trainer.py 文件源码

def _init_variables(self):
        """ Create the initialization operation for the variables """
        # Adam optimizer uses two variables that can only be accessed through the use of a protected
        # function since the variables aren't scoped in anyway. Trying to add a tf.variable_scope
        # around apply_gradients where the variables are created did not help.
        var_list = set(self.optimizer._get_beta_accumulators()) # pylint: disable=protected-access
        slot_names = self.optimizer.get_slot_names()
        for tower in self.towers:
            variables = tower.global_variables
            var_list.update(variables)

            for slot_name in slot_names:
                for variable in variables:
                    slot = self.optimizer.get_slot(variable, slot_name)
                    if slot is not None:
                        var_list.add(slot)

        # Initialize all the variables
        self.initialization_operation = tf.group(
            tf.variables_initializer(var_list),

            # Apparently local variables are not part of 'all' variables... go figure
            # This is needed for metrics for example
            tf.local_variables_initializer())