Cosmetics.

[dagnn.git] / test-dagnn.lua

diff --git a/test-dagnn.lua b/test-dagnn.lua
index 5b266da..a41d880 100755 (executable)
--- a/test-dagnn.lua
+++ b/test-dagnn.lua
@@ -21,9 +21,12 @@
 
 require 'torch'
 require 'nn'
-
 require 'dagnn'
 
+-- torch.setnumthreads(params.nbThreads)
+torch.setdefaulttensortype('torch.DoubleTensor')
+torch.manualSeed(2)
+
 function checkGrad(model, criterion, input, target)
    local params, gradParams = model:getParameters()
 
@@ -81,61 +84,34 @@ function printTensorTable(t)
    end
 end
 
--- torch.setnumthreads(params.nbThreads)
-torch.setdefaulttensortype('torch.DoubleTensor')
-torch.manualSeed(2)
+--               +- Linear(10, 10) -> ReLU ---> d --+
+--              /                              /     \
+--             /                              /       \
+--  --> a --> b -----------> c --------------+         e -->
+--                            \                       /
+--                             \                     /
+--                              +-- Mul(-1) --------+
 
---                     +--> c ----> e --+
---                    /            /     \
---                   /            /       \
---  input --> a --> b ---> d ----+         g --> output
---                          \             /
---                           \           /
---                            +--> f ---+
+model = nn.DAG()
 
-a = nn.Linear(10, 10)
+a = nn.Linear(50, 10)
 b = nn.ReLU()
-c = nn.Linear(10, 3)
-d = nn.Linear(10, 3)
-e = nn.CMulTable()
-f = nn.Linear(3, 3)
-g = nn.CAddTable()
-
-model = nn.DAG()
+c = nn.Linear(10, 15)
+d = nn.CMulTable()
+e = nn.CAddTable()
 
 model:addEdge(a, b)
-model:addEdge(b, c)
-model:addEdge(b, d)
-model:addEdge(c, e)
+model:addEdge(b, nn.Linear(10, 15), nn.ReLU(), d)
 model:addEdge(d, e)
-model:addEdge(d, f)
-model:addEdge(e, g)
-model:addEdge(f, g)
+model:addEdge(b, c)
+model:addEdge(c, d)
+model:addEdge(c, nn.Mul(-1), e)
 
 model:setInput(a)
-model:setOutput(g)
-
-input = torch.Tensor(3, 10):uniform()
-
-print('******************************************************************')
-print('** updateOutput **************************************************')
-print('******************************************************************')
-
-output = model:updateOutput(input):clone()
-
-printTensorTable(output)
-
-print('******************************************************************')
-print('** updateGradInput ***********************************************')
-print('******************************************************************')
-
-gradInput = model:updateGradInput(input, output)
-
-printTensorTable(gradInput)
+model:setOutput(e)
 
-print('******************************************************************')
-print('** checkGrad *****************************************************')
-print('******************************************************************')
+local input = torch.Tensor(30, 50):uniform()
+local output = model:updateOutput(input):clone()
 
 output:uniform()