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 ---> FullyConnected 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 omitthe bias values from your calculations.

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

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

Section: Chapter Questions

Problem 1PE

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