(a) Consider a convolutional neural network (CNN) with the following structure for classification of color images: • Input layer that takes a 32x32 pixel color image as input • Convolutional layer 1 (CL1) that uses kernels of size 3x3x3 and 6 feature maps, with a stride of 1 and no padding • Pooling layer 1 (PL1) that performs max pooling using kernels of size 2x2 with a stride of 2 • Convolutional layer 2 (CL2) that uses kernels of size 5x5x2 and 15 feature maps, with a stride of 2 and no padding

(a) Consider a convolutional neural network (CNN) with the following structure for classification of color images: • Input layer that takes a 32x32 pixel color image as input • Convolutional layer 1 (CL1) that uses kernels of size 3x3x3 and 6 feature maps, with a stride of 1 and no padding • Pooling layer 1 (PL1) that performs max pooling using kernels of size 2x2 with a stride of 2 • Convolutional layer 2 (CL2) that uses kernels of size 5x5x2 and 15 feature maps, with a stride of 2 and no padding

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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Use of XOR function

XOR function or exclusive OR function was introduced in Excel 2013. This function is available under excel logical functions. For the given statement, the XOR function will return true only when either of the statements is true. It returns false when both the statements are true or when both the statements are false.

Question

4. (a) Consider a convolutional neural network (CNN) with the following structure for classification of color
images:
• Input layer that takes a 32x32 pixel color image as input
Convolutional layer 1 (CL1) that uses kernels of size 3x3x3 and 6 feature maps, with a stride of 1
and no padding
• Pooling layer 1 (PL1) that performs max pooling using kernels of size 2x2 with a stride of 2
• Convolutional layer 2 (CL2) that uses kernels of size 5x5x2 and 15 feature maps, with a stride of
2 and no padding
Pooling layer (PL2) that performs max pooling using kernels of size 2x2 with a stride of 2
Output layer with 5 nodes.
Determine the number of unique weight parameters (excluding the bias parameters) to be estimated.
(b) Briefly explain the inception module used in GoogLe Net. State the main purpose of the inception
module. State how the kernel factorization method can be used to reduce the number of parameters in
the inception module.

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