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Forward propagation Notation Input Output n : number of features

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Presentation on theme: "Forward propagation Notation Input Output n : number of features"— Presentation transcript:

1 Forward propagation Notation Input Output n : number of features
Z1 Z2 x1 Notation Input n : number of features P : number of data Output m : number of features x2 xn Zm

2 Matrix multiplication
(2 X 3) (3X 2)  (2 X 2) 일반적으로 (A X B) (B X C)  (A X C)

3

4 Z1 w11 x1 w12 Number of input feature : 2 Number of output feature : 3 Number of parameters : 2 X 3 Number of input feature : n Number of output feature : m Number of parameters : n X m w13 Z2 w21 w22 x2 w23 Z3

5 Z1 = x1*w11 + x2*w21 Number of input feature : 2 Number of output feature : 3 Number of parameters : 2 X 3 w11 x1 w12 w13 Z2 = x1*w12+x2*w22 w21 w11 w12 w13 w21 w22 w23 w22 [x1, x2] X x2 = [X1*w11 + x2*w21, x1*w12+x2*w22, x1*w13+x2*w23] w23 Z3 = X1*w13+x2*w23 (1 X 2) (2 X 3)  (1 X 3)

6 Number of input feature : 2
Number of output feature : 3 Number of parameters : 2 X 3 Number of input feature : n Number of output feature : m Number of parameters : n X m (1 X 2) (2 X 3)  (1 X 3) (1 X n) (n X m)  (1 X m) [x1, x2] X w11 w12 w13 w21 w22 w23 = [X1*w11 + x2*w21, x1*w12+x2*w22, x1*w13+x2*w23]

7 17 Z1 x1 23 x2 4 Z2 10 1 11 x5 Z3

8 Forward propation Notation Input Output n : number of features
1st data: Notation Input n : number of features p : number of data Output m : number of features 2nd data : [ ] [ ]

9 1st data: 2nd data : [ ] [ ]

10 2 X 5 5 X 3 2 X 3 n : number of input feature p : number of data p : number of data m : number of output feature

11

12 Forward propagation Apply activation Sigmoid. ….. sigmoid Z1 Z2 x1 x2
xn Zm

13 parameter input sigmoid output

14 W1 W2 W3 W4 input hidden1 hidden2 hidden3 output n features m features
o features p features q features n X m m X o o X p p X q Z1 = np.dot(input, W1) O1 = sigmoid(Z1) Z2 = np.dot(O1, W2) O2 = sigmoid(Z2) Z3 = np.dot(O2, W3) O3 = sigmoid(Z3) Z4 = np.dot(O3, W4) O4 = sigmoid(Z4)


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