Dataset utilities UX improvements

- Display number of files used for training/validation when validation_split is used
- Refuse to perform validation split if the data is shuffled and not seeded

PiperOrigin-RevId: 308750122
Change-Id: I07f9090e714d1290532c7b7b7f51417f7193c797

tensorflow/python/keras/preprocessing/dataset_utils.py

@@ -170,19 +170,16 @@ def get_training_or_validation_split(samples, labels, validation_split, subset):
   Returns:
     tuple (samples, labels), potentially restricted to the specified subset.
   """
-  if validation_split:
+  if not validation_split:
-    if not 0 < validation_split < 1:
-      raise ValueError(
-          '`validation_split` must be between 0 and 1, received: %s' %
-          (validation_split,))
-  if subset is None:
     return samples, labels
 
   num_val_samples = int(validation_split * len(samples))
   if subset == 'training':
+    print('Using %d files for training.' % (len(samples) - num_val_samples,))
     samples = samples[:-num_val_samples]
     labels = labels[:-num_val_samples]
   elif subset == 'validation':
+    print('Using %d files for validation.' % (num_val_samples,))
     samples = samples[-num_val_samples:]
     labels = labels[-num_val_samples:]
   else:
@@ -199,3 +196,22 @@ def labels_to_dataset(labels, label_mode, num_classes):
   elif label_mode == 'categorical':
     label_ds = label_ds.map(lambda x: array_ops.one_hot(x, num_classes))
   return label_ds
+
+
+def check_validation_split_arg(validation_split, subset, shuffle, seed):
+  """Raise errors in case of invalid argument values."""
+  if validation_split and not 0 < validation_split < 1:
+    raise ValueError(
+        '`validation_split` must be between 0 and 1, received: %s' %
+        (validation_split,))
+  if (validation_split or subset) and not (validation_split and subset):
+    raise ValueError(
+        'If `subset` is set, `validation_split` must be set, and inversely.')
+  if subset not in ('training', 'validation', None):
+    raise ValueError('`subset` must be either "training" '
+                     'or "validation", received: %s' % (subset,))
+  if validation_split and shuffle and seed is None:
+    raise ValueError(
+        'If using `validation_split` and shuffling the data, you must provide '
+        'a `seed` argument, to make sure that there is no overlap between the '
+        'training and validation subset.')

tensorflow/python/keras/preprocessing/image_dataset.py

@@ -167,6 +167,8 @@ def image_dataset_from_directory(directory,
         '`color_mode` must be one of {"rbg", "rgba", "grayscale"}. '
         'Received: %s' % (color_mode,))
   interpolation = image_preprocessing.get_interpolation(interpolation)
+  dataset_utils.check_validation_split_arg(
+      validation_split, subset, shuffle, seed)
 
   if seed is None:
     seed = np.random.randint(1e6)

tensorflow/python/keras/preprocessing/image_dataset_test.py

@@ -199,13 +199,13 @@ class ImageDatasetFromDirectoryTest(keras_parameterized.TestCase):
     directory = self._prepare_directory(num_classes=2, count=10)
     dataset = image_dataset.image_dataset_from_directory(
         directory, batch_size=10, image_size=(18, 18),
-        validation_split=0.2, subset='training')
+        validation_split=0.2, subset='training', seed=1337)
     batch = next(iter(dataset))
     self.assertLen(batch, 2)
     self.assertEqual(batch[0].shape, (8, 18, 18, 3))
     dataset = image_dataset.image_dataset_from_directory(
         directory, batch_size=10, image_size=(18, 18),
-        validation_split=0.2, subset='validation')
+        validation_split=0.2, subset='validation', seed=1337)
     batch = next(iter(dataset))
     self.assertLen(batch, 2)
     self.assertEqual(batch[0].shape, (2, 18, 18, 3))
@@ -285,6 +285,14 @@ class ImageDatasetFromDirectoryTest(keras_parameterized.TestCase):
       _ = image_dataset.image_dataset_from_directory(
           directory, validation_split=0.2, subset='other')
 
+    with self.assertRaisesRegex(ValueError, '`validation_split` must be set'):
+      _ = image_dataset.image_dataset_from_directory(
+          directory, validation_split=0, subset='training')
+
+    with self.assertRaisesRegex(ValueError, 'must provide a `seed`'):
+      _ = image_dataset.image_dataset_from_directory(
+          directory, validation_split=0.2, subset='training')
+
 
 if __name__ == '__main__':
   v2_compat.enable_v2_behavior()

tensorflow/python/keras/preprocessing/text_dataset.py

@@ -131,6 +131,8 @@ def text_dataset_from_directory(directory,
     raise ValueError(
         '`label_mode` argument must be one of "int", "categorical", "binary", '
         'or None. Received: %s' % (label_mode,))
+  dataset_utils.check_validation_split_arg(
+      validation_split, subset, shuffle, seed)
 
   if seed is None:
     seed = np.random.randint(1e6)

tensorflow/python/keras/preprocessing/text_dataset_test.py

@@ -142,12 +142,14 @@ class TextDatasetFromDirectoryTest(keras_parameterized.TestCase):
   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')
+        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')
+        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,))
@@ -212,6 +214,14 @@ class TextDatasetFromDirectoryTest(keras_parameterized.TestCase):
       _ = 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')
+
 
 if __name__ == '__main__':
   v2_compat.enable_v2_behavior()