tfr.data.make_parsing_fn

Returns a parsing fn for a standard data format.

tfr.data.make_parsing_fn(
    data_format,
    list_size=None,
    context_feature_spec=None,
    example_feature_spec=None,
    size_feature_name=None,
    mask_feature_name=None,
    shuffle_examples=False,
    seed=None
)

data_format (string) See RankingDataFormat.
list_size (int) The number of examples to keep per ranking instance. If specified, truncation or padding may happen. Otherwise, the output Tensors have a dynamic list size.
context_feature_spec (dict) A mapping from feature keys to FixedLenFeature or VarLenFeature values for context.
example_feature_spec (dict) A mapping from feature keys to FixedLenFeature or VarLenFeature values for the list of examples.
size_feature_name (str) Name of feature for example list sizes. Populates the feature dictionary with a tf.int32 Tensor of shape [batch_size] for this feature name. If None, which is default, this feature is not generated.
mask_feature_name (str) Name of feature for example list masks. Populates the feature dictionary with a tf.bool Tensor of shape [batch_size, list_size] for this feature name. If None, which is default, this feature is not generated.
shuffle_examples (bool) A boolean to indicate whether examples within a list are shuffled before the list is trimmed down to list_size elements (when list has more than list_size elements).
seed (int) A seed passed onto random_ops.uniform() to shuffle examples.

A parsing function with signature parsing_fn(serialized), where serialized is a string Tensor representing the serialized data in the specified data_format and the function returns a feature map.