diff options
Diffstat (limited to 'nerv/layer')
| -rw-r--r-- | nerv/layer/affine.lua | 67 | ||||
| -rw-r--r-- | nerv/layer/init.lua | 2 | ||||
| -rw-r--r-- | nerv/layer/lstm.lua | 8 | ||||
| -rw-r--r-- | nerv/layer/lstm_gate.lua | 97 | ||||
| -rw-r--r-- | nerv/layer/lstmp.lua | 12 | ||||
| -rw-r--r-- | nerv/layer/projection.lua | 70 |
6 files changed, 58 insertions, 198 deletions
diff --git a/nerv/layer/affine.lua b/nerv/layer/affine.lua index 16250fd..8b4751c 100644 --- a/nerv/layer/affine.lua +++ b/nerv/layer/affine.lua @@ -87,7 +87,11 @@ local AffineLayer = nerv.class('nerv.AffineLayer', 'nerv.Layer') -- @param global_conf see `self.gconf` of `nerv.Layer.__init` -- @param layer_conf a table providing with settings dedicated for the layer, -- for `layer_conf` fields that are shared by all layers, see --- `nerv.Layer.__init`. The affine layer requires parameters to be bound, the +-- `nerv.Layer.__init`. This fields can be specified: +-- * `activation`: the type of the activation function layer, also known as \sigma in \sigma(Wx + b). Default value none (no activation function). +-- * `no_bias`: a bool value indicates use bias parameter or not. Default value false. +-- * `param_type`: a string table has the same length with `dim_in`, indicates the parameter type for every input. 'D' for diagonal weight matrix, 'N' for normal weight matrix. Default 'N' for every input. +-- The affine layer requires parameters to be bound, the -- following parameter names will be looked up while binding: -- -- * `ltp`: the linear transformation parameter, also known as the weight matrix, W in Wx + b @@ -95,7 +99,11 @@ local AffineLayer = nerv.class('nerv.AffineLayer', 'nerv.Layer') function AffineLayer:__init(id, global_conf, layer_conf) nerv.Layer.__init(self, id, global_conf, layer_conf) + self.param_type = layer_conf.param_type or table.vector(#self.dim_in, 'N') self:check_dim_len(-1, 1) -- exactly one output, allow multiple inputs + if layer_conf.activation then + self.activation = layer_conf.activation('', global_conf, {dim_in = {self.dim_out[1]}, dim_out = {self.dim_out[1]}}) + end self:bind_params() end @@ -108,19 +116,24 @@ function AffineLayer:bind_params() self["ltp" .. i] = self:find_param(pid_list, lconf, self.gconf, nerv.LinearTransParam, {self.dim_in[i], self.dim_out[1]}) + if self.param_type[i] == 'D' then + self['ltp' .. i].trans:diagonalize() + end local no_update = lconf["no_update_ltp" .. i] if (no_update ~= nil) and no_update or lconf.no_update_all then self["ltp" .. i].no_update = true end end self.ltp = self.ltp1 -- alias of ltp1 - self.bp = self:find_param("bp", lconf, self.gconf, - nerv.BiasParam, - {1, self.dim_out[1]}, - nerv.Param.gen_zero) - local no_update = lconf["no_update_bp"] - if (no_update ~= nil) and no_update or lconf.no_update_all then - self.bp.no_update = true + if not self.no_bias then + self.bp = self:find_param("bp", lconf, self.gconf, + nerv.BiasParam, + {1, self.dim_out[1]}, + nerv.Param.gen_zero) + local no_update = lconf["no_update_bp"] + if (no_update ~= nil) and no_update or lconf.no_update_all then + self.bp.no_update = true + end end end @@ -137,7 +150,15 @@ function AffineLayer:init(batch_size) end self["ltp" .. i]:train_init() end - self.bp:train_init() + if not self.no_bias then + self.bp:train_init() + end + if self.activation then + self.act_bak = self.mat_type(batch_size, self.dim_out[1]) + self.act_bak:fill(0) + self.err_bak = self.mat_type(batch_size, self.dim_out[1]) + self.err_bak:fill(0) + end end function AffineLayer:batch_resize(batch_size) @@ -148,25 +169,39 @@ function AffineLayer:update() for i = 1, #self.dim_in do self["ltp" .. i]:update_by_err_input() end - self.bp:update_by_gradient() + if not self.no_bias then + self.bp:update_by_gradient() + end end function AffineLayer:propagate(input, output) + local result = self.activation and self.act_bak or output[1] -- apply linear transform - output[1]:mul(input[1], self.ltp1.trans, 1.0, 0.0, 'N', 'N') + result:mul(input[1], self.ltp1.trans, 1.0, 0.0, 'N', 'N') for i = 2, #self.dim_in do - output[1]:mul(input[i], self["ltp" .. i].trans, 1.0, 1.0, 'N', 'N') + result:mul(input[i], self["ltp" .. i].trans, 1.0, 1.0, 'N', 'N') end -- add bias - output[1]:add_row(self.bp.trans, 1.0) + if not self.no_bias then + result:add_row(self.bp.trans, 1.0) + end + if self.activation then + self.activation:propagate({self.act_bak}, output) + end end function AffineLayer:back_propagate(bp_err, next_bp_err, input, output) + if self.activation then + self.activation:back_propagate(bp_err, {self.err_bak}, {self.act_bak}, output) + end + local result = self.activation and self.err_bak or bp_err[1] for i = 1, #self.dim_in do - next_bp_err[i]:mul(bp_err[1], self["ltp" .. i].trans, 1.0, 0.0, 'N', 'T') - self["ltp" .. i]:back_propagate_by_err_input(bp_err[1], input[i]) + next_bp_err[i]:mul(result, self["ltp" .. i].trans, 1.0, 0.0, 'N', 'T') + self["ltp" .. i]:back_propagate_by_err_input(result, input[i]) + end + if not self.no_bias then + self.bp:back_propagate_by_gradient(result:colsum()) end - self.bp:back_propagate_by_gradient(bp_err[1]:colsum()) end function AffineLayer:get_params() diff --git a/nerv/layer/init.lua b/nerv/layer/init.lua index d175d02..054784b 100644 --- a/nerv/layer/init.lua +++ b/nerv/layer/init.lua @@ -272,13 +272,11 @@ nerv.include('combiner.lua') nerv.include('softmax.lua') nerv.include('elem_mul.lua') nerv.include('lstm.lua') -nerv.include('lstm_gate.lua') nerv.include('dropout.lua') nerv.include('gru.lua') nerv.include('rnn.lua') nerv.include('duplicate.lua') nerv.include('identity.lua') -nerv.include('projection.lua') nerv.include('lstmp.lua') nerv.include('relu.lua') diff --git a/nerv/layer/lstm.lua b/nerv/layer/lstm.lua index 3de3453..e568ee8 100644 --- a/nerv/layer/lstm.lua +++ b/nerv/layer/lstm.lua @@ -23,16 +23,14 @@ function LSTMLayer:__init(id, global_conf, layer_conf) }, ['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}, }, ['nerv.TanhLayer'] = { mainTanh = {dim_in = {dout}, dim_out = {dout}}, outputTanh = {dim_in = {dout}, dim_out = {dout}}, }, - ['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}}, diff --git a/nerv/layer/lstm_gate.lua b/nerv/layer/lstm_gate.lua deleted file mode 100644 index 39a3ff7..0000000 --- a/nerv/layer/lstm_gate.lua +++ /dev/null @@ -1,97 +0,0 @@ -local LSTMGateLayer = nerv.class('nerv.LSTMGateLayer', 'nerv.Layer') --- NOTE: this is a full matrix gate - -function LSTMGateLayer:__init(id, global_conf, layer_conf) - nerv.Layer.__init(self, id, global_conf, layer_conf) - self.param_type = layer_conf.param_type - self:check_dim_len(-1, 1) --accept multiple inputs - self:bind_params() -end - -function LSTMGateLayer:bind_params() - local lconf = self.lconf - lconf.no_update_ltp1 = lconf.no_update_ltp1 or lconf.no_update_ltp - for i = 1, #self.dim_in do - local pid = "ltp" .. i - local pid_list = i == 1 and {pid, "ltp"} or pid - self["ltp" .. i] = self:find_param(pid_list, lconf, self.gconf, - nerv.LinearTransParam, - {self.dim_in[i], self.dim_out[1]}) - if self.param_type[i] == 'D' then - self["ltp" .. i].trans:diagonalize() - end - local no_update = lconf["no_update_ltp" .. i] - if (no_update ~= nil) and no_update or lconf.no_update_all then - self["ltp" .. i].no_update = true - end - end - self.ltp = self.ltp1 -- alias of ltp1 - self.bp = self:find_param("bp", lconf, self.gconf, - nerv.BiasParam, {1, self.dim_out[1]}, - nerv.Param.gen_zero) - local no_update = lconf["no_update_bp"] - if (no_update ~= nil) and no_update or lconf.no_update_all then - self.bp.no_update = true - end -end - -function LSTMGateLayer:init(batch_size) - if self.dim_out[1] ~= self.bp.trans:ncol() then - nerv.error("mismatching dimensions of linear transform and bias paramter") - end - for i = 1, #self.dim_in do - if self.dim_in[i] ~= self["ltp" .. i].trans:nrow() then - nerv.error("mismatching dimensions of linear transform parameter and input") - end - if self.dim_out[1] ~= self["ltp" .. i].trans:ncol() then - nerv.error("mismatching dimensions of linear transform parameter and output") - end - self["ltp" .. i]:train_init() - end - self.bp:train_init() - self.err_bakm = self.mat_type(batch_size, self.dim_out[1]) -end - -function LSTMGateLayer:batch_resize(batch_size) - if self.err_m:nrow() ~= batch_size then - self.err_bakm = self.mat_type(batch_size, self.dim_out[1]) - end -end - -function LSTMGateLayer:propagate(input, output) - -- apply linear transform - output[1]:mul(input[1], self.ltp1.trans, 1.0, 0.0, 'N', 'N') - for i = 2, #self.dim_in do - output[1]:mul(input[i], self["ltp" .. i].trans, 1.0, 1.0, 'N', 'N') - end - -- add bias - output[1]:add_row(self.bp.trans, 1.0) - output[1]:sigmoid(output[1]) -end - -function LSTMGateLayer:back_propagate(bp_err, next_bp_err, input, output) - self.err_bakm:sigmoid_grad(bp_err[1], output[1]) - for i = 1, #self.dim_in do - next_bp_err[i]:mul(self.err_bakm, self["ltp" .. i].trans, 1.0, 0.0, 'N', 'T') - self["ltp" .. i]:back_propagate_by_err_input(self.err_bakm, input[i]) - end - self.bp:back_propagate_by_gradient(self.err_bakm:colsum()) -end - -function LSTMGateLayer:update() - for i = 1, #self.dim_in do - self["ltp" .. i]:update_by_err_input() - if self.param_type[i] == 'D' then - self["ltp" .. i].trans:diagonalize() - end - end - self.bp:update_by_gradient() -end - -function LSTMGateLayer:get_params() - local pr = nerv.ParamRepo({self.bp}, self.loc_type) - for i = 1, #self.dim_in do - pr:add(self["ltp" .. i]) - end - return pr -end diff --git a/nerv/layer/lstmp.lua b/nerv/layer/lstmp.lua index bbb2091..dc30797 100644 --- a/nerv/layer/lstmp.lua +++ b/nerv/layer/lstmp.lua @@ -24,24 +24,20 @@ function LSTMPLayer:__init(id, global_conf, layer_conf) }, ['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}, + projection = {dim_in = {dcell}, dim_out = {dout}, pr = pr, no_bias = true}, }, ['nerv.TanhLayer'] = { mainTanh = {dim_in = {dcell}, dim_out = {dcell}}, outputTanh = {dim_in = {dcell}, dim_out = {dcell}}, }, - ['nerv.LSTMGateLayer'] = { - forgetGate = {dim_in = {din, dout, dcell}, dim_out = {dcell}, param_type = {'N', 'N', 'D'}, pr = pr}, - inputGate = {dim_in = {din, dout, dcell}, dim_out = {dcell}, param_type = {'N', 'N', 'D'}, pr = pr}, - outputGate = {dim_in = {din, dout, dcell}, dim_out = {dcell}, param_type = {'N', 'N', 'D'}, pr = pr}, - }, ['nerv.ElemMulLayer'] = { inputGateMul = {dim_in = {dcell, dcell}, dim_out = {dcell}}, forgetGateMul = {dim_in = {dcell, dcell}, dim_out = {dcell}}, outputGateMul = {dim_in = {dcell, dcell}, dim_out = {dcell}}, }, - ['nerv.ProjectionLayer'] = { - projection = {dim_in = {dcell}, dim_out = {dout}, pr = pr}, - }, } local connections = { diff --git a/nerv/layer/projection.lua b/nerv/layer/projection.lua deleted file mode 100644 index 077125b..0000000 --- a/nerv/layer/projection.lua +++ /dev/null @@ -1,70 +0,0 @@ -local ProjectionLayer = nerv.class('nerv.ProjectionLayer', 'nerv.Layer') - ---- The constructor. -function ProjectionLayer:__init(id, global_conf, layer_conf) - nerv.Layer.__init(self, id, global_conf, layer_conf) - self:check_dim_len(-1, 1) -- exactly one output, allow multiple inputs - self:bind_params() -end - -function ProjectionLayer:bind_params() - local lconf = self.lconf - lconf.no_update_ltp1 = lconf.no_update_ltp1 or lconf.no_update_ltp - for i = 1, #self.dim_in do - local pid = "ltp" .. i - local pid_list = i == 1 and {pid, "ltp"} or pid - self["ltp" .. i] = self:find_param(pid_list, lconf, self.gconf, - nerv.LinearTransParam, - {self.dim_in[i], self.dim_out[1]}) - local no_update = lconf["no_update_ltp" .. i] - if (no_update ~= nil) and no_update or lconf.no_update_all then - self["ltp" .. i].no_update = true - end - end - self.ltp = self.ltp1 -- alias of ltp1 -end - -function ProjectionLayer:init(batch_size) - for i = 1, #self.dim_in do - if self.dim_in[i] ~= self["ltp" .. i].trans:nrow() then - nerv.error("mismatching dimensions of linear transform parameter and input") - end - if self.dim_out[1] ~= self["ltp" .. i].trans:ncol() then - nerv.error("mismatching dimensions of linear transform parameter and output") - end - self["ltp" .. i]:train_init() - end -end - -function ProjectionLayer:batch_resize(batch_size) - -- do nothing -end - -function ProjectionLayer:update() - for i = 1, #self.dim_in do - self["ltp" .. i]:update_by_err_input() - end -end - -function ProjectionLayer:propagate(input, output) - -- apply linear transform - output[1]:mul(input[1], self.ltp1.trans, 1.0, 0.0, 'N', 'N') - for i = 2, #self.dim_in do - output[1]:mul(input[i], self["ltp" .. i].trans, 1.0, 1.0, 'N', 'N') - end -end - -function ProjectionLayer:back_propagate(bp_err, next_bp_err, input, output) - for i = 1, #self.dim_in do - next_bp_err[i]:mul(bp_err[1], self["ltp" .. i].trans, 1.0, 0.0, 'N', 'T') - self["ltp" .. i]:back_propagate_by_err_input(bp_err[1], input[i]) - end -end - -function ProjectionLayer:get_params() - local pr = nerv.ParamRepo({self.ltp1}, self.loc_type) - for i = 2, #self.dim_in do - pr:add(self["ltp" .. i]) - end - return pr -end |