Improve variable name resolution by making more variables local.

train.lua

@@ -209,7 +209,7 @@ for name,proto in pairs(protos) do
 end
 
 -- preprocessing helper function
-function prepro(x,y)
+local function prepro(x,y)
     x = x:transpose(1,2):contiguous() -- swap the axes for faster indexing
     y = y:transpose(1,2):contiguous()
     if opt.gpuid >= 0 and opt.opencl == 0 then -- ship the input arrays to GPU
@@ -271,22 +271,23 @@ function feval(x)
     local rnn_state = {[0] = init_state_global}
     local predictions = {}           -- softmax outputs
     local loss = 0
+    local local_clones = clones
     for t=1,opt.seq_length do
-        clones.rnn[t]:training() -- make sure we are in correct mode (this is cheap, sets flag)
-        local lst = clones.rnn[t]:forward{x[t], unpack(rnn_state[t-1])}
+        local_clones.rnn[t]:training() -- make sure we are in correct mode (this is cheap, sets flag)
+        local lst = local_clones.rnn[t]:forward{x[t], unpack(rnn_state[t-1])}
         rnn_state[t] = {unpack(lst, i, #init_state)} -- extract the state, without output
         predictions[t] = lst[#lst] -- last element is the prediction
-        loss = loss + clones.criterion[t]:forward(predictions[t], y[t])
+        loss = loss + local_clones.criterion[t]:forward(predictions[t], y[t])
     end
     loss = loss / opt.seq_length
     ------------------ backward pass -------------------
     -- initialize gradient at time t to be zeros (there's no influence from future)
     local drnn_state = {[opt.seq_length] = clone_list(init_state, true)} -- true also zeros the clones
     for t=opt.seq_length,1,-1 do
         -- backprop through loss, and softmax/linear
-        local doutput_t = clones.criterion[t]:backward(predictions[t], y[t])
+        local doutput_t = local_clones.criterion[t]:backward(predictions[t], y[t])
         drnn_state[t][#drnn_state[t]+1] = doutput_t
-        local dlst = clones.rnn[t]:backward({x[t], unpack(rnn_state[t-1])}, drnn_state[t])
+        local dlst = local_clones.rnn[t]:backward({x[t], unpack(rnn_state[t-1])}, drnn_state[t])
         -- dlst[1] is the gradient on x, which we don't need
         -- using unpack should slide the values into the correct indexes, allowing us to forego a loop.
         drnn_state[t-1] = {unpack(dlst, 2)}