En un ejercicio de machine translation anterior hemos visto la parte 1 de esta práctica, correspondiente a la carga de datos. En este apartado seguiremos con la realización del mismo ejercicio en su segunda fase: la de preprocesado y entrenamiento.
Ejercicio de machine translation
Preprocesado
#Ejercicio de machine translation
def tokenize (x):
" " "
Tokenize x
:param x: List of sentences / strings to be tokenized
:return: Tuple of (tokenized x data, tokenizer used to tokenize x)
" " "
t = Tokenizer ( )
#fit the tokenizer on the documents
t.fit_on_texts (x)
return t.texts_to_sequences (x), t
#Tokenize Example output
text_sentences = [
'The quick brown fox jumps over the lazy dog .',
'By Jove, my quick study of lexicography won a prize .'
'This is a short sentence .']
text_tokenized, text_tokenizer = tokenize (text_sentences)
print (text_tokenizer.word_index)
print ( )
for sample_i, (sent, token_sent) in enumerate (zip (text_sentences, text_tokenized)):
print ('Sequence { } in x'.format (sample_i + 1))
print ('Input: { }'.format (sent))
print ('Output: { }'.format (token_sent))
#Ejercicio de machine translation
def pad (x_length = None):
" " "
Pad x
:param x: List of sequences.
:param length: Length to pad the sequence to. If None, use length of longest sequence in x.
:return: Padded numpy array of sequences
" " "
if length is None:
length = max ([len (sentence) for sentence in x])
return pad_sequences (x, maxlen = length, padding = 'post')
#Pad Tokenized output
test_pad = pad (text_tokenized)
for sample_i, (token_sent, pad_sent) in enumerate (zip (text_tokenized, test_pad)):
print ('Sequence { } in x'.format (sample_i + 1))
print ('Input: { }'.format (np.array (token_sent)))
print ('Output: { }'.format (pad_sent))
Entrenamiento
Primero usaremos una implementación de RNN many to many:
#Ejercicio de machine translation
def logits_to_text (logits, tokenizer):
" " "
Turn logits from a neural network into text using the tokenizer
:param logits: Logits from a neural network
:param tokenizer: Keras Tokenizer fit on the labels
:return: String that represents the text of the logits
" " "
index_to_words = {id: word for word, id in tokenizer.word_index.items ()}
index_to_words [0] = '<PAD>'
return ' '.join ([index_to_words [prediction] for prediction in np.argmax (logits, 1)])
print (' 'logits_to_text' function loaded. ')#Ejercicio de machine translation
import tensorflow as tf
gpu_devices = tf.config.experimental.list_physical_devices ('GPU')
for device in gpu_devices:
tf.config.experimental.set_memory_growth (device, True)#Ejercicio de machine translation
#Reshaping the input to work with a basic RNN
tmp_x = pad (preproc_english_sentences, max_french_sequence_length)
tmp_x = tmp_x.reshape ((-1, preproc_french_sentences.shape [-2], 1))
learning_rate = 1e - 3
input:seq = tf.keras.layers.Input (tmp_x.shape [1:])
rnn = tf.compat.v1.keras.layers.CuDNNLSTM (100, return_sequences = True) (input_seq)
#french_vocab_size
logits = tf.keras.layers.TimeDistributed (tf.keras.layers.Dense (french_vocab_size)) (rnn)
model = tf.keras.models.Model (input_seq, tf.keras.layers.Activation ('softmax') (logits))
model.compile (loss = tf.keras.losses.sparse_categorical_crossentropy, optimizer = tf. keras.optimizers.Adam (learning_rate), metrics = ['accuracy'])
model.fit (tmp_x, preproc_french_sentences, batch_size = 64, epochs = 1, validation_split = 0.2)
#Print prediction (s)
print (logits_to_text (model.predict (tmp_x [:1]) [0], french_tokenizer))
Añadimos una capa de embedding previa a la capa de GRU:
#Ejercicio de machine translation
learning_rate = 1e - 3
input_seq = tf.keras.layers.Input (tmpX-shape [2:])
embedding = tf.keras.layers.Embedding (input_dim = english_vocab_size, output_dim = 256 (input_seq)
rnn = tf.keras.layers.GRU (100, return_sequences = True) (embedding)
#french vocab size
logits = tf.keras.layers.TimeDistributed (tf.keras.layers.Dense (french_vocab_size)) (rnn)
model = tf.keras.models.Model (input_seq, tf.keras.layers.Activation ('softmax') (logits))
model.compile (loss = tf.keras.losses.sparse_ categorical_ crossentropy, optimizer = tf. keras.optimizers.Adam (learning_rate), metrics = ['accuracy'])
model.fit (tmp_x, preproc_french_sentences, batch_size = 64, epochs = 1, validation_split = 0.2)
#Print prediction (s)
print (logits_to_text (model.predict (tmp_x [:1]) [0], french_tokenizer))Una vez aquí, podríamos seguir optimizando más este ejercicio por medio de la inserción de un modelo LSTM.
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