Alert dont submit AI generated answer.data mining According to the Neural Network structure shown in the following figure, and assumethat:1. All weights are set as: 0.50.2*x, x < 02. Activation function is LeakyReLU: f(x) = {0.2 *n-x, x ≥03. Loss function is defined as: L = ½-½ 11(y; — ŷ;)², where y; is the ground truth and ŷiis the output of the Neural Network.W₁₁(Σ(Σx1ΣΣΣŷ(Σ(Σ)Given a dataset D = {(x1,y1), (x2, y2), (X3, Y3), (x4, Y4)}, and• x₁ = (1,3), y₁ = 5• x2 = (-1,2), y₂ = 3• x3 = (2,6), y3 = 7•x4 = (4,2), y = 2Q1: [15%] [Forward Propagation] calculate the corresponding output ŷ1, ŷ2, Ŷ3, ŷ4JLQ2: [20%] [Backward Propagation] calculate the loss function and gradient: (redweight in the structure)aw 1

Q1: For X₁ = (1,3): z₁ = (1 * 0.5) + (3 * 0.5) = 2 ŷ₁ = f(z₁) = f(2) = 2 For X₂ =…

Answered: According to the 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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The neural network given below takes two binary valued inputs x1,x2 ϵ{0, 1} and the activation function is the binary threshold function (h(x)=l if x>0;O otherwise). The OR logical functions does it compute Select one: True False
Two layers neural networks consist of 3 input neurons and one output neuron. The input vector is (x1, x2, x3) where x3 is always =1.0. x1, x2 and the desired output d are given by the table below. When using the initial weight vector w = (1.0, 1.0, -3.0) there was an error as shown in the figure. The equation for the decision boundary was: x1*1 + x2*1 - 1*3=0. , (i.e. net = x1*w1+x2*w2+x3*w3), f(net) is the sign of net What are the new values of the vector w after the first input (1,1,1), note that input x3 is always 1.0? What are the new values of the vector w after the second input (9.4, 6.4, 1.0)? Suggest a vector w such that the total error is ZERO for the 10 points.
giving the following weight matrix for a vector quantization neural network: w = [ 0.6000 0.6300 0.8000 0.5000 1.0000 -0.7000] and given the following input x= [0.3 , 0.5] decide the winning node and hence update the weight matrix given a training rate of 0.1
The language must be in python. Neural Network Units weights of [-1.2, -1.1, 3.3, -2.1] two training examples:Example 1: [0.9, 10.0, 3.1, 1]Example 2: [0.9, 2.1, 3.7, 1] Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold. Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the sigmoid with respect to net input for both examples Calculate the derivative of the ReLU with respect to net input for both examples
Write a code for a neural network that can learn XOR gate using backpropagation learning algorithm. Use two neurons in the input layer, two neurons in the hidden layer, and one neuron in the output layer. PS: Use python.
Consider a fully-connected artificial neural network with one hidden layer, i.e., a multilayer perceptron (MLP), which has 5 inputs, 3 neurons in the hidden layer, and 1 output neuron. The relation between the output y and the inputs x = [x1, . . . , x5] is given by y(x) = f (w, φ(x)), where φ(x) = [φ1(x), φ2(x), φ3(x)] 1. Draw the diagram that shows the inputs, nuerons, connections, correspond-ing weight parameters, and activation functions. 2. Explain the relation y(x) = f (w, φ(x)): write the explicit relation, explainthe role of functions f and φ(x), and state examples of functions.
(i). Consider a 3-layer perceptron neural network consisting of 1 input neuron, 2hidden neurons with sigmoid activation functions, and 1 output neuron with linearactivation function. Assuming v1 and v2 represent the connection weights fromthe hidden neurons to the output neuron, v0 represents the bias for the outputneuron, w11 and w21 represent the connection weights from the input neuron tothe hidden neurons, and w10 and w20 represent the biases for the 2 hiddenneurons. Let x represent the input to the neural network and y represent the outputfrom the neural network.Suppose the vector of neural network weights is:w = [v0 v1 v2 w10 w11 w20 w21]T = [0 0.1 0.5 0 -1 1 0.2]T. Calculate the output y if input x = 0. Calculate y again for x = 1. (ii). For the same neural network as in (1), calculate the derivative of y with respect the neural network weight parameters v0, v1, v2 , w10 , w11 , w20, and w21 for x = 0. Calculate the derivatives again for x = 1.
Using the nnet library to build neural networks in R, you can set the __________ parameter to ensure that the model will stop training even if it doesn’t identify reliably predictive pathways. maxit seed logit limit
By using neural network algorithm solve the XOR problem. Where X1 and X2 are inputs Y is the output. Learning rate is 0.5. Threshold is 1 if weighted sum >= 0.5 otherwise 0 and weights w1 and w2 = 0.9 Add 17 with learning rate and w1, w2 before start solving the question. X1 X2 Y 0 0 0 0 1 1 1 0 1 1 1 0
Draw a geed forward neural network with 3 input neurons, 1 hidden layer including 3 neurons and 1 output neuron. Consider all the weights as -1 and thresholds 0. Find output for vector [1 -1 1 ]
Give an example of a difficulty that develops when a neural network has several layers.Discuss overfitting and how to prevent it in the following paragraphs.
Define the neural network 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

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