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Neural Networks Learning the Network: Part 2

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Neural Networks Learning the network: Part 2 11-785, Fall 2020 Lecture 4 1 Recap: Universal approximators • Neural networks are universal approximators – Can approximate any function • Provided they have sufficient architecture – We have to determine the weights and biases to make them model the function 2 Recap: Approach • Define a divergence between the actual output and desired output of the network – Must be differentiable: can quantify how much a miniscule change of changes • Make all neuronal activations differentiable – Differentiable: can quantify how much a miniscule change of changes • Differentiability – enables us to determine if a small change in any parameter of the network is increasing or decreasing – Will let us optimize the network 3 Recap: The expected divergence • Minimize the expected “divergence” between the output of the net and the desired function over the input space 4 Recap: Emipirical Risk Minimization • Problem: Computing the expected divergence requires knowledge of at all which we will not have • Solution: Approximate it by the average divergence over a large number of “training” samples drawn from • Estimate the parameters to minimize this “loss” instead 5 Problem Statement • Given a training set of input-output pairs • Minimize the following function w.r.t • This is problem of function minimization – An instance of optimization 6 • A CRASH COURSE ON FUNCTION OPTIMIZATION 7 A brief note on derivatives.. derivative • A derivative of a function at any point tells us how much a minute increment to the argument of the function will increment the value of the function . For any expressed as a multiplier to a tiny increment to obtain the increments to the output . Based on the fact that at a fine enough resolution, any smooth, continuous function is locally linear at any point 8 Scalar function of scalar argument • When and are scalar . Derivative: . Often represented (using somewhat inaccurate notation) as . Or alternately (and more reasonably) as 9 Multivariate scalar function: Scalar function of vector argument Note: is now a vector • Giving us that is a row vector: • The partial derivative gives us how increments when only is incremented • Often represented as 10 Multivariate scalar function: Scalar function of vector argument Note: is now a vector We will be using this • Where symbol for vector and matrix derivatives o You may be more familiar with the term “gradient” which is actually defined as the transpose of the derivative 11 Caveat about following slides • The following slides speak of optimizing a function w.r.t a variable “x” • This is only mathematical notation. In our actual network optimization problem we would be optimizing w.r.t. network weights “w” • To reiterate – “x” in the slides represents the variable that we’re optimizing a function over and not the input to a neural network • Do not get confused! 12 The problem of optimization f(x) global maximum inflection point local minimum global minimum x • General problem of optimization: find the value of x where f(x) is minimum 13 Finding the minimum of a function f(x) x • Find the value at which = 0 – Solve • The solution is a “turning point” – Derivatives go from positive to negative or vice versa at this point • But is it a minimum? 14 Turning Points 0 0 - + - + - - + - + - + - + - + - + - -- + - - 0 • Both maxima and minima have zero derivative • Both are turning points 15 Derivatives of a curve f’(x) f(x) x • Both maxima and minima are turning points • Both maxima and minima have zero derivative 16 Derivative of the derivative of the curve f’’(x) f’(x) f(x) x • Both maxima and minima are turning points • Both maxima and minima have zero derivative • The second derivative f’’(x) is –ve at maxima and +ve at minima! 17 Solution: Finding the minimum or maximum of a function f(x) x • Find the value at which = 0: Solve • The solution is a turning point • Check the double derivative at : compute • If is positive is a minimum, otherwise it is a maximum 18 A note on derivatives of functions of single variable maximum • All locations with zero Inflection point derivative are critical points minimum – These can be local maxima, local minima, or inflection points Critical points • The second derivative is – Positive (or 0) at minima – Negative (or 0) at maxima – Zero at inflection points • It’s a little more complicated for Derivative is 0 functions of multiple variables 19 A note on derivatives of functions of single variable maximum • All locations with zero Inflection point derivative are critical points minimum – These can be local maxima, local minima, or inflection points • The second derivative is – at minima – at maxima – Zero at inflection points positive zero • It’s a little more complicated for functions of multiple variables.. negative 20 What about functions of multiple variables? • The optimum point is still “turning” point – Shifting in any direction will increase the value – For smooth functions, miniscule shifts will not result in any change at all • We must find a point where shifting in any direction by a microscopic amount will not change the value of the function 21 A brief note on derivatives of multivariate functions 22 The Gradient of a scalar function • The derivative of a scalar function of a multi-variate input is a multiplicative factor that gives us the change in for tiny variations in – The gradient is the transpose of the derivative 23 Gradients of scalar functions with multivariate inputs • Consider • Relation: This is a vector inner product. To understand its behavior lets consider a well-known property of inner products 24 A well-known vector property • The inner product between two vectors of fixed lengths is maximum when the two vectors are aligned – i.e. when 25 Properties of Gradient vs angle of Blue arrow is • • For an increment of any given length is max if is aligned with T – The function f(X) increases most rapidly if the input increment T is exactly in the direction of • The gradient is the direction of fastest increase in f(X) 26 Gradient Gradient 푇 vector 27 Gradient Gradient 푇 vector Moving in this direction increases fastest 28 Gradient Gradient 푇 vector Moving in this 푇 direction increases Moving in this fastest direction decreases fastest 29 Gradient Gradient here is 0 Gradient here is 0 30 Properties of Gradient: 2 푇 • The gradient vector is perpendicular to the level curve 31 The Hessian • The Hessian of a function is given by the second derivative 2 f 2 f 2 f 2 . x x x x x1 1 2 1 n 2 f 2 f 2 f . 2 2 x2x1 x2 x2xn X f (x1,..., xn ) : . . 2 f 2 f 2 f . 2 xnx1 xnx2 xn 32 Returning to direct optimization… 33 Finding the minimum of a scalar function of a multivariate input • The optimum point is a turning point – the gradient will be 0 34 Unconstrained Minimization of function (Multivariate) 1. Solve for the where the derivative (or gradient) equals to zero X f (X ) 0 2. Compute the Hessian Matrix at the candidate solution and verify that – Hessian is positive definite (eigenvalues positive) -> to identify local minima – Hessian is negative definite (eigenvalues negative) -> to identify local maxima 35 Closed Form Solutions are not always available f(X) X • Often it is not possible to simply solve – The function to minimize/maximize may have an intractable form • In these situations, iterative solutions are used – Begin with a “guess” for the optimal and refine it iteratively until the correct value is obtained 36 Iterative solutions f(X) X x0 x1 x2 x5 x3 x4 • Iterative solutions – Start from an initial guess for the optimal – Update the guess towards a (hopefully) “better” value of – Stop when no longer decreases • Problems: – Which direction to step in – How big must the steps be 37 The Approach of Gradient Descent • Iterative solution: – Start at some point – Find direction in which to shift this point to decrease error • This can be found from the derivative of the function – A negative derivative moving right decreases error – A positive derivative moving left decreases error – Shift point in this direction 38 The Approach of Gradient Descent • Iterative solution: Trivial algorithm . Initialize . While • If is positive: 푥 = 푥 − 푠푡푒푝 • Else 푥 = 푥 + 푠푡푒푝 – What must step be to ensure we actually get to the optimum?39 The Approach of Gradient Descent • Iterative solution: Trivial algorithm . Initialize . While • Identical to previous algorithm 40 The Approach of Gradient Descent • Iterative solution: Trivial algorithm . Initialize . While • is the “step size” 41 Gradient descent/ascent (multivariate) • The gradient descent/ascent method to find the minimum or maximum of a function iteratively – To find a maximum move in the direction of the gradient – To find a minimum move exactly opposite the direction of the gradient • Many solutions for step size 42 Gradient descent convergence criteria • The gradient descent algorithm converges when one of the following criteria is satisfied k+1 k f (x )- f (x ) < e1 • Or k x f (x ) < e 2 43 Overall Gradient Descent Algorithm • Initialize: . • do . • while 44 Convergence of Gradient Descent • For appropriate step size, for convex (bowl- shaped) functions gradient descent will always find the minimum. • For non-convex functions it will find a local minimum or an inflection point 45 • Returning to our problem.. 46 Problem Statement • Given a training set of input-output pairs • Minimize the following function w.r.t • This is problem of function minimization – An instance
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