This document provides an introduction and overview of machine learning and TensorFlow. It discusses the different types of machine learning including supervised learning, unsupervised learning, and reinforcement learning. It then explains concepts like logistic regression, softmax, and cross entropy that are used in neural networks. It covers how to evaluate models using metrics like accuracy, precision, and recall. Finally, it introduces TensorFlow as an open source machine learning framework and discusses computational graphs, automatic differentiation, and running models on CPU or GPU.

1. TensorFlow深度學習快速上⼿手班
⼀一、機器學習
By Mark Chang

2. ⼤大綱
• 機器學習簡介
• Tensorflow簡介
• Tensorflow安裝
• 單層感知器實作

3. 機器學習簡介

4. 機器學習種類
監督式學習
Supervised Learning
⾮非監督式學習
Unsupervised Learning
增強式學習
Reinforcement Learning

5. 監督式學習
• 影像識別
http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf

6. 監督式學習
機器學習模型
機器學習模型 ship
ship
資料
⼈人⼯工標記
資料
答案

7. ⾮非監督式學習
• 語意理解
https://papers.nips.cc/paper/5021-distributed-representations-of-
words-and-phrases-and-their-compositionality.pdf

8. ⾮非監督式學習
機器學習模型
Beijing is the capital of China.
As China's capital, Beijing is a large and vibrant city.
Tokyo is the capital of Japan.
As Japan’s capital, Tokyo is a large and vibrant city.
…….
資料
結果

9. 增強式學習
• 打電動
http://arxiv.org/pdf/1312.5602v1.pdf

10. 增強式學習
機器學習模型環境
訊息
動作

11. 監督式機器學習
訓練資料
機器學習模型
輸出值
正確答案
對答案
如果答錯了，
要修正模型
機器學習模型
測試資料
訓練
完成
輸出值

12. 符號慣例
訓練資料
全部：X , Y
單筆：x(i), y(i)
機器學習模型
h
模型參數
w
輸出值
h(X)
正確答案
Y
對答案
E(h(X),Y)
如果答錯了，
要修正模型
X
Y

13. 邏輯迴歸（Logistic Regression）
• ⽤用Sigmoid曲線去逼近資料的分佈情形
x
y
x
y
訓練
完成

14. 邏輯迴歸（Logistic Regression）
• ⽤用Sigmoid曲線去逼近資料的分佈情形
x
y

15. 訓練資料
X
Y
-0.47241379 0
-0.35344828 0
-0.30148276 0
0.33448276 1
0.35344828 1
0.37241379 1
0.39137931 1
0.41034483 1
0.44931034 1
0.49827586 1
0.51724138 1
…. ….

16. 機器學習模型
Sigmoid function
w0 + w1x 0
h(x) ⇡ 0
w0 + w1x 0
h(x) ⇡ 1
h(x) =
1
1 + e (w0+w1x)

17. 修正模型
• Error function : Cross Entropy
E(h(X), Y ) =
1
m
(
mX
i
y(i)
log(h(x(i)
)) + (1 y(i)
)log(1 h(x(i)
)))
h(x(i)
) ⇡ 0 and y(i)
= 0 ) E(h(X), Y ) ⇡ 0
h(x(i)
) ⇡ 1 and y(i)
= 1 ) E(h(X), Y ) ⇡ 0
h(x(i)
) ⇡ 0 and y(i)
= 1 ) E(h(X), Y ) ⇡ 1
h(x(i)
) ⇡ 1 and y(i)
= 0 ) E(h(X), Y ) ⇡ 1

18. w1
w0
修正模型
• 梯度下降:
w0 w0–⌘
@E(h(X), Y )
@w0
w1 w1–⌘
@E(h(X), Y )
@w1
(
@E(h(X), Y )
@w0
,
@E(h(X), Y )
@w1
)

19. 修正模型

20. 多類別分類
• Softmax
n1
n2
n3
n1,out =
en1,in
en1,in + en2,in + en3,in
n2,out =
en2,in
en1,in + en2,in + en3,in
n3,out =
en3,in
en1,in + en2,in + en3,in
n1,in
n2,in
n3,in

21. 多類別分類
• Softmax
n1,out =
en1,in
en1,in + en2,in + en3,in
n1
n2
n3
n1,in
n2,in
n3,in
n1,in n2,in and
n1,in n3,in
n1,in ⌧ n2,in or
n1,in ⌧ n3,in

22. 多類別分類
nin =
2
4
1
3
2
3
5 nout =
2
6
4
e1
e1+e3+e2
e3
e1+e3+e2
e2
e1+e3+e2
3
7
5 =
2
4
0.09
0.67
0.24
3
5
nin =
2
4
1
1
1
3
5 nout =
2
6
4
e1
e1+e1+e1
e1
e1+e1+e1
e1
e1+e1+e1
3
7
5 =
2
4
0.33
0.33
0.33
3
5
nout =
2
6
4
e1
e1+e5+e2
e5
e1+e5+e2
e2
e1+e5+e2
3
7
5 =
2
4
0.01
0.94
0.05
3
5nin =
2
4
1
5
2
3
5 softmax
softmax
softmax

23. 多類別分類
• One-hot Encoding
Class 1 Class 2 Class 3
1
0
0
0
1
0
0
0
1
n1
n2
n3

24. 多類別分類
• One-hot Encoding
One hot
encoding
2
6
6
4
y(1)
y(2)
y(3)
y(4)
3
7
7
5 =
2
6
6
4
1 0 0
0 0 1
0 1 0
1 0 0
3
7
7
5Y =
2
6
6
4
y(1)
y(2)
y(3)
y(4)
3
7
7
5 =
2
6
6
4
1
3
2
1
3
7
7
5

25. 多類別分類
• Cross-Entropy:
1
0
0
n1
n2
n3
log(n1,out)
E(h(X), Y ) =
1
m
(
mX
i
log(h(x(i)
))y(i) )
y(i)
= 1
n1,out ⇡ 1 and y(i)
= 1 ) E ⇡ 0
n1,out ⇡ 0 and y(i)
= 1 ) E ⇡ 1

26. 多類別分類
• Cross-Entropy:
E(h(X), Y ) =
1
m
(
mX
i
log(h(x(i)
))y(i) )
E(h(X), Y) =
1
4
(log(n
(1)
1,out) + log(n
(2)
3,out) + log(n
(3)
2,out) + log(n
(4)
1,out))
h(X) =
2
6
6
4
h(x(1)
)
h(x(2)
)
h(x(3)
)
h(x(4)
)
3
7
7
5 =
2
6
6
6
4
n
(1)
1,out n
(1)
2,out n
(1)
3,out
n
(2)
1,out n
(2)
2,out n
(2)
3,out
n
(3)
1,out n
(3)
2,out n
(3)
3,out
n
(4)
1,out n
(4)
2,out n
(4)
3,out
3
7
7
7
5
Y =
2
6
6
4
y(1)
y(2)
y(3)
y(4)
3
7
7
5 =
2
6
6
4
1 0 0
0 0 1
0 1 0
1 0 0
3
7
7
5

27. 成效評估
訓練資料
機器學習模型
機器學習模型
測試資料
訓練
完成
輸出值
資料集
成效評估

28. 成效評估
• 準確度（Accurarcy）
Y
h(X)
0 0
1 0
0 0
1 0
0 1
1 0
1 1
1 1
Accurarcy (A) :
A =
counts(Y = h(x))
total
Accurarcy (A)
= 4/8 = 50%

29. 成效評估
h(x)
1 0
Y
1
True
Positive
(TP)
False
Negative
(FN)
0
False
Positive
(FP)
True
Negative
(TN)
P =
TP
TP + FP
R =
TP
TP + FN
F =
2
1
P + 1
R
Precision (P) :
Recall (R) :
F-score (F) :

30. 成效評估
Y
h(X)
0 0
1 0
0 0
1 0
0 1
1 0
1 1
1 1
h(x)
1 0
Y
1 2 3
0 1 2
Precision (P) = 2 / (1+2) = 66.67%
Recall (R) = 2 / (2+3) = 40%
F-score(F) = 2/(1/0.6667+1/0.4) = 50%

31. 成效評估
Y
h(X)
0 1
1 1
1 1
1 1
0 1
1 1
1 1
1 1
h(x)
1 0
Y
1 6 0
0 2 0
Precision (P) = 6 / (6+2) = 75%
Recall (R) = 6 / (6+0) = 100%
F-score(F) = 2/(1/0.75+1/1) = 85.71%

32. 成效評估
Precision of L1: A / (A+D+G)
Precision of L2: E / (B+E+H)
Recall of L1: A / (A+B+C)
Recall of L2: E/ (D+E+F)
h(x)
L1 L2 L3
Y
L1 A B C
L2 D E F
L3 G H I
• 多類別分類
– Confusion matrix

33. 成效評估
• 多類別分類
Y
h(X)
1 1
2 3
3 1
1 1
2 2
3 3
1 2
2 3
3 3
1 3
2 3
3 1
1 1
h(x)
1 2 3
Y
1 3 1 1
2 0 1 3
3 2 0 2
Confusion Matrix

34. 成效評估
• Precision
h(x)
1 2 3
Y
1 3 1 1
2 0 1 3
3 2 0 2
h(x)
1 2 3
Y
1 60% 50% 16.7%
2 0 50% 50%
3 40% 0 33.3%

35. 成效評估
• Recall
h(x)
1 2 3
Y
1 3 1 1
2 0 1 3
3 2 0 2
h(x)
1 2 3
Y
1 60% 20% 20%
2 0 25% 75%
3 50% 0 50%

36. Tensorflow簡介

37. Tensorflow
• https://www.tensorflow.org/
• TensorFlow 是 Google 開發的開源機器學習⼯工具。
• 透過使⽤用Computational Graph，來進⾏行數值演算。
• ⽀支援程式語⾔言：python、C++
• LICENSE: Apache 2.0 open source license
• 系統需求：
– 作業系統必須為Mac或Linux
– Python 2.7 或 3.3 （含以上）

38. Computational Graph

39. Tensorflow
機器學習Library
(ex, scikit-learn)
TensorFlow 從頭開始寫
彈性
技術門檻
把資料整理好後，剩
下的就直接呼叫API
自行定義
Computational Graph，
並交由TensorFlow計算。
自己推導微分公式，
自己寫整個流程
低
低
高
高

40. Tensorflow
• 彈性
– 只要是可以⽤用Computational Graph來表達的運
算，都可以⽤用Tensorflow來解。
• ⾃自動微分
– ⾃自動計算Computational Graph微分後的結果。
• 平台相容性
– 同樣的程式碼可⽤用CPU執⾏行，亦可⽤用GPU執⾏行。

41. CPU V.S GPU
http://allegroviva.com/gpu-computing/difference-between-gpu-and-cpu/

42. Tensorflow安裝

43. Tensorflow安裝
• 1. 安裝pyenv
• 2. 安裝anaconda-2.x.x
• 3. 安裝tensorflow

44. 1.安裝pyenv
• Mac OSX Homebrew
– 輸⼊入指令：
• From Github
– 輸⼊入指令： （使⽤用Ubuntu者請將.bash_profile 改為.bashrc）
cd ~
git clone https://github.com/yyuu/pyenv.git ~/.pyenv
echo 'export PYENV_ROOT=$HOME/.pyenv' ~/.bash_profile
echo 'export PATH=$PYENV_ROOT/bin:$PATH' ~/.bash_profile
echo 'eval $(pyenv init -)' ~/.bash_profile
source ~/.bash_profile
brew update
brew install pyenv

45. 1.安裝pyenv
• 安裝完後，輸⼊入：
• 若顯⽰示以下訊息，則表⽰示pyenv安裝成功
pyenv
pyenv 20160422
Usage: pyenv command [args]
Some useful pyenv commands are:
commands List all available pyenv commands
local Set or show the local application-specific Python version
global Set or show the global Python version
shell Set or show the shell-specific Python version
install Install a Python version using python-build
……

46. 2. 安裝anaconda-2.x.x
• 輸⼊入指令，以安裝anaconda-2.4.0
• 安裝完後，輸⼊入指令，切換環境到anaconda-2.4.0
• 切換完之後，輸⼊入：
• 若顯⽰示anaconda-2.4.0，則表⽰示安裝成功
pyenv install anaconda-2.4.0
pyenv global anaconda-2.4.0
pyenv global
anaconda-2.4.0

47. 3. 安裝Tensorflow
• Ubuntu/Linux 64-bit, CPU only
– 輸⼊入指令：
• Mac OS X, CPU only:
– 輸⼊入指令：
sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/
cpu/tensorflow-0.7.1-cp27-none-linux_x86_64.whl
sudo easy_install --upgrade six
sudo pip install --upgrade pip
sudo pip install --upgrade https://storage.googleapis.com/tensorflow/mac/
tensorflow-0.7.1-cp27-none-any.whl

48. 3. 安裝tensorflow
• 安裝完Tensorflow後，輸⼊入指令：
• 進⼊入python shell後，輸⼊入指令：
• 若無任何錯誤訊息，則表⽰示Tensorflow安裝成功。
python
Import tensorflow as tf

49. 單層感知器實作

50. 單層感知器實作
https://github.com/ckmarkoh/ntc_deeplearning_tensorflow/
blob/master/sec1/mnist_train.ipynb

51. MNIST
• 數字識別
• 多元分類：0~9
https://www.tensorflow.org/versions/r0.7/images/MNIST.png

52. 訓練資料
https://www.tensorflow.org/versions/r0.7/images/MNIST-Matrix.png
28x28 = 784
28
28

53. 訓練資料
https://www.tensorflow.org/versions/r0.7/images/mnist-train-xs.png
https://www.tensorflow.org/versions/r0.7/images/mnist-train-ys.png
X
Y

54. 模型
• 單層感知器（One-layer Perceptron）
https://www.tensorflow.org/versions/r0.7/images/softmax-regression-scalargraph.png

55. 訓練後
5
0
4

56. Tensorflow
x_ = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x_, W) + b)
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
optimizer = tf.train.GradientDescentOptimizer(0.01)
trainer = optimizer.minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
sess.run(train, feed_dict={x_:x_data,y_:y_data})
print sess.run(cross_entropy, feed_dict={x_:x_data,y_:y_data})
sess.close()
Computational
Graph
Session

57. Computation Graph
# placeholder
x_ = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
# variable
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# operations
y = tf.nn.softmax(tf.matmul(x_, W) + b)
# error function
cross_entropy = -tf.reduce_sum(y_* tf.log(y))
# trainer
optimizer = tf.train.GradientDescentOptimizer(0.01)
trainer = optimizer.minimize(cross_entropy)
# initalizer
init = tf.initialize_all_variables()

58. Placeholder
0.
…
0.
0.
…
0.
0.
…
0.1
0.
…
0.
0.
…
0.2
0.
…
0.
…
…
…
x_ y_
x_ = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
0.
…
0.
0.
…
0.
0.
…
1.
1.
…
0.
0.
…
0.
0.
…
0.
…
…
…
784 10
n

59. Variable
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
bw
0.
……..
0.
0.
……..
0.
0.
……..
0.
……..
……..
……..
0.
……..
0.
0.
……..
0.
0.
0.
……..
0.
0.
784
10
10

60. Matrix Multiplication
y = tf.nn.softmax(tf.matmul(x_, W) + b)
https://www.tensorflow.org/versions/r0.8/images/softmax-regression-scalarequation.png

61. Matrix Multiplication
y = tf.nn.softmax(tf.matmul(x_, W) + b)
https://www.tensorflow.org/versions/r0.8/images/softmax-regression-vectorequation.png

62. Batch
• 為了提升運算速度，通常會同時平⾏行計算好幾
筆訓練資料。
• 同時計算的訓練資料，稱為BATCH
• Ex, BATCH⼤大⼩小為4：
x =
2
6
6
6
4
x
(1)
1 x
(1)
2 x
(1)
3
x
(2)
1 x
(2)
2 x
(2)
3
x
(3)
1 x
(3)
2 x
(3)
3
x
(4)
1 x
(4)
2 x
(4)
3
3
7
7
7
5

63. Matrix Multiplication with Batch
y = tf.nn.softmax(tf.matmul(x_, W) + b)
x =
2
6
6
6
4
x
(1)
1 x
(1)
2 x
(1)
3
x
(2)
1 x
(2)
2 x
(2)
3
x
(3)
1 x
(3)
2 x
(3)
3
x
(4)
1 x
(4)
2 x
(4)
3
3
7
7
7
5
W =
2
4
W1,1 W1,2 W1,3
W2,1 W2,2 W2,3
W3,1 W3,2 W3,3
3
5 b =
2
4
b1
b2
b3
3
5
matmul(x , W) + b
=
2
6
6
6
4
x
(1)
1 W1,1 + x
(1)
2 W2,1 + x
(1)
3 W3,1 + b1 · · · x
(1)
1 W1,3 + x
(1)
2 W2,3 + x
(1)
3 W3,3 + b3
x
(2)
1 W1,1 + x
(2)
2 W2,1 + x
(2)
3 W3,1 + b1 · · · x
(2)
1 W1,3 + x
(2)
2 W2,3 + x
(2)
3 W3,3 + b3
x
(3)
1 W1,1 + x
(3)
2 W2,1 + x
(3)
3 W3,1 + b1 · · · x
(3)
1 W1,3 + x
(3)
2 W2,3 + x
(3)
3 W3,3 + b3
x
(4)
1 W1,1 + x
(4)
2 W2,1 + x
(4)
3 W3,1 + b1 · · · x
(4)
1 W1,3 + x
(4)
2 W2,3 + x
(4)
3 W3,3 + b3
3
7
7
7
5

64. Matrix Multiplication with Batch
y = tf.nn.softmax(tf.matmul(x_, W) + b)
w
x_
tf.matmul(x_,w)+b
b
0.
…..
0.
0.
…..
0.
…..
…..
…..
784
0.
…..
0.
…..
…..
…..
0.
…..
0.
10
10
0.
…..
0.
784
n
0.
…..
0.
…..
…..
…..
0.
…..
0.
10
n

65. Softmax
y = tf.nn.softmax(X)
X =
2
6
6
6
4
X
(1)
1 X
(1)
2 X
(1)
3
X
(2)
1 X
(2)
2 X
(2)
3
X
(3)
1 X
(3)
2 X
(3)
3
X
(4)
1 X
(4)
2 X
(4)
3
3
7
7
7
5
y = softmax(X)
=
2
6
6
6
6
6
6
6
4
X
(1)
1
X
(1)
1 +X
(1)
2 +X
(1)
3
X
(1)
2
X
(1)
1 +X
(1)
2 +X
(1)
3
X
(1)
3
X
(1)
1 +X
(1)
2 +X
(1)
3
X
(2)
1
X
(2)
1 +X
(2)
2 +X
(2)
3
X
(2)
2
X
(2)
1 +X
(2)
2 +X
(2)
3
X
(2)
3
X
(2)
1 +X
(2)
2 +X
(2)
3
X
(3)
1
X
(3)
1 +X
(3)
2 +X
(3)
3
X
(3)
2
X
(3)
1 +X
(3)
2 +X
(3)
3
X
(3)
3
X
(3)
1 +X
(3)
2 +X
(3)
3
X
(4)
1
X
(4)
1 +X
(4)
2 +X
(4)
3
X
(4)
2
X
(4)
1 +X
(4)
2 +X
(4)
3
X
(4)
3
X
(4)
1 +X
(4)
2 +X
(4)
3
3
7
7
7
7
7
7
7
5

66. Softmax
y = tf.nn.softmax(tf.matmul(x_, W) + b)
tf.nn.sigmoid
0.
…..
0.
…..
…..
…..
0.
…..
0.
10
n
0.
…..
0.
…..
…..
…..
0.
…..
0.
10
n

67. Error Function
cross_entropy = -tf.reduce_sum(y_* tf.log(y))
y =
2
6
6
4
1 0 0
0 1 0
1 0 0
0 0 1
3
7
7
5 log(y) =
2
6
6
6
4
log(y
(1)
1 ) log(y
(1)
2 ) log(y
(1)
3 )
log(y
(2)
1 ) log(y
(2)
2 ) log(y
(2)
3 )
log(y
(3)
1 ) log(y
(3)
2 ) log(y
(3)
3 )
log(y
(4)
1 ) log(y
(4)
2 ) log(y
(4)
3 )
3
7
7
7
5
y ⇤ log(y) =
2
6
6
6
4
log(y
(1)
1 ) 0 0
0 log(y
(2)
2 ) 0
log(y
(3)
1 ) 0 0
0 0 log(y
(4)
3 )
3
7
7
7
5

68. Error Function
cross_entropy = -tf.reduce_sum(y_* tf.log(y))
y ⇤ log(y) =
2
6
6
6
4
log(y
(1)
1 ) 0 0
0 log(y
(2)
2 ) 0
log(y
(3)
1 ) 0 0
0 0 log(y
(4)
3 )
3
7
7
7
5
reduced sum(y ⇤ log(y))
= log(y
(1)
1 ) + log(y
(2)
2 ) + log(y
(3)
1 ) + log(y
(4)
3 )

69. Error Function
cross_entropy = -tf.reduce_sum(y_* tf.log(y))
y_ y
1.4331052
-tf.reduce_sum(y_*tf.log(y))
0.
…..
0.
…..
…..
…..
0.
…..
0.
10
n
0.
…..
0.
…..
…..
…..
0.
…..
0.
10
n

70. Trainer
optimizer = tf.train.GradientDescentOptimizer(0.1)
train = optimizer.minimize(cross_entropy)

71. Trainer
w w ⌘
@E(h(X), Y )
@w
b b ⌘
@E(h(X), Y )
@b

72. Computation Graph
• Initializer
init = tf.initialize_all_variables()
w
b
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
0.
……..
0.
……..
……..
……..
0.
……..
0.
0.
……..
0.

73. Session
# create session
sess = tf.Session()
# initialize variable
sess.run(init)
# gradient descent
for step in xrange(1000):
sess.run(train, feed_dict={x_:x_data,y_:y_data})
# fetch variable
print sess.run(cross_entropy, feed_dict={x_:x_data,y_:y_data})
# release resource
sess.close()

74. Run Operations
sess.run(init)
the Node in
Computational
Graph

75. Run Operations
for step in xrange(1000):
sess.run(train, feed_dict={x_:x_data,y_:y_data} )
the Node in
Computational
Graph
Input
Data
x_data y_data
0.
…
0.
0.
…
0.
0.
…
0.1
0.
…
0.
0.
…
0.2
0.
…
0.
…
…
…
0.
…
0.
0.
…
0.
0.
…
1.
1.
…
0.
0.
…
0.
0.
…
0.
…
…
…

76. Run Operations
print sess.run(cross_entropy, feed_dict={x_:x_data,y_:y_data})
the Node in
Computational
Graph
Input
Data
x_data y_data
Results
2.4564333 0.
…
0.
0.
…
0.
0.
…
0.1
0.
…
0.
0.
…
0.2
0.
…
0.
…
…
…
0.
…
0.
0.
…
0.
0.
…
1.
1.
…
0.
0.
…
0.
0.
…
0.
…
…
…

77. 成效評估
# computational graph
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# session
result_accurarcy = sess.run(accuracy, feed_dict={x_: mnist.test.images,
y_: mnist.test.labels})

78. 成效評估
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
y =
2
6
6
4
0.8 0.2 0
0 1.0 0.0
0.4 0.5 0.1
0 0.1 0.9
3
7
7
5 y =
2
6
6
4
1 0 0
0 1 0
1 0 0
0 0 1
3
7
7
5
argmax(y) =
2
6
6
4
0
1
1
2
3
7
7
5 argmax(y ) =
2
6
6
4
0
1
0
2
3
7
7
5
equal(argmax(y), argmax(y )) =
2
6
6
4
True
True
False
True
3
7
7
5

79. 成效評估
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
correct prediction =
2
6
6
4
True
True
False
True
3
7
7
5
cast(correct prediction, float32) =
2
6
6
4
1.0
1.0
0.0
1.0
3
7
7
5
reduced mean(cast(correct prediction, float32)) = 0.75

80. 儲存模型參數
# 開啟新的json檔
f = open(model1.json,w”)
# 取得W和b的數值
result_W = sess.run(W)
result_b = sess.run(b)
# 轉換成json string
json_to_write = json.dumps({
W: result_W.tolist(),
b: result_b.tolist(),
},indent=4)
# 寫入json檔案
f.write(json_to_write)
# 關閉json檔案
f.close()

81. 單層感知器模型載⼊入
https://github.com/ckmarkoh/ntc_deeplearning_tensorflow/
blob/master/sec1/mnist_load.ipynb

82. 載⼊入模型參數
# 建立新的Variable
W = tf.Variable(model_json[W], name=W)
b = tf.Variable(model_json[b], name=b”)
# 更改既有的Variable
sess.run( W.assign(model_json[W”]) )
sess.run( b.assign(model_json[b”]) )

83. 單層感知器Tensorboard
https://github.com/ckmarkoh/ntc_deeplearning_tensorflow/
blob/master/sec1/mnist_board.ipynb

84. Tensorboard
Histogram Summary
Scalar Summary Computational Graph

85. Scalar Summary
summ_ce = tf.scalar_summary(cross_entropy, cross_entropy)
summ_acc = tf.scalar_summary(accuracy, accuracy)
cross_entropy accurarcy

86. Histogram Summary
summ_W = tf.histogram_summary(weights, W)
summ_b = tf.histogram_summary(biases, b)
weights biases

87. Summary Writer
summ_merged = tf.merge_summary([summ_W, summ_b, summ_ce])
writer = tf.train.SummaryWriter(./, sess.graph_def)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
result1 = sess.run(tf.log(y), feed_dict={x_: batch_xs, y_: batch_ys})
result2 = sess.run(y_ * tf.log(y), feed_dict={x_: batch_xs, y_: batch_ys})
sess.run(trainer, feed_dict={x_: batch_xs, y_: batch_ys})
summ_str = sess.run(summ_merged,feed_dict={x_:batch_xs,y_:batch_ys})
writer.add_summary(summ_str, i)
if (i+1)%5 == 0:
summary_str = sess.run(summ_acc,feed_dict={x_:mnist.test.images,
y_:mnist.test.labels})
writer.add_summary(summ_str, i)

88. name_scope
with tf.name_scope(cross_entropy) as scope:
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))

89. Launch Tensorboard
tensorboard --logdir=./
Starting TensorBoard on port 6006
(You can navigate to http://0.0.0.0:6006)

90. 講師資訊
• Email: ckmarkoh at gmail dot com
• Blog: http://cpmarkchang.logdown.com
• Github: https://github.com/ckmarkoh
Mark Chang
• Facebook: https://www.facebook.com/ckmarkoh.chang
• Slideshare: http://www.slideshare.net/ckmarkohchang
• Linkedin:
https://www.linkedin.com/pub/mark-chang/85/25b/847
90