[Shell] The example shell script of automation way to build the software or library required

I don't like to preach at people. But, if someone has a task that has been done more than two times, then he/she should consider using an automation way to ease the burden. One of the solutions is by writing a shell script. Here is an example of building OpenCV 3.4.1 on TX2 using a shell script. The software or the target platform is not the key part. The most important part is to adopt this sort of shell script to become the one of your version.

[Tool] Visdom is a great visualization tool

I cannot use my word to describe Visdom because it is so amazingly awesome. Referencing the introduction from Facebook AI's official site,Visdom is a visualization tool that generates rich visualizations of live data to help researchers and developers stay on top of their scientific experiments that are run on remote servers. Visualizations in Visdom can be viewed in browsers and easily shared with others.

[TensorRT] How to use TensorRT to do inference with TensorFlow model ?

TensorRT is a high-performance deep learning inference optimizer and runtime that delivers low latency, high-throughput inference for deep learning applications. Here I am going to demonstrate that how to use TensorRT to do inference with TensorFlow model.


Install TensorRT
Please refer to this official website first:
https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html#installing
After downloading TensorRT 4.0 ( in my case ), we can install it.

$ dpkg -i nv-tensorrt-repo-ubuntu1604-cuda9.0-ga-trt4.0.1.6-20180612_1-1_amd64.deb
$ apt-get update
$ apt-get install tensorrt
$ apt-get install python-libnvinfer-dev
$ apt-get install uff-converter-tf

[TensorFlow] How to write op with gradient in python?

Recently for some reasons, I studied the Domain-Adversarial Training of Neural Networks and it can be downloaded from http://jmlr.org/papers/volume17/15-239/15-239.pdf

In this paper, there is the key point that we should implement "Gradient Reversal Layer" for Discriminator to use it to connect the feature extractor. I found the source to implement it by replacing Identity op's gradient function as follows:

[TensorFlow] How to generate the Memory Report from Grappler?

In the previous post, I introduce the way to generate cost and model report from Grappler.
https://danny270degree.blogspot.com/2019/01/tensorflow-how-to-generate-cost-and.html
In this post, I will continue to introduce the memory report which I think that is very useful. Please refer to my previous post to get the model code.

[TensorFlow] How to generate the Cost and Model Report from Grappler?

General speaking, Grappler in Tensorflow has several optimizers to do the specific area optimizations, such as for reducing the peak memory usage in GPU. So, I want to introduce some useful functions inside Grappler which are used for Simple Placer mechanism. And, these functions are also partially used in Grappler's optimizers.

[TensorFlow] My example of using SavedModelBuilder to do inference in TensorFlow

The purpose of this post is to show my example of SavedModelBuilder to do inference in TensorFlow. From my experiment, this approach can save a model with the signature that has input and output node name. And SavedModelBuilder can restore the graph based on the previously saved model pb file and the signature definition. Once, the restore is done, the inference task can be executed directly without GPU device needed if the training task is on GPU device.

[Reinforcement Learning] Get started to learn Actor Critic for reinforcement learning

Actor-Critic is basically combined with Policy Gradient (Actor)  and Function Approximation (Critic) based algorithm together. Actor is based on the probability given by policy to act and Critic judges the performance of Actor and gives the score. So, Actor will improve its probability given by policy based on Critic's judge and score. The following diagram is the concept: