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authorQi Liu <[email protected]>2016-03-11 20:11:00 +0800
committerQi Liu <[email protected]>2016-03-11 20:11:00 +0800
commite2a9af061db485d4388902d738c9d8be3f94ab34 (patch)
tree468d6c6afa0801f6a6bf794b3674f8814b8827f7 /nerv/layer/lstm.lua
parent2f46a5e2b37a054f482f76f4ac3d26b144cf988f (diff)
add recipe and fix bugs
Diffstat (limited to 'nerv/layer/lstm.lua')
-rw-r--r--nerv/layer/lstm.lua191
1 files changed, 66 insertions, 125 deletions
diff --git a/nerv/layer/lstm.lua b/nerv/layer/lstm.lua
index 641d5dc..5dbcc20 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}, pr = pr},
+ inputGate = {dim_in = {din, dout, dout}, dim_out = {dout}, pr = pr},
+ outputGate = {dim_in = {din, dout, dout}, dim_out = {dout}, 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