evaluate.py 文件源码

def main(_):
  # Configuration.
  num_unrolls = FLAGS.num_steps

  if FLAGS.seed:
    tf.set_random_seed(FLAGS.seed)

  # Problem.
  problem, net_config, net_assignments = util.get_config(FLAGS.problem,
                                                         FLAGS.path)

  # Optimizer setup.
  if FLAGS.optimizer == "Adam":
    cost_op = problem()
    problem_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
    problem_reset = tf.variables_initializer(problem_vars)

    optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
    optimizer_reset = tf.variables_initializer(optimizer.get_slot_names())
    update = optimizer.minimize(cost_op)
    reset = [problem_reset, optimizer_reset]
  elif FLAGS.optimizer == "L2L":
    if FLAGS.path is None:
      logging.warning("Evaluating untrained L2L optimizer")
    optimizer = meta.MetaOptimizer(**net_config)
    meta_loss = optimizer.meta_loss(problem, 1, net_assignments=net_assignments)
    _, update, reset, cost_op, _ = meta_loss
  else:
    raise ValueError("{} is not a valid optimizer".format(FLAGS.optimizer))

  with ms.MonitoredSession() as sess:
    # Prevent accidental changes to the graph.
    tf.get_default_graph().finalize()

    total_time = 0
    total_cost = 0
    for _ in xrange(FLAGS.num_epochs):
      # Training.
      time, cost = util.run_epoch(sess, cost_op, [update], reset,
                                  num_unrolls)
      total_time += time
      total_cost += cost

    # Results.
    util.print_stats("Epoch {}".format(FLAGS.num_epochs), total_cost,
                     total_time, FLAGS.num_epochs)