This method combines multiple consecutive elements of this dataset, which
might have different shapes, into a single element. The tensors in the
resulting element have an additional outer dimension, and are padded to the
respective shape in padded_shapes.
dataset_padded_batch(
dataset,
batch_size,
padded_shapes,
padding_values = NULL,
drop_remainder = FALSE
)A dataset
An integer, representing the number of consecutive elements of this dataset to combine in a single batch.
A nested structure of tf$TensorShape or integer vector
tensor-like objects representing the shape to which the respective
component of each input element should be padded prior to batching. Any
unknown dimensions (e.g. tf$Dimension(NULL) in a tf$TensorShape or -1
in a tensor-like object) will be padded to the maximum size of that
dimension in each batch.
(Optional) A nested structure of scalar-shaped tf$Tensor, representing the padding values to use for the respective components. Defaults are 0 for numeric types and the empty string for string types.
Ensure that batches have a fixed size by omitting any final smaller batch if it's present. Note that this is required for use with the Keras tensor inputs to fit/evaluate/etc.
A dataset
Other dataset methods:
dataset_batch(),
dataset_cache(),
dataset_collect(),
dataset_concatenate(),
dataset_decode_delim(),
dataset_filter(),
dataset_interleave(),
dataset_map_and_batch(),
dataset_map(),
dataset_prefetch_to_device(),
dataset_prefetch(),
dataset_reduce(),
dataset_repeat(),
dataset_shuffle_and_repeat(),
dataset_shuffle(),
dataset_skip(),
dataset_take(),
dataset_window()