diff options
author | nicholas-leonard <nick@nikopia.org> | 2015-01-04 20:49:21 +0300 |
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committer | nicholas-leonard <nick@nikopia.org> | 2015-01-10 00:43:06 +0300 |
commit | 517c6c0e36046f13167a4b77b06c464747ddc0fc (patch) | |
tree | 563a836e9232debec0ba88742bf8398b15ac2267 /test.lua | |
parent | 27acf6315e30181936a309e9831e18baec1a3f28 (diff) |
WeightedEuclidean optimizations
Diffstat (limited to 'test.lua')
-rw-r--r-- | test.lua | 93 |
1 files changed, 68 insertions, 25 deletions
@@ -495,44 +495,87 @@ function nntest.Euclidean() end function nntest.WeightedEuclidean() - local ini = math.random(3,5) - local inj = math.random(13,5) - local input = torch.Tensor(ini):zero() + local ini = math.random(5,7) + local inj = math.random(5,7) + local input = torch.randn(ini) + local gradOutput = torch.randn(inj) local module = nn.WeightedEuclidean(ini,inj) + local output = module:forward(input):clone() + + local output2 = torch.Tensor(inj):zero() + local temp = input:clone() + for o = 1,module.weight:size(2) do + temp:copy(input):add(-1,module.weight:select(2,o)) + temp:cmul(temp) + temp:cmul(module.diagCov:select(2,o)):cmul(module.diagCov:select(2,o)) + output2[o] = math.sqrt(temp:sum()) + end + mytester:assertTensorEq(output, output2, 0.000001, 'WeightedEuclidean forward 1D err') + + local input2 = torch.randn(8, ini) + input2[2]:copy(input) + local output2 = module:forward(input2) + mytester:assertTensorEq(output2[2], output, 0.000001, 'WeightedEuclidean forward 2D err') + + local output = module:forward(input):clone() + module:zeroGradParameters() + local gradInput = module:backward(input, gradOutput, 1):clone() + local gradInput2 = torch.zeros(ini) + for o = 1,module.weight:size(2) do + temp:copy(input) + temp:add(-1,module.weight:select(2,o)) + temp:cmul(module.diagCov:select(2,o)):cmul(module.diagCov:select(2,o)) + temp:mul(gradOutput[o]/output[o]) + gradInput2:add(temp) + end + mytester:assertTensorEq(gradInput, gradInput2, 0.000001, 'WeightedEuclidean updateGradInput 1D err') + + local gradWeight = module.gradWeight:clone():zero() + local gradDiagCov = module.gradDiagCov:clone():zero() + for o = 1,module.weight:size(2) do + if output[o] ~= 0 then + temp:copy(module.weight:select(2,o)):add(-1,input) + temp:cmul(module.diagCov:select(2,o)):cmul(module.diagCov:select(2,o)) + temp:mul(gradOutput[o]/output[o]) + gradWeight:select(2,o):add(temp) + + temp:copy(module.weight:select(2,o)):add(-1,input) + temp:cmul(temp) + temp:cmul(module.diagCov:select(2,o)) + temp:mul(gradOutput[o]/output[o]) + gradDiagCov:select(2,o):add(temp) + end + end + mytester:assertTensorEq(gradWeight, module.gradWeight, 0.000001, 'WeightedEuclidean accGradParameters gradWeight 1D err') + mytester:assertTensorEq(gradDiagCov, module.gradDiagCov, 0.000001, 'WeightedEuclidean accGradParameters gradDiagCov 1D err') + + local input2 = input:view(1, -1):repeatTensor(8, 1) + local gradOutput2 = gradOutput:view(1, -1):repeatTensor(8, 1) + local output2 = module:forward(input2) + module:zeroGradParameters() + local gradInput2 = module:backward(input2, gradOutput2, 1/8) + mytester:assertTensorEq(gradInput2[2], gradInput, 0.000001, 'WeightedEuclidean updateGradInput 2D err') + + mytester:assertTensorEq(gradWeight, module.gradWeight, 0.000001, 'WeightedEuclidean accGradParameters gradWeight 2D err') + mytester:assertTensorEq(gradDiagCov, module.gradDiagCov, 0.000001, 'WeightedEuclidean accGradParameters gradDiagCov 2D err') + + input:zero() + module.fastBackward = false + local err = jac.testJacobian(module,input) mytester:assertlt(err,precision, 'error on state ') local err = jac.testJacobianParameters(module, input, module.weight, module.gradWeight) mytester:assertlt(err,precision, 'error on weight ') - local err = jac.testJacobianParameters(module, input, module.bias, module.gradBias) + local err = jac.testJacobianParameters(module, input, module.diagCov, module.gradDiagCov) mytester:assertlt(err,precision, 'error on bias ') local ferr,berr = jac.testIO(module,input) mytester:asserteq(ferr, 0, torch.typename(module) .. ' - i/o forward err ') mytester:asserteq(berr, 0, torch.typename(module) .. ' - i/o backward err ') - -- test batch - local bs = math.random(3,5) - input:uniform(0,1) - local output = module:forward(input):clone() - module:zeroGradParameters() - local gradInput = module:backward(input, output):clone() - local params, gradParams = module:parameters() - for i=1,#params do - params[i] = params[i]:clone() - end - local input2 = input:view(1, -1):repeatTensor(bs, 1) - local output2 = module:forward(input2) - module:zeroGradParameters() - local gradInput2 = module:backward(input2, output2, 1/bs) - local params2, gradParams2 = module:parameters() - mytester:assertTensorEq(output2[bs-1], output, 0.000001, "error in batch updateOutput") - mytester:assertTensorEq(gradInput2[bs-1], gradInput, 0.000001, "error in batch updateGradInput") - mytester:assertTensorEq(gradParams[1], gradParams2[1], 0.000001, "error in batch accGradParameters (gradTemplates)") - mytester:assertTensorEq(gradParams[2], gradParams2[2], 0.000001, "error in batch accGradParameters (gradDiagCov)") - input:zero() module:zeroGradParameters() local err = jac.testJacobian(module,input) @@ -541,7 +584,7 @@ function nntest.WeightedEuclidean() local err = jac.testJacobianParameters(module, input, module.weight, module.gradWeight) mytester:assertlt(err,precision, 'error on weight ') - local err = jac.testJacobianParameters(module, input, module.bias, module.gradBias) + local err = jac.testJacobianParameters(module, input, module.diagCov, module.gradDiagCov) mytester:assertlt(err,precision, 'error on bias ') local ferr,berr = jac.testIO(module,input2) |