1 files changed, 0 insertions, 124 deletions
diff --git a/nerv/tnn/layersT/lstm_t.lua b/nerv/tnn/layersT/lstm_t.lua
deleted file mode 100644
index 04d0600..0000000
--- a/nerv/tnn/layersT/lstm_t.lua
+++ /dev/null
@@ -1,124 +0,0 @@
-local LSTMLayerT = nerv.class('nerv.LSTMLayerT', 'nerv.LayerT')
-
-function LSTMLayerT:__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 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], 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]}, ["lambda"] = {1}}},
-            [ap("inputCDup")] = {{}, {["dim_in"] = {self.dim_in[3]}, 
-                ["dim_out"] = {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], self.dim_in[3]},
-                ["lambda"] = {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]}}},
-            [ap("outputTanhL")] = {{}, {["dim_in"] = {self.dim_out[1]}, ["dim_out"] = {self.dim_out[1]}}},
-        },
-        ["nerv.GateFLayer"] = {
-            [ap("forgetGateL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2], self.dim_in[3]}, 
-                ["dim_out"] = {self.dim_in[3]}, ["pr"] = pr}},
-            [ap("inputGateL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2], self.dim_in[3]}, 
-                ["dim_out"] = {self.dim_in[3]}, ["pr"] = pr}},
-            [ap("outputGateL")] = {{}, {["dim_in"] = {self.dim_in[1], self.dim_in[2], self.dim_in[3]}, 
-                ["dim_out"] = {self.dim_in[3]}, ["pr"] = pr}},
-
-        },
-        ["nerv.ElemMulLayer"] = {
-            [ap("inputGMulL")] = {{}, {["dim_in"] = {self.dim_in[3], self.dim_in[3]}, ["dim_out"] = {self.dim_in[3]}}},
-            [ap("forgetGMulL")] = {{}, {["dim_in"] = {self.dim_in[3], self.dim_in[3]}, ["dim_out"] = {self.dim_in[3]}}},
-            [ap("outputGMulL")] = {{}, {["dim_in"] = {self.dim_in[3], self.dim_in[3]}, ["dim_out"] = {self.dim_in[3]}}},
-        },
-    }
-    
-    local layerRepo = nerv.LayerRepo(layers, pr, global_conf)
-
-    local connections_t = {
-        ["<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.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, chunk_size)
-    self.dagL:init(batch_size, chunk_size)
-end
-
-function LSTMLayerT:batch_resize(batch_size, chunk_size)
-    self.dagL:batch_resize(batch_size, chunk_size)
-end
-
-function LSTMLayerT:update(bp_err, input, output, t)
-    self.dagL:update(bp_err, input, output, t)
-end
-
-function LSTMLayerT:propagate(input, output, t)
-    self.dagL:propagate(input, output, t)
-end
-
-function LSTMLayerT:back_propagate(bp_err, next_bp_err, input, output, t)
-    self.dagL:back_propagate(bp_err, next_bp_err, input, output, t)
-end
-
-function LSTMLayerT:get_params()
-    return self.dagL:get_params()
-end