TensorFlow에서 훈련된 모델을 효과적으로 저장하고 복원하는 방법은 무엇입니까?

import tensorflow as tf

# Prepare input placeholders
w1 = tf.placeholder("float", name="w1")
w2 = tf.placeholder("float", name="w2")

# Define test operation
w3 = tf.add(w1, w2)
w4 = tf.multiply(w3, tf.Variable(2.0, name="bias"), name="op_to_restore")

# Initialize variables and run session
sess = tf.Session()
sess.run(tf.global_variables_initializer())

# Create saver object
saver = tf.train.Saver()

# Save the model
saver.save(sess, 'my_test_model', global_step=1000)

# Restore model
saver = tf.train.import_meta_graph('my_test_model-1000.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))

# Get placeholders and feed data
w1 = sess.graph.get_tensor_by_name("w1:0")
w2 = sess.graph.get_tensor_by_name("w2:0")
feed_dict = {w1: 13.0, w2: 17.0}

# Run saved operation
op_to_restore = sess.graph.get_tensor_by_name("op_to_restore:0")
result = sess.run(op_to_restore, feed_dict)