diff --git a/src/plugins/ImportTorch/nn.js b/src/plugins/ImportTorch/nn.js
index 4c5cab8c7..c3d085ca6 100644
--- a/src/plugins/ImportTorch/nn.js
+++ b/src/plugins/ImportTorch/nn.js
@@ -145,6 +145,7 @@ define([
         var CreateLayer = function(type) {
             var res = luajs.newContext()._G,
                 attrs = [].slice.call(arguments, 1),
+                ltGet = luajs.types.LuaTable.prototype.get,
                 node;
 
             if (LAYERS[type]) {
@@ -165,6 +166,16 @@ define([
                     }
                 }
             }
+
+            // Override get
+            res.get = function noNilGet(value) {
+                var result = ltGet.call(this, value);
+                if (!result) {
+                    throw Error(`"${value}" is not supported for ${type}`);
+                }
+                return result;
+            };
+
             return res;
         };
 
diff --git a/test/test-cases/code/googlenet-setters.lua b/test/test-cases/code/googlenet-setters.lua
new file mode 100644
index 000000000..a8ede7ad8
--- /dev/null
+++ b/test/test-cases/code/googlenet-setters.lua
@@ -0,0 +1,83 @@
+-- Copy of googlenet.lua which uses setters (the other googlenet has them removed)
+require 'nn'
+nGPU = 10
+local function inception(input_size, config)
+   local concat = nn.Concat(2)
+   if config[1][1] ~= 0 then
+      local conv1 = nn.Sequential()
+      conv1:add(nn.SpatialConvolution(input_size, config[1][1],1,1,1,1)):add(nn.ReLU(true))
+      concat:add(conv1)
+   end
+
+   local conv3 = nn.Sequential()
+   conv3:add(nn.SpatialConvolution(  input_size, config[2][1],1,1,1,1)):add(nn.ReLU(true))
+   conv3:add(nn.SpatialConvolution(config[2][1], config[2][2],3,3,1,1,1,1)):add(nn.ReLU(true))
+   concat:add(conv3)
+
+   local conv3xx = nn.Sequential()
+   conv3xx:add(nn.SpatialConvolution(  input_size, config[3][1],1,1,1,1)):add(nn.ReLU(true))
+   conv3xx:add(nn.SpatialConvolution(config[3][1], config[3][2],3,3,1,1,1,1)):add(nn.ReLU(true))
+   conv3xx:add(nn.SpatialConvolution(config[3][2], config[3][2],3,3,1,1,1,1)):add(nn.ReLU(true))
+   concat:add(conv3xx)
+
+   local pool = nn.Sequential()
+   pool:add(nn.SpatialZeroPadding(1,1,1,1)) -- remove after getting nn R2 into fbcode
+   if config[4][1] == 'max' then
+      pool:add(nn.SpatialMaxPooling(3,3,1,1):ceil())
+   elseif config[4][1] == 'avg' then
+      pool:add(nn.SpatialAveragePooling(3,3,1,1):ceil())
+   else
+      error('Unknown pooling')
+   end
+   if config[4][2] ~= 0 then
+      pool:add(nn.SpatialConvolution(input_size, config[4][2],1,1,1,1)):add(nn.ReLU(true))
+   end
+   concat:add(pool)
+
+   return concat
+end
+
+local features = nn.Sequential()
+features:add(nn.SpatialConvolution(3,64,7,7,2,2,3,3)):add(nn.ReLU(true))
+features:add(nn.SpatialMaxPooling(3,3,2,2):ceil())
+features:add(nn.SpatialConvolution(64,64,1,1)):add(nn.ReLU(true))
+features:add(nn.SpatialConvolution(64,192,3,3,1,1,1,1)):add(nn.ReLU(true))
+features:add(nn.SpatialMaxPooling(3,3,2,2):ceil())
+features:add(inception( 192, {{ 64},{ 64, 64},{ 64, 96},{'avg', 32}})) -- 3(a)
+features:add(inception( 256, {{ 64},{ 64, 96},{ 64, 96},{'avg', 64}})) -- 3(b)
+features:add(inception( 320, {{  0},{128,160},{ 64, 96},{'max',  0}})) -- 3(c)
+features:add(nn.SpatialConvolution(576,576,2,2,2,2))
+features:add(inception( 576, {{224},{ 64, 96},{ 96,128},{'avg',128}})) -- 4(a)
+features:add(inception( 576, {{192},{ 96,128},{ 96,128},{'avg',128}})) -- 4(b)
+features:add(inception( 576, {{160},{128,160},{128,160},{'avg', 96}})) -- 4(c)
+features:add(inception( 576, {{ 96},{128,192},{160,192},{'avg', 96}})) -- 4(d)
+
+local main_branch = nn.Sequential()
+main_branch:add(inception( 576, {{  0},{128,192},{192,256},{'max',  0}})) -- 4(e)
+main_branch:add(nn.SpatialConvolution(1024,1024,2,2,2,2))
+main_branch:add(inception(1024, {{352},{192,320},{160,224},{'avg',128}})) -- 5(a)
+main_branch:add(inception(1024, {{352},{192,320},{192,224},{'max',128}})) -- 5(b)
+main_branch:add(nn.SpatialAveragePooling(7,7,1,1))
+main_branch:add(nn.View(1024):setNumInputDims(3))
+main_branch:add(nn.Linear(1024,nClasses))
+main_branch:add(nn.LogSoftMax())
+
+-- add auxillary classifier here (thanks to Christian Szegedy for the details)
+local aux_classifier = nn.Sequential()
+aux_classifier:add(nn.SpatialAveragePooling(5,5,3,3):ceil())
+aux_classifier:add(nn.SpatialConvolution(576,128,1,1,1,1))
+aux_classifier:add(nn.View(128*4*4):setNumInputDims(3))
+aux_classifier:add(nn.Linear(128*4*4,768))
+aux_classifier:add(nn.ReLU())
+aux_classifier:add(nn.Linear(768,nClasses))
+aux_classifier:add(nn.LogSoftMax())
+
+local splitter = nn.Concat(2)
+splitter:add(main_branch):add(aux_classifier)
+local model = nn.Sequential():add(features):add(splitter)
+
+model.imageSize = 256
+model.imageCrop = 224
+
+
+return model
diff --git a/test/test-cases/code/vgg.lua b/test/test-cases/code/vgg.lua
index a837f60ec..7ee0eee03 100644
--- a/test/test-cases/code/vgg.lua
+++ b/test/test-cases/code/vgg.lua
@@ -51,7 +51,7 @@ classifier:add(nn.LogSoftMax())
 
 local model = nn.Sequential()
 model:add(features):add(classifier)
-model.imageSize = 256
-model.imageCrop = 224
+--model.imageSize = 256
+--model.imageCrop = 224
 
 return model