Fixed the Linear stage when the output size is 1.

1 files changed, 49 insertions, 46 deletions

diff --git a/test/test.lua b/test/test.lua
index 5e4bce7..6832885 100644
--- a/test/test.lua
+++ b/test/test.lua
@@ -239,69 +239,72 @@ end
 
 function nntest.Linear()
    local ini = math.random(5,7)
-   local inj = math.random(5,7)
+   local inj_vals = {math.random(5,7), 1}  -- Also test the inj = 1 spetial case
    local input = torch.Tensor(ini):zero()
-   local module = nn.Linear(ini,inj)
 
-   -- 1D
-   local err = jac.testJacobian(module,input)
-   mytester:assertlt(err,precision, 'error on state ')
+   for ind, inj in pairs(inj_vals) do
+     local module = nn.Linear(ini,inj)
 
-   local err = jac.testJacobianParameters(module, input, module.weight, module.gradWeight)
-   mytester:assertlt(err,precision, 'error on weight ')
+     -- 1D
+     local err = jac.testJacobian(module,input)
+     mytester:assertlt(err,precision, 'error on state ')
 
-   local err = jac.testJacobianParameters(module, input, module.bias, module.gradBias)
-   mytester:assertlt(err,precision, 'error on bias ')
+     local err = jac.testJacobianParameters(module, input, module.weight, module.gradWeight)
+     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.testJacobianParameters(module, input, module.bias, module.gradBias)
+     mytester:assertlt(err,precision, 'error on bias ')
 
-   local err = jac.testJacobianUpdateParameters(module, input, module.bias)
-   mytester:assertlt(err,precision, 'error on bias [direct update] ')
+     local err = jac.testJacobianUpdateParameters(module, input, module.weight)
+     mytester:assertlt(err,precision, 'error on weight [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
+     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, 'bias', 'gradBias')) do
-      mytester:assertlt(err, precision, string.format(
-                         'error on bias [%s]', t))
-   end
+     for t,err in pairs(jac.testAllUpdate(module, input, 'weight', 'gradWeight')) do
+        mytester:assertlt(err, precision, string.format(
+                           'error on weight [%s]', t))
+     end
 
-   -- 2D
-   local nframe = math.random(50,70)
-   local input = torch.Tensor(nframe, ini):zero()
+     for t,err in pairs(jac.testAllUpdate(module, input, 'bias', 'gradBias')) do
+        mytester:assertlt(err, precision, string.format(
+                           'error on bias [%s]', t))
+     end
 
-   local err = jac.testJacobian(module,input)
-   mytester:assertlt(err,precision, 'error on state ')
+     -- 2D
+     local nframe = math.random(50,70)
+     local input = torch.Tensor(nframe, ini):zero()
 
-   local err = jac.testJacobianParameters(module, input, module.weight, module.gradWeight)
-   mytester:assertlt(err,precision, 'error on weight ')
+     local err = jac.testJacobian(module,input)
+     mytester:assertlt(err,precision, 'error on state ')
 
-   local err = jac.testJacobianParameters(module, input, module.bias, module.gradBias)
-   mytester:assertlt(err,precision, 'error on weight ')
+     local err = jac.testJacobianParameters(module, input, module.weight, module.gradWeight)
+     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.testJacobianParameters(module, input, module.bias, module.gradBias)
+     mytester:assertlt(err,precision, 'error on weight ')
 
-   local err = jac.testJacobianUpdateParameters(module, input, module.bias)
-   mytester:assertlt(err,precision, 'error on bias [direct update] ')
+     local err = jac.testJacobianUpdateParameters(module, input, module.weight)
+     mytester:assertlt(err,precision, 'error on weight [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
+     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, 'bias', 'gradBias')) do
-      mytester:assertlt(err, precision, string.format(
-                         'error on bias [%s]', t))
-   end
+     for t,err in pairs(jac.testAllUpdate(module, input, 'weight', 'gradWeight')) do
+        mytester:assertlt(err, precision, string.format(
+                           'error on weight [%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 ')
+     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 ')
+  end  -- for ind, inj in pairs(inj_vals) do
 end
 
 function nntest.SparseLinear()