Suppose we have the following CNN network: Input (225X225X3) ---> 5X5-filter (512-filters),Stride- 2,padding-2 ---> 3X3-max-pooling,stride-1 ---> 3X3-filter (256-filters),Stride-2 ---> 2X2-max- pooling,stride-2 --->3X3-filter (128-filters),Stride-2,padding-0 --> 2X2 max-pooling,stride-2 ---> Fully

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
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Suppose we have the following CNN network: Input (225X225X3) ---> 5X5-filter (512-filters),Stride-
2,padding-2 ---> 3X3-max-pooling,stride-1 ---> 3X3-filter (256-filters),Stride-2 ---> 2X2-max-
pooling,stride-2 --->3X3-filter (128-filters),Stride-2,padding-0 --> 2X2 max-pooling,stride-2 ---> Fully
Connected with 512 neurons ---> Fully Connected with 512 neurons ---> Fully Connected with 10 neurons.
How many parameters do the Gradient-descent algorithm need to learn for this network? You may omit
the bias values from your calculations.
Transcribed Image Text:Suppose we have the following CNN network: Input (225X225X3) ---> 5X5-filter (512-filters),Stride- 2,padding-2 ---> 3X3-max-pooling,stride-1 ---> 3X3-filter (256-filters),Stride-2 ---> 2X2-max- pooling,stride-2 --->3X3-filter (128-filters),Stride-2,padding-0 --> 2X2 max-pooling,stride-2 ---> Fully Connected with 512 neurons ---> Fully Connected with 512 neurons ---> Fully Connected with 10 neurons. How many parameters do the Gradient-descent algorithm need to learn for this network? You may omit the bias values from your calculations.
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