Dimensionality Reduction and Scattered Data Visualization with MNIST

We live in 3D world, and we can only view scattered data in 1D, 2D, or 3D. Yet, we deal with data that have very large dimension. Consider MNIST dataset, which is considered to be a toy example in deep learning field, consists of 28 X 28 gray images; that is 784 dimensions.

How would this MNIST data look like in 2D or 3D after dimensionality reduction? Let's figure it out! I am going to write the code in Pytorch. I have to say, Pytorch is so much better than other deep learning libraries, such as Theano or Tensorflow. Of course, it is just my personal opinion, so let's not get into this argument here.

What I want to do is to take Pytorch's MNIST example found here, and make some modifications to reduce the data dimension to 2D and plot scattered data. This will be a very good example that shows how to do all the following in Pytorch:
1. Create a custom network
2. Create a custom layer
3. Transfer learning from an existing model
4. Save and load a model

Here is the plot I get from running the code below.

This code is tested on Pytorch 0.3.1.

Three Minutes Daily Vim Tip: Highlight Current Cursor Line

In Vim, it is often difficult to locate the current cursor, especially when you jump around. Here is a tip to easily locate the cursor.

Simply add the following line to ~/.vimrc
set cursorline

That's it!

Python IDE with YouCompleteMe

OK, I have tried YouCompleteMe, but then I stopped using it after some time. Well, I am going to give it a try once more. The reason is that I am usually developing on a server through ssh. I could launch GUI-based IDE, such as PyCharm through X-forwarding, but the response is too slow. I realized that editing with Vim on a remote server is probably the best option here.

In any case, I explained how to install YouCompleteMe plugin in my previous post. Here, I will talk about a few more useful tips.

One of the must-have feature in IDE is perhaps GoTo feature, where one can go to the definition of certain method of variable. YouCompleteMe also provides this feature. Formally, this is mapped with
:YcmCompleter GoTo

We can create a shortcut. Add the following in the ~/.vimrc file
nnoremap <C-P> :YcmCompleter GoTo<CR>

In the source file that YouCompleteMe understands, you can simply move the cursor to the symbol and press <Control> + p key to go to the definition. To go back, you can press <Control> + o key.

Also, add the following to ~/.vimrc file
let g:ycm_python_binary_path = 'python'

This will load the default python first found on PATH environment.

Happy coding!

Android Camera2 Bare Minimum Code Sample

Google has replaced camera API with camera2 API a few years ago. I guess it is a good thing that Google is working hard to improve Android, personally I think camera2 API is just so difficult. Compared to the deprecated camera API, it requires so much more code.

I am trying to understand this new camera2 API, and it is just not easy. I looked at Google's Camera2Basic sample code, it is still daunting for Android beginners like myself. Well, here is my attempt to trim down this fat sample code into the bare minimum so that it is easier to understand the fundamentals of the camera2 API.

All it does is to connect to a camera and display its preview to the screen. No capture, no manual focus, no error checks, etc. I am not happy that even this bare-bone app requires so many lines. Anyways, I will add more functionalities in the upcoming post.

OpenCV on PyCharm

PyCharm is such a nice IDE for developing in Python. I really love it. However, with OpenCV module, I realized that PyCharm didn't recognize cv2 module.

After some search, here is a very simple solution! Credit goes to here.
When you import cv2, simply copy paste the whole thing below:

import cv2

# this is just to unconfuse pycharm
try:
    from cv2 import cv2
except ImportError:
    pass

That's it!

Redirecting Outputs from Parallel Subprocess Runs

Say you are call some executable from python in parallel. You don't want to clog the stdout, so you want to save each stdout from executable as a file. Below is what you can do.