change RNN/LSTM/LSTMP parameter order, which is compatible to old version

3 files changed, 47 insertions, 47 deletions

diff --git a/nerv/layer/lstm.lua b/nerv/layer/lstm.lua
index 5d73ad2..d6437e5 100644
--- a/nerv/layer/lstm.lua
+++ b/nerv/layer/lstm.lua
@@ -24,13 +24,13 @@ function LSTMLayer:__init(id, global_conf, layer_conf)
             cellDup = {dim_in = {dout}, dim_out = {dout, dout, dout, dout, dout}},
         },
         ['nerv.AffineLayer'] = {
-            mainAffine = {dim_in = table.connect({dout}, din), dim_out = {dout}, pr = pr},
-            forgetGate = {dim_in = table.connect({dout, dout}, din), dim_out = {dout},
-                param_type = table.connect({'N', 'D'}, table.vector(#din, 'N')), pr = pr, activation = nerv.SigmoidLayer},
-            inputGate = {dim_in = table.connect({dout, dout}, din), dim_out = {dout},
-                param_type = table.connect({'N', 'D'}, table.vector(#din, 'N')), pr = pr, activation = nerv.SigmoidLayer},
-            outputGate = {dim_in = table.connect({dout, dout}, din), dim_out = {dout},
-                param_type = table.connect({'N', 'D'}, table.vector(#din, 'N')), pr = pr, activation = nerv.SigmoidLayer},
+            mainAffine = {dim_in = table.connect(din, {dout}), dim_out = {dout}, pr = pr},
+            forgetGate = {dim_in = table.connect(din, {dout, dout}), dim_out = {dout},
+                param_type = table.connect(table.vector(#din, 'N'), {'N', 'D'}), pr = pr, activation = nerv.SigmoidLayer},
+            inputGate = {dim_in = table.connect(din, {dout, dout}), dim_out = {dout},
+                param_type = table.connect(table.vector(#din, 'N'), {'N', 'D'}), pr = pr, activation = nerv.SigmoidLayer},
+            outputGate = {dim_in = table.connect(din, {dout, dout}), dim_out = {dout},
+                param_type = table.connect(table.vector(#din, 'N'), {'N', 'D'}), pr = pr, activation = nerv.SigmoidLayer},
         },
         ['nerv.TanhLayer'] = {
             mainTanh = {dim_in = {dout}, dim_out = {dout}},
@@ -51,20 +51,20 @@ function LSTMLayer:__init(id, global_conf, layer_conf)
         --{'<input>[1 .. n]', 'inputDup(1 .. n)[1]', 0},
 
         -- input gate
-        {'outputDup[1]', 'inputGate[1]', 1},
-        {'cellDup[1]', 'inputGate[2]', 1},
-        --{'inputDup(1 .. n)[1]', 'inputGate[3 .. n + 2]', 0},
+        {'outputDup[1]', 'inputGate[' .. (#din + 1) .. ']', 1},
+        {'cellDup[1]', 'inputGate[' .. (#din + 2) .. ']', 1},
+        --{'inputDup(1 .. n)[1]', 'inputGate[1 .. n]', 0},
 
         -- forget gate
-        {'outputDup[2]', 'forgetGate[1]', 1},
-        {'cellDup[2]', 'forgetGate[2]', 1},
-        --{'inputDup(1 .. n)[2]', 'forgetGate[3 .. n + 2]', 0},
+        {'outputDup[2]', 'forgetGate[' .. (#din + 1) .. ']', 1},
+        {'cellDup[2]', 'forgetGate[' .. (#din + 2) .. ']', 1},
+        --{'inputDup(1 .. n)[2]', 'forgetGate[1 .. n]', 0},
 
         -- lstm cell
         {'forgetGate[1]', 'forgetGateMul[1]', 0},
         {'cellDup[3]', 'forgetGateMul[2]', 1},
-        {'outputDup[3]', 'mainAffine[1]', 1},
-        --{'inputDup(1 .. n)[3]', 'mainAffine[2 .. n + 1]', 0},
+        {'outputDup[3]', 'mainAffine[' .. (#din + 1) .. ']', 1},
+        --{'inputDup(1 .. n)[3]', 'mainAffine[1 .. n]', 0},
         {'mainAffine[1]', 'mainTanh[1]', 0},
         {'inputGate[1]', 'inputGateMul[1]', 0},
         {'mainTanh[1]', 'inputGateMul[2]', 0},
@@ -73,9 +73,9 @@ function LSTMLayer:__init(id, global_conf, layer_conf)
         {'mainCombine[1]', 'cellDup[1]', 0},
 
         -- forget gate
-        {'outputDup[4]', 'outputGate[1]', 1},
-        {'cellDup[4]', 'outputGate[2]', 0},
-        --{'inputDup(1 .. n)[4]', 'outputGate[2 .. n + 1]', 0},
+        {'outputDup[4]', 'outputGate[' .. (#din + 1) .. ']', 1},
+        {'cellDup[4]', 'outputGate[' .. (#din + 2) .. ']', 0},
+        --{'inputDup(1 .. n)[4]', 'outputGate[1 .. n]', 0},
 
         -- lstm output
         {'cellDup[5]', 'outputTanh[1]', 0},
@@ -86,10 +86,10 @@ function LSTMLayer:__init(id, global_conf, layer_conf)
     }
     for i = 1, #din do
         table.insert(connections, {'<input>[' .. i .. ']', 'inputDup' .. i .. '[1]', 0})
-        table.insert(connections, {'inputDup' .. i .. '[1]', 'inputGate[' .. (i + 2) .. ']', 0})
-        table.insert(connections, {'inputDup' .. i .. '[2]', 'forgetGate[' .. (i + 2) .. ']', 0})
-        table.insert(connections, {'inputDup' .. i .. '[3]', 'mainAffine[' .. (i + 1) .. ']', 0})
-        table.insert(connections, {'inputDup' .. i .. '[4]', 'outputGate[' .. (i + 2) .. ']', 0})
+        table.insert(connections, {'inputDup' .. i .. '[1]', 'inputGate[' .. i .. ']', 0})
+        table.insert(connections, {'inputDup' .. i .. '[2]', 'forgetGate[' .. i .. ']', 0})
+        table.insert(connections, {'inputDup' .. i .. '[3]', 'mainAffine[' .. i .. ']', 0})
+        table.insert(connections, {'inputDup' .. i .. '[4]', 'outputGate[' .. i .. ']', 0})
     end
 
     self:add_prefix(layers, connections)
diff --git a/nerv/layer/lstmp.lua b/nerv/layer/lstmp.lua
index 49c9516..c16dbd4 100644
--- a/nerv/layer/lstmp.lua
+++ b/nerv/layer/lstmp.lua
@@ -25,13 +25,13 @@ function LSTMPLayer:__init(id, global_conf, layer_conf)
             cellDup = {dim_in = {dcell}, dim_out = {dcell, dcell, dcell, dcell, dcell}},
         },
         ['nerv.AffineLayer'] = {
-            mainAffine = {dim_in = table.connect({dout}, din), dim_out = {dcell}, pr = pr},
-            forgetGate = {dim_in = table.connect({dout, dcell}, din), dim_out = {dcell},
-                param_type = table.connect({'N', 'D'}, table.vector(#din, 'N')), pr = pr, activation = nerv.SigmoidLayer},
-            inputGate = {dim_in = table.connect({dout, dcell}, din), dim_out = {dcell},
-                param_type = table.connect({'N', 'D'}, table.vector(#din, 'N')), pr = pr, activation = nerv.SigmoidLayer},
-            outputGate = {dim_in = table.connect({dout, dcell}, din), dim_out = {dcell},
-                param_type = table.connect({'N', 'D'}, table.vector(#din, 'N')), pr = pr, activation = nerv.SigmoidLayer},
+            mainAffine = {dim_in = table.connect(din, {dout}), dim_out = {dcell}, pr = pr},
+            forgetGate = {dim_in = table.connect(din, {dout, dcell}), dim_out = {dcell},
+                param_type = table.connect(table.vector(#din, 'N'), {'N', 'D'}), pr = pr, activation = nerv.SigmoidLayer},
+            inputGate = {dim_in = table.connect(din, {dout, dcell}), dim_out = {dcell},
+                param_type = table.connect(table.vector(#din, 'N'), {'N', 'D'}), pr = pr, activation = nerv.SigmoidLayer},
+            outputGate = {dim_in = table.connect(din, {dout, dcell}), dim_out = {dcell},
+                param_type = table.connect(table.vector(#din, 'N'), {'N', 'D'}), pr = pr, activation = nerv.SigmoidLayer},
             projection = {dim_in = {dcell}, dim_out = {dout}, pr = pr, no_bias = true},
         },
         ['nerv.TanhLayer'] = {
@@ -53,20 +53,20 @@ function LSTMPLayer:__init(id, global_conf, layer_conf)
         --{'<input>[1 .. n]', 'inputDup(1 .. n)[1]', 0},
 
         -- input gate
-        {'outputDup[1]', 'inputGate[1]', 1},
-        {'cellDup[1]', 'inputGate[2]', 1},
-        --{'inputDup(1 .. n)[1]', 'inputGate[3 .. n + 2]', 0},
+        {'outputDup[1]', 'inputGate[' .. (#din + 1) .. ']', 1},
+        {'cellDup[1]', 'inputGate[' .. (#din + 2) .. ']', 1},
+        --{'inputDup(1 .. n)[1]', 'inputGate[1 .. n]', 0},
 
         -- forget gate
-        {'outputDup[2]', 'forgetGate[1]', 1},
-        {'cellDup[2]', 'forgetGate[2]', 1},
-        --{'inputDup(1 .. n)[2]', 'forgetGate[3 .. n + 2]', 0},
+        {'outputDup[2]', 'forgetGate[' .. (#din + 1) .. ']', 1},
+        {'cellDup[2]', 'forgetGate[' .. (#din + 2) .. ']', 1},
+        --{'inputDup(1 .. n)[2]', 'forgetGate[1 .. n]', 0},
 
         -- lstm cell
         {'forgetGate[1]', 'forgetGateMul[1]', 0},
         {'cellDup[3]', 'forgetGateMul[2]', 1},
-        {'outputDup[3]', 'mainAffine[1]', 1},
-        --{'inputDup(1 .. n)[3]', 'mainAffine[2 .. n + 1]', 0},
+        {'outputDup[3]', 'mainAffine[' .. (#din + 1) .. ']', 1},
+        --{'inputDup(1 .. n)[3]', 'mainAffine[1 .. n]', 0},
         {'mainAffine[1]', 'mainTanh[1]', 0},
         {'inputGate[1]', 'inputGateMul[1]', 0},
         {'mainTanh[1]', 'inputGateMul[2]', 0},
@@ -75,9 +75,9 @@ function LSTMPLayer:__init(id, global_conf, layer_conf)
         {'mainCombine[1]', 'cellDup[1]', 0},
 
         -- forget gate
-        {'outputDup[4]', 'outputGate[1]', 1},
-        {'cellDup[4]', 'outputGate[2]', 0},
-        --{'inputDup(1 .. n)[4]', 'outputGate[2 .. n + 1]', 0},
+        {'outputDup[4]', 'outputGate[' .. (#din + 1) .. ']', 1},
+        {'cellDup[4]', 'outputGate[' .. (#din + 2) .. ']', 0},
+        --{'inputDup(1 .. n)[4]', 'outputGate[1 .. n]', 0},
 
         -- lstm output
         {'cellDup[5]', 'outputTanh[1]', 0},
@@ -89,10 +89,10 @@ function LSTMPLayer:__init(id, global_conf, layer_conf)
     }
     for i = 1, #din do
         table.insert(connections, {'<input>[' .. i .. ']', 'inputDup' .. i .. '[1]', 0})
-        table.insert(connections, {'inputDup' .. i .. '[1]', 'inputGate[' .. (i + 2) .. ']', 0})
-        table.insert(connections, {'inputDup' .. i .. '[2]', 'forgetGate[' .. (i + 2) .. ']', 0})
-        table.insert(connections, {'inputDup' .. i .. '[3]', 'mainAffine[' .. (i + 1) .. ']', 0})
-        table.insert(connections, {'inputDup' .. i .. '[4]', 'outputGate[' .. (i + 2) .. ']', 0})
+        table.insert(connections, {'inputDup' .. i .. '[1]', 'inputGate[' .. i .. ']', 0})
+        table.insert(connections, {'inputDup' .. i .. '[2]', 'forgetGate[' .. i .. ']', 0})
+        table.insert(connections, {'inputDup' .. i .. '[3]', 'mainAffine[' .. i .. ']', 0})
+        table.insert(connections, {'inputDup' .. i .. '[4]', 'outputGate[' .. i .. ']', 0})
     end
 
     self:add_prefix(layers, connections)
diff --git a/nerv/layer/rnn.lua b/nerv/layer/rnn.lua
index 63e0b55..8d5a07c 100644
--- a/nerv/layer/rnn.lua
+++ b/nerv/layer/rnn.lua
@@ -17,7 +17,7 @@ function RNNLayer:__init(id, global_conf, layer_conf)
 
     local layers = {
         ['nerv.AffineLayer'] = {
-            main = {dim_in = table.connect({dout}, din), dim_out = {dout}, pr = pr, activation = layer_conf.activation},
+            main = {dim_in = table.connect(din, {dout}), dim_out = {dout}, pr = pr, activation = layer_conf.activation},
         },
         ['nerv.DuplicateLayer'] = {
             duplicate = {dim_in = {dout}, dim_out = {dout, dout}},
@@ -26,11 +26,11 @@ function RNNLayer:__init(id, global_conf, layer_conf)
 
     local connections = {
         {'main[1]', 'duplicate[1]', 0},
-        {'duplicate[1]', 'main[1]', 1},
+        {'duplicate[1]', 'main[' .. (#din + 1) .. ']', 1},
         {'duplicate[2]', '<output>[1]', 0},
     }
     for i = 1, #din do
-        table.insert(connections, {'<input>[' .. i .. ']', 'main[' .. (i + 1) .. ']', 0})
+        table.insert(connections, {'<input>[' .. i .. ']', 'main[' .. i .. ']', 0})
     end
 
     self:add_prefix(layers, connections)