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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
  • Quotes
    • Use double quotes
  • Symbol Descriptions
    • timestep t（not t timestep）
  • 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 (``)
    • backward function
      • 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 dataset
      • data point (preferred)/data instance/training example/testing example/example of the dataset
    • Distinguish：
      • hyperparameter vs parameter
      • minibatch stochastic gradient descent vs stochastic gradient descent
    • List
  • Use numerals when they are explaining or part of code or math.
  • Acceptable abbreviations
    • MLP, CNN, RNN, GRU, LSTM, model names (e.g., ELMo, GPT, BERT)
    • We spell out full names in most cases to be clear (e.g., NLP -> natural language processing)

Math

  • Be consistent in math notation
  • Place punctuations within equations if necessary
    • e.g., comma and period
  • Assignment symbol
    • \leftarrow
  • Use mathematical numerals only when they are part of math: "$x$ is either $1$ or $-1$", "the greatest common divisor of $12$ and $18$ is $6$".

Figure

  • Software
    • Use OmniGraffle to make figures.
      • Export pdf (infinite canvas) in 100%, then use pdf2svg to convert to svg
        • ls *.pdf | 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 (for text), STIXGeneral (for math), 9pt（subscripts/superscripts：6pt）
      • Do not italicize numbers or parentheses in subscripts or superscripts
    • Color：
      • Blue as background (text is black)
        • (Try to avoid) Extra Dark：3FA3FD
        • Dark：66BFFF
        • Light：B2D9FF
        • (Try to avoid) Extra Light: CFF4FF
  • Be careful about copyright
  • matplotlib

Code

  • Each line must have <=78 characters (limited by page width)
  • 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
    • No period at the end of comments.
    • For clarity, surround variable names with accents, e.g., # shape of X
  • 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
    • Use f-strings. To break a long f-string into multi-lines, just use one f-string per line.
  • Other items
    • nd.f(x) → x.nd
    • random_normal → random.normal
    • multiple imports
    • .1 → 1.0
      1. → 1.0
    • remove namescope

References

  • Refer to d2lbook on how to add references for figure, table and equations.

QR Code

Citations

  1. Run pip install git+https://github.com/d2l-ai/d2l-book
  2. Use bibtool to generate consistent keys for bibtex entries. Install it by brew install bib-tool
  3. Add an bibtex entry to d2l.bib on the root directory. Say the original entry is
@article{wood2011sequence,
  title={The sequence memoizer},
  author={Wood, Frank and Gasthaus, Jan and Archambeau, C{\'e}dric and James, Lancelot and Teh, Yee Whye},
  journal={Communications of the ACM},
  volume={54},
  number={2},
  pages={91--98},
  year={2011},
  publisher={ACM}
}
  1. Run bibtool -s -f "%3n(author).%d(year)" d2l.bib -o d2l.bib. Now the added entry will have consistent keys. And as a side-effect, it'll appear in alphabetically sorted order relative to all other papers in the file:
@Article{	  Wood.Gasthaus.Archambeau.ea.2011,
  title		= {The sequence memoizer},
  author	= {Wood, Frank and Gasthaus, Jan and Archambeau, C{\'e}dric
		  and James, Lancelot and Teh, Yee Whye},
  journal	= {Communications of the ACM},
  volume	= {54},
  number	= {2},
  pages		= {91--98},
  year		= {2011},
  publisher	= {ACM}
}
  1. In the text, use the following to cite the added paper:
:cite:`Wood.Gasthaus.Archambeau.ea.2011`

Edit and Test Code in One Framework

  1. Say we want to edit and test MXNet code in xx.md, run d2lbook activate default xx.md. Then code of other frameworks is deactivated in xx.md.
  2. Open xx.md using Jupyter notebook, edit code and use "Kernel -> Restart & Run All" to test code.
  3. Run d2lbook activate all xx.md to re-activate code of all the frameworks. Then git push.

Likewise, d2lbook activate pytorch/tensorflow xx.md will only activate PyTorch/TensorFlow code in xx.md.