Python variables.create_global_step方法代码示例

# 需要导入模块: from tensorflow.contrib.framework.python.ops import variables [as 别名]
# 或者: from tensorflow.contrib.framework.python.ops.variables import create_global_step [as 别名]
def _infer_model(self,
                   input_fn,
                   feed_fn=None,
                   outputs=None,
                   as_iterable=True,
                   iterate_batches=False):
    # Check that model has been trained.
    checkpoint_path = saver.latest_checkpoint(self._model_dir)
    if not checkpoint_path:
      raise NotFittedError("Couldn't find trained model at %s."
                           % self._model_dir)

    with ops.Graph().as_default() as g:
      random_seed.set_random_seed(self._config.tf_random_seed)
      contrib_framework.create_global_step(g)
      features = self._get_features_from_input_fn(input_fn)
      infer_ops = self._get_predict_ops(features)
      predictions = self._filter_predictions(infer_ops.predictions, outputs)
      mon_sess = monitored_session.MonitoredSession(
          session_creator=monitored_session.ChiefSessionCreator(
              checkpoint_filename_with_path=checkpoint_path,
              scaffold=infer_ops.scaffold,
              config=self._session_config))
      if not as_iterable:
        with mon_sess:
          if not mon_sess.should_stop():
            return mon_sess.run(predictions, feed_fn() if feed_fn else None)
      else:
        return self._predict_generator(mon_sess, predictions, feed_fn,
                                       iterate_batches) 

# 需要导入模块: from tensorflow.contrib.framework.python.ops import variables [as 别名]
# 或者: from tensorflow.contrib.framework.python.ops.variables import create_global_step [as 别名]
def _infer_model(self,
                   input_fn,
                   feed_fn=None,
                   outputs=None,
                   as_iterable=True,
                   iterate_batches=False):
    # Check that model has been trained.
    checkpoint_path = saver.latest_checkpoint(self._model_dir)
    if not checkpoint_path:
      raise NotFittedError("Couldn't find trained model at %s."
                           % self._model_dir)

    with ops.Graph().as_default() as g:
      random_seed.set_random_seed(self._config.tf_random_seed)
      contrib_framework.create_global_step(g)
      features = self._get_features_from_input_fn(input_fn)
      infer_ops = self._call_legacy_get_predict_ops(features)
      predictions = self._filter_predictions(infer_ops.predictions, outputs)
      mon_sess = monitored_session.MonitoredSession(
          session_creator=monitored_session.ChiefSessionCreator(
              checkpoint_filename_with_path=checkpoint_path))
      if not as_iterable:
        with mon_sess:
          if not mon_sess.should_stop():
            return mon_sess.run(predictions, feed_fn() if feed_fn else None)
      else:
        return self._predict_generator(mon_sess, predictions, feed_fn,
                                       iterate_batches) 

# 需要导入模块: from tensorflow.contrib.framework.python.ops import variables [as 别名]
# 或者: from tensorflow.contrib.framework.python.ops.variables import create_global_step [as 别名]
def _train_model(self, input_fn, hooks):
    all_hooks = []
    self._graph = ops.Graph()
    with self._graph.as_default() as g, g.device(self._device_fn):
      random_seed.set_random_seed(self._config.tf_random_seed)
      global_step = contrib_framework.create_global_step(g)
      features, labels = input_fn()
      self._check_inputs(features, labels)
      model_fn_ops = self._call_legacy_get_train_ops(features, labels)
      ops.add_to_collection(ops.GraphKeys.LOSSES, model_fn_ops.loss)
      all_hooks.extend([
          basic_session_run_hooks.NanTensorHook(model_fn_ops.loss),
          basic_session_run_hooks.LoggingTensorHook(
              {
                  'loss': model_fn_ops.loss,
                  'step': global_step
              },
              every_n_iter=100)
      ])
      all_hooks.extend(hooks)

      scaffold = model_fn_ops.training_scaffold or monitored_session.Scaffold()
      if not (scaffold.saver or ops.get_collection(ops.GraphKeys.SAVERS)):
        ops.add_to_collection(
            ops.GraphKeys.SAVERS,
            saver.Saver(
                sharded=True,
                max_to_keep=self._config.keep_checkpoint_max,
                defer_build=True))

      chief_hooks = []
      if (self._config.save_checkpoints_secs or
          self._config.save_checkpoints_steps):
        saver_hook_exists = any([
            isinstance(h, basic_session_run_hooks.CheckpointSaverHook)
            for h in (all_hooks + model_fn_ops.training_hooks + chief_hooks +
                      model_fn_ops.training_chief_hooks)
        ])
        if not saver_hook_exists:
          chief_hooks = [
              basic_session_run_hooks.CheckpointSaverHook(
                  self._model_dir,
                  save_secs=self._config.save_checkpoints_secs,
                  save_steps=self._config.save_checkpoints_steps,
                  scaffold=scaffold)
          ]
      with monitored_session.MonitoredTrainingSession(
          master=self._config.master,
          is_chief=self._config.is_chief,
          checkpoint_dir=self._model_dir,
          scaffold=scaffold,
          hooks=all_hooks + model_fn_ops.training_hooks,
          chief_only_hooks=chief_hooks + model_fn_ops.training_chief_hooks,
          save_checkpoint_secs=0,  # Saving is handled by a hook.
          save_summaries_steps=self._config.save_summary_steps,
          config=self.config.tf_config) as mon_sess:
        loss = None
        while not mon_sess.should_stop():
          _, loss = mon_sess.run([model_fn_ops.train_op, model_fn_ops.loss])
      summary_io.SummaryWriterCache.clear()
      return loss