tf.contrib.data.read_batch_features

Reads batches of Examples. (deprecated)

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
)

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`.
`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.

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Example:

We can use arguments:

Args

Returns

tf.contrib.data.read_batch_features