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Layer INPUT CONV-9-32 POOL-2 CONV-5-64 POOL-2 CONV-5-64 POOL-2 FC-3 Activation map dimensions Number of weights Number of biases 128x128 x 3 0 0

Layer INPUT CONV-9-32 POOL-2 CONV-5-64 POOL-2 CONV-5-64 POOL-2 FC-3 Activation map dimensions Number of weights Number of biases 128x128 x 3 0 0

Database System Concepts
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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You come up with a CNN classifier. For each layer, calculate the number
of weights, number of biases and the size of the associated feature maps.
The notation follows the convention:
• CONV-K-N denotes a convolutional layer with N filters, each them of size K×K,
Padding and stride parameters are always 0 and 1 respectively.
• POOL-K indicates a K × K pooling layer with stride K and padding 0.
• FC-N stands for a fully-connected layer with N neurons.

Layer INPUT CONV-9-32 POOL-2 CONV-5-64 POOL-2 CONV-5-64 POOL-2 FC-3 Activation map dimensions Number of weights Number of biases 128x128 x 3 0 0
Transcribed Image Text:Layer INPUT CONV-9-32 POOL-2 CONV-5-64 POOL-2 CONV-5-64 POOL-2 FC-3 Activation map dimensions Number of weights Number of biases 128x128 x 3 0 0
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