1 files changed, 0 insertions, 80 deletions
diff --git a/nerv/layer/affine_recurrent.lua b/nerv/layer/affine_recurrent.lua
deleted file mode 100644
index fd6f38f..0000000
--- a/nerv/layer/affine_recurrent.lua
+++ /dev/null
@@ -1,80 +0,0 @@
-local Recurrent = nerv.class('nerv.AffineRecurrentLayer', 'nerv.Layer')
-
---id: string
---global_conf: table
---layer_conf: table
---Get Parameters
-function Recurrent:__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.log_pre = self.id .. "[LOG]"
-
-    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("ltphh", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[2], self.dim_out[1]}) --layer_conf.ltp_hh --from hidden to hidden
-    self.ltp_ih = self:find_param("ltpih", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[1], 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
-
-    self.clip = layer_conf.clip --clip error in back_propagate
-    if self.clip ~= nil then
-        nerv.info("%s creating, will clip the error by %f", self.log_pre, self.clip)
-    end
-end
-
---Check parameter 
-function Recurrent:init(batch_size)
-    if self.ltp_hh.trans:ncol() ~= self.bp.trans:ncol() or
-       self.ltp_ih.trans:ncol() ~= self.bp.trans:ncol() then
-        nerv.error("mismatching dimensions of ltp and bp")
-    end
-    if self.dim_in[1] ~= self.ltp_ih.trans:nrow() or
-        self.dim_in[2] ~= self.ltp_hh.trans:nrow() then
-        nerv.error("mismatching dimensions of ltp and input")
-    end
-    if (self.dim_out[1] ~= self.bp.trans:ncol()) then
-        nerv.error("mismatching dimensions of bp and output")
-    end
-    
-    self.ltp_hh:train_init()
-    self.ltp_ih:train_init()
-    self.bp:train_init()
-end
-
-function Recurrent:batch_resize(batch_size)
-    -- do nothing
-end
-
-function Recurrent:update(bp_err, input, output)
-    self.ltp_ih:update_by_err_input(bp_err[1], input[1])       
-    self.ltp_hh:update_by_err_input(bp_err[1], input[2])       
-    self.bp:update_by_gradient(bp_err[1]:colsum())
-end
-
-function Recurrent:propagate(input, output)
-    output[1]:mul(input[1], self.ltp_ih.trans, 1.0, 0.0, 'N', 'N')
-    output[1]:mul(input[2], self.ltp_hh.trans, 1.0, 1.0, 'N', 'N')
-    output[1]:add_row(self.bp.trans, 1.0)
-end
-
-function Recurrent:back_propagate(bp_err, next_bp_err, input, output)
-    next_bp_err[1]:mul(bp_err[1], self.ltp_ih.trans, 1.0, 0.0, 'N', 'T')
-    next_bp_err[2]:mul(bp_err[1], self.ltp_hh.trans, 1.0, 0.0, 'N', 'T')
-    --[[
-    for i = 0, next_bp_err[2]:nrow() - 1 do
-        for j = 0, next_bp_err[2]:ncol() - 1 do
-            if (next_bp_err[2][i][j] > 10) then next_bp_err[2][i][j] = 10 end
-            if (next_bp_err[2][i][j] < -10) then next_bp_err[2][i][j] = -10 end
-        end
-    end
-    ]]--
-    if self.clip ~= nil then
-        next_bp_err[2]:clip(-self.clip, self.clip)
-    end
-end
-
-function Recurrent:get_params()
-    return nerv.ParamRepo({self.ltp_ih, self.ltp_hh, self.bp})
-end