Training the model with validation

Here you will learn how to train the neural machine translator model with a validation step.

You are provided with the nmt model that you created in the last chapter. Furthermore, you will train the model on a English and French sentences obtained from the Udacity Github Repo. You are provided with training English text (tr_en) and French text (tf_fr) as well as validation English text (v_en) and French text (v_fr) from the previous exercise.

Training the model takes a bit of time so your code will take a little longer to run.

This exercise is part of the course

Machine Translation with Keras

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Exercise instructions

Create validation data by transforming v_en and v_fr using sents2seqs function.
Get a properly transformed batch of inputs and outputs using the sents2seqs function.
Use the inputs (en_x) and outputs (de_y) to train the nmt on a single batch.
Use v_en_x and v_de_y along with valid_size as batch_size to evaluate the nmt model and obtain the validation accuracy.

Hands-on interactive exercise

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

# Convert validation data to onehot
v_en_x = ____(____, ____, onehot=____, reverse=____)
v_de_y = ____(____, ____, onehot=True)

n_epochs, bsize = 3, 250
for ei in range(n_epochs):
  for i in range(0,train_size,bsize):
    # Get a single batch of inputs and outputs
    en_x = ____(____, tr_en[____:____], onehot=____, reverse=____)
    de_y = sents2seqs(____, tr_fr[____:____], onehot=True)
    # Train the model on a single batch of data
    nmt.____(____, ____)    
  # Evaluate the trained model on the validation data
  res = nmt.evaluate(____, ____, batch_size=valid_size, verbose=0)
  print("{} => Loss:{}, Val Acc: {}".format(ei+1,res[0], res[1]*100.0))

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