar but much simpler encoder-decoder based neural machine translator model. Specifically, you will use the previously defined inputs and outputs and define a Keras Model object and compile the model with a given loss function and an optimizer.

Here you are provided with en_inputs (encoder input layer), en_out and en_state (encoder GRU output), de_out (decoder GRU output) and de_pred (decoder prediction) that you previously defined.

This exercise is part of the course

Machine Translation with Keras

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Exercise instructions

Define a Keras Model which takes in the en_inputs as inputs and the decoder predictions (de_pred) as the output.
Compile the defined model by calling <model>.compile with the 'adam' optimizer, cross entropy loss and accuracy (acc) as a metric.
Print the summary of the model.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

from tensorflow.keras.models import Model
# Define a model with encoder input and decoder output
nmt = ____(____=____, outputs=____)

# Compile the model with an optimizer and a loss
nmt.____(optimizer=____, ____='categorical_crossentropy', metrics=[____])

# View the summary of the model 
nmt.____()

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