Added oneHot encoding and softmax

model/RNN.lua

@@ -1,21 +1,28 @@
 local RNN = {}
 
-function RNN.rnn(input_size, rnn_size, n)
+function RNN.rnn(input_size, rnn_size, n, dropout)
 
   -- there are n+1 inputs (hiddens on each layer and x)
   local inputs = {}
   table.insert(inputs, nn.Identity()()) -- x
   for L = 1,n do
     table.insert(inputs, nn.Identity()()) -- prev_h[L]
+
   end
 
   local x, input_size_L
   local outputs = {}
   for L = 1,n do
 
     local prev_h = inputs[L+1]
-    if L == 1 then x = inputs[1] else x = outputs[L-1] end
-    if L == 1 then input_size_L = input_size else input_size_L = rnn_size end
+    if L == 1 then 
+      x = OneHot(input_size)(inputs[1])
+      input_size_L = input_size
+    else 
+      x = outputs[(L-1)] 
+      if dropout > 0 then x = nn.Dropout(dropout)(x) end -- apply dropout, if any
+      input_size_L = rnn_size
+    end
 
     -- RNN tick
     local i2h = nn.Linear(input_size_L, rnn_size)(x)
@@ -24,6 +31,12 @@ function RNN.rnn(input_size, rnn_size, n)
 
     table.insert(outputs, next_h)
   end
+-- set up the decoder
+  local top_h = outputs[#outputs]
+  if dropout > 0 then top_h = nn.Dropout(dropout)(top_h) end
+  local proj = nn.Linear(rnn_size, input_size)(top_h)
+  local logsoft = nn.LogSoftMax()(proj)
+  table.insert(outputs, logsoft)
 
   return nn.gModule(inputs, outputs)
 end