Install PyTorch on Window 10

Great news! PyTorch now is supporting Windows!

If you have a PC with suitable Nvidia graphics card and installed CUDA 9.0 and Anaconda, type the following commands;

conda install pytorch cuda90 -c pytorch
pip3 install torchvision

It is about 500 MB, so be patient!

Screen Shot 2018-04-29 at 21.44.50

Underline is the old post.

PyTorch is a deep learning framework that puts Python first. Currently, it only supports MacOS or Linux.Capture1

But, can we use it on WIN10 without changing the system/computer?

Yes,  we can. 

Continue reading “Install PyTorch on Window 10”

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.

You could download my compiled Apk file here and install on your android device (>Android 7.0) (22-May-2018).

Update: 25-May-2018, fixed the bug of object tracking function in TF Detect. PS: While running the activities, pressing the volume keys on your device will
toggle debug visualizations on/off, rendering additional info to the screen that
may be useful for development purposes.

 

Feel free to let me know if there are any bugs 🙂

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”

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”

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)”

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.

Capture

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?”