Consider a convolutional neural network whose inputs are an RGB color image ofsize 39 x 39 (i.e., the input volume size is 39 x 39 × 3 ). The network has threeconvolutional layers and the parameters of each convolutional layer are given in Figure 1.Compute the output volume size of the feature maps at each convolutional layer.9.Conv1Conv2Conv3Filter size = 3 x 3 Filter size = 5 x 5 Filter size = 5 x 5# filters = 10Stride = 1# filters = 20# filters = 40Stride = 2Stride = 2Input: 39 x 39 x 3Padding = 0Padding = 0Padding = 0Figure 1. CNN model.

Conv1 Conv2 Conv3 Filter size = 3 x 3 Filter size = 5 x 5 Filter size = 5 x 5 # filters = 40 Stride = 2 # filters = 10 # filters = 20 Stride = 1 Stride = 2 Input: 39 x 39 x 3 Padding = 0 Padding = 0 Padding = 0 Figure 1. CNN model.

Conv1 Conv2 Conv3 Filter size = 3 x 3 Filter size = 5 x 5 Filter size = 5 x 5 # filters = 40 Stride = 2 # filters = 10 # filters = 20 Stride = 1 Stride = 2 Input: 39 x 39 x 3 Padding = 0 Padding = 0 Padding = 0 Figure 1. CNN model.

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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Question

Consider a convolutional neural network whose inputs are an RGB color image of
size 39 x 39 (i.e., the input volume size is 39 x 39 × 3 ). The network has three
convolutional layers and the parameters of each convolutional layer are given in Figure 1.
Compute the output volume size of the feature maps at each convolutional layer.
9.
Conv1
Conv2
Conv3
Filter size = 3 x 3 Filter size = 5 x 5 Filter size = 5 x 5
# filters = 10
Stride = 1
# filters = 20
# filters = 40
Stride = 2
Stride = 2
Input: 39 x 39 x 3
Padding = 0
Padding = 0
Padding = 0
Figure 1. CNN model.

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