LSTM backwardThroughTime unit tested

1 files changed, 6 insertions, 4 deletions

diff --git a/test/test-all.lua b/test/test-all.lua
index df51302..a824737 100644
--- a/test/test-all.lua
+++ b/test/test-all.lua
@@ -347,6 +347,10 @@ function nnxtest.Recurrent()
    mytester:assert(isRecursable(mlp.initialModule, torch.randn(inputSize)), "Recurrent isRecursable() initial error")
    mytester:assert(isRecursable(mlp.recurrentModule, {torch.randn(inputSize), torch.randn(outputSize)}), "Recurrent isRecursable() recurrent error")
    
+   -- test that the above test actually works
+   local euclidean = nn.Euclidean(inputSize, outputSize)
+   mytester:assert(not isRecursable(euclidean, torch.randn(batchSize, inputSize)), "AbstractRecurrent.isRecursable error")
+   
    local gradOutputs, outputs = {}, {}
    -- inputs = {inputN, {inputN-1, {inputN-2, ...}}}}}
    local inputs
@@ -610,7 +614,6 @@ function nnxtest.LSTM()
    for step=1,nStep do
       -- iteratively build an LSTM out of non-recurrent components
       local lstm = lstmStep:clone()
-      lstm:share(lstmStep)
       lstm:share(lstmStep, 'weight', 'gradWeight', 'bias', 'gradBias')
       if step == 1 then
          mlp2 = lstm
@@ -624,7 +627,6 @@ function nnxtest.LSTM()
          mlp2 = rnn
       end
       
-      
       -- prepare inputs for mlp2
       if inputs then
          inputs = {input[step], inputs}
@@ -633,7 +635,6 @@ function nnxtest.LSTM()
       end
    end
    mlp2:add(nn.SelectTable(1)) --just output the output (not cell)
-   
    local output2 = mlp2:forward(inputs)
    
    mlp2:zeroGradParameters()
@@ -646,7 +647,8 @@ function nnxtest.LSTM()
    mytester:assert(#params == #params2, "LSTM parameters error "..#params.." ~= "..#params2)
    for i, gradParam in ipairs(gradParams) do
       local gradParam2 = gradParams2[i]
-      mytester:assertTensorEq(gradParam, gradParam2, 0.000001, "LSTM gradParam "..i.." error "..tostring(gradParam).." "..tostring(gradParam2))
+      mytester:assertTensorEq(gradParam, gradParam2, 0.000001, 
+         "LSTM gradParam "..i.." error "..tostring(gradParam).." "..tostring(gradParam2))
    end
 end