Artificial Intelligence代写、CNN留学生代做、Python编程设计调试、代做Python代写

日期：2019-05-12 09:51

School of Computer Science

The University of Adelaide

Artificial Intelligence

Assignment 2

Semester 1, 2019

Due 11:55pm, Friday 17th May 2019

Introduction

In this assignment, you will develop classification models to classify noisy input images

into two classes: square or circle. Examples are shown in Fig. 1.

Figure 1: Samples of noisy images labelled as square (left) and circle (right).

Your classification models will use the training and testing sets (thatare available with

this assignment at https://myuni.adelaide.edu.au/courses/45386/assignments)

containing many image samples labelled as square or circle.

Assignment

Your task is to write Python code which will train and validate the following two

classification models:

1) K-Nearest neighbour (KNN) classifier [30 marks].

For the KNN classifier, you can only use standard Python libraries (e.g., numpy)

in order to implement all aspects of the training and testing algorithms.

Using matplotlib, plot a graph of the evolution of classification accuracy for

the training and testing sets as a function of K, where K = 1 to 10.

Clearly identify the value of K for which generalisation is best.

Undergraduates can use whatever algorithm they see fit (including exhaustive

search) and all 30 marks will be available.

Post-graduates who implement exhaustive search will be eligible for only 15/30

marks. To be eligible for 30/30 you must implement a K-d tree to store and

search the database.

2) Convolutional neural network (CNN) classifier [30 marks].

For the convolutional neural network, you should use Tensorflow within Jupyter

Notebook by modifying the Multilayer Perceptron program supplied with this

assignment. Instructions for installation of Python 3.7, Jupyter and TensorFlow

(via a package called miniconda) are in a separate sheet supplied with this

assignment.

You should modify the code so that it implements the LeNet CNN structure to

that was presented in lectures. In particular, the LeNet architecture should

comprise two convolutional layers (5x5 convolutions), and two hidden fullconnected

(dense) layers in addition to the output layer. After each convolutional

layer the architecture should use max pooling to reduce the size by a factor of 2

in each axis. After each pooling operation you should use a RELU (Rectified

Linear Unit) activation function. The LeNet will also have three dense layers

forming a Multilayer Perceptron (MLP) classifier (you can use the ones already in

the sample implementation. The size of the two hidden-layers in the MLP must

be 2x and x (where you will need to test different values of x by changing the

code or writing a suitable function). Of course the output layer will have a single

neuron.

Undergraduates should experiment training two LeNets, with ‘x’ = 20, ‘x’ = 50

and ensure the results are written up carefully, comparing the two networks.

Postgraduates should experiment training three LeNets, with ‘x’ = 20, ‘x’ = 50.

and ‘x’ = 100 and report the results for all three variants, commenting carefully on

each.

Using matplotlib, plot a graph of the evolution of accuracy for the training and

testing sets as a function of the number of epochs, for each of the CNNs you

train (up to a maximum of 200 epochs).

Sample python code that trains and tests a multi-layer perceptron classifier (and can run

in a Jupyter Notebook session) is provided with the assignment specification at

https://myuni.adelaide.edu.au/courses/45386/assignments. You should modify this code

to produce your own program.

Submission

You must submit via MyUni, by the due date, two files:

1. A zip file or Jupyter notebook file (.ipynb) containing your code with the two

classifiers and all implementations described above.

2. A pdf file with a short (no more than 2 pages) written report detailing your

implementation, your results, and an interpretation of the results. The results you

should include are:

a. The training and testing accuracies at for KNN, K=1 to 10

b. The training and testing accuracies for CNN, x=20, x=50 (and x=100 for

postgrads)

This should take the form of a table:

Classifier Training Accuracy Testing Accuracy

K-NN (k = 1)

...

K-NN (k = 10)

CNN (x = 20)

CNN (x = 50)

CNN (x = 100)

The implementations are worth 30 marks each (see above) and the report is worth 40

marks. For full marks your report should include a careful and critical analysis of your

observations about the performance of the different algorithms and algorithm settings.

This assignment is due 11.55pm on Friday 17th May, 2019. If your submission

Is late, the maximum mark you can obtain will be reduced by 25% per day (or part

thereof) past the due date or any extension you are granted.