6688

Fashion-MNISTis a dataset of Zalando’s article images — consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28×28 grayscale image, associated with a label from 10 classes. Fashion- MNIST is a replacement for the original MNIST dataset (handwritten digits) for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits. The web pagehttps://github.com/ zalandoresearch/fashion-mnisthas more information.

Document Preview:

Predictive Analytics Assignment — 2018SubmissionThe assignment solution should be submitted electronically, and can be a combinationof R code and PDF document, by email to O.Obst@westernsydney.edu.au.Include the completed cover sheet that you can ?nd at the end of the document.Submission is due on 18 Nov 2018, 11:59pm.1. Fashion-MNIST is a dataset of Zalando’s article images — consisting of atraining set of 60,000 examples and a test set of 10,000 examples. Each exampleis a 28×28 grayscale image, associated with a label from 10 classes. Fashion-MNIST is a replacement for the original MNIST dataset (handwritten digits) forbenchmarking machine learning algorithms. It shares the same image size andstructure of training and testing splits. The web pagehttps://github.com/zalandoresearch/fashion-mnist has more information.Figure 1: An example for how some the data looks like (each class takes three-rows).1In the vuws data directory for the assignment is a zip archive (fashionmnist.zip)that contains 4 data ?les (training and test sets, split into data and labels), plussome R code (loader.R).You can use the functions inloader.R to load the unzipped ?les, and also to dis-1play individual images while you work on your code . The functionload mnist()in this ?le loads all the data (must be in the same directory), and creates variablestrain$n,train$x,train$y, andtest$n,test$x,test$y.After loading,train$x is a 60000 x 784 matrix, each row is one digit (28×28).thYou can use the callshow digit(train$x[5,]) to display the5 trainingexample. The labels (0–9) for the data are in the train$y and test$y vari-ables.(a) Using the nn2 function in the RANN library, implement a 3 nearest neigh-bour classi?cation. Submit your R code, and the achieved accuracy onthe test set (accuracy: proportion of correct results from total number ofclassi?cations).(b) Thenn2 function also returns the distance to thek nearest neighbours (fordetails, see thenn2 help text)….

Save your time - order a paper!

Get your paper written from scratch within the tight deadline. Our service is a reliable solution to all your troubles. Place an order on any task and we will take care of it. You won’t have to worry about the quality and deadlines

Order Paper Now

Attachments:

Assignment.pdf