Tensorflow 同时载入多个模型的实例讲解

class Predict:
 def __init__(self....):
  创建sess
  创建恢复器tf.train.Saver
  从恢复点恢复参数：tf.train.Saver.restore(...)


 def predict(self,...):
  sess.run(output,feed_dict={输入})

 File "/home/jiangminghao/.local/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
 pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [256,512] rhs shape= [640,512]
   [[Node: save/Assign_14 = Assign[T=DT_FLOAT, _class=["loc:@fullcont/Variable"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/cpu:0"](fullcont/Variable, save/RestoreV2_14)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
 File "PREDICT_WITH_SPARK_DATAFLOW_WA.py", line 121, in <module>
 pre2=Predict(label=new_list[1])
 File "PREDICT_WITH_SPARK_DATAFLOW_WA.py", line 47, in __init__
 self.saver.restore(self.sess,self.ckpt.model_checkpoint_path)
 File "/home/jiangminghao/.local/lib/python3.5/site-packages/tensorflow/python/training/saver.py", line 1560, in restore
 {self.saver_def.filename_tensor_name: save_path})
 File "/home/jiangminghao/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 895, in run
 run_metadata_ptr)
 File "/home/jiangminghao/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1124, in _run
 feed_dict_tensor, options, run_metadata)
 File "/home/jiangminghao/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
 options, run_metadata)
 File "/home/jiangminghao/.local/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call
 raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [256,512] rhs shape= [640,512]

class Predict:
 def __init__(self....):
  self.graph=tf.Graph()#为每个类(实例)单独创建一个graph
  with self.graph.as_default():
    self.saver=tf.train.import_meta_graph(...)#创建恢复器
    #注意！恢复器必须要在新创建的图里面生成,否则会出错。
  self.sess=tf.Session(graph=self.graph)#创建新的sess
  with self.sess.as_default():
    with self.graph.as_default():
     self.saver.restore(self.sess,...)#从恢复点恢复参数

 def predict(self,...):
  sess.run(output,feed_dict={输入})