diff options
author | txh18 <[email protected]> | 2015-11-24 22:06:45 +0800 |
---|---|---|
committer | txh18 <[email protected]> | 2015-11-24 22:06:45 +0800 |
commit | 8e590ba284bfee414659f1845e175b41cac05d45 (patch) | |
tree | a812e760e3631263c18144c7c6bb4f7a332732af /nerv/layer/affine.lua | |
parent | 914a026734db6608e04987e9fcec9c82612e8673 (diff) |
let affine supported multiple inputs
Diffstat (limited to 'nerv/layer/affine.lua')
-rw-r--r-- | nerv/layer/affine.lua | 36 |
1 files changed, 28 insertions, 8 deletions
diff --git a/nerv/layer/affine.lua b/nerv/layer/affine.lua index e24a0c6..d56fcb8 100644 --- a/nerv/layer/affine.lua +++ b/nerv/layer/affine.lua @@ -64,25 +64,35 @@ function AffineLayer:__init(id, global_conf, layer_conf) 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 - self.bp = self:find_param("bp", layer_conf, global_conf, nerv.BiasParam, {1, self.dim_out[1]})--layer_conf.bp + 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 +101,30 @@ 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 end |