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.

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
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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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