CNN: In this example we try to understand how convolution works in a Convolutional Neural Network (CNN) by manually applying a 3 × 3 filter on a 4 × 4 image matrix shown in the Fig. We also try to understand forward and backward pass in CNN by manually calculating for the given image. 1. Consider the input image with only 1 channel in Fig.2. Assume there is no padding for the image, compute the output matrix when the given convolution filter W is applied. 2. After the convolution computation in the above sub problem, a max pool of 2 × 2 is applied. Find the final output ˆy after the pooling layer.
CNN: In this example we try to understand how convolution works in a Convolutional Neural Network (CNN) by manually applying a 3 × 3 filter on a 4 × 4 image matrix shown in the Fig. We also try to understand forward and backward pass in CNN by manually calculating for the given image. 1. Consider the input image with only 1 channel in Fig.2. Assume there is no padding for the image, compute the output matrix when the given convolution filter W is applied. 2. After the convolution computation in the above sub problem, a max pool of 2 × 2 is applied. Find the final output ˆy after the pooling layer.
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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CNN: In this example we try to understand how convolution works in a Convolutional Neural Network (CNN) by manually applying a 3 × 3 filter on a 4 × 4 image matrix shown in the Fig. We also try to understand forward and
backward pass in CNN by manually calculating for the given image.
1. Consider the input image with only 1 channel in Fig.2. Assume there is no padding for the image, compute the output matrix when the given convolution filter W is applied.
2. After the convolution computation in the above sub problem, a max pool of 2 × 2 is applied. Find the final output ˆy after the pooling layer.
Transcribed Image Text:1230
Output
|0 2 2 1
0 300
0121
Max Pool 2 x 2
w
1
0 -1
True Output
010
2
-1
1
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