1 input = layers. Input (shape=(28, 28, 1)) 2 3 X = layers. Conv2D(32, (3, 3), activation="relu", padding="same") (input) 4 5 x = layers.MaxPooling2D((2, 2), padding="same") (x) 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) Le 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() W NH

1 input = layers. Input (shape=(28, 28, 1)) 2 3 X = layers. Conv2D(32, (3, 3), activation="relu", padding="same") (input) 4 5 x = layers.MaxPooling2D((2, 2), padding="same") (x) 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) Le 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() W NH

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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