第一步,读一读这篇博客
https://www.jb51.net/article/138932.htm (浅谈Tensorflow模型的保存与恢复加载)
第二步:
参考博客:
- https://blog.csdn.net/u011734144/article/details/82107610
按照上述教程配置好相关文件之后(模型是下面tensorflow-serving中产生的,直接移到textcnnrnn中的)然后再执行下面命令:
首先启动:
ljj@debian:~$ docker run -p 8501:8501 --mount type=bind,source=/home/ljj/serving/tensorflow_serving/servables/tensorflow/testdata/textcnnrnn,target=/models/find_lemma_category -e MODEL_NAME=find_lemma_category -t tensorflow/serving
然后调用:
ljj@debian:~$ curl --tlsv1.2 -d '{"instances": [10,10,10,8,6,1,8,9,1]}' -X POST http://0.0.0.0:8501/v1/models/find_lemma_category:predict
但是出现错误:
{ "error": "instances is a plain list, but expecting list of objects as multiple input tensors required as per tensorinfo_map" }ljj@debian:~$
事故现场:
- https://www.jianshu.com/p/2fffd0e332bc
- https://blog.csdn.net/SEUer_jeff/article/details/75578732
- https://blog.csdn.net/wangjian1204/article/details/68928656
参考教程:
- https://hub.docker.com/r/bitnami/tensorflow-serving
在这个教程中:https://github.com/tobegit3hub/tensorflow_template_application
在Ubuntu中命令应如下
python sparse_classifier.py train_file ./data/cancer/cancer_train.csv.tfrecords validate_file ./data/cancer/cancer_test.csv.tfrecords feature_size 4 label_size 3 enable_colored_log
python dense_classifier.py train_file ./data/cancer/cancer_train.csv.tfrecords validate_file ./data/cancer/cancer_test.csv.tfrecords feature_size 4 label_size 3 enable_colored_log
使用dense_classifier.py 即可产生checkpoint文件夹,可供后续在http_service中使用rest_server进行调用。但是具体调用浏览器端仍存在数组越界的问题
安装一个命令行下使用的文本浏览器便于测试使用
https://www.cnblogs.com/tsdxdx/p/7221132.html
Debian/Ubuntu: apt-get install w3m w3m-img
Centos: yum install w3m w3m-img
常见问题
不再支持export
- https://www.jianshu.com/p/91aae37f1da6