decorator
soruce
基本的装饰器
概括的讲，装饰器的作用就是为已经存在的函数或对象添加额外的功能。
def debug(func):
def wrapper(*args, **kwargs): # 指定宇宙无敌参数
print "[DEBUG]: enter {}()".format(func.__name__)
print 'Prepare and say...',
return func(*args, **kwargs)
return wrapper # 返回
@debug
def say(something):
print "hello {}!".format(something)
带参数的装饰...
import
soruce
import 整个模块(需要使用前缀)
# 导入sys整个模块
import sys
# 使用sys模块名作为前缀来访问模块中的成员
print(sys.argv[0])
# 导入sys、os两个模块
import sys,os
设置别名(需要使用前缀)
# 导入sys整个模块，并指定别名为s
import sys as s
# 使用s模块别名作为前缀来访问模块中的成员
print(s.argv[0])
# 导入sys、os两个模块，并为sys指定别名s，为os指定别名o
import sys as s,os as o
from xx import x1（无须使用任何前缀）
# 导入sys模块的argv成员
from sys import argv
# 使用...
pandas
soruce
import
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
read file
train = pd.read_csv("/kaggle/input/store-sales-time-series-forecasting/train.csv")
test = pd.read_csv("/kaggle/...
fastai notebook - NLP
soruce
Tokenization
Word-based:: Split a sentence on spaces, as well as applying language-specific rules to try to separate parts of meaning even when there are no spaces (such as turning “don’t” into “do n’t”). Generally, punctuation marks are also split into separate tokens.
Subword based:: Split words into smaller parts, based on the most co...
fastai notebook - midlevel data
soruce
Transform
# When we studied tokenization and numericalization in the last chapter, we started by grabbing a bunch of texts:
files = get_text_files(path, folders = ['train', 'test'])
txts = L(o.open().read() for o in files[:2000])
# We then showed how to tokenize them with a Tokenizer:
tok = Tokenizer.from_folder(path)
tok.setup(txts)
to...
fastai notebook - write a custom model
soruce
write a custom model
from fastai.vision.all import *
path = untar_data(URLs.PETS)
files = get_image_files(path/"images")
class SiameseImage(fastuple):
def show(self, ctx=None, **kwargs):
img1,img2,same_breed = self
if not isinstance(img1, Tensor):
if img2.size != img1.size: img2 = img2.resize(img1.size)
...
fastai notebook - vision
soruce
Single-label classification
Cats vs Dogs
from fastai.vision.all import *
path = untar_data(URLs.PETS)
path.ls()
files = get_image_files(path/"images")
len(files)
def label_func(f): return f[0].isupper()
dls = ImageDataLoaders.from_name_func(path, files, label_func, item_tfms=Resize(224))
dls.show_batch()
learn = vision_learner(dls,...
fastai notebook - transformer
soruce
Text transfer learning
!pip install -Uq transformers
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
from fastai.text.all import *
pretrained_weights = 'gpt2'
tokenizer = GPT2TokenizerFast.from_pretrained(pretrained_weights)
model = GPT2LMHeadModel.from_pretrained(pretrained_weights)
ids = tokenizer.encode('This is an exam...
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