1 files changed, 0 insertions, 93 deletions
diff --git a/nerv/examples/lmptb/lmptb/layer/affine_recurrent.lua b/nerv/examples/lmptb/lmptb/layer/affine_recurrent.lua
deleted file mode 100644
index 0a762f0..0000000
--- a/nerv/examples/lmptb/lmptb/layer/affine_recurrent.lua
+++ /dev/null
@@ -1,93 +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.bp = layer_conf.bp
-    self.ltp_ih = layer_conf.ltp_ih --from input to hidden
-    self.ltp_hh = layer_conf.ltp_hh --from hidden to hidden
-    
-    self:check_dim_len(2, 1)
-    self.direct_update = layer_conf.direct_update
-end
-
---Check parameter 
-function Recurrent:init(batch_size)
-    if (self.ltp_ih.trans:ncol() ~= self.bp.trans:ncol() or 
-        self.ltp_hh.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_ih_grad = self.ltp_ih.trans:create()
-    self.ltp_hh_grad = self.ltp_hh.trans:create()
-    self.ltp_ih:train_init()
-    self.ltp_hh:train_init()
-    self.bp:train_init()
-end
-
-function Recurrent:update(bp_err, input, output)
-    if (self.direct_update == true) then
-        local ltp_ih = self.ltp_ih.trans
-        local ltp_hh = self.ltp_hh.trans
-        local bp = self.bp.trans
-        local ltc_ih = self.ltc_ih
-        local ltc_hh = self.ltc_hh
-        local bc = self.bc
-        local gconf = self.gconf
-        -- momentum gain
-        local mmt_gain = 1.0 / (1.0 - gconf.momentum);
-        local n = input[1]:nrow() * mmt_gain
-        -- update corrections (accumulated errors)
-        self.ltp_ih.correction:mul(input[1], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
-        self.ltc_hh.correction:mul(input[2], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
-        self.bp.correction:add(bc, bp_err[1]:colsum(), gconf.momentum, 1.0)
-        -- perform update
-        ltp_ih:add(ltp_ih, self.ltp_ih.correction, 1.0, -gconf.lrate / n)
-        ltp_hh:add(ltp_hh, self.ltp_hh.correction, 1.0, -gconf.lrate / n)
-        bp:add(bp, self.bp.correction, 1.0, -gconf.lrate / n)
-        -- weight decay
-        ltp_ih:add(ltp_ih, ltp_ih, 1.0, -gconf.lrate * gconf.wcost)
-        ltp_hh:add(ltp_hh, ltp_hh, 1.0, -gconf.lrate * gconf.wcost)
-    else
-        self.ltp_ih_grad:mul(input[1], bp_err[1], 1.0, 0.0, 'T', 'N') 
-        self.ltp_ih:update(self.ltp_ih_grad)
-        self.ltp_hh_grad:mul(input[2], bp_err[1], 1.0, 0.0, 'T', 'N') 
-        self.ltp_hh:update(self.ltp_hh_grad)       
-        self.bp:update(bp_err[1]:colsum())
-    end
-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
-end
-
-function Recurrent:get_params()
-    return {self.ltp_ih, self.ltp_hh, self.bp}
-end