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.
Args |
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).
|
Returns |
A tf.estimator.export.ServingInputReceiver object, which packages the
placeholders and the resulting feature Tensors together.
|
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2023-09-29 UTC.
[null,null,["Last updated 2023-09-29 UTC."],[],[]]