Add export savedmodel to wide_deep

official/mnist/mnist.py

@@ -246,7 +246,6 @@ def __init__(self):
     super(MNISTArgParser, self).__init__(parents=[
         parsers.BaseParser(),
         parsers.ImageModelParser(),
-        parsers.ExportParser(),
     ])
 
     self.set_defaults(

official/resnet/resnet_run_loop.py

@@ -465,7 +465,6 @@ def __init__(self, resnet_size_choices=None):
         parsers.BaseParser(),
         parsers.PerformanceParser(),
         parsers.ImageModelParser(),
-        parsers.ExportParser(),
         parsers.BenchmarkParser(),
     ])
 

official/utils/arg_parsers/parsers.py

@@ -104,12 +104,13 @@ class BaseParser(argparse.ArgumentParser):
     batch_size: Create a flag to specify the batch size.
     multi_gpu: Create a flag to allow the use of all available GPUs.
     hooks: Create a flag to specify hooks for logging.
+    export_dir: Create a flag to specify where a SavedModel should be exported.
   """
 
   def __init__(self, add_help=False, data_dir=True, model_dir=True,
                train_epochs=True, epochs_between_evals=True,
                stop_threshold=True, batch_size=True, multi_gpu=True,
-               hooks=True):
+               hooks=True, export_dir=True):
     super(BaseParser, self).__init__(add_help=add_help)
 
     if data_dir:
@@ -176,6 +177,15 @@ def __init__(self, add_help=False, data_dir=True, model_dir=True,
           metavar="<HK>"
       )
 
+    if export_dir:
+      self.add_argument(
+          "--export_dir", "-ed",
+          help="[default: %(default)s] If set, a SavedModel serialization of "
+               "the model will be exported to this directory at the end of "
+               "training. See the README for more details and relevant links.",
+          metavar="<ED>"
+      )
+
 
 class PerformanceParser(argparse.ArgumentParser):
   """Default parser for specifying performance tuning arguments.
@@ -292,29 +302,6 @@ def __init__(self, add_help=False, data_format=True):
       )
 
 
-class ExportParser(argparse.ArgumentParser):
-  """Parsing options for exporting saved models or other graph defs.
-
-  This is a separate parser for now, but should be made part of BaseParser
-  once all models are brought up to speed.
-
-  Args:
-    add_help: Create the "--help" flag. False if class instance is a parent.
-    export_dir: Create a flag to specify where a SavedModel should be exported.
-  """
-
-  def __init__(self, add_help=False, export_dir=True):
-    super(ExportParser, self).__init__(add_help=add_help)
-    if export_dir:
-      self.add_argument(
-          "--export_dir", "-ed",
-          help="[default: %(default)s] If set, a SavedModel serialization of "
-               "the model will be exported to this directory at the end of "
-               "training. See the README for more details and relevant links.",
-          metavar="<ED>"
-      )
-
-
 class BenchmarkParser(argparse.ArgumentParser):
   """Default parser for benchmark logging.
 

official/wide_deep/README.md

@@ -47,6 +47,37 @@ Run TensorBoard to inspect the details about the graph and training progression.
 tensorboard --logdir=/tmp/census_model
 ```
 
+## Inference with SavedModel
+You can export the model into Tensorflow [SavedModel](https://www.tensorflow.org/programmers_guide/saved_model) format by using the argument `--export_dir`:
+
+```
+python wide_deep.py --export_dir /tmp/wide_deep_saved_model
+```
+
+After the model finishes training, use [`saved_model_cli`](https://www.tensorflow.org/programmers_guide/saved_model#cli_to_inspect_and_execute_savedmodel) to inspect and execute the SavedModel.
+
+Try the following commands to inspect the SavedModel:
+
+**Replace `${TIMESTAMP}` with the folder produced (e.g. 1524249124)**
+```
+# List possible tag_sets. Only one metagraph is saved, so there will be one option.
+saved_model_cli show --dir /tmp/wide_deep_saved_model/${TIMESTAMP}/
+
+# Show SignatureDefs for tag_set=serve. SignatureDefs define the outputs to show.
+saved_model_cli show --dir /tmp/wide_deep_saved_model/${TIMESTAMP}/ \
+    --tag_set serve --all
+```
+
+### Inference
+Let's use the model to predict the income group of two examples:
+```
+saved_model_cli run --dir /tmp/wide_deep_saved_model/${TIMESTAMP}/ \
+--tag_set serve --signature_def="predict" \
+--input_examples='examples=[{"age":[46.], "education_num":[10.], "capital_gain":[7688.], "capital_loss":[0.], "hours_per_week":[38.]}, {"age":[24.], "education_num":[13.], "capital_gain":[0.], "capital_loss":[0.], "hours_per_week":[50.]}]'
+```
+
+This will print out the predicted classes and class probabilities. Class 0 is the <=50k group and 1 is the >50k group.
+
 ## Additional Links
 
 If you are interested in distributed training, take a look at [Distributed TensorFlow](https://www.tensorflow.org/deploy/distributed).

official/wide_deep/wide_deep.py

@@ -175,6 +175,27 @@ def parse_csv(value):
   return dataset
 
 
+def export_model(model, model_type, export_dir):
+  """Export to SavedModel format.
+
+  Args:
+    model: Estimator object
+    model_type: string indicating model type. "wide", "deep" or "wide_deep"
+    export_dir: directory to export the model.
+  """
+  wide_columns, deep_columns = build_model_columns()
+  if model_type == 'wide':
+    columns = wide_columns
+  elif model_type == 'deep':
+    columns = deep_columns
+  else:
+    columns = wide_columns + deep_columns
+  feature_spec = tf.feature_column.make_parse_example_spec(columns)
+  example_input_fn = (
+      tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec))
+  model.export_savedmodel(export_dir, example_input_fn)
+
+
 def main(argv):
   parser = WideDeepArgParser()
   flags = parser.parse_args(args=argv[1:])
@@ -216,6 +237,10 @@ def eval_input_fn():
         flags.stop_threshold, results['accuracy']):
       break
 
+  # Export the model
+  if flags.export_dir is not None:
+    export_model(model, flags.model_type, flags.export_dir)
+
 
 class WideDeepArgParser(argparse.ArgumentParser):
   """Argument parser for running the wide deep model."""