TensorFlow模型保存/载入的两种方法

import tensorflow as tf 
import numpy as np 

W = tf.Variable([[1,1,1],[2,2,2]],dtype = tf.float32,name='w') 
b = tf.Variable([[0,1,2]],dtype = tf.float32,name='b') 

init = tf.initialize_all_variables() 
saver = tf.train.Saver() 
with tf.Session() as sess: 
  sess.run(init) 
  save_path = saver.save(sess,"save/model.ckpt") 

import tensorflow as tf 
import numpy as np 

W = tf.Variable(tf.truncated_normal(shape=(2,3)),dtype = tf.float32,name='w') 
b = tf.Variable(tf.truncated_normal(shape=(1,3)),dtype = tf.float32,name='b') 

saver = tf.train.Saver() 
with tf.Session() as sess: 
  saver.restore(sess,"save/model.ckpt") 

tf.train.import_meta_graph

import_meta_graph(
 meta_graph_or_file,
 clear_devices=False,
 import_scope=None,
 **kwargs
)

### 定义模型
input_x = tf.placeholder(tf.float32, shape=(None, in_dim), name='input_x')
input_y = tf.placeholder(tf.float32, shape=(None, out_dim), name='input_y')

w1 = tf.Variable(tf.truncated_normal([in_dim, h1_dim], stddev=0.1), name='w1')
b1 = tf.Variable(tf.zeros([h1_dim]), name='b1')
w2 = tf.Variable(tf.zeros([h1_dim, out_dim]), name='w2')
b2 = tf.Variable(tf.zeros([out_dim]), name='b2')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
hidden1 = tf.nn.relu(tf.matmul(self.input_x, w1) + b1)
hidden1_drop = tf.nn.dropout(hidden1, self.keep_prob)
### 定义预测目标
y = tf.nn.softmax(tf.matmul(hidden1_drop, w2) + b2)
# 创建saver
saver = tf.train.Saver(...variables...)
# 假如需要保存y，以便在预测时使用
tf.add_to_collection('pred_network', y)
sess = tf.Session()
for step in xrange(1000000):
 sess.run(train_op)
 if step % 1000 == 0:
  # 保存checkpoint, 同时也默认导出一个meta_graph
  # graph名为'my-model-{global_step}.meta'.
  saver.save(sess, 'my-model', global_step=step)

with tf.Session() as sess:
 new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta')
 new_saver.restore(sess, 'my-save-dir/my-model-10000')
 # tf.get_collection() 返回一个list. 但是这里只要第一个参数即可
 y = tf.get_collection('pred_network')[0]

 graph = tf.get_default_graph()

 # 因为y中有placeholder，所以sess.run(y)的时候还需要用实际待预测的样本以及相应的参数来填充这些placeholder，而这些需要通过graph的get_operation_by_name方法来获取。
 input_x = graph.get_operation_by_name('input_x').outputs[0]
 keep_prob = graph.get_operation_by_name('keep_prob').outputs[0]

 # 使用y进行预测 
 sess.run(y, feed_dict={input_x:...., keep_prob:1.0})