Training the model
Did you know that in 2017, Google translate served more than 500 million users daily?
Here, you will train your first Teacher Forced model. Teacher Forcing is commonly used in sequence to sequence models like your neural machine translator to achieve better performance.
You will be provided with the sents2seqs() function, English sentences en_text and French sentences fr_text.
Questo esercizio fa parte del corso
Machine Translation with Keras
Istruzioni dell'esercizio
- Get the decoder input which contains the onehot encoded sequences of French words (except for the last word in every sequence).
- Get the decoder output which contains the onehot encoded sequences of French words (except for the first word in every sequence).
- Train the model on a single batch of data.
- Obtain the evaluation metrics (loss and accuracy) for training data
en_x,de_xandde_y.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
n_epochs, bsize = 3, 250
for ei in range(n_epochs):
for i in range(0,data_size,bsize):
en_x = sents2seqs('source', en_text[i:i+bsize], onehot=True, reverse=True)
de_xy = sents2seqs('target', fr_text[i:i+bsize], onehot=True)
# Separate the decoder inputs from de_xy
de_x = ____[:,____,:]
# Separate the decoder outputs from de_xy
de_y = ____[____]
# Train the model on a single batch of data
nmt_tf.____([____,____], ____)
# Obtain the eval metrics for the training data
res = _____.evaluate([____,____], ____, batch_size=bsize, verbose=0)
print("{} => Train Loss:{}, Train Acc: {}".format(ei+1,res[0], res[1]*100.0))