tfr.data.build_sequence_example_serving_input_receiver_fn

Creates a serving_input_receiver_fn for SequenceExample inputs.

tfr.data.build_sequence_example_serving_input_receiver_fn(
    input_size,
    context_feature_spec,
    example_feature_spec,
    default_batch_size=None
)

A string placeholder is used for inputs. Note that the context_feature_spec and example_feature_spec shouldn't contain weights, labels or training only features in general.

input_size (int) The number of frames to keep in a SequenceExample. If specified, truncation or padding may happen. Otherwise, set it to None to allow dynamic list size (recommended).
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.
default_batch_size (int) Number of query examples expected per batch. Leave unset for variable batch size (recommended).

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