์ ํ๊ท
์ ๊ธ์
๋ฆฌ์๋ฒฝ
๋ฆฌ์ถํ
์ ์ฑํ
| // modified by glizzy (8/25/2017) | |
| // ๋ธ๋ผ์ฐ์ ์ GreaseMonkey(Firefox) TamperMonkey(Chrome) ์ ์ค์นํ๊ณ , | |
| // https://gist.github.com/foriequal0/154e73d3289d808e8ce94603f5eff6a4/raw/yaminjeongeum.user.js | |
| // ์ ๋งํฌ๋ฅผ ๋ธ๋ผ์ฐ์ ์ ์ ๋ ฅํ๋ฉด ์๋์ผ๋ก ์คํฌ๋ฆฝํธ๋ฅผ ์ค์นํ๊ฒ ๋๋๋ ์ฐฝ์ด ๋น๋๋ค. | |
| // ==UserScript== | |
| // @name yaminjeongeum | |
| // @namespace yaminjeongeum.kr | |
| // @description ์ผ๋ฏผ์ ์ |
| Sub ๋งคํฌ๋ก1() | |
| ' | |
| ' Excel ์ธ๋ก์ค ๋ถ์ฌ๋ฃ๊ธฐ | |
| ' | |
| ' ๋ฐ๋ก ๊ฐ๊ธฐ ํค: Ctrl+E | |
| ' | |
| Selection.Copy | |
| ActiveCell.Offset(1, 0).Select | |
| Range(Selection, Selection.End(xlDown).Offset(-1, 0)).Select | |
| ActiveSheet.Paste |
| // Javascript for GreaseMonkey(Firefox) TamperMonkey(Chrome) | |
| // cited from foriequal0/yaminjeongeum.user.js | |
| // ==UserScript== | |
| // @name textreplacer | |
| // @namespace textreplacer.kr | |
| // @description ์ฐํฉ๋ด์ค์ฉ ํ์ ๋ณํ๊ธฐ | |
| // @include * | |
| // @exclude file://* | |
| // @version 1 |
| 10t [์ดํค] 10t [์ญํค] | |
| 1kg [ํํค๋ก๊ทธ๋] 1kg [์ผํฌ๋ก๊ทธ๋จ] | |
| 2๋ง๋๋ถํฌ ์ดํญ๋ถํฌ | |
| 2์ค์์ถฉ ๋๋ธ ๋ฒํผ๋ง | |
| 3.14 [์ฌ๋ฐ์ธ] 3.14 [์ผ์ผ์ธ] | |
| 3.1์ธ๋ฏผ๋ด๊ธฐ 3.1์ด๋ | |
| 3๊ทน์์ ํธ๋์ง์คํฐ | |
| 4.19์ธ๋ฏผ๋ด๊ธฐ 4.19ํ๋ช | |
| 5๋ถ์ 2์ 2์ 5๋ถ์ | |
| 70๋์ [์ผํ๋์ฌ] 70์ฃผ๋ ์ |
| ============================== | |
| Unique Surnames in North Korea | |
| ๊ณ(ๆก) | |
| ๊ถ(ๅผ) | |
| ๋ ๊ณ (็จๅญค) | |
| ๋(่ฃ) | |
| ๋ขฐ(้ท)* | |
| ๋ง(่ฌ)* | |
| ๋ฌต(ๅขจ)* |
| Code Hanja Hangul | |
| \u9fa6 ้พฆ ์ | |
| \u9fa7 ้พง ์ | |
| \u9fa8 ้พจ ํ | |
| \u9fa9 ้พฉ ๊ทผ | |
| \u9faa ้พช ์ ,๋ถ | |
| \u9fab ้พซ ๊ฐ,์ | |
| \u9fac ้พฌ ๋ง | |
| \u9fad ้พญ ์ด | |
| \u9fae ้พฎ ๊ธฐ |
| // ==UserScript== | |
| // @name Text Replacement | |
| // @namespace Text Replacement | |
| // @description ์ฐํฉ๋ด์ค์ฉ ๊ฐ์ฒด์/์ฝ์/ํ์ฅํ์ ๋ณํ๊ธฐ | |
| // @include * | |
| // @exclude file://* | |
| // @version 1 | |
| // @grant none | |
| // @author Damheo Lee ([email protected]) | |
| // @run-at Tampermonkey (Greasemonkey) ์ค์นํ ์ฌ์ฉ |
| import tensorflow as tf | |
| mnist = tf.keras.datasets.mnist | |
| (x_train, y_train),(x_test, y_test) = mnist.load_data() | |
| x_train, x_test = x_train / 255.0, x_test / 255.0 | |
| # ValueError in here | |
| # The first layer in a Sequential model must get an `input_shape` argument. | |
| # MNIST data has the array with shape 28 by 28 tuple | |
| # put 60K array into 1D-convolution layer |