Multi-Layer Perceptron

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Multi-Layer Perceptron (MLP)

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Multi-Layer Perceptron Ranga Rodrigo February 8, 2014

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.

N1xN2 weights Layer L (output layer) Layer 1 Layer 2 Layer k

Description of the MLP In each layer, there are Nk elements (neurons), k = 1, ...,L, denoted as Nki , 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 The output signal of i-th neuron in k-th layer is denoted as , .

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

i-th neuron in k-th layer

Forward Pass Output signals of Lth layer Output desired signals

Backpropagation Weights update