Merge pull request #31 from liuq901/master

modfiy param generate & rewrite LSTM layer

1 files changed, 66 insertions, 125 deletions

diff --git a/nerv/layer/lstm.lua b/nerv/layer/lstm.lua
index 641d5dc..56f674a 100644
--- a/nerv/layer/lstm.lua
+++ b/nerv/layer/lstm.lua
@@ -1,144 +1,85 @@
-local LSTMLayer = nerv.class('nerv.LSTMLayer', 'nerv.Layer')
+local LSTMLayer = nerv.class('nerv.LSTMLayer', 'nerv.GraphLayer')
 
 function LSTMLayer:__init(id, global_conf, layer_conf)
-    -- input1:x
-    -- input2:h
-    -- input3:c
     nerv.Layer.__init(self, id, global_conf, layer_conf)
-    -- prepare a DAGLayer to hold the lstm structure
+    self:check_dim_len(1, 1)
+
+    local din = layer_conf.dim_in[1]
+    local dout = layer_conf.dim_out[1]
+
     local pr = layer_conf.pr
     if pr == nil then
         pr = nerv.ParamRepo({}, self.loc_type)
     end
-    
-    local function ap(str)
-        return self.id .. '.' .. str
-    end
-    local din1, din2, din3 = self.dim_in[1], self.dim_in[2], self.dim_in[3]
-    local dout1, dout2, dout3 = self.dim_out[1], self.dim_out[2], self.dim_out[3]
-    local layers = {
-        ["nerv.CombinerLayer"] = {
-            [ap("inputXDup")] = {dim_in = {din1},
-                                      dim_out = {din1, din1, din1, din1},
-                                      lambda = {1}},
 
-            [ap("inputHDup")] = {dim_in = {din2},
-                                      dim_out = {din2, din2, din2, din2},
-                                      lambda = {1}},
-
-            [ap("inputCDup")] = {dim_in = {din3},
-                                      dim_out = {din3, din3, din3},
-                                      lambda = {1}},
-
-            [ap("mainCDup")] = {dim_in = {din3, din3},
-                                     dim_out = {din3, din3, din3},
-                                     lambda = {1, 1}},
+    local layers = {
+        ['nerv.CombinerLayer'] = {
+            mainCombine = {dim_in = {dout, dout}, dim_out = {dout}, lambda = {1, 1}},
         },
-        ["nerv.AffineLayer"] = {
-            [ap("mainAffineL")] = {dim_in = {din1, din2},
-                                        dim_out = {dout1},
-                                        pr = pr},
+        ['nerv.DuplicateLayer'] = {
+            inputDup = {dim_in = {din}, dim_out = {din, din, din, din}},
+            outputDup = {dim_in = {dout}, dim_out = {dout, dout, dout, dout, dout}},
+            cellDup = {dim_in = {dout}, dim_out = {dout, dout, dout, dout, dout}},
         },
-        ["nerv.TanhLayer"] = {
-            [ap("mainTanhL")] = {dim_in = {dout1}, dim_out = {dout1}},
-            [ap("outputTanhL")] = {dim_in = {dout1}, dim_out = {dout1}},
+        ['nerv.AffineLayer'] = {
+            mainAffine = {dim_in = {din, dout}, dim_out = {dout}, pr = pr},
         },
-        ["nerv.LSTMGateLayer"] = {
-            [ap("forgetGateL")] = {dim_in = {din1, din2, din3},
-                                        dim_out = {din3}, pr = pr},
-            [ap("inputGateL")] = {dim_in = {din1, din2, din3},
-                                        dim_out = {din3}, pr = pr},
-            [ap("outputGateL")] = {dim_in = {din1, din2, din3},
-                                        dim_out = {din3}, pr = pr},
-
+        ['nerv.TanhLayer'] = {
+            mainTanh = {dim_in = {dout}, dim_out = {dout}},
+            outputTanh = {dim_in = {dout}, dim_out = {dout}},
         },
-        ["nerv.ElemMulLayer"] = {
-            [ap("inputGMulL")] = {dim_in = {din3, din3},
-                                        dim_out = {din3}},
-            [ap("forgetGMulL")] = {dim_in = {din3, din3},
-                                        dim_out = {din3}},
-            [ap("outputGMulL")] = {dim_in = {din3, din3},
-                                        dim_out = {din3}},
+        ['nerv.LSTMGateLayer'] = {
+            forgetGate = {dim_in = {din, dout, dout}, dim_out = {dout}, param_type = {'N', 'N', 'D'}, pr = pr},
+            inputGate = {dim_in = {din, dout, dout}, dim_out = {dout}, param_type = {'N', 'N', 'D'}, pr = pr},
+            outputGate = {dim_in = {din, dout, dout}, dim_out = {dout}, param_type = {'N', 'N', 'D'}, pr = pr},
+        },
+        ['nerv.ElemMulLayer'] = {
+            inputGateMul = {dim_in = {dout, dout}, dim_out = {dout}},
+            forgetGateMul = {dim_in = {dout, dout}, dim_out = {dout}},
+            outputGateMul = {dim_in = {dout, dout}, dim_out = {dout}},
         },
     }
     
-    self.lrepo = nerv.LayerRepo(layers, pr, global_conf)
-
     local connections = {
-        ["<input>[1]"] = ap("inputXDup[1]"),
-        ["<input>[2]"] = ap("inputHDup[1]"),
-        ["<input>[3]"] = ap("inputCDup[1]"),
-
-        [ap("inputXDup[1]")] = ap("mainAffineL[1]"),
-        [ap("inputHDup[1]")] = ap("mainAffineL[2]"),
-        [ap("mainAffineL[1]")] = ap("mainTanhL[1]"),
-
-        [ap("inputXDup[2]")] = ap("inputGateL[1]"),
-        [ap("inputHDup[2]")] = ap("inputGateL[2]"),
-        [ap("inputCDup[1]")] = ap("inputGateL[3]"),
-        
-        [ap("inputXDup[3]")] = ap("forgetGateL[1]"),
-        [ap("inputHDup[3]")] = ap("forgetGateL[2]"),
-        [ap("inputCDup[2]")] = ap("forgetGateL[3]"),
-
-        [ap("mainTanhL[1]")] = ap("inputGMulL[1]"),
-        [ap("inputGateL[1]")] = ap("inputGMulL[2]"),
-        
-        [ap("inputCDup[3]")] = ap("forgetGMulL[1]"),
-        [ap("forgetGateL[1]")] = ap("forgetGMulL[2]"),
-
-        [ap("inputGMulL[1]")] = ap("mainCDup[1]"),
-        [ap("forgetGMulL[1]")] = ap("mainCDup[2]"),
-
-        [ap("inputXDup[4]")] = ap("outputGateL[1]"),
-        [ap("inputHDup[4]")] = ap("outputGateL[2]"),
-        [ap("mainCDup[3]")] = ap("outputGateL[3]"),
-
-        [ap("mainCDup[2]")] = "<output>[2]",
-        [ap("mainCDup[1]")] = ap("outputTanhL[1]"),
-        
-        [ap("outputTanhL[1]")] = ap("outputGMulL[1]"),
-        [ap("outputGateL[1]")] = ap("outputGMulL[2]"),
-
-        [ap("outputGMulL[1]")] = "<output>[1]",
+        -- lstm input
+        {'<input>[1]', 'inputDup[1]', 0},
+
+        -- input gate
+        {'inputDup[1]', 'inputGate[1]', 0},
+        {'outputDup[1]', 'inputGate[2]', 1},
+        {'cellDup[1]', 'inputGate[3]', 1},
+
+        -- forget gate
+        {'inputDup[2]', 'forgetGate[1]', 0},
+        {'outputDup[2]', 'forgetGate[2]', 1},
+        {'cellDup[2]', 'forgetGate[3]', 1},
+
+        -- lstm cell
+        {'forgetGate[1]', 'forgetGateMul[1]', 0},
+        {'cellDup[3]', 'forgetGateMul[2]', 1},
+        {'inputDup[3]', 'mainAffine[1]', 0},
+        {'outputDup[3]', 'mainAffine[2]', 1},
+        {'mainAffine[1]', 'mainTanh[1]', 0},
+        {'inputGate[1]', 'inputGateMul[1]', 0},
+        {'mainTanh[1]', 'inputGateMul[2]', 0},
+        {'inputGateMul[1]', 'mainCombine[1]', 0},
+        {'forgetGateMul[1]', 'mainCombine[2]', 0},
+        {'mainCombine[1]', 'cellDup[1]', 0},
+
+        -- forget gate
+        {'inputDup[4]', 'outputGate[1]', 0},
+        {'outputDup[4]', 'outputGate[2]', 1},
+        {'cellDup[4]', 'outputGate[3]', 0},
+
+        -- lstm output
+        {'cellDup[5]', 'outputTanh[1]', 0},
+        {'outputGate[1]', 'outputGateMul[1]', 0},
+        {'outputTanh[1]', 'outputGateMul[2]', 0},
+        {'outputGateMul[1]', 'outputDup[1]', 0},
+        {'outputDup[5]', '<output>[1]', 0},
     }
-    self.dag = nerv.DAGLayer(self.id, global_conf,
-                                {dim_in = self.dim_in,
-                                 dim_out = self.dim_out,
-                                 sub_layers = self.lrepo,
-                                 connections = connections})
-    
-    self:check_dim_len(3, 2) -- x, h, c and h, c
-end
-
-function LSTMLayer:bind_params()
-    local pr = layer_conf.pr
-    if pr == nil then
-        pr = nerv.ParamRepo({}, self.loc_type)
-    end
-    self.lrepo:rebind(pr)
-end
-
-function LSTMLayer:init(batch_size, chunk_size)
-    self.dag:init(batch_size, chunk_size)
-end
-
-function LSTMLayer:batch_resize(batch_size, chunk_size)
-    self.dag:batch_resize(batch_size, chunk_size)
-end
-
-function LSTMLayer:update(bp_err, input, output, t)
-    self.dag:update(bp_err, input, output, t)
-end
-
-function LSTMLayer:propagate(input, output, t)
-    self.dag:propagate(input, output, t)
-end
-
-function LSTMLayer:back_propagate(bp_err, next_bp_err, input, output, t)
-    self.dag:back_propagate(bp_err, next_bp_err, input, output, t)
-end
 
-function LSTMLayer:get_params()
-    return self.dag:get_params()
+    self:add_prefix(layers, connections)
+    local layer_repo = nerv.LayerRepo(layers, pr, global_conf)
+    self:graph_init(layer_repo, connections)
 end