policy.py 文件源码

def initialize_variables(self, save_file=None):
        self.session.run(tf.global_variables_initializer())
        if save_file is not None:
            try:
                self.saver.restore(self.session, save_file)
            except:
                # some wizardry here... basically, only restore variables
                # that are in the save file; otherwise, initialize them normally.
                from tensorflow.python.framework import meta_graph
                meta_graph_def = meta_graph.read_meta_graph_file(save_file + '.meta')
                stored_var_names = set([n.name
                    for n in meta_graph_def.graph_def.node
                    if n.op == 'VariableV2'])
                print(stored_var_names)
                var_list = [v for v in tf.global_variables()
                    if v.op.name in stored_var_names]
                # initialize all of the variables
                self.session.run(tf.global_variables_initializer())
                # then overwrite the ones we have in the save file
                # by using a throwaway saver, saved models are automatically
                # "upgraded" to the latest graph definition.
                throwaway_saver = tf.train.Saver(var_list=var_list)
                throwaway_saver.restore(self.session, save_file)