tfr.data.build_ranking_serving_input_receiver_fn

Returns a serving input receiver fn for a standard data format.

tfr.data.build_ranking_serving_input_receiver_fn(
    data_format,
    context_feature_spec,
    example_feature_spec,
    list_size=None,
    size_feature_name=None,
    mask_feature_name=None,
    receiver_name='input_ranking_data',
    default_batch_size=None
)

Args
`data_format`	(string) See RankingDataFormat.
`context_feature_spec`	(dict) Map from feature keys to `FixedLenFeature` or `VarLenFeature` values.
`example_feature_spec`	(dict) Map from feature keys to `FixedLenFeature` or `VarLenFeature` values.
`list_size`	(int) The number of examples to keep. If specified, truncation or padding may happen. Otherwise, set it to None to allow dynamic list size (recommended).
`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.
`receiver_name`	(string) The name for the receiver tensor.
`default_batch_size`	(int) Number of instances expected per batch. Leave unset for variable batch size (recommended).

Returns
A `tf.estimator.export.ServingInputReceiver` object, which packages the placeholders and the resulting feature Tensors together.

tfr.data.build_ranking_serving_input_receiver_fn

Args

Returns