Defined in tensorflow/contrib/data/python/ops/

Returns a Dataset of feature dictionaries from Example protos.


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]),


  • file_pattern: List of files or patterns of file paths containing Example records. See tf.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.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
  • 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 (default is 1).
  • reader_num_threads: Number of threads used to read Example records. If >1, the results will be interleaved.
  • parser_num_threads: Number of threads to use for parsing Example tensors into a dictionary of Feature tensors.
  • 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.


A dataset of dict elements. Each dict maps feature keys to Tensor or SparseTensor objects.