Tensorflow 同时载入多个模型的实例讲解
有时我们希望在一个python的文件空间同时载入多个模型，例如 我们建立了10个CNN模型，然后我们又写了一个预测类Predict，这个类会从已经保存好的模型restore恢复相应的图结构以及模型参数。然后我们会创建10个Predict的对象Instance，每个Instance负责一个模型的预测。
Predict的核心为：
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})
如果我们直接轮流生成10个不同的Predict 对象的话，我们发现tensorflow是会报类似于下面的错误：
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]
关键就是：
Assign requires shapes of both tensors to match.意思是载入模型的时候 赋值失败。主要是因为不同对象里面的不同sess使用了同一进程空间下的相同的默认图graph。
正确的解决方法：
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})
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