tf.data.experimental.make_batched_features_dataset

Returns a Dataset of feature dictionaries from Example protos.

Used in the notebooks

Used in the tutorials

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]),
}

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