Adding Batch L2 Normalization Layer that makes all rows of input Tensor unit L2 norm

1 files changed, 23 insertions, 0 deletions

diff --git a/test.lua b/test.lua
index 9414a66..959c369 100644
--- a/test.lua
+++ b/test.lua
@@ -3554,6 +3554,29 @@ function nntest.Padding()
    mytester:assertTensorEq(gradInput, input, 0.00001, "Padding backward error")
 end
 
+function nntest.L2Normalize()
+   local ini = math.random(6,8)
+   local inj = math.random(3,5)
+   local input = torch.randn(ini, inj)
+
+   local module = nn.L2Normalize()
+
+   -- test correctness of output
+   local output = module:forward(input)
+   local norms = torch.norm(output, 2, 2)
+   local desired_norms = torch.ones(ini)
+   mytester:assertTensorEq(norms, desired_norms, 0.000001, 'L2Normalize forward err')
+
+   -- test the Jacobian
+   local err = jac.testJacobian(module,input)
+   mytester:assertlt(err, precision, 'error on state ')
+
+   -- test IO correctness
+   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