fastai Abbreviation Guide

As mentioned in the fastai style, we name symbols following the Huffman Coding principle, which basically means

Commonly used and generic concepts should be named shorter. You shouldn’t waste short sequences on less common concepts.

fastai also follows the life-cycle naming principle: > The shorter life a symbol, the shorter name it should have.

which means: - Aggressive Abbreviations are used in list comprehensions, lambda functions, local helper functions. - Aggressive Abbreviations are sometimes used for local temporary variables inside a function. - Common Abbreviations are used most elsewhere, especially for function arguments, function names, and variables - Light or No Abbreviations are used for module names, class names or constructor methods, since they basically live forever. However, when a class or module is very popular, we could consider using abbreviations to shorten its name.

This document lists abbreviations of common concepts that are consistently used across the whole fastai project. For naming of domain-specific concepts, you should check their corresponding module documentations. Concepts are grouped and listed by semantic order. Note that there are always exceptions, especially when we try to comply with the naming convention in a library.

	Concept	Abbr.	Combination Examples
Suffix
	multiple of something (plural)	s	xs, ys, tfms, args, ss
	internal property or method	_	data_, V_()
Prefix
	check if satisfied	is_	is_reg, is_multi, is_single, is_test, is_correct
	On/off a feature	use_	use_bn
	Number of something (plural)	n_	n_embs, n_factors, n_users, n_items
	count something	num_	num_features(), num_gpus()
	convert to something	to_	to_gpu(), to_cpu(), to_np()
Infix
	Convert between concepts	2	name2idx(), label2idx(), seq2seq
Aggressive
	function	f
	torch input	x
	key, value	k,v	for k,v in d.items()
	other pairs of short scope	p,q	listify(p,q) (same as python’s stdlib)
	index	i
	generic object argument	o	[o for o in list], lambda o: o
	variable	v	V(), VV()
	tensor	t	T()
	array	a	A()
	use first letter		weight -> w, model -> m
Generic
	function	fn	opt_fn, init_fn, reg_fn
	process	proc	proc_col
	transform	tfm	tfm_y, TfmType
	evaluate	eval	eval()
	argument	arg
	input	x
	input / output	io
	object	obj
	string	s
	class	cl	cl, classes
	source	src
	destination	dst
	directory	dir
	percentage	p
	ratio, proportion of something	r
	count	cnt
	configuration	cfg
	random	rand
	utility	util
	filename	fname
	threshold	thresh
Data
	number of elements	n
	length	len
	size	sz
	array	arr	label_arr
	dictionary	dict
	sequence	seq
	dataset	ds	train_ds
	dataloader	dl	train_dl
	dataframe	df	train_df
	train	train	train_ds, train_dl, train_x, train_y
	validation	valid	valid_ds, valid_dl, valid_x, valid_y
	test	test	test_ds, test_dl
	number of classes	c
	batch	b
	batch’s x parts	xb
	batch’s y parts	yb
	batch size	bs
	multiple targets	multi	is_multi
	regression	reg	is_reg
	iterate, iterator	iter	train_iter, valid_iter
	torch input	x
	target	y
	dependent var tensor	dep
	independent var tensor	indep
	prediction	pred
	output	out
	column	col	dep_col
	continuous var	cont	conts
	category var	cat	cat, cats
	continuous columns	cont_cols
	category columns	cat_cols
	dependent column	dep_col
	index	idx
	identity	id
	first element	head
	last element	tail
	unique	uniq
	residual	res
	label	lbl	(not common)
	augment	aug
	padding	pad
	probability	pr
	image	img
	rectangle	rect
	color	colr
	anchor box	anc
	bounding box	bb
Modeling
	initialize	init
	language model	lm
	recurrent neural network	rnn
	convolutional neural network	convnet
	model data	md
	linear	lin
	embedding	emb
	batch norm	bn
	dropout	drop
	fully connected	fc
	convolution	conv
	hidden	hid
	optimizer (e.g. Adam)	opt
	layer group learning rate optimizer	layer_opt
	criteria	crit
	weight decay	wd
	momentum	mom
	cross validation	cv
	learning rate	lr
	schedule	sched
	cycle length	cl
	multiplier	mult
	activation	actn
CV	computer vision
	figure	fig
	image	im
	transform image using opencv	_cv	zoom_cv(), rotate_cv(), stretch_cv()
NLP	natural language processing (nlp)
	token	tok
	sequence length	sl
	back propagation through time	bptt