1 files changed, 114 insertions, 0 deletions
diff --git a/nerv/examples/lmptb/lmptb/layer/gru_t.lua b/nerv/examples/lmptb/lmptb/layer/gru_t.lua
new file mode 100644
index 0000000..8f15cc8
--- /dev/null
+++ b/nerv/examples/lmptb/lmptb/layer/gru_t.lua
@@ -0,0 +1,114 @@
+local GRULayerT = nerv.class('nerv.GRULayerT', 'nerv.LayerT')
+
+function GRULayerT:__init(id, global_conf, layer_conf)
+    --input1:x input2:h input3:c(h^~)
+    self.id = id
+    self.dim_in = layer_conf.dim_in
+    self.dim_out = layer_conf.dim_out
+    self.gconf = global_conf
+
+    if self.dim_in[2] ~= self.dim_out[1] then
+        nerv.error("dim_in[2](%d) mismatch with dim_out[1](%d)", self.dim_in[2], self.dim_out[1])
+    end
+
+    --prepare a DAGLayerT to hold the lstm structure
+    local pr = layer_conf.pr
+    if pr == nil then
+        pr = nerv.ParamRepo()
+    end
+    
+    local function ap(str)
+        return self.id .. '.' .. str
+    end
+
+    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]}, ["lambda"] = {1}}},
+            [ap("inputHDup")] = {{}, {["dim_in"] = {self.dim_in[2]}, 
+                ["dim_out"] = {self.dim_in[2], self.dim_in[2], self.dim_in[2], self.dim_in[2], self.dim_in[2]}, ["lambda"] = {1}}},
+            [ap("updateGDup")] = {{}, {["dim_in"] = {self.dim_in[2]},
+                ["dim_out"] = {self.dim_in[2], self.dim_in[2]}, ["lambda"] = {1}}},
+            [ap("updateMergeL")] = {{}, {["dim_in"] = {self.dim_in[2], self.dim_in[2], self.dim_in[2]}, ["dim_out"] = {self.dim_out[1]},
+                ["lambda"] = {1, -1, 1}}},
+        },
+        ["nerv.AffineLayer"] = {
+            [ap("mainAffineL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2]}, ["dim_out"] = {self.dim_out[1]}, ["pr"] = pr}},
+        },
+        ["nerv.TanhLayer"] = {
+            [ap("mainTanhL")] = {{}, {["dim_in"] = {self.dim_out[1]}, ["dim_out"] = {self.dim_out[1]}}},
+        },
+        ["nerv.GateFLayer"] = {
+            [ap("resetGateL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2]}, 
+                ["dim_out"] = {self.dim_in[2]}, ["pr"] = pr}},
+            [ap("updateGateL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2]}, 
+                ["dim_out"] = {self.dim_in[2]}, ["pr"] = pr}},
+        },
+        ["nerv.ElemMulLayer"] = {
+            [ap("resetGMulL")] = {{}, {["dim_in"] = {self.dim_in[2], self.dim_in[2]}, ["dim_out"] = {self.dim_in[2]}}},
+            [ap("updateGMulCL")] = {{}, {["dim_in"] = {self.dim_in[2], self.dim_in[2]}, ["dim_out"] = {self.dim_in[2]}}},
+            [ap("updateGMulHL")] = {{}, {["dim_in"] = {self.dim_in[2], self.dim_in[2]}, ["dim_out"] = {self.dim_in[2]}}},
+        },
+    }
+    
+    local layerRepo = nerv.LayerRepo(layers, pr, global_conf)
+
+    local connections_t = {
+        ["<input>[1]"] = ap("inputXDup[1]"), 
+        ["<input>[2]"] = ap("inputHDup[1]"),
+
+        [ap("inputXDup[1]")] = ap("resetGateL[1]"),
+        [ap("inputHDup[1]")] = ap("resetGateL[2]"),
+        [ap("inputXDup[2]")] = ap("updateGateL[1]"),
+        [ap("inputHDup[2]")] = ap("updateGateL[2]"),
+        [ap("updateGateL[1]")] = ap("updateGDup[1]"),
+
+        [ap("resetGateL[1]")] = ap("resetGMulL[1]"),
+        [ap("inputHDup[3]")] = ap("resetGMulL[2]"),
+
+        [ap("inputXDup[3]")] = ap("mainAffineL[1]"),
+        [ap("resetGMulL[1]")] = ap("mainAffineL[2]"),
+        [ap("mainAffineL[1]")] = ap("mainTanhL[1]"),
+
+        [ap("updateGDup[1]")] = ap("updateGMulHL[1]"),
+        [ap("inputHDup[4]")] = ap("updateGMulHL[2]"),
+        [ap("updateGDup[2]")] = ap("updateGMulCL[1]"),
+        [ap("mainTanhL[1]")] = ap("updateGMulCL[2]"),
+ 
+        [ap("inputHDup[5]")] = ap("updateMergeL[1]"),
+        [ap("updateGMulHL[1]")] = ap("updateMergeL[2]"),
+        [ap("updateGMulCL[1]")] = ap("updateMergeL[3]"),
+
+        [ap("updateMergeL[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(2, 1) -- x, h and h
+end
+
+function GRULayerT:init(batch_size, chunk_size)
+    self.dagL:init(batch_size, chunk_size)
+end
+
+function GRULayerT:batch_resize(batch_size, chunk_size)
+    self.dagL:batch_resize(batch_size, chunk_size)
+end
+
+function GRULayerT:update(bp_err, input, output, t)
+    self.dagL:update(bp_err, input, output, t)
+end
+
+function GRULayerT:propagate(input, output, t)
+    self.dagL:propagate(input, output, t)
+end
+
+function GRULayerT:back_propagate(bp_err, next_bp_err, input, output, t)
+    self.dagL:back_propagate(bp_err, next_bp_err, input, output, t)
+end
+
+function GRULayerT:get_params()
+    return self.dagL:get_params()
+end