Push
commit
a89c1b812f
|
@ -0,0 +1 @@
|
||||||
|
.venv/
|
|
@ -0,0 +1,78 @@
|
||||||
|
import tensorflow as tf
|
||||||
|
import nlpaug.augmenter.word as naw
|
||||||
|
|
||||||
|
|
||||||
|
class DataLoader:
|
||||||
|
def __init__(self, path, buffer_size, batch_size, max_length, test_ratio=0.2):
|
||||||
|
self.path = path
|
||||||
|
self.buffer_size = buffer_size
|
||||||
|
self.batch_size = batch_size
|
||||||
|
self.max_length = max_length
|
||||||
|
self.test_ratio = test_ratio
|
||||||
|
self.aug = naw.SynonymAug(aug_src="wordnet")
|
||||||
|
|
||||||
|
def _split_input_target(self, sequence):
|
||||||
|
parts = tf.strings.split(sequence, "\t")
|
||||||
|
index = int(parts[0])
|
||||||
|
sentence = tf.strings.reduce_join(parts[1:], separator=" ")
|
||||||
|
return sentence, index
|
||||||
|
|
||||||
|
def augment_data(self, sentence, index):
|
||||||
|
aug_sentence = self.aug.augment(sentence.numpy().decode())
|
||||||
|
return sentence, aug_sentence, index
|
||||||
|
|
||||||
|
def tf_augment_data(self, sentence, index):
|
||||||
|
sentence, aug_sentence, index = tf.py_function(
|
||||||
|
self.augment_data, [sentence, index], [tf.string, tf.string, tf.int32]
|
||||||
|
)
|
||||||
|
return sentence, aug_sentence, index
|
||||||
|
|
||||||
|
def load_dataset(self):
|
||||||
|
lines_dataset = tf.data.TextLineDataset(self.path)
|
||||||
|
dataset = lines_dataset.map(self._split_input_target)
|
||||||
|
dataset = dataset.map(self.tf_augment_data)
|
||||||
|
|
||||||
|
# Split dataset into train and test
|
||||||
|
dataset_size = tf.data.experimental.cardinality(dataset).numpy()
|
||||||
|
test_size = int(dataset_size * self.test_ratio)
|
||||||
|
train_size = dataset_size - test_size
|
||||||
|
train_dataset = dataset.take(train_size)
|
||||||
|
test_dataset = dataset.skip(train_size)
|
||||||
|
|
||||||
|
# Shuffle and batch
|
||||||
|
train_dataset = train_dataset.shuffle(self.buffer_size).batch(self.batch_size)
|
||||||
|
test_dataset = test_dataset.shuffle(self.buffer_size).batch(self.batch_size)
|
||||||
|
|
||||||
|
return train_dataset, test_dataset
|
||||||
|
|
||||||
|
|
||||||
|
def test():
|
||||||
|
# Hyperparameters
|
||||||
|
buffer_size = 10000
|
||||||
|
batch_size = 64
|
||||||
|
max_length = 100 # Or any other value depending on your data
|
||||||
|
|
||||||
|
# Create DataLoader
|
||||||
|
data_loader = DataLoader(
|
||||||
|
"../datasets/deu_mixed-typical_2011_1M/deu_mixed-typical_2011_1M-sentences.txt",
|
||||||
|
buffer_size,
|
||||||
|
batch_size,
|
||||||
|
max_length,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Load the datasets
|
||||||
|
train_dataset, test_dataset = data_loader.load_dataset()
|
||||||
|
|
||||||
|
# Test the data loader on the training dataset
|
||||||
|
print("First 5 batches from the training dataset:")
|
||||||
|
for sent, aug, indxs in train_dataset.take(1):
|
||||||
|
print(f"Indices: {indxs}, Sentences: {sent}, Augmented: {aug}")
|
||||||
|
|
||||||
|
# Test the data loader on the test dataset
|
||||||
|
# print("\nFirst 5 batches from the test dataset:")
|
||||||
|
# for sentences, indices in test_dataset.take(5):
|
||||||
|
# print(f"Indices: {indices}, Sentences: {sentences}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
test()
|
Loading…
Reference in New Issue