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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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NETWORK ARCHITECTURE
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NETWORK ARCHITECTURE
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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NETWORK ARCHITECTURE
Type of connection between layers:
Fully connected
Partially connected
Bi-directional
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NETWORK ARCHITECTURE
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
+H2 +H3 +H4+H
EntreeEntree
sortiesortie
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PRUNING ALGORITHM
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Adjusting the weight to follow the equations :
To simplify:To simplify: