saver = tf.train.Saver(tf.global_variables(),max_to_keep=1)  #max_to_keep这个保证只保存最后一次training的训练数据

saver = tf.train.Saver(tf.global_variables())
moudke_file=tf.train.latest_checkpoint('PATH')
saver.restore(sess,moudke_file)

def read_checkpoint():
  w = []
  checkpoint_path = '/home/ximao/models/resnet3/variable_logs/model.ckpt-17000'
  reader = tf.train.NewCheckpointReader(checkpoint_path)
  var = reader.get_variable_to_shape_map()
  for key in var:
    if 'weights' in key and 'conv' in key and 'Mo' not in key:
      print('tensorname:', key)
  #   # print(reader.get_tensor(key))

var=[v for v in weight_pruned if v.op.name=='WRN/conv1/weights']
conv1_temp=tf.convert_to_tensor(conv1,dtype=tf.float32)
sess.run(tf.assign(var[0],conv1_temp))