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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
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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Suppose that we train a neural network to classify images. The inputs are 3-by-3 grayscale images (ie. 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?
Transcribed Image Text:Suppose that we train a neural network to classify images. The inputs are 3-by-3 grayscale images (ie. 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?
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