Example of tensorflow.contrib.learn.ExportStrategy

问题:

Can someone provide examples of full working code for Tensorflow

tf.contrib.learn.ExportStrategy

The documentation lacks examples. I also could not find any examples on Github or Stackoverflow for this seemingly obscure Tensorflow operation.

Documentation: https://www.tensorflow.org/api_docs/python/tf/contrib/learn/ExportStrategy

回答1:

Google CloudML has a good working example here: https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/census/customestimator/trainer

You'll need to use their full code to get the example to work, but here is the gist of how to use ExportStrategy:

import tensorflow as tf
from tensorflow.contrib.learn.python.learn import learn_runner
from tensorflow.contrib.learn.python.learn.utils import (
    saved_model_export_utils)
from tensorflow.contrib.training.python.training import hparam

def csv_serving_input_fn():
    """Build the serving inputs."""
    csv_row = tf.placeholder(
        shape=[None],
        dtype=tf.string
    )
    features = parse_csv(csv_row)
    # Ignore label column
    features.pop(LABEL_COLUMN)
    return tf.estimator.export.ServingInputReceiver(
        features, {'csv_row': csv_row})


def example_serving_input_fn():
    """Build the serving inputs."""
    example_bytestring = tf.placeholder(
        shape=[None],
        dtype=tf.string,
    )
    features = tf.parse_example(
        example_bytestring,
        tf.feature_column.make_parse_example_spec(INPUT_COLUMNS)
    )
    return tf.estimator.export.ServingInputReceiver(
        features, {'example_proto': example_bytestring})


def json_serving_input_fn():
  """Build the serving inputs."""
  inputs = {}
  for feat in INPUT_COLUMNS:
    inputs[feat.name] = tf.placeholder(shape=[None], dtype=feat.dtype)
  return tf.estimator.export.ServingInputReceiver(inputs, inputs)


SERVING_FUNCTIONS = {
    'JSON': json_serving_input_fn,
    'EXAMPLE': example_serving_input_fn,
    'CSV': csv_serving_input_fn
}

# Run the training job
# learn_runner pulls configuration information from environment
# variables using tf.learn.RunConfig and uses this configuration
# to conditionally execute Experiment, or param server code
learn_runner.run(
  generate_experiment_fn(
      min_eval_frequency=args.min_eval_frequency,
      eval_delay_secs=args.eval_delay_secs,
      train_steps=args.train_steps,
      eval_steps=args.eval_steps,
      export_strategies=[saved_model_export_utils.make_export_strategy(
          SERVING_FUNCTIONS[args.export_format],
          exports_to_keep=1
      )]
  ),
  run_config=tf.contrib.learn.RunConfig(model_dir=args.job_dir),
  hparams=hparam.HParams(**args.__dict__)
)