diff options
Diffstat (limited to 'nerv/layer/lstm.lua')
| -rw-r--r-- | nerv/layer/lstm.lua | 192 |
1 files changed, 67 insertions, 125 deletions
diff --git a/nerv/layer/lstm.lua b/nerv/layer/lstm.lua index d8eee71..56f674a 100644 --- a/nerv/layer/lstm.lua +++ b/nerv/layer/lstm.lua @@ -1,143 +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 - self.id = id - self.dim_in = layer_conf.dim_in - self.dim_out = layer_conf.dim_out - self.gconf = global_conf + nerv.Layer.__init(self, id, global_conf, layer_conf) + self:check_dim_len(1, 1) + + local din = layer_conf.dim_in[1] + local dout = layer_conf.dim_out[1] - -- 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 + pr = nerv.ParamRepo({}, self.loc_type) end - local din1, din2, din3 = self.dim_in[1], self.dim_in[2], self.dim_in[3] - local dout1, dout2 = self.dim_out[1], self.dim_out[2] - 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, - param_type = {'N', 'N', 'D'}}}, - [ap("inputGateL")] = {{}, {dim_in = {din1, din2, din3}, - dim_out = {din3}, pr = pr, - param_type = {'N', 'N', 'D'}}}, - [ap("outputGateL")] = {{}, {dim_in = {din1, din2, din3}, - dim_out = {din3}, pr = pr, - param_type = {'N', 'N', 'D'}}}, - + ['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}, param_type = {'N', 'N', 'D'}, pr = pr}, + inputGate = {dim_in = {din, dout, dout}, dim_out = {dout}, param_type = {'N', 'N', 'D'}, pr = pr}, + outputGate = {dim_in = {din, dout, dout}, dim_out = {dout}, param_type = {'N', 'N', 'D'}, 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}}, }, } - 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]", + -- 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 = 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() + self:add_prefix(layers, connections) + local layer_repo = nerv.LayerRepo(layers, pr, global_conf) + self:graph_init(layer_repo, connections) end |