投稿時間:2022-03-06 22:17:52 RSSフィード2022-03-06 22:00 分まとめ(21件)

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python	Pythonタグが付けられた新着投稿 - Qiita	venvを使ってみる【Python】	https://qiita.com/yyyyy__78/items/f0c14c64b4d31e1411fc	pyenvなどで仮想環境作成後にPythonのバージョンを切り替えたいときはこちらの記事が参考になります仮想環境を作成するpythonmvenvsampleenvプロジェクトのフォルダに「sampleenv」ディレクトリが作成されます。	2022-03-06 21:53:34
python	Pythonタグが付けられた新着投稿 - Qiita	"Line Notify"を利用してPythonでLineに通知を送る	https://qiita.com/hk512/items/cf780de8cdbc323e0820	quotLineNotifyquotを利用してPythonでLineに通知を送るはじめに仮想通貨自動取引botの取引状況をスマホに通知する際に利用させて頂いてます。	2022-03-06 21:43:53
python	Pythonタグが付けられた新着投稿 - Qiita	突然jupyterが使えなくなったので解決方法メモ	https://qiita.com/shuki/items/3b873cb019195afdc601	これで一応インストールはできたのだがjupyterコマンドjupyternotebookなどが使えなかった。	2022-03-06 21:04:10
js	JavaScriptタグが付けられた新着投稿 - Qiita	JavaScript学習 - 値の型・計算関連	https://qiita.com/Kiku-cha/items/402699c97359d8434552	もし、文字列で足し算をさせたいというのであれば、parseIntという命令を使って、文字列を進数の整数値に変換させることで計算できるようになります。	2022-03-06 21:44:27
js	JavaScriptタグが付けられた新着投稿 - Qiita	Vue.js　超入門	https://qiita.com/kakuteki/items/4bab38e9e9a36c935cad	タスクの文字データとチェックボックスが選択されているかどうかを配列に入れます。	2022-03-06 21:14:55
技術ブログ	Developers.IO	無人島でサバイバルしてみた	https://dev.classmethod.jp/articles/how-to-survive-in-uninhabited-island/	twitter	2022-03-06 12:46:05
海外TECH	Ars Technica	The war in Ukraine is keeping Chinese social media censors busy	https://arstechnica.com/?p=1838387	russia	2022-03-06 12:01:32
海外TECH	MakeUseOf	The Quick Guide to Mining Litecoin	https://www.makeuseof.com/quick-guide-mining-litecoin/	cryptocurrencies	2022-03-06 12:30:13
海外TECH	DEV Community	Deep Learning Library From Scratch 4: Automatic differentiation	https://dev.to/ashwinscode/deep-learning-library-from-scratch-4-automatic-differentiation-96i	Deep Learning Library From Scratch Automatic differentiationWelcome to part of this series where we will talk about automatic differentiation Github repo to code for this series ashwins code Zen Deep Learning Library Deep Learning library written in Python Contains code for my blog series on building a deep learning library Last post What is automatic differentiation Firstly we need to recap on what a derivative is In simple terms a derivative of a function with respect to a variable measures how much the result of the function would change with a change in the variable It essentially measures how sensitive the function is to a change in that variable This is an essential part of training neural networks So far in our library we have been calculating derivatives of variables by hand However in practice deep learning libraries rely on automatic differentiation Automatic differentiation is the process of accurately calculating derivates of any numerical function expressed as code In simpler terms for any calculations we perform in our code we should be able to calculate the derivates of any variables used in that calculation y x y grad x what is the gradient of x Forward mode autodiff and reverse mode autodiffThere are two popular methods of performing automatic differentiation forward mode and reverse mode Forward mode utilises dual numbers to compute derivatives A dual number is anything number in the form x a bεx a bεx a bεwhere εεε is a really small number close to such that ε ε ε If we apply a function to a dual number as such x a bεf x f a bε f a f′ a ⋅b εx a bε newlinef x f a bε f a f a cdot b εx a bεf x f a bε f a f′ a ⋅b εyou can see we calculate both the result of f a f a f a and the gradient of aa a given by the coefficient of εεε Forward mode is preferred when the input dimensions are smaller than the output dimensions of the function however in a deep learning setting the input dimensions would be larger than that of the output Reverse mode is preferred for this situation In our library we will implement reverse mode differentiation for this reason Reverse mode differentiation is a bit more difficult to implement As calculations are performed a computation graph is built For example the following diagram shows the computation graph for f x x yf x frac x y f x x y​After this graph is built the function is evaluated Using the function evaluation and the graph derivatives of all variables used in the function can be calculated This is because each operator node would each come with a mechanism to calculate the partial derivatives of the nodes that it involves If we look at the bottom right node of the diagram the yy y node the multiplier node should be able to calculate it s derivative with respect to the y node and the node Each operator node would have different mechanisms since the way a derivative is calculated depends on the operation involved When using the graph to calculate derivatives I find it easier to traverse the graph in a depth first manner You start at the very top node and calculate it s derivative with respect to the next node remember you are traversing depth first and record that node s gradient Move down to that node and repeat the process Each time you move down a level in the graph multiply the gradient you just calculated by the gradient you calculated in the previous level this is due to the chain rule Repeat until all the nodes gradients have been recorded Note it is not necessary to calculate all the gradients in the graph If you want to find the gradient of a single variable you can stop once it s gradient has been calculated However we d usually want to find the gradients many variables so calculating all the gradients in the graph all at once would be much computationally cheaper since it would only require one graph evaluation If you wanted to find the gradients of all the variables you wanted ONLY you would have to do an evaluation of the graph for each variable which would turn out to be much more computationally expensive to do Differentiation rulesHere are the different differentiation rules used by each node which are used in calculating the derivates in the computation graph Note all of these will show the partial derivative meaning everything that is not the variable we are finding the gradient of is treated as a constant In the following think of xxx and yyy as nodes in the graph and zzz as the result of the operation applied between these nodes At multiplication nodes z xydzdx ydzdy xz xy newline frac dz dx y newline frac dz dy xz xydxdz​ ydydz​ xAt division nodes z xydzdx ydzdy xy z frac x y newline frac dz dx frac y newline frac dz dy xy z yx​dxdz​ y​dydz​ xy At addition nodes z x ydzdx dzdy z x y newline frac dz dx newline frac dz dy z x ydxdz​ dydz​ At subtraction nodes z x ydzdx dzdy z x y newline frac dz dx newline frac dz dy z x ydxdz​ dydz​ At power nodes z xydzdx yxy dzdy xy⋅ln x z x y newline frac dz dx yx y newline frac dz dy x y cdot ln x z xydxdz​ yxy dydz​ xy⋅ln x The chain rule is then used to backpropogate all the gradients in the graph y f g x dydx f′ g x ⋅g′ x y f g x newline frac dy dx f g x cdot g x y f g x dxdy​ f′ g x ⋅g′ x However when matrix multiplying the chain rule get a bit different z x⋅ydzdx f′ z ⊗yTdzdy xT⊗f′ z z x cdot y newline frac dz dx f z otimes y T newline frac dz dy x T otimes f z z x⋅ydxdz​ f′ z ⊗yTdydz​ xT⊗f′ z where f z f z f z is a function that involves zzz meaning f′ z f z f′ z would be the gradient calculated in the previous layer of the graph By default aka if z is the highest node in the graph f z zf z zf z z meaning f′ z f z f′ z would be a matrix of s with the same shape as zzz The codeThe Github repo I linked at the start contains all the code for the automatic differentiation part of the library and has updated all the neural network layers optimisers and loss function to use automatic differentiation to calculate gradients To avoid this post being too long I will show and explain the code in the next post Thank you for reading	2022-03-06 12:05:58
海外ニュース	Japan Times latest articles	Ukrainian refugees near 1.5 million as Russian assault enters 11th day	https://www.japantimes.co.jp/news/2022/03/06/world/ukraine-russia-declaration-war-putin/	Ukrainian refugees near million as Russian assault enters th dayMoscow and Kyiv traded blame over a failed cease fire on Saturday that would have let civilians flee Mariupol and Volnovakha two southern cities besieged by	2022-03-06 21:21:27
ニュース	BBC News - Home	Ukraine: Russia has attacked schools and hospitals, says deputy PM	https://www.bbc.co.uk/news/world-europe-60638042?at_medium=RSS&at_campaign=KARANGA	february	2022-03-06 12:49:40
ニュース	BBC News - Home	Ukraine war: Residents run from Russian shelling in Irpin, near Kyiv	https://www.bbc.co.uk/news/world-europe-60637845?at_medium=RSS&at_campaign=KARANGA	irpin	2022-03-06 12:20:31
ニュース	BBC News - Home	Don't fight in Ukraine - military boss tells Britons	https://www.bbc.co.uk/news/uk-60637185?at_medium=RSS&at_campaign=KARANGA	britonsthe	2022-03-06 12:49:01
ニュース	BBC News - Home	Shell defends 'difficult' decision to buy Russian crude oil	https://www.bbc.co.uk/news/business-60638255?at_medium=RSS&at_campaign=KARANGA	crude	2022-03-06 12:03:23
ニュース	BBC News - Home	Ukraine war: Investigate claim PM intervened to help Evgeny Lebedev get peerage, says Starmer	https://www.bbc.co.uk/news/uk-politics-60638289?at_medium=RSS&at_campaign=KARANGA	fears	2022-03-06 12:46:07
北海道	北海道新聞	道南で１０６人感染　新型コロナ	https://www.hokkaido-np.co.jp/article/653500/	道南	2022-03-06 21:19:46
北海道	北海道新聞	舞、人形劇　アイヌ文化に触れる　道がフォーラム　札幌	https://www.hokkaido-np.co.jp/article/653559/	理解	2022-03-06 21:18:00
北海道	北海道新聞	東日本大震災１１年　被災地と連帯舞台から訴え　札幌　演劇と和太鼓がコラボ公演	https://www.hokkaido-np.co.jp/article/653556/	東日本大震災	2022-03-06 21:13:00
北海道	北海道新聞	平和祈るバレエ　小樽の高橋さん撮影　ロシアのウクライナ侵攻に抗議	https://www.hokkaido-np.co.jp/article/653554/	小樽市内	2022-03-06 21:06:07
北海道	北海道新聞	制圧都市に怒りの国旗　世界で反戦デモ、ロシアでも	https://www.hokkaido-np.co.jp/article/653555/	黄色	2022-03-06 21:07:00
海外TECH	reddit	/r/WorldNews Live Thread: Russian Invasion of Ukraine Day 11, Part 2 (Thread #114)	https://www.reddit.com/r/worldnews/comments/t7xoli/rworldnews_live_thread_russian_invasion_of/	r WorldNews Live Thread Russian Invasion of Ukraine Day Part Thread submitted by u WorldNewsMods to r worldnews link comments	2022-03-06 12:23:22