According to the Tensorflow Dataset docs the approach you presented is now supported. Splitting is possible by passing split parameter to tfds.load
like so split="test[:70%]"
.
(training_set, validation_set), dataset_info = tfds.load(
'tf_flowers',
split=['train[:70%]', 'train[70%:]'],
with_info=True,
as_supervised=True,
)
With the above code the training_set
has 2569 entries, while validation_set
has 1101.
Thank you Saman for the comment on API deprecation:
In previous Tensorflow version it was possible to use tfds.Split
API which is now deprecated:
(training_set, validation_set), dataset_info = tfds.load(
'tf_flowers',
split=[
tfds.Split.TRAIN.subsplit(tfds.percent[:70]),
tfds.Split.TRAIN.subsplit(tfds.percent[70:])
],
with_info=True,
as_supervised=True,
)
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