一、rom ._ellip_harm_2 import _ellipsoid, _ellipsoid_norm ImportError: cannot import name '_ellipsoid'
Python中可能会遇到 cannot import name ‘XXX’ 错误, 其实这有可能出现再模块导入的顺序问题上， 比如：在A文件头执行到语句 from B import XXX ，程序马上就会转到B文件中去，从头到尾顺序寻找B文件中的XXX函数，而A文件就暂停执行，直到把XXX函数复制到内存中，但B文件中的文件头可能也有导入， 如果B文件头中又导入了A文件中的函数，由于XXX函数还没有被复制。所以于A文件因为暂停执行而无法导入，就会出现上面的错误了。
二、libstdc++.so.6: version `CXXABI_1.3.9' not found (required by..... )
ibstdc++.so.6在系统中存在于/usr/lib/libstdc++.so.6 或者/usr/lib/x86_64-linux-gnu/libstdc++.so.6。导致这个问题的出现可能是你在别的库中（例如anaconda）也存在该动态库文件。
1、查看
strings /usr/lib/libstdc++.so.6 | grep 'CXXABI'
或者
strings /usr/lib/x86_64-linux-gnu/libstdc++.so.6 | grep 'CXXABI'
可以看到
CXXABI_1.3
CXXABI_1.3.1
CXXABI_1.3.2
CXXABI_1.3.3
CXXABI_1.3.4
CXXABI_1.3.5
CXXABI_1.3.6
CXXABI_1.3.7
CXXABI_1.3.8
CXXABI_1.3.9
CXXABI_TM_1
CXXABI_FLOAT128
有CXXABI_1.3.9这一项，而同样的方式查看anaconda3/lib/下libstdc++.so.6文件
strings anaconda3/lib/libstdc++.so.6 | grep 'CXXABI'
最高才存在CXXABI_1.3.7
2、复制动态库
#删除原来的libstdc++.so.6
sudo rm -rf anaconda3/lib/libstdc++.so.6
#拷贝新的动态库文件,注意自己的是哪个版本
sudo cp /usr/lib/libstdc++.so.6.0.21 /home/ubuntu/anaconda3/lib/
3、建立软连接
cd anaconda3/lib/
sudo chmod +r libstdc++.so.6.0.21
sudo ln -sf libstdc++.so.6.0.21 libstdc++.so.6
sudo ldconfig
//////////////////////////////////////////////////////////////////上述原因具体事情，具体分析。
/home/jdmdx/anaconda3/envs/tensorflow/bin/python "/home/jdmdx/tensorflow code/MobileNetV2-master/train_mobilenetv2.py"
2018-04-18 01:01:04.704523: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-04-18 01:01:04.744722: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at example_parsing_ops.cc:240 : Invalid argument: Feature: image/encoded (data type: string) is required but could not be found.
2018-04-18 01:01:04.750963: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at example_parsing_ops.cc:240 : Invalid argument: Feature: image/encoded (data type: string) is required but could not be found.
2018-04-18 01:01:04.751038: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at example_parsing_ops.cc:240 : Invalid argument: Feature: image/encoded (data type: string) is required but could not be found.
2018-04-18 01:01:04.751253: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at example_parsing_ops.cc:240 : Invalid argument: Feature: image/encoded (data type: string) is required but could not be found.
WARNING:tensorflow:From /home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Use the retry module or similar alternatives.
[*] Try to load trained model...
[*] Reading checkpoints...
Traceback (most recent call last):
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1327, in _do_call
return fn(*args)
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1312, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1420, in _call_tf_sessionrun
status, run_metadata)
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 516, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [1,1,1280,9] rhs shape= [1,1,1280,10]
[[Node: save/Assign_24 = Assign[T=DT_FLOAT, _class=["loc:@mobilenetv2/logits/w"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](mobilenetv2/logits/w, save/RestoreV2:24)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/jdmdx/tensorflow code/MobileNetV2-master/train_mobilenetv2.py", line 118, in <module>
main()
File "/home/jdmdx/tensorflow code/MobileNetV2-master/train_mobilenetv2.py", line 86, in main
could_load, step = load(sess, saver, args.checkpoint_dir)
File "/home/jdmdx/tensorflow code/MobileNetV2-master/train_mobilenetv2.py", line 18, in load
saver.restore(sess, os.path.join(checkpoint_dir, ckpt_name))
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1775, in restore
{self.saver_def.filename_tensor_name: save_path})
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 905, in run
run_metadata_ptr)
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1140, in _run
feed_dict_tensor, options, run_metadata)
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
run_metadata)
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/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= [1,1,1280,9] rhs shape= [1,1,1280,10]
[[Node: save/Assign_24 = Assign[T=DT_FLOAT, _class=["loc:@mobilenetv2/logits/w"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](mobilenetv2/logits/w, save/RestoreV2:24)]]
Caused by op 'save/Assign_24', defined at:
File "/home/jdmdx/tensorflow code/MobileNetV2-master/train_mobilenetv2.py", line 118, in <module>
main()
File "/home/jdmdx/tensorflow code/MobileNetV2-master/train_mobilenetv2.py", line 81, in main
saver = tf.train.Saver()
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1311, in __init__
self.build()
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1320, in build
self._build(self._filename, build_save=True, build_restore=True)
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1357, in _build
build_save=build_save, build_restore=build_restore)
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 809, in _build_internal
restore_sequentially, reshape)
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 470, in _AddRestoreOps
assign_ops.append(saveable.restore(saveable_tensors, shapes))
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 162, in restore
self.op.get_shape().is_fully_defined())
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/state_ops.py", line 281, in assign
validate_shape=validate_shape)
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/gen_state_ops.py", line 61, in assign
use_locking=use_locking, name=name)
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3290, in create_op
op_def=op_def)
File "/home/jdmdx/anaconda3/envs/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1654, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [1,1,1280,9] rhs shape= [1,1,1280,10]
[[Node: save/Assign_24 = Assign[T=DT_FLOAT, _class=["loc:@mobilenetv2/logits/w"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](mobilenetv2/logits/w, save/RestoreV2:24)]]
\\\\\\\\\\\\\\\\\\\\\\\\\\\不知道什么错误,在sess上加入了sess=tf.Session()导致mobilenetv2训练卡死
IndexError: list index out of range
在config文件中出现这种错误，是因为输入参数的时候没有输入全。
Tensorflow FIFOQueue '_4_batch_join/fifo_queue' is closed and has insufficient elements
在跑mobileNet模型时，出现的错误，原因是在sess.run之前 没有写sess = tf.InteractiveSession()
protobuf 转换python代码时发生 Expected "required", "optional", or "repeated".错误解决方法
Google Protocol Buffers 简称 Protobuf，它提供了一种灵活、高效、自动序列化结构数据的机制，可以联想 XML，但是比 XML 更小、更快、更简单。仅需要自定义一次你所需的数据格式，然后用户就可以使用 Protobuf 编译器自动生成各种语言的源码，方便的读写用户自定义的格式化的数据。与语言无关，与平台无关，还可以在不破坏原数据格式的基础上，依据老的数据格式，更新现有的数据格式。
在很多谷歌开源的程序中都大部分用到了protobuf，比如最新开源出来的object_detection中就存在这样的定义。最近想着编译一下这个目标检测识别的程序，发现protobuf居然报了个错误，错误码即如下：
.proto:386:3: Expected "required", "optional", or "repeated".
针对这个问题，才发现自己电脑上的protobuf版本是2.5版本(通过命令：protoc --version进行查看)，可能原因还是新版本又更新了一些参数，查看了下需求，也发现需要用到2.6版本，所以只能重新进行编译protobuf.高的版本。
于是从https://github.com/google/protobuf/releases?after=v2.6.1下载2.6的数据包。
直接按照传统的 ./configure, make -j4 , sudo make install等方式来进行安装。
这时候还需要一个步骤，需要在/etc/profile中配置一下参数：
[plain] view plain copy
export LD_LIBRARY_PATH=/usr/local/lib
不然就会报如下错误：
protoc: error while loading shared libraries: libprotoc.so.9: cannot open shared object file: No such file or directory
完成安装后，再重新进行进行代码转换，即可成功。
[html] view plain copy
protoc ./object_detection/protos/*.proto --python_out=.
dscbigdata-Lenovo-Product:~/work/tensorflow/models-master$
这时候，对应的python文件已经生成。
在object_detection中的mobilev1训练时，出现failed to find any matching files for .......................（ckpt保存地址）
此训练使用迁移学习进行的训练，需要下载别人的训练节点进行训练加速。
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ubuntu16.04电脑异常关机后分辨率出现的特别低，设置里分辨率只有800*600。查资料发现是因为异常关机导致驱动出现错误。
解决方法为：在设置中选择更新：
然后选择驱动设备：
由于上传的电脑没有GPU，上面显示没有。有GPU的 选择英伟达驱动进行更新，重启计算机即可。。