готово Final_Al copy (4 и³ 4) Q6) Autoencoder and VAE a) Explain how autoencoder works and mention some of its applications b) Explain the following code input = layers.Input (shape=(28, 28, 1)) x = layers. Conv2D(32, (3, 3), activation="relu", padding="same") (input) x = layers.MaxPooling2D((2, 2), padding="same") (x) x = layers. Conv2D(32, (3, 3), activation="relu", padding="same") (x) x = layers.MaxPooling2D((2, 2), padding="same") (x) x = layers. Conv2DTranspose (32, (3, 3), strides=2, activation="relu", padding="same") (x) x = layers. Conv2DTranspose (32, (3, 3), strides=2, activation="relu", padding="same") (x) 10 x = layers. Conv2D(1, (3, 3), activation="sigmoid", padding="same") (x) 11 12 autoencoder= Model(input, x) 13 autoencodex.compile(optimizer="adam", loss="binary_crossentropy") 14 autoencodex.summary()

Computer Networking: A Top-Down Approach (7th Edition)
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ISBN:9780133594140
Author:James Kurose, Keith Ross
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Chapter1: Computer Networks And The Internet
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Готово
Final_Al copy (4 и³ 4)
Q6) Autoencoder and VAE
a) Explain how autoencoder works and mention some of its
applications
b) Explain the following code
1
input = layers. Input (shape=(28, 28, 1))
2
3
x = layers. Conv2D(32, (3, 3), activation="relu",
padding="same") (input)
4
x = layers.MaxPooling2D((2, 2), padding="same") (x)
5
x = layers. Conv2D(32, (3, 3), activation="relu",
padding="same") (x)
6
x = layers. MaxPooling2D((2, 2), padding="same") (x)
7
8
x = layers. Conv2DTranspose (32, (3, 3), strides=2,
activation="relu", padding="same") (x)
9
x = layers. Conv2DTranspose (32, (3, 3), strides=2,
activation="relu", padding="same") (x)
10
x = layers. Conv2D(1, (3, 3), activation="sigmoid",
padding="same") (x)
11
12 autoencoder = Model (input, x)
13 autoencoder.compile (optimizer="adam",
loss="binary_crossentropy")
14 autoencoder.summary()
Transcribed Image Text:Готово Final_Al copy (4 и³ 4) Q6) Autoencoder and VAE a) Explain how autoencoder works and mention some of its applications b) Explain the following code 1 input = layers. Input (shape=(28, 28, 1)) 2 3 x = layers. Conv2D(32, (3, 3), activation="relu", padding="same") (input) 4 x = layers.MaxPooling2D((2, 2), padding="same") (x) 5 x = layers. Conv2D(32, (3, 3), activation="relu", padding="same") (x) 6 x = layers. MaxPooling2D((2, 2), padding="same") (x) 7 8 x = layers. Conv2DTranspose (32, (3, 3), strides=2, activation="relu", padding="same") (x) 9 x = layers. Conv2DTranspose (32, (3, 3), strides=2, activation="relu", padding="same") (x) 10 x = layers. Conv2D(1, (3, 3), activation="sigmoid", padding="same") (x) 11 12 autoencoder = Model (input, x) 13 autoencoder.compile (optimizer="adam", loss="binary_crossentropy") 14 autoencoder.summary()
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