代写159.740编程、代做c/c++，Python程序

日期：2024-11-03 09:52

159.740 Intelligent Systems

Assignment #2

N.H.Reyes

Letter Recognition using Deep Neural Nets with Softmax Units

Deadline: 4th of November

Instructions:

You are allowed to work in a group of 2 members for this assignment.

Your task is to write a program that implements and tests a multi-layer feed-forward network for

recognising characters defined in the UCI machine learning repository:

http://archive.ics.uci.edu/ml/datasets/Letter+Recognition

Requirements:

1. Use QT to develop your Neural Network application. A short tutorial on QT, and a start-up

code that will help you get started quickly with the assignment is provided via Stream.

2. You may utilise/consult codes available in books and websites provided that you cite them

properly, explain the codes clearly, and incorporate them with the start-up codes provided.

3. Implement a multi-layer feed-forward network using backpropagation learning and test it on the

given problem domain using different network configurations and parameter settings. There

should be at least 2 hidden layers in your neural network.

h21 h11 X1

X2

F1

F2 h12 h22

OF1

OF2

δh21

δh22 δh12

δf1

δf2

δh11

… … … …

X16

Fm Hi Hj

OFm

Input node

Legend:

hidden node

output node = softmax unit

Note that all nodes, except the input nodes have a bias node attached to it.

159.740 Intelligent Systems

Assignment #2

N.H.Reyes

A. Inputs

16 primitive numerical attributes (statistical moments and edge counts)

The input values in the data set have been scaled to fit into a range of integer values

from 0 through 15. It is up to you if you want to normalise the inputs before feeding

them to your network.

B. Data sets

Use the data set downloadable from:

Training set: use the first 16,000

Test set/Validation set: use the remaining 4,000

Submit your training data, validation/test data in separate files.

C. Performance measure:

Mean Squared Error (MSE)

Percentage of Good Classification (PGC)

Confusion Matrix (only for the best Neural Network configuration found)

D. Training

Provide a facility for shuffling data before feeding it to the network during training

Provide a facility for continuing network training after loading weights from file (do not

reset the weights).

Provide a facility for training the network continuously until either the maximum

epochs have been reached, or the target percentage of good classification has been met.

For each training epoch, record the Mean Squared Error and the Percentage of Good

Classification in a text file. You need this to plot the results of training later, to

compare the effects of the parameter settings and the architecture of your network.

E. Testing the Network

Calculate the performance of the network on the Test set in terms of both the MSE and

PGC.

F. Network Architecture

It is up to you to determine the number of hidden layers and number of hidden nodes

per hidden layer in your network. The minimum number of hidden layers is 2.

Use softmax units at the output layer

Experiment with ReLU and tanh as the activation functions of your hidden units

Determine the weight-update formulas based on the activation functions used

4. Provide an interface in your program for testing the network using an input string consisting of

the 16 attributes. The results should indicate the character classification, and the 26 actual

numeric outputs of the network. (the start-up codes partly include this functionality already, for

a simple 3-layer network (1 hidden layer), but you need to modify it to make it work for the

multiple hidden layer architecture that you have designed).

5. Provide an interface in your program for:

A. Reading the entire data set

B. Initialising the network

C. Loading trained weights

D. Saving trained weights

E. Training the network up to a maximum number of epochs

159.740 Intelligent Systems

Assignment #2

F. Testing the network on a specified test set (from a file)

G. Shuffling the training set.

6. Set the default settings of the user interface (e.g. learning rate, weights, etc.) to the best

configuration that delivered the best experiment results.

7. Use a fixed random seed number (123) so that any randomisation can be replicated empirically.

8. It is up to you to write the main program, and any classes or data structures that you may

require.

9. You may choose to use a momentum term or regularization term, as part of backpropagation

learning. Indicate in your documentation, if you are using this technique.

10. You need to modify the weight-update rules to reflect the correct derivatives of the activation

function used in your network architecture.

11. Provide graphs in Excel showing the network performance on training data and test data

(similar to the graphs discussed in the lecture).

12. Provide the specifications of your best trained network. Fill-up Excel workbook

(best_network_configuration.xlsx).

13. Provide a confusion matrix for the best NN classifier system found in your experiments.

14. Provide a short user guide for your program.

15. Fill-up the Excel file, named checklist.xlsx, to allow for accurate marking of your assignment.

Criteria for marking

Documentation – 30%

o Submit the trained weights of your best network (name it as best_weights.txt)

o Generate a graph of the performance of your best performing network (MSE vs.

Epochs) on the training set and test set.

o Generate a confusion matrix of your best network

o fill-up the Excel file, named checklist.xlsx

o fill-up the Excel file, named best_network_configuration.xlsx

o provide a short user guide for your program

System implementation – 70%

Nothing follows.

N.H.Reyes