tensorflow训练权重的保存和读取

'''import tensorflow as tf#Prepare to feed input, i.e. feed_dict and placeholdersw1 = tf.placeholder("float", name="w1")w2 = tf.placeholder("float", name="w2")b1= tf.Variable(2.0,name="bias")feed_dict ={w1:4,w2:8}#Define a test operation that we will restorew3 = tf.add(w1,w2)w4 = tf.multiply(w3,b1,name="op_to_restore")sess = tf.Session()sess.run(tf.global_variables_initializer())#Create a saver object which will save all the variablessaver = tf.train.Saver()#Run the operation by feeding inputprint (sess.run(w4,feed_dict))#Prints 24 which is sum of (w1+w2)*b1 #Now, save the graphsaver.save(sess, 'D:/a_ssd/',global_step=1000)'''import tensorflow as tfsess=tf.Session()    #First let's load meta graph and restore weightssaver = tf.train.import_meta_graph('-1000.meta')saver.restore(sess,tf.train.latest_checkpoint('./'))# Access saved Variables directlyprint(sess.run('bias:0'))# This will print 2, which is the value of bias that we saved# Now, let's access and create placeholders variables and# create feed-dict to feed new datagraph = tf.get_default_graph()w1 = graph.get_tensor_by_name("w1:0")w2 = graph.get_tensor_by_name("w2:0")feed_dict ={w1:13.0,w2:17.0}#Now, access the op that you want to run. op_to_restore = graph.get_tensor_by_name("op_to_restore:0")print( sess.run(op_to_restore,feed_dict))