tensorforce/models/model.py
def setup_saver(self):
        """
        Creates the tf.train.Saver object and stores it in self.saver.
        """
        if self.execution_type == "single":
            global_variables = self.get_variables(include_submodules=True, include_nontrainable=True)
        else:
            global_variables = self.global_model.get_variables(include_submodules=True, include_nontrainable=True)

        # global_variables += [self.global_episode, self.global_timestep]

        for c in self.get_savable_components():
            c.register_saver_ops()

        # TensorFlow saver object
        # TODO potentially make other options configurable via saver spec.
        self.saver = tf.train.Saver(
            var_list=global_variables,  # should be given?
            reshape=False,
            sharded=False,
            max_to_keep=5,
            keep_checkpoint_every_n_hours=10000.0,
            name=None,
            restore_sequentially=False,
            saver_def=None,
            builder=None,
            defer_build=False,
            allow_empty=True,
            write_version=tf.train.SaverDef.V2,
            pad_step_number=False,
            save_relative_paths=True
            # filename=None
        )
def setup_saver(self):
        """
        Creates the tf.train.Saver object and stores it in self.saver.
        """
        if self.execution_type == "single":
            global_variables = self.get_variables(include_submodules=True, include_nontrainable=True)
        else:
            global_variables = self.global_model.get_variables(include_submodules=True, include_nontrainable=True)

        # global_variables += [self.global_episode, self.global_timestep]

        for c in self.get_savable_components():
            c.register_saver_ops()

        # TensorFlow saver object
        # TODO potentially make other options configurable via saver spec.
        self.saver = tf.train.Saver(
            var_list=global_variables,  # should be given?
            reshape=False,
            sharded=False,
            max_to_keep=5,
            keep_checkpoint_every_n_hours=10000.0,
            name=None,
            restore_sequentially=False,
            saver_def=None,
            builder=None,
            defer_build=False,
            allow_empty=True,
            write_version=tf.train.SaverDef.V2,
            pad_step_number=False,
            save_relative_paths=True
            # filename=None
        )
def setup_saver ( self ) : if self . execution_type == "single" : global_variables = self . get_variables ( include_submodules = True , include_nontrainable = True ) else : global_variables = self . global_model . get_variables ( include_submodules = True , include_nontrainable = True ) # global_variables += [self.global_episode, self.global_timestep] for c in self . get_savable_components ( ) : c . register_saver_ops ( ) # TensorFlow saver object # TODO potentially make other options configurable via saver spec. self . saver = tf . train . Saver ( var_list = global_variables , # should be given? reshape = False , sharded = False , max_to_keep = 5 , keep_checkpoint_every_n_hours = 10000.0 , name = None , restore_sequentially = False , saver_def = None , builder = None , defer_build = False , allow_empty = True , write_version = tf . train . SaverDef . V2 , pad_step_number = False , save_relative_paths = True # filename=None )
Creates the tf.train.Saver object and stores it in self.saver.
Creates the tf . train . Saver object and stores it in self . saver .