Suppose that we train a neural network to classify images. The inputs are 3-by-3 grayscale images (Le. there are 9 pixels in each image), and the labels contain 10 classes. Connected with the input layer (with the image as a flattened 1-dimensional vector), there are 5 nodes in the fırst hidden layer, 4 in the second hidden layer, and then followed with the softmax output layer. Each node is given an activation function of ReLU(x). How many trainable parameters are there in this neural network?

Suppose that we train a neural network to classify images. The inputs are 3-by-3 grayscale images (Le. there are 9 pixels in each image), and the labels contain 10 classes. Connected with the input layer (with the image as a flattened 1-dimensional vector), there are 5 nodes in the fırst hidden layer, 4 in the second hidden layer, and then followed with the softmax output layer. Each node is given an activation function of ReLU(x). How many trainable parameters are there in this neural network?

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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