/usr/share/lua/5.1/optim/checkgrad.lua is in lua-torch-optim 0~20170208-g656c42a-3.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | --[[ An implementation of a simple numerical gradient checker.
ARGS:
- `opfunc` : a function that takes a single input (X), the point of
evaluation, and returns f(X) and df/dX
- `x` : the initial point
- `eps` : the epsilon to use for the numerical check (default is 1e-7)
RETURN:
- `diff` : error in the gradient, should be near tol
- `dC` : exact gradient at point
- `dC_est` : numerically estimates gradient at point
]]--
-- function that numerically checks gradient of NCA loss:
function optim.checkgrad(opfunc, x, eps)
-- compute true gradient:
local Corg,dC = opfunc(x)
dC:resize(x:size())
local Ctmp -- temporary value
local isTensor = torch.isTensor(Corg)
if isTensor then
Ctmp = Corg.new(Corg:size())
end
-- compute numeric approximations to gradient:
local eps = eps or 1e-7
local dC_est = torch.Tensor():typeAs(dC):resizeAs(dC)
for i = 1,dC:size(1) do
local tmp = x[i]
x[i] = x[i] + eps
local C1 = opfunc(x)
if isTensor then
Ctmp:copy(C1)
C1 = Ctmp
end
x[i] = x[i] - 2 * eps
local C2 = opfunc(x)
x[i] = tmp
dC_est[i] = (C1 - C2) / (2 * eps)
end
-- estimate error of gradient:
local diff = torch.norm(dC - dC_est) / torch.norm(dC + dC_est)
return diff,dC,dC_est
end
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