diff options
Diffstat (limited to 'nerv/layer/gate_fff.lua')
-rw-r--r-- | nerv/layer/gate_fff.lua | 73 |
1 files changed, 0 insertions, 73 deletions
diff --git a/nerv/layer/gate_fff.lua b/nerv/layer/gate_fff.lua deleted file mode 100644 index 6082e27..0000000 --- a/nerv/layer/gate_fff.lua +++ /dev/null @@ -1,73 +0,0 @@ -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 |