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A) Given the following CNN network architecture, calculate the shape of the outputs for cach layer. CONV is a convolution layer, and POOL is a max pooling layer. The outputs for earch layers feeds into the next layer going downwards. Use the 3rd dimension for the number of depth/channels. Show your work. INPUT [256x256x3] CONVI: 32 11xl1 filters at stride 4, padding 0 POOLI: 3x3 filters at stride 2, padding 0 CONV2: 64 5x5 filters at stride 1, padding 0 POOL2: 3x3 filters at stride 4, padding 0 CONV3: 128 5x5 filters at stride 1, padding 1

A) Given the following CNN network architecture, calculate the shape of the outputs for cach layer. CONV is a convolution layer, and POOL is a max pooling layer. The outputs for earch layers feeds into the next layer going downwards. Use the 3rd dimension for the number of depth/channels. Show your work. INPUT [256x256x3] CONVI: 32 11xl1 filters at stride 4, padding 0 POOLI: 3x3 filters at stride 2, padding 0 CONV2: 64 5x5 filters at stride 1, padding 0 POOL2: 3x3 filters at stride 4, padding 0 CONV3: 128 5x5 filters at stride 1, padding 1

Introductory Circuit Analysis (13th Edition)
Introductory Circuit Analysis (13th Edition)
13th Edition
ISBN:9780133923605
Author:Robert L. Boylestad
Publisher:Robert L. Boylestad
Chapter1: Introduction
Section: Chapter Questions
Problem 1P: Visit your local library (at school or home) and describe the extent to which it provides literature...
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A) Given the following CNN network architecture, calculate the shape of the outputs for each layer. CONV is a convolution layer, and POOL is a max pooling layer. The outputs for earch layers feeds into the next layer going downwards. Use the 3rd dimension for the number of depth/channels. Show your work. INPUT [256x256x3] CONVI: 32 11x11 filters at stride 4, padding 0 POOL1: 3x3 filters at stride 2, padding 0 CONV2: 64 5x5 filters at stride 1, padding 0 POOL2: 3x3 filters at stride 4, padding 0 CONV3: 128 5x5 filters at stride 1, padding 1
Transcribed Image Text:A) Given the following CNN network architecture, calculate the shape of the outputs for each layer. CONV is a convolution layer, and POOL is a max pooling layer. The outputs for earch layers feeds into the next layer going downwards. Use the 3rd dimension for the number of depth/channels. Show your work. INPUT [256x256x3] CONVI: 32 11x11 filters at stride 4, padding 0 POOL1: 3x3 filters at stride 2, padding 0 CONV2: 64 5x5 filters at stride 1, padding 0 POOL2: 3x3 filters at stride 4, padding 0 CONV3: 128 5x5 filters at stride 1, padding 1
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