- Focus on user intent and audience.
- Use every-day words, and keep sentences short.
- Use consistent sentence construction, wording, and capitalization.
- Make your article easy to scan.
- Show empathy.
We aspire to follow these principles when we write technical content for TensorFlow docs. We might not always get there, but we keep trying.
With a few exceptions, TensorFlow uses the standard Markdown rules. This section explains the primary differences between standard Markdown rules and the Markdown rules that TensorFlow documentation uses.
Write about code
Inline mentions of code
`backticks` around these things when they're used in
- Argument names:
- Returned tensor names:
- Data types:
- Other op names reference in text:
- Class names:
- File name:
- Math expressions or conditions:
`-1-input.dims() <= dim <= input.dims()`
Use three backticks before and after code block. In the opening backtick line, specify the language.
```python # some python code here ```
Links in Markdown
Links between files in this repository
For links between files in this repository, use relative links:
Eager Basics. These
links will work on both GitHub and http://tensorflow.org.
Links to API documentation
API links are converted when the site is published.
To link to the Python API, enclose the full symbol path in backticks:
For the C++ API, use the namespace path:
tensorflow::Tensorto produce tensorflow::Tensor
For external links, including files on http://tensorflow.org that are not in the
tensorflow/docs repository, just use regular Markdown links with the full URI.
To link to source code, use a link starting with https://www.github.com/tensorflow/tensorflow/blob/master/, followed by the file name starting at the GitHub root.
This URI naming scheme ensures that http://tensorflow.org can forward the link to the branch of the code corresponding to the version of the documentation you're viewing.
Do not include URI query parameters in the link.
If you are going to write or edit substantial portions of the narrative documentation, please read the Google style guide.
Principles of good style
Check the spelling and grammar in your contributions. Most editors include a spell checker or have an available spell-checking plugin. You can also paste your text into a Google Doc or other document software for a more robust spelling and grammar check.
Use a casual and friendly voice. Write TensorFlow documentation like a conversation — as if you're talking to another person one-on-one. Use a supportive tone in the article.
Note: Being less formal does not mean being less technical. Simplify your prose, not the technical content.
Avoid disclaimers, opinions, and value judgements. Words like "easily", "just", and "simple" are loaded with assumptions. Something might seem easy to you, but be difficult for another person. Try to avoid these whenever possible.
Use simple, to the point sentences without complicated jargon. Compound sentences, chains of clauses, and location-specific idioms can make text hard to understand and translate. If a sentence can be split in two, it probably should. Avoid semicolons. Use bullet lists when appropriate.
Provide context. Don't use abbreviations without explaining them. Don't mention non-TensorFlow projects without linking to them. Explain why the code is written the way it is.
# ⇒ instead of a single equal sign when you want to show what an op
# 'input' is a tensor of shape [2, 3, 5](tf.expand_dims(input, 0) ) # ⇒ [1, 2, 3, 5]
When you're talking about a tensor in general, don't capitalize the word
tensor. When you're talking about the specific object that's provided to or
returned from an op, then you should capitalize the word Tensor and add
backticks around it because you're talking about a
Don't use the word Tensors (plural) to describe multiple
unless you really are talking about a
Tensors object. Instead, say "a list (or
Use the term dimensions to refer to the shape of a tensor. If you need to be specific about the size, use these conventions:
- Refer to a scalar as a 0-D tensor.
- Refer to a vector as a 1-D tensor.
- Refer to a matrix as a 2-D tensor.
- Refer to a tensor with 3 or more dimensions as a 3-D tensor or n-D tensor. Use the word rank only if it's unambiguous in that context. Never use the word order to describe the size of a tensor.
Use the word shape to detail the dimensions of a tensor, and show the shape in square brackets with backticks. For example:
If `input` is a 3-D tensor with shape `[3, 4, 3]`, this operation returns a 3-D tensor with shape `[6, 8, 6]`.