[TensorFlow] How to print the timestamp of a node/operation of computation graph in run-time?

When some people first time tries to debug or print out information of some result from a node/operation in the computation graph in TensorFlow, they maybe confuse about how to do it. Fortunately, someone in Google gave a great explanation of the print function:
https://towardsdatascience.com/using-tf-print-in-tensorflow-aa26e1cff11e
After reading it, you should understand how tf.Print() function works and to use it.

[ONNX] Use ONNX_TF and nGraph_ONNX to do inference/prediction with ONNX model

Here I try to use the pre-trained model from ONNX model zoo, which the models are already converted from some deep learning framework. So I download the Resnet50 model from the following URL and untar it:

wget https://s3.amazonaws.com/download.onnx/models/opset_8/resnet50.tar.gz
tar -xzvf resnet50.tar.gz 

P.S: pre-trained ONNX models: https://github.com/onnx/models

Then, I can do the inference/prediction using this ONNX model in two ways:

[ONNX] Train in Tensorflow and export to ONNX (Part I)

From my point of view, ONNX is a model description spec and ONNX model needs Deep Learning framework or backend tool/compiler which supports it to run.
The advantage of ONNX as I know is about portable and exchangeable between DL frameworks.
Here I will use this tutorial to convert TensorFlow's model to ONNX model by myself.

https://github.com/onnx/tutorials/blob/master/tutorials/OnnxTensorflowExport.ipynb

[Fun] compress and composite dataset to one image file

If you want to compress and composite the images in a dataset, like MNIST or CIFAR-10, you probably can refer to my example.

People need to see it to believe it. Here you go:

[Confusion Matrix] How to calculate confusion matrix, precision and recall list from scratch

I directly give an example which is with 10 categories, such as CIFAR-10 and MNIST. It explains how to calculate the confusion matrix, precision and recall list from scratch in Python. My data is generated at random. You should replace by yours. Here it goes:

[Qt5] How to develop Qt5 GUI with TensorFlow C++ library?

Here I give a simple and complete example of how to develop Qt5 GUI with TensorFlow C++ library on Linux platform. Please check out my GitHub's repository as follow:
https://github.com/teyenliu/tf_inference_gui

[TensorFlow] How to implement LMDBDataset in tf.data API?

I have finished implementing the LMDBDataset in tf.data API.  It could be not the bug-free component, but at least it's my first time to try to implement C++ and Python function in TensorFlow. The API architecture looks like this:

[TensorFlow] How to build your C++ program or application with TensorFlow library using CMake

When you want to build your  C++ program or application using TensorFlow library or functions, you probably will encounter some header file missed issues or linking problems. Here is the step list that I have verified and it works well.

1. Prepare TensorFlow ( v1.10) and its third party's library

$ git clone --recursive https://github.com/tensorflow/tensorflow
$ cd tensorflow/contrib/makefile
$ ./build_all_linux.sh

2. Modify .tf_.tf_configure.bazelrc

$ cd tensorflow/
$ vim .tf_configure.bazelrc
  append this line in the bottom of the file
  ==>
  build --define=grpc_no_ares=true