### Neural Networks and Image Classification#### Problem StatementSuppose that we train a neural network to classify images. The inputs are 5-by-5 grayscale images (i.e., there are 25 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 first 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? #### Explanation and Calculations- **Input Layer**: 25 pixels (flattened)- **First Hidden Layer**: 5 nodes- **Second Hidden Layer**: 4 nodes- **Output Layer**: 10 classes (nodes)To compute the number of trainable parameters, we need to calculate weights and biases between each layer:1. **Between Input and First Hidden Layer**: - Weights: 25 (input nodes) * 5 (hidden nodes) = 125 - Biases: 5 - Total: 125 + 5 = 1302. **Between First Hidden and Second Hidden Layer**: - Weights: 5 * 4 = 20 - Biases: 4 - Total: 20 + 4 = 243. **Between Second Hidden and Output Layer**: - Weights: 4 * 10 = 40 - Biases: 10 - Total: 40 + 10 = 50**Total Trainable Parameters**:130 (first connection) + 24 (second connection) + 50 (third connection) = 204Therefore, there are **204 trainable parameters** in this neural network.

Given Data : Number of inputs = 25 Number of output = 10 Number of nodes in first hidden layers =5…

1 pts Suppose that we train a neural network to classify images. The inputs are 5-by-5 grayscale images (Le. there are 25 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 first 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). How many trainable parameters are there in this neural network?

1 pts Suppose that we train a neural network to classify images. The inputs are 5-by-5 grayscale images (Le. there are 25 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 first 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). 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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