import argparse
import os

import model

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

#Returns an experiment function
def create_experiment(**experiment_args):

  #Creates an experiment using config args and hparams
  def _experiment(run_config, hparams):
    train_input = lambda: model.input_fn(
        hparams.train_files,
        batch_size=hparams.train_batch_size,
    )
    eval_input = lambda: model.input_fn(
        hparams.eval_files,
        batch_size=hparams.eval_batch_size,
    )
    experiment = tf.contrib.learn.Experiment(
        model.create_estimator(
            config=run_config,
            model_dir=args.job_dir
        ),
        train_input_fn=train_input,
        eval_input_fn=eval_input,
        **experiment_args
    )
    return experiment
  return _experiment


if __name__ == '__main__':
  parser = argparse.ArgumentParser()

  parser.add_argument(
      '--train-files',
      nargs='+',
      required=True
  )
  parser.add_argument(
      '--train-batch-size',
      type=int,
      default=32
  )
  parser.add_argument(
      '--eval-batch-size',
      type=int,
      default=32
  )
  parser.add_argument(
      '--eval-files',
      nargs='+',
      required=True
  )
  parser.add_argument(
      '--job-dir',
      required=False
  )
  parser.add_argument(
      '--verbosity',
      choices=[
          'DEBUG',
          'ERROR',
          'FATAL',
          'INFO',
          'WARN'
      ],
      default='INFO',
  )
  # Experiment arguments
  parser.add_argument(
      '--eval-delay-secs',
      default=10,
      type=int
  )
  parser.add_argument(
      '--min-eval-frequency',
      default=None,
      type=int
  )
  parser.add_argument(
      '--train-steps',
      type=int
  )
  parser.add_argument(
      '--eval-steps',
      default=100,
      type=int
  )
  parser.add_argument(
      '--export-format',
      choices=['JSON', 'CSV', 'EXAMPLE'],
      default='JSON'
  )

  args = parser.parse_args()

  # Set python level verbosity
  tf.logging.set_verbosity(args.verbosity)

  learn_runner.run(
      create_experiment(
          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(
              model.SERVING_FUNCTIONS[args.export_format],
              exports_to_keep=1,
              default_output_alternative_key=None,
          )]
      ),
      run_config=run_config.RunConfig(model_dir=args.job_dir),
      hparams=hparam.HParams(**args.__dict__)
  )

9f52180610dfca5d55bf10bf70391a3ead5c0d3b
<mask token>

<mask token>


def create_experiment(**experiment_args):

    def _experiment(run_config, hparams):
        train_input = lambda : model.input_fn(hparams.train_files,
            batch_size=hparams.train_batch_size)
        eval_input = lambda : model.input_fn(hparams.eval_files, batch_size
            =hparams.eval_batch_size)
        experiment = tf.contrib.learn.Experiment(model.create_estimator(
            config=run_config, model_dir=args.job_dir), train_input_fn=
            train_input, eval_input_fn=eval_input, **experiment_args)
        return experiment
    return _experiment


<mask token>

<mask token>


def create_experiment(**experiment_args):

    def _experiment(run_config, hparams):
        train_input = lambda : model.input_fn(hparams.train_files,
            batch_size=hparams.train_batch_size)
        eval_input = lambda : model.input_fn(hparams.eval_files, batch_size
            =hparams.eval_batch_size)
        experiment = tf.contrib.learn.Experiment(model.create_estimator(
            config=run_config, model_dir=args.job_dir), train_input_fn=
            train_input, eval_input_fn=eval_input, **experiment_args)
        return experiment
    return _experiment


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--train-files', nargs='+', required=True)
    parser.add_argument('--train-batch-size', type=int, default=32)
    parser.add_argument('--eval-batch-size', type=int, default=32)
    parser.add_argument('--eval-files', nargs='+', required=True)
    parser.add_argument('--job-dir', required=False)
    parser.add_argument('--verbosity', choices=['DEBUG', 'ERROR', 'FATAL',
        'INFO', 'WARN'], default='INFO')
    parser.add_argument('--eval-delay-secs', default=10, type=int)
    parser.add_argument('--min-eval-frequency', default=None, type=int)
    parser.add_argument('--train-steps', type=int)
    parser.add_argument('--eval-steps', default=100, type=int)
    parser.add_argument('--export-format', choices=['JSON', 'CSV',
        'EXAMPLE'], default='JSON')
    args = parser.parse_args()
    tf.logging.set_verbosity(args.verbosity)
    learn_runner.run(create_experiment(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(
        model.SERVING_FUNCTIONS[args.export_format], exports_to_keep=1,
        default_output_alternative_key=None)]), run_config=run_config.
        RunConfig(model_dir=args.job_dir), hparams=hparam.HParams(**args.
        __dict__))

import argparse
import os
import model
import tensorflow as tf
from tensorflow.contrib.learn.python.learn import learn_runner
from tensorflow.contrib.learn.python.learn.estimators import run_config
from tensorflow.contrib.learn.python.learn.utils import saved_model_export_utils
from tensorflow.contrib.training.python.training import hparam


def create_experiment(**experiment_args):

    def _experiment(run_config, hparams):
        train_input = lambda : model.input_fn(hparams.train_files,
            batch_size=hparams.train_batch_size)
        eval_input = lambda : model.input_fn(hparams.eval_files, batch_size
            =hparams.eval_batch_size)
        experiment = tf.contrib.learn.Experiment(model.create_estimator(
            config=run_config, model_dir=args.job_dir), train_input_fn=
            train_input, eval_input_fn=eval_input, **experiment_args)
        return experiment
    return _experiment


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--train-files', nargs='+', required=True)
    parser.add_argument('--train-batch-size', type=int, default=32)
    parser.add_argument('--eval-batch-size', type=int, default=32)
    parser.add_argument('--eval-files', nargs='+', required=True)
    parser.add_argument('--job-dir', required=False)
    parser.add_argument('--verbosity', choices=['DEBUG', 'ERROR', 'FATAL',
        'INFO', 'WARN'], default='INFO')
    parser.add_argument('--eval-delay-secs', default=10, type=int)
    parser.add_argument('--min-eval-frequency', default=None, type=int)
    parser.add_argument('--train-steps', type=int)
    parser.add_argument('--eval-steps', default=100, type=int)
    parser.add_argument('--export-format', choices=['JSON', 'CSV',
        'EXAMPLE'], default='JSON')
    args = parser.parse_args()
    tf.logging.set_verbosity(args.verbosity)
    learn_runner.run(create_experiment(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(
        model.SERVING_FUNCTIONS[args.export_format], exports_to_keep=1,
        default_output_alternative_key=None)]), run_config=run_config.
        RunConfig(model_dir=args.job_dir), hparams=hparam.HParams(**args.
        __dict__))

import argparse
import os

import model

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

#Returns an experiment function
def create_experiment(**experiment_args):

  #Creates an experiment using config args and hparams
  def _experiment(run_config, hparams):
    train_input = lambda: model.input_fn(
        hparams.train_files,
        batch_size=hparams.train_batch_size,
    )
    eval_input = lambda: model.input_fn(
        hparams.eval_files,
        batch_size=hparams.eval_batch_size,
    )
    experiment = tf.contrib.learn.Experiment(
        model.create_estimator(
            config=run_config,
            model_dir=args.job_dir
        ),
        train_input_fn=train_input,
        eval_input_fn=eval_input,
        **experiment_args
    )
    return experiment
  return _experiment


if __name__ == '__main__':
  parser = argparse.ArgumentParser()

  parser.add_argument(
      '--train-files',
      nargs='+',
      required=True
  )
  parser.add_argument(
      '--train-batch-size',
      type=int,
      default=32
  )
  parser.add_argument(
      '--eval-batch-size',
      type=int,
      default=32
  )
  parser.add_argument(
      '--eval-files',
      nargs='+',
      required=True
  )
  parser.add_argument(
      '--job-dir',
      required=False
  )
  parser.add_argument(
      '--verbosity',
      choices=[
          'DEBUG',
          'ERROR',
          'FATAL',
          'INFO',
          'WARN'
      ],
      default='INFO',
  )
  # Experiment arguments
  parser.add_argument(
      '--eval-delay-secs',
      default=10,
      type=int
  )
  parser.add_argument(
      '--min-eval-frequency',
      default=None,
      type=int
  )
  parser.add_argument(
      '--train-steps',
      type=int
  )
  parser.add_argument(
      '--eval-steps',
      default=100,
      type=int
  )
  parser.add_argument(
      '--export-format',
      choices=['JSON', 'CSV', 'EXAMPLE'],
      default='JSON'
  )

  args = parser.parse_args()

  # Set python level verbosity
  tf.logging.set_verbosity(args.verbosity)

  learn_runner.run(
      create_experiment(
          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(
              model.SERVING_FUNCTIONS[args.export_format],
              exports_to_keep=1,
              default_output_alternative_key=None,
          )]
      ),
      run_config=run_config.RunConfig(model_dir=args.job_dir),
      hparams=hparam.HParams(**args.__dict__)
  )
