(1) How many weights in the convolutional layer do we need to learn? Please explain. (2) How many ReLu operations (after pooling, before fully-connected layer) are performed on the forward pass? Please explain. (3) How many weights do we need to learn for the entire network? Please explain.

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
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(1) How many weights in the convolutional layer do we need to learn? Please explain.
(2) How many ReLu operations (after pooling, before fully-connected layer) are performed
on the forward pass? Please explain.
(3) How many weights do we need to learn for the entire network? Please explain.
Transcribed Image Text:(1) How many weights in the convolutional layer do we need to learn? Please explain. (2) How many ReLu operations (after pooling, before fully-connected layer) are performed on the forward pass? Please explain. (3) How many weights do we need to learn for the entire network? Please explain.
The following figure is a diagram of a small convolutional neural network that converts a
16x16 image into 4 output values (16x16 - 4x12x12 → 4x6x6 6x1). The network has
the following layers/operations from input to output: convolution with 4 filters, max pooling,
ReLu (after pooling), and finally a fully-connected layer (with no hidden layer). For this
network we will not be using any bias/offset parameters. Please answer the following
questions about this network.
16x16
Convolution
4 filters 5x5
Stride 1
4@12x12
max pooling
2x2
Stride 2
4@6x6
6x1
fully-
connected
Transcribed Image Text:The following figure is a diagram of a small convolutional neural network that converts a 16x16 image into 4 output values (16x16 - 4x12x12 → 4x6x6 6x1). The network has the following layers/operations from input to output: convolution with 4 filters, max pooling, ReLu (after pooling), and finally a fully-connected layer (with no hidden layer). For this network we will not be using any bias/offset parameters. Please answer the following questions about this network. 16x16 Convolution 4 filters 5x5 Stride 1 4@12x12 max pooling 2x2 Stride 2 4@6x6 6x1 fully- connected
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