diff options
author | Boris Fomitchev <bfomitchev@nvidia.com> | 2015-11-21 08:45:34 +0300 |
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committer | Boris Fomitchev <bfomitchev@nvidia.com> | 2015-11-21 09:07:09 +0300 |
commit | bddfa35bdbe4cc68d86021c83e2b7ef4d7eccca5 (patch) | |
tree | 7f3c317a2ab9685e7290b116e55f82a30b751d95 | |
parent | 0d5095fba39362bb2fe4a064f058dc79b7eee824 (diff) |
fixed conflict leftover
as per Natalia, restored randomization
-rw-r--r-- | test/test.lua | 48 |
1 files changed, 22 insertions, 26 deletions
diff --git a/test/test.lua b/test/test.lua index 9d13dfd..b5991c5 100644 --- a/test/test.lua +++ b/test/test.lua @@ -731,7 +731,6 @@ function cudnntest.LogSoftMax_batch() end function cudnntest.SpatialLogSoftMax() -<<<<<<< HEAD -- batch local numLabels = math.random(5,10) local h = math.random(5,10) @@ -765,34 +764,31 @@ function cudnntest.SpatialLogSoftMax() mytester:assertlt(err, precision_backward, 'error in difference between central difference and :backward') end - function cudnntest.SpatialBatchNormalization() - -- batch - local h = 4 --math.random(5,10) - local w = 4 --math.random(5,10) - local bsz = 4 --math.random(1, 32) - local from = 4 --math.random(1, 32) - local input = torch.randn(bsz,from,h,w):cuda() - local gradOutput = torch.randn(bsz,from,h,w):cuda() - local cbn = cudnn.SpatialBatchNormalization(bsz, 1e-3):cuda() - local gbn = nn.SpatialBatchNormalization(bsz, 1e-3):cuda() - - local rescuda = cbn:forward(input) - local groundtruth = gbn:forward(input) - local resgrad = cbn:backward(input, gradOutput) - local groundgrad = gbn:backward(input, gradOutput) - - - local error = rescuda:float() - groundtruth:float() - mytester:assertlt(error:abs():max(), - precision_forward, 'error in batch normalization (forward) ') - error = resgrad:float() - groundgrad:float() - mytester:assertlt(error:abs():max(), - precision_backward, 'error in batch normalization (backward) ') - + -- batch + local h = math.random(5,10) + local w = math.random(5,10) + local bsz = math.random(1, 32) + local from = math.random(1, 32) + local input = torch.randn(bsz,from,h,w):cuda() + local gradOutput = torch.randn(bsz,from,h,w):cuda() + local cbn = cudnn.SpatialBatchNormalization(from, 1e-3):cuda() + local gbn = nn.SpatialBatchNormalization(from, 1e-3):cuda() + cbn.weight:copy(gbn.weight) + cbn.bias:copy(gbn.bias) + local rescuda = cbn:forward(input) + local groundtruth = gbn:forward(input) + local resgrad = cbn:backward(input, gradOutput) + local groundgrad = gbn:backward(input, gradOutput) + + local error = rescuda:float() - groundtruth:float() + mytester:assertlt(error:abs():max(), + precision_forward, 'error in batch normalization (forward) ') + error = resgrad:float() - groundgrad:float() + mytester:assertlt(error:abs():max(), + precision_backward, 'error in batch normalization (backward) ') end - function cudnntest.SpatialCrossEntropyCriterion() -- batch local numLabels = math.random(5,10) |