我们通过tf.train.Saver()来保存和重载变量

实现是保存

# Create some variables.v1 = tf.get_variable("v1", shape=[3], initializer = tf.zeros_initializer)v2 = tf.get_variable("v2", shape=[5], initializer = tf.zeros_initializer)inc_v1 = v1.assign(v1+1)dec_v2 = v2.assign(v2-1)# Add an op to initialize the variables.init_op = tf.global_variables_initializer()# Add ops to save and restore all the variables.saver = tf.train.Saver()# Later, launch the model, initialize the variables, do some work, and save the# variables to disk.with tf.Session() as sess:  sess.run(init_op)  # Do some work with the model.  inc_v1.op.run()  dec_v2.op.run()  # Save the variables to disk.  save_path = saver.save(sess, "/tmp/model.ckpt")  print("Model saved in path: %s" % save_path)

通过调用saver的save方法来保存，返回一个str，代表了路径。

然后展示的是我们保存部分变量和重载：

tf.reset_default_graph()# Create some variables.v1 = tf.get_variable("v1", [3], initializer = tf.zeros_initializer)v2 = tf.get_variable("v2", [5], initializer = tf.zeros_initializer)# Add ops to save and restore only `v2` using the name "v2"saver = tf.train.Saver({
   "v2": v2})# Use the saver object normally after that.with tf.Session() as sess:  # Initialize v1 since the saver will not.  v1.initializer.run()  saver.restore(sess, "/tmp/model.ckpt")  print("v1 : %s" % v1.eval())  print("v2 : %s" % v2.eval())

如果Saver中不传入参数，则会将所有的变量都保存。传入字典，则会按照字典中的key-value对变量进行保存。

对于不需要feed数据就可以获取的值，比如Variable。我们可以直接使用variable.eval()将变量的值打印出来。

它相当于：

tf.get_default_session().run(t)