We consider the 2-variable scalar function f(x)=exp(x1)(x^2_1 +x^2_2)^3 generate the computational graph of this expression...
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We consider the 2-variable scalar function f(x)=exp(x1)(x^2_1 +x^2_2)^3 • generate the computational graph of this expression (graph starts with c1 = x1, c2 = x2, and ends with f = c7) • use forward mode differentiation to compute Vf (0, 2). Show your intermediate values on the graph. • use reverse mode differentiation to compute the same gradient. Show your intermediate values (values of the adjoint variables cī = af/aci). • use the backward() method of pytorch to verify your answers. • comment briefly on the computational effectiveness of forward vs reverse modes of computing these derivatives. We consider the 2-variable scalar function f(x)=exp(x1)(x^2_1 +x^2_2)^3 • generate the computational graph of this expression (graph starts with c1 = x1, c2 = x2, and ends with f = c7) • use forward mode differentiation to compute Vf (0, 2). Show your intermediate values on the graph. • use reverse mode differentiation to compute the same gradient. Show your intermediate values (values of the adjoint variables cī = af/aci). • use the backward() method of pytorch to verify your answers. • comment briefly on the computational effectiveness of forward vs reverse modes of computing these derivatives.
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Lets break down the steps to compute the gradient of the given function fxexpx1x12x223 at the point 0 2 using both forward mode differentiation and re... View the full answer
Related Book For
Microeconomics An Intuitive Approach with Calculus
ISBN: 978-0538453257
1st edition
Authors: Thomas Nechyba
Posted Date:
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