original_docstring: Creates the hash of the model used for the server keeping track of what is being used
docstring: Creates the hash of the model used for the server keeping track of what is being used
docstring_tokens: Creates the hash of the model used for the server keeping track of what is being used
code: def create_model_hash(self):
          """Creates the hash of the model used for the server keeping track of what is being used"""
          all_saved_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
          print(len(all_saved_variables))
          for i in all_saved_variables:
              print(self.player_index, i.name)
          saved_variables = self.sess.run(all_saved_variables)
          saved_variables = np.array(saved_variables)
          return int(hex(hash(str(saved_variables.data))), 16) % 2 ** 64
code_tokens: def create_model_hash ( self ) : all_saved_variables = tf . get_collection ( tf . GraphKeys . TRAINABLE_VARIABLES ) print ( len ( all_saved_variables ) ) for i in all_saved_variables : print ( self . player_index , i . name ) saved_variables = self . sess . run ( all_saved_variables ) saved_variables = np . array ( saved_variables ) return int ( hex ( hash ( str ( saved_variables . data ) ) ) , 16 ) % 2 ** 64
short_docstring: Creates the hash of the model used for the server keeping track of what is being used
short_docstring_tokens: Creates the hash of the model used for the server keeping track of what is being used
comment: """Creates the hash of the model used for the server keeping track of what is being used"""