tf.data.experimental.make_batched_features_dataset     View source on GitHub    Returns a Dataset of feature dictionaries from Example protos. 
tf.data.experimental.make_batched_features_dataset(
    file_pattern, batch_size, features, reader=None, 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 of the wrong type.  
  ValueError   If label_key is not one of the features keys.