After Inoreader change the free plan, which limit the max subscription to 150, I begin to find an alternative. Finally, I found Tiny Tiny RSS. It has a nice website and has the fever API Plugin which was supported by most of the RSS reader app, so you can read RSS on all of you devices.

This post will tell you how to deploy it on your server.

Prerequisite

You need to install Docker and Docker Compose before using docker-compose.yml

Using the right Emacs Version

I failed to preview LaTeX with emacs-plus. If you have installed d12frosted/emacs-plus, uninstall it and use emacs-mac.

brew tap railwaycat/emacsmacport
brew install emacs-mac

If you like the fancy spacemacs icon, install it with cask: brew cask install emacs-mac-spacemacs-icon

Install Tex

• Download and install BasicTeX.pkg here.
• Add /Library/TeX/texbin to PATH.
• Install dvisvgm by sudo tlmgr update --self && sudo tlmgr install dvisvgm collection-fontsrecommended

Emacs settings

• Add TeX related bin to path: (setenv "PATH" (concat (getenv "PATH") ":/Library/TeX/texbin"))
• Tell Org Mode to create svg images: (setq org-latex-create-formula-image-program 'dvisvgm)

Now you can see the rendered LaTeX equation by calling org-preview-latex-fragment or using shortcut ,Tx.

PyTorch provide a simple DQN implementation to solve the cartpole game. However, the code is incorrect, it diverges after training (It has been discussed here).

The official code’s training data is below, it’s high score is about 50 and finally diverges.

There are many reason that lead to divergence.

First it use the difference of two frame as input in the tutorial, not only it loss the cart’s absolute information(This information is useful, as game will terminate if cart moves too far from centre), but also confused the agent when difference is the same but the state is varied.

Recently, I found a really good example code for Python circular import, and I’d like to record it here.

Here is the code:

# X.py
def X1():
    return "x1"

from Y import Y2

def X2():
    return "x2"

# Y.py
def Y1():
    return "y1"

from X import X1

def Y2():
    return "y2"

Guess what will happen if you run python X.py and python Y.py?

Overview

1. CPython allocation memory to save dictionary, the initial table size is 8, entries are saved as <hash,key,value> in each slot(The slot content changed after Python 3.6).
2. When a new key is added, python use i = hash(key) & mask where mask=table_size-1 to calculate which slot it should be placed. If the slot is occupied, CPython using a probing algorithm to find the empty slot to store new item.
3. When 2/3 of the table is full, the table will be resized.
4. When getting item from dictionary, both hash and key must be equal.

Resizing

When elements size is below 50000, the table size will increase by a factor of 4 based on used slots. Otherwise, it will increase by a factor of 2. The dictionary size is always \(2^{n}\).

PyTorch is a really powerful framework to build the machine learning models. Although some features is missing when compared with TensorFlow (For example, the early stop function, History to draw plot), its code style is more intuitive.

Torchtext is a NLP package which is also made by pytorch team. It provide a way to read text, processing and iterate the texts.

Google Colab is a Jupyter notebook environment host by Google, you can use free GPU and TPU to run your modal.