def test_text_dataset_from_directory_binary(self):
directory = self._prepare_directory(num_classes=2)
dataset = text_dataset.text_dataset_from_directory(
directory, batch_size=8, label_mode='int', max_length=10)
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8,))
self.assertEqual(batch[0].dtype.name, 'string')
self.assertEqual(len(batch[0].numpy()[0]), 10) # Test max_length
self.assertEqual(batch[1].shape, (8,))
self.assertEqual(batch[1].dtype.name, 'int32')
dataset = text_dataset.text_dataset_from_directory(
directory, batch_size=8, label_mode='binary')
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8,))
self.assertEqual(batch[0].dtype.name, 'string')
self.assertEqual(batch[1].shape, (8, 1))
self.assertEqual(batch[1].dtype.name, 'float32')
dataset = text_dataset.text_dataset_from_directory(
directory, batch_size=8, label_mode='categorical')
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8,))
self.assertEqual(batch[0].dtype.name, 'string')
self.assertEqual(batch[1].shape, (8, 2))
self.assertEqual(batch[1].dtype.name, 'float32')
def test_text_dataset_from_directory_multiclass(self):
directory = self._prepare_directory(num_classes=4, count=15)
dataset = text_dataset.text_dataset_from_directory(
directory, batch_size=8, label_mode=None)
batch = next(iter(dataset))
self.assertEqual(batch.shape, (8,))
dataset = text_dataset.text_dataset_from_directory(
directory, batch_size=8, label_mode=None)
sample_count = 0
iterator = iter(dataset)
for batch in dataset:
sample_count += next(iterator).shape[0]
self.assertEqual(sample_count, 15)
dataset = text_dataset.text_dataset_from_directory(
directory, batch_size=8, label_mode='int')
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8,))
self.assertEqual(batch[0].dtype.name, 'string')
self.assertEqual(batch[1].shape, (8,))
self.assertEqual(batch[1].dtype.name, 'int32')
dataset = text_dataset.text_dataset_from_directory(
directory, batch_size=8, label_mode='categorical')
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8,))
self.assertEqual(batch[0].dtype.name, 'string')
self.assertEqual(batch[1].shape, (8, 4))
self.assertEqual(batch[1].dtype.name, 'float32')
def test_text_dataset_from_directory_errors(self):
directory = self._prepare_directory(num_classes=3, count=5)
with self.assertRaisesRegex(ValueError, '`labels` argument should be'):
_ = text_dataset.text_dataset_from_directory(directory,
labels=None)
with self.assertRaisesRegex(ValueError,
'`label_mode` argument must be'):
_ = text_dataset.text_dataset_from_directory(directory,
label_mode='other')
with self.assertRaisesRegex(
ValueError,
'only pass `class_names` if the labels are inferred'):
_ = text_dataset.text_dataset_from_directory(
directory,
labels=[0, 0, 1, 1, 1],
class_names=['class_0', 'class_1', 'class_2'])
with self.assertRaisesRegex(
ValueError,
'Expected the lengths of `labels` to match the number of files'
):
_ = text_dataset.text_dataset_from_directory(directory,
labels=[0, 0, 1, 1])
with self.assertRaisesRegex(ValueError,
'`class_names` passed did not match'):
_ = text_dataset.text_dataset_from_directory(
directory, class_names=['class_0', 'class_2'])
with self.assertRaisesRegex(ValueError,
'there must exactly 2 classes'):
_ = text_dataset.text_dataset_from_directory(directory,
label_mode='binary')
with self.assertRaisesRegex(
ValueError, '`validation_split` must be between 0 and 1'):
_ = text_dataset.text_dataset_from_directory(directory,
validation_split=2)
with self.assertRaisesRegex(ValueError,
'`subset` must be either "training" or'):
_ = text_dataset.text_dataset_from_directory(directory,
validation_split=0.2,
subset='other')
with self.assertRaisesRegex(ValueError,
'`validation_split` must be set'):
_ = text_dataset.text_dataset_from_directory(directory,
validation_split=0,
subset='training')
with self.assertRaisesRegex(ValueError, 'must provide a `seed`'):
_ = text_dataset.text_dataset_from_directory(directory,
validation_split=0.2,
subset='training')
def test_text_dataset_from_directory_validation_split(self):
directory = self._prepare_directory(num_classes=2, count=10)
dataset = text_dataset.text_dataset_from_directory(
directory, batch_size=10, validation_split=0.2, subset='training',
seed=1337)
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (8,))
dataset = text_dataset.text_dataset_from_directory(
directory, batch_size=10, validation_split=0.2, subset='validation',
seed=1337)
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertEqual(batch[0].shape, (2,))
def test_text_dataset_from_directory_standalone(self):
# Test retrieving txt files without labels from a directory and its subdirs.
# Save a few extra files in the parent directory.
directory = self._prepare_directory(count=7, num_classes=2)
for i in range(3):
filename = 'text_%s.txt' % (i,)
f = open(os.path.join(directory, filename), 'w')
text = ''.join([random.choice(string.printable) for _ in range(20)])
f.write(text)
f.close()
dataset = text_dataset.text_dataset_from_directory(
directory, batch_size=5, label_mode=None, max_length=10)
batch = next(iter(dataset))
# We just return the texts, no labels
self.assertEqual(batch.shape, (5,))
self.assertEqual(batch.dtype.name, 'string')
# Count samples
batch_count = 0
sample_count = 0
for batch in dataset:
batch_count += 1
sample_count += batch.shape[0]
self.assertEqual(batch_count, 2)
self.assertEqual(sample_count, 10)
def test_text_dataset_from_directory_manual_labels(self):
directory = self._prepare_directory(num_classes=2, count=2)
dataset = text_dataset.text_dataset_from_directory(
directory, batch_size=8, labels=[0, 1], shuffle=False)
batch = next(iter(dataset))
self.assertLen(batch, 2)
self.assertAllClose(batch[1], [0, 1])
def test_sample_count(self):
directory = self._prepare_directory(num_classes=4, count=15)
dataset = text_dataset.text_dataset_from_directory(
directory, batch_size=8, label_mode=None)
sample_count = 0
for batch in dataset:
sample_count += batch.shape[0]
self.assertEqual(sample_count, 15)
def test_text_dataset_from_directory_follow_links(self):
directory = self._prepare_directory(num_classes=2, count=25,
nested_dirs=True)
dataset = text_dataset.text_dataset_from_directory(
directory, batch_size=8, label_mode=None, follow_links=True)
sample_count = 0
for batch in dataset:
sample_count += batch.shape[0]
self.assertEqual(sample_count, 25)
def test_text_dataset_from_directory_no_files(self):
directory = self._prepare_directory(num_classes=2, count=0)
with self.assertRaisesRegex(ValueError, 'No text files found.'):
_ = text_dataset.text_dataset_from_directory(directory)
from tensorflow.keras.layers import Input, Layer, MultiHeadAttention, Dense
from tensorflow.keras.layers import LayerNormalization, Dropout, Embedding, GlobalMaxPooling1D
from tensorflow.keras.models import Model
from tensorflow.python.data.experimental import cardinality
from tensorflow.python.keras.preprocessing.text_dataset import text_dataset_from_directory
from tensorflow.keras.layers.experimental.preprocessing import TextVectorization
from tensorflow.python.keras.utils.vis_utils import plot_model
# descargar el dataset desde: https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
# una vez descomprimido, borrar la carpeta 'unsup' (sólo útil para aprendizaje no supervisado)
data_path = os.path.join('data', 'nlp', 'aclImdb')
batch_size = 32
raw_train_ds = text_dataset_from_directory(os.path.join(data_path, 'train'),
batch_size=batch_size,
validation_split=0.2,
subset='training',
seed=42)
raw_val_ds = text_dataset_from_directory(os.path.join(data_path, 'train'),
batch_size=batch_size,
validation_split=0.2,
subset='validation',
seed=42)
raw_test_ds = text_dataset_from_directory(os.path.join(data_path, 'test'),
batch_size=batch_size)
print(f'Number of batches in raw_train_ds: {cardinality(raw_train_ds)}')
print(f'Number of batches in raw_val_ds: {cardinality(raw_val_ds)}')
print(f'Number of batches in raw_test_ds: {cardinality(raw_test_ds)}')
# Imprimimos 5 instancias para ver que aspecto tienen los datos
