This document summarizes a machine learning workshop on image classification. The workshop covers introduction to machine learning concepts like neural networks, deep neural networks, and convolutional neural networks. It then demonstrates image classification on the Fashion MNIST dataset using both dense neural networks and convolutional neural networks. Finally, it guides attendees on preparing their own image data and training a classification model on their images.

1. Machine Learning Workshop I
Image Classification
Tao Chen
taochen@qnap.com

2. 關於講師
陳雲濤 Tao Chen
taochen@qnap.com
Software Engineer,
Data Intelligence & Application Div., QNAP
Experience on IoT with computer vision, image processing,
image classification, object detection & segmentation.

3. 課前準備
● 本課程會實作一個客製化的照片分類模型
● 請大家準備兩種類別照片，每種至少 50 張以上，分別存放在兩個資料夾中
● 適合對象：
1. 未接觸過 Machine Learning 的開發者
2. 有使用ML相關經驗，想認識 Deep Learning 方法者
3. 有興趣的想實作自己照片分類器的 QNAPers

4. 課程大綱
● Introduction to Machine Learning
● Neural Network
● Deep Neural Network (DNN)
● Convolutional Neural Network (CNN)
● Image Classification on Fashion MNIST Dataset
● Prepare Your Data for Image Classification
● Train Your Image Classification Model

5. Introduction to Machine Learning

6. Introduction to Machine Learning
● Traditional:
Data
Data
● Machine Learning:
Output

7. Machine Learning ≈ Looking for a Function
this is a cat
Training Testing
“cat”
“not a
cat”
You write the
program for learning
Ref: 出自台大電機系李宏毅老師 Machine Learning 課程投影片

8. Type of Machine Learning Task
● Classification → output class
● Regression → output value
“cat”
影像辨識
88.87mm
本月總降雨量預測
中央氣象局觀測資料

9. Goodness of Machine Learning Function
● Classification → output class
● Regression → output value
“cat”
88.87 mm中央氣象局觀測資料 87.00 mm
“cat”
Ground Truth
evaluate by accuracy
evaluate by MSE (mean
squared error)

10. Machine Learning Process
“cat”
Ref: 出自台大電機系李宏毅老師 Machine Learning 課程投影片

11. Performance Evaluation (Accuracy)
● 假設100個病患使用某模型做疾病檢測
○ 檢測有病為 Positive
○ 檢測沒病為 Negative
● 前提(病患之真實狀況)
○ 15 個病患有病
○ 85 的病患沒病
● 某模型之檢測結果
○ 20 個病患檢測有病 (其中只有 10 個病患真的有病，10 個誤判)
○ 80 個病患檢測沒病 (其中只有 75 個病患真的沒病，5 個誤判)
檢測有病 檢測沒病
真實有病 10 5
真實沒病 10 75
Accuracy: (10+75)/100 = 85%

12. Precision, Recall and F1-score
對的說成對的
錯的說成錯的
錯的說成對的
對的說成錯的

13. ● 假設100個病患使用某模型做疾病檢測
○ 檢測有病為 Positive
○ 檢測沒病為 Negative
● 前提(病患之真實狀況)
○ 15 個病患有病
○ 85 的病患沒病
● 某模型之檢測結果
○ 20 個病患檢測有病 (其中只有 10 個病患真的有病，10 個誤判)
○ 80 個病患檢測沒病 (其中只有 75 個病患真的沒病，5 個誤判)
檢測有病 檢測沒病
真實有病 10 5
真實沒病 10 75
→ TP: 10, TN: 75, FP: 10, FN: 5
→ Precision: 10/(10+10)= 50%, Recall: 10/(10+5)= 66.66%
→ F1-score: 2*0.5*0.66/(0.5+0.66) = 57.14%
Accuracy: (10+75)/100 = 85%
Performance Evaluation (F1-score)

14. from：http://speech.ee.ntu.edu.tw/~tlkagk/courses/ML_2017_2/Lecture/introduction.pdf

15. NN = Neural Network
(one kind of ML function)

16. Neural Network
from cs231n：http://cs231n.stanford.edu/

17. Basic NN
input Activation
Function
bias
weight
z = input(s) * weight(s) + bias
from NTU ML course：https://www.youtube.com/watch?v=Dr-WRlEFefw

18. 2
-3
0.5
0.5
0.6
0.5
0.1
0.6
0
0.3
0.6
0.4
0.15
0.2
0.1
0.5
Activation Function f :
0.4
0.5
f(-0.6)
f(0.3)
input
input
weight
bias
0.27
0.38
f(0.27)
f(0.38)
0.217
(output)
Basic NN
[ 2 ✕ 0.4 + (-3) ✕ 0.5 ] + 0.1 =
-0.6
[ 2 ✕ 0.6 + (-3) ✕ 0.5 ] + 0.6 =
0.3

19. from cs231n：http://cs231n.stanford.edu/
Activation Functions

20. 2
-3
0.5
0.5
0.6
0.5
0.1
0.6
0
0.3
0.6
0.4
0.15
0.2
0.27
0.38
0.217
0.4
0.5
0.1
0.5
f(-0.6)
f(0.3)
f(0.27)
f(0.38)
0.3
Ground
Truth
Judge by Loss function
e.g. MSE
Loss value = MSE(0.217, 0.3) = 0.006889
Training NN

21. new
bias
new
bias
new
bias
new
bias
0.217
new
weight
0.3
Ground
Truth
Judge by Loss function
e.g. MSE
Loss value = MSE(0.217, 0.3) = 0.006889
Update Weights and Biases with Loss value
new
weight
new
weights
new
weights
Learn by
next data
Training NN
(By Backpropogation Algorithm)

22. 2
-3
0.5
0.5
0.6
0.5
0.1
0.6
0
0.3
0.6
0.4
0.15
0.2
0.27
0.38
0.308
0.4
0.5
1
0.5
f(-0.6)
f(0.3)
f(0.27)
f(0.38) 0.71
2
0.1
Ground T.
0
1
Cross Entropy Loss
dog
cat
Sleep
Walk
with U
Basic NN (Classification)

23. from cs231n：http://cs231n.stanford.edu/

24. 圖片出處：http://lucenaresearch.com/deep-neural-networks/

25. DNN = Deep Neural Network

26. Ref : https://achintavarna.wordpress.com/2017/11/17/keras-tutorial-for-beginners-a-simple-neural-network-to-identify-numbers-mnist-data/
…
0
0
0
0
0
0
0
0
1
0
count loss with Ground T.
Update Weights and Biases with Loss value
0.04
0.01
0.02
0.11
0.01
0.06
0.21
0.01
0.35
0.18

27. CNN = Convolutional Neural Network

28. Basic Architecture of CNN
Ref: 出自台大電機系李宏毅老師 Machine Learning 課程投影片
Convolution
Max Pooling
Flatten
特徵擷取
Feature Extraction
降維度
Reduce Resolution
將所有擷取到的特徵攤
平
→ 降為一維向量
to one-dimension

29. Convolution
Ref: 出自台大電機系李宏毅老師 Machine Learning 課程投影片

30. Convolution
Ref: 出自台大電機系李宏毅老師 Machine Learning 課程投影片

31. ● Convolution Filters 的長相
Convolution
Ref: https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148
from Stanford CS231n CNN: http://cs231n.github.io/convolutional-networks/

32. from cs231n：http://cs231n.stanford.edu/
Pooling

33. Ref: https://stats385.github.io/poolinglayers
Pooling

34. Ref: https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148
Convolutional Neural Network

35. 64 convolution filters
pooling
R, G, B
Ref: https://hk.saowen.com/a/d2cde0a5e42606aae41a53e7ddb55ad7eb3208dc4cd218067344246a9a8ebf54
Convolutional Neural Network
● Understanding Neural Networks
Through Deep Visualization

36. Frameworks

37. Image Classification on Fashion MNIST Dataset

38. Environment Setting on Google Colab
● Google Colab:
https://colab.research.google.com/notebooks/welcome.ipynb#recent=true
● 文件 -> 新建 python3 Notebook
● 修改 -> 筆記本設置 -> GPU

39. Fashion MNIST Dataset
● Fashion MNIST Github
○ Total images 70000 (training dataset 60000, testing dataset 10000)
○ Resolution 28x28
○ Gray-scale
○ 10 classes:

40. Lab #1: Train DNN Model on Fashion MNIST
● Colab Notebook:
https://colab.research.google.com/drive/174_6xXG1up-GD6Ui
rXYMzMJYxyCdaPSD

41. Lab #2: Train CNN Model on Fashion MNIST
● Colab Notebook:
https://colab.research.google.com/drive/1eXKrKBe0lbsV9Ex6
amryF0Pml8QsySqY

42. Prepare Your Data for Image Classification

43. Prepare Data
Sample data:
https://drive.google.com/drive/folders/1-Wc10SNWg8UZ77wTmIs9JU2x_UYWvpI5?usp=s
haring

44. Train Your Image Classification Model

45. Lab #3: Train Your Image Classificaiton Model
● Colab Notebook:
https://colab.research.google.com/drive/1Sct4c6UUse5heNgF
fgdP-MckjCJ3umFM

46. 照片辨識PK - 你能比 AI 準嗎？
● ImageNet Validation Set Competition
https://cs.stanford.edu/people/karpathy/ilsvrc/

47. Thank You