tf.contrib.data.read_batch_features

tf.contrib.data.read_batch_features(
    file_pattern,
    batch_size,
    features,
    reader=tf.data.TFRecordDataset,
    reader_args=None,
    randomize_input=True,
    num_epochs=None,
    capacity=10000
)

Defined in tensorflow/contrib/data/python/ops/readers.py.

Reads batches of Examples. (deprecated)

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.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 tf.data.TFRecordDataset.
• reader_args: Additional arguments to pass to the reader class.
• randomize_input: Whether the input should be randomized.
• num_epochs: Integer specifying the number of times to read through the dataset. If None, cycles through the dataset forever.
• capacity: Buffer size of the ShuffleDataset. A large capacity ensures better shuffling but would increase memory usage and startup time.

Returns:

A dict from keys in features to Tensor or SparseTensor objects.