1 files changed, 93 insertions, 0 deletions
diff --git a/nerv/examples/lmptb/lmptb/layer/affine_recurrent.lua b/nerv/examples/lmptb/lmptb/layer/affine_recurrent.lua
new file mode 100644
index 0000000..0a762f0
--- /dev/null
+++ b/nerv/examples/lmptb/lmptb/layer/affine_recurrent.lua
@@ -0,0 +1,93 @@
+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