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| author | Jonathan Tompson <tompson@cims.nyu.edu> | 2013-10-20 19:51:07 +0400 |
|---|---|---|
| committer | Jonathan Tompson <tompson@cims.nyu.edu> | 2013-10-20 19:51:07 +0400 |
| commit | 7286784cbd0af841501bfadb98a4a2cee28199b6 (patch) | |
| tree | 898cebbef05e7e99dfa05af345f180c971ee702b /test | |
| parent | e1fbc0cccab633fd0615dc64ba9fd52f64072622 (diff) | |
fixed a bug in Pairwise distance when the output Lp norm is zero (which results in a divide by zero issue). Rewrote PairwiseDistance following Clement's suggestion to only have one codepath. Fixed a small bug in extra/test/test.lua where the input to the non-batch fprop test was zero.
Diffstat (limited to 'test')
| -rw-r--r-- | test/test.lua | 7 |
1 files changed, 4 insertions, 3 deletions
diff --git a/test/test.lua b/test/test.lua index 5a1d469..0d54e3d 100644 --- a/test/test.lua +++ b/test/test.lua @@ -1536,7 +1536,7 @@ function nntest.PairwiseDistance() -- I assume both SplitTable and Sequential do not have bugs, otherwise this -- test will break. for p = 1,4 do -- test a few Lp norms - -- TEST CASE 1: non-batch inputs + -- TEST CASE 1: non-batch input, same code path but includes a resize local ini = math.random(10,20) local input = torch.Tensor(2, ini):zero() local module = nn.Sequential() @@ -1550,7 +1550,8 @@ function nntest.PairwiseDistance() mytester:asserteq(ferr, 0, torch.typename(module)..' - i/o forward err ') mytester:asserteq(berr, 0, torch.typename(module)..' - i/o backward err ') - -- Also check that the forward prop result is correct + -- Also check that the forward prop result is correct. + input = torch.rand(2, ini) err = torch.dist(input:select(1,1), input:select(1,2), p) - module:forward(input)[1] mytester:assertlt(err,precision, ' error on non-batch fprop ') @@ -1567,7 +1568,7 @@ function nntest.PairwiseDistance() err = jac.testJacobian(module,input) mytester:assertlt(err,precision, ' error on state ') - -- Also check that the forward prop result is correct + -- Also check that the forward prop result is correct. -- manually calculate each distance separately local inputa = torch.rand(inj,ini) local inputb = torch.rand(inj,ini) |