A Presentation On: By: Edutechlearners

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By:
Edutechlearners
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 The perceptron was first proposed by Rosenblatt (1958) is a simple
neuron that is used to classify its input into one of two categories.
 A perceptron is a single processing unit of a neural network. A
perceptron uses a step function that returns +1 if weighted sum of its
input  0 and -1 otherwise.
b (bias)
x1
w1
v y
x2 w2 (v)
wn
xn
 While in actual neurons the dendrite receives electrical signals from the
axons of other neurons, in the perceptron these electrical signals are
represented as numerical values. At the synapses between the dendrite
and axons, electrical signals are modulated in various amounts. This is
also modeled in the perceptron by multiplying each input value by a
value called the weight.
 An actual neuron fires an output signal only when the total strength of
the input signals exceed a certain threshold. We model this
phenomenon in a perceptron by calculating the weighted sum of the
inputs to represent the total strength of the input signals, and applying a
step function on the sum to determine its output. As in biological neural
networks, this output is fed to other perceptrons.
 Perceptron can be defined as a single artificial neuron that
computes its weighted input with the help of the threshold activation
function or step function.
 It is also called as a TLU (Threshold Logical Unit).
x1 w1 w0
w2
x2
.  n o
. wn  w x
i=0 i i
x.n
n
1 if  wi xi >0
f(xi)= { i=0
-1 otherwise
Supervised learning is used when we have a set of training data.This
training data consists of some input data that is connected with some
correct output values. The output values are often referred to as target
values. This training data is used by learning algorithms like back
propagation or genetic algorithms.
 In machine learning, the perceptron is an algorithm
for supervised classification of an input into one of several possible
non-binary outputs.
 Perceptron can be defined as a single artificial neuron that computes its
weighted input with the help of the threshold activation function or step
function.
 The Perceptron is used for binary Classification.
 The Perceptron can only model linearly separable classes.
 First train a perceptron for a classification task.
- Find suitable weights in such a way that the training examples are
correctly classified.
- Geometrically try to find a hyper-plane that separates the examples of
the two classes.
 Linear separability is the concept wherein the separation of the input
space into regions is based on whether the network response is positive
or negative.
 When the two classes are not linearly separable, it may be desirable to
obtain a linear separator that minimizes the mean squared error.
 Definition : Sets of points in 2-D space are linearly separable if the sets
can be separated by a straight line.
 Generalizing, a set of points in n-dimensional space are linearly
separable if there is a hyper plane of (n-1) dimensions separates the
sets.
 Consider a network having positive response in the first quadrant and
negative response in all other quadrants (AND function) with either
binary or bipolar data, then the decision line is drawn separating the
positive response region from the negative response region.
 The net input to the output Neuron is:
Yin = w0 + Ʃi xi wi
Where Yin = The net inputs to the ouput neurons.

i = any integer
w0 = initial weight
 The following relation gives the boundary region of net

input.
b + Ʃi xi wi = 0
 The equation can be used to determine the decision
boundary between the region where Yin> 0 and Yin < 0.
 Depending on the number of input neurons in the network.

this equation represents a line, a plane or a hyper-plane.
 If it is possible to find the weights so that all of the training

input vectors for which the correct response is 1. lie on the
either side of the boundary, then the problem is called
linearly separable.
 Otherwise. If the above criteria is not met, the problem is

called linearly non-separable.
 Even parity means even number of 1 bits in the input
 Odd parity means odd number of 1 bits in the input
 There is no way to draw a single straight line so that the circles are on
one side of the line and the dots on the other side.
 Perceptron is unable to find a line separating even parity input patterns
from odd parity input patterns.
 The perceptron can only model linearly separable functions,
− those functions which can be drawn in 2-dim graph and single
straight line separates values in two part.
Boolean functions given below are linearly separable:
− AND
− OR
− COMPLEMENT
It cannot model XOR function as it is non linearly separable.
− When the two classes are not linearly separable, it may be desirable
to obtain a linear separator that minimizes the mean squared error.
 A Single Layer Perceptron consists of an input and an output layer. The
activation function employed is a hard limiting function.
 Definition : An arrangement of one input layer of neurons feed forward
to one output layer of neurons is known as Single Layer Perceptron.
 Step 1 : Create a perceptron with (n+1) input neurons x0 , x1 , . . . . . , . xn ,
where x0 = 1 is the bias input. Let O be the output neuron.
 Step 2 : Initialize weight W = (w0, w1, . . . . . , . wn ) to random weights.
 Step 3 :Iterate through the input patterns xj of the training set using the
weight set; i.e compute the weighted sum of inputs
net j = Ʃ Xi wi For i=1 to n
for each input pattern j .
 Step 4 : Compute the output Yj using the step function
 Step 5 :Compare the computed output yj with the target output yj
for each input pattern j .
 If all the input patterns have been classified correctly, then output
(read) the weights and exit.
 Step 6 : Otherwise, update the weights as given below : If the
computed outputs yj is 1 but should have been 0,
 Then wi = wi - α xi , i= 0, 1, 2, . . . . , n
 If the computed outputs yj is 0 but should have been 1,Then wi =
wi + α xi , i= 0, 1, 2, . . . . , n
 where α is the learning parameter and is constant.
 Step 7 : goto step 3
 END
 Multilayer perceptrons (MLP) are the most popular type of neural
networks in use today. They belong to a general class of structures
called feedforward neural networks, a basic type of neural network
capable of approximating generic classes of functions, including
continuous and integrable functions.
 A multilayer perceptron:
has one or more hidden layers with any number of units.
uses linear combination functions in the input layers.
uses generally sigmoid activation functions in the hidden layers.
has any number of outputs with any activation function.
has connections between the input layer and the first hidden layer,
between the hidden layers, and between the last hidden layer and the
output layer.
x1
x2
Input
Output
xn
Hidden layers
 The input layer:
• Introduces input values into the network.
• No activation function or other processing.
The hidden layer(s):

• Performs classification of features.
• Two hidden layers are sufficient to solve any problem.
• Features imply more layers may be better.
The output layer:

• Functionally is just like the hidden layers.
• Outputs are passed on to the world outside the neural network.
 In 1959, Bernard Widrow and Marcian Hoff of Stanford
developed models they called ADALINE (Adaptive Linear
Neuron) and MADALINE (Multilayer ADALINE). These
models were named for their use of Multiple ADAptive
LINear Elements. MADALINE was the first neural network to
be applied to a real world problem. It is an adaptive filter
which eliminates echoes on phone lines.
 Initialize Initialize
• Assign random weights to all links
Training
• Feed-in known inputs in random sequence
• Simulate the network
• Compute error between the input and the Training
output (Error Function)
• Adjust weights (Learning Function)
• Repeat until total error < ε
Thinking
• Simulate the network Thinking
• Network will respond to any input
• Does not guarantee a correct solution even for trained
inputs
 Training patterns are presented to the network's inputs; the
output is computed. Then the connection weights wj are
modified by an amount that is proportional to the product of the
difference between the actual output, y, and the desired
output, d, and the input pattern, x.
 The algorithm is as follows:
 Initialize the weights and threshold to small random numbers.
 Present a vector x to the neuron inputs and calculate the output.
 Update the weights according to:
 where
 d is the desired output,
 t is the iteration number, and
 eta is the gain or step size, where 0.0 < n < 1.0
 Repeat steps 2 and 3 until:
 the iteration error is less than a user-specified error threshold
or
 a predetermined number of iterations have been completed.
 Training of Network : Given a set of inputs ‘x’, and output/target
values ‘y’, the network finds the best linear mapping from x to y.
 Given an unpredicted ‘x’ value, we train our network to predict

what the most likely ‘y’ value will be.
 Classification of pattern is also a technique of training the

network, in which we assign a physical object, event or
phenomenon to one set of pre-specified classes (or categories).
 Let us consider an example to illustrate the concept, with 2
inputs (x1 and x2) and 1 output node, classifying input into 2
Classes (class 0 and class 1).

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Where Yin = The net inputs to the ouput neurons.

 The following relation gives the boundary region of net

 Depending on the number of input neurons in the network.

 If it is possible to find the weights so that all of the training

 Otherwise. If the above criteria is not met, the problem is

The hidden layer(s):

The output layer:

 Given an unpredicted ‘x’ value, we train our network to predict

 Classification of pattern is also a technique of training the

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