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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