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STYLE_GUIDE.md

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STYLE GUIDE

In General

  • Be clear, engaging, pragmatic, and consistent

Text

  • Chapters and Sections
    • Provide an overview at the beginning of each chapter
    • Be consistent in the structure of each section
      • Summary
      • Exercises
      • Scan the QR Code to Access Discussions
      • References (if any)
  • Quotes
    • Use double quotes
  • Symbol Descriptions
    • time step t(not t time step)
  • Tools, Class, and Functions
    • Gluon, MXNet, NumPy, spaCy, NDArray, Symbol, Block, HybridBlock, ResNet-18, Fashion-MNIST, matplotlib
      • Consider these as words without accents (``)
    • Sequential class/instance, HybridSequential class/instance
      • Without accents (``)
    • backwardfunction
      • not backward() function
    • for loop
  • Terminologies
    • Consistently use
      • function (not method)
      • instance (not object)
      • weight, bias, label
      • model training, model prediction (model inference)
      • training/testing/validation data set
    • Distinguish:
      • hyperparameter vs parameter
      • mini-batch stochastic gradient descent vs stochastic gradient descent
    • List

Math

Figure

  • Software
    • Use OmniGraffle to make figures.
      • Export pdf (infinite canvas) in 100%, then use pdf2svg to convert to svg
        • ls | while read f; do pdf2svg $f ${f%.pdf}.svg; done
      • Do not export svg directly from Omnigraffle (font size may slightly change)
  • Style
    • Size:
      • Horizontal:<= 400 pixels (limited by page width)
      • Vertical:<= 200 pixels (exceptions may be made)
    • Thickness:
      • StickArrow
      • 1pt
      • arrow head size: 50%
    • Font:
      • Arial, 9pt(subscripts:7pt)
    • Color:
      • Blue as background (text is black)
        • Dark:66BFFF
        • Light:B2D9FF
  • Be careful about copyright
  • Reference
    • e.g., Figure 7.1 (manually)
  • matplotlib

Code

  • Each line must have <=80 characters (limited by page width)
  • Use utils.py to encapsulate classes/functions that are repetitively used
    • Give full implementation when it is used for the first time
  • Python
  • To save space, put several assignments on the same line
    • e.g, num_epochs, lr = 5, 0.1
  • Be consistent in variable names
    • num_epochs
      • number of epochs
    • num_hiddens
      • number of hidden units
    • num_inputs
      • number of inputs
    • num_outputs
      • number of outputs
    • net
      • model
    • lr
      • learning rate
    • acc
      • accuracy
    • During iterations
      • features:X
      • labels:y, y_hat or Y, Y_hat
      • for X, y in data_iter
    • Data sets:
      • features:features or images
      • labels:labels
      • DataLoader instance:train_iter, test_iter, data_iter
  • Comments
    • Add period at the end of comments.
  • imports
    • import alphabetically
    • from mxnet.gluon import data as gdata, loss as gloss, nn, utils as gutils
  • Print outputs
    • epoch, lr, loss, train acc, time
    • Around 5 lines
  • Print variables
    • if possible use x, y instead of print('x:', x, 'y:', y) at the end of the code block
  • String
    • Use single quotes
  • Other items
    • nd.f(x)x.nd
    • random_normalrandom.normal
    • multiple imports
    • .11.0
      1. 1.0
    • remove namescope

Hyperlinks

QR Code

References

  • Append references at the end of each section
    • Google Scholar: APA format
    • All references are to be consolidated by the Press