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
multiple of something (plural) s xs, ys, tfms, args, ss
internal property or method _ data_, V_()
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()
Convert between concepts 2 name2idx(), label2idx(), seq2seq
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
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
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
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