### 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

See similar textbooks

Related questions

Q: Find the shortest ancestral path between v and w given a DAG and two vertices, v and w. A common…

A: In a directed acyclic graph (DAG), the issue is to determine the shortest ancestral path connecting…

Q: Answer questions a–d for the graph defined by the following sets:N = {1, 2, 3}N0 = {1}Nf = {3}E =…

A: Let's go through the questions step-by-step based on the provided graph and paths. Definitions: N:…

Q: Fill in the blanks so the program prints

A: Answer is explained below

Q: Let’s consider the operation of a learning switch in the context of a network in which 6 nodes…

A: The answer is in step 2:

Q: explain with proper working that how we can come up with these answers.

A: Consider the given :

Q: Suppose that we train a neural network to classify images. The inputs are 3-by-3 grayscale images…

A: Actually, given question regarding Machine Learning.

Q: For a fixed integer C > 0, given a sequence of {r;}{1, and {y:}E=1 with y: = (E=max{t-C,1} T3)°,…

A: Code: #include <bits/stdc++.h>using namespace std;int main(){ int A,B;…

Q: re said to be equivalent if L(M1) = L(M2). In other words, two machines are equivalent if they…

A: To change epsilon-NFA to NFA without epsilon, check epsilon closure of each state. As state q1 can…

Q: Consider a convolutional neural network which takes as input a 42-by-54 color image (i.e. with three…

A: A Convolutional Neural Network (CNN) is a deep learning architecture designed to process and analyze…

Q: Which of the following statements about various neural network layers is true? (Multiple Answers…

A: Neural network layers are fundamental building blocks in deep learning models. Each layer serves a…

Q: Find the total number Q of tunable parameters in a general L-hidden-layer neural network, in terms…

A: Answer:- a feedforward neural network is the simplest type of artificial neural network in which…

Q: We consider a neural network with 100 neurons in the input layer and 10 neurons in the hidden layer…

A: Dear Student, Weight Parameters = number of neurons in the input layer * number of neurons in the…

Q: Try to classify the following dataset with three classes by implementing a multiclass classification…

A: This is a challenging challenge that calls for an in-depth knowledge of both the NumPy library and…

Q: Implement Algorithm (highest label preflow push algorithm). Let N = (G,c,s,t) be a flow network,…

A: highest label preflow push algorithm:-

Q: Suppose we are fitting a neural network with three hidden layers to a training set. It is found that…

A: A neural network is a computational model inspired by the structure and functioning of the human…

Q: Consider a block stacking robot with the following actions: 1 Stack(x, y) • Preconditions: Clear(y),…

A: The solution for the above given question is given below:

Q: Vertex S denotes the start state and vertices G1 and G2 denote the two goal states. Directed edges…

Concept explainers

Polynomial Time

The problems for which the deterministic polynomial-time algorithm exists belong to the complexity class (p) and it is central in the field of computational complexity theory. According to Cobham’s thesis, polynomial time is a synonym for “feasible”, “fast”, and “efficient”.

Question

### Neural Networks and Image Classification

#### Problem Statement
Suppose 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 = 130

2. **Between First Hidden and Second Hidden Layer**:
- Weights: 5 * 4 = 20
- Biases: 4
- Total: 20 + 4 = 24

3. **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) = 204

Therefore, there are **204 trainable parameters** in this neural network.

Expert Solution

This question has been solved!

Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.

SEE SOLUTION Check out a sample Q&A here

Problem Desciption :

VIEW

Solution :

VIEW

Step by step

Solved in 2 steps

SEE SOLUTION Check out a sample Q&A here

Knowledge Booster

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.

Similar questions

Define a fully connected network with two hidden layers with Nh1 and Nh2 features, respectively. The hidden layers should use relu activation and the final layer should not have any activation. a linear network with no hidden layer. We will use this network to study the benefit of depth, or equivalently using a non-linear network instead of a linear network. The final layer should not have any activation class NeuralNet(N.Module): # YOUR CODE HERE class LinearNet(N.Module): #YOUR CODE HERE
Q3. The weights, activation function, and set of input values for a Neural Network are given below: Weights: wl = 2, w2 = -3, w3 = 4, w4 = -2, w5 = 5 1 when p > 0 O when p = 0 -1 when p <0 Set of input values (Set S1, S2, S3 and input values x1, x2, x3, x4, x5): Activation function: f (p): where p is weighted sum of inputs S1 S2 S3 x1 2 4 x2 3 2 3 x3 3 6 -2 x4 -4 -3 3 X5 -3 -2 3 What is the final output value for each set of input values?
Full explain this question very fast solution sent me step by step Not :- Text typing work only do u and all part work Not allow paper work Thanks
1) design an artificial neural network with two inputs (X1 and X2). She won't have the bias. This network will have a hidden layer with two neurons (N1 and N2), plus an output layer, with one neuron (N3). The output of the network is called Y. Make the weights (W's) of each neuron explicit in the network, as well as the interconnections between all parts of the ANN.
Suppose we have two industrial control systems S1 and S2 that are workingconcurrently. We need to forbid some sequence of actions from taking placebetween the two systems. One such action is allowing S1 and S2 to be onat the same time. Consider the alphabet:Σ = {On(S1), On(S2), Off(S1), Off(S2)}A sequence of actions is a word over Σ∗.i. Convert the property on sequences given above into a language, andthen define an NFA that accepts it.ii. A second property is that S2 must power off after S1, but S2 mustpower on before S1. Define a language which captures this property(and this property only), and then define a NFA that accepts it.
10
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
Suppose that the road network is defined by the undirected graph, where the vertices represent cities and edges represent the road between two cities. The Department of Highways (DOH) decides to install the cameras to detect the bad driver. To reduce the cost, the cameras are strategically installed in the cities that a driver must pass through in order to go from one city to another city. For example, if there are two cities A and B such that the path that goes from A to B and the path that goes from B to A must pass the city C, then C must install the camera. Suppose that there are m cities and n roads. Write an O (m + n) to list all cities where cameras should be installed.