Task 1: Feed Forward Neural NetworkHere is a problem consisting of inputs P and targets T to be solved with a network.P = [012345678910];T = [01234321 2 3 4];Here a network is created with one hidden layer of five neurons.net = newff(P,T,5);The network is simulated and its output plotted against the targets.Y = sim(net,P);plot(P,T,P,Y,'o')The network is trained for 50 epochs. Again the network's output is plotted.net.trainParam.epochs = 50;net = train(net,P,T);Y = sim(net,P);plot(P,T,P,Y,'o')Change the training epochs to 100 and 1000, and plot again. Copy the plotted figures (4figures) and prepare a one-page report including your comments under each figure.Task 2: Repeat task 1 fora) simplefit_dataset (use the command load simplefit_dataset)b) simplecluster dataset (use the command load simplecluster_dataset)Using 50 and 1000 epochs in the training, each with 5 and 20 hidden layers and report thefigures (8 figures in total) obtained with your comments.▪ Descriptionnewff(P,T,[S1 S2...S(N-1)],{TF1 TF2...TFNI), BTF,BLF, PF,IPF,OPF,DDF) takes several argumentsPR x Q1 matrix of Q1 sample R-element input vectorsTSN x Q2 matrix of Q2 sample SN-element target vectorsSiSize of ith layer, for N-1 layers, default = [].(Output layer size SN is determined from T.)TFiTransfer function of ith layer. (Default='tansig' forhidden layers and 'purelin' for output layer.)BTFBackpropagation network training function(default = 'trainlm')BLFBackpropagation weight/bias learning function (default='learngdm')PFPerformance function. (Default = 'mse')IPFRow cell array of input processingfunctions. (Default = {'fixunknowns','removeconstantrows','mapminmax'})OPFRow cell arrayof output processing functions. (Default = {'removeconstantrows','mapminmax'}) DDFDatadivison function (default = 'dividerand'),and returns an N-layer feed-forward backpropagation network.The transfer functions TFi can be any differentiable transfer function such as tansig, logsig,or purelin.The training function BTF can be any of the backpropagation training functions such astrainim, trainbfg, trainrp, traingd, etc.

Task 1 requires that the dataset be loaded first with the commands P = [0123456789 10] and T =…

Task 1: Feed Forward Neural Network Here is a problem consisting of inputs P and targets T to be solved with a network. P=[0 1 2 3 4 5 6 7 8 9 10]; T = [0 1 2 3 4 3 2 1 2 3 4]; Here a network is created with one hidden layer of five neurons. net = newff(P.T,5); The network is simulated and its output plotted against the targets. = sim(net,P); plot(P.T.P.Y,'o') The network is trained for 50 epochs. Again the network's output is plotted. net.trainParam.epochs = 50; met = train(net,P,T); Incim/not Pl

Task 1: Feed Forward Neural Network Here is a problem consisting of inputs P and targets T to be solved with a network. P=[0 1 2 3 4 5 6 7 8 9 10]; T = [0 1 2 3 4 3 2 1 2 3 4]; Here a network is created with one hidden layer of five neurons. net = newff(P.T,5); The network is simulated and its output plotted against the targets. = sim(net,P); plot(P.T.P.Y,'o') The network is trained for 50 epochs. Again the network's output is plotted. net.trainParam.epochs = 50; met = train(net,P,T); Incim/not Pl

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

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
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.
Suppose an employee has to be classified as a good worker or bad worker for a job promotion application. The characteristics for the problem are: Degree: {Bachelors, Masters, Doctorate}, Gender: {male, female}, Income: {[25K-50K], [50K-100K], [100K+]}, Age: {[25-50], [50-65], [65+]}. Design a neural network that can be trained to predict an employee application for promotion as a good worker or bad worker.
Program important
There are many sensors on a wind turbine, based on each sensor's reading many alerts are generated as (start, end). For easier monitoring, our customer is asking to raise alerts on the turbine level instead of on each sensor. If any sensor is having an alert, there should be an alert for the entire wind turbine. Suppose the alerts created with two sensors readings are [(0,3),(5,10),(1,4),(10,15)], then the turbine level alert should be [(0,4),(5,15)]. Return the alerts sorted by the start timestamp. If there is no alert from the sensor level, return an empty array []. Code needs to be in Python?
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?
V1 Implement a multilayer perceptron based neural network (two hidden layers) for 3-class classification. Both holdout (70, 10, and 20%) and 5-fold cross-validation can be used to evaluate the accuracy (both overall and individual accuracy). You can select the number of hidden neurons of each hidden layer and other MLP parameters using grid-search method. The dataset (use any input file, or I can send separately) contains 7 features and the last column is the output (class labels). Packages such as tensorflow, keras, Scikitlearn etc are not allowed
Q4:- Perform three steps of the learning algorithm of the ART network for n=9 and m-3 assuming that the training vectors are: x1 = [1 0 0 0 1 0 0 0 1] x2 = [ 1 1 0 0 1 0 0 1 1] x3 = [10 1 0 1 0 10 1] Select an appropriate vigilance threshold p so that all three patterns are cluster separately when the sequence x1, x2, and x3 are presented .Compute the final weights of the trained network.
plz do
dont post unnecessary answer dont answer if you dont know else direct dislike dont post copied answer
Consider the following training set input values x' = (1, x? = (0, х3 (- 1, -0.5, -1)" No. desired output value 1. 0, - 1, 1)" - 1)" -1 2. 1 3. 1 Assume the learning rate is 0.1 and the initial weight vector is w, = (1, -1, 0)". After training draw the neural network.
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