ea c461 - artificial intelligence neural networks
TRANSCRIPT
EA C461 - Artificial Intelligence
Neural Networks
Topics
Connectionist Approach to Learning Perceptron, Perceptron Learning
Neural Net example: ALVINN
Autonomous vehicle controlled by Artificial Neural Network
Drives up to 70mph on public highways
Note: most images are from the online slides for Tom Mitchell’s book “Machine Learning”
Neural Net example: ALVINN
Input is 30x32 pixels= 960 values
1 input pixel
4 hidden units
30 output units
Sharp
right
Straight
ahead
Sharp left
Learning means adjusting weight
values
Neural Net example: ALVINN
Output is array of 30 values
This corresponds to steering instructions
E.g. hard left, hard right
This shows one hidden node
Input is 30x32 array of pixel values
= 960 values Note: no special visual
processing
Size/colour corresponds to weight on link
Neural Networks
Mathematical representations of information processing in biological systems? Efficient models for statistical pattern recognition
Multi Layer Perceptron Model comprises multiple layers of logistic
regression models (with continuous nonlinearities) Compact models, comparing to SVM with similar
generalization performances Likelihood function is no longer convex!!!
Substantial resources requirement for training , often Quicker processing of new data
Feed-forward Network Functions
Linear models for regression and classification
Neural networks use basis functions that follow similar form Each basis function is itself a nonlinear function of a
linear combination of the inputs, The coefficients in the linear combination are
adaptive parameters Can be modeled as a series of functional
transformations
Feed-forward Network Functions
First construct M linear combinations of the input variables x1 , . . . , xD in the form
aj is called as activation, wj0 is bias, wji are weights
h(.) – non linear differentiable transformation Generally sigmoid function : logic sigmoid, tanh
Feed-forward Network Functions
Proceed to do the same with the second layer
The choice of activation function at second layer (corresponds to output ) is determined by the nature of the data and the assumed distribution of target variables
Feed-forward Network Functions
Evaluating this equation can be interpreted as a forward propagation of information through the network
Bias can be absorbed into the input
Feed-forward Network
Activation functions
Activation functions are linear for perceptrons Activation functions are not linear for MLP
Composition of successive linear transformations is itself a linear transformation
We can always find an equivalent network without hidden units
If the number of hidden units is smaller than either the number of input or output units, then the information is lost in the dimensionality reduction at the
hidden units. the transformations that the network can generate are not
the most general possible linear transformations from inputs to outputs because
Little / no interest in MLP’s with linear activation for hidden layers
Output layer
For regression we use linear outputs and a sum-of-squares error, for (multiple independent)
For binary classifications we use logistic sigmoid outputs and a cross-entropy error function, and for multiclass classification we use softmax outputs with the corresponding multiclass cross-entropy error function
For classification problems involving two classes, we can use a single logistic sigmoid output, or a network with two outputs having a softmax output activation function
Universal Approximators
A two-layer network with linear outputs can uniformly approximate any continuous function on a compact input domain to arbitrary accuracy provided the network has a sufficiently large number of hidden units
Universal approximators
Parameter optimization
In the neural networks literature, it is usual to consider the minimization of an error function rather than the maximization of the (log) likelihood
Maximizing the likelihood function is equivalent to minimizing the sum-of-squares error function
Parameter optimization
The value of w found by minimizing E( w ) will be denoted wML because it corresponds to the maximum likelihood solution.
The nonlinearity of the network function y( xn, w ) causes the error E( w ) to be nonconvex In practice local maxima of the likelihood may be
found,
Parameter optimization
Parameter optimization
If we make a small step in weight space from w to w+δ w then the change in the error function is
δE ≈ δwT ∇E(w)where
∇E(w) points in the direction of greatest rate of increase of the error function.
A step in the direction of −∇E(w) reduces the
error
Parameter optimization
E(w) is a smooth continuous function of w It’s value will be smaller where the gradient of the error
function vanishes , i.e E(w) = 0 , stationary point Stationary points can be minima, maxima & saddle points
Many points in weight space at which the gradient vanishes
For any point w that is a local minimum, there will be other points in weight space that are equivalent minima In a two-layer network with M hidden units, each point
in weight space is a member of a family of M!2M equivalent points (plus) multiple inequivalent stationary points and multiple inequivalent minima
Parameter optimization
Not always feasible to find the global minimum Also, it will not be known whether the global minimum
has been found It may be necessary to compare several local minima
in order to find a sufficiently good solution Iterative numerical procedures
Choose some initial value w(0) for the weight vector Navigate through weight space in a succession of
steps of the form
w (τ +1) = w(τ) + ∆w(τ)
τ – Iteration Step The value of ∇E(w) is evaluated at the new weight
vector w(τ+1)
Gradient descent optimization
Update weight to make a small step in the direction of the negative gradient
Error function is defined with respect to a training set Each step requires that the entire training set be
processed to evaluate ∇E Batch methods It is necessary to run gradient-based algorithm
multiple times Each time using a different randomly chosen starting point Comparing the resulting performance on an independent
validation set
Gradient descent optimization
Error functions based on ML principle for a set of independent observations comprise a sum of terms, one for each data point
On-line gradient descent / sequential gradient descent / stochastic gradient descent, makes an update to the weight vector based on one data point at a time
Cycle through each point/ pick random points with replacement
Back propagation
Back propagation – misc slides
Regularization in Neural Networks
The generalization error is not a simple function of M due to the presence of local minima in the error function
Not always feasible to choose M by plotting
Regularization in Neural Networks
The number of input and outputs units in a neural network is determined by the dimensionality of the data set
The number M of hidden units is a free parameter that can be adjusted to give the best predictive performance
Regularization in Neural Networks
Choose a relatively large value for M and then control the complexity by the addition of a regularization term to the error function
The simplest regularizer is the quadratic
Weight decay regularizer The effective model complexity is determined
by the choice of the regularization coefficient λ
Early Stopping
Training can therefore be stopped at the point of smallest error with respect to the validation data set
Invariance
In the classification of objects in two-dimensional images, such as handwritten digits, a particular object should be assigned the same classification irrespective of its position within the image (translation invariance) its size (scale invariance)
If sufficiently large numbers of training patterns are available, then neural network can learn the invariance(at least approximately)
Invariance
Can we augment the training set using replicas of the training patterns, transformed according to the desired invariances
Invariance
We can simply ignore the invariance in the neural network Invariance is built into the pre-processing by extracting
features that are invariant under the required transformations Any subsequent regression or classification system that uses
such features as inputs will necessarily also respect these invariances
Build the invariance properties into the structure of a neural network Convolutional neural networks
Idea: Extracting local features that depend only on small
subregions Merge these info in later stages of processing in order to
detect higher-order features ultimately as the image as a whole
Convolutional neural networks
Build the invariance properties into the structure of a neural network Convolutional neural networks
Idea: Extracting local features that depend only on
small subregions Merge these info in later stages of processing in
order to detect higher-order features ultimately as the image as a whole
Radial Basis Function
An approach to function approximation Learned hypothesis takes the form
k user provided constant (Number of Kernels) xu is an intance from X. Ku will decrease with d increases, and generally it is
a Gaussian Kernel, centered at xu
k
uuuu xxdKwwxf
10 )),(()(ˆ
),(2
1 22
)),((xxd
uu
uuexxdK
This function can
be used to describe a two-layer network
The width of each kernel σ2 can be separately specified
The network training procedure learns wi.
Radial Basis Functions
Radial Basis Functions
Choosing kernels One fixed width kernel for each training point
Each kernel influences the only its neighborhood Fits training data exactly
Choose smaller number of kernels in comparison with the number of training examples Each kernel distributed uniformly across the space (or)
guided by the EM Algorithm
Radial Basis Function
Summarization on RBF Provides a global approximation to the target function Represented by a linear combination of many local kernel
functions To neglect the values out of defined region(region/width) Can be trained more efficiently