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EE459EE459Neural NetworksNeural NetworksBackpropagationBackpropagation
EE459EE459Neural NetworksNeural NetworksBackpropagationBackpropagation
Kasin PrakobwaitayakitKasin PrakobwaitayakitDepartment of Electrical EngineeringDepartment of Electrical Engineering
Chiangmai UniversityChiangmai University
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BackgroundArtificial neural networks (ANNs) provide a general, practical method for learning real-valued, discrete-valued, and vector-valued functions from examples. Algorithms such as BACKPROPAGATION use gradient descent to tune network parameters to best fit a training set of input-output pairs. ANN learning is robust to errors in the training data and has been successfully applied to problems such as face recognition/detection, speech recognition, and learning robot control strategies.
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Autonomous Vehicle Steering
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Characteristics of ANNs•Instances are represented by many attribute-value pairs.
•The target function output may be discrete-valued, real-valued, or a vector of several real- or discrete-valued attributes.
•The training examples may contain errors.
•Long training times are acceptable.
•Fast evaluation of the learned target function may be required.
•The ability of humans to understand the learned target function is not important.
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Very simple example
0 1
0
-0.10.4
net input = 0.4 0 + -0.1 1 = -0.1
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Learning problem to be solved
• Suppose we have an input pattern (0 1)• We have a single output pattern (1)• We have a net input of -0.1, which
gives an output pattern of (0)• How could we adjust the weights, so
that this situation is remedied and the spontaneous output matches our target output pattern of (1)?
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Answer• Increase the weights, so that the
net input exceeds 0.0• E.g., add 0.2 to all weights• Observation: Weight from input
node with activation 0 does not have any effect on the net input
• So we will leave it alone
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One type of ANN system is based on a unit called a perceptron.
The perceptron function can sometimes be written as
The space H of candidate hypotheses considered in perceptron learning is the set of all possible real-valued weight vectors.
Perceptrons
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Representational Power of Perceptrons
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Decision surface
linear decision surface nonlinear decision surface
Programming Example of Decision Surface
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The Perceptron Training Rule
One way to learn an acceptable weight vector is to begin with random weights, then iteratively apply the perceptron to each training example, modifying the perceptron weights whenever it misclassifies an example. This process is repeated, iterating through the training examples as many times as needed until the perceptron classifies all training examples correctly. Weights are modified at each step according to the perceptron training rule, which revises the weight associated with input according to the rule
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Gradient Descent and Delta Rule
The delta training rule is best understood by considering the task of training an unthresholded perceptron; that is, a linear unit for which the output o is given by
In order to derive a weight learning rule for linear units, let us begin by specifying a measure for the training error of a hypothesis (weight vector), relative to the training examples.
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Visualizing the Hypothesis Space
minimum error
initial weight vector by random
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Derivation of the Gradient Descent Rule
The vector derivative is called the gradient of E with respect to , written
The gradient specifies the direction that produces the steepest increase in E. The negative of this vector therefore gives the direction of steepest decrease. The training rule for gradient descent is
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Derivation of the Gradient Descent Rule (cont.)
The negative sign is presented because we want to move the weight vector in the direction that decreases E. This training rule can also written in its component form
which makes it clear that steepest descent is achieved by altering each component of in proportion to .
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The vector of derivatives that form the gradient can be obtained by differentiating E
Derivation of the Gradient Descent Rule (cont.)
The weight update rule for standard gradient descent can be summarized as
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Stochastic Approximation to Gradient DescentEECP0720 Expert Systems – Artificial Neural Networks
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Summary of PerceptronPerceptron training rule guaranteed to succeed if
•training examples are linearly separable
•sufficiently small learning rate
Linear unit training rule uses gradient descent
•guaranteed to converge to hypothesis with minimum squared error
•given sufficiently small learning rate
•even when training data contains noise
EECP0720 Expert Systems – Artificial Neural Networks
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BACKPROPAGATION Algorithm
EECP0720 Expert Systems – Artificial Neural Networks
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Error FunctionThe Backpropagation algorithm learns the weights for a multilayer network, given a network with a fixed set of units and interconnections. It employs gradient descent to attempt to minimize the squared error between the network output values and the target values for those outputs. We begin by redefining E to sum the errors over all of the network output units
where outputs is the set of output units in the network, and tkd and okd are the target and output values associated with the kth output unit and training example d.
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Architecture of Backpropagation
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Backpropagation Learning Algorithm
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Backpropagation Learning Algorithm (cont.)
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Backpropagation Learning Algorithm (cont.)
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Backpropagation Learning Algorithm (cont.)
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Backpropagation Learning Algorithm (cont.)
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Inputs To Neurons• Arise from other neurons or from
outside the network• Nodes whose inputs arise outside the
network are called input nodes and simply copy values
• An input may excite or inhibit the response of the neuron to which it is applied, depending upon the weight of the connection
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Weights• Represent synaptic efficacy and may be
excitatory or inhibitory• Normally, positive weights are
considered as excitatory while negative weights are thought of as inhibitory
• Learning is the process of modifying the weights in order to produce a network that performs some function
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Output• The response function is normally
nonlinear• Samples include
– Sigmoid
– Piecewise linear
xexf
1
1)(
xif
xifxxf
,0
,)(
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Backpropagation Preparation
• Training SetA collection of input-output patterns that are used to train the network
• Testing SetA collection of input-output patterns that are used to assess network performance
• Learning Rate-ηA scalar parameter, analogous to step size in numerical integration, used to set the rate of adjustments
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Network Error• Total-Sum-Squared-Error (TSSE)
• Root-Mean-Squared-Error (RMSE)
patterns outputs
actualdesiredTSSE 2)(2
1
outputspatterns
TSSERMSE
*##
*2
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A Pseudo-Code Algorithm
• Randomly choose the initial weights• While error is too large
– For each training pattern• Apply the inputs to the network• Calculate the output for every neuron from the input
layer, through the hidden layer(s), to the output layer• Calculate the error at the outputs• Use the output error to compute error signals for pre-
output layers• Use the error signals to compute weight adjustments• Apply the weight adjustments
– Periodically evaluate the network performance
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Face Detection using Neural Networks
Neural
Network
Face Database
Non-Face Database
Training ProcessOutput=1, for face database
Output=0, for non-face database
Face
orNon-
Face?
Test
ing P
roc e
ss
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Backpropagation Using Gradient Descent
• Advantages– Relatively simple implementation– Standard method and generally works well
• Disadvantages– Slow and inefficient– Can get stuck in local minima resulting in
sub-optimal solutions
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Local Minima
Local Minimum
Global Minimum
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Alternatives To Gradient Descent
• Simulated Annealing– Advantages
• Can guarantee optimal solution (global minimum)
– Disadvantages• May be slower than gradient descent• Much more complicated implementation
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Alternatives To Gradient Descent
• Genetic Algorithms/Evolutionary Strategies– Advantages
• Faster than simulated annealing• Less likely to get stuck in local minima
– Disadvantages• Slower than gradient descent• Memory intensive for large nets
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Enhancements To Gradient Descent
• Momentum– Adds a percentage of the last
movement to the current movement
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Enhancements To Gradient Descent
• Momentum– Useful to get over small bumps in the error
function– Often finds a minimum in less steps– w(t) = -n*d*y + a*w(t-1)
• w is the change in weight• n is the learning rate• d is the error• y is different depending on which layer we are
calculating• a is the momentum parameter
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Enhancements To Gradient Descent
• Adaptive Backpropagation Algorithm– It assigns each weight a learning rate– That learning rate is determined by the sign
of the gradient of the error function from the last iteration
• If the signs are equal it is more likely to be a shallow slope so the learning rate is increased
• The signs are more likely to differ on a steep slope so the learning rate is decreased
– This will speed up the advancement when on gradual slopes
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Enhancements To Gradient Descent
• Adaptive Backpropagation– Possible Problems:
• Since we minimize the error for each weight separately the overall error may increase
– Solution:• Calculate the total output error after each
adaptation and if it is greater than the previous error reject that adaptation and calculate new learning rates
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Enhancements To Gradient Descent
• SuperSAB(Super Self-Adapting Backpropagation)– Combines the momentum and adaptive methods.– Uses adaptive method and momentum so long as
the sign of the gradient does not change• This is an additive effect of both methods resulting in a
faster traversal of gradual slopes– When the sign of the gradient does change the
momentum will cancel the drastic drop in learning rate
• This allows for the function to roll up the other side of the minimum possibly escaping local minima
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Enhancements To Gradient Descent
• SuperSAB– Experiments show that the SuperSAB
converges faster than gradient descent
– Overall this algorithm is less sensitive (and so is less likely to get caught in local minima)
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Other Ways To Minimize Error
• Varying training data– Cycle through input classes– Randomly select from input classes
• Add noise to training data– Randomly change value of input node (with
low probability)• Retrain with expected inputs after
initial training– E.g. Speech recognition
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Other Ways To Minimize Error
• Adding and removing neurons from layers– Adding neurons speeds up learning
but may cause loss in generalization– Removing neurons has the opposite
effect
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