a.) Let's consider first 5 layers of CNN AlexNet and calculate: size and number of activation maps in each layer as well as number of weights (only in 5 layers) that needs to be optimized using back-propagation algorithm. b.) Which type on machine learning is used by CNNs (supervised or unsupervised learning) Case Study: AlexNet [Krizhevsky et al. 2012] Full (simplified) AlexNet architecture: [227x227x3] INPUT Stride Max pooling 3224 128 Max pooling 282 128 dense 128 Max pooling CONV1: 96 11x11 filters at stride 4, pad 0 MAX POOL1: 3x3 filters at stride 2 NORM1: Normalization layer CONV2: 256 5x5 filters at stride 1, pad 2 MAX POOL2: 3x3 filters at stride 2 (only relevant part of AlexNet is listed above however the figure shows full AlexNet) fo dense
a.) Let's consider first 5 layers of CNN AlexNet and calculate: size and number of activation maps in each layer as well as number of weights (only in 5 layers) that needs to be optimized using back-propagation algorithm. b.) Which type on machine learning is used by CNNs (supervised or unsupervised learning) Case Study: AlexNet [Krizhevsky et al. 2012] Full (simplified) AlexNet architecture: [227x227x3] INPUT Stride Max pooling 3224 128 Max pooling 282 128 dense 128 Max pooling CONV1: 96 11x11 filters at stride 4, pad 0 MAX POOL1: 3x3 filters at stride 2 NORM1: Normalization layer CONV2: 256 5x5 filters at stride 1, pad 2 MAX POOL2: 3x3 filters at stride 2 (only relevant part of AlexNet is listed above however the figure shows full AlexNet) fo dense
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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![a.) Let's consider first 5 layers of CNN AlexNet and calculate: size and number of activation
maps in each layer as well as number of weights (only in 5 layers) that needs to be optimized
using back-propagation algorithm.
b.) Which type on machine learning is used by CNNs (supervised or unsupervised learning)
Case Study: AlexNet
[Krizhevsky et al. 2012]
Full (simplified) AlexNet architecture:
[227x227x3] INPUT
224Stride
Max
pooling
27
128
128
Max
pooling
192
192
192
128
dense dense
128 Max
pooling
2048 25 dense
CONV1: 96 11x11 filters at stride 4, pad 0
MAX POOL1: 3x3 filters at stride 2
NORM1: Normalization layer
CONV2: 256 5x5 filters at stride 1, pad 2
MAX POOL2: 3x3 filters at stride 2
(only relevant part of AlexNet is listed above however the figure shows full AlexNet)
2048 2048
1000](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3831194d-a4dd-43eb-950f-f7d241d3bcca%2Fe57d8b08-2c71-4c14-99d5-271055f2d9f2%2Fkk8wkbrj_processed.jpeg&w=3840&q=75)
Transcribed Image Text:a.) Let's consider first 5 layers of CNN AlexNet and calculate: size and number of activation
maps in each layer as well as number of weights (only in 5 layers) that needs to be optimized
using back-propagation algorithm.
b.) Which type on machine learning is used by CNNs (supervised or unsupervised learning)
Case Study: AlexNet
[Krizhevsky et al. 2012]
Full (simplified) AlexNet architecture:
[227x227x3] INPUT
224Stride
Max
pooling
27
128
128
Max
pooling
192
192
192
128
dense dense
128 Max
pooling
2048 25 dense
CONV1: 96 11x11 filters at stride 4, pad 0
MAX POOL1: 3x3 filters at stride 2
NORM1: Normalization layer
CONV2: 256 5x5 filters at stride 1, pad 2
MAX POOL2: 3x3 filters at stride 2
(only relevant part of AlexNet is listed above however the figure shows full AlexNet)
2048 2048
1000
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