(Optional.) A tf.string scalar evaluating to one of
"" (no compression), "ZLIB", or "GZIP".
(Optional.) A tf.int64 scalar representing the number of
bytes in the read buffer. If your input pipeline is I/O bottlenecked,
consider setting this parameter to a value 1-100 MBs. If None, a
sensible default for both local and remote file systems is used.
(Optional.) A tf.int64 scalar representing the
number of files to read in parallel. If greater than one, the records of
files read in parallel are outputted in an interleaved order. If your
input pipeline is I/O bottlenecked, consider setting this parameter to a
value greater than one to parallelize the I/O. If None, files will be
If any argument does not have the expected type.
If any argument does not have the expected shape.
The type specification of an element of this dataset.
The components of the resulting element will have an additional outer
dimension, which will be batch_size (or N % batch_size for the last
element if batch_size does not divide the number of input elements N
evenly and drop_remainder is False). If your program depends on the
batches having the same outer dimension, you should set the drop_remainder
argument to True to prevent the smaller batch from being produced.
A tf.int64 scalar tf.Tensor, representing the number of
consecutive elements of this dataset to combine in a single batch.
(Optional.) A tf.bool scalar tf.Tensor, representing
whether the last batch should be dropped in the case it has fewer than
batch_size elements; the default behavior is not to drop the smaller