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2021-11-02
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Nov 3 In-Class Exercise Thread .

Please post to this thread your solution to the Nov 2 In-Class Exercise Thread.

Best,

Chris

(Edited: 2021-11-08)
Please post to this thread your solution to the Nov 2 In-Class Exercise Thread. Best, Chris
2021-11-03
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-- Nov 2 In-Class Exercise Thread

Our First Layer was

Conv2D(64, (3,3), padding="same", input_shape=(64, 64, 1))

Input m: 64 * 64 = 4096

Output n: No. of filters * Output shape = 64 * (64 * 64) = 262144

Therefore, using Glorot and Bengio initialization

W_(i,j) ~ U (-(6/(4096+262144))^0.5, (6/(4096+262144))^0.5) W_(i,j) ~ U (-0.00475, 0.00475)

(Edited: 2021-11-03)
Our First Layer was Conv2D(64, (3,3), padding="same", input_shape=(64, 64, 1)) Input m: 64 * 64 = 4096 Output n: No. of filters * Output shape = 64 * (64 * 64) = 262144 Therefore, using Glorot and Bengio initialization @BT@W_(i,j) ~ U (-(6/(4096+262144))^0.5, (6/(4096+262144))^0.5)@BT@ @BT@W_(i,j) ~ U (-0.00475, 0.00475)@BT@
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-- Nov 2 In-Class Exercise Thread

Resource Description for Screenshot 2021-11-03 165701.png

((resource:Screenshot 2021-11-03 165701.png|Resource Description for Screenshot 2021-11-03 165701.png))
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-- Nov 2 In-Class Exercise Thread

Resource Description for WhatsApp Image 2021-11-03 at 4.56.55 PM.jpeg

((resource:WhatsApp Image 2021-11-03 at 4.56.55 PM.jpeg|Resource Description for WhatsApp Image 2021-11-03 at 4.56.55 PM.jpeg))
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-- Nov 2 In-Class Exercise Thread

Resource Description for WhatsApp Image 2021-11-03 at 4.55.38 PM.jpeg

((resource:WhatsApp Image 2021-11-03 at 4.55.38 PM.jpeg|Resource Description for WhatsApp Image 2021-11-03 at 4.55.38 PM.jpeg))
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-- Nov 2 In-Class Exercise Thread

Conv2D(32, kernel_size=(3, 3), input_shape=(64,64,3)) <br> m=64 x 64 =4096 <br> n= 64 x 64 x 32 = 131072 <br> Wi,jU(-6/(m+n), 6/(m+n) <br> Wi,jU(-6/135168, 6/135168) <br> Wi,j~U(-0.0066, 0.0066)

(Edited: 2021-11-03)
Conv2D(32, kernel_size=(3, 3), input_shape=(64,64,3)) <br> m=64 x 64 =4096 <br> n= 64 x 64 x 32 = 131072 <br> Wi,j~U(-√6/(m+n), √6/(m+n) <br> Wi,j~U(-√6/135168, √6/135168) <br> Wi,j~U(-0.0066, 0.0066)
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-- Nov 2 In-Class Exercise Thread

Resource Description for 7, Giorot.jpg

((resource:7, Giorot.jpg|Resource Description for 7, Giorot.jpg))
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-- Nov 2 In-Class Exercise Thread

Resource Description for 20211103_171826.jpg

((resource:20211103_171826.jpg|Resource Description for 20211103_171826.jpg))
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-- Nov 2 In-Class Exercise Thread

model.add(Conv2D(32, (3, 3), activation=&#039;relu&#039;, kernel_initializer=&#039;he_uniform&#039;, input_shape=(64, 64, 1)))

Input and Output (m,n): m= 6464= 4096 n= 6464*32= 131072

Resource Description for WhatsApp Image 2021-11-03 at 5.06.06 PM.jpeg

model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', input_shape=(64, 64, 1))) Input and Output (m,n): m= 64*64= 4096 n= 64*64*32= 131072 ((resource:WhatsApp Image 2021-11-03 at 5.06.06 PM.jpeg|Resource Description for WhatsApp Image 2021-11-03 at 5.06.06 PM.jpeg))
2021-11-06
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-- Nov 2 In-Class Exercise Thread

First layer: Conv2D(64, (3,3), padding=&quot;same&quot;, input_shape=(64, 64, 1))

which gives m = 64 * 64 and n = 64 * 64 * 64

Resource Description for inclass cnn layer.jpg

First layer: Conv2D(64, (3,3), padding="same", input_shape=(64, 64, 1)) which gives m = 64 * 64 and n = 64 * 64 * 64 ((resource:inclass cnn layer.jpg|Resource Description for inclass cnn layer.jpg))
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