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Suppose the input to a convolution NN is a 32 x 32 color image. We want to apply eight convolution filters to it, all with the shape 5 x 5. We are using Valid padding and a stride of two both vertically and horizontally. (a) What is the shape of the variable in which we store the filters' values? (b) What is the shape of the output of tf.nn.conv2d?

Suppose the input to a convolution NN is a 32 x 32 color image. We want to apply eight convolution filters to it, all with the shape 5 x 5. We are using Valid padding and a stride of two both vertically and horizontally. (a) What is the shape of the variable in which we store the filters' values? (b) What is the shape of the output of tf.nn.conv2d?

Computer Networking: A Top-Down Approach (7th Edition)
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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Suppose the input to a convolution NN is a 32 x 32 color image. We want to apply eight convolution filters to it, all with the shape 5 x 5. We are using Valid padding and a stride of two both vertically and horizontally. (a) What is the shape of the variable in which we store the filters' values? (b) What is the shape of the output of tf.nn.conv2d?
Transcribed Image Text:Suppose the input to a convolution NN is a 32 x 32 color image. We want to apply eight convolution filters to it, all with the shape 5 x 5. We are using Valid padding and a stride of two both vertically and horizontally. (a) What is the shape of the variable in which we store the filters' values? (b) What is the shape of the output of tf.nn.conv2d?
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