tf.compat.v2.data.experimental.make_batched_features_dataset

Returns a Dataset of feature dictionaries from Example protos.

tf.compat.v2.data.experimental.make_batched_features_dataset(
    file_pattern, batch_size, features, reader=tf.data.TFRecordDataset,
    label_key=None, reader_args=None, num_epochs=None, shuffle=True,
    shuffle_buffer_size=10000, shuffle_seed=None, prefetch_buffer_size=None,
    reader_num_threads=None, parser_num_threads=None, sloppy_ordering=False,
    drop_final_batch=False
)

If label_key argument is provided, returns a Dataset of tuple comprising of feature dictionaries and label.

Example:

serialized_examples = [
  features {
    feature { key: "age" value { int64_list { value: [ 0 ] } } }
    feature { key: "gender" value { bytes_list { value: [ "f" ] } } }
    feature { key: "kws" value { bytes_list { value: [ "code", "art" ] } } }
  },
  features {
    feature { key: "age" value { int64_list { value: [] } } }
    feature { key: "gender" value { bytes_list { value: [ "f" ] } } }
    feature { key: "kws" value { bytes_list { value: [ "sports" ] } } }
  }
]

We can use arguments:

features: {
  "age": FixedLenFeature([], dtype=tf.int64, default_value=-1),
  "gender": FixedLenFeature([], dtype=tf.string),
  "kws": VarLenFeature(dtype=tf.string),
}

And the expected output is:

{
  "age": [[0], [-1]],
  "gender": [["f"], ["f"]],
  "kws": SparseTensor(
    indices=[[0, 0], [0, 1], [1, 0]],
    values=["code", "art", "sports"]
    dense_shape=[2, 2]),
}

Args
`file_pattern`	List of files or patterns of file paths containing `Example` records. See `tf.io.gfile.glob` for pattern rules.
`batch_size`	An int representing the number of records to combine in a single batch.
`features`	A `dict` mapping feature keys to `FixedLenFeature` or `VarLenFeature` values. See `tf.io.parse_example`.
`reader`	A function or class that can be called with a `filenames` tensor and (optional) `reader_args` and returns a `Dataset` of `Example` tensors. Defaults to `tf.data.TFRecordDataset`.
`label_key`	(Optional) A string corresponding to the key labels are stored in `tf.Examples`. If provided, it must be one of the `features` key, otherwise results in `ValueError`.
`reader_args`	Additional arguments to pass to the reader class.
`num_epochs`	Integer specifying the number of times to read through the dataset. If None, cycles through the dataset forever. Defaults to `None`.
`shuffle`	A boolean, indicates whether the input should be shuffled. Defaults to `True`.
`shuffle_buffer_size`	Buffer size of the ShuffleDataset. A large capacity ensures better shuffling but would increase memory usage and startup time.
`shuffle_seed`	Randomization seed to use for shuffling.
`prefetch_buffer_size`	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.
`reader_num_threads`	Number of threads used to read `Example` records. If >1, the results will be interleaved. Defaults to `1`.
`parser_num_threads`	Number of threads to use for parsing `Example` tensors into a dictionary of `Feature` tensors. Defaults to `2`.
`sloppy_ordering`	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`.
`drop_final_batch`	If `True`, and the batch size does not evenly divide the input dataset size, the final smaller batch will be dropped. Defaults to `False`.

Returns
A dataset of `dict` elements, (or a tuple of `dict` elements and label). Each `dict` maps feature keys to `Tensor` or `SparseTensor` objects.

Raises
`TypeError`	If `reader` is a `tf.compat.v1.ReaderBase` subclass.
`ValueError`	If `label_key` is not one of the `features` keys.

tf.compat.v2.data.experimental.make_batched_features_dataset

Example:

We can use arguments:

Args

Returns

Raises