A Taste of TensorFlow on My Android Phone (II)

This post is a follow-up to my last post: A Taste of TensorFlow on My Android Phone.

TensorFlow Mobile

In the last post, I just tried the TensorFlow for object classification (TF Classify). This time I installed all four demos of the TensorFlow Mobile for Android according to this tutorial: TensorFlow Lite Demo for Android. They are awesome 😛

Continue reading “A Taste of TensorFlow on My Android Phone (II)”


A Taste of TensorFlow on My Android Phone

If you like Google’s open-source machine learning framework, TensorFlow, do not miss this “TensorFlow For Poets“.  I went through the tutorial this afternoon and found it is super Awesome. See the photos below, I first tested it on the coffee mug from my Intern company, Aurecon Group. I used the virtual device, Nexus 5X, from Android Studio 3.0.1 on MacBook Air 11′  (Do not do this unless you have enough SSD 😛 ).

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Then, I successfully installed the compiled app (TF_Classify) on my XIAO MI – 4C (MIUI 9.0 – Android 7.0) and tested it on my coffee mug at home.
You can download and install it on your own Android devices from the following link:

Continue reading “A Taste of TensorFlow on My Android Phone”


Fast Neural Style Transfer by PyTorch (Mac OS)

Continue my last post Image Style Transfer Using ConvNets by TensorFlow (Windows), this article will introduce the Fast Neural Style Transfer by PyTorch on MacOS.

The original program is written in Python, and uses [PyTorch], [SciPy]. A GPU is not necessary but can provide a significant speedup especially for training a new model. Regular sized images can be styled on a laptop or desktop using saved models.

More details about the algorithm could be found in the following papers:

  1. Perceptual Losses for Real-Time Style Transfer and Super-Resolution ;
  2. Instance Normalization: The Missing Ingredient for Fast Stylization.

If you could not download the papers, here are the Papers.

Continue reading “Fast Neural Style Transfer by PyTorch (Mac OS)”


Image Style Transfer Using ConvNets by TensorFlow (Windows)

This post is talking about how to setup a basic developing environment of Google’s TensorFlow on Windows 10 and apply the awesome application called “Image style transfer”, which is using the convolutional neural networks to create artistic images based on the content image and style image provided by the users.

The early research paper is “A Neural Algorithm of Artistic Style” by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge on arXiv. You could also try their website application on DeepArt where there are a lot of amazing images uploaded by the people all over the world. DeepArt has apps on Google Play and App Store, but I suggest you use a much faster app Prisma, which is as awesome as DeepArt! [More to read: Why-does-the-Prisma-app-run-so-fast].

Moreover, I strongly recommend the formal paper Image Style Transfer Using Convolutional Neural Networks by the same authors published on CVPR-2016. This paper gives more details about how this method works. In short, the following two figures cover the idea behind the magic.

Continue reading “Image Style Transfer Using ConvNets by TensorFlow (Windows)”

Starting My First Intern at Melbourne Australia Tomorrow

Dear All,

I am using the song above to thank you all for your help and support in the past. You know that I have spent the last three years (2014-2017) in pursuing my Ph.D. degree in Computer Science and got a plan to be graduated in 2018.

Continue reading “Starting My First Intern at Melbourne Australia Tomorrow”

Building ConvNets on MNIST dataset by TensorFlow with the new WIN10 GPU Monitor

A few days ago, I updated my  Windows 10 to version 1709 and found out that Microsoft added the GPU monitor in the Task Manager which I thought is awesome for ML developers and researchers.

Here is a screen capture of the official MNIST codes running Tensorflow-GPU on my Desktop.  It is clear to see that the GTX 960 uses about 3.5GB memory out of 4.0GB to train the ConvNets, which is much faster than the CPU computing.


You can find more models from the TensorFlow Models. This repository contains a number of different models implemented in TensorFlow.


What do I think about PyTorch and TensorFlow?

As we all know, the TensorFlow is very powerful and mature deep learning library with strong visualization capabilities and several options to use for high-level model development. PyTorch is still young framework which is getting momentum fast.

I strongly suggest CS and IT researchers/engineers learn both of them.

Tensorflow will be a good option if you are developing models for production or on mobile platforms, maybe in the future for large-scale distributed model training. Because it has good community support and comprehensive documentation, it is easier to find answers and get helps online.

Well, PyTorch is a good fit if you are doing research or your production are not very demanding.

Personly, I think Pytorch has better development and debugging experience.

Continue reading “What do I think about PyTorch and TensorFlow?”

TensorFlow Neural Network Playground in Matlab

The amazing website http://playground.tensorflow.org  can help you open a Neural Network on your Web Browser. The GUI is mind blowing, and you could download all the codes to study or to build your own project.

Now,   The Good News!  Amro and Ray Phan have created the MATLAB version of the NN playground, it looks just like the GUI of the Tensorflow version. However, it is not tensorflow-based, it is built on the Neural Networks Toolbox of Matlab (>R2009b). The authors said they are inspired by the [TensorFlow Neural Networks Playground] interface readily available online, so they created a MATLAB implementation of the same Neural Network interface for using Artificial Neural Networks for regression and classification of highly nonlinear data. Continue reading “TensorFlow Neural Network Playground in Matlab”