tf.data.experimental.pad_to_cardinality

Pads a dataset with fake elements to reach the desired cardinality.

tf.data.experimental.pad_to_cardinality(
    cardinality, mask_key='valid'
)

The dataset to pad must have a known and finite cardinality and contain dictionary elements. The mask_key will be added to differentiate between real and padding elements -- real elements will have a <mask_key>=True entry while padding elements will have a <mask_key>=False entry.

Example usage:

ds = tf.data.Dataset.from_tensor_slices({&#x27;a': [1, 2]})
ds = ds.apply(tf.data.experimental.pad_to_cardinality(3))
list(ds.as_numpy_iterator())
[{&#x27;a': 1, 'valid': True}, {'a': 2, 'valid': True}, {'a': 0, 'valid': False}]

This can be useful, e.g. during eval, when partial batches are undesirable but it is also important not to drop any data.

ds = ...
# Round up to the next full batch.
target_cardinality = -(-ds.cardinality() // batch_size) * batch_size
ds = ds.apply(tf.data.experimental.pad_to_cardinality(target_cardinality))
# Set `drop_remainder` so that batch shape will be known statically. No data
# will actually be dropped since the batch size divides the cardinality.
ds = ds.batch(batch_size, drop_remainder=True)

cardinality The cardinality to pad the dataset to.
mask_key The key to use for identifying real vs padding elements.

A dataset transformation that can be applied via Dataset.apply().