diff options
Diffstat (limited to 'test.lua')
| -rw-r--r-- | test.lua | 106 |
1 files changed, 106 insertions, 0 deletions
@@ -3195,6 +3195,112 @@ function nntest.CosineEmbeddingCriterion() equal(grads[2], zero, 'gradient should be zero') end +function nntest.BatchNormalization() + local nframes = torch.random(50,70) + local indim = torch.random(1,10) + local input = torch.zeros(nframes, indim):uniform() + local module = nn.BatchNormalization(indim) + + 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) + mytester:assertlt(err,precision, 'error on weight ') + + local err = jac.testJacobianUpdateParameters(module, input, module.weight) + mytester:assertlt(err,precision, 'error on weight [direct update] ') + + local err = jac.testJacobianUpdateParameters(module, input, module.bias) + mytester:assertlt(err,precision, 'error on bias [direct update] ') + + for t,err in pairs(jac.testAllUpdate(module, input, + 'weight', 'gradWeight')) do + mytester:assertlt(err, precision, string.format( + 'error on weight [%s]', t)) + end + + for t,err in pairs(jac.testAllUpdate(module, input, + 'bias', 'gradBias')) do + mytester:assertlt(err, precision, string.format( + 'error on bias [%s]', t)) + end + + -- IO + 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 ') + + -- batch norm without affine transform + module = nn.BatchNormalization(0) + + local err = jac.testJacobian(module,input) + mytester:assertlt(err,precision, 'error on state ') + + -- IO + 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 ') +end + +function nntest.SpatialBatchNormalization() + local nframes = torch.random(1,10) + local indim = torch.random(1,4) + local ini = torch.random(1,5) + local inj = torch.random(1,5) + local input = torch.zeros(nframes, indim, ini, inj):uniform() + local module = nn.SpatialBatchNormalization(indim) + + 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) + mytester:assertlt(err,precision, 'error on weight ') + + local err = jac.testJacobianUpdateParameters(module, input, module.weight) + mytester:assertlt(err,precision, 'error on weight [direct update] ') + + local err = jac.testJacobianUpdateParameters(module, input, module.bias) + mytester:assertlt(err,precision, 'error on bias [direct update] ') + + for t,err in pairs(jac.testAllUpdate(module, input, + 'weight', 'gradWeight')) do + mytester:assertlt(err, precision, string.format( + 'error on weight [%s]', t)) + end + + for t,err in pairs(jac.testAllUpdate(module, input, + 'bias', 'gradBias')) do + mytester:assertlt(err, precision, string.format( + 'error on bias [%s]', t)) + end + + -- IO + 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 ') + + -- batch norm without affine transform + module = nn.SpatialBatchNormalization(0) + + local err = jac.testJacobian(module,input) + mytester:assertlt(err,precision, 'error on state ') + + -- IO + 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 ') +end + mytester:add(nntest) if not nn then |