投稿時間:2022-01-08 20:16:21 RSSフィード2022-01-08 20:00 分まとめ(18件)

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js	JavaScriptタグが付けられた新着投稿 - Qiita	GIF画像のデータ形式とHTML+JavaScript (ローカル生成)のサンプルプログラム	https://qiita.com/ikiuo/items/69de0087e358a2ec6949	図形制御拡張GraphicControlExtensionバイト位置図形制御拡張内容「a」版拡張ExtensionIntroducerxDescriptor図形制御表題GraphicControlLabelxF情報の大きさx副塊の後続バイト数混合情報ltPackedFieldsgt遅延時間秒単位の下位ビットDelayTime遅延時間秒単位の上位ビットDelayTime透明色とする色番号TransparetColorIndex終端x副塊の後続バイト数混合情報ltPackedFieldsgtビット位置ビット数内容予約Reserved消去方法DisposalMethodユーザー操作による遅延時間の短縮UserInputFlag透明色が有効TransparentColorFlag消去方法DisposalMethod値内容未定義デコーダは何もしない消去しない背景色論理画面記述子で指定を使う一つ前の画像に戻す予約注釈拡張CommentExtensionバイト位置注釈拡張内容「a」版拡張ExtensionIntroducerxDescriptor注釈表題CommentLabelxFE注釈情報副塊情報形式DataSubblocks〃文字画面拡張PlainTextExtensionバイト位置文字画面拡張内容「a」版拡張ExtensionIntroducerxDescriptor文字画面表題PlainTextLabelx文字画面設定のサイズ副塊情報形式DataSubblocksxC文字画面の位置左の下位ビットTextGridLeftPosition文字画面の位置左の上位ビットTextGridLeftPosition文字画面の位置上の下位ビットTextGridTopPosition文字画面の位置上の上位ビットTextGridTopPosition文字画面の幅の下位ビットTextGridWidth文字画面の幅の上位ビットTextGridWidth文字画面の高さの下位ビットTextGridHeight文字画面の高さの上位ビットTextGridHeight文字の幅CharacterCellWidth文字の高さCharacterCellHeight文字の共通色番号TextForegroundColorIndex文字背景の共通色番号TextBackgroundColorIndex任意の文字列副塊情報形式DataSubblocksPlainTextData〃応用拡張ApplicationExtensionバイト位置応用拡張内容「a」版拡張ExtensionIntroducerxDescriptor応用拡張表題ApplicationExtensionxFF識別子と認識コードのサイズ副塊情報形式DataSubblocksxBバイトの識別子ApplicationIdentifierバイトの認識コードApplicationAuthenticationCode任意の応用拡張情報副塊情報形式DataSubblocksApplicationData〃終端子Trailerバイト位置オフセット内容終端子TrailerxBGIFにおけるLZWのパラメータLZW最小コード幅LZWMinimumCodeSizeをLMCSとします。	2022-01-08 19:09:00
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	HTMLでのformタグで送信できるものについて	https://teratail.com/questions/377207?rss=all	表示しているデータをそのまま返しているためformタグの中でinputタグに初期値を入れて表示しています。	2022-01-08 19:46:21
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	iPhoneでアクセスするとページ途中から表示される	https://teratail.com/questions/377206?rss=all	iPhoneでアクセスするとページ途中から表示される下記URLのサイトなのですが、PCやAndroidスマホでは問題無く表示されるのですが、iPhoneでアクセスするとSafari・Chrome関係なく、施工事例のページ以外全てページの途中へリンクしてしまいます。	2022-01-08 19:45:53
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	OpenCVでの画像を読み込む際、一部しか読み込まれない	https://teratail.com/questions/377205?rss=all	openCVを用いて画像を読み込み、表示をしました。	2022-01-08 19:43:35
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	指定時間後にfalseに移行させるにはどうしたらよいですか。	https://teratail.com/questions/377204?rss=all	指定時間後にfalseに移行させるにはどうしたらよいですか。	2022-01-08 19:37:54
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Flutter null許容でないインスタンスフィールド「メッセージ」を初期化する必要があります。	https://teratail.com/questions/377203?rss=all	Flutternull許容でないインスタンスフィールド「メッセージ」を初期化する必要があります。	2022-01-08 19:12:10
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	React の map ループ内で JSON を取得し HTML を更新した。	https://teratail.com/questions/377202?rss=all	Reactのmapループ内でJSONを取得しHTMLを更新した。	2022-01-08 19:02:53
Docker	dockerタグが付けられた新着投稿 - Qiita	(Nuxt.js)共通部分をcomponent化して使い回す	https://qiita.com/Bezzi05791520/items/17bb2fea1c1d25b6eba6		2022-01-08 19:40:23
海外TECH	DEV Community	Build the organizational chart with Angular	https://dev.to/101samovar/build-the-organizational-chart-with-angular-5bae	Build the organizational chart with AngularDear friends My video turned out to be too heavy to upload to the site Therefore I propose a link to YouTube In this video we will create an Angular single page application The application to create organizational charts We will create the layout with Angular components the service to communicate with the data repository We will define the routing to change content screens We will create the chart component The chart component will have the ability to add chart elements to remove chart elements to edit chart elements Enjoy your viewing and I will be glad for your feedback on the content Code	2022-01-08 10:53:13
海外TECH	DEV Community	Deep Learning Library From Scratch 3: More optimisers	https://dev.to/ashwinscode/deep-learning-library-from-scratch-3-more-optimisers-4l23	Deep Learning Library From Scratch More optimisersWelcome to part of this series where we build a deep learning library from scratch In this post we will add more optimisation functions and loss functions to our library Here is the Github repo 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 Optimisation functionsThe goal of an optimisation function is to tweak the network parameters to minimise the neural network s loss It does this by taking the gradient of the parameters with respect to the loss and using this gradient to update the parameters Different loss functions use the gradients in different ways which leads to an acceleration in the training process If we graph out the loss function as seen in the image above optimisers aim to change the parameters of the neural network so that the minimum loss value is produced aka the lowest dip in the graph The path which the optimisers take during the training process is represented by the black ball MomentumMomentum is an optimisation function which extends the gradient descent algorithm which we looked at in the last post It is designed to accelerate the training process meaning it would minimise the loss in a fewer number of epochs If we think about our black ball momentum causes this black ball to accelerate quickly towards the minimum like rolling a ball down from the top of a hill Momentum accumulates the gradients calculated in previous epochs which helps it to determine the direction to go to in order to minimise the loss The formula it uses to update parameters is as follows dt β⋅dt l⋅gpt pt dtd t beta cdot d t l cdot g newlinep t p t d t newlinedt​ β⋅dt ​ l⋅gpt ​ pt​ dt​ ptp t pt​ is the parameter value at epoch t dtd t dt​ is the direction to go at epoch t calculated from previous epochs gradients It is initialised at ll l is the learning rate β beta β is a predetermined value usually chosen to be gg g is the gradient of the parameter with respect to this lossHere is our python implementation of this optimiser optim py class Momentum def init self lr beta self lr lr self beta beta def momentum average self prev grad return self beta prev self lr grad def call self model loss grad loss backward for layer in tqdm tqdm model layers grad layer backward grad if isinstance layer layers Layer if not hasattr layer momentum layer momentum w b layer momentum w self momentum average layer momentum w layer w gradient layer momentum b self momentum average layer momentum b layer b gradient layer w layer momentum w layer b layer momentum b RMSPropRMSProp works by taking an exponential average of the squares of the previous gradients An exponential average is used to give recent gradients more weight than earlier gradients This exponential average is used to determine the update in the parameter RMSProp aims to minimise the oscillations in the training step In terms of our black ball the ball would take a smooth straight path towards the minimum instead of zig zagging towards it which often happens with other optimisers Here are the equations for parameter updates dt β⋅dt β ⋅gΔpt ldt ϵ⋅gpt pt Δptd t beta cdot d t beta cdot g newline varDelta p t frac l sqrt d t epsilon cdot g newlinep t p t varDelta p t newlinedt​ β⋅dt ​ β ⋅gΔpt​ dt​ ϵ​l​⋅gpt ​ pt​ Δpt​ ptp t pt​ is the parameter value at epoch t dtd t dt​ is the exponential squared average of previous gradients It is initialised at ll l is the learning rate β beta β is a predetermined value usually chosen to be gg g is the gradient of the parameter with respect to this loss ϵ epsilon ϵ is a predetermined value to avoid division by Usually set at As seen in the second equation we divide the learning rate by the exponential average This leads to parameters in later epochs having a larger training step since the exponential average gets smaller as more epochs occur RMSProp also automatically slows down as it approaches the minima which is ideal since a too large step size would cause an overcorrection in the updating of parameters Here is our python implementation optim py class RMSProp def init self lr beta epsilon self lr lr self beta beta self epsilon epsilon def rms average self prev grad return self beta prev self beta grad def call self model loss grad loss backward for layer in tqdm tqdm model layers grad layer backward grad if isinstance layer layers Layer if not hasattr layer rms layer rms w b layer rms w self rms average layer rms w layer w gradient layer rms b self rms average layer rms b layer b gradient layer w self lr np sqrt layer rms w self epsilon layer w gradient layer b self lr np sqrt layer rms b self epsilon layer b gradient AdamAdam combines the ideas in RMSProp and Momentum together Here are the update equations vt β⋅vt β ⋅gst β⋅st β ⋅gΔpt l⋅vtst ϵpt pt Δptv t beta cdot v t beta cdot g newlines t beta cdot s t beta cdot g newline varDelta p t l cdot frac v t sqrt s t epsilon newlinep t p t varDelta p t vt​ β​⋅vt ​ β​ ⋅gst​ β​⋅st ​ β​ ⋅gΔpt​ l⋅st​​ ϵvt​​pt ​ pt​ Δpt​ ptp t pt​ is the parameter value at epoch t vtv t vt​ is the exponential average of previous gradients It is initialised at sts t st​ is the exponential squared average of previous gradients It is initialised at ll l is the learning rate β beta β​ is a predetermined value usually chosen to be β beta β​ is a predetermined value usually chosen to be gg g is the gradient of the parameter with respect to this loss ϵ epsilon ϵ is a predetermined value to avoid division by Usually set at Here is our python implementation optim py class Adam def init self lr beta beta epsilon self lr lr self beta beta self beta beta self epsilon epsilon def rms average self prev grad return self beta prev self beta grad def momentum average self prev grad return self beta prev self beta grad def call self model loss grad loss backward for layer in tqdm tqdm model layers grad layer backward grad if isinstance layer layers Layer if not hasattr layer adam layer adam w b w b layer adam w self momentum average layer adam w layer w gradient layer adam b self momentum average layer adam b layer b gradient layer adam w self rms average layer adam w layer w gradient layer adam b self rms average layer adam b layer b gradient w adjust layer adam w self beta b adjust layer adam b self beta w adjust layer adam w self beta b adjust layer adam b self beta layer w self lr w adjust np sqrt w adjust self epsilon layer b self lr b adjust np sqrt b adjust self epsilon Using our new optimisers This is how we d use our new optimisers in our library training a model for the same problem we described last post XOR gate import layersimport lossimport optimimport numpy as npx np array y np array net layers Model layers Linear layers Linear layers Sigmoid layers Linear layers Softmax net train x y optim optim RMSProp lr loss loss MSE epochs print net x epoch loss ████████████████████████████████████████████████████████████████████████████████████████████ lt it s epoch loss ████████████████████████████████████████████████████████████████████████████████████████████ lt it s epoch loss ████████████████████████████████████████████████████████████████████████████████████████████ lt it s epoch loss ██████████████████████████████████████████████████████████████████████████████████ lt it s epoch loss ████████████████████████████████████████████████████████████████████████████████████████████ lt it s epoch loss ████████████████████████████████████████████████████████████████████████████████████████████ lt it s epoch loss ████████████████████████████████████████████████████████████████████████████████████████████ lt it s epoch loss ████████████████████████████████████████████████████████████████████████████████████████████ lt it s epoch loss e ████████████████████████████████████████████████████████████████████████████████████████████ lt it s epoch loss e ████████████████████████████████████████████████████████████████████████████████████████████ lt it s epoch loss e As you can see compared to the last post our model has trained much much better thanks to our new optimiser Thanks for reading Next post we will apply our library so far to a more advanced problem handwritten digit recognition	2022-01-08 10:19:58
ニュース	BBC News - Home	Novak Djokovic: Having Covid gave tennis star vaccine exemption - lawyers	https://www.bbc.co.uk/news/world-australia-59920379?at_medium=RSS&at_campaign=KARANGA	australia	2022-01-08 10:43:56
ニュース	BBC News - Home	Cladding: More flat owners to be freed from bills	https://www.bbc.co.uk/news/uk-59916812?at_medium=RSS&at_campaign=KARANGA	buildings	2022-01-08 10:37:22
ニュース	BBC News - Home	Djokovic court case: Could he argue his way to the Open?	https://www.bbc.co.uk/news/world-australia-59904833?at_medium=RSS&at_campaign=KARANGA	australian	2022-01-08 10:51:22
ニュース	BBC News - Home	GB's Bankes earns second World Cup victory of season	https://www.bbc.co.uk/sport/winter-sports/59921081?at_medium=RSS&at_campaign=KARANGA	GB x s Bankes earns second World Cup victory of seasonBriton Charlotte Bankes preparations for February s Winter Olympics continue to gather momentum as she claims a second snowboard cross World Cup win of the season	2022-01-08 10:31:14
ニュース	BBC News - Home	'It's better than the M25' - Billings drives 500 miles to join England squad in Sydney	https://www.bbc.co.uk/sport/cricket/59912811?at_medium=RSS&at_campaign=KARANGA	x It x s better than the M x Billings drives miles to join England squad in SydneySam Billings answers the call to solve England s Ashes injury crisis by driving more than miles from the Gold Coast to Sydney	2022-01-08 10:11:28
サブカルネタ	ラーブロ	博多長浜らーめん いっき＠谷在家	http://ra-blog.net/modules/rssc/single_feed.php?fid=195348	営業時間	2022-01-08 10:00:38
北海道	北海道新聞	三浦綾子原作の映画完成、２９日札幌で試写会　「われ弱ければ　矢嶋楫子伝」	https://www.hokkaido-np.co.jp/article/631353/	三浦綾子	2022-01-08 19:12:44
北海道	北海道新聞	かるた日本一へ札舞う、滋賀　近江神宮、クイーン初防衛	https://www.hokkaido-np.co.jp/article/631389/	小倉百人一首	2022-01-08 19:07:00