Download Multi-Layer Perceptron

MULTI-LAYER PERCEPTRON
Ranga Rodrigo
February 8, 2014
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INTRODUCTION
• Perceptron can only be a linear classifier.
• We can have a network of neurons (perceptron-like
structures) with an input layer, one or more hidden
layers, and an output layer.
• Each layer consists of many neurons and the output
of a layer is fed as inputs to all neurons of the next
layer.
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N1xN2
weights
Layer 1
Layer 2
Layer k
Layer L
(output
layer)
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DESCRIPTION OF THE MLP
• In each layer, there are Nk elements (neurons), k =
1, ...,L, denoted as Nki , N ik , i  1, , N k
• Each neuron may be a sigmoidal neuron.
• There are N0 inputs, to which signals x1(t), ..., xN0(t),
are applied, notated in the form of a vector
x  [ x1 (t ),, x N0 (t )]T , t  1,2,
• The output signal of i-th neuron in k-th layer is
(k )
y
denoted as i (t ) i  1, N k , k  1,  , L .
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DESCRIPTION OF PARAMETERS
Input vector for kth layer
Input for kth layer from
the output of (k-1) layer
(except for k=1, i = 0)
weights of neuron
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i-TH NEURON IN k-TH LAYER
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FORWARD PASS
Output signals of Lth layer
Output desired signals
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BACKPROPAGATION
Weights update
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