+ All Categories
Home > Documents > Neural Networks- Introduction

Neural Networks- Introduction

Date post: 23-Dec-2015
Category:

Documents
Upload: naeemkashif
View: 225 times
Download: 0 times
Download Report this document
Share this document with a friend
Description:
Neural Networks by Muhammad Amjad
22
Introduction to Neural Network toolbox in Matlab Matlab stands for MATrix LABoratory.
Transcript
Page 1: Neural Networks- Introduction

Introduction to Neural Network toolbox in Matlab

Matlab stands for MATrix LABoratory.

Page 2: Neural Networks- Introduction

Programming Language : Matlab

High-level script language with interpreter.Huge library of function and scripts.Act as an computing environment that combines

numeric computation, advanced graphics and visualization.

Page 3: Neural Networks- Introduction

Entrance of matlab

Type matlab in unix command prompt• e.g. sparc76.cs.cuhk.hk:/uac/gds/username> matlab

• If you will find an command prompt ‘>>’ and you have successfully entered matlab.

>>

Page 4: Neural Networks- Introduction

Ask more information about software

>> info – contacting the company

• eg. Technique support, bugs.

>> ver – version of matlab and its toolboxes– licence number

>> whatsnew– what’s new of the version

Page 5: Neural Networks- Introduction

Function for programmer

help : Detail of function provided.– >> help nnet, help sumsqr

lookfor : Find out a function by giving some keyword.– >> lookfor sum

•TRACE Sum of diagonal elements.•CUMSUM Cumulative sum of elements.•SUM Sum of elements.•SUMMER Shades of green and yellow colormap.•UIRESUME Resume execution of blocked M-file.•UIWAIT Block execution and wait for resume.……………...

Page 6: Neural Networks- Introduction

Function for programmer (cont’d)

which : the location of function in the system(similar to whereis in unix shell)

– >> which sum– sum is a built-in function.

– >> which sumsqr– /opt1/matlab-5.3.1/toolbox/nnet/nnet/sumsqr.m

So that you can save it in your own directory and

modify it.

Page 7: Neural Networks- Introduction

Function for programmer (cont’d)

! : calling unix command in matlab system– >> !ls– >> !netscape

Page 8: Neural Networks- Introduction

Plotting graph

Visualisation of the data and result.Most important when handing in the report.plot : plot the vector in 2D or 3D

– >> y = [1 2 3 4]; figure(1); plot(power(y,2));

Index of the vector (you can make another vector for the x-axis)

x = [2 4 6 8]; plot(x,power(y,2));

Add vector x as the x-axis index

Page 9: Neural Networks- Introduction

Implementation of Neural Network using NN Toolbox Version

3.0.1

1. Loading data source.2. Selecting attributes required.3. Decide training, validation, and testing data.4. Data manipulations and Target generation.

– (for supervised learning)5. Neural Network creation (selection of network architecture) and initialisation.6. Network Training and Testing.7. Performance evaluation.

Page 10: Neural Networks- Introduction

Loading data

load: retrieve data from disk.– In ascii or .mat format.

Save variables in matlab environment and load back

>> data = load(‘wtest.txt’);>> whos data;Name Size Bytes Classdata 826x7 46256 double array

Page 11: Neural Networks- Introduction

Matrix manipulation

stockname = data(:,1);

training = data([1:100],:)a=[1;2]; a*a’ => [1,2;2,4];

a=[1,2;2,4]; a.*a => [1,4;4,16];

for all

Start for 1

1 2 2 4

1 4 4 16

Page 12: Neural Networks- Introduction

Neural Network Creation and Initialisation

net = newff(PR,[S1 S2...SNl],{TF1 TF2...TFNl},BTF,BLF,PF) Description NEWFF(PR,[S1 S2...SNl],{TF1 TF2...TFNl},BTF,BLF,PF) takes, PR - Rx2 matrix of min and max values for R input elements. Si - Size of ith layer, for Nl layers. TFi - Transfer function of ith layer, default = 'tansig'. BTF - Backprop network training function, default = 'trainlm'. BLF - Backprop weight/bias learning function, default = 'learngdm'. PF - Performance function, default = 'mse’ and returns an N layer feed-forward backprop network.

Number of inputs decided by PR

S1: number hidden neurons

S2: number of ouput neuron

>> PR = [-1 1; -1 1; -1 1; -1 1];

-1 1 -1 1 -1 1 -1 1

Min Max

neuron 1

Page 13: Neural Networks- Introduction

Neural Network Creation

newff : create a feed-forward network. Description NEWFF(PR,[S1 S2...SNl],{TF1 TF2...TFNl},BTF,BLF,PF) takes, PR - Rx2 matrix of min and max values for R input elements. Si - Size of ith layer, for Nl layers. TFi - Transfer function of ith layer, default = 'tansig'. BTF - Backprop network training function, default = 'trainlm'. BLF - Backprop weight/bias learning function, default = 'learngdm'. PF - Performance function, default = 'mse’ and returns an N layer feed-forward backprop network.

TF1: logsig

TF2: logsig

>> net = newff([-1 1; -1 1; -1 1; -1 1], [4,1], {‘logsig’ ‘logsig’}); Number of inputs decided by PR

Page 14: Neural Networks- Introduction

Network Initialisation

Initialise the net’s weighting and biases>> net = init(net); % init is called after newff

re-initialise with other function:– net.layers{1}.initFcn = 'initwb';– net.inputWeights{1,1}.initFcn = 'rands';– net.biases{1,1}.initFcn = 'rands';– net.biases{2,1}.initFcn = 'rands';

Page 15: Neural Networks- Introduction

Network Training

The overall architecture of your neural network is store in the variable net;

We can reset the variable inside.

net.trainParam.epochs =1000; (Max no. of epochs to train) [100]

net.trainParam.goal =0.01; (stop training if the error goal hit) [0]

net.trainParam.lr =0.001; (learning rate, not default trainlm) [0.01]

net.trainParam.show =1; (no. epochs between showing error) [25]

net.trainParam.time =1000; (Max time to train in sec) [inf]

Page 16: Neural Networks- Introduction

Network Training(cont’d)

train : train the network with its architecture. Description TRAIN(NET,P,T,Pi,Ai) takes, NET - Network. P - Network inputs. T - Network targets, default = zeros. Pi - Initial input delay conditions, default = zeros. Ai - Initial layer delay conditions, default = zeros.

>> p = [-0.5 1 -0.5 1; -1 0.5 -1 0.5; 0.5 1 0.5 1; -0.5 -1 -0.5 -1];

-0.5 1 -0.5 1 -1 0.5 -1 0.5 0.5 1 0.5 1 -0.5 -1 -0.5 -1Training

pattern 1

For neuron 1

Page 17: Neural Networks- Introduction

Network Training(cont’d)

train : train the network with its architecture. Description TRAIN(NET,P,T,Pi,Ai) takes, NET - Network. P - Network inputs. T - Network targets, default = zeros. Pi - Initial input delay conditions, default = zeros. Ai - Initial layer delay conditions, default = zeros.

>> p = [-0.5 1 -0.5 1; -1 0.5 -1 0.5; 0.5 1 0.5 1; -0.5 -1 -0.5 -1];

>> net = train(net, p, t);

>> t = [-1 1 -1 1];

-1 1 -1 1

Training pattern 1

Page 18: Neural Networks- Introduction

Simulation of the network

[Y] = SIM(model, UT)

Y : Returned output in matrix or structure format. model : Name of a block diagram model. UT : For table inputs, the input to the model is interpolated.

>> UT = [-0.5 1 ; -0.25 1; -1 0.25 ; -1 0.5];

-0.5 1.00 -0.25 1.00 -1.00 0.25 -1.00 0.50Training

pattern 1

For neuron 1

>> Y = sim(net,UT);

Page 19: Neural Networks- Introduction

Performance Evaluation

Comparison between target and network’s output in testing set.(generalisation ability)

Comparison between target and network’s output in training set. (memorisation ability)

Design a function to measure the distance/similarity of the target and output, or simply use mse for example.

Page 20: Neural Networks- Introduction

Write them in a file(Adding a new function)

Create a file as fname.m (extension as .m)>> fname

loading.mfunction [Y , Z] = othername(str)

Y = load(str);

Z = length(Y);

>> [A,B] = loading('wtest.txt');

Page 21: Neural Networks- Introduction

Reference

Neural Networks Toolbox User's Guide– http://www.cse.cuhk.edu.hk/corner/tech/doc/manual/matlab-5.3.1/help/pdf_doc/nnet/nnet.pdf

Matlab Help Desk– http://www.cse.cuhk.edu.hk/corner/tech/doc/manual/matlab-5.3.1/help/helpdesk.html

Mathworks ower of Matlab– http://www.mathworks.com/

Page 22: Neural Networks- Introduction

Recommended
Artificial Neural Networks : An Introduction

Artificial Neural Networks : An Introduction

Documents

Introduction to Neural Networks - Perceptron

Introduction to Neural Networks - Perceptron

Technology

Introduction to Neural Networks - Research Blog...Introduction to Neural Networks Terrance DeVries Contents 1. Brief overview of neural networks 2. Introduction to PyTorch (Jupyter

Introduction to Neural Networks - Research Blog...Introduction to Neural Networks Terrance DeVries Contents 1. Brief overview of neural networks 2. Introduction to PyTorch (Jupyter

Documents

An introduction to neural networks for beginners€¦ · Part 1 – Introduction to neural networks 1.1 WHAT ARE ARTIFICIAL NEURAL NETWORKS? Artificial neural networks (ANNs) are

An introduction to neural networks for beginners€¦ · Part 1 – Introduction to neural networks 1.1 WHAT ARE ARTIFICIAL NEURAL NETWORKS? Artificial neural networks (ANNs) are

Documents

Neural Networks Introduction - GitHub Pages

Neural Networks Introduction - GitHub Pages

Documents

Introduction to Neural Networks - in.tum.de · Introduction to Neural Networks Freek Stulp. 2 Overview Biological Background Artificial Neuron Classes of Neural Networks 1. Perceptrons

Introduction to Neural Networks - in.tum.de · Introduction to Neural Networks Freek Stulp. 2 Overview Biological Background Artificial Neuron Classes of Neural Networks 1. Perceptrons

Documents

Convolutional Neural Networks for Deep Learning: An Introduction · 2017-10-05 · Introduction: Convolutional Neural Networks (CNN) Convolutional Neural Networks: A deep learning

Convolutional Neural Networks for Deep Learning: An Introduction · 2017-10-05 · Introduction: Convolutional Neural Networks (CNN) Convolutional Neural Networks: A deep learning

Documents

Introduction to Neural Networks - CPS Summer School › ... › 10 › Introduction-to-Neural-Networks.pdf · 2018-10-22 · Introduction to Neural Networks Danilo Pau Advanced System

Introduction to Neural Networks - CPS Summer School › ... › 10 › Introduction-to-Neural-Networks.pdf · 2018-10-22 · Introduction to Neural Networks Danilo Pau Advanced System

Documents

Convolutional Neural Networks: An Introduction

Convolutional Neural Networks: An Introduction

Technology

Introduction to Neural Networks - Databricks · • Introduction to Neural Networks • Training Neural Networks • Applying your Neural Networks This series will be make use of

Introduction to Neural Networks - Databricks · • Introduction to Neural Networks • Training Neural Networks • Applying your Neural Networks This series will be make use of

Documents

An Introduction to Neural Networks

An Introduction to Neural Networks

Documents

Introduction to Artificial Neural Networks · Introduction to Artificial Neural Networks 24/06/2018 AMLSS Ahmed Guessoum – Intro. to Neural Networks Ahmed Guessoum Natural Language

Introduction to Artificial Neural Networks · Introduction to Artificial Neural Networks 24/06/2018 AMLSS Ahmed Guessoum – Intro. to Neural Networks Ahmed Guessoum Natural Language

Documents

Brief Introduction to Neural Networks

Brief Introduction to Neural Networks

Documents

NEURAL NETWORKS Biological analogy Introduction to Artificial Neural Networks Typical architectures.

NEURAL NETWORKS Biological analogy Introduction to Artificial Neural Networks Typical architectures.

Documents

Introduction To Neural Networks

Introduction To Neural Networks

Documents

Introduction to neural networks

Introduction to neural networks

Technology

ARTIFICIAL NEURAL NETWORKS. Introduction to Neural Networks.

ARTIFICIAL NEURAL NETWORKS. Introduction to Neural Networks.

Documents

Introduction to Neural Networks Supervised learning: neural networks ...vision.psych.umn.edu/users/kersten/kersten-lab/courses/Psy5038WF... · Introduction to Neural Networks Supervised

Introduction to Neural Networks Supervised learning: neural networks ...vision.psych.umn.edu/users/kersten/kersten-lab/courses/Psy5038WF... · Introduction to Neural Networks Supervised

Documents