Python tensorflow.python.training.evaluation._evaluate_once() Examples

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Example #1
Source File: estimator.py    From estimator with Apache License 2.0 5 votes vote down vote up 
def _evaluate_run(self, checkpoint_path, scaffold, update_op, eval_dict,
                    all_hooks, output_dir):
    """Run evaluation."""
    eval_results = evaluation._evaluate_once(  # pylint: disable=protected-access
        checkpoint_path=checkpoint_path,
        master=self._config.evaluation_master,
        scaffold=scaffold,
        eval_ops=update_op,
        final_ops=eval_dict,
        hooks=all_hooks,
        config=self._session_config)

    current_global_step = eval_results[tf.compat.v1.GraphKeys.GLOBAL_STEP]

    _write_dict_to_summary(
        output_dir=output_dir,
        dictionary=eval_results,
        current_global_step=current_global_step)

    if checkpoint_path:
      _write_checkpoint_path_to_summary(
          output_dir=output_dir,
          checkpoint_path=checkpoint_path,
          current_global_step=current_global_step)

    return eval_results
Example #2
Source File: estimator.py    From lambda-packs with MIT License 4 votes vote down vote up 
def _evaluate_model(self,
                      input_fn,
                      hooks=None,
                      checkpoint_path=None,
                      name=''):
    """Evaluates the model using the training.evaluation library."""
    # Check that model has been trained (if nothing has been set explicitly).
    if not checkpoint_path:
      latest_path = saver.latest_checkpoint(self._model_dir)
      if not latest_path:
        raise ValueError('Could not find trained model in model_dir: {}.'.
                         format(self._model_dir))
      checkpoint_path = latest_path

    # Setup output directory.
    eval_dir = os.path.join(self._model_dir, 'eval' if not name else
                            'eval_' + name)

    with ops.Graph().as_default() as g:
      random_seed.set_random_seed(self._config.tf_random_seed)
      global_step_tensor = training.create_global_step(g)
      features, labels = input_fn()
      estimator_spec = self._call_model_fn(
          features, labels, model_fn_lib.ModeKeys.EVAL)

      if model_fn_lib.MetricKeys.LOSS in estimator_spec.eval_metric_ops:
        raise ValueError(
            'Metric with name "%s" is not allowed, because Estimator ' % (
                model_fn_lib.MetricKeys.LOSS) +
            'already defines a default metric with the same name.')
      estimator_spec.eval_metric_ops[
          model_fn_lib.MetricKeys.LOSS] = metrics_lib.mean(estimator_spec.loss)

      update_op, eval_dict = _extract_metric_update_ops(
          estimator_spec.eval_metric_ops)

      if ops.GraphKeys.GLOBAL_STEP in eval_dict:
        raise ValueError(
            'Metric with name `global_step` is not allowed, because Estimator '
            'already defines a default metric with the same name.')
      eval_dict[ops.GraphKeys.GLOBAL_STEP] = global_step_tensor

      eval_results = evaluation._evaluate_once(  # pylint: disable=protected-access
          checkpoint_path=checkpoint_path,
          master=self._config.evaluation_master,
          scaffold=estimator_spec.scaffold,
          eval_ops=update_op,
          final_ops=eval_dict,
          hooks=hooks,
          config=self._session_config)

      _write_dict_to_summary(
          output_dir=eval_dir,
          dictionary=eval_results,
          current_global_step=eval_results[ops.GraphKeys.GLOBAL_STEP])

    return eval_results
Example #3
Source File: estimator_v2.py    From boxnet with GNU General Public License v3.0 4 votes vote down vote up 
def _evaluate_model(self,
                      input_fn,
                      hooks=None,
                      checkpoint_path=None,
                      name=''):
    """Evaluates the model using the training.evaluation library."""
    # Check that model has been trained (if nothing has been set explicitly).
    if not checkpoint_path:
      latest_path = saver.latest_checkpoint(self._model_dir)
      if not latest_path:
        raise ValueError('Could not find trained model in model_dir: {}.'.
                         format(self._model_dir))
      checkpoint_path = latest_path

    # Setup output directory.
    eval_dir = os.path.join(self._model_dir, 'eval' if not name else
                            'eval_' + name)

    with ops.Graph().as_default() as g:
      random_seed.set_random_seed(self._config.tf_random_seed)
      global_step_tensor = self._create_and_assert_global_step(g)
      features, labels = self._get_features_and_labels_from_input_fn(
          input_fn, model_fn_lib.ModeKeys.EVAL)
      estimator_spec = self._call_model_fn(
          features, labels, model_fn_lib.ModeKeys.EVAL, self.config)

      if model_fn_lib.LOSS_METRIC_KEY in estimator_spec.eval_metric_ops:
        raise ValueError(
            'Metric with name "%s" is not allowed, because Estimator ' % (
                model_fn_lib.LOSS_METRIC_KEY) +
            'already defines a default metric with the same name.')
      estimator_spec.eval_metric_ops[
          model_fn_lib.LOSS_METRIC_KEY] = metrics_lib.mean(estimator_spec.loss)

      update_op, eval_dict = _extract_metric_update_ops(
          estimator_spec.eval_metric_ops)

      if ops.GraphKeys.GLOBAL_STEP in eval_dict:
        raise ValueError(
            'Metric with name `global_step` is not allowed, because Estimator '
            'already defines a default metric with the same name.')
      eval_dict[ops.GraphKeys.GLOBAL_STEP] = global_step_tensor

      all_hooks = list(hooks or [])
      all_hooks.extend(list(estimator_spec.evaluation_hooks or []))

      eval_results = evaluation._evaluate_once(  # pylint: disable=protected-access
          checkpoint_path=checkpoint_path,
          master=self._config.evaluation_master,
          scaffold=estimator_spec.scaffold,
          eval_ops=update_op,
          final_ops=eval_dict,
          hooks=all_hooks,
          config=self._session_config)

      _write_dict_to_summary(
          output_dir=eval_dir,
          dictionary=eval_results,
          current_global_step=eval_results[ops.GraphKeys.GLOBAL_STEP])

    return eval_results
Example #4
Source File: estimator.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 4 votes vote down vote up 
def _evaluate_model(self,
                      input_fn,
                      hooks=None,
                      checkpoint_path=None,
                      name=''):
    """Evaluates the model using the training.evaluation library."""
    # Check that model has been trained (if nothing has been set explicitly).
    if not checkpoint_path:
      latest_path = saver.latest_checkpoint(self._model_dir)
      if not latest_path:
        raise ValueError('Could not find trained model in model_dir: {}.'.
                         format(self._model_dir))
      checkpoint_path = latest_path

    # Setup output directory.
    eval_dir = os.path.join(self._model_dir, 'eval' if not name else
                            'eval_' + name)

    with ops.Graph().as_default() as g:
      random_seed.set_random_seed(self._config.tf_random_seed)
      global_step_tensor = self._create_and_assert_global_step(g)
      features, labels = self._get_features_and_labels_from_input_fn(
          input_fn, model_fn_lib.ModeKeys.EVAL)
      estimator_spec = self._call_model_fn(
          features, labels, model_fn_lib.ModeKeys.EVAL, self.config)

      if model_fn_lib.LOSS_METRIC_KEY in estimator_spec.eval_metric_ops:
        raise ValueError(
            'Metric with name "%s" is not allowed, because Estimator ' % (
                model_fn_lib.LOSS_METRIC_KEY) +
            'already defines a default metric with the same name.')
      estimator_spec.eval_metric_ops[
          model_fn_lib.LOSS_METRIC_KEY] = metrics_lib.mean(estimator_spec.loss)

      update_op, eval_dict = _extract_metric_update_ops(
          estimator_spec.eval_metric_ops)

      if ops.GraphKeys.GLOBAL_STEP in eval_dict:
        raise ValueError(
            'Metric with name `global_step` is not allowed, because Estimator '
            'already defines a default metric with the same name.')
      eval_dict[ops.GraphKeys.GLOBAL_STEP] = global_step_tensor

      all_hooks = list(hooks or [])
      all_hooks.extend(list(estimator_spec.evaluation_hooks or []))

      eval_results = evaluation._evaluate_once(  # pylint: disable=protected-access
          checkpoint_path=checkpoint_path,
          master=self._config.evaluation_master,
          scaffold=estimator_spec.scaffold,
          eval_ops=update_op,
          final_ops=eval_dict,
          hooks=all_hooks,
          config=self._session_config)

      _write_dict_to_summary(
          output_dir=eval_dir,
          dictionary=eval_results,
          current_global_step=eval_results[ops.GraphKeys.GLOBAL_STEP])

    return eval_results