1 files changed, 20 insertions, 20 deletions
diff --git a/nerv/examples/lmptb/tnn/layersT/lstm_t.lua b/nerv/examples/lmptb/tnn/layersT/lstm_t.lua
index 4ec2e54..d7d8a20 100644
--- a/nerv/examples/lmptb/tnn/layersT/lstm_t.lua
+++ b/nerv/examples/lmptb/tnn/layersT/lstm_t.lua
@@ -19,10 +19,12 @@ function LSTMLayerT:__init(id, global_conf, layer_conf)
 
     local layers = {
         ["nerv.CombinerLayer"] = {
-            [ap("inputXDup")] = {{}, {["dim_in"] = {self.dim_in[1]}, ["dim_out"] = {self.dim_in[1], self.dim_in[1], self.dim_in[1]}}},
-            [ap("inputHDup")] = {{}, {["dim_in"] = {self.dim_in[2]}, ["dim_out"] = {self.dim_in[2], self.dim_in[2], self.dim_in[2]}}},
+            [ap("inputXDup")] = {{}, {["dim_in"] = {self.dim_in[1]}, 
+                ["dim_out"] = {self.dim_in[1], self.dim_in[1], self.dim_in[1]}, ["lambda"] = {1}}},
+            [ap("inputHDup")] = {{}, {["dim_in"] = {self.dim_in[2]}, 
+                ["dim_out"] = {self.dim_in[2], self.dim_in[2], self.dim_in[2]}, ["lambda"] = {1}}},
             [ap("inputCDup")] = {{}, {["dim_in"] = {self.dim_in[3]}, 
-                ["dim_out"] = {self.dim_in[3], self.dim_in[3], self.dim_in[3], self.dim_in[3]}}},
+                ["dim_out"] = {self.dim_in[3], self.dim_in[3], self.dim_in[3], self.dim_in[3]}, ["lambda"] = {1}}},
             [ap("mainCDup")] = {{}, {["dim_in"] = {self.dim_in[3], self.dim_in[3]}, ["dim_out"] = {self.dim_in[3], self.dim_in[3]},
                 ["lambda"] = {1, 1}}},
         },
@@ -76,39 +78,37 @@ function LSTMLayerT:__init(id, global_conf, layer_conf)
         [ap("forgetGMul[1]")] = ap("mainCDup[2]"),
 
         [ap("mainCDup[2]")] = "<output>[2]",
-    }
+        [ap("mainCDup[1]")] = ap("outputTanhL[1]"),
 
+        [ap("outputTanhL[1]")] = "<output>[1]",
+    }
+    self.dagL = nerv.DAGLayerT(self.id, global_conf, 
+            {["dim_in"] = self.dim_in, ["dim_out"] = self.dim_out, ["sub_layers"] = layerRepo, 
+            ["connections"] = connections_t})
+    
     self:check_dim_len(3, 2) -- x, h, c and h, c
 end
 
-function LSTMLayerT:init(batch_size)
-    if self.ltp.trans:ncol() ~= self.bp.trans:ncol() then
-        nerv.error("mismatching dimensions of linear transform and bias paramter")
-    end
-    if self.dim_in[1] ~= self.ltp.trans:nrow() then
-        nerv.error("mismatching dimensions of linear transform parameter and input")
-    end
-    if self.dim_out[1] ~= self.ltp.trans:ncol() then
-        nerv.error("mismatching dimensions of linear transform parameter and output")
-    end
-    self.ltp_grad = self.ltp.trans:create()
-    self.ltp:train_init()
-    self.bp:train_init()
+function LSTMLayerT:init(batch_size, chunk_size)
+    self.dagL:init(batch_size, chunk_size)
 end
 
-function LSTMLayerT:batch_resize(batch_size)
-    -- do nothing
+function LSTMLayerT:batch_resize(batch_size, chunk_size)
+    self.dagL:batch_resize(batch_size, chunk_size)
 end
 
 function LSTMLayerT:update(bp_err, input, output)
+    self.dagL:update(bp_err, input, output)
 end
 
 function LSTMLayerT:propagate(input, output)
+    self.dagL:propagate(input, output)
 end
 
 function LSTMLayerT:back_propagate(bp_err, next_bp_err, input, output)
+    self.dagL:back_propagate(bp_err, next_bp_err, input, output)
 end
 
 function LSTMLayerT:get_params()
-    return nerv.ParamRepo({self.ltp, self.bp})
+    return self.dagL:get_params()
 end