Python tpu_config.RunConfig方法代碼示例

# 需要導入模塊: from tensorflow.contrib.tpu.python.tpu import tpu_config [as 別名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_config import RunConfig [as 別名]
def test_create_estimator_and_inputs(self):
    """Tests that Estimator and input function are constructed correctly."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    train_steps = 20
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps)
    estimator = train_and_eval_dict['estimator']
    train_steps = train_and_eval_dict['train_steps']
    self.assertIsInstance(estimator, tf.estimator.Estimator)
    self.assertEqual(20, train_steps)
    self.assertIn('train_input_fn', train_and_eval_dict)
    self.assertIn('eval_input_fns', train_and_eval_dict)
    self.assertIn('eval_on_train_input_fn', train_and_eval_dict) 

# 需要導入模塊: from tensorflow.contrib.tpu.python.tpu import tpu_config [as 別名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_config import RunConfig [as 別名]
def test_create_tpu_estimator_and_inputs(self):
    """Tests that number of train/eval defaults to config values."""

    run_config = tpu_config.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    train_steps = 20
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps,
        use_tpu_estimator=True)
    estimator = train_and_eval_dict['estimator']
    train_steps = train_and_eval_dict['train_steps']

    self.assertIsInstance(estimator, tpu_estimator.TPUEstimator)
    self.assertEqual(20, train_steps) 

# 需要導入模塊: from tensorflow.contrib.tpu.python.tpu import tpu_config [as 別名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_config import RunConfig [as 別名]
def test_create_estimator_and_inputs(self):
    """Tests that Estimator and input function are constructed correctly."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    train_steps = 20
    eval_steps = 10
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps,
        eval_steps=eval_steps)
    estimator = train_and_eval_dict['estimator']
    train_steps = train_and_eval_dict['train_steps']
    eval_steps = train_and_eval_dict['eval_steps']
    self.assertIsInstance(estimator, tf.estimator.Estimator)
    self.assertEqual(20, train_steps)
    self.assertEqual(10, eval_steps)
    self.assertIn('train_input_fn', train_and_eval_dict)
    self.assertIn('eval_input_fn', train_and_eval_dict)
    self.assertIn('eval_on_train_input_fn', train_and_eval_dict) 

# 需要導入模塊: from tensorflow.contrib.tpu.python.tpu import tpu_config [as 別名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_config import RunConfig [as 別名]
def test_create_estimator_with_default_train_eval_steps(self):
    """Tests that number of train/eval defaults to config values."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    config_train_steps = configs['train_config'].num_steps
    config_eval_steps = configs['eval_config'].num_examples
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config, hparams, pipeline_config_path)
    estimator = train_and_eval_dict['estimator']
    train_steps = train_and_eval_dict['train_steps']
    eval_steps = train_and_eval_dict['eval_steps']

    self.assertIsInstance(estimator, tf.estimator.Estimator)
    self.assertEqual(config_train_steps, train_steps)
    self.assertEqual(config_eval_steps, eval_steps) 

# 需要導入模塊: from tensorflow.contrib.tpu.python.tpu import tpu_config [as 別名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_config import RunConfig [as 別名]
def test_create_tpu_estimator_and_inputs(self):
    """Tests that number of train/eval defaults to config values."""

    run_config = tpu_config.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    train_steps = 20
    eval_steps = 10
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps,
        eval_steps=eval_steps,
        use_tpu_estimator=True)
    estimator = train_and_eval_dict['estimator']
    train_steps = train_and_eval_dict['train_steps']
    eval_steps = train_and_eval_dict['eval_steps']

    self.assertIsInstance(estimator, tpu_estimator.TPUEstimator)
    self.assertEqual(20, train_steps)
    self.assertEqual(10, eval_steps) 

# 需要導入模塊: from tensorflow.contrib.tpu.python.tpu import tpu_config [as 別名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_config import RunConfig [as 別名]
def test_create_estimator_with_default_train_eval_steps(self):
    """Tests that number of train/eval defaults to config values."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    config_train_steps = configs['train_config'].num_steps
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config, hparams, pipeline_config_path)
    estimator = train_and_eval_dict['estimator']
    train_steps = train_and_eval_dict['train_steps']

    self.assertIsInstance(estimator, tf.estimator.Estimator)
    self.assertEqual(config_train_steps, train_steps) 

# 需要導入模塊: from tensorflow.contrib.tpu.python.tpu import tpu_config [as 別名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_config import RunConfig [as 別名]
def test_create_train_and_eval_specs(self):
    """Tests that `TrainSpec` and `EvalSpec` is created correctly."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    train_steps = 20
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps)
    train_input_fn = train_and_eval_dict['train_input_fn']
    eval_input_fns = train_and_eval_dict['eval_input_fns']
    eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
    predict_input_fn = train_and_eval_dict['predict_input_fn']
    train_steps = train_and_eval_dict['train_steps']

    train_spec, eval_specs = model_lib.create_train_and_eval_specs(
        train_input_fn,
        eval_input_fns,
        eval_on_train_input_fn,
        predict_input_fn,
        train_steps,
        eval_on_train_data=True,
        final_exporter_name='exporter',
        eval_spec_names=['holdout'])
    self.assertEqual(train_steps, train_spec.max_steps)
    self.assertEqual(2, len(eval_specs))
    self.assertEqual(None, eval_specs[0].steps)
    self.assertEqual('holdout', eval_specs[0].name)
    self.assertEqual('exporter', eval_specs[0].exporters[0].name)
    self.assertEqual(None, eval_specs[1].steps)
    self.assertEqual('eval_on_train', eval_specs[1].name) 

# 需要導入模塊: from tensorflow.contrib.tpu.python.tpu import tpu_config [as 別名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_config import RunConfig [as 別名]
def test_experiment(self):
    """Tests that the `Experiment` object is constructed correctly."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    experiment = model_lib.populate_experiment(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=10,
        eval_steps=20)
    self.assertEqual(10, experiment.train_steps)
    self.assertEqual(None, experiment.eval_steps) 

# 需要導入模塊: from tensorflow.contrib.tpu.python.tpu import tpu_config [as 別名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_config import RunConfig [as 別名]
def test_create_train_and_eval_specs(self):
    """Tests that `TrainSpec` and `EvalSpec` is created correctly."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    train_steps = 20
    eval_steps = 10
    eval_on_train_steps = 15
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps,
        eval_steps=eval_steps)
    train_input_fn = train_and_eval_dict['train_input_fn']
    eval_input_fn = train_and_eval_dict['eval_input_fn']
    eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
    predict_input_fn = train_and_eval_dict['predict_input_fn']
    train_steps = train_and_eval_dict['train_steps']
    eval_steps = train_and_eval_dict['eval_steps']

    train_spec, eval_specs = model_lib.create_train_and_eval_specs(
        train_input_fn,
        eval_input_fn,
        eval_on_train_input_fn,
        predict_input_fn,
        train_steps,
        eval_steps,
        eval_on_train_data=True,
        eval_on_train_steps=eval_on_train_steps,
        final_exporter_name='exporter',
        eval_spec_name='holdout')
    self.assertEqual(train_steps, train_spec.max_steps)
    self.assertEqual(2, len(eval_specs))
    self.assertEqual(eval_steps, eval_specs[0].steps)
    self.assertEqual('holdout', eval_specs[0].name)
    self.assertEqual('exporter', eval_specs[0].exporters[0].name)
    self.assertEqual(eval_on_train_steps, eval_specs[1].steps)
    self.assertEqual('eval_on_train', eval_specs[1].name) 

# 需要導入模塊: from tensorflow.contrib.tpu.python.tpu import tpu_config [as 別名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_config import RunConfig [as 別名]
def export_estimator_savedmodel(estimator,
                                export_dir_base,
                                serving_input_receiver_fn,
                                assets_extra=None,
                                as_text=False,
                                checkpoint_path=None,
                                strip_default_attrs=False):
  """Export `Estimator` trained model for TPU inference.

  Args:
    estimator: `Estimator` with which model has been trained.
    export_dir_base: A string containing a directory in which to create
      timestamped subdirectories containing exported SavedModels.
    serving_input_receiver_fn: A function that takes no argument and returns a
      `ServingInputReceiver` or `TensorServingInputReceiver`.
    assets_extra: A dict specifying how to populate the assets.extra directory
      within the exported SavedModel, or `None` if no extra assets are needed.
    as_text: whether to write the SavedModel proto in text format.
    checkpoint_path: The checkpoint path to export.  If `None` (the default),
      the most recent checkpoint found within the model directory is chosen.
    strip_default_attrs: Boolean. If `True`, default-valued attributes will be
      removed from the NodeDefs.

  Returns:
    The string path to the exported directory.
  """
  # `TPUEstimator` requires `tpu_config.RunConfig`, so we cannot use
  # `estimator.config`.
  config = tpu_config.RunConfig(model_dir=estimator.model_dir)
  est = TPUEstimator(
      estimator._model_fn,  # pylint: disable=protected-access
      config=config,
      params=estimator.params,
      use_tpu=True,
      train_batch_size=2048,  # Does not matter.
      eval_batch_size=2048,  # Does not matter.
  )
  return est.export_savedmodel(export_dir_base, serving_input_receiver_fn,
                               assets_extra, as_text, checkpoint_path,
                               strip_default_attrs) 

# 需要導入模塊: from tensorflow.contrib.tpu.python.tpu import tpu_config [as 別名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_config import RunConfig [as 別名]
def test_experiment(self):
    """Tests that the `Experiment` object is constructed correctly."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    experiment = model_lib.populate_experiment(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=10,
        eval_steps=20)
    self.assertEqual(10, experiment.train_steps)
    self.assertEqual(20, experiment.eval_steps) 

# 需要導入模塊: from tensorflow.contrib.tpu.python.tpu import tpu_config [as 別名]
# 或者: from tensorflow.contrib.tpu.python.tpu.tpu_config import RunConfig [as 別名]
def test_create_train_and_eval_specs(self):
    """Tests that `TrainSpec` and `EvalSpec` is created correctly."""
    run_config = tf.estimator.RunConfig()
    hparams = model_hparams.create_hparams(
        hparams_overrides='load_pretrained=false')
    pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
    train_steps = 20
    eval_steps = 10
    train_and_eval_dict = model_lib.create_estimator_and_inputs(
        run_config,
        hparams,
        pipeline_config_path,
        train_steps=train_steps,
        eval_steps=eval_steps)
    train_input_fn = train_and_eval_dict['train_input_fn']
    eval_input_fn = train_and_eval_dict['eval_input_fn']
    eval_on_train_input_fn = train_and_eval_dict['eval_on_train_input_fn']
    predict_input_fn = train_and_eval_dict['predict_input_fn']
    train_steps = train_and_eval_dict['train_steps']
    eval_steps = train_and_eval_dict['eval_steps']

    train_spec, eval_specs = model_lib.create_train_and_eval_specs(
        train_input_fn,
        eval_input_fn,
        eval_on_train_input_fn,
        predict_input_fn,
        train_steps,
        eval_steps,
        eval_on_train_data=True,
        final_exporter_name='exporter',
        eval_spec_name='holdout')
    self.assertEqual(train_steps, train_spec.max_steps)
    self.assertEqual(2, len(eval_specs))
    self.assertEqual(eval_steps, eval_specs[0].steps)
    self.assertEqual('holdout', eval_specs[0].name)
    self.assertEqual('exporter', eval_specs[0].exporters[0].name)
    self.assertEqual(eval_steps, eval_specs[1].steps)
    self.assertEqual('eval_on_train', eval_specs[1].name)