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

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 x 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. Conv1 Conv2 Conv3 Filter size = 3 x 3 Filter size 5 x 5 Filter size 5 x 5 # filters = 40 Stride - 2 # filters = 20 # filters = 10 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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