Da Nang University of Technology
Course: CSE446
LABORATORY 1
Application of NN for Fish Behavior Understanding
Class:13ECE Name of students:
Nguyen Le Huan (13ECE2) Nguyen Anh Nguyen (13ECE1) Ngo Thanh Truong Giang (13ECE1)
Da Nang
September 24st, 2017
1. Project Objective:
The objectives of this lab are the following:
To implement supervised learning in the form of neural networks and error backpropagation. The aim of this lab is to done completely in Matlab starting from zero. For this purpose, creating network is built and tested in the application of Fish Behavior Understanding.
2. Project Scope:
a. b. c. d. e.
To meet project requirements, you will need to: Get used with Neural Network Toolbox included in Matlab. Setup Neural Network to learn specific learning problem. Get used with confusion matrix as evaluation method. Analyzed the performance of the network since changing the configuration parameter. 3. Get Matrix File = dir; %File = File(~cellfun('isempty', {File.date})); inputFile = []; outputFile = []; for i = 3 : 72 fileName = File(i).name; load(fileName); input = statistic'; inputFile = [inputFile input]; if (dodoc == 0) output = [-1 -1]'; else output = [1 1]'; end outputFile = [outputFile output]; end First of all, we should input file for training. We use the data in the folder “Train”, put the same location on the computer. We using loop to get file.
4. Train inputs = inputFile; targets = outputFile; hiddenLayerSize = [7 3]; net = fitnet(hiddenLayerSize,'traincgb'); net.trainParam.epochs=4000; net.trainParam.goal=1e-15; net.trainParam.show = 10; net.divideParam.trainRatio = 80/100; net.divideParam.valRatio = 0/100; net.divideParam.testRatio = 20/100; [net,tr] = train(net,inputs,targets); view(net)
After input training file, we should create a hidden layer size, prepare for NN. - Input: 9 properties. - Output: 2 properties corresponds to clean water and polluted water. -
We use Conjugate gradient backpropagation with Polak-Ribiére (cgb). Set up the division of data: - 80% used for training. -20% used for testing.
error=15; while (error > 5) net = fitnet(hiddenLayerSize,'traincgb'); net.trainParam.epochs=4000; net.trainParam.goal=1e-15; net.trainParam.show = 10; net.divideParam.trainRatio = 80/100; net.divideParam.valRatio = 0/100; net.divideParam.testRatio = 20/100; error = 0; [net,tr] = train(net,inputs,targets); myFolder = dir; myFolder = myFolder(~cellfun('isempty', {myFolder.date})); outputCompare = []; outputTestFile = []; for i = 3 : 22 file = myFolder(i).name; load(file); inputTest = statistic'; if (dodoc == 0) output = [-1 -1]'; else output = [1 1]'; end outputCompare = [outputCompare output]; outputTest = net(inputTest); outputTest = hardlims(outputTest); outputTestFile = [outputTestFile outputTest] end for j = 1 : 20 if(outputTestFile(1,j) ~= outputCompare(1,j)) error = error + 1; end end error end From the idea train again if number of error unsatisfactory conditions, we using a loop until number of error less or equal 4.
5. Result
After several time training, we get result which is 4 errors.
The accuracy (AC) is the proportion of the total number of predictions that were correct. AC=75% The recall or true positive rate (TP) is the proportion of positive cases that were correctly identified TP =86.6% The false positive rate (FP) is the proportion of negatives cases that were incorrectly classified as positive FP=60% The true negative rate (TN) is defined as the proportion of negatives cases that were classified correctly TN=40% The false negative rate (FN) is the proportion of positives cases that were incorrectly classified as negative FN=13.4% Finally, precision (P) is the p roportion of the predicted positive cases that were correct P=18.75%
In the epoch 767, we get best resul. Through each training, the performance increases.
Until we run the testing with 5 states but k = 100 and get a worse precision and accuracy: Recall = 1.000000; Precision = 1.000000; Accuracy = 1.000000 1.2
K-mean algorithm 1 0.8 y t il i b
Recall
0.6 a p o r
Precision P
0.4
Accuracy
0.2
0 0
20
40
60
80
100
120
140
Number of Observation
Then we try to run with other cases and get the results:
State 2 3 4 5 6 7 8 9 10
Numbers of obversation 100 100 100 100 100 100 100 100 100
Recall 1 1 1 1 1 1 1 1 0
Precision 0.923077 0.857143 0.8 1 0.925926 0.75 0.75 1 0
Accuracy 0.96296 0.925926 0.888889 1 0.96296 0.851852 0.851852 1 0.444444
1.2
1
0.8 ti il
0.6
Series1
r
Series2
Series3
0.4
0.2
0 0
2
4
6
8
10
12
Number of state
We found out that we can improve the precision if we take k = 100. Although with 9 states we get the same results but it takes time to run algorithm. Therefore, experimental results showed that the 5-state left-to-right hidden Markov model provided the highest performance.
