diff options
author | Clement Farabet <clement.farabet@gmail.com> | 2011-10-27 21:47:29 +0400 |
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committer | Clement Farabet <clement.farabet@gmail.com> | 2011-10-27 21:47:29 +0400 |
commit | 0adcff9732ba532c27cda507b644b4b2f11e9a1a (patch) | |
tree | c56603144ebcd38e01d518016aacd9ebe87e6c8d | |
parent | 2b400a4dcd4d438d63d9af6650dda72271746d72 (diff) | |
parent | 947a61dcea4d0ca854953fa3168f0a9b71f90e6f (diff) |
Merge branch 'master' of github.com:clementfarabet/lua---nnx
-rw-r--r-- | ASGDOptimization.lua | 1 | ||||
-rw-r--r-- | CMakeLists.txt | 1 | ||||
-rw-r--r-- | DiagHessian.lua | 2 | ||||
-rw-r--r-- | SGDOptimization.lua | 27 | ||||
-rw-r--r-- | init.lua | 1 | ||||
-rw-r--r-- | newCGOptimization.lua | 194 |
6 files changed, 216 insertions, 10 deletions
diff --git a/ASGDOptimization.lua b/ASGDOptimization.lua index 28e1131..892d740 100644 --- a/ASGDOptimization.lua +++ b/ASGDOptimization.lua @@ -46,6 +46,7 @@ function ASGD:optimize() self.parameters:add(-self.eta_t, self.gradParameters) end -- (3) Average part + -- a := a + mu_t [ w - a ] self.a = self.a or self.parameters.new():resizeAs(self.parameters):zero() if self.mu_t ~= 1 then self.tmp = self.tmp or self.a.new():resizeAs(self.a) diff --git a/CMakeLists.txt b/CMakeLists.txt index 5b90102..5484b9c 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -120,6 +120,7 @@ install_files(${INSTALL_PREFIX} SpatialRecursiveFovea.lua) install_files(${INSTALL_PREFIX} Optimization.lua) install_files(${INSTALL_PREFIX} LBFGSOptimization.lua) install_files(${INSTALL_PREFIX} CGOptimization.lua) +install_files(${INSTALL_PREFIX} newCGOptimization.lua) install_files(${INSTALL_PREFIX} SGDOptimization.lua) install_files(${INSTALL_PREFIX} ASGDOptimization.lua) install_files(${INSTALL_PREFIX} GeneticSGDOptimization.lua) diff --git a/DiagHessian.lua b/DiagHessian.lua index dfcdcaf..26aed3e 100644 --- a/DiagHessian.lua +++ b/DiagHessian.lua @@ -1,7 +1,7 @@ -- Module function nn.Module.backwardDiagHessian(self, input, diagHessianOutput) - self.diagHessianInput = self.diagHessianInput or input + self.diagHessianInput = self.diagHessianInput or diagHessianOutput return self.diagHessianInput end diff --git a/SGDOptimization.lua b/SGDOptimization.lua index e26c6ed..8ae804d 100644 --- a/SGDOptimization.lua +++ b/SGDOptimization.lua @@ -78,9 +78,11 @@ end function SGD:diagHessian(inputs, targets) if not self.learningRates then + print('<SGD> creating learningRates, initDiagHessian') -- do initialization - self.diagHessianEpsilon = self.diagHessianEpslion or 1e-3 + self.diagHessianEpsilon = self.diagHessianEpsilon or 1e-2 self.learningRates = torch.Tensor():typeAs(self.parameters):resizeAs(self.parameters):fill(1) + -- we can call this multiple times as it will only create the tensors once. self.module:initDiagHessianParameters() self.diagHessianParameters = nnx.flattenParameters(nnx.getDiagHessianParameters(self.module)) @@ -109,23 +111,30 @@ function SGD:diagHessian(inputs, targets) module:accDiagHessianParameters(inputs, critDiagHessian) self.diagHessianParameters:div(inputs:size(1)) end - -- protect diag hessian (the proper way of doing it is the commented code, - -- but for speed reasons, the uncommented code just works) + print('<diagHessian>') + print(' + before max ') + print(' + epsilon: '..self.diagHessianEpsilon) + print(' + norm of dhP: '..self.diagHessianParameters:norm()) + print(' + max dhP : '..self.diagHessianParameters:max()) + print(' + min dhp: '.. self.diagHessianParameters:min()) + -- protect diag hessian self.diagHessianParameters:apply( - function(x) - return math.max(x, self.diagHessianEpsilon) + function(x) + local out = math.max(math.abs(x), self.diagHessianEpsilon) + if (x < 0) then out = -out end + return out end) - --self.diagHessianParameters:add(self.diagHessianEpsilon) -- now learning rates are obtained like this: self.learningRates:cdiv(self.diagHessianParameters) - print('<diagHessian>') + -- test + print(' + after max') print(' + norm of dhP: '..self.diagHessianParameters:norm().. ' norm of LR: '..self.learningRates:norm()) print(' + max dhP : '..self.diagHessianParameters:max() .. - ' max LR: '..self.learningRates:max()) - print(' + min dhp: '.. self.diagHessianParameters:min() .. ' min LR: '..self.learningRates:min()) + print(' + min dhp: '.. self.diagHessianParameters:min() .. + ' max LR: '..self.learningRates:max()) -- self.learningRates:div(self.learningRates:norm()) end @@ -108,6 +108,7 @@ torch.include('nnx', 'SGDOptimization.lua') torch.include('nnx', 'ASGDOptimization.lua') torch.include('nnx', 'LBFGSOptimization.lua') torch.include('nnx', 'CGOptimization.lua') +torch.include('nnx', 'newCGOptimization.lua') torch.include('nnx', 'GeneticSGDOptimization.lua') torch.include('nnx', 'DiagHessian.lua') diff --git a/newCGOptimization.lua b/newCGOptimization.lua new file mode 100644 index 0000000..71b1c6e --- /dev/null +++ b/newCGOptimization.lua @@ -0,0 +1,194 @@ +local CG,parent = torch.class('nn.newCGOptimization', 'nn.BatchOptimization') +-- +-- wrapper around Koray's cg function which implements rasmussen's +-- matlab cg in pure lua. +-- Author: Marco Scoffier +-- +function CG:__init(...) + parent.__init(self, ...) + xlua.unpack_class(self, {...}, + 'cgOptimization', nil, + {arg='rho', type='number', default=0.1}, + {arg='sig', type='number', default=0.5}, + {arg='int', type='number', default=0.1}, + {arg='ext', type='number', default=3.0}, + {arg='max', type='number', default=20}, + {arg='ratio', type='number', default=100}, + {arg='length', type='number', default=25}, + {arg='red', type='number', default=1}, + {arg='verbose', type='number', default=0} + ) + + + + -- we need three points for the interpolation/extrapolation stuff + self.df1, self.df2, self.df3 = torch.Tensor(),torch.Tensor(),torch.Tensor() + + self.df1:resizeAs(self.parameters) + self.df2:resizeAs(self.parameters) + self.df3:resizeAs(self.parameters) + + -- search direction + self.s = torch.Tensor():resizeAs(self.parameters) + + -- we need a temp storage for X + self.x0 = torch.Tensor():resizeAs(self.parameters) + self.df0 = torch.Tensor():resizeAs(self.parameters) + +end + +function CG:optimize() + local rho = self.rho + local sig = self.sig + local int = self.int + local ext = self.ext + local max = self.max + local ratio = self.ratio + local length = self.length + local red = self.red + local verbose = self.verbose + + local i = 0 + local ls_failed = 0 + local fx = {} + + -- we need three points for the interpolation/extrapolation stuff + local z1, z2, z3 = 0,0,0 + local d1, d2, d3 = 0,0,0 + local f1, f2, f3 = 0,0,0 + local df1,df2,df3 = self.df1, self.df2, self.df3 + + local x = self.parameters + local s = self.s + + local x0 = self.x0 + local f0 = 0 + local df0 = self.df0 + + -- the magic function: will update the parameter vector using CG + -- evaluate at initial point + f1 = self.evaluate() + df1:copy(self.gradParameters) + i=i+1 + + -- initial search direction + s:copy(df1):mul(-1) + + d1 = -s:dot(s ) -- slope + z1 = red/(1-d1) -- initial step + + while i < math.abs(length) do + + x0:copy(x) + f0 = f1 + df0:copy(df1) + x:add(z1,s) + f2 = self.evaluate() + df2:copy(self.gradParameters) + i=i+1 + d2 = df2:dot(s) + f3,d3,z3 = f1,d1,-z1 -- init point 3 equal to point 1 + local m = math.min(max,length-i) + local success = 0 + local limit = -1 + + while true do + while (f2 > f1+z1*rho*d1 or d2 > -sig*d1) and m > 0 do + limit = z1 + if f2 > f1 then + z2 = z3 - (0.5*d3*z3*z3)/(d3*z3+f2-f3) + else + local A = 6*(f2-f3)/z3+3*(d2+d3) + local B = 3*(f3-f2)-z3*(d3+2*d2) + z2 = (math.sqrt(B*B-A*d2*z3*z3)-B)/A + end + if z2 ~= z2 or z2 == math.huge or z2 == -math.huge then + z2 = z3/2; + end + z2 = math.max(math.min(z2, int*z3),(1-int)*z3); + z1 = z1 + z2; + x:add(z2,s) + f2 = self.evaluate() + df2:copy(self.gradParameters) + i=i+1 + m = m - 1 + d2 = df2:dot(s) + z3 = z3-z2; + end + if f2 > f1+z1*rho*d1 or d2 > -sig*d1 then + break + elseif d2 > sig*d1 then + success = 1; + break; + elseif m == 0 then + break; + end + local A = 6*(f2-f3)/z3+3*(d2+d3); + local B = 3*(f3-f2)-z3*(d3+2*d2); + z2 = -d2*z3*z3/(B+math.sqrt(B*B-A*d2*z3*z3)) + + if z2 ~= z2 or z2 == math.huge or z2 == -math.huge or z2 < 0 then + if limit < -0.5 then + z2 = z1 * (ext -1) + else + z2 = (limit-z1)/2 + end + elseif (limit > -0.5) and (z2+z1) > limit then + z2 = (limit-z1)/2 + elseif limit < -0.5 and (z2+z1) > z1*ext then + z2 = z1*(ext-1) + elseif z2 < -z3*int then + z2 = -z3*int + elseif limit > -0.5 and z2 < (limit-z1)*(1-int) then + z2 = (limit-z1)*(1-int) + end + f3=f2; d3=d2; z3=-z2; + z1 = z1+z2; + + x:add(z2,s) + + f2 = self.evaluate() + df2:copy(self.gradParameters) + i=i+1 + m = m - 1 + d2 = df2:dot(s) + end + if success == 1 then + f1 = f2 + fx[#fx+1] = f1; + local ss = (df2:dot(df2)-df2:dot(df1)) / df1:dot(df1) + s:mul(ss) + s:add(-1,df2) + local tmp = df1:clone() + df1:copy(df2) + df2:copy(tmp) + d2 = df1:dot(s) + if d2> 0 then + s:copy(df1) + s:mul(-1) + d2 = -s:dot(s) + end + + z1 = z1 * math.min(ratio, d1/(d2-1e-320)) + d1 = d2 + ls_failed = 0 + else + x:copy(x0) + f1 = f0 + df1:copy(df0) + if ls_failed or i>length then + break + end + local tmp = df1:clone() + df1:copy(df2) + df2:copy(tmp) + s:copy(df1) + s:mul(-1) + d1 = -s:dot(s) + z1 = 1/(1-d1) + ls_failed = 1 + end + end + self.output = f1 -- self.evaluate(x) + collectgarbage() +end |