INPUT CONVI: 32 11x11 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
INPUT CONVI: 32 11x11 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
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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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](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F753416b6-ea52-42ce-8e81-5c5a3d522944%2F4a3b2b49-d292-4843-96c9-bd9dbada0912%2Fn9pynw_processed.png&w=3840&q=75)
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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