開啓Linux 核心功能的配置選單,
Step1: 得先安裝ncurses 套件, 才能產生選單畫面>> sudo apt-get install libncurses5-dev libncursesw5-dev 若以上無法成功安裝, 則先下這個指令後再安裝套件, 應該就能順利安裝 >> sudo apt-get update --allow-releaseinfo-change
Step2: 進入到核心原始碼的資料匣, 執行 make menuconfig
1.) How can I choose between normalization and standardization in different situations?
如何儲存Fine-tune BERT model 的網路權重及架構?
Custom mask layers require a config and must override get_config ...........
1. ) 若pre-trained bert model 只是用來作為sentence embedding 的話,. 那就只儲存後面自己接的網路架構..不用整個儲存(即不必含BERT model), 因此可忽略載入pre-trained bert model . 那就回到原本傳統的model.save() , load_models('xxxx.h5')
2.) pre-trained bert model 串接自己網路架構, 一起訓練, 如果是這種的, 就用方法2
只存weight (model.save_weights), 用原本model 架構去產生一個空的new_model, 然後new_model.load_weights
model.save_weights('my_model_weights.h5')
...
new_model = <build your model with your model building code>
new_model.load_weights('my_model_weights.h5')
下載並安裝wacom driver
https://drive.google.com/drive/folders/1fXPbY3cqNh8vPpvjJj2LdIFp2uv-_L6Z?usp=sharing
試著熟悉數位筆的使用感覺, 同時熟悉幾個常用的快速鍵,會讓你的講課流暢度不會因為數位筆的關係,而不斷停頓,
3.) 有時可能需要寫字在電腦螢幕上, 如軟體操作畫面, 則可以使用Pointofix 這類的軟體.
安裝Pointofix , 預設是德文, 中文化方法==> 將 pointofix_translation.ini 複製到 C:\Program Files (x86)\Pointofix . 更多 Pointofix 使用說明 https://briian.com/6545/
即可. 
在使用Pointofix 時,會發現在書寫螢幕時，使用wacom 筆寫得不是很順暢時, 無法寫得很小字時, 請記進入到 Wacom 數位板內容, "取消使用Windows Ink". (要先裝wacom Driver才會有這個介面)
5.) ScreenShot 分享
若在螢幕上用Pointofix 所寫的內容很多很重要,那麼也可以按下F7 擷最畫面存成.PNG檔, 順便幫同學做個筆記. 之後再利用HFS或 Google drive 分享給同學.
若是使用Google drive 建立一個目錄來分享檔案, 分享的網址可以建立短網址 (https://reurl.cc/main/tw) , 此網址於課程的第一堂課就可以給同學, 後續若有新增其他檔案, 同學也都可以在同一個地方下載或者想回家練習仍可以再次下載.
另外, 此分享目錄最好也能有結構的分類
/
|--code
|-- doucment
|-- scrrenshot
|-- scrrenshot_20200908
| |-- scrrenshot_20200915
6.) 熟悉一下PowerPoint的快速鍵
記住 PowerPoint 在播放投影片的快速鍵. 可以增加講課的流暢度.
可以試著按F8 ,可以直接切換成畫筆模式, 且不斷按下F8 還可以切換筆的顏色
老師的用心是決定課程品質的關鍵! 我們也得換位思考, 若我們是坐在底下的學生. 我們
一定也會期待這門課能獲得什麼, 而不是坐在那裡浪費生命
with open(annotation_file, "r") as f:
data = json.load(f)
annotations=data["annotations"]
images=data["images"]
categories=data["categories"]
print(f"Number of images: {len(annotations)}")
print(f"Number of images: {len(images)}")
print(f"Number of images: {len(categories)}")
The COCO dataset has been downloaded and extracted successfully. Number of images: 36781 Number of images: 5000 Number of images: 80images[60] => 取出index 60 這張圖的資訊{'license': 1, 'file_name': '000000360661.jpg', 'coco_url': 'http://images.cocodataset.org/val2017/000000360661.jpg', 'height': 480, 'width': 640, 'date_captured': '2013-11-18 21:33:43', 'flickr_url': 'http://farm4.staticflickr.com/3670/9709793032_f9ee4f0aa2_z.jpg', 'id': 360661}annotations[60] => 取出index 60 annotations資訊{'segmentation': [[267.51, 222.31, 292.15, 222.31, 291.05, 237.02, 265.67, 237.02]], 'area': 367.89710000000014, 'iscrowd': 0, 'image_id': 525083, 'bbox': [265.67, 222.31, 26.48, 14.71], 'category_id': 72, 'id': 34096}annotation{ "id": int, "image_id": int, "category_id": int, "segmentation": RLE or [polygon], "area": float, "bbox": [x,y,width,height], "iscrowd": 0 or 1, }
每一張圖片會有一個image_id, 而一張圖可能包含一個以上的單一物件或群物件. 針對每一個物件,不論是單一物件或群物件, 都會用一個annotation來表現物件內容.. 一張圖會有多個annotation, 即多個物件annotation{ "id": int, ==> 物件id "image_id": int, ==> 所屬的圖片 "category_id": int, ==>此物件的類別id "segmentation": RLE or [polygon], ==> 單一物件或一群物件的區域描述 "area": float, ==> 物件區域的Pixel總數 "bbox": [x,y,width,height], ==> bounding box的座標 "iscrowd": 0 or 1, ==> 0: 單一物件, 1: 一群物件 (如:一群觀眾)}
其中"segmentation": 若為單一物件, 則是以一個多邊形的座標點 [X1,Y1,X2,Y2, ....] 來描述此物件的區塊位置.
若是一群物件的區域描述, 如要描述一群蘋果,則會用Mask的方式來描述,如下圖所示。一群物件的區域描述, 即iscrowd=1, 則segmentation的內容為{'counts': [671, 10, 2, 2, 4, 22, 6, 31, 1, 11, 1, 10, 379, 16, 1, 25, 5, 55, 378, 43, 4, 55, 378, 44, 3, 55, 378, 44, 3, 55, 378, 44, 3, 55, 378, 44, 3, 55, 378, 44, 4, 54, 379, 29, 1, 16, 1, 54, 380, 28, 2, 15, 2, 53, 382, 23, 6, 15, 1, 8, 21, 1, 3, 3, 5, 12, 384, 20, 8, 16, 40, 12, 384, 16, 14, 15, 40, 10, 386, 10, 21, 14, 40, 8, 388, 8, 22, 15, 41, 3, 393, 3, 25, 15, 465, 15, 465, 15, 466, 14, 467, 13, 468, 12, 469, 10, 471, 8, 474, 3, 983, 7, 472, 9, 470, 11, 454, 6, 1, 20, 452, 28, 451, 30, 449, 31, 448, 33, 447, 34, 446, 35, 445, 35, 445, 35, 445, 35, 445, 36, 445, 36, 445, 35, 447, 33, 450, 30, 450, 30, 450, 12, 1, 17, 451, 10, 3, 16, 452, 8, 6, 13, 455, 4, 12, 8, 474, 3, 50865, 6, 459, 6, 8, 11, 454, 27, 452, 29, 450, 31, 448, 32, 448, 32, 448, 32, 448, 32, 448, 32, 448, 32, 448, 31, 450, 29, 452, 20, 2, 4, 456, 7, 1, 3, 1, 4, 7174, 6, 2, 6, 4, 14, 447, 34, 445, 36, 443, 38, 442, 38, 442, 38, 442, 38, 442, 38, 442, 38, 442, 38, 443, 36, 445, 34, 446, 6, 19, 6, 450, 3, 478, 1, 42714, 6, 473, 8, 471, 10, 469, 15, 465, 18, 462, 19, 461, 21, 459, 22, 458, 24, 456, 26, 455, 25, 456, 24, 458, 22, 461, 18, 463, 16, 466, 3, 5, 3, 3840, 7, 463, 20, 459, 22, 457, 27, 448, 33, 446, 35, 437, 44, 435, 46, 433, 47, 432, 48, 432, 48, 432, 48, 432, 48, 432, 30, 4, 14, 432, 29, 7, 12, 432, 29, 8, 10, 433, 29, 9, 8, 435, 13, 1, 1, 2, 10, 12, 3, 439, 12, 6, 7, 456, 10, 471, 3, 3, 2, 474, 1, 478, 2, 477, 3, 476, 4, 476, 9, 470, 11, 469, 12, 468, 13, 467, 14, 7, 1, 458, 23, 457, 23, 457, 23, 458, 22, 459, 21, 461, 19, 463, 18, 462, 19, 461, 20, 1, 9, 450, 33, 447, 34, 446, 36, 444, 37, 443, 38, 443, 38, 443, 37, 445, 35, 450, 7, 2, 21, 459, 21, 460, 20, 461, 19, 463, 3, 3, 10, 471, 8, 474, 3, 18209, 1, 479, 2, 478, 3, 477, 4, 476, 5, 475, 6, 474, 7, 474, 10, 471, 8, 474, 4, 5302, 7, 447, 4, 26, 4, 445, 6, 1, 15, 11, 3, 443, 29, 6, 3, 442, 32, 4, 2, 442, 38, 442, 38, 442, 38, 442, 38, 442, 38, 442, 37, 444, 16, 1, 18, 446, 8, 1, 3, 6, 3, 4, 8, 449, 3, 22, 4, 46076, 6, 468, 13, 466, 15, 461, 20, 459, 21, 458, 22, 457, 23, 457, 23, 457, 23, 457, 23, 457, 22, 458, 21, 459, 20, 460, 20, 461, 19, 462, 18, 463, 17, 462, 18, 461, 19, 460, 19, 461, 19, 461, 19, 461, 19, 461, 19, 461, 19, 461, 19, 461, 18, 463, 16, 465, 8, 1, 3, 470, 4, 31194, 12, 9, 6, 452, 29, 445, 36, 443, 38, 441, 39, 435, 45, 434, 46, 427, 53, 426, 54, 425, 55, 424, 55, 425, 54, 426, 53, 427, 51, 429, 25, 1, 24, 430, 22, 5, 22, 210], 'size': [480, 640]}References:
 |
| 7 × 7 mm QFN-56 package |
RP2040 Chip features:
 |
| RP2040的晶片架構圖 |
 |
| Raspberry Pi Pico |
