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Copy pathJacobian.lua
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249 lines (216 loc) · 7.78 KB
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nn.Jacobian = {}
function nn.Jacobian.backward (module, input, param, dparam)
local doparam = 0
if param then
doparam = 1
end
param = param or input
-- output deriv
module:forward(input)
local dout = module.output.new():resizeAs(module.output)
-- 1D view
local sdout = module.output.new(dout:storage(),1,dout:nElement())
-- jacobian matrix to calculate
local jacobian = torch.Tensor(param:nElement(),dout:nElement()):zero()
for i=1,sdout:nElement() do
dout:zero()
sdout[i] = 1
module:zeroGradParameters()
local din = module:updateGradInput(input, dout)
module:accGradParameters(input, dout)
if doparam == 1 then
jacobian:select(2,i):copy(dparam)
else
jacobian:select(2,i):copy(din)
end
end
return jacobian
end
function nn.Jacobian.backwardUpdate (module, input, param)
-- output deriv
module:forward(input)
local dout = module.output.new():resizeAs(module.output)
-- 1D view
local sdout = module.output.new(dout:storage(),1,dout:nElement())
-- jacobian matrix to calculate
local jacobian = torch.Tensor(param:nElement(),dout:nElement()):zero()
-- original param
local params = module:parameters()
local origparams = {}
for j=1,#params do
table.insert(origparams, params[j]:clone())
end
for i=1,sdout:nElement() do
for j=1,#params do
params[j]:copy(origparams[j])
end
dout:zero()
sdout[i] = 1
local din = module:updateGradInput(input, dout)
module:accUpdateGradParameters(input, dout, 1)
jacobian:select(2,i):copy(param)
end
for j=1,#params do
params[j]:copy(origparams[j])
end
return jacobian
end
function nn.Jacobian.forward(module, input, param)
param = param or input
-- perturbation amount
local small = 1e-6
-- 1D view of input
--local tst = param:storage()
local sin = param.new(param):resize(param:nElement())--param.new(tst,1,tst:size())
-- jacobian matrix to calculate
local jacobian = torch.Tensor():resize(param:nElement(),module:forward(input):nElement())
local outa = torch.Tensor(jacobian:size(2))
local outb = torch.Tensor(jacobian:size(2))
for i=1,sin:nElement() do
sin[i] = sin[i] - small
outa:copy(module:forward(input))
sin[i] = sin[i] + 2*small
outb:copy(module:forward(input))
sin[i] = sin[i] - small
outb:add(-1,outa):div(2*small)
jacobian:select(1,i):copy(outb)
end
return jacobian
end
function nn.Jacobian.forwardUpdate(module, input, param)
-- perturbation amount
local small = 1e-6
-- 1D view of input
--local tst = param:storage()
local sin = param.new(param):resize(param:nElement())--param.new(tst,1,tst:size())
-- jacobian matrix to calculate
local jacobian = torch.Tensor():resize(param:nElement(),module:forward(input):nElement())
local outa = torch.Tensor(jacobian:size(2))
local outb = torch.Tensor(jacobian:size(2))
for i=1,sin:nElement() do
sin[i] = sin[i] - small
outa:copy(module:forward(input))
sin[i] = sin[i] + 2*small
outb:copy(module:forward(input))
sin[i] = sin[i] - small
outb:add(-1,outa):div(2*small)
jacobian:select(1,i):copy(outb)
jacobian:select(1,i):mul(-1)
jacobian:select(1,i):add(sin[i])
end
return jacobian
end
function nn.Jacobian.testJacobian (module, input, minval, maxval)
minval = minval or -2
maxval = maxval or 2
local inrange = maxval - minval
input:copy(torch.rand(input:nElement()):mul(inrange):add(minval))
local jac_fprop = nn.Jacobian.forward(module,input)
local jac_bprop = nn.Jacobian.backward(module,input)
local error = jac_fprop-jac_bprop
return error:abs():max()
end
function nn.Jacobian.testJacobianParameters (module, input, param, dparam, minval, maxval)
minval = minval or -2
maxval = maxval or 2
local inrange = maxval - minval
input:copy(torch.rand(input:nElement()):mul(inrange):add(minval))
param:copy(torch.rand(param:nElement()):mul(inrange):add(minval))
local jac_bprop = nn.Jacobian.backward(module, input, param, dparam)
local jac_fprop = nn.Jacobian.forward(module, input, param)
local error = jac_fprop - jac_bprop
return error:abs():max()
end
function nn.Jacobian.testJacobianUpdateParameters (module, input, param, minval, maxval)
minval = minval or -2
maxval = maxval or 2
local inrange = maxval - minval
input:copy(torch.rand(input:nElement()):mul(inrange):add(minval))
param:copy(torch.rand(param:nElement()):mul(inrange):add(minval))
local params_bprop = nn.Jacobian.backwardUpdate(module, input, param)
local params_fprop = nn.Jacobian.forwardUpdate(module, input, param)
local error = params_fprop - params_bprop
return error:abs():max()
end
function nn.Jacobian.testIO(module,input, minval, maxval)
minval = minval or -2
maxval = maxval or 2
local inrange = maxval - minval
-- run module
module:forward(input)
local go = module.output:clone():copy(torch.rand(module.output:nElement()):mul(inrange):add(minval))
module:zeroGradParameters()
module:updateGradInput(input,go)
module:accGradParameters(input,go)
local fo = module.output:clone()
local bo = module.gradInput:clone()
-- write module
local f = torch.DiskFile('tmp.bin','w'):binary()
f:writeObject(module)
f:close()
-- read module
local m = torch.DiskFile('tmp.bin'):binary():readObject()
m:forward(input)
m:zeroGradParameters()
m:updateGradInput(input,go)
m:accGradParameters(input,go)
-- cleanup
os.remove('tmp.bin')
local fo2 = m.output:clone()
local bo2 = m.gradInput:clone()
local errf = fo - fo2
local errb = bo - bo2
return errf:abs():max(), errb:abs():max()
end
function nn.Jacobian.testAllUpdate(module, input, weight, gradWeight)
local gradOutput
local lr = torch.uniform(0.1, 1)
local errors = {}
-- accGradParameters
local maccgp = module:clone()
local weightc = maccgp[weight]:clone()
maccgp:forward(input)
gradOutput = torch.rand(maccgp.output:size())
maccgp:zeroGradParameters()
maccgp:updateGradInput(input, gradOutput)
maccgp:accGradParameters(input, gradOutput)
maccgp:updateParameters(lr)
errors["accGradParameters"] = (weightc-maccgp[gradWeight]*lr-maccgp[weight]):norm()
-- accUpdateGradParameters
local maccugp = module:clone()
maccugp:forward(input)
maccugp:updateGradInput(input, gradOutput)
maccugp:accUpdateGradParameters(input, gradOutput, lr)
errors["accUpdateGradParameters"] = (maccugp[weight]-maccgp[weight]):norm()
-- shared, accGradParameters
local macsh1 = module:clone()
local macsh2 = module:clone()
macsh2:share(macsh1, weight)
macsh1:forward(input)
macsh2:forward(input)
macsh1:zeroGradParameters()
macsh2:zeroGradParameters()
macsh1:updateGradInput(input, gradOutput)
macsh2:updateGradInput(input, gradOutput)
macsh1:accGradParameters(input, gradOutput)
macsh2:accGradParameters(input, gradOutput)
macsh1:updateParameters(lr)
macsh2:updateParameters(lr)
local err = (weightc-maccgp[gradWeight]*(lr*2)-macsh1[weight]):norm()
err = err + (weightc-maccgp[gradWeight]*(lr*2)-macsh2[weight]):norm()
errors["accGradParameters [shared]"] = err
-- shared, accUpdateGradParameters
local macshu1 = module:clone()
local macshu2 = module:clone()
macshu2:share(macshu1, weight)
macshu1:forward(input)
macshu2:forward(input)
macshu1:updateGradInput(input, gradOutput)
macshu2:updateGradInput(input, gradOutput)
macshu1:accUpdateGradParameters(input, gradOutput, lr)
macshu2:accUpdateGradParameters(input, gradOutput, lr)
local err = (weightc-maccgp[gradWeight]*(lr*2)-macshu1[weight]):norm()
err = err + (weightc-maccgp[gradWeight]*(lr*2)-macshu2[weight]):norm()
errors["accUpdateGradParameters [shared]"] = err
return errors
end