Ml Ch2 Neuralnetwork

Bộ môn KHMT, Khoa CNTT, HUAHanoi, Nov. 2010

Trí tuệ nhân tạo nâng cao

Chuyên đề: Một số phương pháp học máy tiên tiến và ứng dụng(Advanced machine learning techniques and applications)

GV: Nguyễn Thị Thủy

Advanced AI – Neural Network 2 HUA, Nov. 2010

Nội dung

♦ Ch1: Tổng quan về học máy (Machine learning)♦ Ch2: Mạng nơ-ron và Giải thuật di truyền

– Mạng nơ-ron (Artificial neural network)– Giải thuật di truyền (Genetic algorithm*)

♦ Ch3: Support Vector Machines (SVMs)– Lý thuyết– Ứng dụng

♦ Ch4: Phương pháp học tổ hợp (Ensemble learning)– Boosting and bagging– Online boosting

♦ Ch5: Case study– Ứng dụng của ML trong Thị giác máy tính, trong khai phá dữ liệu, trong

Nông nghiệp


Nội dung

1. Giới thiệu về NN

2. Cấu trúc và phân loại mạng nơ-ron

3. Vấn đề học của NN

4. Sử dụng ANN Toolbox trong Matlab

Giải thuật Di truyền (Genetic Algorithm, GA – nếu có thời gian)


I- Artificial Neural Network (ANN)


Introduction

♦ People are much better than computers at recognizing patterns. How do they do it?

– Neurons in the perceptual system represent features of the sensory input. – The brain learns to extract many layers of features. Features in one layer represent

combinations of simpler features in the layer below.

♦ Can we train computers mimicking the way the brain does it?– Nobody knows how the brain does it, so this requires both engineering insights and

scientific discoveries.


Mạng nơ ron sinh học

♦ ~ 100 billion neurons♦ Mỗi neuron có hàng

ngàn kết nối:– Thu nhận tín hiệu– Lan truyền thông

tin


Mạng nơ-ron nhân tạo (Artificial Neural Network)

♦ Neural network: information processing paradigm inspired by biological nervous systems, such as our brain~Computational model of real neurons and an integral part of the disciplines

of machine learning and computational intelligence.♦ Structure: large number of highly interconnected processing elements

(neurons) working together♦ Like people, they learn from experience (by examples)

http://en.wikipedia.org/wiki/Machine_learning

http://en.wikipedia.org/wiki/Computational_intelligence


Cấu trúc mạng nơ-ron

♦ Neural networks are configured for a specific application, such as pattern recognition or data classification, through a learning process

♦ In a biological system, learning involves adjustments to the synaptic connections between neurons (weights)same for artificial neural networks (ANNs)


Mathematical representation

The neuron calculates a weighted sum of inputs and compares it to a threshold. If the sum is higher than the threshold, the output is set to 1, otherwise to -1.

Non-linearity


♦ Neuron model

♦ Transfer functions:


Network architecture

♦ Two or more of the neurons can be combined in a layer, ♦ An ANN could contain one or more such layers. ♦ A single layer NN with R inputs, S neurons is


♦ Multi layers of neurons


Training the network

♦ A simple perceptron: It’s a single-unit network♦ Change the weight by an amount proportional

to the difference between the desired output and the actual output.

Δ Wi = η * (D-Y).Ii

Perceptron Learning Rule

Learning rateDesired output

Input

Actual output


Multilayer Networks and Backpropagation Training

♦ Feedforward network



♦ The process of tuning the values of the weights and biases of the network to optimize network performance, i.e. mean square error mse

♦ mse = the average squared error between the network outputs a and the target outputs t.

♦ 2 training modes– Incremental mode: the gradient is computed and the weights are updated

after each input is applied to the network.– Batch mode: all the inputs in the training set are applied to the network

before the weights are updated.

♦ Any optimization algorithm can be used to optimize the performance function!



♦ Backpropagation algorithm: performing computations backward through the network.

♦ Training Algorithms:– simplest optimization algorithm — gradient descent (updates the network

weights and biases in the direction in which the performance function decreases most rapidly, the negative of the gradient).

– One iteration:


♦ Training functions in Matlab


ANN Learning

♦ From experience: examples / training data♦ Strength of connection between the neurons is stored as a weight-value

for the specific connection♦ Learning the solution to a problem = changing the connection weights


ANN

♦ Power: Model complex functions, nonlinearity built into the network ♦ Ease of use:

– Learn by example– Very little user domain-specific expertise needed

♦ Intuitively appealing: based on model of biology, will it lead to genuinely intelligent computers/robots?

Neural networks cannot do anything that cannot be done using traditional computing techniques, BUT they can do some things which would

otherwise be very difficult.


Applications

♦ Classification– Image processing– Predict bankruptcy for credit card companies– Risk assessment

♦ Prediction: learning from past experience– pick the best stocks in the market– predict weather– identify people with cancer risk

♦ Recognition– Pattern recognition: SNOOPE (bomb detector in U.S. airports)– Character recognition



Dự báo thời tiết: chuồn chuồn bay thấp thì mưa…♦ Input: ?♦ Output: trời mưa hay nắng?

♦ Input: Nhiệt độ, áp suất, độ ẩm, sức gió…


Các thế hệ ANN

♦ Search for “Artificial neural network”

♦ 3 thế hệ:– Thế hệ 1: Culloch&Pitts, threshold gates– Thế hệ 2: firing rate, sigmoid functions– Mạng nơron thế hệ thứ 3: temporal factor, Spiking neuron (W. Maass,

Henry Markram)

♦ Não “nhân tạo”

– http://ditwww.epfl.ch/cgi-perl/EPFLTV/home.pl?page=channel_one&lang=2&connected=0&channel_id=29

http://ditwww.epfl.ch/cgi-perl/EPFLTV/home.pl?page=channel_one<=2&connected=0&channel_id=29




ANN – thế hệ 1

♦ McCulloch & Pitts, 1944: first model of an artificial neurons ~ basic computational elements

♦ Perceptrons (~1960)

Activation function: binary 0, 1 ~ in computer

non-adaptivehand-codedfeatures

output units e.g. class labels

input units e.g. pixels

Bomb Toy


Back-propagate error signal to get derivatives for learning

input vector

hidden layers

outputs

Compare outputs with correct answerto get error signal

ANN – thế hệ 2

Activation function:continous


ANN thế hệ 3

♦ See EPFL clips


ANN thế hệ 3


♦ Ứng dụng:♦ Thế hệ 1 và 2: là các hàm/công cụ tính toán (có thể học), giải quyết bất

kỳ bài toán thực tế nào mà các mô hình tính toán khác có thể làm được♦ Thế hệ 3: Neural microcircuit: hardware implementations, “sillicon

neron”


Ví dụ ứng dụng

♦ Bài toán phân loại/nhận dạng sản phẩm sản xuất (ví dụ: wine dataset, UCI)– http://archive.ics.uci.edu/ml/datasets/Wine

♦ Bài toán nhận dạng ký tự quang (click)

♦ Bài toán đấu thầu thi công (click)

♦ Bài toán xử lý nước thải (click)

http://archive.ics.uci.edu/ml/datasets/Wine

http://fita.hua.edu.vn/ntthuy/courses/NN_DauthauThicong.pdf


Thực hành

♦ Matlab tutorial:– http://www.mathworks.com/academia/student_center/tutorials/launchpad.ht

ml

♦ ANN Toolbox in Matlab (click here)–

♦ Demo some applications!!

♦ Need Matlab tutorial? Search for “matlab tutorial”, all online, e.g:– http://www.cyclismo.org/tutorial/matlab/– http://www.math.siu.edu/matlab/tutorials.html

http://www.mathworks.com/academia/student_center/tutorials/launchpad.html

http://www.mathworks.com/academia/student_center/tutorials/launchpad.html

http://fita.hua.edu.vn/ntthuy/courses/nnet_Matlab.pdf

http://www.cyclismo.org/tutorial/matlab/

http://www.math.siu.edu/matlab/tutorials.html

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Ml Ch2 Neuralnetwork