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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
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