(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 performedon 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 a16x16 image into 4 output values (16x16 - 4x12x12 → 4x6x6 6x1). The network hasthe 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 thisnetwork we will not be using any bias/offset parameters. Please answer the followingquestions about this network.16x16Convolution4 filters 5x5Stride 14@12x12max pooling2x2Stride 24@6x66x1fully-connected

(1) The convolutional layer in the diagram uses 4 filters, and each filter is applied to the entire…

(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.

(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

Problem 1PE

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Please answer only correct.
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