unit tests complete

1 files changed, 14 insertions, 22 deletions

diff --git a/test/test.lua b/test/test.lua
index 91b38e8..cea1a53 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -1650,52 +1650,44 @@ function nntest.LookupTable()
    local totalIndex = math.random(10,100)
    local nIndex = math.random(5,7)
    local entry_size = math.random(5,7)
-   local input = torch.Tensor(nIndex):zero()
+   local input = torch.IntTensor(nIndex):zero()
    local module = nn.LookupTable(totalIndex, entry_size)
    local minval = 1
    local maxval = totalIndex
 
    -- 1D
    local err = jac.testJacobianParameters(module, input, module.weight, module.gradWeight, minval, maxval)
-   mytester:assertlt(err,precision, 'error on weight ')
+   mytester:assertlt(err,precision, '1D error on weight ')
 
    local err = jac.testJacobianUpdateParameters(module, input, module.weight, minval, maxval)
-   mytester:assertlt(err,precision, 'error on weight [direct update] ')
+   mytester:assertlt(err,precision, '1D error on weight [direct update] ')
    
    module.gradWeight:zero()
    for t,err in pairs(jac.testAllUpdate(module, input, 'weight', 'gradWeight')) do
       mytester:assertlt(err, precision, string.format(
-                         'error on weight [%s]', t))
+                         '1D error on weight [%s]', t))
    end
 
    -- 2D
    local nframe = math.random(50,70)
-   local input = torch.Tensor(nframe, nIndex):zero()
-
-   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 input = torch.IntTensor(nframe, nIndex):zero()
 
-   local err = jac.testJacobianUpdateParameters(module, input, module.bias)
-   mytester:assertlt(err,precision, 'error on bias [direct update] ')
+   local err = jac.testJacobianParameters(module, input, module.weight, module.gradWeight, minval, maxval)
+   mytester:assertlt(err,precision, '2D error on weight ')
+   
+   local err = jac.testJacobianUpdateParameters(module, input, module.weight, minval, maxval)
+   mytester:assertlt(err,precision, '2D error on weight [direct update] ')
 
+   module.gradWeight:zero()
    for t,err in pairs(jac.testAllUpdate(module, input, 'weight', 'gradWeight')) do
       mytester:assertlt(err, precision, string.format(
-                         'error on weight [%s]', t))
+                         '2D 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)
+   module.gradInput = torch.Tensor(3,4):zero() --fixes an error
+   local ferr,berr = jac.testIO(module,input,minval,maxval)
    mytester:asserteq(ferr, 0, torch.typename(module) .. ' - i/o forward err ')
    mytester:asserteq(berr, 0, torch.typename(module) .. ' - i/o backward err ')
 end