An int representing the number of records to combine in a
If True, and the batch size does not evenly divide the
input dataset size, the final smaller batch will be dropped. Defaults to
Integer specifying the number of times to read through the
dataset. If None, cycles through the dataset forever. Defaults to
A boolean, indicates whether the input should be shuffled.
Defaults to True.
Buffer size of the items to shuffle. The size is the
number of items (i.e. records for a record based TFXIO) to hold. Only
data read into the buffer will be shuffled (there is no shuffling across
buffers). A large capacity ensures better shuffling but would increase
memory usage and startup time.
Randomization seed to use for shuffling.
Number of feature batches to prefetch in order to
improve performance. Recommended value is the number of batches consumed
per training step. Defaults to auto-tune.
Number of threads used to read records. If >1, the
results will be interleaved. Defaults to tf.data.experimental.AUTOTUNE.
Number of threads to use for parsing Example tensors
into a dictionary of Feature tensors (if applicable). Defaults to
If True, reading performance will be improved at the
cost of non-deterministic ordering. If False, the order of elements
produced is deterministic prior to shuffling (elements are still
randomized if shuffle=True. Note that if the seed is set, then order
of elements after shuffling is deterministic). Defaults to False.
name of the label tensor. If provided, the returned dataset
will yield Tuple[Dict[str, Tensor], Tensor], where the second term in
the tuple is the label tensor and the dict (the first term) will not
contain the label feature.