diff options
Diffstat (limited to 'nerv/layer')
| -rw-r--r-- | nerv/layer/affine.lua | 39 | ||||
| -rw-r--r-- | nerv/layer/affine_recurrent.lua | 4 | ||||
| -rw-r--r-- | nerv/layer/elem_mul.lua | 38 | ||||
| -rw-r--r-- | nerv/layer/gate_fff.lua | 73 | ||||
| -rw-r--r-- | nerv/layer/init.lua | 23 | ||||
| -rw-r--r-- | nerv/layer/tanh.lua | 35 |
6 files changed, 201 insertions, 11 deletions
diff --git a/nerv/layer/affine.lua b/nerv/layer/affine.lua index 6903c51..566e9bc 100644 --- a/nerv/layer/affine.lua +++ b/nerv/layer/affine.lua @@ -61,28 +61,38 @@ end function AffineLayer:__init(id, global_conf, layer_conf) self.id = id - self.ltp = layer_conf.ltp - self.bp = layer_conf.bp self.dim_in = layer_conf.dim_in self.dim_out = layer_conf.dim_out + self.ltp = self:find_param("ltp", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[1], self.dim_out[1]}) --layer_conf.ltp + for i = 2, #self.dim_in do + self["ltp" .. i] = self:find_param("ltp" .. i, layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[i], self.dim_out[1]}) + end + self.bp = self:find_param("bp", layer_conf, global_conf, nerv.BiasParam, {1, self.dim_out[1]}) --layer_conf.bp self.gconf = global_conf - self:check_dim_len(1, 1) -- exactly one input and one output - -- self.direct_update = layer_conf.direct_update or global_conf.direct_update + self:check_dim_len(-1, 1) -- exactly one output, allow multiple inputs end function AffineLayer:init(batch_size) if self.ltp.trans:ncol() ~= self.bp.trans:ncol() then nerv.error("mismatching dimensions of linear transform and bias paramter") end + self.bp:train_init() if self.dim_in[1] ~= self.ltp.trans:nrow() then nerv.error("mismatching dimensions of linear transform parameter and input") end if self.dim_out[1] ~= self.ltp.trans:ncol() then nerv.error("mismatching dimensions of linear transform parameter and output") end - self.ltp_grad = self.ltp.trans:create() self.ltp:train_init() - self.bp:train_init() + for i = 2, #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 AffineLayer:batch_resize(batch_size) @@ -91,20 +101,31 @@ end function AffineLayer:update(bp_err, input, output) self.ltp:update_by_err_input(bp_err[1], input[1]) + for i = 2, #self.dim_in do + self["ltp" .. i]:update_by_err_input(bp_err[1], input[i]) + end self.bp:update_by_gradient(bp_err[1]:colsum()) end function AffineLayer:propagate(input, output) - -- apply linear transform output[1]:mul(input[1], self.ltp.trans, 1.0, 0.0, 'N', 'N') - -- add bias + for i = 2, #self.dim_in do + output[1]:mul(input[i], self["ltp" .. i].trans, 1.0, 1.0, 'N', 'N') + end output[1]:add_row(self.bp.trans, 1.0) end function AffineLayer:back_propagate(bp_err, next_bp_err, input, output) next_bp_err[1]:mul(bp_err[1], self.ltp.trans, 1.0, 0.0, 'N', 'T') + for i = 2, #self.dim_in do + next_bp_err[i]:mul(bp_err[1], self["ltp" .. i].trans, 1.0, 0.0, 'N', 'T') + end end function AffineLayer:get_params() - return nerv.ParamRepo({self.ltp, self.bp}) + local pr = nerv.ParamRepo({self.ltp, self.bp}) + for i = 2, #self.dim_in do + pr:add(self["ltp" .. i].id, self["ltp" .. i]) + end + return pr end diff --git a/nerv/layer/affine_recurrent.lua b/nerv/layer/affine_recurrent.lua index da189e0..d537f4a 100644 --- a/nerv/layer/affine_recurrent.lua +++ b/nerv/layer/affine_recurrent.lua @@ -10,8 +10,8 @@ function Recurrent:__init(id, global_conf, layer_conf) self.dim_out = layer_conf.dim_out self.gconf = global_conf - self.bp = layer_conf.bp - self.ltp_hh = layer_conf.ltp_hh --from hidden to hidden + self.bp = self:find_param("bp", layer_conf, global_conf, nerv.BiasParam, {1, self.dim_out[1]}) --layer_conf.bp + self.ltp_hh = self:find_param("ltp_hh", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[2], self.dim_out[1]}) --layer_conf.ltp_hh --from hidden to hidden self:check_dim_len(2, 1) self.direct_update = layer_conf.direct_update diff --git a/nerv/layer/elem_mul.lua b/nerv/layer/elem_mul.lua new file mode 100644 index 0000000..c809d3e --- /dev/null +++ b/nerv/layer/elem_mul.lua @@ -0,0 +1,38 @@ +local ElemMulLayer = nerv.class('nerv.ElemMulLayer', 'nerv.Layer') + +function ElemMulLayer:__init(id, global_conf, layer_conf) + self.id = id + self.dim_in = layer_conf.dim_in + self.dim_out = layer_conf.dim_out + self.gconf = global_conf + + self:check_dim_len(2, 1) -- Element-multiply input[1] and input[2] +end + +function ElemMulLayer:init(batch_size) + if self.dim_in[1] ~= self.dim_in[2] or + self.dim_in[1] ~= self.dim_out[1] then + nerv.error("dim_in and dim_out mismatch for ElemMulLayer") + end +end + +function ElemMulLayer:batch_resize(batch_size) + --do nothing +end + +function ElemMulLayer:propagate(input, output) + output[1]:mul_elem(input[1], input[2]) +end + +function ElemMulLayer:back_propagate(bp_err, next_bp_err, input, output) + next_bp_err[1]:mul_elem(bp_err[1], input[2]) + next_bp_err[2]:mul_elem(bp_err[1], input[1]) +end + +function ElemMulLayer:update(bp_err, input, output) + --do nothing +end + +function ElemMulLayer:get_params() + return nerv.ParamRepo({}) +end diff --git a/nerv/layer/gate_fff.lua b/nerv/layer/gate_fff.lua new file mode 100644 index 0000000..6082e27 --- /dev/null +++ b/nerv/layer/gate_fff.lua @@ -0,0 +1,73 @@ +local GateFFFLayer = nerv.class('nerv.GateFLayer', 'nerv.Layer') --Full matrix gate + +function GateFFFLayer:__init(id, global_conf, layer_conf) + self.id = id + self.dim_in = layer_conf.dim_in + self.dim_out = layer_conf.dim_out + self.gconf = global_conf + + for i = 1, #self.dim_in do + self["ltp" .. i] = self:find_param("ltp" .. i, layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[i], self.dim_out[1]}) --layer_conf.ltp + end + self.bp = self:find_param("bp", layer_conf, global_conf, nerv.BiasParam, {1, self.dim_out[1]})--layer_conf.bp + + self:check_dim_len(-1, 1) --accept multiple inputs +end + +function GateFFFLayer:init(batch_size) + for i = 1, #self.dim_in do + if self["ltp" .. i].trans:ncol() ~= self.bp.trans:ncol() then + nerv.error("mismatching dimensions of linear transform and bias paramter") + end + if self.dim_in[i] ~= self["ltp" .. i].trans:nrow() then + nerv.error("mismatching dimensions of linear transform parameter and input") + end + self["ltp"..i]:train_init() + end + + if self.dim_out[1] ~= self.ltp1.trans:ncol() then + nerv.error("mismatching dimensions of linear transform parameter and output") + end + self.bp:train_init() + self.err_bakm = self.gconf.cumat_type(batch_size, self.dim_out[1]) +end + +function GateFFFLayer:batch_resize(batch_size) + if self.err_m:nrow() ~= batch_size then + self.err_bakm = self.gconf.cumat_type(batch_size, self.dim_out[1]) + end +end + +function GateFFFLayer: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 GateFFFLayer: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') + end +end + +function GateFFFLayer:update(bp_err, input, output) + self.err_bakm:sigmoid_grad(bp_err[1], output[1]) + for i = 1, #self.dim_in do + self["ltp" .. i]:update_by_err_input(self.err_bakm, input[i]) + end + self.bp:update_by_gradient(self.err_bakm:colsum()) +end + +function GateFFFLayer:get_params() + local pr = nerv.ParamRepo({self.bp}) + for i = 1, #self.dim_in do + pr:add(self["ltp" .. i].id, self["ltp" .. i]) + end + return pr +end diff --git a/nerv/layer/init.lua b/nerv/layer/init.lua index 6861b0e..23606e1 100644 --- a/nerv/layer/init.lua +++ b/nerv/layer/init.lua @@ -70,8 +70,29 @@ function Layer:get_dim() return self.dim_in, self.dim_out end +function Layer:find_param(pid, l_conf, gconf, p_type, p_dim) + if l_conf[pid] ~= nil then + nerv.info("Param [%s] of layer [%s] found in layer_conf.", pid, self.id) + return l_conf[pid] + end + local pid_g = self.id .. '_' .. pid --global identifier + local pr = l_conf.pr + local p + if pr ~= nil and pr:has_param(pid_g) == true then + nerv.info("Param [%s] of layer [%s] found in layer_conf.paramRepo.", pid, self.id) + p = pr:get_param(pid_g) + return p + end + nerv.info("Param [%s] of layer [%s] is not found in layer_conf or layer_conf.paramRepo, switch to auto-generate.", pid, self.id) + p = p_type(pid_g, gconf) + p.trans = gconf.cumat_type(unpack(p_dim)) + p.trans:generate(gconf.param_random) + return p +end + nerv.include('affine.lua') nerv.include('sigmoid.lua') +nerv.include('tanh.lua') nerv.include('softmax_ce.lua') nerv.include('bias.lua') nerv.include('window.lua') @@ -79,3 +100,5 @@ nerv.include('mse.lua') nerv.include('combiner.lua') nerv.include('affine_recurrent.lua') nerv.include('softmax.lua') +nerv.include('elem_mul.lua') +nerv.include('gate_fff.lua') diff --git a/nerv/layer/tanh.lua b/nerv/layer/tanh.lua new file mode 100644 index 0000000..e1c32f2 --- /dev/null +++ b/nerv/layer/tanh.lua @@ -0,0 +1,35 @@ +local TanhLayer = nerv.class("nerv.TanhLayer", "nerv.Layer") + +function TanhLayer:__init(id, global_conf, layer_conf) + self.id = id + self.gconf = global_conf + self.dim_in = layer_conf.dim_in + self.dim_out = layer_conf.dim_out + self:check_dim_len(1, 1) +end + +function TanhLayer:init() + if self.dim_in[1] ~= self.dim_out[1] then + nerv.error("mismatching dimensions of input and output") + end +end + +function TanhLayer:batch_resize(batch_size) + -- do nothing +end + +function TanhLayer:update(bp_err, input, output) + -- no params, therefore do nothing +end + +function TanhLayer:propagate(input, output) + output[1]:tanh(input[1]) +end + +function TanhLayer:back_propagate(bp_err, next_bp_err, input, output) + next_bp_err[1]:tanh_grad(bp_err[1], output[1]) +end + +function TanhLayer:get_params() + return nerv.ParamRepo({}) +end |