diff options
Diffstat (limited to 'nerv/layer')
| -rw-r--r-- | nerv/layer/lstm.lua | 53 | ||||
| -rw-r--r-- | nerv/layer/lstmp.lua | 55 | ||||
| -rw-r--r-- | nerv/layer/rnn.lua | 15 |
3 files changed, 72 insertions, 51 deletions
diff --git a/nerv/layer/lstm.lua b/nerv/layer/lstm.lua index e568ee8..5d73ad2 100644 --- a/nerv/layer/lstm.lua +++ b/nerv/layer/lstm.lua @@ -2,9 +2,12 @@ local LSTMLayer = nerv.class('nerv.LSTMLayer', 'nerv.GraphLayer') function LSTMLayer:__init(id, global_conf, layer_conf) nerv.Layer.__init(self, id, global_conf, layer_conf) - self:check_dim_len(1, 1) + self:check_dim_len(-1, 1) + if #self.dim_in == 0 then + nerv.error('LSTM layer %s has no input', self.id) + end - local din = layer_conf.dim_in[1] + local din = layer_conf.dim_in local dout = layer_conf.dim_out[1] local pr = layer_conf.pr @@ -17,15 +20,17 @@ function LSTMLayer:__init(id, global_conf, layer_conf) mainCombine = {dim_in = {dout, dout}, dim_out = {dout}, lambda = {1, 1}}, }, ['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.AffineLayer'] = { - mainAffine = {dim_in = {din, dout}, dim_out = {dout}, pr = pr}, - forgetGate = {dim_in = {din, dout, dout}, dim_out = {dout}, param_type = {'N', 'N', 'D'}, pr = pr, activation = nerv.SigmoidLayer}, - inputGate = {dim_in = {din, dout, dout}, dim_out = {dout}, param_type = {'N', 'N', 'D'}, pr = pr, activation = nerv.SigmoidLayer}, - outputGate = {dim_in = {din, dout, dout}, dim_out = {dout}, param_type = {'N', 'N', 'D'}, pr = pr, activation = nerv.SigmoidLayer}, + mainAffine = {dim_in = table.connect({dout}, din), dim_out = {dout}, pr = pr}, + forgetGate = {dim_in = table.connect({dout, dout}, din), dim_out = {dout}, + param_type = table.connect({'N', 'D'}, table.vector(#din, 'N')), pr = pr, activation = nerv.SigmoidLayer}, + inputGate = {dim_in = table.connect({dout, dout}, din), dim_out = {dout}, + param_type = table.connect({'N', 'D'}, table.vector(#din, 'N')), pr = pr, activation = nerv.SigmoidLayer}, + outputGate = {dim_in = table.connect({dout, dout}, din), dim_out = {dout}, + param_type = table.connect({'N', 'D'}, table.vector(#din, 'N')), pr = pr, activation = nerv.SigmoidLayer}, }, ['nerv.TanhLayer'] = { mainTanh = {dim_in = {dout}, dim_out = {dout}}, @@ -37,26 +42,29 @@ function LSTMLayer:__init(id, global_conf, layer_conf) outputGateMul = {dim_in = {dout, dout}, dim_out = {dout}}, }, } + for i = 1, #din do + layers['nerv.DuplicateLayer']['inputDup' .. i] = {dim_in = {din[i]}, dim_out = {din[i], din[i], din[i], din[i]}} + end local connections = { -- lstm input - {'<input>[1]', 'inputDup[1]', 0}, + --{'<input>[1 .. n]', 'inputDup(1 .. n)[1]', 0}, -- input gate - {'inputDup[1]', 'inputGate[1]', 0}, - {'outputDup[1]', 'inputGate[2]', 1}, - {'cellDup[1]', 'inputGate[3]', 1}, + {'outputDup[1]', 'inputGate[1]', 1}, + {'cellDup[1]', 'inputGate[2]', 1}, + --{'inputDup(1 .. n)[1]', 'inputGate[3 .. n + 2]', 0}, -- forget gate - {'inputDup[2]', 'forgetGate[1]', 0}, - {'outputDup[2]', 'forgetGate[2]', 1}, - {'cellDup[2]', 'forgetGate[3]', 1}, + {'outputDup[2]', 'forgetGate[1]', 1}, + {'cellDup[2]', 'forgetGate[2]', 1}, + --{'inputDup(1 .. n)[2]', 'forgetGate[3 .. n + 2]', 0}, -- lstm cell {'forgetGate[1]', 'forgetGateMul[1]', 0}, {'cellDup[3]', 'forgetGateMul[2]', 1}, - {'inputDup[3]', 'mainAffine[1]', 0}, - {'outputDup[3]', 'mainAffine[2]', 1}, + {'outputDup[3]', 'mainAffine[1]', 1}, + --{'inputDup(1 .. n)[3]', 'mainAffine[2 .. n + 1]', 0}, {'mainAffine[1]', 'mainTanh[1]', 0}, {'inputGate[1]', 'inputGateMul[1]', 0}, {'mainTanh[1]', 'inputGateMul[2]', 0}, @@ -65,9 +73,9 @@ function LSTMLayer:__init(id, global_conf, layer_conf) {'mainCombine[1]', 'cellDup[1]', 0}, -- forget gate - {'inputDup[4]', 'outputGate[1]', 0}, - {'outputDup[4]', 'outputGate[2]', 1}, - {'cellDup[4]', 'outputGate[3]', 0}, + {'outputDup[4]', 'outputGate[1]', 1}, + {'cellDup[4]', 'outputGate[2]', 0}, + --{'inputDup(1 .. n)[4]', 'outputGate[2 .. n + 1]', 0}, -- lstm output {'cellDup[5]', 'outputTanh[1]', 0}, @@ -76,6 +84,13 @@ function LSTMLayer:__init(id, global_conf, layer_conf) {'outputGateMul[1]', 'outputDup[1]', 0}, {'outputDup[5]', '<output>[1]', 0}, } + for i = 1, #din do + table.insert(connections, {'<input>[' .. i .. ']', 'inputDup' .. i .. '[1]', 0}) + table.insert(connections, {'inputDup' .. i .. '[1]', 'inputGate[' .. (i + 2) .. ']', 0}) + table.insert(connections, {'inputDup' .. i .. '[2]', 'forgetGate[' .. (i + 2) .. ']', 0}) + table.insert(connections, {'inputDup' .. i .. '[3]', 'mainAffine[' .. (i + 1) .. ']', 0}) + table.insert(connections, {'inputDup' .. i .. '[4]', 'outputGate[' .. (i + 2) .. ']', 0}) + end self:add_prefix(layers, connections) local layer_repo = nerv.LayerRepo(layers, pr, global_conf) diff --git a/nerv/layer/lstmp.lua b/nerv/layer/lstmp.lua index dc30797..49c9516 100644 --- a/nerv/layer/lstmp.lua +++ b/nerv/layer/lstmp.lua @@ -2,9 +2,12 @@ local LSTMPLayer = nerv.class('nerv.LSTMPLayer', 'nerv.GraphLayer') function LSTMPLayer:__init(id, global_conf, layer_conf) nerv.Layer.__init(self, id, global_conf, layer_conf) - self:check_dim_len(1, 1) + self:check_dim_len(-1, 1) + if #self.dim_in == 0 then + nerv.error('LSTMP layer %s has no input', self.id) + end - local din = layer_conf.dim_in[1] + local din = layer_conf.dim_in local dcell = layer_conf.cell_dim local dout = layer_conf.dim_out[1] @@ -18,15 +21,17 @@ function LSTMPLayer:__init(id, global_conf, layer_conf) mainCombine = {dim_in = {dcell, dcell}, dim_out = {dcell}, lambda = {1, 1}}, }, ['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 = {dcell}, dim_out = {dcell, dcell, dcell, dcell, dcell}}, }, ['nerv.AffineLayer'] = { - mainAffine = {dim_in = {din, dout}, dim_out = {dcell}, pr = pr}, - forgetGate = {dim_in = {din, dout, dcell}, dim_out = {dcell}, param_type = {'N', 'N', 'D'}, pr = pr, activation = nerv.SigmoidLayer}, - inputGate = {dim_in = {din, dout, dcell}, dim_out = {dcell}, param_type = {'N', 'N', 'D'}, pr = pr, activation = nerv.SigmoidLayer}, - outputGate = {dim_in = {din, dout, dcell}, dim_out = {dcell}, param_type = {'N', 'N', 'D'}, pr = pr, activation = nerv.SigmoidLayer}, + mainAffine = {dim_in = table.connect({dout}, din), dim_out = {dcell}, pr = pr}, + forgetGate = {dim_in = table.connect({dout, dcell}, din), dim_out = {dcell}, + param_type = table.connect({'N', 'D'}, table.vector(#din, 'N')), pr = pr, activation = nerv.SigmoidLayer}, + inputGate = {dim_in = table.connect({dout, dcell}, din), dim_out = {dcell}, + param_type = table.connect({'N', 'D'}, table.vector(#din, 'N')), pr = pr, activation = nerv.SigmoidLayer}, + outputGate = {dim_in = table.connect({dout, dcell}, din), dim_out = {dcell}, + param_type = table.connect({'N', 'D'}, table.vector(#din, 'N')), pr = pr, activation = nerv.SigmoidLayer}, projection = {dim_in = {dcell}, dim_out = {dout}, pr = pr, no_bias = true}, }, ['nerv.TanhLayer'] = { @@ -39,26 +44,29 @@ function LSTMPLayer:__init(id, global_conf, layer_conf) outputGateMul = {dim_in = {dcell, dcell}, dim_out = {dcell}}, }, } - + for i = 1, #din do + layers['nerv.DuplicateLayer']['inputDup' .. i] = {dim_in = {din[i]}, dim_out = {din[i], din[i], din[i], din[i]}} + end + local connections = { -- lstm input - {'<input>[1]', 'inputDup[1]', 0}, + --{'<input>[1 .. n]', 'inputDup(1 .. n)[1]', 0}, -- input gate - {'inputDup[1]', 'inputGate[1]', 0}, - {'outputDup[1]', 'inputGate[2]', 1}, - {'cellDup[1]', 'inputGate[3]', 1}, + {'outputDup[1]', 'inputGate[1]', 1}, + {'cellDup[1]', 'inputGate[2]', 1}, + --{'inputDup(1 .. n)[1]', 'inputGate[3 .. n + 2]', 0}, -- forget gate - {'inputDup[2]', 'forgetGate[1]', 0}, - {'outputDup[2]', 'forgetGate[2]', 1}, - {'cellDup[2]', 'forgetGate[3]', 1}, + {'outputDup[2]', 'forgetGate[1]', 1}, + {'cellDup[2]', 'forgetGate[2]', 1}, + --{'inputDup(1 .. n)[2]', 'forgetGate[3 .. n + 2]', 0}, -- lstm cell {'forgetGate[1]', 'forgetGateMul[1]', 0}, {'cellDup[3]', 'forgetGateMul[2]', 1}, - {'inputDup[3]', 'mainAffine[1]', 0}, - {'outputDup[3]', 'mainAffine[2]', 1}, + {'outputDup[3]', 'mainAffine[1]', 1}, + --{'inputDup(1 .. n)[3]', 'mainAffine[2 .. n + 1]', 0}, {'mainAffine[1]', 'mainTanh[1]', 0}, {'inputGate[1]', 'inputGateMul[1]', 0}, {'mainTanh[1]', 'inputGateMul[2]', 0}, @@ -67,9 +75,9 @@ function LSTMPLayer:__init(id, global_conf, layer_conf) {'mainCombine[1]', 'cellDup[1]', 0}, -- forget gate - {'inputDup[4]', 'outputGate[1]', 0}, - {'outputDup[4]', 'outputGate[2]', 1}, - {'cellDup[4]', 'outputGate[3]', 0}, + {'outputDup[4]', 'outputGate[1]', 1}, + {'cellDup[4]', 'outputGate[2]', 0}, + --{'inputDup(1 .. n)[4]', 'outputGate[2 .. n + 1]', 0}, -- lstm output {'cellDup[5]', 'outputTanh[1]', 0}, @@ -79,6 +87,13 @@ function LSTMPLayer:__init(id, global_conf, layer_conf) {'projection[1]', 'outputDup[1]', 0}, {'outputDup[5]', '<output>[1]', 0}, } + for i = 1, #din do + table.insert(connections, {'<input>[' .. i .. ']', 'inputDup' .. i .. '[1]', 0}) + table.insert(connections, {'inputDup' .. i .. '[1]', 'inputGate[' .. (i + 2) .. ']', 0}) + table.insert(connections, {'inputDup' .. i .. '[2]', 'forgetGate[' .. (i + 2) .. ']', 0}) + table.insert(connections, {'inputDup' .. i .. '[3]', 'mainAffine[' .. (i + 1) .. ']', 0}) + table.insert(connections, {'inputDup' .. i .. '[4]', 'outputGate[' .. (i + 2) .. ']', 0}) + end self:add_prefix(layers, connections) local layer_repo = nerv.LayerRepo(layers, pr, global_conf) diff --git a/nerv/layer/rnn.lua b/nerv/layer/rnn.lua index fd6e753..63e0b55 100644 --- a/nerv/layer/rnn.lua +++ b/nerv/layer/rnn.lua @@ -4,12 +4,7 @@ function RNNLayer:__init(id, global_conf, layer_conf) nerv.Layer.__init(self, id, global_conf, layer_conf) self:check_dim_len(-1, 1) if #self.dim_in == 0 then - nerv.error('RNN Layer %s has no input', self.id) - end - - self.activation = layer_conf.activation - if self.activation == nil then - self.activation = 'nerv.SigmoidLayer' + nerv.error('RNN layer %s has no input', self.id) end local din = layer_conf.dim_in @@ -22,10 +17,7 @@ function RNNLayer:__init(id, global_conf, layer_conf) local layers = { ['nerv.AffineLayer'] = { - main = {dim_in = table.connect({dout}, din), dim_out = {dout}, pr = pr}, - }, - [self.activation] = { - activation = {dim_in = {dout}, dim_out = {dout}}, + main = {dim_in = table.connect({dout}, din), dim_out = {dout}, pr = pr, activation = layer_conf.activation}, }, ['nerv.DuplicateLayer'] = { duplicate = {dim_in = {dout}, dim_out = {dout, dout}}, @@ -33,8 +25,7 @@ function RNNLayer:__init(id, global_conf, layer_conf) } local connections = { - {'main[1]', 'activation[1]', 0}, - {'activation[1]', 'duplicate[1]', 0}, + {'main[1]', 'duplicate[1]', 0}, {'duplicate[1]', 'main[1]', 1}, {'duplicate[2]', '<output>[1]', 0}, } |