How to install and use TensorFlow

This article mainly introduces how to install and use TensorFlow, has a certain reference value, interested friends can refer to, I hope you can learn a lot after reading this article, the following let the editor take you to understand it.

Installation

(1) installation package installation: pip install tensorflow==1.14-I https://pypi.douban.com/simple

Virtualenv-p / usr/bin/python2.7 venv-python2.7-tf1.14.0source. / venv-python2.7-tf1.14.0/bin/activatepip listpythonpip install numpy==1.16.5 opt-einsum==2.3.2 future-I https://pypi.douban.com/simplepip install tensorflow==1.14.0-I https://pypi.douban.com/simple

(2) Source code compilation and installation: https://tensorflow.google.cn/install/source

Install bazel-0.25.2# wget https://github.com/bazelbuild/bazel/releases/download/0.25.2/bazel-0.25.2-linux-x86_64# chmod uplix bazel-0.25.2-linux-x86_64# ln-s / path/bazel-0.25.2-linux-x86_64 / usr/bin/bazel# bazel versionBuild label: 0.25.2Install tensorflow-1.14.0# git clone https://github.com/tensorflow/tensorflow.git # cd tensorflow# git checkout v1.14.0#. / configure # / usr/bin/python3 Others are default# bazel build-- config=opt / / tensorflow/tools/pip_package:build_pip_package#. / bazel-bin/tensorflow/tools/pip_package/build_pip_package / tmp/tensorflow_pkg# mv / tmp/tensorflow_pkg/tensorflow-1.14.0-cp36-cp36m-linux_x86_64.whl. / # python3-m pip install. / tensorflow-1.14.0-cp36-cp36m-linux_x86_64.whlinstall tensorflow-1.14.0 with MKL&Patch# Git clone https://github.com/tensorflow/tensorflow.git# cd tensorflow/# git checkout v1.14. Patch-p0

< /path/tf-mkl.patch# ./configure # /usr/bin/python3, others are default# bazel build --config=mkl --config=opt //tensorflow/tools/pip_package:build_pip_package# ./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg# mv /tmp/tensorflow_pkg/tensorflow-1.14.0-cp36-cp36m-linux_x86_64.whl ./# python3 -m pip install ./tensorflow-1.14.0-cp36-cp36m-linux_x86_64.whl# python3 -m pip listtensorboard (1.14.0)tensorflow (1.14.0)tensorflow-estimator (1.14.0)使用模型优化 （1）查看 saved_model 模型的输入和输出 # bazel build tensorflow/python/tools:saved_model_cli# saved_model_cli show --dir detection/ --all或者# python3 /usr/local/lib/python3.6/site-packages/tensorflow/python/tools/saved_model_cli.py show --dir detection/ --allMetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:signature_def['serving_default']: The given SavedModel SignatureDef contains the following input(s):inputs['image'] tensor_info:dtype: DT_UINT8shape: (1, -1, -1, 3)name: image:0inputs['true_image_shape'] tensor_info:dtype: DT_INT32shape: (1, 3)name: true_image_shape:0 The given SavedModel SignatureDef contains the following output(s):outputs['detection_boxes'] tensor_info:dtype: DT_FLOATshape: (1, -1, 4)name: ChangeCoordToOriginalImage/stack:0outputs['detection_classes'] tensor_info:dtype: DT_INT32shape: (1, -1)name: add:0outputs['detection_keypoints'] tensor_info:dtype: DT_FLOATshape: (1, -1, 4, 2)name: TextKeypointPostProcess/Reshape_2:0outputs['detection_scores'] tensor_info:dtype: DT_FLOATshape: (1, -1)name: strided_slice_3:0outputs['num_detections'] tensor_info:dtype: DT_INT32shape: (1)name: BatchMultiClassNonMaxSuppression/stack_8:0 Method name is: tensorflow/serving/predict （2）将 tf 的 saved_model 保存成 frozen_model # bazel build tensorflow/python/tools:freeze_graph# freeze_graph --input_saved_model_dir detection/ --output_graph detection_frozen_model.pb --output_node_names ChangeCoordToOriginalImage/stack,add,TextKeypointPostProcess/Reshape_2,strided_slice_3,BatchMultiClassNonMaxSuppression/stack_8或者# python3 /usr/local/lib/python3.6/site-packages/tensorflow/python/tools/freeze_graph.py --input_saved_model_dir detection/ --output_graph detection_frozen_model.pb --output_node_names ChangeCoordToOriginalImage/stack,add,TextKeypointPostProcess/Reshape_2,strided_slice_3,BatchMultiClassNonMaxSuppression/stack_8 （3）将 frozen_model 通过优化得到 optimized_model # bazel build tensorflow/python/tools:optimize_for_inference // ouput: bazel-bin/tensorflow/python/tools/optimize_for_inference# optimize_for_inference --input detection_frozen_model.pb --output detection_optimized_model.pb --input_names image,true_image_shape --output_names ChangeCoordToOriginalImage/stack,add,TextKeypointPostProcess/Reshape_2,strided_slice_3,BatchMultiClassNonMaxSuppression/stack_8 --frozen_graph true --placeholder_type_enum 4,3,1,3,1,1,3或者# python3 /usr/local/lib/python3.6/site-packages/tensorflow/python/tools/optimize_for_inference.py --input detection_frozen_model.pb --output detection_optimized_model.pb --input_names image,true_image_shape --output_names ChangeCoordToOriginalImage/stack,add,TextKeypointPostProcess/Reshape_2,strided_slice_3,BatchMultiClassNonMaxSuppression/stack_8 --frozen_graph true --placeholder_type_enum 4,3,1,3,1,1,3 其中 placeholder_type_enum 详情如下: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/types.proto （4）将 pb 模型输出成 TensorFlow 的可视化 graph # bazel build tensorflow/python/tools:import_pb_to_tensorboard# import_pb_to_tensorboard --model_dir ./recognition_frozen_model.pb --log_dir ./recognition_log或者# python3 /usr/local/lib/python3.6/site-packages/tensorflow/python/tools/import_pb_to_tensorboard.py --model_dir ./recognition_frozen_model.pb --log_dir ./recognition_frozen_model.graph# nohup tensorboard --logdir=./recognition_frozen_model.graph --port=6006 2>

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Visualization tool TensorBoard usage: https://blog.csdn.net/gg_18826075157/article/details/78440766

(5) official manual for quantifying, solidifying and optimizing pb models: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms intel quantification manual: https://github.com/IntelAI/tools/tree/master/tensorflow_quantization

# bazel build tensorflow/tools/graph_transforms:transform_graph# bazel-bin/tensorflow/tools/graph_transforms/transform_graph-- in_graph= ". / detection_frozen_model.pb"-- out_graph= ". / detection_transformed_model.pb"-- inputs= "image,true_image_shape"-- outputs= "ChangeCoordToOriginalImage/stack,add,TextKeypointPostProcess/Reshape_2,strided_slice_3,BatchMultiClassNonMaxSuppression/stack_8"-- transforms=' add_default_attributes strip_unused_nodes () remove_nodes (op=Identity Op=CheckNumerics) fold_constants (ignore_errors=true) fold_batch_norms fold_old_batch_norms quantize_weights'

PS: model optimization refer: https://blog.csdn.net/qq_14845119/article/details/78846372 Model quantization: https://www.jianshu.com/p/d2637646cda1

Log and vlog output configuration for tf

There are two flags, similarly named, but with somewhat different semantics: TF_CPP_MIN_LOG_LEVEL-which has 3 or 4 basic levels-low numbers = more messages.

0 outputs Information, Warning, Error, and Fatals (default) 1 outputs Warning, and above 2 outputs Errors and above. Etc... I didn't check edge cases

TF_CPP_MIN_VLOG_LEVEL-which causes very very many extra Information errors-really for debugging only-low numbers = less messages.

3 Outputs lots and lots of stuff 2 Outputs less 1 Outputs even less 0 Outputs nothing extra (default)

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