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)”

Fast Neural Style Transfer by PyTorch (Mac OS)

2021-Jan-31: The git repo has been upgraded from PyTorch-0.3.0 to PyTorch-1.7.0. with Python=3.8.3.


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 (2016).
  2. Instance Normalization: The Missing Ingredient for Fast Stylization (2017).


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

You can find all the source code and images (updated in 2021) at my GitHub: fast_neural_style .

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

Sharing the opinion about Generative Adversarial Networks (GAN)

Generative models, like Generative Adversarial Networks (GAN),  are a rapidly advancing area of research for computer science and machine intelligence nowadays. It’s hard to keep track of them all, not to mention the incredibly creative ways in which researchers have achieved and been working on.

The following figures demonstrate some results of the current works ( Images from https://blog.openai.com/generative-models/).

gen_models_anim_2
GAN learning to generate images (linear time)

gen_models_anim_1
VAE learning to generate images (log time)

I think it is necessary to understand the basic pros and cons of it, and it may be very helpful to your own research. I have not fully reviewed the theory and papers, but after skimmed a few papers, I got the impression that the training process of GAN models is very tricky as well as any neural networks model. Thus, there must be a huge improving space for people to make.

Thanks to the internet!  There are papers and codes everywhere and nobody will be left behind in these days unless he/she wants to.  So working hard and to be a better man (or women or anything good for humanity), cheers!

Here are some papers and blogs that summarized the literature very well.

Here is my old group slide meeting note and download links.

This slideshow requires JavaScript.

Extra Source:

Continue reading “Sharing the opinion about Generative Adversarial Networks (GAN)”