réseau de neurons

8/7/2019 Rseau de neurons

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RSEAU DE NEURONS

Octobre 2010


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NETWORK ARCHITECTURE

Normally the neural network is divided into:

Input layer (data is presented to the NN)

Output layer (presents the result to the user)

Hidden layers (refer to one or more layer)


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What is a neuron ?

Neuron is a parameterized algebraic

function with borders.w1

w2

w3

w4

w5

b

hActivation

function

Sum

weight


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Type of connection between layers:

Fully connected

Partially connected

Bi-directional


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Number of nodes:

Input and output layers automatically determinedby the number of inputs and outputs.

For hidden layer

no steadfast rule for determining the neuronsnumber.

There is 2 approach to do it:Start with few and then increase until the overall

results are improved.

Start with twice the number of input nodes plus one

([x*2]+1)


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NETWORK ALGORITHMS

First we must see the activation function inside

the neurons:

Threshold

Linear

Sigmoid

Note: the sigmoid function is the most common

function used.


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NETWORK ALGORITHMS

While training of artificial neural network:

1. First the connection between neurons are set torandom weight values.

2. During the training process the input-output dataare fed into the network.

3. Difference between training output and actualoutput is then calculated.

4. Considering this deference as error, using thetraining algorithm the weight is updated to reducethis error.

5. Once trained, the network is hopefully ready to

predict accurate output.


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NETWORK ALGORITHMS

Training ANN: Training is defined as a search process for the optimized

set of weight values, witch can minimize the squared error

between the estimation and the experimental data of unitsin output layer.

Learning a neural network can be supervised or

unsupervised.

The most common algorithm is supervised training called

standard-back Propagation.


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NETWORK DATA SETS

Data set is often divided into training set and

test set.

Training set is for learning (weights)

Test set is to tune parameters (architecture not

weight number of hidden units)


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TRAINING SETS

A large training set is required with a variety of

data.

Numeric and nominal variables can be handled, all

other sets need to be converted or discarded.

Redundant variables are minimized or eliminated.

Cases with missing values can be used but outliers

may cause problems. (if necessary)

Choose independent variables ???


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PRUNING ALGORITHM

Training algorithms


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PRUNING ALGORITHM BASIC IDEA

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PRUNING ALGORITHM

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Adjusting the weight to follow the equations :

To simplify:To simplify:

réseau de neurons

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