3 files changed, 5 insertions, 6 deletions
diff --git a/nerv/layer/duplicate.lua b/nerv/layer/duplicate.lua
index 137472b..2621cdf 100644
--- a/nerv/layer/duplicate.lua
+++ b/nerv/layer/duplicate.lua
@@ -20,10 +20,7 @@ function DuplicateLayer:batch_resize()
 end
 
 function DuplicateLayer:propagate(input, output)
-    for i = 1, #self.dim_out do
-        output[i]:copy_from(input[1])
-        -- FIXME: use reference copy to speed up
-    end
+    -- do nothing, use reference copy in nn/network.lua
 end
 
 function DuplicateLayer:back_propagate(bp_err, next_bp_err)
diff --git a/nerv/layer/rnn.lua b/nerv/layer/rnn.lua
index 0b5ccaa..333be9e 100644
--- a/nerv/layer/rnn.lua
+++ b/nerv/layer/rnn.lua
@@ -20,7 +20,7 @@ function RNNLayer:__init(id, global_conf, layer_conf)
         ['nerv.AffineLayer'] = {
             main = {dim_in = {din, dout}, dim_out = {dout}, pr = pr},
         },
-        [layers.activation] = {
+        [layer_conf.activation] = {
             activation = {dim_in = {dout}, dim_out = {dout}},
         },
         ['nerv.DuplicateLayer'] = {
diff --git a/nerv/layer/softmax_ce.lua b/nerv/layer/softmax_ce.lua
index 7b4a80c..acd4ee6 100644
--- a/nerv/layer/softmax_ce.lua
+++ b/nerv/layer/softmax_ce.lua
@@ -61,14 +61,16 @@ function SoftmaxCELayer:propagate(input, output, t)
     end
     ce:mul_elem(ce, label)
     ce = ce:rowsum()
+    ce:set_values_by_mask(self.gconf.mask[t], 0)
     if output[1] ~= nil then
         output[1]:copy_from(ce)
     end
     -- add total ce
     self.total_ce = self.total_ce - ce:colsum()[0][0]
-    self.total_frames = self.total_frames + softmax:nrow()
+    self.total_frames = self.total_frames + self.gconf.mask[t]:colsum()[0][0]
     -- TODO: add colsame for uncompressed label
     if self.compressed then
+        classified:set_values_by_mask(self.gconf.mask[t], -1)
         self.total_correct = self.total_correct + classified:colsame(input[2])[0][0]
     end
 end