1 files changed, 140 insertions, 0 deletions
diff --git a/nerv/layer/lstm.lua b/nerv/layer/lstm.lua
new file mode 100644
index 0000000..500bd87
--- /dev/null
+++ b/nerv/layer/lstm.lua
@@ -0,0 +1,140 @@
+local LSTMLayer = nerv.class('nerv.LSTMLayer', 'nerv.Layer')
+
+function LSTMLayer:__init(id, global_conf, layer_conf)
+    -- input1:x
+    -- input2:h
+    -- input3:c
+    self.id = id
+    self.dim_in = layer_conf.dim_in
+    self.dim_out = layer_conf.dim_out
+    self.gconf = global_conf
+
+    -- prepare a DAGLayer 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 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}}},
+        },
+        ["nerv.AffineLayer"] = {
+            [ap("mainAffineL")] = {{}, {dim_in = {din1, din2},
+                                        dim_out = {dout1},
+                                        pr = pr}},
+        },
+        ["nerv.TanhLayer"] = {
+            [ap("mainTanhL")] = {{}, {dim_in = {dout1}, dim_out = {dout1}}},
+            [ap("outputTanhL")] = {{}, {dim_in = {dout1}, dim_out = {dout1}}},
+        },
+        ["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.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}}},
+        },
+    }
+    
+    local layerRepo = 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]",
+    }
+    self.dag = nerv.DAGLayer(self.id, global_conf,
+                                {dim_in = self.dim_in,
+                                 dim_out = self.dim_out,
+                                 sub_layers = layerRepo,
+                                 connections = connections})
+    
+    self:check_dim_len(3, 2) -- x, h, c and h, c
+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()
+end